Saccharomyces cerevisiae are a phenotypically diverse species that adapt to a wide variety of environments by exploiting standing genetic diversity and selecting for advantageous mutations. Glyphosate and copper-based herbicides/ fungicides affect non-target organisms, these incidental exposures can impact microbial populations. In this study, glyphosate resistance was found in the historical collection of yeast which was collected over the last century, but only in yeast isolated after the introduction of glyphosate. The highest glyphosate-resistant yeasts were isolated from agricultural sites. However, herbicide application at these sites was not recorded. In an effort to assess glyphosate resistance and impact on non-target microorganisms, yeast were harvested from 15 areas with known herbicidal histories, including an organic farm, conventional farm, remediated coal mine, suburban locations, state park, and a national forest. Yeast representing 23 genera were isolated from 237 samples of plant, soil, spontaneous fermentation, nut, flower, fruit, feces, and tree material samples. Saccharomyces, Candida, Metschnikowia, Klyveromyces, Hanseniaspora, and Pichia were other genera commonly found across our sampled environments. Managed areas had less species diversity and at the brewery, only Saccharomyces and Pichia were isolated. A conventional farm growing RoundUp Ready™ corn had the lowest phylogenetic diversity and the highest glyphosate resistance. The mine was sprayed with multiple herbicides including a commercial formulation of glyphosate; however, the yeast did not have elevated glyphosate resistance. In contrast to the conventional farm, the mine was exposed to glyphosate only one year prior to sample isolation. Glyphosate resistance is an example of the anthropogenic selection of nontarget organisms.

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins and carbohydrates in their seeds, distinctly dominate modern agronomic practice. Indeed, these plants play an essential role in the food industry and fuel production. Therefore, the seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, not only seed development and germination, but also age- and stress-related alterations in seed vigor, longevity, nutritional value and safety can be addressed by a broad panel of analytical, biochemical and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes, accompanying plant development, senescence and response to biotic or environmental stress. Among individual methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. Here we discuss the main methodological approaches employed by seed proteomics in the context of physiological changes related to seed development, ageing and response to environmental stress.

The type of fertilization used influences plant compounds and pest infestation. We measured, through chemical stimuli, the preference of Melanaphis sacchari for sorghum plants fertilized by means of conventional fertilization (CF) or organic fertilization (OF). Leaves were collected from sorghum plants fertilized with 200 kg N ha-1 using ammonium sulfate and poultry manure. Extracts were obtained using Soxhlet extraction, and the compounds were identified using a gas chromatograph coupled with mass spectrometry (GC/MS). Sorghum extracts were individually tested through bioassays to determine M. sacchari preference. The abundance and number of compounds in sorghum differed depending on the type of fertilization used. M. sacchari showed a preference for the extract from CF sorghum plants (76.66%) over the extract from OF plants (23.34%). Therefore, the type of fertilization can be used as a tactic to prevent higher infestations of M. sacchari. The biological activity of the compounds identified here with M. sacchari should be determined for future pest management strategies using allelochemicals, given that the sugarcane aphid uses chemical signals to locate its host plant.

The intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance underscores the need for rigorous dataset evaluation and selection protocols to ensure the reliability and generalizability of classification outcomes. This study involved a thorough examination of selected publicly available plant diseases datasets, with an emphasis on how well they performed as measured by GLCM metrics. After first classifying the datasets according to their GLCM metrics, dataset_2 (D2) and dataset_5 (D5) were found, respectively, to be the best-performing dataset in all GLCM analyses. The same datasets were then used to train deep learning models, and their classification performances were assessed. A noteworthy association was observed between the results of training deep learning models and the performance ratings derived from GLCM studies. More specifically, dataset_2 (D2) performed best in both GLCM analysis and deep learning model performance, indicating a strong correlation between the accuracy of classification and the textural qualities that GLCM captured. In the context of plant disease identification, in particular, these results highlight the significance of clearly defined dataset selection criteria in deep learning applications. Scholars can improve the accuracy and dependability of deep learning models for diagnosing plant diseases by giving preference to datasets with favorable GLCM metrics. The research also emphasizes the importance of texture features being taken into account in addition to conventional image features, highlighting the necessity of transparency and rigor in dataset selection procedures.

Botanical living collections within botanical gardens represent a crucial global asset for plant diversity. Particular emphasis should be given to dendrological collections, considering their key role in contribution to biodiversity conservation, support scientific inquiry, enhance educational initiatives and support on involvement of the public. The introduction of plants, especially woody plants, is one of the important problems in botanical science, solved in botanical gardens and arboretums. Establishment and development of the Dendrological collections in Botanical gardens is a way to have a comprehensive representation of diverse plant species from different biogeographical regions and continents. Current paper first time presents establishment, development, current status and future perspective of dendrological collections of Yerevan Botanical Garden in the linkage to introduction of woody plants in Armenia.

Isothermal Nucleic Acid Amplification based-Lateral Flow Testing (INAA-LFT) has emerged as a robust technique for on-site pathogen detection, providing a visible indication of pathogen nucleic acid amplification that rivals or even surpasses the sensitivity of real-time quantitative PCR. The isothermal nature of INAA-LFT ensures consistent conditions for nucleic acid amplification, establishing it as a crucial technology for rapid on-site pathogen detection. However, despite its considerable promise, the widespread application of isothermal INAA amplification-based lateral flow testing faces several challenges. This review provides an overview of the INAA-LFT procedure, highlighting its advancements in detecting plant viruses. Moreover, the review underscores the imperative to address existing limitations and emphasizes ongoing research efforts dedicated to enhancing the applicability and performance of this technology in the realm of rapid on-site testing.

Maintaining sufficient levels of plant nutrients in the soil and controlling certain heavy metals, which can be toxic to the environment, are critical to ensure sustainable agricultural production. The study aimed to assess the linkage of crop-specific agricultural practices established by farmers in the Kvemo Kartli region (Georgia) with metal accumulation in soils of agricultural lands being subject to influence from polluted irrigation water in the past. In particular, we tried to identify the primary sources of micronutrients, including iron (Fe), copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn), and toxic elements such as cadmium (Cd) and lead (Pb), and share of the contaminated irrigation water and other factors related to agricultural practices under different land uses, such as intensive and extensive arable farming, vineyards, orchards, and permanent pastures having the least disturbed soil. Based on principal component analysis, five primary sources were identified and categorized according to farmers' interviews and previous studies conducted in the region. The results showed that increased concertation of plant-available Cu, Zn, Cd and Pb were mainly associated with irrigation water and intensive use of fungicides; Fe, Mn, and Ni were closely linked to several factors, such as the mineralogical composition of soils, mineral and organic fertilizers inputs, and atmospheric deposition from diffuse sources, where exhausts from transport are probably the primary source. During our study, we attempted to differentiate irrigation water inputs from fungicides using simulation based on irrigation pattern and irrigation water quality on the one hand and fungicide application rate and their metal content on the other. The simulation revealed that the intensive application of fungicides, especially in vineyards, is more significant in enriching soils with Cu and Zn than irrigation water. Identification of factorial dependences was supported by statistical analysis and application of a number of contamination assessment methods: Contamination Factor (CF), Pollution Load Index (PLI), single-factor Pollution Index (PI), Nemerow’s comprehensive pollution index (PIN), Enrichment Factor (EF), and Geo-Accumulation Index (Igeo). Applied environmental indices indicate that the soils under the former and existing vineyards are the most enriched with Cu and Zn, highlighting the significance of agricultural practices on heavy metals accumulation in soils of agricultural lands.

This review provides an overview of advancements in plant genomics, emphasizing key stages in genomics projects and addressing associated challenges. Long read sequencing enables the cost-effective sequencing of plant DNA and assembly of highly continuous genome sequences - often even separating haplophases. Incorporating external hints, such as cDNA sequences from RNA-seq or full length cDNA sequencing, enhances the identification of gene models. While these steps enable high-throughput exploration of numerous plant genomes, a significant bottleneck lies in elucidating gene functions. The classical approach based on wet lab methods is impractical when dealing with thousands of genes in a new genome sequence. To overcome this challenge, computational tools harnessing existing information for cross species knowledge transfer are essential for expediting the functional annotation process. In support of researchers entering the field of plant genomics, a collection of recommended tools has been curated and is accessible at https://github.com/bpucker/ToolOverview.

Owing to the rising demand for vegetable soybean products, there is an increasing need for high-yield soybean varieties to raise the output. However, complex correlation patterns among quantitative traits with genetic interactions bring a challenge for vegetable soybean breeding. Here, a genome-wide association study (GWAS) was applied for six yield-related traits with 188 vegetable soybean accessions. A total of 81 SNPs were identified for pod length, plant height, pods number, pod thickness, pod width, and seed weight per plant by Blink model. Among these significant SNPs, 79 were novel SNPs, while 2 overlapped with previously reported SNPs. A total of 220 genes were found in 100 kb upstream and downstream regions of significant SNPs, of which 17 genes were functional proteins. Among these function proteins, four candidate genes, Glyma.13G109100, Glyma.03G183200, Glyma.09G102200, and Glyma.09G102300 were analyzed as significant haplotype variations, which encode MYB-related transcription factor, auxin-responsive family protein, F-box protein and CYP450, respectively. In addition, the relative expression with four candidate genes in the V030 and V071 (Among them, the plant height, pod number and fresh pod weight of V030 were lower than V071 strains) vegetable soybean were significantly different and these genes could be involved in plant growth and development via various pathways. Therefore, we proposed four genes from vegetable soybean germplasm that could be the key candidate genes for pod yield and plant height and these studies will accelerate the process of high-yield vegetable soybean breeding.

The emergence of multidrug-resistant strains of many pathogens is a serious threat and makes chemotherapy more difficult. The main objective of this study was to evaluate the antibacterial activity of Senna singueana and Cyphostemma junceum plant extracts against Staphylococcus aureus and Pseudomonas aeruginosa. The crude extracts of the selected medicinal plants were prepared and subjected to phytochemical screening using standard methods. Antibacterial activity was determined using the disc diffusion method. Therefore, the highest (15.41%) and lowest (0.91%) percentage yields were obtained from ethanol stem bark extracts and chloroform root extracts of Senna singueana, respectively. The maximum (14±1.0 mm) and minimum (7.5±0.5 mm) zones of inhibition were obtained from chloroform and distilled water leaf extracts of Senna singueana against Pseudomonas aeruginosa, respectively. In addition, the highest (13.5±1.0) zone of inhibition was obtained from Cyphostemma junceun root extract at 125 µg/ml. No inhibition zones were recorded in the root extract of Cyphostemma junceun at 500 µg/ml. More rich secondary metabolites, such as flavonoids, tennins, phenols, glycosides, terpenoids and sapnoids, were screened from distilled water leaf extracts, while flavonoids, tennis, phenols, glycosides, terpenoids, sapnoids and coumarins were screened in ethanol root extracts of Cyphostemma junceum. The leaves, stem bark and root of Senna singueana as well as the root of Cyphostemma junceun have strong antibacterial activity against both bacterial species. Thus, the two plant extracts could be used for healing and killing bacterial agents that can be potential sources for drug development.

Polygonatum cyrtonema Hua. is an important medicinal and edible food in China and the traditional processing method of nine-steaming and nine-drying has been used to process its rhizome. However, the global dynamic changes in secondary metabolites during the processing and whether this method could enhance the accumulation of functional components remain unclear. This study aimed to explore the processing-induced mechanisms involved in the accumulation of secondary metabolites and provide theoretical support for the pharmacological study on P. cyrtonema. Results showed the first 6 processing cycles induced drastic changes in the accumulation of secondary metabolites, while the last 3 did not induce further changes. The accumulation of most secondary metabolites significantly increased after the first 3 cycles and stabilized after 6 cycles. The first 3 cycles led to numerous newly added metabolites while the last 6–9 cycles led to a small number of disappeared metabolites. Processing enhanced the biosynthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. Together, a short period of processing was helpful for enhancing the biosynthesis of secondary metabolites. However, the enhancing effects will be reversed or weakened under continuous processing. The processing cycles should be reduced to 3–6 to maintain the enhancing effects.

