Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on the plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of highly-priority transcription factors (TFs) dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We revealed distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficients nodes. Dynamic transcriptional regulatory modeling on integrated TF-target and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that can potentially be used in future genetic crop engineering programs.

MicroRNA (miRNA) is a typical class of small RNAs that could modulate gene expression in trans at the post-transcriptional level. miRNAs bind to the miRNA binding sites (MBSs) in target mRNAs by sequence complementarity. Alternative splicing (AS) is another commonly occurred process in pre-mRNAs that changes the isoforms of a gene. It is hypothesized that there should be an interaction for gene regulation that involves both AS and miRNA targeting. Studies have verified this hypothesis in the model organism Arabidopsis thaliana. High-throughput sequencing data suggested that in Arabidopsis a considerably large fraction of MBSs are affected by AS events. The overlapping between MBS and AS exceeds the randomly simulated number. Functional experiments have indicated that the AS events are required for the gene expression changes of miRNA targets. Therefore, AS and MBS are mutually favored. The observed expression changes caused by miRNAs could also be contributed by AS events. In the present perspective article, we propose that the AS analysis should be incorporated in the differential-expression analysis of miRNA studies. When defining a differentially-expressed gene, it should be clarified whether the change in gene expression is caused by AS events or solely by miRNA targeting.

Carotid intima-media thickness (CIMT) is a surrogate indicator for atherosclerosis and has been shown to predict cardiovascular risk in multiple large studies. Identifying and characterizing molecular markers for carotid atheroma plaque formation can be critical for early intervention and prevention of atherosclerosis. This study performed transcription factor (TF) network analysis of transcriptomic data focusing on two TF genes, ZNF385D, HAND2, whose polymorphisms reportedly associate with carotid atherosclerosis. Gene expression data were measured from pieces of carotid endarterectomy of hypertensive patients each paired with one sample of distant macroscopically intact tissue. TF networks were reconstructed for ZNF385D and HAND2 and their association with carotid atheroma progression examined using gene-set enrichment analysis with high statistical significance (p<6.95e-7) suggesting involvement of expression quantitative loci (eQTL). Examination of the regulation and correlation patterns suggests high clinical and interventional impacts of the two TF genes on impairing carotid atheroma plaque formation and preventing carotid atherosclerosis.

As one of the important components of underwater propulsion systems, water lubricated bearings are often failure due to mechanical wearing and vibrating, especially under high loads and prolonged friction. In this paper, a dual-network co-crosslinking strategy based on HNBR is proposed, in which the epoxy network connects with the rubber network through Epoxidized Eommia ulmoides gum. The damping, tribological and mechanical properties of the prepared composite are systematically investigated. The results show that this material has excellent friction and vibration damping properties, with a water-lubricated coefficient of friction as low as 0.022 and a wear resistance as high as 3.87 × 10-6mm3/Nm. Preparing HNBR-based composite by dual network co-crosslinked is proved being a feasible solution to improve the reliability and service life of water-lubricated bearings.

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that “chatgpt” occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

Micro-expression (ME) is one of the key psychological stress reactions. It is a modest, spontaneous facial mechanism. ME has significant applicability in a variety of psychologically-related sectors because to its precision and unpredictability with regard to psychological manifestations. Nevertheless, the current Micro-expression recognition (MER) algorithms have poor accuracy and a limited quantity of ME data, and this study issue has not been thoroughly investigated. Therefore, we present an approach for deep learning based on a Spatio-temporal capsule network (STCP-Net). STCP-Net has four components: a jitter reduction module, a differential feature extraction module, an STCP module, and a fully linked layer. The first two modules are aimed to extract diversifying differential features more precisely and to limit the influence of head jitter. The STCP module is used to extract Spatio-temporal features layer by layer, taking the temporal and geographical connection between features into account. This research runs sufficient trials using the Leave One Subject Out (LOSO) methodology for cross-validation using the CASMEII dataset. The conclusion and analysis demonstrate that the algorithm is innovative and efficient.

Nine crossbred finishing barrows randomly assigned to 3 dietary treatments were used to investigate the effects of dietary lysine on muscle growth related metabolic and signaling pathways. Muscle samples were collected from the longissimus dorsi of individual pigs after feeding the lysine-deficient, lysine-adequate, or lysine-excess diet for 5 weeks, and the total RNA was extracted afterwards. Affymetrix Porcine Gene 1.0 ST Array was used to quantify the expression levels of 19,211 genes. A total of 674 transcripts were differentially expressed (P ≤ 0.05); 60 out of 131 transcripts (P ≤ 0.01) were annotated in the NetAffx database. Ingenuity pathway analysis showed that dietary lysine deficiency may lead to (1) increased muscle protein degradation via the ubiquitination pathway as indicated by the up-regulated DNAJA1, HSP90AB1 and UBE2B mRNA, (2) reduced muscle protein synthesis via the up-regulated RND3 and ZIC1 mRNA, (3) increased serine and glycine synthesis via the up-regulated PHGDH and PSPH mRNA, and (4) increased lipid accumulation via the up-regulated ME1, SCD, and CIDEC mRNA. Dietary lysine excess may lead to (1) decreased muscle protein degradation via the down-regulated DNAJA1, HSP90AA1, HSPH1, and UBE2D3 mRNA, and (2) reduced lipid biosynthesis via the down-regulated CFD and ME1 mRNA.

Logistics Engineering has transcended over the decades into an approach for competitive benefits in organisational performance and logistics efficiency. Most industries are recognising that significant savings are available to companies that are able to ordinate and improve within their logistics operations. Companies today face great challenges because the successful supply of many products and services needs to the effective integration of logistics activities across a prolongation supply chain and an increasing geographical separation. Moreover, logistics integration approach involves both internal integration for an ordinated, unified process as well as relationships to react flexibly, changeability, and responsibility to customer's demands. In this study, by using of Fuzzy Vikor (F-Vikor), the best combination of RIR was selected and RIR Locating was done. The main aim of this paper is survey and design of the integrated operational logistics network (IOLN) and also, a proposed IOLN to integration of the Iran Khodro Co Supply Network.

Deeper understanding of biological processes, disease mechanisms, and prospective therapeutic targets can be attained by Gene Expression Network Analysis (GENA), which offers a potent framework for revealing the intricate regulatory mechanisms controlling gene expression. GENA is a computational method used to understand the intricate interactions and relationships between the genes in a biological system. It entails using network theory, statistical analysis, and gene expression data to pinpoint functional modules, regulatory linkages, and important genes or pathways involved in a particular biological process or illness. This chapter broadly outlines the principles and practice of GENA, to an novice reader and outlines a simple method of performing a GENA using online Rice gene expression datasets available in various websites.

Coordination of activities between nuclei and organelles in plant cells involves information exchange, in which phytohormones may play an essential role. Therefore, dissection of the mechanisms of hormone-related integration between phytohormones and mitochondria is an important and challenging task. Here, we found that inputs from multiple hormones may cause changes in transcript accumulation of mitochondrial-encoded genes and nuclear genes encoding mitochondrial (mt) proteins. In particular, treatments with exogenous hormones induced changes in GUS expression in the reporter line possessing a 5'-deletion fragment of the RPOTmp promoter. These changes corresponded in part to up- or downregulation of RPOTmp in wild-type plants, which affected the transcription of mt-encoded genes, implying that promoter fragments of the RPOTmp gene are functionally involved in responses to IAA (indole-3-acetic acid), ACC (1-aminocyclopropane-1-carboxylic acid), and ABA (abscisic acid). Hormone-dependent modulations in the expression of mt-encoded genes can also be mediated through mitochondrial transcription termination factors 15, 17, and 18 of the mTERF family and genes for tetratricopeptide repeat proteins that are coexpressed with mTERF genes, in addition to SWIB5 encoding a mitochondrial SWI/SNF (nucleosome remodelling) complex B protein. These genes specifically responded to hormone treatment, displaying both negative and positive regulation in a context-dependent manner. According to bioinformatic resources, their promoter regions possess putative cis-acting elements involved in responses to phytohormones. Alternatively, hormone-related transcriptional activity of these genes may be modulated indirectly, which is especially relevant for brassinosteroids (BS). In general, the results of the study indicate that hormones are essential mediators that are able to cause alterations in the transcript accumulation of mt-related nuclear genes, which in turn trigger the expression of mt genes.

Abstract: An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion, involving the decomposition of the organic matter in two or more substrates by bacteria and archaea, in the absence of oxygen. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab, with Monod kinetics involving two substrates and an intermediate (M2SI model), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi-Sugeno approach available in the ANFIS package of Matlab. The biomethane production results simulated by M2SI were used in neural network modeling, firstly employing a “generic” network applicable to all 8 scenarios. A very good fit was obtained (R²>0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition). The parameters of the M2SI model for the 8 different conditions were also mapped using a neural network, as a function of the organic material composition. A fit with R²>0.99 was obtained using 25 neurons. In the case of the fuzzy logic, RMSE of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks).

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

This work presents an approach for detecting mathematical formulas in scanned document images. The proposed approach is end-to-end trainable. Since many OCR engines cannot reliably work with the formulas, it is essential to isolate them to obtain the clean text for information extraction from the document. Our proposed pipeline comprises a hybrid task cascade network with deformable convolutions and a Resnext101 backbone. Both of these modifications help in better detection. We evaluate the proposed approaches on the ICDAR-2017 POD and Marmot datasets and achieve an overall accuracy of 96% for the ICDAR-2017 POD dataset. We achieve an overall reduction of error of 13%. Furthermore, the results on Marmot datasets are improved for the isolated and embedded formulas. We achieved an accuracy of 98.78% for the isolated formula and 90.21% overall accuracy for embedded formulas. Consequently, it results in an error reduction rate of 43% for isolated and 17.9% for embedded formulas.

Plastid-encoded RNA polymerase (PEP) forms a multisubunit complex in operating chloroplasts, where PEP subunits and a sigma factor are tightly associated with 12 additional nuclear-encoded proteins. Mutants with disrupted genes encoding PEP-associated proteins (PAPs) provide unique tools for deciphering mutual relationships among phytohormones. A block of chloroplast biogenesis in Arabidopsis pap mutants specifying highly altered metabolism in white tissues induced dramatic fluctuations in the content of major phytohormones and their metabolic genes, whereas hormone signaling circuits mostly remained functional. Reprogramming of the expression of biosynthetic and metabolic genes contributed to a greatly increased content of salicylic acid (SA) and a concomitant decrease in 1-aminocyclopropane-1-carboxylic acid (ACC) and oxophytodienoic acid (OPDA), precursors of ethylene and jasmonic acid, respectively, in parallel to reduced levels of abscisic acid (ABA). The lack of differences in the free levels of indole-3-acetic acid (IAA) between the pap mutants and wild-type plants was accompanied by fluctuations in the contents of IAA precursors and conjugated forms as well as multilayered changes in the expression of IAA metabolic genes. Along with cytokinin (CK) overproduction, all of these compensatory changes aim to balance plant growth and defense systems to ensure viability under highly modulated conditions.