Helicobacter pylori infections are still an urgent and hot topic. This is related to the involvement of H. pylori in human diseases (including gastric diseases, gastric adenocarcinoma, mucosa-associated lymphoid tissue lymphoma, and diabetes), the number of which is constantly increasing. At the same time, agents effective against these bacteria are decreasing due to spreading resistance. Our research focused on extracts of the Rubus genus, commonly present in Central and Eastern Europe and very useful in folk medicine. Results have shown that the extract from Rubus idaeus 'Willamette' shoots, and the extract from R. idaeus 'Poranna Rosa' fruits and R. idaeus 'Laszka' R. occidentalis Litacz' fruits prevent the formation of biofilm by H. pylori on intravenous infusions (they reduce living cells, which was detected using triphenyl tetrazolium chloride (TTC) as a dye) at concentrations of 6.65 and 16.65 mg/mL, respectively. However, the living bacteria in the formed biofilm are affected by the extract from the shoots of R. idaeus 'Willamette' and the fruit of R. idaeus 'Poranna Rosa' at a concentration of 16.65 mg/ml, and the remaining extracts at a concentration of 33.3 mg/ml. The results of the study of interactions of extracts from Rubus sp. and the 2 antibiotics against H. pylori planktonic bacteria showed doxycycline synergism with all tested extracts, but only the extract of R. occidentalis 'Litacz' with levofloxacin. The remaining tested extracts showed indifference with levofloxacin. However, studies on the interaction between antibiotics and extracts of R. idaeus 'Willamette' shoot showed synergy with doxycycline and levofloxacin, additivity with amoxicillin and clarithromycin, and indifference with metronidazole. The tests were carried out on fragments of intravenous infusions with TTC as a dye. The tested extracts may be elements of broadly understood anti-H. pylori therapy.

Mechanical damage of plant tissues leads to the activation of methanol production and its release in the atmosphere. The gaseous methanol or vapors emitted by the damaged plant induce resistance in neighboring intact plants to bacterial pathogens but create favorable conditions for the viral infection spread. Among Nicotiana benthamiana methanol-inducible genes (MIGs) most are associated with plant defense and intercellular transport. Here we characterize NbMIG21 that encodes a 209-aa protein (NbMIG21p) which does not share any homology with annotated proteins. NbMIG21p was demonstrated to contain nucleolus localization signals (NoLS). Co-localization studies with fibrillarin and coilin, nucleolus and Cajal bodies marker proteins, revealed that NbMIG21p is distributed between these subnuclear structures. Our results show that recombinant NbMIG21 possess DNA-binding properties . Similar to gaseous methanol effect an increased NbMIG21 expression leads to the downregulation of the nuclear import of NLS-containing proteins as was demonstrated on the model GFP-NLS protein. Moreover, upregulated NbMIG21 facilitates tobacco mosaic virus (TMV) intercellular transport. We identified NbMIG21 promoter (PrMIG21) and showed that it is methanol-sensitive thus induction of NbMIG21 expression occurs at the level of transcription. Our findings suggest that methanol-activated NbMIG21 might participate in creating favorable conditions for the viral reproduction and spread.

Anthracnose, caused by the fungus Colletotrichum lindemuthianum, poses a significant and widespread threat to the common bean crop. The use of plant genetic resistance has proven to be the most effective strategy for managing anthracnose disease. The Amendoim Cavalo (AC) Andean cultivar has resistance against multiple races of C. lindemuthianum, which is conferred by the Co-AC gene. Fine mapping of this resistance gene to common bean chromosome Pv01 enabled the identification of Phvul.001G244300, Phvul.001G244400, and Phvul.001G244500 candidate genes for further validation. In this study, we assessed the relative expression of Co-AC candidate genes, as well as other putative genes in vicinity of this locus and known resistance genes, in the AC cultivar following inoculation with the race 73 of C. lindemuthianum. Gene expression analysis revealed significantly higher expression levels of Phvul.001G244500. Notably, Phvul.001G244500 encodes a putative Basic Helix-Loop-Helix transcription factor, suggesting its involvement in the regulation of defense responses. Furthermore, we observed a significant modulation of the expression of defense related genes PR1a, PR1b, and PR2, in a time-course experiment. These findings contribute to the development of improved strategies for breeding anthracnose-resistant common bean cultivars, thereby mitigating the impact of this pathogen on crop yields and ensuring sustainable bean production.

Goji berries, long valued in Chinese medicine and cuisine for their wide range of medicinal benefits, are considered a 'superfruit' and functional food. Out of the nearly 100 Lycium species known for their genetic diversity, L. barbarum and L. chinense currently dominate the market. Due to increasing market demand and concerns about food safety and sustainability, Europe and the Americas are expanding the local goji berry production, using as starting material plants originated from China. European breeding programs are focusing on Lycium to develop varieties adapted to local conditions, especially in response to climate change. By 2023, Romania registered seven goji berry varieties, both from L. barbarum and L. chinense species, without incorporating local germplasm. Advanced genomic studies, including NGS sequencing of five cultivated and three wild goji berry plant genomes, have been conducted to analyse their genetic variation, with a particular focus on the BODYGUARD 3 and 4 genes. This research uncovered significant differences between cultivated and wild genotypes, both in the entire genome and specifically in the BODYGUARD genes, providing crucial insights for goji berry breeders to support the development of goji berry cultivation in Romania.

In the wake of the rapid deployment of Small Modular Reactors (SMRs), this study aims to enhance the efficiency, reliability, and safety of SMR operations through a deeper understanding of human factors in their interaction within digital control room systems. Recognizing the pivotal role of human understanding in this new era of nuclear power, we employed electroencephalogram (EEG)-based monitoring to provide an unparalleled real-time view into operators' cognitive states. By interfacing detailed human models, informed by EEG metrics, with specific operational tasks, we recreate potential operational scenarios using an SMR simulator and capture intricate human responses therein. Our results elucidated the intricate relationship between EEG-derived data and human performance shaping factors, indicating a marked correlation between certain EEG patterns and operational efficiencies. Conclusively, these findings underscore the potential of EEG monitoring not only as a diagnostic tool but as an instrumental aid in the design and operation of future SMR digital control rooms. The insights derived offer a roadmap for the development of practical strategies, ensuring more effective and safer SMR operations.

The increased cultivation of Cannabis sativa L. in North America, represented by high Δ9-tetrahydrocannabinol-containing (high-THC) cannabis genotypes and low THC-containing hemp genotypes, has been impacted by an increasing number of plant pathogens. These include fungi which destroy roots, stems and leaves, in some cases causing a build-up of populations and mycotoxins in the inflorescences that can negatively impact quality. Viroids and viruses have also increased in prevalence and severity and can reduce plant growth and product quality. Rapid diagnosis of the occurrence and spread of these pathogens is critical. Techniques in the area of molecular diagnostics have been applied to study these pathogens in both cannabis and hemp. These include polymerase chain reaction (PCR)-based technologies, including RT-PCR, multiplex RT-PCR, RT-qPCR, and ddPCR, as well as whole genome sequencing (NGS) and bioinformatics. In this study, examples of how these technologies have enhanced the rapidity and sensitivity of pathogen diagnosis on cannabis and hemp will be illustrated. These molecular tools have also enabled studies on the diversity and origins of specific pathogens, specifically viruses and viroids, and these will be illustrated. Comparative studies on the genomics and metabolomics of healthy and diseased plants are urgently needed to provide insight into their impact on quality and composition of cannabis and hemp-derived products. Management of these pathogens will require monitoring of their spread and survival using the appropriate technologies to allow accurate detection, followed by appropriate implementation of disease control measures.

Plant Growth Promoting Rhizobacteria (PGPR) is beneficial microorganisms that inhabit the rhizosphere of plants and play a crucial role in enhancing plant growth and development. This review provides an overview of the significant role of PGPR in sustainable agriculture. The mechanisms that promote plant growth, such as nitrogen fixation, phosphate solubilization, and growth hormones, are discussed. The potential of PGPR in improving plant tolerance to biotic and abiotic stresses, reducing the use of chemical fertilizers and pesticides, and enhancing nutrient availability, soil fertility, and uptake is also highlighted. Moreover, the ecological and economic benefits of using PGPR for sustainable agriculture include improving crop productivity, reducing environmental pollution, and ensuring food security.

Although the COVID-19 pandemic appears to be diminishing, the emergence of SARS-CoV-2 variants represents a threat to humans due to its inherent transmissibility, immunological evasion, virulence, and invulnerability to existing therapies. The previous COVID-19 pandemic affected more than 500 million people and caused over 6 million deaths. Vaccines are essential, but in circumstances when vaccination is not accessible or in individuals with compromised immune systems, drugs can give additional protection. Targeting host signaling pathways is recommended due to their genomic stability and resistance barriers. Plant-derived products, which have been used in traditional medicine for treating pathological conditions, offer structurally novel therapeutic compounds including anti-viral. In addition, plant-derived bioactive substances might serve as the ideal basis for developing sustainable/efficient/cost-effective anti-viral alternatives. Epigenetic modifications in the genome can affect gene expression without altering DNA sequence. Host cells can use epigenetic gene regulation as a mechanism to silence incoming viral DNA molecules, while viruses have recruited cellular epi transcriptomic (covalent modifications of RNAs) modifiers to increase the translational efficiency and transcript stability of viral transcripts to enhance viral gene expression and replication. Moreover, viruses manipulate host cells' epigenetic machinery to ensure productive viral infections. Environmental factors, such as natural products, may influence epigenetic modifications. In this review, we are exploring the potential of plant-derived substances as epigenetic modifiers for broad-spectrum anti-viral activity, reviewing modulation processes and anti-viral effects on DNA and RNA viruses, and addressing future research objectives in this rapidly emerging field.

This review article explores the significance of beneficial microorganisms in crop improvement, highlighting their roles in promoting plant growth, enhancing nutrient uptake, and protecting against pathogens. The symbiotic interactions between plants and microorganisms have been extensively studied, revealing the vast potential of these beneficial partnerships in sustainable agriculture. The article discusses various types of beneficial microorganisms, including mycorrhizal fungi, rhizobia, and plant growth-promoting bacteria, elucidating their mechanisms of action and their impact on plant health and productivity. Furthermore, it examines the application of microbial inoculants and biofertilizers in crop production systems, emphasizing their potential to enhance crop yield, nutrient use efficiency, and stress tolerance. The review also explores emerging technologies, such as metagenomics and synthetic biology, in harnessing the full potential of beneficial microorganisms for crop improvement. Overall, we highlight the importance of beneficial microorganisms in sustainable agriculture and provide insights into their promising applications in crop enhancement.

Commercial formulates of beneficial microbes have been used to enrich the rhizosphere microbiome of tomato plants grown in pots located in a glasshouse. These plants have been subjected to attacks by soil-borne parasites, such as root-knot nematodes (RKNs), and herbivores, such as the miner insect Tuta absoluta. The development of both parasites and the symptoms of their parasitism were restricted in these plants with respect to plants left untreated. A mixture, named in the text as Myco, containing plant growth promoting rhizobacteria (PGPR), opportunistic biocontrol fungi (BCF), and arbuscular mycorrhizal fungi (AMF) was more effective in limiting pest damages than a formulate containing the sole AMF (Ozor). Therefore, Myco-treated plants inoculated with RKNs were taken as a model to further study which biocontrol agent (BCA) was specifically responsible of plant immunization and the molecular pathways exploited to achieve such an immunization. The PGPR contained in Myco were not able to reduce nematode infection, rather, they worsened symptoms on plants, compared with those observed on untreated plants. When plants were treated with suspensions of Bacillus subtilis at concentrations similar to those provided with Myco and then inoculated with RKNs, infection was actually more severe than that on untreated plants. Therefore, it was argued that both BCF and AMFwere the microorganisms that, colonizing roots, synergistically stimulate plant immune system against RKNs. Beneficial fungi lowered the activities of the defense supporting enzymes endochitinases and β-1,3-glucanase to be able to colonize the roots. However, as early as three days after nematode inoculation, these enzyme activities, and the expression of the encoding pathogenesis related genes (PR-2, PR-3) were found to be enhanced in roots with respect to not inoculated plants, thus indicating that plants had been primed against RKNs. Addition of paclobutrazol, which reduced salicylic acid (SA) levels in cells, and of diphenyliodonium chloride, which inhibits superoxides generation, completely abolished the repressive effect of Myco on nematode infection and presumably plant immune reaction. Inhibitors of copper enzymes and of the alternative cyanide-resistant respiration did not significantly alter resistance induction by Myco. When Myco-treated plants were subjected to a moderate water stress and inoculated with nematodes, they retained numbers of developed individuals in the roots similar to those present in regularly watered plants, in contrast to what occurred in roots of untreated stressed plants that hosted very few individuals because of poor nutrient availability.

Nutrients are the driving force of plant and herbivore interactions. Different elements and fertilization levels in soil, or artificially fertilized substrates can have a direct impact on plant defense responses. Meanwhile, nutrient levels mediated by root associated microbes can indirectly affect plant-herbivore interactions. Nutrient uptake promoted by beneficial mycorrhizal fungi or rhizobacteria interacting with roots can stimulate plant tolerance to herbivore attack, as evidenced by herbivore performance, plant nutrient status, plant biomass allocation and compensatory growth. In return, herbivory affects nutrient fluxes between plants and their associated beneficial microbes. Furthermore, aboveground and belowground herbivory can affect the nutrient exchange between plants and soil microbial communities, mainly for carbon-and nitrogen-based resources supporting herbivores´ life cycles. These ecological interactions are governed by jasmonic acid (JA) and key enzymes on the molecular level. This review summarizes the current scientific literature on the role of nutrients in plant-herbivore interactions. This will support efforts to optimize plant defense strategy via managing nutrient variance.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

The high-mountain and arctic plants are considered especially sensitive to the climate changes because of close adaptations to the cold environment. Kalmia procumbens, the typical arctic-alpine species reaches southernmost European localities in the Pyrenees and Carpathians. The aim of the study was the assessment and comparison of K. procumbens current potential niche areas in the Pyrenees and Carpathians and their possible reduction due to climate change, depending on the scenario. Realized niches of K. procumbens in the Pyrenees are compact while in the Carpathians dispersed. In both mountain chains, the species occurs in the alpine and subalpine vegetation belts, going down to elevations of about 1500-1600 m, while the most elevated localities in the Pyrenees are at ca 3000 m, about 500 m higher than in the Carpathians. The localities of K. procumbens in the Carpathians have a more continental climate than in the Pyrenees, with lower precipitation and temperatures but higher seasonality of temperature and of precipitation. The species covered a larger area of geographic distribution during Last Glacial, and reduced area during mid Holocene. Due to the climate warming, the strong reduction of potential area of occurrence to 2100 is expected in the Carpathians and moderate reduction in in the Pyrenees.

Phytoalexins are naturally-occurring molecules with antimicrobial activity deriving from the secondary metabolism of plants, which are synthesized in response to physical agents or stresses, phytopathogenic agents (fungi, bacteria and viruses) as well as numerous chemical compounds and biological control agents. Among these, grapevine phytoalexins, which belong to the chem-ical group of stilbenes, exhibit biocidal activity against a large number and a variety of plant pathogens. An important issue in research on these compounds is to be able to stimulate their synthesis in planta and to determine whether the induction of their production can serve as a basis for protecting that plant against its pathogens. Quite a few chemical compounds, derivatives of phytohormones bio-elicitors as well as biocontrol agents have been used to induce the synthesis of stilbene phytoalexins with the aim of conferring protection to grapevine against its main dis-eases (gray mold, downy mildew, powdery mildew and esca). This article reviews the experi-ments that have been carried out in this direction during the last thirty years and shows that the observed protective effects towards pathogens are generally linked to induction and priming of the grapevine phytoalexin response, confirming the interest of using, in a more general way, stimulation of the production of phytoalexins in plants as a basis for crop protection.

Although jojoba (Simmondsia chinensis) has been cultivated for years, information on its N re-quirements is limited. A 6-year study of mature jojoba plants grown under field conditions with an intensive management regime evaluated the effect of N-application rate on plant nutrient status, growth and productivity, and nitrate accumulation in the soil. Five levels of N application were tested: 50, 150, 250, 370 and 500 kg N ha-1. The fertilizers were provided all along the growing season via subsurface drip irrigation system. Leaf N concentration, in both spring and summer, reflected the level of N applied. Diagnostic leaf (youngest leaf that has reached full size) concentration of 1.3% N was identified as the threshold for N deficiency. Increasing rates of N application resulted in higher P levels in young leaves. Plant K status, as reflected by leaf analysis, was not affected by N treatment but was strongly affected by fruit load. Vegetative growth was inhibited when only 50 kg N ha-1 was applied. Soil analysis at the end of the fertilization season showed substantial accumulation of nitrate for the two highest application rates. Considering productivity, N costs, and environmental risk, 150 kg N ha-1 is the recommended dosage for in-tensively grown jojoba. N deficiencies can be identified using leaf analysis and excess N can be detected by soil sampling toward the end of the growing season. These results and tools will fa-cilitate precise N fertilization in intensive jojoba plantations.

Jatropha multifida L. is a plant belong to Euphorbiaceae family used in Benin pharmacopoeia because of its medicinal properties. The objective of this work is to appreciate the therapeutic power of this plant through the evaluation of some of its biological activities. After the preliminary phytochemistry, the polyphenols and flavonoids were quantified and identified by the LC-MS-ESI. The antimicrobial power of the extracts was investigated by agar medium diffusion. The antioxidant power of the extracts was evaluated by the reduction of the DPPH radical, the ABTS radical cation, the feric ion (FRAP) and the lipid peroxidation (LPO). Anti-inflammatory activity was assessed by inhibition of protein (albumin) denaturation. Indeed, different phenolic and flavonoid compounds namely, 2-Hydroxybenzoic acid, o-Coumaroylquinic acid, Apigenin-apiosyl-glucoside, Luteolin-galactoside, Luteolin-glucoside, Luteolin-rhamnoside, Quercetin-glucoside, Quercetin-arabinoside, Dicaffeoyquinic acid, Kaempferol-rhamnoside were identify. The J. mutifida extracts has a bactericidal effect with reference strains of wich CMBs vary from 22.67 mg/ml (S. aureus, S. enteridis, L. monocytogenes and C. albicans) to 47.61 mg/ml (E. coli) and with the meat isolated strains (S. equorum, S. saprophiticus, S. haemoliticus, S. cohnii and S. lentus). Ethanolic extract show the highest DPPH radical scavenging activity (IC₅₀ = 0.72±0.03 mg/ml) while methanolic extract showed the highest ferric ion reduction (46.23±1.10 µgEAA/g). Contrary to the FRAP method, the reducing power with ABTS method of water-ethanolic extract was greater (0.49±0.11 mol ET/g). The same extract show the highest albumin denaturation inhibition power (97.31±0.35%) at 1000µg/ml. J. multifida extracts are rich in bioactive compound with good biological activity

The variability observed in the annual seed production of perennial plants can be seen as an indication of changes in the allocation of resources between growth and reproduction, which can be attributed to fluctuations in the environment. However, a significant knowledge gap exists concerning the impacts of nitrogen addition on the interannual seed production patterns of perennial plant. We hypothesized that the addition of nitrogen would impact the annual variations in the seed production of perennial plants, ultimately affecting their overall reproductive efficiency. A multiyear field experiment was conducted to investigate the effects of varying nitrogen supply levels (e.g., 0, 4, 8 kg N ha−1 yr−1 of N0, N4, and N8) on the vegetative and floral traits, pollinator visitation rates, and seed traits over a period of four consecutive years. The results showed that the N0 treatment exhibited the highest levels of seed production and reproductive efficiency within the initial two years. In contrast, the N4 treatment displayed its highest level of performance in these metrics in the second and third years, whereas the N8 treatment showcased its most favorable outcomes in the third and fourth years. The similar patterns were found in the number of flowers per capitulum and the number of capitula per plant. There exists a positive correlation between aboveground biomass and several factors, encompassing the number of flowers per capitulum, the number of capitula per plant, the volume of nectar per capitulum, and the seed production per plant. This implies that the addition of N affected the maintenance of plant aboveground biomass, flower traits stability and, subsequently, the frequency of seed production, and the reproductive efficiency. Our results suggest that augmenting the nitrogen content in the soil may have the capacity to modify the inherent variability in seed production that is observed across various years. These findings have the potential to enhance our comprehension of the impact of nitrogen addition on the reproductive performance of perennial herbaceous plants, and the underlying mechanisms of biodiversity in the context of global environmental changes.

Vernonia amygdalina is an important nutritional and medicinal plant that is used to treat various diseases such as malaria and diabetes in Nigeria. In this study, we sequenced, assembled, and annotated the complete nucleotide sequence of the chloroplast genome of a V. amygdalina species isolated in Awka, Nigeria. The chloroplast genome is 153 149 bp in length and has a conserved structure that includes the LSC (84261 bp) region, the SSC (13152 bp) region, and the IR (27868 bp) regions. A total of 131 genes were identified, including 86 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. Comparative analysis of the genome revealed species variations in the LSC region associated with the geographical location of the V. amygdalina plant. Phylogenetic analysis of the chloroplast genomes using FastTree showed that V. amygdalina clustered closest to the chloroplast of the Crepidiastrum genus. This study identifies the characteristics of the V. amygdalina chloroplast genome, which will improve our understanding of intraspecies diversity and the identification of potential molecular markers to determine geographical origin.

In order to explore the genetic diversity of the extremely small population wild plant Habenaria dentata and protect and optimize its wild resources. The genetic diversity of 133 samples from 6 wild populations of H.dentata was analyzed by ISSR molecular marker technique to provide a scientific basis for the screening of improved germplasm resources. The results show that the average number of alleles (Na) was 1.765, the average number of effective alleles (Ne) was 1.424, the average Nei 's gene diversity index (He) was 0.252, the average Shannon information diversity index (I) was 0.381, and the average percentage of polymorphic loci (PPL) was 76.499%. The variation within populations was 77.34%, and the variation between populations was 22.66%. Gene flow (Nm) was 1.705 8, greater than 1. The results of cluster analysis showed that the six populations were mainly divided into four branches and not classified according to geographical location. There was no significant correlation between geographical location and genetic distance between populations (r=0.557, P>0.05). The genetic diversity of H.dentata is high. Among the six wild populations, the genetic diversity of Mulun population is the highest and this population can be used as a key protection unit.

The advancement of mass spectrometry technologies has revolutionised plant metabolomics research by enabling the acquisition of raw metabolomics data. However, the identification, analysis, and visualisation of these data require specialised tools. Existing solutions lack a dedicated plant-specific metabolite database and pose usability challenges. To address these limitations, we developed PlantMetSuite, a web-based tool for comprehensive metabolomics analysis and visualisation. PlantMetSuite encompasses interactive bioinformatics tools and databases specifically tailored for plant metabolomics data, facilitating upstream-to-downstream analysis in metabolomics and supporting integrative multi-omics investigations. PlantMetSuite can be accessed directly through a user's browser without the need for installation or programming skills. The tool is freely available at https://plantmetsuite.verygenome.com/ and will undergo regular updates and expansions to incorporate additional libraries and newly published metabolomics analysis methods. The tool's significance lies in empowering researchers with an accessible and customisable platform for unlocking plant metabolomics insights.

Plant endophytic fungi and rhizosphere soil fungi are often reported as biocontrol agents against plant pathogens or with plant growth promotion potential. Four treatments were performed in field and greenhouse experiments where cucumber plants were inoculated with Trichoderma harzianum and Fusarium oxysporum in 2022. The roots, stems and leaves of cucumber plants and their rhizosphere soil were collected twice individually from the field and greenhouse for isolation of cucumber endophytic and rhizosphere soil fungi. All 74 genera and 1275 fungal strains were sequenced by rDNA ITS. The PDA media yielded the highest number of genera isolated from cucumber plants, rhizosphere soil and both compared to other media. There were no significant differences among the 4 media for the isolation of cucumber endophytic fungi. The best media for isolating fungi from roots and stems were MRBA and PDA, respectively. PDA and CZA had higher isolation efficiency for the rhizosphere soil fungi than MRBA. The 28 fungal genera had high isolation efficiency, and the endophytic Trichoderma strains were best isolated by MEA. It is suggested that PDA can be used as a basic medium, and different media can be considered for specific fungal genera.

MicroRNAs (MiRNAs) are a class of non-coding single-stranded RNA molecules of approximately 20-24 nucleotides in plants that play an important regulatory role in a variety of biological processes such as plant growth and development and response to various abiotic stresses. For example Drought, Salt, Cold, High temperature, Heavy metals and Nutrition. MiRNAs affect gene expression by manipulating the cleavage, translational expression or DNA methylation of target mRNAs. This review describes the current progress made on the way miRNAs are produced and regulated and the way miRNA/target gene is used in plant responses to abiotic stresses. Studying the molecular mechanism of action of miRNAs downstream target genes can optimize the genetic manipulation of crop growth and development conditions that provide a more theoretical basis for improving crop production.