With advances in next-generation sequencing(NGS) technologies, large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer mechanism is to identify the driver genes from the mutation genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profilings and copy number variation(CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.

The underground injection and geological sequestration of carbon dioxide (CO₂) is a promising approach for reducing the levels of CO₂ in the atmosphere. To mitigate the leakage of CO₂ resulting from natural fracture networks in rock masses, the sequestration process is commonly accompanied by the injection of reactive Portland cement representing a coupled hydro-chemo-mechanical process. In this work, a numerical approach based on the unified pipe network method (UPM) is presented that considers the coupled permeation and diffusion processes with chemical precipitation. Most input parameters are derived from the published literature. The proposed approach is validated through a comparison with analytical solutions, which are applied to simulate the CO₂ sequestration process in a fractured rock mass. The results indicate that the long-term sequestration effect, which is highly influenced by the fracture distribution, can be captured effectively by the model. Consequently, the presented approach can assist engineers in properly designing the arrangement of boreholes and determining the concentration of the grouting material.

Europium is one of the most reactive lanthanides and humans use it in many different applica-tions, but we still know little about its potential toxicity and cellular response to its exposure. Two strains of the eukaryotic microorganism model Tetrahymena thermophila adapted to high concentrations of two Eu(III) compounds (EuCl3 or Eu2O3) were obtained and compared to a control strain and cultures treated with both compounds. In this ciliate, EuCl3 is more toxic than Eu2O3. LC50 values show that this microorganism is more resistant to these Eu(III) compounds than other microorganisms. Oxidative stress originated mainly by Eu2O3 is minimized by overexpression of genes encoding important antioxidant enzymes. The overexpression of metallothionein genes under treatment with Eu(III) compounds supports the possibility that this lanthanide may interact with the -SH groups of the cysteine residues from metallothi-oneins and/or displace essential cations of these proteins during their homeostasis function. Both lipid metabolism (lipid droplets that fuse with europium-containing vacuoles) and au-tophagy are involved in the cellular response to europium stress. Bioaccumulation, together with a possible biomineralization to europium phosphate, seems to be the main mechanism of Eu(III) detoxification in these cells.

Human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. As in dynamic system concepts, one can consider cluster formation as the system attractor, cell seeding as the initial condition, and density change over time as a path within the basin of attraction. Herein, Cellular Automata, which is a class of Agent-Based Models, and Boolean Networks are discrete modeling methods used in population dynamics such that running the dynamics of cellular automata backward reveals an underlying network in terms of basin of attraction, also known as global dynamics. Thus, we hypothesize that one can estimate the local dynamic of an agent-based model from the basin of attraction of a boolean network. Here, we propose an approach to estimating these agent-based model rules, which consists of comparing the density states within each state transition and reaching a consensus among state transitions belonging to a basin of attraction of a boolean network. The objectives of this study are: (1) to infer a boolean network from the co-culture growth curve; (2) to estimate the rules of agent-based models; and (3) to implement spatial dynamics simulations. The binarization of the growth curve shows high population density after four days; the estimated agent-based model rules were compatible with cell proliferation and migration in agreement with literature, so we had to propose individual rules for survival and death. Spatial dynamics shows that (1) keratinocytes exhibit a higher density in neighborhoods where melanoma is present; (2) the chance of keratinocyte migration increases until the fourth day, then decreases, and the probability of survival increases substantially after four days; and (3) cells with low proliferation capacity die and free space for those with high ones. Our approach suggests that the attractor state of cell co-culture is induced mainly by an increase in keratinocyte migration and survival. Our approach has the potential to offer valuable clues about microenvironmental interactions or configurations that drive population dynamics.

Intramuscular fat content (IFC) and meat color are vital indicators affecting pork quality. A significant positive correlation between IFC and redness meat color (a* value) indicates that these two traits are likely to be regulated by shared molecular pathways. To identify candidate genes, hub genes, and signaling pathways that regulate these two traits, we measured the IFC and a* value in 147 hybrid pigs and selected individuls with extreme phenotypes for transcriptome analysis. The results revealed 485 and 394 overlapping differentially expressed genes (DEGs) using DESeq2, limma, and edgeR packages affecting IFC and a* value, respectively. Weighted gene co-expression network analysis (WGCNA) identified four modules significantly correlated with IFC and a* value. Moreover, we integrated functional enrichment analysis results based on DEGs, GSEA, and WGCNA conditions to identify candidate genes, and identified 47 and 53 candidate genes affecting IFC and a* value, respectively. The protein-protein interaction (PPI) network analysis of candidate genes showed that 6 and 14 hub genes affecting IFC and a* value, respectively. These genes mainly participated in various pathways related to lipid metabolism and redox reactions. Notably, four crucial hub genes (MYC, SOX9, CEBPB, and PPAGRC1A) were shared for these two traits. After functional annotation of these four hub genes, we hypothesized that the SOX9/CEBPB/PPARGC1A axis could co-regulate lipid metabolism and myoglobin redox response. Further research on these hub genes, especially the SOX9/CEBPB/PPARGC1A axis, will help to understand the molecular mechanism of the co-regulation of IFC and a* value, which will provide the theoretical basis for improving pork quality.

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.

Numerous in vitro studies on isolated cells have been conducted to uncover the molecular mechanisms of action of Panax ginseng Meyer root extracts and purified ginsenosides. However, the concentrations of ginsenosides and the extracts used in these studies were much higher than detected in pharmacokinetic studies in humans and animals orally administered with ginseng preparations at therapeutic doses. Our study aimed to assess: (a) the effects of ginsenoside Rg5, the major "rare" ginsenoside of Red Ginseng, on gene expression in the murine neuronal cell line HT22 in a wide range of concentrations, from 10-4 to 10-18 M, and (b) the effects of differentially expressed genes on cellular and physiological functions in organismal disorders and diseases. Gene expression profiling was performed by transcriptome-wide mRNA microarray analyses in HT22 cells after treatment with ginsenoside Rg5. Ginsenoside Rg5 exhibits soft-acting effects on gene expression of neuronal cells in a wide range of physiological concentrations and strong reversal impact at high (toxic) concentration: significant up- or downregulation of expression of about 300 genes at concentrations from 10-6 M to 10-18 M, and dramatically increased both the number of differentially expressed target genes (up to 1670) and the extent of their expression (fold changes compared to unexposed cells) at a toxic concentration of 10-4 M. Network pharmacology analyses of genes expression profiles using Ingenuity pathway analysis (IPA) software showed that at low physiological concentrations, ginsenoside Rg5 has the potential to activate the biosynthesis of cholesterol and to exhibit predictable effects in senescence, neuroinflammation, apoptosis, and immune response, suggesting soft-acting, beneficial effects on organismal death, movement disorders, and cancer.

Acetamiprid is a broad-spectrum neonicotinoid insecticide used in agriculture to control aphids. While recent studies have documented resistance to acetamiprid in several aphid species, the underlying mechanisms are still not fully understood. In this study, we analyzed the transcriptome and metatranscriptome of a laboratory strain of the pea aphid, Acyrthosiphon pisum (Harris, 1776), with reduced susceptibility to acetamiprid after nine generations of exposure to identify candidate genes and the microbiome involved in the adaptation process. Sequencing of the transcriptome of both selected (RS) and non-selected (SS) strains allowed the identification of 14858 genes and 4938 new transcripts. Most of the differentially expressed genes were associated with catalytic activities and metabolic pathways involving carbon and fatty acids. Specifically, the alcohol-forming fatty acyl-CoA reductase (FAR) and acyl-CoA synthetase (ACSF2), both involved in the synthesis of epidermal wax layer components, were significantly upregulated in RS, suggesting that adaptation to acetamiprid involves the synthesis of a thicker protective layer. Metatranscriptomic analysis revealed subtle shifts in the microbiome of RS. These results contribute to a deeper understanding of acetamiprid adaptation by the pea aphid and provide new insights for aphid control strategies.

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies GO terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g. weakly, moderately or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, SEA and GSEA. These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server https://webfsgor.sysbio.cytogen.ru/ .

Originating in the Mediterranean, B. oleracea is grown all over the world and is highly valued for its nutritional value and variety in cooking. Its susceptibility to pests, diseases, and environmental stressors, however, poses significant challenges to sustaining consistent crop quality and productivity. The BASIC PENTACYSTEINE gene significantly impacts plant growth and responses to abiotic stresses. Nevertheless, little is known about its precise role and the underlying biological processes in B. oleracea. Eleven BPC genes in B. oleracea were identified by a thorough genome-wide investigation and categorized into three categories according to their structural traits and gene motifs. There were eight different chromosomes on which these BoBPCs were located. Comparative synteny analysis was used to reveal the evolutionary traits of potatoes, Arabidopsis, and B. oleracea. The BoBPC family genes in B. oleracea were found to have specific cis-elements linked to growth, stress responses, and hormone regulation when their promoter regions were studied. Cuticular wax synthesis expression revealed that all BoBZR1 genes, from wild to mutant, expressed at varied amounts in distinct types. Moreover, distinctive BoBPC gene expression patterns were found in the seven distinct tissues that were studied. Our thorough analysis shows that the BoBPC family is widely involved in B. oleracea developmental processes. The present findings provide an established theoretical basis for further exploration into the composition and roles of BoBPC in B. oleracea.

The fusion of "innovation theory" and "ecology" gave birth to a large number of studies on "innovation ecology", which mainly studies how to build an industrial ecological chain at the regional level, focusing on self-evolution, achieving ecological balance, and enabling the regional economy to take the path of sustainable innovation. This type of research borrows a lot of concepts from ecology, and very vividly describes the competition and cooperation relationships formed by various agents in the innovation system, laying a good foundation for qualitative analysis of the inherent dynamics of innovation development. However, many studies focus on the analogous description of ecosystems and economic systems, lacking scientifically and rigorously quantitative empirical research as a support. This paper uses network-based indicators such as degree, cluster coefficient, and betweenness centrality to measure the function and position of high-tech enterprises in the Z-Park in a business environment, so as to clarify the socio-economic meaning of the topological structure of the regional innovation system. On this basis, it provides theoretical references for regional innovation development and sustainable development policy formulation.