Plant breeding aims to develop elite crop varieties appropriate for various environments with higher quality and quantity of production. Researchers use quantitative trait loci (QTL) mapping and association studies to identify regions in the genome responsible for the variation of the quantitative traits of interest. However, mapped regions do not always translate to functional proteins, which makes it challenging to identify genes associated with traits of interest. The biological functions of proteins are strongly dependent on their 3D structure. Alternatively, if proteins can be directly linked with the phenotypes, the effect of mutations on phenotypic changes can be assessed. Innovation of deep learning models in biology opens new avenues of exploration. AlphaFold is an AI system that predicts the 3D structure of a protein from its amino acid sequence with near experimental accuracy and was used in this study. Point mutations with a significant influence on the 3D structure of a protein can capture the effect on phenotypes through association study, and this provides insights into the regions that are of functional importance. In the current study, 534 plants were selected based on plant vigor, and 154 missense variants that change amino acid sequences, including 5 significant hits from previous study, were included. The changes in protein 3D structure were assessed by association with the phenotype. The analysis identified five significant associations, four of which were also identified in previous study of SNPs GWAS, however, a new fifth association was also identified which was annotated as disease resistance gene in Medicago truncatula. This study helps to associate SNPs that could be missed by GWAS due to stringent Bonferroni corrected p-values by providing a more robust filter for SNPs using features from predicted protein 3D structures.

Plant Cysteine Oxidase (PCO) is a plant O2-sensing enzyme of catalyzing oxidation of cysteine to Cys-sulfinic acid at the N-termini of target proteins. To better understand the Brassica napus PCO gene family, PCO genes in B. napus and related species were analyzed. In this study, 20, 7 and 8 PCO genes were identified in Brassica napus, Brassica rapa and Brassica oleracea, respectively. According to phylogenetic analysis, the PCOs were divided into five groups: PCO1, PCO2, PCO3, PCO4 and PCO5. Gene organization and motif distribution analysis suggested that PCO gene family was relatively conserved during evolution. Expression analysis showed that PCO genes were expressed in different tissues at different developmental stages. Interestingly, most of the Bna/Br/BoPCO5 members were expressed in leaves, roots, flowers, buds and siliques, suggesting an important role in both vegetative and reproductive development. Expression of BnaPCO was induced by various abiotic stress, especially waterlogging stress, which was consistent with the result of cis-element analysis. In this study, the PCO gene family of Brassicaceae was analyzed for the first time, which contributes to a comprehensive understanding of the origin and evolution of PCO genes in Brassicaceae and the function of BnaPCO in regulating plant responses to abiotic stresses.

Olfaction plays a crucial role in many important behavioral activities of insects, such as finding sexual partners, locating hosts, and selecting oviposition sites. Ionotropic receptors (IRs) play a central role in detecting chemosensory information from the environment and guiding insect behaviors and are potential target genes for pest control. Empoasca onukii Matsuda is a major pest of the tea plant Camellia sinensis (L.) O. Ktze, and seriously influences tea yields and quality. In this study, the ionotropic receptor gene EnouIR25a in E. onukii was cloned, and the expression pattern of EnouIR25a was detected in various tissues. Behavioral responses of E. onukii to volatile compounds emitted by tea plants were determined by the olfactometer bioassay and field trials. To further explore the function of EnouIR25a in olfactory recognition of compounds, RNAi (RNA interference) of EnouIR25a was carried out by ingestion of in vitro synthesized dsRNAs. The CDS length of EnouIR25a was 1266 bp and it encoded a 48.87 kD protein. EnouIR25a was enriched in the antennae of E. onukii. E. onukii were more significantly attracted by 1-phenylethanol at concentration of 100 µl/ml. Feeding with dsRNA-IR25a significantly downregulated the expression level of EonuIR25a, after 3 h of treatment, which disturbed the behavioral responses of E. onukii to 1-phenylethanol at concentration of 100µl/ml. The response rate of E. onukii to 1-phenylethanol was significantly decreased after dsRNA-IR25a treatment for 12 h. In summary, the ionotropic receptor gene EnouIR25a was highly expressed in the antennae of E. onukii and was involved in olfactory recognition of the tea plant volatile 1-phenylethanol. The present study may help us to use the ionotropic receptor gene as a target for the behavioral manipulation of E. onukii in the future.

Pyrolyzed waste biomass, or biochar, has been suggested as a means to increase plant growth and to mitigate soil salinization, which is a widespread agricultural issue and can reach extreme levels in urban soils impacted by de-icing salts. Soil mixing is enhanced by reduced biochar particle size; however, biochar properties vary with particle size and recent studies suggest that plant growth responses may be maximized at intermediate particle sizes. We examined the responses of two plant species (cowpea (Vigna unguiculata) and velvetleaf (Abutilon theophrasti)) to biochar amendments that spanned a wide range of particle sizes obtained by sieving, with and without de-icing salt additions. The smallest size fractions of biochar reduced plant growth relative to unamended controls. Plant biomass production was generally maximized at intermediate biochar particle size treatments, with particles sizes of 0.5-2.0 mm showing the best response. Mitigation of salt effects was also improved at intermediate biochar particle sizes in this particles size range. Our results emphasize the importance of optimizing biochar particle size to best enhance plant responses to biochar, with particular reference to saline soils.

The rhizosphere is a rich source of actinomycetes which can produce several potential biologically-active secondary metabolites. The principal goals for this research are to extract, purify and characterize the bioactive secondary metabolites produced by three different strains of actinomycetes isolated from the rhizosphere of rosemary, black locust and olive. The plant growth-promoting effect (PGPE) of the studied strains of actinomycetes on Ocimum basilicum L. (basil) and disease-control effect on necrotic stem lesions of “black leg” caused by Fusarium tabacinum on basil, were in silico evaluated. The cell-free culture filtrates from the studied actinomycetes isolates were evaluated in vitro for their antimicrobial activity against some common phytopathogens. The secondary metabolites obtained from the cell-free culture filtrates have been chemically characterized using high‐resolution electrospray ionization of liquid-chromatography/mass-spectrometric detection (ESI-(HR)Orbitrap-MS). Results of in silico trial showed that all studied isolates demonstrated PGPE on basil seedlings improving some Eco-physiological characteristics and reduced the disease incidence of F. tabacinum. The extracted metabolites from the studied actinomycetes demonstrated antimicrobial activity in Petri-plates assay. The chemical analysis revealed the presence of totally 20 different components. This research emphasizes how valuable the examined isolates are for producing bioactive compounds, indicating their putative antimicrobial activity and their potential employment as fungal-biocontrol agents. In particular, the obtained results revealed the possibility of green-synthesis of some important secondary metabolites such as N-Acetyl-L-histidinol, Rhizocticin A and Eponemycin from actinomycetes. The bioactive metabolites may be successively used to develop novel bio-formulations for both crop protection and/or PGPE.

Plants are constantly exposed to a variety of different environmental stresses, including drought, salinity, and elevated temperatures. These stress cues are assumed to aggravate in the future driven by the global climate change scenario which we are currently experiencing. These stressors have largely detrimental effects on plant growth and development and, therefore, put global food security in jeopardy. For this reason, it is necessary to expand our understanding of the underlying mechanisms by which plants respond to abiotic stresses. Especially boosting our insight into the ways by which plants balance their growth and their defense programs appears to be of paramount importance, as this may lead to novel perspectives that can pave the way to increase agricultural productivity in a sustainable manner. In this review, our aim was to present a detailed overview of different factettes of the crosstalk between the antagonistic plant hormones abscisic acid (ABA) and auxin, two phytohormones that are the main drivers of plant stress responses, on the one hand, and plant growth, on the other.

Due to an increase in the consumption of food, feed, and fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to obtain high-yielding crops that can adapt to future climate changes. Currently, the main feed source used for ruminant livestock production is forage grasses. In temperate climate zones, perennial grasses grown for feed are widely distributed and tend to suffer under unfavorable environmental conditions. Gene editing has been shown to be an effective tool for the development of abiotic stress-resistant plants. The highly versatile CRISPR-Cas system enables increasingly complex modifications in genomes while maintaining precision and low off-target frequency mutations. In this review, we provide an overview of forage grass species that have been subjected to gene editing. We offer a perspective view on the generation of plants resilient to abiotic stresses. Due to the broad factors contributing to these stresses the review focuses on drought, salt, heat, and cold stresses. The application of new genomic techniques (e.g., CRISPR-Cas) allows addressing several challenges caused by climate change and abiotic stresses for developing forage grass cultivars with improved adaptation to the future climatic conditions. Gene editing will contribute towards developing safe and sustainable food systems.

About a decade ago, active optical crop canopy sensors are being used to manage in-season variable nitrogen (N) fertilization in cornfields to match the plant demand that occurs mid season, increasing the efficiency compared to broadcast N applications. There were also initiatives of using ultrasonic sensors to measure plant height on-the-go for N application and crop water demand estimation, but no studies have integrated the optical, ultrasonic and canopy temperature for crop water stress assessment. The objective of this chapter is to evaluate the crop water status using infrared thermometry integrated with optical and ultrasonic sensors. Specifics objectives are: (i) evaluate the corn canopy temperature under different previous crop, N rates and irrigation levels; (ii) test a procedure for water stress assessment in commercial cornfields using the integration of sensors, (iii) correlate plant based sensor measurements (N status, plant height and canopy temperature) with grain yield, soil attributes and detailed topographical features, and (iv) study the spatial dependence of canopy temperature. This study was conducted in one small plot study area and on three producer’s fields in 2010. The small plot experiment consisted of two irrigation levels (70 and 100% of evapotranspiration – ET), two previous crop schemes (corn after corn – CC and corn after soybeans – CS), and four N rates (0, 75, 150, 225 kg N ha^-1). Canopy temperature, optical reflectance and plant height was measured from R2 until R6 in the small plots. At the producer’s fields, three long strips across center pivots were used to have a non-limited N and water crop and then continuous georeferenced sensors measurements were taken during side-dress (V11 growth stage) in about 10 hectares in each field. In the small plot study the crop canopy temperature was influenced by the irrigation levels and N rates. The procedure proposed could be used to identify zones in the producer’s field where water stress can be a yield limiting factor other than N derived. Inside the zones considered that water stress played a major whole, there were low correlations between plant height, plant N status and canopy temperature, indicating that the canopy temperature had more influence from water stress than vegetation cover. Concave and lower elevation areas had higher yields compared to convex and high elevation, showing that the detailed elevation mapping can be beneficial to delineate stables zones that possibly could be used in variable irrigation systems. The spatial dependence of canopy temperature was over 65 meters across producers’ sites, showing that the commercial high clearance applicator’s swath width was adequate to obtain accurate maps. The integration of plant N status, plant height and canopy temperature was beneficial to detect water stressed zones in the field. Opportunities can be foresee also for on-the-go N fertilization using integration of these sensors because is likely that water stress can be confounded with different N supply during the growing season and in different zones in the field.

Global crop yield and food security are being threatened by phytophagous insects. Innovative methods are required to increase agricultural output while reducing reliance on hazardous synthetic insecticides. It appears to be quite effective at reducing production costs and boosting farm profitability to use the ground-breaking CRISPR-Cas technology to create plants that are insect resistant. In contrast, this new technique can modify an insect's genome to either produce gene drive or get beyond an insect's tolerance to various insecticides. This paper reviews and critically discusses the use of CRISPR-Cas genome editing technology in long-term insect pest management. The emphasis of this review is on the prospective uses of the CRISPR-Cas system for insect stress management in crop production by creating genome-edited crops and insects. The potential and difficulties of using CRISPR-Cas technology to reduce pest stress in crop plants are critically examined and discussed.

The plant microbiome is involved in enhancing nutrient acquisition, plant growth, stress tolerance and reduces chemical inputs. The identification of microbial functional diversity offers the chance to comprehend and engineer them for various agricultural processes. Using a shotgun metagenomics technique, this study examined the functional diversity and metabolic potentials of microbial communities in the rhizosphere soybean. 18 genera were selected out of which six are prominent in sample AB, the prominent genera are Geobacter, Nitrobacter, Burkholderia, Candidatus, Bradyrhizobium and Streptomyces. Twenty-one functional categories were present with 14 of the functions being dominant. The dominant functions include carbohydrates, fatty acids, lipids and isoprenoids, amino acids and derivatives, sulfur metabolism, and nitrogen metabolism. Kruskal- Wallis test was used to test samples’ diversity differences. There was a significant difference in the diversity with p-value of 0.04. ANOSIM was used to analyse the similarities of the samples, p-values and R-values of the samples were 0.01 and 0.5835 respectively. Phosphorus with p-value of0.718 and 64.3% contribution was more prominent among the soil properties that have influence on functional diversity of the samples. Given the functional groups reported in this study, it is clear that soil characteristics had an impact on on the functions role of the rhizospheric microbiome of soybean

Magnesium transporters (MGTs) play a prominent role in the absorption, transport and storage of magnesium in plant cells. In the present study, MGT gene family members were identified and characterized in two species of cucurbitaceae, including C. sativus and C. lanatus. 20, 19, and 20 MGT genes were recognized in C. lanatus, C. sativus, and C. melo, respectively. According to physicochemical properties, the members of each sub-class of MGTs in the species of cucurbitaceae showed the close relationship. Proteins from NIPA were identified as hydrophilic proteins with high stability. Based on phylogenetic analysis, MGT family members were classified into three groups, and NIPAs showed more diversity. Besides, duplication events were not identified between the MGT members in C. lanatus, and C. sativus. According to pocket analysis, residues such as L, V, S, I, and A were frequently observed in the binding sites of MGT proteins in both species. The prediction of post-translation modifications revealed that MSR2 proteins have high phosphorylation potential than other sub-classes in both studied plants. The expression profile of MGTs showed that MGTs are more expressed in root tissues. In addition, MGTs showed differential expression in response to abiotic/biotic stresses as well as hormone application and NIPAs were more induced in response to stimuli in watermelon. The results of this study, as the primary work of MGT gene family, can be used in programs related to cucurbitaceae breeding.

The tri-trophic interactions between plants, insects, and insect predators and parasitoids are often mediated by chemical cues. The attraction to Herbivore-Infested Plant Volatiles (HIPVs) has been well documented for arthropod predators and parasitoids, and more recently for insectivorous birds. The attraction to plant volatiles induced by the exogenous application of methyl jasmonate (MeJA), a phytohormone typically produced in response to an attack of chewing herbivores, have provided controversial results, both in arthropod and avian predators. In this study, we aimed to examine whether potential differences in the composition of bouquets of volatiles produced by Herbivore-infested and MeJA-treated Pyrenean oak trees (Quercus pyrenaica) were related to differential avian attraction, as results from a previous study suggested. Results showed that the overall emission of volatiles produced by MeJA-treated and Herbivore-infested trees did not differ, and were higher than emissions of Control trees, although MeJA treatment showed more significant reaction and released several specific compounds in contrast to Herbivore-induced trees. These slight differences in the volatile composition may explain why avian predators were not so attracted to MeJA-treated trees as observed in a previous study in this plant-herbivore system. Unfortunately, the lack of avian visits to the experimental trees in the current study does not allow us to confirm this result and points out the need to perform more robust predator studies.

Abstract: A lower bed single row for pineapple cultivation could protect pineapple from soil erosion in rainy season and during drought, however, disease problem could arise due to water logging. Two experiments using a lower bed single row was done to understand the ability of gypsum providing soil calcium (Ca) available to pineapple plant, resistance to heart rot disease, and give better effect on crop growth and fruit quality of the pineapple in Ultisol soil. In the first trial, four level dosis of gypsum (0, 1.0, 1.5, 2.0 Mg ha-1) and dolomite 2 Mg ha-1 were applied by spreading and incorporated into the soil which have saturated with inoculums of Phytophthora nicotianae. In the second trial, gypsum treatments (0, 1.0, 1.5, 2.0, 2.5 Mg ha-1) were applied in the row between the single row beds as a basic fertilizer. The result showed that P. nicotianae attacked the pineapple plants in all treatments at 6 weeks after planting (WAP), and at 10 WAP, the mortality of dolomite treatment reached 63.8%, significantly different than that for gypsum treatments (3.3-14.3%). In the second experiment, gypsum increased plant weight significantly at 3 until 9 months after planting especially when it was applied 1.5-2.5 Mg ha-1. Fruit texture, total soluble solid (TSS), titratable acidity (TA) were not significant different among the treatment but all meet the standards for grades of canned pineapple. Result showed that soil applied gypsum before planting provides soil calcium and met the plant Ca requirement during a period of early and fast growth step and safe for heart rot disease.

Graphene is a remarkable material with numerous applications. Due to its thin and lightweight design, it is ideal for a variety of applications. The synthesis of high-quality graphene in a cost-effective and environmentally friendly manner continues to be a significant challenge. Chemical reduction is considered to be the most advantageous method for preparing reduced graphene oxide (rGO). However, this process necessitates the use of toxic and harmful substances, which can have a detrimental effect on the environment and human health. Thus, to accomplish the objective, the green synthesis principle has prompted researchers worldwide to develop a simple method for green reduction of graphene oxide (GO), which is readily accessible, sustainable, economical, renewable, and environmentally friendly in nature. For example, the use of natural materials such as plants is generally considered safe. Furthermore, plants contain reducing and capping agents. The current review will focus on the discovery and application of rGO synthesis using extracts from a variety of different parts of the plant. The review aims to aid current and future researchers in their search for a novel plant extract that acts as a reductant in the green synthesis of rGO. The review aims to assist current and future researchers in their research for a novel plant extract that acts as a reductant in the green synthesis of rGO as well as their potential applications in a variety of industries.

Typhonium flagelliforme is an Indonesian herbal plant used and applied traditionally to treat cancer diseases. Gamma rays have irradiated rodent tuber mutant plant at six doses gray to in-crease the chemical compounds of anticancer activity. The effect of isolated compounds from ro-dent tuber mutant plants has never been studied and published yet. Our study unveiled the poten-tial of stigmasterol as a remarkable cytotoxic agent and the significant contribution of NMR spectroscopy, IR, Mass spectra, QTOF MS towards the isolation and identification of this anti-cancer agent. Stigmasterol was isolated from T. flagelliforme mutant plant. Stigmasterol was more effective against MCF-7 cells with an IC50 value of 0.1623 µM than Cisplatin with IC50 value 13.2 µM. It is the most potential and active fraction in the human breast cancer cell line. The mo-lecular docking study analyzed the chemical profile of stigmasterol to confirm the receptor in agonist binding sites. The prediction of the toxicity of stigmasterol compounds using in silico and analysis of its interaction with the receptor can act as a competitive regulator with a high-affinity binding site on FXR. Stigmasterol has potential as a candidate for an anticancer drug that pro-moting further clinical action.

Extracellular vesicles (EVs) can transfer diverse RNA cargo for intercellular signalling. EV-associated RNAs have been found in diverse fungi and were proposed to be relevant for pathogenesis in animal hosts. In plant-pathogen interactions, small RNAs are exchanged in a cross-kingdom RNAi warfare and EVs were considered to be a delivery mechanism. To extend the search for EV-associated molecules involved in plants-pathogen communication, we have characterised the repertoire of EV-associated mRNAs secreted by the maize smut pathogen, Ustilago maydis. For this initial survey, EVs were isolated from axenic filamentous cultures that mimic infectious hyphae. The EV-associated RNAs were resistant to degradation by RNases and the presence of intact mRNAs was evident. The set of mRNAs enriched inside EVs relative to the fungal cells are functionally distinct from those that are depleted from EVs, particularly overrepresented in metabolic enzyme activities. Intriguingly, mRNAs of some known effectors and other proteins linked to virulence were found in EVs. Furthermore, several mRNAs enriched in EVs are also upregulated during infection, suggesting that EV-associated mRNAs may participate in plant-pathogen interaction.

The drought is one of all phenomena at local to a global scale that caused climate change impacts, alongside it also the human activities related, which were deforested and land use changed that caused to ecological disturbance, which one is hydrological changes. Hence, it’s a lot of lost productivity, chiefly to farming land aspects and the other, so the land restoration by reforestation is needed in framework to water cycle (hydrology) process kindly. The aim of this research has identified and analyzed ecological restoration to ecosystem services, chiefly in wastewater management (treatment) to conservation, especially in Al Zaytun areas (Pesantren), so observing and in-depth individual interview (with some of personage and pesantren of boards) is one method that used to data collected, alongside land survey management to classified type development in Al Zaytun areas. The result of this research revealed that Al Zaytun successfully in water management to conservation by wastewater treated management by Eichhornia crassipes (Enceng gondok), afterward to the reservoir as water saved development. Those conservation types considered capability in reviving to ecological systems to ecosystem services increased kindly, alongside to land restoration by some of the plant species or trees to grow and developed in which Tectona grandis L. f is more dominantly and it's favorite because of investment economic that advantage based on ecological perspective. These were type management that did by Al Zaytun, alongside its able in coping to both drought and climate change impacts kindly and adaptively, and of course, it’s part of an obligation as the follower’s Islam religion in preserving and maintaining natural resources, chiefly to water resources.

Polyploidy means having more than two basic sets of chromosomes. Polyploid plants may be artificially obtained through chemical, physical and biological (2n gametes) methods. This approach allows an increased gene scope and expression, thus resulting in phenotypic changes such as yield and product quality. Nonetheless, breeding new cultivars through induced polyploidy should overcome deleterious effects that are partly contributed by genome and epigenome instability after polyploidization. Furthermore, shortening the time required from early chromosome set doubling to the final selection of high yielding superior polyploids is a must. Despite these hurdles, plant breeders have successfully obtained polyploid bred-germplasm in broad range of forages after optimizing methods, concentration and time, particularly when using colchicine. These experimental polyploids are a valuable tool for understanding gene expression, which seems to be driven by dosage dependent gene expression, altered gene regulation and epigenetic changes. Isozymes and DNA-based markers facilitated the identification of rare alleles for particular loci when compared with diploids, and also explained their heterozygosity, phenotypic plasticity and adaptability to diverse environments. Experimentally induced polyploid germplasm could enhance fresh herbage yield and quality, e.g. leaf protein content, leaf total soluble solids, water soluble carbohydrates and sucrose content. Offspring of experimentally obtained hybrids should undergo selection for several generations to improve their performance and stability.

This paper presents preliminary operational performance results of a pilot grid-connected photovoltaic (PV) system designed and installed on the rooftop of the Ministry of Petroleum, Energy and Mining headquarter in Nouakchott (latitude of 18.1°N and the longitude of 16.0°W), Mauritania. The aim is for the government to demonstrate the relevance of using solar energy and to encourage the uptake of solar PV technology for commercial and residential building applications in Mauritania.. In this study, the grid-connected PV system has a peak power of 48 kW and the performance monitoring was carried out during one year, with a system that allow to measure DC power, inverter and system conversion efficiency, energy generated by the PV arrays, solar radiation in the inclination plane of panels, ambient temperature and module temperature. During this period, the PV plant was found to supply 65,668 kWh to the grid. The final yield ranged from 3.91 to 5.09 kWh/kWp/day. The performance ratio was found to vary from 69.69% to 89.35% and the annual capacity factor was found to be 19%. Finally, performance parameters were compared with other PV plants installed in the same region of Northern Africa. The outcome of this work is deemed important in assisting accurate PV system design and decision-making.

Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., N_Pop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Individual plant cells are the building blocks for all plantae and artificially constructed plant biomaterials, like biocomposites. Secondary cell walls (SCWs) are a key component for mediating mechanical strength and stiffness in both living vascular plants and biocomposite materials. In this paper, we study the structure and biomechanics of cultured plant cells during the cellular developmental stages associated with SCW formation. We use a model culture system that induces transdifferentiation of Arabidopsis thaliana cells to xylem vessel elements, upon treatment with dexamethasone (DEX). We group the transdifferentiation process into three distinct stages, based on morphological observations of the cell walls. The first stage includes cells with only a primary cell wall (PCW), the second covers cells that have formed a SCW, and the third stage includes cells with a ruptured tonoplast and partially or fully degraded PCW. We adopt a multi-scale approach to study the mechanical properties of cells in these three stages. We perform large-scale indentations with a micro-compression system and nanoscale indentations through atomic force microscopy (AFM), in three different osmotic conditions. We introduce a spring-based model to deconvolve the competing stiffness contributions from turgor pressure, PCW, SCW and cytoplasm in the stiffness of differentiating cells. Prior to triggering differentiation, cells in hypotonic pressure conditions are significantly stiffer than cells in isotonic or hypertonic conditions, highlighting the dominant role of turgor pressure. Plasmolyzed cells with a SCW reach similar levels of stiffness as cells with maximum turgor pressure. The stiffness of the PCW in all of these conditions is lower than the stiffness of the fully-formed SCW. Our results provide the first experimental characterization of the mechanics of SCW formation at single cell level.