The excellent properties of nanomaterials have been confirmed in many fields, but the effects on plants are still unclear. In this study, we have found that bismuth vanadium promoted the growth of taproots and the development of lateral roots, increased the roots activity by regulating the expression of polyamine or hormone-related genes and reducing the accumulation of reactive oxygen species in Arabisdopsis thaliana. Meantime, BV had higher antimicrobial effect than that of graphene oxide (GO). These results showed us a new perspective possibility of nanomaterials assisting the development of plants and crops, which is expected to become one of the ways to solve the problem of controlling and promoting the development of plants and crops.

Viral co-infections, where a host is infected by multiple viruses simultaneously, are common in the human population. Human viral co-infections can lead to complex interactions between viruses and the host immune system, affecting the clinical outcome and posing challenges for treatment. Understanding the types, mechanisms, impacts, and identification methods of human viral co-infections is crucial for the prevention and control of viral diseases. In this review, we first introduce the significance of studying human viral co-infections and summarize the current research progress and gaps in this field. We then classify human viral co-infections into four types based on the pathogenic properties and species of the viruses involved. Next, we discuss the molecular mechanisms of viral co-infections, focusing on virus-virus interactions, host immune responses, and clinical manifestations. We also summarize the experimental and computational methods for identifying viral co-infections, emphasizing the latest advances in high-throughput sequencing and bioinformatics approaches. Finally, we highlight the challenges and future directions in human viral co-infection research, aiming to provide new insights and strategies for the prevention, control, diagnosis, and treatment of viral diseases. This review provides a comprehensive overview of the current knowledge and future perspectives on human viral co-infections, and underscores the need for interdisciplinary collaborations to address this complex and important topic.

The present study was performed to examine that transforming growth factor beta (TGF-β) in root-surrounding tissues on deciduous teeth during the physiological root resorption regulates the differentiation induction into odontoclast. We prepared root-surrounding tissues with (R) or without (N) physiological root resorption scraped off at three regions (R1-R3 or N1-N3) from the cervical area to the apical area of the tooth and measured both TGF-β and the tartrate-resistant acid phosphatase (TRAP) activities. The TGF-β activity level was increased in N1-N3, whereas the TRAP activity was increased in R2 and R3. In vitro experiments for RANKL-mediated osteoclast differentiation revealed that TGF-β in N1-N3 and R1-R3 enhanced the TRAP activity in RAW264 cells. A genetic study indicated that the mRNA level of TGF-β1 in N1 and N2 was significantly increased, and corresponded with that of osteoprotegerin (OPG). In contrast, the expression level of receptor activator of NF-κB ligand (RANKL) was increased in R2 and R3. Our findings suggest that TGF-β is closely related to the regulation of OPG induction and RANKL-mediated odontoclast differentiation depending on the timing of RANKL and OPG mRNA expression in the root-surrounding tissues of deciduous teeth during physiological root resorption.

Drought stress on soybean is a research-demanding matter for negative influence that agricultural drought brings about. This study was designated to evaluate the effect of drought stress on some gene expression in flowering and pod elongation stages in soybean. This experiment was carried out in split-plot format with RCBD design with four replicates. Drought stress as the main factor included three levels (irrigation after 50, 100, and 150 millimeters evaporation from the A-class evaporation pot) of which 50 millimeters evaporation is considered as control. The sub-factor included a factorial combination of 3 varieties (DPX, Sari and WE6) and two sampling stages (flowering and pod elongation). The gene expression analysis was carried out by using the QRT-PCR technique. According to our results, all genes have shown overexpression in drought stress despite this result was not the same for all genotypes and stress levels. Some genes have up-regulated in mediate stress (treatment 100) level (like as Gmdreb 2, Gmdreb 5, GmRD20A, GmaxACD2) and other genes up-regulated in serve stress (treatment 150) level. Between genotypes, DPX cultivar and WE6 line were better than of the sari cultivar for all genes up-regulated.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

Alfalfa, an important legume forage, is an ideal crop for sustainable agriculture and a potential bioenergy plant. Drought, one of the most common environmental stresses, substantially affects plants’ growth, development and productivity. MicroRNAs (miRNAs) are newly discovered gene expression regulators that have been linked to several plant stress responses. To elucidate the role of miRNAs in drought stress regulation of alfalfa, a high-throughput sequencing approach was used to analyze 12 small RNA libraries comprising of 4 samples, each with 3 biological replicates. We identified 348 known miRNAs, belonging to 80 miRNA families, from the 12 libraries and 281 novel miRNAs using Mireap software. 18 known miRNAs in roots and 12 known miRNAs in leaves were screened out as drought-responsive miRNAs. Except for miR319d and miR157a which were upregulated under drought stress, the expression pattern of drought-responsive miRNAs were different between roots and leaves in alfalfa. This is the first study discovering miR157a, miR1507, miR3512, miR3630, miR5213, miR5294, miR5368 and miR6173 are drought-responsive miRNAs. Target transcripts of drought-responsive miRNAs were computationally predicted. All 447 target genes for the known miRNAs were predicted using an online tool. This study provides a significant insight on understanding drought-responsive mechanisms of alfalfa.

Thymomas (TH) are a unique group of heterogeneous tumors of the thymic epithelium. In particular, the subtypes B2 and B3 tend to be aggressive and metastatic. Radical tumor resection remains the only curative option for localized tumors, while more advanced TH require multimodal treatment. Deep sequencing analyses have failed to identify known oncogenic driver mutations in TH, with the notable exception of the GTF2I mutation, which occurs predominantly in type A and AB TH. However, there are multiple alternative non-mutational mechanisms (e.g., perturbed thymic developmental programs, metabolism, non-coding RNA networks) that control cellular behavior and tumorigenesis through deregulation of critical molecular pathways. Here, we attempted to show how the results of studies investigating such alternative mechanisms could be integrated into a current model of TH biology. This model could be used to focus ongoing research and therapeutic strategies.

Long non-coding RNA (lncRNA) can regulate several aspects of gene expression, being associated with complex phenotypes in humans and livestock species. In taurine beef cattle, recent evidence points to the involvement of lncRNA in feed efficiency (FE), a proxy for increased productivity and sustainability. Here, we hypothesized specific regulatory roles of lncRNA in FE of indicine cattle. Using RNA-Seq data from liver, muscle, hypothalamus, pituitary and adrenal gland from Nellore bulls with divergent FE, we submitted new transcripts to a series of filters to confidently predict lncRNA. Then, we identified lncRNA that were differentially expressed (DE) and/or key regulators of FE. Finally, we explored lncRNA genomic location and interactions with miRNA and mRNA to infer potential function. We were able to identify 126 relevant lncRNA for FE in Bos indicus, some with high homology to previously identified lncRNA in Bos taurus and some possible specific regulators of FE in indicine cattle. Moreover, lncRNA identified here were linked to previously described mechanisms related to FE in hypothalamus-pituitary-adrenal axis and are expected to help elucidate this complex phenotype. This study contributes to expanding the catalogue of lncRNA, particularly in indicine cattle, and identifies candidates for further studies in animal selection and management.

Panax ginseng C.A.Mey. is an adaptogenic plant traditionally used to enhance mental and physical capacities in cases of weakness, exhaustion, tiredness, loss of concentration, and during recovery. According to ancient records, Red Ginseng root preparations enhance longevity with long-term intake. Recent pharmacokinetic studies of ginsenosides in humans and our in vitro study in neuronal cells suggest that ginsenosides are effective when their level in blood is shallow - at concentrations from 10-6 to 10-18 M. In the present study, we compared the effects of Red Ginseng root preparation HRG80TM(HRG) at concentrations from 0.01 to 10,000 ng/ml with effects of White Ginseng (WG) and purified ginsenosides Rb1, Rg3, Rg5 and Rk1 on gene expression of isolated hippocampal neurons. The aim of this study was to predict the effects of differently expressed genes on cellular and physiological functions in organismal disorders and diseases. Gene expression profiling was performed by transcriptome-wide mRNA microarray analyses in murine HT22 cells after treatment with ginseng preparations. Ingenuity pathway downstream/upstream analysis (IPA) was performed with datasets of significantly up-or downregulated genes, and expected effects on cellular function and disease were identified by IPA software. Ginsenosides Rb1, Rg3, Rg5, and Rk1 have substantially various effects on gene expression profiles (signatures) and are different from signatures of HRG and WG. Furthermore, the signature of HRG is changed significantly with dilution from 10000 to 0.01 ng/ml. Network pharmacological analyses of gene expression profiles showed that HRG exhibits predictable positive effects in neuroinflammation, senescence, apoptosis, and immune response, suggesting beneficial soft-acting effects in cancer, gastrointestinal, and endocrine systems diseases and disorders in a wide range of low concentrations in blood.

Cell identity is determined by chromatin structure and the profiles of gene expression, which are dependent on chromatin accessibility and DNA methylation of regions critical for gene expression, such as enhancers and promoters. These epigenetic modifications are required for mammalian development and are essential for the establishment and the maintenance of the cellular identity. DNA methylation was once thought to be a permanent repressive epigenetic mark, but systematic analysis in various genomic contexts reveals a more dynamic regulation than previously thought. In fact, both active DNA methylation and demethylation occur during cell fate commitment and terminal differentiation. To link methylation signatures of specific genes to their expression profiles, we have determined the methyl-CpG configurations of the promoters of five genes switched on and off during murine postnatal brain differentiation by bisulfite-targeted sequencing. We report here the structure of significant, dynamic and stable methyl-CpG profiles associated with silencing or activation of expression of genes during neural stem cell and brain postnatal differentiation. Strikingly, these methylation cores mark different mouse brain areas and cell types derived from the same areas during differentiation.

Transporter associated with antigen processing 1 (TAP1) gene codes for a transporter protein, which is responsible for tumor antigen presentation in the MHC I or HLA complex. A defect in the gene results in an inadequate tumor tracking. TAP1 may also influence multi drug resistance, which is an extreme threat in providing treatment by drugs which are chemotherapeutic. The gene of TAP1 was analyzed bioinformatically. It gave us prognostic data as a confirmation of whether it should be used as a biomarker. The expression level and pattern analysis were conducted using ONCOMINE, GENT2 and GEPIA2 online platforms. Samples with different clinical outcomes were investigated for expression and promoter methylation analysis was done in cancer vs normal tissues using UALCAN. The copy number alteration and mutation frequency and expression in different cancer studies were analyzed using cBioPortal. The PrognoScan and KM plotter survival analysis of significant data (p-value<0.05) was representing graphically. Pathway and Gene ontology analysis of gene correlated to TAP1 gene was presented using bar charts. After arranging the data in a single panel and correlating expression to prognosis, understanding mutational and alterations and comparing pathways, TAP1 may be a potential novel target to evade a threat against chemotherapy and the study gives new aspects to consider for immunotherapy in human breast, lung, liver and ovarian cancer.