Tomato (Solanum lycopersicum L.) is a valuable horticultural crop grown and consumed worldwide. Optimum production is hindered by several factors of which Verticillium dahliae, the cause of Verticillium wilt, is one of the major biological constraints in temperate production regions. V. dahliae is difficult to manage because it is a vascular pathogen, has a broad host range and worldwide distribution, and can persist in soil for years. Understanding the pathogen virulence and genetic diversity, host resistance, and plant-pathogen interactions can ultimately inform the development of integrated strategies to manage the disease. In recent years, considerable research has focused on providing new insight into these processes as well as the development and integration of environment-friendly management approaches. In this review, we discuss and summarize the recent findings on the race and population structure of V. dahliae; pathogenicity factors; host genes, proteins, and enzymes involved in defense; the emergent management strategies, and recent approaches to managing Verticillium wilt in tomatoes.

The present study provides an observation and documentation on the effect of static magnetic field on the growth of Triticum aestivum. The seeds are allowed to germinate and the seedlings are grown in an environment of the controlled static magnetic field while other physical factors such as temperature, light exposure, soil pH, etc. are held constant. Also, a comparison of influence on the average height between both the poles of the magnet is estimated and north pole oriented magnet is found to imply a significant difference. Finally some of the potential applications of the study in non-invasive protein silencing, GMO production, Vertical farming and Terraforming are briefly discussed.

Rice is consumed as a staple food by majority of the people in the world and failure in rice crop, due to any reason, poses a severe threat of starvation. Rice blast, caused by a fungus blast has been ranked among the most important plant diseases. It is by far the most threatening disease of ric crop and it is found wherever rice is grown. All of the rice blast disease management strategies that have been employed have limited success and rice blast has never been eliminated from a region in which rice is grown. Hence there is need to look for the best remedy in terms of effectiveness and organic nature of the method etc. This study was aimed to determine the plant growth promoting and biopesticidal effects of bioactive components present in a mixture of Piper caninum and Piper betle var. Nigra leaf extracts. . The extracts were applied in the field to determine their inhibition effects against blast disease, growth and yield improvement.. Extract of both the plants promoted plant growth and exhibited antifungal activity against rice blast fungus, Pyricularia oryzae. However the synergistic effect of the mixture of the two extracts exhibited greater effects than an effect of a single extract. . All treatments reduced the intensity of blast disease on week 15 with disease intensity by 7.90%. The extracts could increase plant height, the numbers of tillers, number of leaves, number of grains per panicle number of heads per panicle, and the full-grain weight hill.. The highest potential yield (t/ha) was observed in the 2% extract treatment, and all treatment results significantly differed from that of the control. The potential grain yield was 3.23 t/ha in the control, while that in the treatment ranged from 3.81 t/ha to 5.61 t/ha. The high grain yield observed with the treatment was caused by the low intensity of blast disease.

Background: Although a cholesterol-lowering diet and the addition of plant sterols and stanols are suggested for the lipid management of children and adults with familial hypercholesterolemia, there is limited evidence evaluating such interventions in this population. Objectives: To investigate the impact of cholesterol-lowering diet and other dietary interventions on the incidence or mortality of cardiovascular disease and lipid profile of patients with familial hypercholesterolemia. Search methods: Relevant trials were identified by searching US National Library of Medicine National Institutes of Health Metabolism Trials Register and clinicaltrials.gov.gr using the following terms: diet, dietary, plant sterols, stanols, omega-3 fatty acids, fiber and familial hypercholesterolemia. Selection criteria: Randomized controlled trials evaluating the effect of cholesterol-lowering diet or other dietary interventions in children and adults with familial hypercholesterolemia were included. Data collection and analysis: Two authors independently assessed the trial eligibility and bias risk and one extracted the data, with independent verification of data extraction by a colleague. Results: A total of 17 trials were finally included, with a total of 376 participants across 8 comparison groups. The included trials had either a low or unclear bias risk for most of the parameters used for risk assessment. Cardiovascular incidence or mortality were not evaluated in any of the included trials. Among the planned comparisons regarding patients’ lipidemic profile, a significant difference was noticed for the following comparisons and outcomes: omega-3 fatty acids reduced triglycerides (mean difference [MD]: -0.27 mmol/L, 95% confidence interval [CI]: -0.47 to -0.07, p<0.01) when compared with placebo. A non-significant trend towards a reduction in subjects’ total cholesterol (MD: -0.34, 95% CI: -0.68 to 0, mmol/L, p=0.05) and low-density lipoprotein cholesterol (MD: -0.31, 95% CI: -0.61 to 0, mmol/L, p=0.05) was noticed. In comparison with cholesterol-lowering diet, the additional consumption of plant stanols decreased total cholesterol (MD: -0.62 mmol/l, 95% CI: -1.13 to -0.11, p=0.02) and low-density lipoprotein cholesterol (MD: -0.58 mmol/l, 95% CI: -1.08 to -0.09, p=0.02). The same was by plant sterols (MD: -0.46 mmol/l, 95% CI: -0.76 to -0.17, p<0.01 for cholesterol, and MD: -0.45 mmol/l, 95% CI: -0.74 to -0.16, p<0.01 for low-density lipoprotein cholesterol). No heterogeneity was noticed among the studies included in these analyses. Conclusions: Available trials confirm that the addition of plant sterols or stanols has a cholesterol-lowering effect on such individuals. On the other hand, supplementation with omega-3 fatty acids effectively reduces triglycerides and might have a role in lowering the cholesterol of patients with familial hypercholesterolemia. Additional studies are needed to investigate the effectiveness of a cholesterol-lowering diet or the addition of soya protein and dietary fibers to a cholesterol-lowering diet in familial hypercholesterolemia.

Selenium (Se) is an essential element of the human diet. Therefore, it is necessary to implement Se in agricultural fertilization, although it is not considered as an essential element for plants, Se provides benefits at the level of redox metabolism, increasing the resistance of plants to various stress factors. The increase of the availability of selenium with the use of biopolymer complexes was sought in Great Lakes lettuce grown in substrate pots treated with SeO₂ (5 mg L Se), Cs-PAA + Se (5 mg L Se), and Cs-PAA. The redox metabolism was modified by increasing the enzymatic activity of glutathione peroxidase. The use of Cs-PAA + Se biopolymer complexes increase selenium up to 24 mg/Kg dry weight (DW) in plant tissues.

Rhizosphere microbiomes which have been implicated to enhance plant growth and yield are modulated or influenced by a couple of environmental factors such as soil type, plant cultivar, climate change and anthropogenic activities. In particular, anthropogenic activity such as the use of nitrogen-based chemical fertilizers is associated with environmental destruction and this call for a more ecofriendly strategy to increase nitrogen level of agricultural land. This feat is attainable by harnessing nitrogen-fixing endophytic and free-living rhizobacteria. Rhizobium, Pseudomonas, Azospirillum and Bacillus have been found to have positive impacts on crops by enhancing both above and belowground biomass and could therefore play positive roles in achieving sustainable agriculture. Thus, it is needful to study these rhizosphere microbiomes with more sophisticated culture-independent technologies such as next generation sequencing (NGS) with the prospect of discovering novel bacteria with plant growth promoting traits. This review is therefore aimed at discussing factors that can modulate rhizosphere microbiomes with focus on the contributions of nitrogen fixing bacteria towards sustainable agricultural development and the techniques that can be used for their study.

Pinus patula is one of the most planted wood conifer species worldwide; however, no foliar nutrient standards exist for this species up to date. The objective of the present study was to generate and verify two sets of foliar nutrient standards for nearly ten-year-old P. patula trees: critical nutrient concentrations and DRIS norms. Nutrients studied were N, P, K, Ca, Mg, Fe, Cu, Zn, Mn, and B. The reference standards were verified experimentally by installing two fertilization trials; one of them located in Huayacocotla, state of Veracruz and the other one in Aquixtla, state of Puebla, Mexico. Nutrient status of each fertilization trial was correctly predicted by critical nutrient values and DRIS as well. Both standards were able to detect the secondary growth-limiting nutrient deficiency in the Huayacocotla trial, where the primary limitation for growth was scarcity of solar radiation within tree crowns. The limiting nutrient in both experimental trials was K.

There is evidence that optimal nutrition is fundamental to human health and in the prevention of non-communicable diseases (NCDs) later in adult life. The identification, production and consumption of functional foods worldwide can increase health benefits for all who can access and afford such foods subject to advice from nutritionists. Recent meetings organized by the health agencies, give a crucial opportunity to make nutrition, a central part of the post-2015 sustainable human and agricultural development agenda. The aim of discussions in these meetings was to provide functional crops and foods to achieve optimal health by prevention of NCDs. It is possible that these efforts might ensure that the goals and targets set in the agenda are adequate to address the many challenges of global undernutrition as well as obesity which are major risk factors of NCDs. In many developing and middle income countries, food security provided by the governments, in one sense understandably, gave least consideration to functional foods supply and the prevention of obesity and metabolic syndrome, resulting in to emergence of NCDs. The Thailand Declaration reiterates that commitments to eradicate hunger and undernutrition as well as over-nutrition, and to increase investments in effective interventions; designers foods and designers crops. However, in planning coherent policies, our past experience on rapidly absorbed, energy-rich processed foods should be taken in to account while developing sustainable food systems. The food industry should be educated to exploit the expertise of food scientists and health professionals in designing functional foods taking cognizance of manufacturing and processing. Similarly, agriculture scientists may be actively involved in educating farmers so as to grow cash crops providing functional foods. The aim should be to achieve an increase in the availability of functional foods to an extent, or by a policy, by which such foods are available to poors, at affordable cost to prevent hunger and undernutrition and related diseases as well as NCDs. In addition our efforts might help in developing an international consensus on how to approach the development of new designer foods by farmers and food industry to produce low glycemic index foods. Such efforts may establish an international framework for the prevention of NCDs, so that human susceptibility to these diseases is substantially diminished.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

Driven by environmental sustainability concerns, the integration of biobased components in curtain wall systems is gaining traction in both research and the construction market. This paper explores the development and validation of a bio-based façade system within the Basajaun H2020 project. The project aimed to demonstrate the feasibility of introducing environmentally friendly bio-based components into the mature curtain wall façade industry. The paper focuses on identifying technological solutions for replacing key components such as profiles, insulation, and the tightness system with bio-based and less environmental impact alternatives presenting the results achieved in the façade system design of the Basajaun project funded by European Union. These solutions aimed at creating a bio-composite-based curtain wall façade that adheres to current building envelope standards and normative; to implement diverse façade typologies for vision panels, opaque sections, and integrated windows; to engineer the prefabrication process for industrialization, enabling wider market replication and simplified transport and installation. The results demonstrate that the Basajaun façade successfully integrates selected components and meets the performance requirements set by regulations. However, questions remain regarding the workability of bio-based profiles as a commercially viable, ready-to-market solution that can replace traditional aluminum profiles in the curtain wall façade industry.

This review aims to consolidate varied information regarding Port Scanning, and examine the tools, techniques, and detection algorithms used. It explores TCP SYN, Full TCP, UDP, and stealth scans, and shows their functions and abilities. Paired with these techniques, it also shows the tools that they can be used by, such as NMap, Zmap, and MASSCAN. It discusses the various detection techniques such as Signature and Anomaly based detection, and discusses the real world impact of this technology, while commenting on the legal and ethical issues posed.

This study reports the synthesis and characterization of novel per-fluorinated organic polymers with azo- and azomethine-based linkers using nucleophilic aromatic substitution. The polymers reveal variations in the fluorine content via the incorporation of decafluorobiphenyl and hexafluorobenzene linkers, which enhanced their hydrophobic nature. The rich fluorine polymers were slightly soluble in THF and have shown molecular weights between 4886 to 11948 g/mol. All polymers exhibit thermal stability in the range of 350-500°C, with varying thermal stability depending on the fluorine and nitrogen content. Conjugation of the polymers was confirmed through changes in the UV-Vis spectra, with a hypsochromic shift observed in all cases, more pronounced in azo-based fluorinated chains due to H-bonding on the nitrogen sites, chain conformations, and planarity. The optical band gap (Eg) of the polymers was determined from the UV-Vis data, with the Eg values of the azo-based fluorinated polymers being 1 eV higher than those of their corresponding linkers. The cross-linking formation was characterized by porosity measurements, with the azo-based polymer exhibiting the highest surface area of 770 m²/g with a pore volume of 0.35 cm³/g, while the open-chain azomethine-based polymer exhibited the lowest surface area of 285 m²/g with a pore volume of 0.0872 cm³/g. Porous structures with varied hydrophobicities were investigated as adsorbents for separating water-benzene and water-phenol mixtures and selectively binding methane/carbon dioxide gases from the air. The most hydrophobic polymers containing the decafluorbiphenyl linker were suitable for benzene separation, while the best methane uptake values were 6.14 and 3.46 mg/g for DAB-Z-1O and DAB-A-1O, respectively. DAB-Z-1h, with the highest surface area and rich in nitrogen sites, exhibited the highest CO₂ uptake at 298 K (17.25).

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. 3D pharmacophore models are powerful computational tools in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning will be highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

The stele concept is one of the oldest enduring concepts in plant biology. This paper reviews the concept and its foundations, and builds an argument for an updated view of steles and their evolution. The history of studies of stelar organization has generated a widely ranging array of definitions of the stele that determine the way we classify steles and construct scenarios about the evolution of stelar architecture. Because at the level of the organism biological evolution proceeds by, and is reflected in, changes in development, concepts of structure need to be grounded in development in order to be relevant in an evolutionary perspective. For the stele, most of the traditional definitions that incorporate development have viewed it as the totality of tissues that either originate from procambium – currently the prevailing view – or are bordered by a boundary layer (e.g., endodermis). A definition of the stele that would bring consensus between these perspectives recasts the stele as a structural entity of dual nature. Here, I review briefly the history of the stele concept, basic terminology related to stelar organization, and traditional classifications of the steles. I then revisit boundary layers from the perspective of histogenesis as a dynamic mosaic of developmental domains. I use classic and recent anatomical and molecular data to reaffirm and explore the importance of boundary layers for stelar organization. Drawing on data from comparative anatomy, developmental regulation, and the fossil record, I offer a model for a stele concept that integrates both the boundary layer and the procambial perspective, consistent with a dual nature of the stele. The dual stele model posits that stelar architecture is determined in the apical meristem by two major cell fate specification events: a first one that specifies a provascular domain and its boundaries, and a second event that specifies a procambial domain (which will mature into conducting tissues) from cell subpopulations of the provascular domain. If the position and extent of the developmental domains defined by the two events are determined by different concentrations of the same morphogen (most likely auxin), then the distribution of this organizer factor in the shoot apical meristem, as modulated by changes in axis size and the effect of lateral organs, can explain the different stelar configurations documented among tracheophytes. This model provides a set of working hypotheses that incorporate assumptions and generate implications that can be tested empirically. The model also offers criteria for an updated classification of steles that is in line with current understanding of plant development. In this classification, steles fall into two major categories determined by the configuration of boundary layers – boundary protosteles and boundary siphonosteles, each with subtypes defined by the architecture of the vascular tissues. Validation the dual stele model and, more generally, in-depth understanding of the regulation of stelar architecture, will necessitate targeted efforts in two areas: (i) the regulation of procambium, vascular tissue, and boundary layer specification in all extant vascular plants, considering that most of the diversity in stelar architecture is hosted by seed-free plants, which are the least explored in terms of developmental regulation; (ii) the configuration of vascular tissues and, especially, boundary layers, in as many extinct species and lineages as possible.

The generation at the helm faces an unprecedented responsibility in the near future of artificial intelligence. The implications of setting up the founding rules that will regulate the operation of AI are heavy since after they’re set they last forever. Once this first AI is commenced, it can be such that no other subsequent AIs could emerge thereby assuming dominion over its own creation stand. As a result, retaining control becomes necessary. Lest humanity surrender agency to its own creation. At this juncture of big talks, critical issue are raised concerning AI administration owners. Is it appropriate for only a few people to have unrestricted control on AI commands while leaving out all precautionary measure? Therefore, we have to always consider between control and constraint when dealing with AI issues which involves authority plays off against morality. The direction Artificial Intelligence takes in the future depends on the decisions made by today’s generation. We will determine how we are viewed historically in terms of technology based on how well we take on such an important duty. There’s a major turning point ahead of us where we who are the stewards of tomorrow must make a choice that protects humanity’s right to self-determination and also exploits the power of AI for change.

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

As belonging to one of the most isolated continents on our planet, the microbial composition of different environments in Antarctica could hold a plethora of undiscovered species with potential for biotechnological applications. This manuscript delineates our discoveries after an expedition to the Bulgarian Antarctic Base "St. Kliment Ohridski" situated on Livingston Island, Antarctica. Amplicon-based metagenomics targeting the 16S rRNA genes and ITS2 region were employed to assess the metagenomes of the bacterial, fungal and archaeal communities across diverse sites within and proximal to the research station. The predominant bacterial assemblages identified included Oxyphotobacteria, Bacteroidia, Gammaprotobacteria and Alphaprotobacteria. A substantial proportion of cyanobacteria reads were attributed to a singular uncultured taxon within the family Leptolyngbyaceae. The bacterial profile of a lagoon near the base exhibited indications of penguin activity, characterized by a higher abundance of Clostridia, similar to lithotelm samples from Hannah Pt. Although most fungal reads in the samples could not be identified at the species level, noteworthy genera, namely Betamyces and Tetracladium, were identified. Archaeal abundance was negligible, with prevalent groups including Woesearchaeales, Nitrosarchaeum, Candidatus Nitrosopumilus and Marine Group II.

Abstract: Background: Dental fear and uncooperative behavior can hinder dental treatment quality. Pediatric Procedural Sedation and Analgesia (PPSA) is used to facilitate treatment when coping capacity is exceeded. Out-of-hospital PPSA has been associated with more adverse outcomes compared to hospital-based settings. Updated Dutch PPSA guidelines recommending two anesthesiologists and a ventilator during out-of-hospital PPSA have increased costs and raised concerns about the accessibility to specialized high quality dental care for children in the Netherlands. Aim: This study aims to investigate the impact of the updated 2017 guideline on the occurrence rate of adverse events during PPSA, as recorded in the anesthesia complication database of twelve participating Dutch dental clinics, with adjustments made for age, weight, and duration of treatment. Method: Data of 25,872 children treated at 12 dental clinics performing out-of-hospital PPSA between 1997 and 2019 were analyzed. Eligible children were aged 2-18 with ASA classification I and II. A logistic two-level mixed-effects model is used to estimate the updated guideline’s impact on adverse events. Results: In the final model we analyzed the data of 21,759 children in nine dental clinics. The OR of the occurrence rate of an adverse event adjusted for age, weight, and duration of treatment was 0.75 (95% CI 0.64 – 0.89) after the implementation of the updated guideline, compared to before the updated guideline. This outcome was significant with a P=0.001, indicating a protective effect. The average odds per clinic of having an adverse event was 0.04 with a 3.81% chance. Conclusion: Our findings demonstrate a significant reduction in adverse events after the implementation of the updated guideline and highlight the importance of adherence to evidence-based guidelines in improving patient safety and the quality of care provided during pediatric procedural sedation and analgesia in out-of-hospital dental clinics. Our study underlines the necessity of considering the complex underlying mechanisms that play a role in the development and implementation of evidence-based practice and safety initiatives on the occurrence of adverse events.

The healing of osteochondral defects (OCD) resulting from injury, osteochondritis, or osteoarthritis in middle- and old-age individuals, bearing lesions in the cartilage and bone, pain, and loss of joint function, presents challenges to the clinical practitioners because of non-regenerative cartilage and the limitations of current therapies. Bioactive peptide-based osteochondral (OC) tissue regeneration is becoming more popular because it does not have the immunogenicity, misfolding, or denaturation problems associated with original proteins. Periodically, reviews are published on the regeneration of bone and cartilage; however, none of them address the simultaneous healing of these tissues in the complicated heterogeneous environment of the OC interface. As regulators of cell adhesion, proliferation, differentiation, angiogenesis, immunomodulation, and antibacterial activity, potential therapeutic strategies for OC defects utilizing bone and cartilage-specific peptides should be examined and investigated. Therefore, the main goal of this review is to study how they contribute to the healing of OC defects, either alone or in conjunction with other peptides and biomaterials.

In the field of modeling social systems, the use of agent-based models (AM) has become a powerful tool for understanding processes occurring in society, in particular, for studying the spread of infections among the population. The agent model is based on a synthetic population consisting of agents, their interactions and sometimes the territory in which the interaction occurs. The choice of an appropriate method for creating such artificial populations and the selection of parameters becomes a key issue affecting the realism of the model and its ability to reflect real-world scenarios. This article delves into the interplay between agent-based modeling, synthetic population construction, and parameter selection, offering our insight into the complex process of modeling infectious disease dynamics. The review concludes with an exploration of extending the Covasim model [1] to experiments in computational epidemiology. Covasim, a dynamic transmission modeling tool, is known for its ability to adapt to different scenarios and offers predictive information critical for managing epidemiological situations. The Covasim model could include improvements to better simulate real-world epidemic conditions, including a revised transmission strategy to account for accumulation through daily contacts. In addition, we propose to add a module for intercity travel and a visualization package that will significantly expand the functionality of Covasim. These improvements make Covasim a more versatile and efficient tool for modeling and analyzing epidemic processes.

AI-based approaches are increasingly being used to revolutionize vaccine development for COVID-19. Small molecules, peptides, and epitopes are being collected for therapy discovery, which could also direct AI-based models, screening, or generation [1]. AI-based models are being used for drug discovery and vaccine development, and pre-existing data is being leveraged through machine learning approaches for COVID-19 drug discovery and vaccine development [1]. The best candidate targets for future treatment development are being identified and evaluated using AI-based models [1]. AI-based approaches can be used to overcome challenges in manufacturing, storage, logistics, and safety and efficacy issues related to different vaccine candidates for COVID-19 [2]. AI algorithms can help identify the best vaccine candidates for COVID-19 while considering the efficiency of antigenic peptides for immune response generation [2]. The presentation of antigenic peptides by major histocompatibility complex (MHC) class II molecules is the first step after COVID-19 vaccine administration for any vaccine-induced immune response [2]. Thus, AI-based models are being used to identify the best vaccine candidates for COVID-19 and to ensure that the vaccine-induced immune response is efficient and safe. The utilization of AI-based methods to address logistical, manufacturing, storage, safety, and efficacy issues regarding several COVID-19 vaccine candidates will be examined in this study. Additionally, while considering the effectiveness of antigenic peptides for the induction of an immune response, we will determine the best potential targets for the next treatment development and assess how AI-based models can help discover the best vaccine candidates for COVID-19. This research ultimately intends to offer insights into how AI-based techniques can transform COVID-19 vaccine development and how they can be applied to address vaccine development issues. In this paper, we focus on recent advances in using artificial intelligence to develop COVID-19 drugs and vaccines, as well as the potential of intelligent training in discovering COVID-19 therapeutics, highlighting potential challenges and solutions.

The coronavirus disease (COVID-19) is a respiratory tract infection caused by the new virus SARS-CoV-2. In March 2020, the World Health Organization (WHO) declared a worldwide COVID-19 pandemic. Since then, there has been a rush to find preventative or curative treatment. This arrived with the appearance of the COVID-19 vaccine, months after the declaration of the pandemic. In the setting of global COVID-19 immunization, there have been reports of rare skin immune-mediated diseases (IMD) after COVID-19 vaccination, particularly systemic sclerosis. Systemic sclerosis (SSc) is the most reported new onset IMD following messenger RNA (mRNA) COVID-19 vaccination. The pathogenesis of SSc is not fully understood, and the diagnosis is based on clinical symptoms. We report a 54-year-old male, with no prior history of autoimmune disorder, with rapid and progressive skin thickening (Modified Rodnan skin score 30/51), weeks after his mRNA COVID-19 vaccination. Antinuclear antibodies (ANA) showed a strongly positive nuclear fine-speckled pattern, but no SSc autoantibodies were found. The patient was diagnosed with SSc based on the persistence of autoantibodies and the clinical criteria according to the 2013 American College of Rheumatology/European League Against Rheumatism classification. Due to the proximity of the two events, we hypothesized a cross-reaction between COVID-19 vaccination and, in genetically predisposed patients, an emergence of systemic sclerosis. Our case suggests a potential relationship between the mRNA COVID-19 vaccine and new-onset autoimmune diseases. Physicians should be aware of this possible association.