Identification of the differentially-expressed genes is important for clarification of the complex molecular mechanisms under drought conditions. In this experiment, transcriptome profiles of sensitive and tolerant tomato genotypes under drought stress were analyzed. Three up-regulated genes were selected, included CAB3 (Chlorophyll a-b binding protein3), SAMDC (S-adenosylmethionine decarboxylase proenzyme), and ACS9 (1-aminocyclopropane-1-carboxylate synthase 9). After bioinformatics analysis, tomato genotypes were subjected to drought stress and gene expression was determined using Real-Time-PCR. Physiological parameters of genotypes were also measured by spectrophotometer-based methods. According to the results, these three genes play a key role in stress tolerance. Expression of the CAB3 gene in both sensitive and tolerant genotypes was not significantly different compared to the control, but the SAMDC gene decreased in both genotypes and the ACS9 gene decreased in sensitive genotype and increased in tolerant genotype. The physiological analysis also showed that under stress conditions, the photosynthetic system of the plant was disrupted and the chlorophyll content was reduced, but, proline content and antioxidant enzymes activity increased, in which their quantity in the tolerant genotype was significantly higher than sensitive. Under drought stress, due to damage to the lipid membrane, Malondialdehyde content also increased, in which the sensitive genotype was more affected.

Myocardial hypertrophy is the most common condition that accompanies heart changings in children. Transcriptional pathways regulating gene expression playing a critical role both in cardiac embryogenesis and in the pathogenesis of congenital hypertrophic cardiomyopathy, neonatal posthypoxic myocardial hypertrophy and congenital heart diseases. This paper describes the state of cardiac gene expression and potential pharmacological modulators at different transcriptional levels. An experimental model of perinatal cardiac hypoxia showed downregulated expression of genes responsible for cardiac muscle integrity and overexpressed genes associated with energy metabolism and apoptosis, which may provide a basis for a therapeutic approach. Current evidence suggests that RNA drugs, theaflavin, neuraminidase, proton pump and histone deacetylase inhibitors are promising pharmacological agents in progressive cardiac hypertrophy. The different points of application of the above drugs makes combined use possible, potentiating the effects of inhibition in specific signaling pathways. The special role of N-acetyl cysteine in both the inhibition of several signaling pathways and the reduction of oxidative stress was emphasized.

Aedes aegypti (L.) is the primary vector of chikungunya, dengue, yellow fever and Zika viruses. The leucine-rich repeats (LRR)-containing domain is evolutionarily conserved in many proteins associated with innate immunity in invertebrates and vertebrates, as well as plants. We focused on the AaeLRIM1 and AaeAPL1 gene expressions in response to Zika virus (ZIKV) and Chikungunya virus (CHIKV) infection using a time course study, as well as the developmental expressions in the eggs, larvae, pupae, and adults. RNA-seq analysis data provided 60 leucine-rich repeat related transcriptions in Ae. aegypti in response to Zika virus (Accession number: GSE118858, https://www.ncbi.nlm.nih.gov/gds/?term=GSE118858). RNA-seq analysis data showed that AaeLRIM1 (AAEL012086-RA) and AaeAPL1 (AAEL009520-RA) were significantly upregulated 2.5 and 3-fold during infection by ZIKV 7-days post infection (dpi) of an Ae. aegypti Key West strain compared to an Orlando strain. The qPCR data showed that LRR-containing proteins AaeLRIM1, AaeAPL1 and five paralogues were expressed 100-fold lower than other nuclear genes, such as defensin, during all developmental stages examined. Together, these data provide insights into transcription profiles of LRR proteins of Ae. aegypti during its development and in response to infection with emergent arboviruses.

Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM was an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.

Primary central nervous system and brain tumors are one of the global burdens that are continuously increasing in cases and requiring more treatment options. Surgery has been the leading treatment option for tumors, however, the localization of the tumor and its infiltrating nature make this option challenging. DNA microarray expression profiles of different CNS tumors provide insight into potential biomarkers for identifying different tumor types and subtypes. Here, we utilized the differentially expressed genes common in four expression profiles, GSE66354, GSE68848, GSE74195, and GSE43290 to reconstruct the gene co-expression network. In this study, we were able to identify preserved cluster genes, hub genes, co-regulating transcription factors, miRNA families, and candidate repurposed drugs. Fourteen identified hub genes, which were, CACNA1A, DNM1, GABRA1, GRIA2, MAPT, SLC17A7, SNAP25, SNAP91, STXBP1, SYT1, COL1A1, COL6A2, FBN2, and FN1 appeared to play a role in tumor progression and may serve as drug targets. We also reported the DEGs in each tumor type, which were ATRT, EPN, PA, MED, PNET, MEN, ACM, ODG, and GBM. Five identified miRNA families, which were, let-7 family, mir-124 family, mir-1 family, mir-103 family, and mir-27 family described in the literature to have tumor-suppressing characteristics. Drug-gene and drug-transcription factor network revealed 32 candidate repurposed drugs and 13 were validated through connectivity map analysis. Here, two main repurposed drugs fit the regulatory requirement for gene interaction, these were, quercetin and vorinostat. Future validation in experimental studies may utilize the future use of the candidate repurposed drugs. Our study provides insights into drug repurposing prospects and understanding tumor expressions.

The complex interplay among pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model organism Yersinia entercolitica (Ye) is one of the five top frequent causes of bacterial gastroenteritis based on the Epidemiological Bulletin of the Robert Koch Institute (RKI) published on September 10, 2020. A fundamental challenge in predicting the course of an infection is to understand whether co-infection with two Yersinia strains differing only in their capacity to resist killing by the host immune system may decrease the overall virulence by competitive exclusion or increase it by acting cooperatively. Herein, we study the primary interactions among Ye, the host immune system and the microbiota, and their influence on Yersinia population dynamics. The employed model considers two host compartments, the intestinal mucosa and lumen, commensal bacteria, the co-existence of wild-type and mutant Yersinia strains, as well the host immune responses. We determine four possible equilibria: the disease-free, wild-type-free, mutant-free, and co-existence of wild-type and mutant equilibrium. We also calculate the reproduction number for each strain as a threshold parameter to determine if the population may either be eradicated or persist within the host. We conclude that the infection should disappear if the reproduction numbers for each strain fall below one, and the commensal bacteria’s growth rate exceeds the pathogens’ growth rates. These findings will help inform public health control strategies. The supplement includes MATLAB source script, Maple workbook, and figures.

COVID-19 manifests regarding extreme acute respiratory conditions caused by a novel beta coronavirus (SARS-CoV-2) which is reported to be the seventh coronavirus to infect humans. Like other SARS-CoVs it has a large positive-stranded RNA genome. But specific furin site in the spike protein, mutation prone and phylogenetically mess Orf1ab separates SARS-CoV-2 from other RNA viruses. Since, the outbreak (February - March 2020) which originated in China, researchers, scientists, and medical professionals are inspecting all possible facts from every possible aspect including its replication, detection, and prevention strategies. This led to the prompt identification of its basic biology, genome characterization, structural based functional information of proteins, and strategies to prevent its spread. Due to the rapid mutation rate, the functional characterization of a few proteins is still lagging. This review summarizes the recent updates on the basic molecular biology of SARS-CoV-2 and prevention strategies undertaken worldwide to tackle COVID-19. This recent information can be implemented for the development and designing of therapeutics against SARS-CoV-2.

Research in various fields of evolutionary biology has shown that divergence in gene expression is a key driver for phenotypic variation. An exceptional contribution of cis-regulatory evolution has for instance been found to contribute to morphological diversification. In the light of these findings, the analysis of genome-wide expression data has become one of the central tools to link genotype and phenotype information on a more mechanistic level. However, in many studies, especially if general conclusions are drawn from such data, a key feature of gene regulation is often neglected. With our article, we want to raise awareness that gene regulation and thus gene expression is highly context dependent. Genes show tissue- and developmental stage-specific expression. We argue that the regulatory context must be considered when studying evolution of gene expression.

Degussa P25 is a benchmark form of TiO₂ used worldwide in photocatalysis studies. Currently no such benchmark exists for co-catalysts, which are essential for many photocatalytic reactions. Here, we present the preparation of Pt nanocluster co-catalysts on TiO₂ using an unmodified commercial source and equipment that is commonly available. Transmission electron microscopy reveals that the procedure produces TiO₂ decorated with Pt atom and nanoclusters (1-5 atoms). Optical reflectance and X-ray diffraction measurements show that the procedure does not affect the TiO₂ polymorph or UV-Vis absorbance. Gas phase photocatalytic splitting of heavy water (D₂O) shows that the Pt nanocluster decorated TiO₂ outperforms Pt nanoparticle (produced by photodeposition) decorated TiO₂ in D₂ production. Pt nanoclusters, produced directly from a commercial source, with high co-catalyst activity are prime candidates to be used in benchmark photocatalytic reactions.

Autophagy is a highly conserved self-degradation process involving the degradation and recycling of cellular components and organelles. Although the involvement of autophagy in metabolic changes during fruit ripening has been preliminarily demonstrated, the variations in autophagic flux and specific functional roles in fruit ripening remain to be elucidated. In this study, we analyzed the occurrence of autophagy during tomato fruit ripening. The results revealed differential expression of SlATG8 family member during tomato fruit ripening. Transmission electron microscopy observations and dansylcadaverine (MDC) staining confirmed the presence of autophagy at the cellular level in tomato fruits. Furthermore, the overexpression of SlATG8f induced the formation of autophagosomes, increased autophagic flux within tomato fruits, effectively enhanced the expression of ATG8 proteins during the color transition phase of fruit ripening, thereby promoting tomato fruit maturation. This overexpression also led to the accumulation of vitamin C (VC) and soluble solids while reducing acidity in the fruit. Collectively, our findings highlight the pivotal role of SlATG8f in enhancing tomato fruit ripening, providing insights into the mechanistic involvement of autophagy in this process. This research contributes to a better understanding of key factors regulating tomato fruit quality and offers a theoretical basis for tomato variety improvement.

In this manuscript we use an exactly solvable stochastic binary model for regulation of gene expression to analyse the dynamics of response to a treatment aiming to modulate the number of transcripts of RKIP gene. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics towards pre-cancerous state: i. to increase the promoter’s ON state duration; ii. to increase the mRNAs’ synthesis rate; iii. to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reducing drug dosage by simultaneously targeting multiple kinetic rates. That enables a reduction of treatment response time and heterogeneity which in principle diminishes the chances of emergence of resistance to treatment. This approach may be useful for inferring kinetic constants related to expression of antimetastatic genes or oncogenes and on the design of multi-drug therapeutic strategies targeting master regulatory genes.

Integrating sustainable processes and innovative waste utilization not only addresses environ-mental challenges but also aligns with evaluating energy sustainability and creating the circular waste management. This study presents a comparative analysis of liquid fuel production from waste tires and water hyacinth using two thermochemical methods: co-pyrolysis (direct method) and co-gasification integrated with the Fischer-Tropsch (FT) process (indirect method). The As-pen Plus simulation software version 9 is employed to develop models, determining optimal operating conditions for maximum liquid fuel, and assessing economic viability. Co-pyrolysis simulations reveal an increase in oil products and a decrease in gas products with higher blending weight ratios of waste tires/water hyacinth (W/B) and lower pyrolyser temperatures. Operating at 400°C with a blending weight ratio of 75:25 provided the maximum liquid fuel rate of 6649.22 gal/day. For co-gasification, optimal conditions (W/B = 50:50, T = 800°C, steam to feed ratio (S/F) = 1) result in the highest syngas yield of 115.92 kmol/h. Fischer-Tropsch simulations demonstrate an increase in gasoline and a decrease in diesel with rising reactor temperatures. At 230°C, the maximum liquid fuel rate reaches 8817.15 gal/day. The economic and production yield analysis indicates that the indirect method better than the direct method, providing higher liquid fuel yields and investment worthiness.

MicroRNAs are used as biomarkers for classification of cancer subtypes since certain miRNAs are differentially expressed in normal and patient samples. Moreover, miRNAs target mRNAs and can heavily influence Gene Expressions. Thus, deregulation of miRNAs is linked to various disorders. Thus, miRNAs can be used for prognosis and developing personalized health solutions for patients. Given the importance of miRNAs, there has been substantial work done in the field. In this paper, recent works in the field of using miRNAs expressions of patients were considered. A total of 20 papers were surveyed which utilized feature selection ensembles, fuzzy logic as well as deep learning. 10 papers have been reported which offer insight into how miRNAs can be utilized for subtype-specific or generalized cancer diagnosis.

The increase in carbon dioxide emissions has a significant impact on human society and the global environment. As the most abundant and cheap C1 resource, the conversion and utilization of carbon dioxide has received extensive attention from researchers. In many methods of carbon dioxide conversion and utilization, the reverse water gas conversion reaction is considered to be one of the most effective ways. In this paper, the research progress of reverse water gas conversion reaction in recent years is introduced.

Inorganic polyphosphate (polyP) is an important factor of stress tolerance in microbial cells. In yeast, the major enzyme of polyP biosynthesis is Vtc4, a subunit of the vacuole transporter chaperone (VTC) complex. In this study, we demonstrated that Vtc4 knockout in Saccharomyces cerevisiae not only decreased polyP content but also caused shifts in the composition of the intracellular polyP pool and changed the stress tolerance profile. In the mutant S. cerevisiae, the level of short-chain acid-soluble polyPs was decreased nearly 10-fold, whereas that of longer acid-insoluble polyPs was decreased only 2-fold, suggesting the existence of other enzymes compensating the production of long-chain polyPs. The Δvtc4 mutant showed inhibition of Mg2+-dependent phosphate uptake and decreased resistance to alkaline stress but increased tolerance to oxidation and heavy metal ions, especially Mn2+. Quantitative PCR revealed the upregulation of the DDR2 gene implicated in multiple stress responses and downregulation of PHO84 encoding a phosphate and Mn2+ transporter, which could account for the effects on phosphate uptake and Mn2+-related stress response in the Δvtc4 mutant. Our study indicates that short-chain polyPs, plays an important role in the regulation of stress response in yeast.

Epitranscriptomics refers to post-transcriptional regulation of gene expression via RNA modifications and editing that affect RNA functions. Many kinds of modifications of mRNA have been described, among which N6-Methyladenosine (m6A), N1-methyladenosine (m1A), 7-methylguanosine (m7G), pseudouridine (Ψ), 5-methylcytidine (m5C). They alter mRNA structure and consequently stability, localization and translation efficiency. Perturbation of the epitranscriptome is associated with human diseases, thus opening the opportunity for potential manipulations as therapeutic approach. In this review, we overview the functional roles of epitranscriptomic marks in the skeletal muscle system, in particular in embryonic myogenesis, muscle cell differentiation and muscle homeostasis processes. Further, we explored high throughput epitranscriptome sequencing data to identify RNA chemical modifications in muscle-specific genes and we discuss the possible functional role and the potential therapeutic applications.

Despite of being an exceptionally biodiversity rich country, the forest coverage of Bangladesh is declining at an alarming rate. Declaration and management of protected areas in this regard is one of the efforts from government side to tackle the loss of biodiversity. The limited numbers of forest-protected areas (FPA), established to conserve the dwindling forest biodiversity of the country with high pressure on them for timber, non-timber forest products, and fuelwood - makes their management challenging. Moreover, most of the FPAs of the country declared only in the recent decades with very limited infrastructure, manpower and policy support for monitoring and governance. Some people-centred approaches for the management of FPAs and alternative livelihood and income generation subsidies although made available through a few project interventions, their number are still inadequate and performance remains less than satisfactory. This chapter provides a critical review of the FPAs of Bangladesh looking at their role in biodiversity conservation, management challenges, and key lessons from previous management interventions with recommendations for the future. It has been revealed that the FPA system of Bangladesh still poorly represents the diverse forest ecosystems with relatively small forest size and lack of corridors for the movement of wildlife. There are ample opportunities to render co-management of FPAs an effective strategy to minimize the conflicts in FPAs management in the country. It is, however, important to ensure the access of local forest-dependent people to different alternative income generating options that may adequately support their livelihoods.

The importance of genome organization at the supranucleosomal scale in the control of gene expression is increasingly recognized today. In mammals, Topologically Associating Domains (TADs) and the active / inactive chromosomal compartments are two of the main nuclear structures that contribute to this organization level. However, recent works reviewed here indicate that, at specific loci, chromatin interactions with nuclear bodies could also be crucial to regulate genome functions, in particular transcription. They moreover suggest that these nuclear bodies are membrane-less organelles dynamically self-assembled and disassembled through mechanisms of phase separation. We have recently developed a novel genome-wide experimental method, High-salt Recovered Sequences sequencing (HRS-seq), which allows the identification of chromatin regions associated with large ribonucleoprotein (RNP) complexes and nuclear bodies. We argue that the physical nature of such RNP complexes and nuclear bodies appears to be central in their ability to promote efficient interactions between distant genomic regions. The development of novel experimental approaches, including our HRS-seq method, is opening new avenues to understand how self-assembly of phase separated nuclear bodies possibly contributes to mammalian genome organization and gene expression.

Traditional methods for discovery and development of new drugs can be a very time-consuming and expensive process because it includes several stages such as compound identification, pre-clinical and clinical trials before the drug is approved by the US Food and Drug Administration (FDA). Therefore, drug repurposing, namely using currently FDA-approved drugs as therapeutics for other diseases than what they are originally prescribed for, is emerging to be a faster and more cost-effective alternative to current drug discovery methods. In this paper, we have described a three-step in silico protocol for analyzing transcriptomics data using online databases and bioinformatics tools for identifying potentially repurposable drugs. The efficacy of this protocol was evaluated by comparing its predictions with the findings of two case studies of recently reported repurposed drugs: HIV treating drug Zidovudine for the treatment of Dry Age-Related Macular Degeneration and the antidepressant Imipramine for Small-Cell Lung Carcinoma. The proposed protocol successfully identified the published findings, thus demonstrating the efficacy of this method. In addition, it also yielded several novel predictions that have not yet been published, including the finding that Imipramine could potentially treat Severe Acute Respiratory Syndrome (SARS), a disease that currently does not have any treatment or vaccine. Since this in-silico protocol is simple to use and does not require advanced computer skills, we believe any motivated participant with access to these databases and tools would be able to apply it to large datasets to identify other potentially repurposable drugs in the future.

The AREB/ABF (ABA response element binding) proteins in plants are vital for plant stress responses, but the understanding of AREB/ABFs in orchid species, an important traditional medicinal and ornamental plants, is limited. Here, twelve AREB/ABF genes were identified within three orchids’ complete genome and classified into three groups (groups I, II, and III) via a phylogenetic analysis, which was further supported by the analysis of their conserved motifs and gene structures. The cis-elements analysis showed that hormone response elements followed by light and stress responses were widely rich in AREB/ABFs. A prediction analysis of orchid ABRE/ABF-mediated regulatory network was further constructed through cis-regulatory elements (CREs) analysis of their promoter regions and revealed that several transcriptional factor (TF) gene family were abundant as the potential regulators for orchid AREB/ABFs. Expression profile analysis based on public transcriptomic data showed that most AREB/ABF genes have distinct tissue-specific expression patterns in orchid plants. Additionally, DcaABI5 as the homolog of ABA INSENSITIVE 5 (ABI5) from Arabidopsis was selected for further analysis. The results showed that Arabidopsis transgenic overexpressing DcaABI5 could rescue the ABA insensitive phenotype in the mutant abi5. Collectively, these findings will provide valuable information on AREB/ABF genes in orchids.

Background: The commonly known assumption that combinations of several herbs in one formulation can have better efficacy due to additive or synergistic effects has not been systematically studied despite some evidence supporting the synergy concept. Study aim: The study aimed to reveal the molecular interactions in situ of host cells in response to the intervention of BHP and justify the benefits of implementing BHP in clinical practice. Results: This overview provides the results of recent clinical and network pharmacology studies of botanical herbal preparations (BHP) of Rhodiola with other plants, including Ashwagandha, Green Tea, Eleutherococcus, Schisandra, Eleutherococcus, Leuzea, Caffeine, Cordyceps, Gingko, Black Cohosh, saffron, and L-carnosine. Conclusions: The most important finding from network pharmacology studies of BHP was the evidence supporting the synergistic interaction of BHP ingredients, revealing unexpected new pharmacological activities unique and specific to the new BHP. Some studies show the superior efficacy of BHP compared with mono-drugs. At the same time, some a priori-designed combinations can fail, presumably due to antagonistic interactions and crosstalk between molecular targets within molecular networks involved in the cellular and overall response of organisms on the intervention. Network pharmacology studies help predict the results of studies to discover new indications and unpredicted adverse events.