The corrosion resistance of two cast CoCr based alloys, with different amounts of chromium and with different alloying elements in the bulk composition of the alloy, was assessed. Herewith, we investigated the corrosion behavior of Co21Cr8Mo7W and Co29Cr7W by open circuit potential (OCP), potentiodynamic polarization (PP) and electrochemical impedance spectroscopy (EIS) in 0.1 M phosphate buffer solution (PBS) at 37°C for long time immersion. After 1000 hours of immersion, the corrosion current density (icor), estimated from anodic polarization tests, is lower for Co21Cr8Mo7W (i.e. 49 nA cm-2) alloy compared to Co29Cr7W alloy (180 nA cm-2). As regards the corrosion potential (Ecor), a nobler value was observed for Co21Cr8Mo7W (i.e. -59 mV vs. Ag/AgCl) compared to Co29Cr7W (i.e. -114 mV vs. Ag/AgCl). These findings suggest a better corrosion resistance of the film formed on the alloy containing lower amount of Cr and two alloying elements, namely Mo and W. These results are promising ones in terms of medical applications because they open new strategies in obtaining alloys with lower content of chromium and with higher protective properties against corrosion attack.

The global construction sector contributes a significant share of total greenhouse gas (GHG) emissions. In Australia, infrastructure activity alone generates 18% of the GHG emissions budget. The use of low-embodied carbon building materials is crucial to achieving sustainability in the construction sector and to fulfill national and international climate goals. Industrial hemp (Cannabis sativa L.) is considered a promising feedstock for sustainable construction materials because of its biogenic carbon content, fast-growing cycles with low agricultural input requirements, and technical functionality which is comparable to traditional materials. This study has applied the life cycle assessment (LCA) guideline of ISO 14040:2006 to estimate the carbon footprint (CF) expressed in carbon dioxide equivalent (CO2eq) emissions of hemp-based building materials in Western Australia. The functional unit is 1 m2 of hemp-based board, and the system boundary includes cradle-to-gate stages, i.e., pre-farm, on-farm, and post-farm activities. The production of 1 m2 of hemp-based board is estimated to be - 2.302 kgCO2eq. Electricity from the public grid for lignin extraction during the post-farm stage is the main contributor to total CO2eq emissions (26%), followed by urea production (14%) during the pre-farm stage. Overall, the use of electricity from the SWIS during the post-farm stage accounts for 45% of total emissions. Sensitivity analysis shows that the CF of hemp-based boards is highly sensitive to the source of energy, i.e., total replacement of the SWIS by solar power decreases the CF from - 2.30 to -6.07 kgCO2eq (164%). The results suggest that hemp-based boards exhibit lower embodied GHG emissions compared to traditional materials, such as gypsum plasterboards.

TiC particles were prepared from Ti and C powders, and TiC-reinforced Cu-based composites were prepared by powder metallurgy with SPS sintering. The effects of SPS sintering temperature and TiC particle reinforcement phase content on the electrical and mechanical properties of Cu-based composites were investigated. The reinforcement of TiC particles by thermal mismatching at increasing sintering temperature has a significant strengthening effect on the composites, but the high temperature causes softening of the Cu matrix and the growth of grains reduces the properties of the composites. In this study, the effects of porosity and density on the composite properties were not significant.

Background. We created agent-based model for short- and longterm forecasting of COVID-19 and for evaluation how the actions of the regulator affected the human and material resources of the healthcare system. Methods. The model was implemented in the AnyLogic software. It includes two state charts – social network and disease transmission. The COVID-19 Essential Supplies Forecasting Tool (COVID-ESFT, version 2.0) was used to determine healthcare resources needed. Results. Satisfactory results were obtained with long-term (up to 50 days) forecasting in the case of a monotonous change in total cases curve. However, if periods of relative stability are accompanied by sudden outbreaks, relatively satisfactory results were obtained with short-term forecasting, up to 10 days. Simulation of various scenarios showed that the most important place for the spread of infection are families. Wherein the maximum number of cases of COVID-19 is observed in the age group of 26-59 years. Due to a set of measures taken by government agencies, the number of cases in Karaganda city was 3.2 times less than was predicted in “no intervention” scenario. Economic effect is estimated at 40 %. Conclusion. Performed model is an attempt to consider as much as possible the peculiarities of the socio-demographic situation in the country. In the future, we will be prepared to some extent for challenges like those we have experienced in the past three years.

Progress in macrophage research is crucial for numerous applications in medicine, including cancer and infectious diseases. However, the existing methods to manipulate living macrophages are labor intense and inconvenient. Here we show that macrophage membranes can be reconstituted after storage for months at 4C, with their CD206 receptor selectivity and specificity being similar to that in the living cells. Then, we have developed a mannose ligand, specific to CD206, linked with PEG as IR spectroscopy marker to detect binding with the macrophage receptor. PEG was selected due to its unique adsorption band of C-O-C group at IR spectra, which does not overlap with other biomolecule’s spectroscopic feature. Next, competitive binding assay versus the PEG-bound ligand, has enabled selection of other higher-affinity ligands specific to CD206. Further, those higher-affinity ligands were used to differentiate activated macrophages in patient’s bronchoalveolar (BAL) or nasopharyngeal (NPL) lavage. CD206- control cells (HEK293T) showed only non-specific binding. Therefore, biochips based on reconstituted macrophage membranes as well as PEG-trimannoside as an IR spectroscopic marker, can be used to develop new methods facilitating macrophage research and macrophage-focused drug discovery.

Background: The incidence of prostate cancer (PC) has been risen annually. Despite the diagnosis is made mainly with non-metastatic PC, mortality is explained by the metastatic disease (mPC). Without a doubt there is an intermediate scenario in which patients have no mPC but will have initiated a metastatic cascade through an epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict what patients will progress in the future to non-localized clinical disease or already have micrometastatic disease, and therefore, will clinically progress after primary treatment. Biomarkers for predicting mPC are still under development; there are few studies and not much evidence of their usefulness. Summary: This review is focused on tissue-based genomic biomarkers (TBGB) for predicting metastatic disease. We developed four main research questions that will attempt to answer according to the current evidence. Why is important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are current prostate cancer treatments? Key Messages: Knowing useful predict tools could help determine which patients may need multimodal or adjuvant treatment even with a localized disease, and in consequence, what patients do not need more than a single modality of treatment. The importance of predicting metastasis is fundamental, given that once metastasis is diagnosed, the quality of life (QoL) and survival drop dramatically.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

The paper explores the potential of a low-cost advanced video-based technique for the assessment of structural damage induced to buildings by seismic loading. A low-cost high-speed video camera was utilized for motion magnification (MM) processing of footages of a two-story reinforced concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e. in terms of modal parameters) and the structural deformations of the building in the MM videos. The results by MM were compared for method validation to damage assessment obtained by the analyses of conventional accelerometers and high-precision optical markers tracked by a passive 3D motion capture system. Also, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometers were also processed and analyzed by using several stationary and non-stationary techniques with the aim to analyze the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed MM-based procedure provided accurate estimate of the main modal frequency and the damage location through the analysis of modal shapes, which were confirmed by advanced analyses of accelerometric data.

The deployment of Electro-Mechanical Actuators plays an important role towards the adoption of the More Electric Aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs in place of more traditional hydraulic solutions is still set back due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA Prognostics and Health Management (PHM) system, capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The authors have developed a model-based prognostic framework for PMSM based EMAs leveraging a metaheuristic algorithm: Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity and proportional gain) are simulated thanks to a Reference Model, acting as a Numerical Test Bench, then detected and identified thanks to the envisioned prognostic method, which leverages a low fidelity Monitoring Model. The employed algorithms showed good results and prove that this strategy could be executed in pre-flight checks or during the flight at specific time intervals, with positive impacts on system safety and availability.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library. The version of SamPy considered in this paper is available at https://github.com/sampy-project/sampy-paper .

Good estimators are characterized as robust, unbiased, efficient, and consistent. However, the commonly used estimators are weak or lack one or more of these properties. In this article, eight (8) estimators for statistical and geometrical estimations of symmetry/asymmetry, similarity/dissimilarity, identity/unidentity, and feature transformation were proposed following Kabirian-based optinalysis and other operations. The proposed estimators are characterized as invariant (robust) under scaling, location shift, and rotation or reflection. A computing code was written in python language for each of the proposed estimators so that peers can have working codes for application and performance evaluation.

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

There is increasing interest in automating train operations of mainline services, e.g. to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but not implemented on a large scale. Before the general introduction of new or adapted technologies can have a transformative effect on the operation of such a complex system as train operation on mainlines, they have to pass functional, interoperability and performance tests. A virtual preliminary analysis is one way to ensure a smooth as well as safe introduction and implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. To demonstrate the developed approach, a straightforward investigation of a case study is conducted using a microscopic train simulator in combination with an ATO algorithm.

Background: In an age where information is generally accessible, most of the interest these days has focused on how accessible and convenient technology can be. So small and personal, mobile devices can transform our perception of learning by combining both mobility and convenience. Mobile learning is part of the digital learning landscape alongside e-learning and serious games. However, knowledge about effective design of mobile learning experiences remains of interest with a focus on appropriate design models and the embodiments that can be implemented to achieve the intended educational outcomes. Exploring the instructor's perspective on mobile learning is essential. Therefore, the aim of this study was to investigate the Moroccan instructors' perception and practice of mobile learning to inform the development of an ecologically valid mobile learning integration model. Methods: Higher education Instructors (n=41) were recruited to the study. The Moroccan instructors' perception and their experiences regarding their adoption of mobile learning were collected using an online survey. The analysis focused on their mobile use, perceived IT competency, and opinions on mobile learning. Results: We described most of the instructors' considerations regarding integrating mobile technologies into their teaching activities. We found that most of the mobile learning activities defined by the respondents corresponded to relatively advanced use of mobile devices. More promising, instructors have found innovative ways to use the educational potential of mobile devices. However, the prospect of mobile devices was still to challenge. No or poor Wi-Fi connection, number of devices or limited access, sometimes fees or applications incompatibility were identified as reasons and obstacles to mobile learning usage. Conclusion: Mobile learning is mostly perceived positively among Moroccan instructors allowing many applications and usage to enhance teaching and learning. In this study, a better understanding of aspects and factors influencing the integration of mobile learning in the Moroccan educational context is exposed, helping further the development of an ecologically valid mobile learning integration model. Future work on mobile learning should consider the highly paced evolution of mobile technologies, emphasizing the flexibility of integration frameworks to support instructors and learners.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.

This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

Has anyone ever missed an event because he was confused in days and dates? Do we remember the date of any day without looking at a calendar? Is the current Gregorian Calendar efficient enough for use, and does it facilitate our life or make it more complicated? Have you ever thought about a much simpler way to calculate days and dates in a year? All these questions are answered in this paper, in which the author proposes original optimization algorithm that creates optimal perennial calendars. Results show that there is more than one way to create a perennial calendar, in which the number of days in each month does not change, neither the dates. Hence, all months have the same sequence of days and dates. In other meaning, Monday becomes the first day of every month, and Sunday becomes the last day. Consequently, the calendars become much easier to memorize and very simple to predict the days and dates in any year.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 0.8x1e6 Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer or simulant Urine) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 1.2x10⁶ Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10^-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5-min and 1-km) radar reflectivity data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used to evaluate the proposed algorithm. The performance of the new algorithm is compared with a radar reflectivity map and verified by two object-oriented methods: structure–amplitude–location (SAL) and geometric index. The verification results suggest that the performance of the new algorithm is good. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

The quadruple burden of diseases, early discharge from hospital and hospital at home have resulted in home-based care services becoming a requirement in South Africa. The home-based care services generate a significant amount of health care risk waste that is mismanaged. However, more attention is given to the health care risk waste generated in hospitals and clinics than to health care risk waste generated by home-based caregivers. Therefore, this study investigates the health care risk waste management practices by home-based caregivers. The study adopted a mixed research approach, qualitative and quantitative methods, using a literature review, interviews, and questionnaires as means of data collection. Results show that there are different types of health care risk waste generated as a result of different activities performed by home-based caregivers, but that the waste was found to be managed in an unsafe manner. The majority of households receiving home-based care did not have basic sanitation facilities such as toilets, running water and waste removal services, aggravating the issue of health care risk waste mismanagement. The study recommends a new policy framework that will lead to safe management practices of generated health care risk waste to be adopted by home-based caregivers.