It was recently demonstrated that a co-planar capacitive sensor could be applied to the evaluation of materials without the disadvantages associated with the other techniques. This technique effectively detects changes in the dielectric properties of the materials due to, for instance, imperfections or variations in the internal structure, by moving a set of simple electrodes on the surface of the specimen. An AC voltage is applied to one or more electrodes and signals are detected by others. This is a promising inspection method for imaging the interior structure of the numerous materials, without the necessity to be in contact with the surface of the sample. In this paper, Finite Element (FE) modelling was employed to simulate the electric field distribution from a co-planar capacitive sensor and the way it interacts with a non-conducting sample. Physical experiments with a prototype capacitive sensor were also performed on a Plexiglas sample with sub-surface defects, to assess the imaging performance of the sensor. A good qualitative agreement was observed between the numerical simulation and experimental result.

Water deficit greatly affects global crop growth and productivity, particularly in water-limited environments like upland rice cultivation, leading to reduced grain yield. Plants activate various defense mechanisms during water deficit, involving numerous genes and complex metabolic pathways. Exploring orthologous genes, linked to enhanced drought tolerance, can aid functional validation in target species using genomic data from model organisms. Here, we studied the OsCPK5 gene in upland rice, an AtCPK6 ortholog from A. thaliana, by overexpressing it in the BRSMG Curinga cultivar. Transformants were assessed under two conditions: water deficit applied 79 days after seeding, lasting 14 days, followed by 7 days of irrigation at 80% field capacity. Physiological data and samples were collected at R3, R6, and R8 stages. GM plants consistently exhibited higher OsCPK5 gene expression across stages, peaking during grain filling. During this phase, GM plants displayed reduced stomatal conductance, photosynthetic rate, and increased water use efficiency compared to non-GM plants under drought. GM plants also exhibited higher filled grain percentage in both irrigation conditions. Their drought susceptibility index was 0.9 times lower than NGM plants, and they maintained a higher chlorophyll a/b index, indicating sustained photosynthesis. NGM plants under water deficit exhibited more leaf senescence, while OsCPK5-overexpressing plants retained green leaves. Overall, OsCPK5 overexpression induced diverse drought tolerance mechanisms, hinting at potential for future development of more drought-tolerant rice cultivars.

To accurately predict the matching relationships between the various components and the engine performance in the whole aero-engine environment, this study introduces a two-dimensional throughflow simulation method for the whole aero-engine. This method is based on individual throughflow solvers for the turbo-machinery and the combustor. It establishes a throughflow simulation model for the whole engine by integrating with the compressor-turbine co-operating equations and boundary conditions. The turbo-machinery throughflow solver employs a circumferentially averaged form of the time-dependent Navier-Stokes equations (N-S) as the governing equation. The combustor solver uses the Reynolds Average Navier-Stokes (RANS) method to solve flow and chemical reaction processes by constructing turbulence, combustion, and radiation models. The accuracy of the component solver is validated using Pratt &amp; Whitney’s three-stage axial compressor (P&amp;W3S1) and General Electric’s high-pressure turbine (GE-EEE HPT), and the predicted results are consistent with the experimental data. Finally, the developed throughflow method is applied to simulate the throttling characteristics of the WZ-X turboshaft engine. The results predicted by the throughflow program are consistent with the GasTurb calculations, including the trends of shaft power delivered, specific fuel consumption (SFC), inlet airflow, and total pressure ratio of the compressor. The developed method to perform throughflow simulation of the whole aero-engine eliminates the dependence on a general component map. It can quickly obtain the meridian flow field parameters and overall engine characteristics, which is expected to guide the design and modification of the engine in the future.

In the end, this paper analyzed the economy from the investment in plant construction, raw material collection, product production and product sales. The resulting products include pyrolytic carbon, pyrolytic oil, pyrolysis gas. The results showed that when the treatment scale was 1 ton h−1, the pyrolysis plants with three loading modes will always be in a state of loss during the 20-year running time. When the processing scale was 9 ton h−1, the pyrolysis plant can be profitable in the first year. In the sensitivity analysis, the influence of three pyrolysis products on the total return on investment was explored. The results showed that the price change of pyrolysis oil has the greatest influence on the total return on investment (Total ROI) under the three loading methods, The price change of pyrolysis gas has minimal impact on the Total ROI. This study provides basic data and basis for the commercialization of coal-biomass co-pyrolysis technology and investment in plant construction.

Under the oxidative stress condition, the small RNA (sRNA) Oxys that acts as essential post-transcriptional regulators of gene expression is produced and plays a regulatory function with the assistance of the RNA chaperone Hfq protein. Interestingly, experimental studies found that the N48A mutation of Hfq protein could enhance the binding affinity with OxyS while resulting in defection of gene regulation. But, how the Hfq protein interacts with sRNA Oxys and the origin of the stronger affinity of N48A mutation are both unclear. In this paper, molecular dynamics (MD) simulations were performed on the complex structure of Hfq and OxyS to explore their binding mechanism. The molecular mechanics generalized Born surface area (MM/GBSA) and interaction entropy (IE) method were combined to calculate the binding free energy between Hfq and OxyS sRNA, and the computational result is in excellent correlation with the experimental result. Per-residue decomposition of the binding free energy revealed that the enhanced binding ability of the N48A mutation mainly comes from the increased van der Waals interactions (vdW). This research explores the binding mechanism between Oxys and chaperone protein Hfq, and revealed the origin of the strong binding affinity of N48A mutation. The results provided important insights on the mechanism of gene expression regulation affected by protein mutations.

The ultrasonic vibration assisted pressing process can improve the fluidity, the uneven distribution of density and particle size of WC-Co powder. However, the microscopic mechanism of ultrasonic vibration on the powder remains unclear. In this paper, WC and Co particles are simulated by three-dimensional spherical models of two particle sizes with the aide of the secondary development of Python. At the same time, the forming process of powder at mesoscale is simulated in the virtue of the finite element analysis software ABAQUS. The influence of vibration amplitude on the fluidity, the filling density and the stress distribution of WC-Co powder is investigated when the ultrasonic vibration is applied to the conventional pressing process. From simulation results, the ultrasonic vibration amplitude has great influences on the density of the compact. With the increase of the ultrasonic amplitude, the compact density also increases gradually. At the initial stage of the compaction, the particles move violently under the action of ultrasonic vibration, the fluidity is obviously enhanced, the arch bridge effect between particles is destroyed, the fine particles quickly fill the pores between the large particles, which increase the density, and the residual stress in the billet decreases after the compaction. From the experimental results, the size distribution of the billet is more uniform, the elastic aftereffect is reduced, the dimensional instability is improved, and the density curves obtained by experiment and simulation are within a reasonable error range.

In recent years, frequent occurrences of low temperature disasters due to global climate change have significantly impacted the normal growth of crops. Research has indicated that inorganic selenium (which is more toxic) can not only enhance the effects of low temperature stress but also increase selenium content on plants; however, there is a scarcity of studies investigating the impacts of organic selenium and nano-selenium on crops. In order to investigate whether organic selenium and nano-selenium can enhance low-temperature tolerance and increase selenium content in pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’), different concentrations (0, 5, 10, 20 mg L-1) of exogenous selenium were applied in this experiment to assess their effects on plant growth, nutritional quality and antioxidant properties. RNA-Seq technology was used to sequence the transcriptome of the leaves. Based on the transcriptome data, a Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a network that associates with physiological traits related to stress resistance. Two highly correlated gene co-expression modules were identified, and within them, nine hub genes associated with endocytosis, antioxidant stress, absorption, transport, and metabolism of selenium were discovered. The results indicated that the beneficial effects on yield and total selenium content under low temperature were attributed to (1) protection of photosynthetic pigments for enhancing photosynthetic capacity by the up-regulation of LHca2, LHcb1, LHca1, LHcb4 in KEGG pathway: photosynthesis-antenna proteins; (2) activation of antioxidant system for efficient ROS homeostasis such as SOD, POD and CAT by the genes such as Superoxide dismutase, Monodehydroascorbate and (3) selenium absorption by endocytosis, seleninum transportation by ABC transporter gene family and selenium metabolism related genes such as Cysteine synthase, Glutaredoxin. These findings provide a foundation for further investigation into the molecular mechanisms underlying the effects of organic selenium and nano-selenium on cyanochloride production at low temperatures.

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

Cysteine cathepsins, a class of proteinaceous enzymes, regulate a wide variety of metabolic processes in human including protein breakdown and turnover and immune functions. Eleven cysteine cathepsins have been identified so far and a wide array of studies related to identifying their specific functions, regulation and distribution patterns in tissues have been conducted. However, in recent past, the association of cysteine cathepsins in occurrence and progression of cancers have been identified and this has caused unrest in scientists triggering them to investigate the physiology, biochemical pathways and interactions of these cathepsins in cancer metastasis and therefore has become a noteworthy topic of intensive research. This review focusses and collects together the published work on molecular functional and structural characterization studies that have been done so far on in vitro expression of genes encoding for cysteine cathepsins in the Escherichia coli bacterial expression system. Accordingly, it was found out that all cathepsins except for cathepsins K, C, H, X and W have been expressed this way and the majority of them were found to be expressed in E. coli BL21(DE3) pLysS expression host via pET3 expression vector. In addition, it was also noted that in most of the expression studies, the substrate that was used to validate the enzymatic activity of the recombinant enzyme that was produced was a cysteine residue along with a benzyloxy-carbonyl salt. Through this review, the authors suggest that there is a very high need that all cysteine cathepsins need to be characterized both structurally and functionally on a molecular platform to better understand their interactions including the biochemical pathways. It is also momentous that the mass production of the recombinant forms of these enzymes are facilitated via expression in such bacterial expression systems and in turn, would also provide a strong platform for the development and progression of studies related to human physiology including oncological studies such as cancer metastasis. Moreover, as per biochemical features of the enzymes that could be identified, the production of efficient inhibitors or inducers as per the necessity to improve health and promote wellbeing among the mankind could be facilitated.

Wild artisanal cultures, such as SCOBY-based membranes (Symbiotic Culture of Bacteria and Yeasts) and water kefir grains (WKG), are complex consortia of microorganisms comprising yeasts, lactic and acetic acid bacteria, with a large strains’ diversity and abundance. The fermented products (FPs) obtained by the microbiome’s contribution can be included into functional products due to their metabiotics (pre-, pro-, post-, and paraprobiotics) as a result of the complex and synergistic association as well as due to the metabolic functionality. In this study, both consortia of SCOBY and WKG were involved in the co-fermentation of a new formulated substrate, that was further analysed aiming at increasing of the postbiotic composition of the FPs. Plackett-Burman (PB) and Response Surface Methodology (RSM) techniques were used for the experimental designs to select and optimize the most important parameters that influence the freeze-dried starter cultures of SCOBY and WKG activity as a multiple inoculum. The tea concentration (1-3%), sugar concentration (5-10%), raisins concentration (3-6%), SCOBY lyophilized culture concentration (0.2-0.5%), WKG lyophilized culture concentration (0.2-0.5%), and fermentation time (5-7 days), were considered the independent variables for mathematical analysis and fermentation conditions’ optimization. The pH, total acidity (TA), antioxidant activity (DPPH), and antimicrobial activity against Escherichia coli ATCC 25922, Bacillus subtilis MIUG B1, Staphylococcus aureus ATCC 25923, and Aspergillus niger MIUG M5 were evaluated as responses. The rich postbiotic bioactive composition of the FP obtained in optimized biotechnological conditions highlighted the usefulness of the artisanal co-cultures, through their symbiotic metabolic interactions for the improvement of bioactive potential.

The epigenetic mechanism of DNA methylation is essential for regulating gene expression and adaptation in plants, especially when exposed to various environmental stressors such as drought, heat, and nutrient availability. The impact of these stressors can lead to alterations in DNA methylation patterns and gene expression, which this study aims to investigate. Specifically, we are interested in understanding how DNA methylation plays a role in controlling the expression of stress-response genes, as well as genes involved in plant growth and development. To accomplish this, we are analyzing the changes in DNA methylation patterns and gene expression in response to different stressors like drought and heat. Our research findings suggest that DNA methylation is a vital regulator of plant adaptation to environmental stress, and we hope to gain further insight into these mechanisms by identifying genes that exhibit differential expression in response to stress and analyzing their DNA methylation status. This study offers valuable insights into the development of crops that can thrive under changing climate conditions.

Combustion power plants emit carbon dioxide (CO₂), a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, recency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant plumes as proxy signals to estimate electric power generation and CO₂ emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, and then an aggregation model predicted plant utilization over a 30-day period. Lastly, emissions factors specific to region, fuel, and plant technology were used to convert estimated electricity generation to CO₂ emissions. Models were trained with reported hourly generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed baseline approaches. In conducting external validation to compare modeled versus reported annual generation and emissions in the countries where available, we calculated the root mean square error overall for modeled countries with validation data as respectively 1.75 TWh (236 plants across 17 countries over 4 years) and 2.04 Mt CO₂ (207 plants across 17 countries over 4 years). Ultimately, we applied our ML method to plants that comprise 32% of global power plant CO₂ emissions averaged over 2015-2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is publicly available at climatetrace.org to support global emissions reduction.

This review deals with the developmental origins of extraocular, jaw and laryngeal muscles, the expression, regulation and functional significance of myosin heavy chains (MyHCs) they express and changes in MyHC expression during phylogeny. Myogenic progenitors from mesoderm in the prechordal plate and branchial arches specify craniofacial muscle allotypes with different repertoires for MyHC expression. To cope with very complex eye movements, extraocular muscles express 11 MyHCs, ranging from the superfast extraocular MyHC to the slowest, non-muscle MyHC IIB. They have distinct global and orbital layers, singly- and multiply-innervated fibres, longitudinal MyHC variations, and palisade endings that mediate axon reflexes. Jaw-closing muscles express the high-force masticatory MyHC, cardiac or limb MyHCs depending on the appropriateness for the acquisition and mastication of their food. Laryngeal muscles express extraocular and limb muscle MyHCs but shift towards slower MyHCs in large animals. During postnatal development, MyHC expression of craniofacial muscles is subject to neural and hormonal modulation. The primary and secondary myotubes of developing EOMs are postulated to induce, via different retrogradely transported neurotrophins, the rich diversity of neural impulse patterns that regulate the specific MyHCs they express. Thyroid hormone shifts 2A MyHC towards 2B in jaw, laryngeal muscles and possibly extraocular muscles.

Collision avoidance is an important feature in advanced driver-assistance systems, aiming at providing correct, timely and reliable warnings before an imminent collision (objects, vehicles, pedestrians, etc.). A co-simulation framework is proposed in this paper to address the design and evaluation of collision avoidances in a cyber-physical system. The co-simulation framework is supported on the interaction between SCANeR and Matlab/Simulink. From the best of authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip LIDAR sensors in a cyber-physical system (CPS) considering traffic scenarios is presented. The CPS is designed and implemented in SCANeR. Secondly, an obstacle recognition library with three specific Artificial Intelligence-based methods is also designed based on sensory information database provided by SCANeR. Three methods for collision avoidance detection are considered, i.e.; a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods for detecting obstacles before different weather conditions is done with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and fog conditions, the support vector machine in rainy and self-organized map in snowy conditions.

This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets, and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between expression of select expansin genes and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.

Apple red flesh coloration is a result of the performance of biochemical pathway involved in the biosynthesis of anthocyanins and anthocyanidins. Based on apple genome analysis, high number of regulatory genes, mainly transcription factors such as MYB, which are components of regulatory complex MYB-bHLH-WD40, and several structural genes (PAL, 4CL, CHS, CHI, F3H, DFR, ANS, UFGT) involved in anthocyanin biosynthesis have been identified so far. In this study, we investigated previously unrecognized genes involved in the constitution of red-flesh apple phenotype. These genes could be concerned as molecular markers for early selection of new apple cultivars. Based on comparative transcriptome analysis of apples with different fruit flesh coloration, we successfully identified and characterized ten genes from plant hormone transduction pathway of: auxin (GH3); cytokinins (B-ARR); gibberellins (DELLA); abscisic acid (SnRK2 and ABF); brassinosteroids (BRI1, BZR1 and TCH4); jasmonic acid (MYC2); and salicylic acid (NPR1). In presented studies an analysis of their expression profiles was performed in immature and ripe fruits of red-fleshed cultivars. Generally, we have uncovered, the set of the novel genes, mediating regulation of the abscisic acid, salicylic acid, cytokinins and jasmonic acid signaling and preliminarily described their role in anthocyanin biosynthesis, accumulation and degradation. Their expression was not characterized in apple fruits so far. Presented study, allowed to underline the relationship of genes from hormone signal transduction pathway with UFGT gene, directly responsible for anthocyanin color transformation as well as confirmed their crucial role of plant hormone regulation and anthocyanin accumulation during apple fruit ripening.

Water is essential for all living things however its pain has become serious. Many industrial activities cause its pollution by the release of polluting byproduct. Waste water treatment is hence necessary. In this context, the waste water of the textile industry containing Red Acid 52 was treated by the solid waste of the washed natural phosphate byproduct. Natural phosphate was also studied. The solid materials were first characterized by chemical analysis, Fourier Transform Infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The phosphate materials were after that, tested in the adsorption of the Red Acid 52. The experimental data indicated that the phosphate waste rock allowed the removal of Red Acid 52. Its maximum retention capacity attained 18.4 mg.g-1. Calcinations of materials inhibits the removal capacity found reduced by 60 to 70%. The adsorption kinetics of the Red Acid 52 on the material is well described by the pseudo second order model while the adsorption isotherms are identified by the Langmuir model. Hereafter, the thermodynamic study revealed that the adsorption process is spontaneous and exothermic. Keywords: Waste water, Phosphate co-product, Adsorption, Red Acid 52.

Escherichia coli and Shigella are common diarrhea-causing pathogens in children and adults. Enteroinvasive Escherichia coli (EIEC) shares a similar pathogenic mechanism with Shigella. However, EIEC are less virulent than Shigella. The aim of this work was to get a better understanding of the virulence differences between EIEC and S. flexneri. We investigated i) the bacterial gene co-expression networks (GCNs) and ii) the the transcriptional modules (WGCNA) of Caco-2 cells infected with EIEC or with S. flexneri during a three-hour period of bacterial infection. The GCN analysis showed that EIEC and S. flexneri networks presented different topologies. Additionally, the EIEC network revealed that pINV genes are not connected with chromosomal genes. WGCNA and eigengene analysis showed enterocyte gene expression variation along the three-hour bacterial post-infection period. Additionally, at one-hour post-infection EIEC induced a higher number of gene expression changes in Caco-2 cells than S. flexneri. Several of these genes are involved in autophagy. This study showed that the lower virulence of EIEC is associated with a lack of functional cooperation between pINV and chromosomal genes, differently from what was observed in S. flexneri. Consequently, EIEC becomes less efficient in subverting host-cell bacterial recognition as well as defense mechanisms such as autophagy.

SARS-CoV2 RNA depended RNA polymerase is an essential enzyme for the survival of the virus in hosts as it helps in the replication of viral RNA. There are no human polymerases that share either sequence or structural homology with viral RNA depended RNA polymerase. These make it a good target for inhibitor discovery, as a specific inhibitor cannot cross-react with the human polymerases. We have used virtual screening, docking, binding energy calculation and simulation to show that valproic acid Co-A, a metabolite from prodrug valproic acid, forms stable interaction with nsP12 of CoV. Our results suggest valproic acid Co-A could be a potential inhibitor of nsP12 of SARS-CoV2.

The requirement for integration of power plants due to the cyclical rise in electrical energy consumption is due to fluctuating load demand with the current grid systems. This integration necessitates effective allocating loads to the power plants for a minimum grid-tied transmission line cost while meeting network constraints. In this paper, we formulate an optimisation problem of minimising the total operational cost of all committed plants transmitted to the grid while meeting network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed Particle Swarm Optimization (PSO) method resolve the optimisation problem using piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction with connected load of 850MW, 1263MW and 2630MW power demand are contrasted with previous PSO application cost values, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), (73.86%, 89.10) respectively, and significant environmental-benefit. The proposed co-optimisation approach can enhance a significant self-consumption ratio compared to the baseline method.

In the present study, we implemented species distribution models (SDMs) to determine the potential invasive distribution range of a cichlid fish, Nile tilapia, Oreochromis niloticus, in a very sensitive inland water system in the southeastern Arabian Peninsula where it is now established and spreading. By using the “Biomod2” package, and the latest CMIP6 climate data, four single species distribution models and EM were built, and the results were evaluated by operating characteristic (ROC) and the true skill statistic (TSS). Based on the relative importance of climate factor analysis the mean diurnal range and precipitation seasonality were useful predictors of habitat suitability for O. niloticus. The response curve analysis indicated that the presence probability of O. niloticus decreased with increasing mean diurnal range and decreasing precipitation seasonality. The suitable distribution ranges for O. niloticus in the studied area were mainly distributed in the northeast of this region. The ensemble model results specified a significant impact of climate change on O. niloticus distribution, so highly suitable areas for this species will be reduced, while areas with low to moderate suitability increase slightly or remain unchanged. Based on the available data, Nile tilapia fulfills all four criteria to be considered as an invasive species: colonization in a new area, direct or indirect impacts of anthropological activity on its distribution range, geographical discontinuity between its native area (Africa) and the Arabian Peninsula (remote dispersal), and finally, its successful breeding in situ without human assistance resulting establishment of this biopollutant. Species distribution modeling provides insights into the factors that can reduce the risks and negative impacts of this biological pollutant on aquatic ecosystems of the southeastern Arabian Peninsula which harbors about 22 recognized fish species in 16 genera, 10 families, seven orders, and a class including 20 natives (seven endemic) species.

It has long been known that phytohormone auxin plays a promoting role in tuber formation and stress tolerance in potatoes. Our study aimed to identify and characterize the complete sets of auxin-related genes that presumably constitute the entire auxin signaling system in potato (Solanum tuberosum L.). The corresponding genes were retrieved from sequenced genomes of the doubled monoploid S. tuberosum DM1-3-516-R44 (DM) of the Phureja group, the heterozygous diploid line RH89-039-16 (RH), and the autotetraploid cultivar Otava. Both canonical and noncanonical auxin signaling pathways were considered. Phylogenetic and domain analyzes of deduced proteins were supplemented by their expression profiling and 3D molecular modeling. The canonical and ABP1-mediated pathways of auxin signaling appeared to be well conserved. Total number of potato genes/proteins presumably involved in canonical auxin signaling is 46 and 108 in monoploid DM and tetraploid Otava, respectively. Among studied potatoes, spectra of expressed genes obviously associated with auxin signaling were partly cultivar-specific and quite different from analogous spectrum in Arabidopsis. Most of the noncanonical pathways found in Arabidopsis appeared to be low probable in potato. This was equally applied to all cultivars used irrespective of their ploidy. Thus, some important features of the (noncanonical) auxin signaling pathways may be variable and species-specific.

Fungal plant diseases are a major threat to food security worldwide. Current efforts to identify and list loci involved in different biological processes are more complicated than originally thought, even when complete genome assemblies are available. Despite numerous experimental and computational efforts to characterize gene functions in plants, about ~ 40% of protein-coding genes in the model plant A. thaliana are still not categorized in the Gene Ontology Biological Process (BP) annotation. In non-model organisms, such as sunflower (Helianthus annuus), the number of BP term annotation is far fewer, ~22%. In the current study we performed gene co-expression network analysis using eight terabytes of public transcriptome datasets and performed expression-based functional prediction to categorize and identify loci involved in the response to fungal pathogens. We were able to construct a reference gene network of healthy green tissue (GreenGCN) and a gene network of healthy and stressed root tissues (RootGCN). Both networks achieved robust high quality scores on the metrics of guilt-by-association and selective constraints versus gene connectivity. We were able to identify eight modules enriched in defense functions, of which, two out of the three modules in the RootGCN were also conserved in the GreenGCN, suggesting similar defense-related expression patterns. We identified 16 WRKY genes involved in defense related functions and 65 previously uncharacterized loci now linked to defense response. In addition, we identified and classified 122 loci previously identified within QTL or near candidate loci reported in GWAS studies of disease resistance in sunflower linked to defense response. In all, we have implemented a valuable strategy to better describe genes within specific biological processes.

A comprehensive investigation was undertaken at Vytautas Magnus University Experimental Station, located at 54°52’50” N latitude and 23°49’41” E longitude on soil (Epieutric Endocalcaric Planosol – PLe-gln-w) since 1999, to understand the impacts of different agrotechnical measures on soil health and crop yield. Two primary factors were assessed. Factor A incorporated practices of straw removal versus straw chopping and spreading, while Factor B evaluated a spectrum of tillage techniques: conventional deep ploughing, shallow ploughing, ploughless tillage, single seedbed discing, and two no-tillage practices, one of which involved cover crops. Findings from this long-term study highlight the significant potential of specific farming systems in enhancing soil organic carbon. It has a positive effect on the release of CO2 emissions from the soil, thus promoting soil resilience and increasing plant productivity. These insights are paramount in devising sustainable agricultural strategies to counter the challenges of climate change on agroecosystems. This research showcases the profound effects of combining residue management and tillage practices, setting a novel standard for sustainable soil management of climatic uncertainties.

We describe the innate and adaptive immune system trajectory in Multi-system inflammatory syndrome of childhood (MIS-C), at acute(within 72 hours of hospitalization), resolution (at clinical improvement) and convalescent phase. In our cohort, in the acute phase, 68% of the children were SARS-CoV-2 seropositive, with hypercytokinenemia (high interleukin(IL)-1beta,IL-6,IL-8,IL-10,IL-17, interferon gamma), procoagulant state, myocardial dysfunction, activated neutrophils and monocytes; differential T and B cell subset lymphopenia; activated chemokine receptor type-7 positive and gamma-delta T cell subsets; antigen presenting cells had reduced HLA-DR expression; and B-cell class-switch responses occurred with illness resolution. MIS-C is an immunopathogenic illness associated with SARS-CoV-2 infections in children.

The Translationally Controlled Tumor Protein (TCTP) is a multifunctional protein found in most eukaryotic taxa. In plants, TCTP regulates cell-cycle progression as well as programmed cell death and is involved in plant regeneration and responses to abiotic and biotic stress, among other functions. Additionally, there is evidence that the transcript and protein are transported over long-distance. Potatoes are a crop of agricultural interest that can reproduce asexually by regeneration from stolons and tubers. Therefore, in this study the effect of transient expression of Solanum tuberosum TCTP (StTCTP) on tuberization was analyzed. StTCTP mRNA was shown to be transported long-distance. Additionally, transient overexpression of StTCTP resulted in tuber bearing shoots with a greater diameter compared to control plants. Furthermore, the early stages of tuberization were induced compared to control plants, in which only mature tubers were observed.

This paper establishes methods that quantify the structure of statistical interactions within a given data set using the characterization of information theory in cohomology by finite methods, and provides their expression in terms of statistical physic and machine learning. Following [1–3], we show directly that k multivariate mutual-informations (I_k) are k-coboundaries. The k-cocycles are given by I_k = 0, which generalize statistical independence to arbitrary dimension k. The topological approach allows to investigate Shannon’s information in the multivariate case without the assumptions of independent identically distributed variables. We develop the computationally tractable subcase of simplicial information cohomology represented by entropy H_k and information I_k landscapes. The I₁ component defines a self-internal energy functional U_k, and (−1)^k I_k,k≥2 components define the contribution to a free energy functional G_k of the k-body interactions. The set of information paths in simplicial structures is in bijection with the symmetric group and random processes, provides a topological expression of the 2nd law and points toward a discrete Noether theorem (1st law). The local minima of free-energy, related to conditional information negativity and the non-Shannonian cone of Yeung [4], characterize a minimum free energy complex. This complex formalizes the minimum free-energy principle in topology, provides a definition of a complex system, and characterizes a multiplicity of local minima that quantifies the diversity observed in biology. Finite data size effects and estimation bias severely constrain the effective computation of the information topology on data, and we provide simple statistical tests for the undersampling bias and for the k-dependences following [5]. We give an example of application of these methods to genetic expression and cell-type classification. The maximal positive I_k identifies the variables that co-vary the most in the population, whereas the minimal negative I_k identifies clusters and the variables that differentiate-segregate the most. The methods unravel biologically relevant I₁₀ with a sample size of 41. It establishes generic methods to quantify the epigenetic information storage and a unified epigenetic unsupervised learning formalism.

Diacylglycerol kinase (DGK) is a lipid kinase that phosphorylates diacylglycerol (DAG) to generate phosphatidic acid (PA). Based on converting one important signaling molecule (DAG) to another (PA), DGK plays an important role in plant responses to abiotic stress including waterlogging stress. However, no studies have been reported on the characterization of the DGK gene family in the waterlogging-tolerant kiwifruit germplasm, Actinidia valvata Dunn. Here, we identified 18 AvDGK genes in the A. valvata genome. The phylogenetic analysis showed that AvDGKs can be classified into three clusters, and members within the same cluster have similar domain distribution, exon-intron structures and conserved motif compositions. Chromosome localization analysis revealed that all the AvDGK genes are located across 18 different chromosomes. There were 29 duplicated gene pairs in kiwifruit and all had undergone purifying selection during evolution. Promoter cis-element analysis revealed that the cis-elements within AvDGK genes are associated with multiple functions, including phytohormone signal transduction, stress response, and plant growth and development. The expression pattern analyses indicated that AvDGK play an important role in the fruit development and plant response to waterlogging stress. AvDGKs gene family in the tetraploid A. valvata genome might promote PA synthesis and subsequent signal transduction both under short-term and long-term waterlogging stress. These results provide information regarding the structural characteristics and potential function of DGK genes within kiwifruit and lay a fundamental basis for further research into breeding for enhancing the kiwifruit’s tolerance to waterlogging stress.

Currently, there is a tenacious need to find new ways to purify water by eliminating bacterial biofilms, textile dyes, and toxic water pollutants. These contaminants pose significant risks to both human health and the environment. To address this issue, in this study, we have developed an eco-friendly approach that involves synthesizing Cobalt-doped Cerium Iron Oxide (CCIO) nanocomposite (NC) using an aqueous extract of Gossypium arboreum L. stalks. The resulting nanoparticles can be used to effectively purify water and tackle the challenges associated with these harmful pollutants. Nanoparticles excel in water pollutant removal by providing high surface area for efficient adsorption, versatile design for simultaneous removal of multiple contaminants, catalytic properties for organic pollutant degradation, and magnetic features for easy separation, offering cost-effective and sustainable water treatment solutions. CCIO nanocomposite has been synthesized by green co-precipitation method due to biomolecules and co-enzymes present in the aqueous extract of Gossypium arboreum L. stalk; a single-step procedure completed in 5 h reaction time. Further, the synthesis of nanocomposites was confirmed by various characterization techniques such as Fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), Field emission scanning electron microscopy (FE-SEM), Transmission electron microscopy (TEM), Thermogravimetric analysis (TGA), Dynamic light scattering (DLS), and Energy Dispersive X-Ray (EDX). CCIO NCs were discovered to have a spherical shape and an average size of 40 nm. Based on DLS zeta potential analysis, CCIO NCs were found to be anionic. Also, CCIO NCs showed significant antimicrobial and antioxidant activity. Overall, considering the physical and chemical properties, the application of CCIO NCs in the adsorption of various dyes (~91 %) and water pollutants (chromium = ~ 60 %) has been considered since they exhibit great adsorption capacity owing to their microporous structure which can be a step forward in water purification.

Abstract: The baculovirus expression vector (BEV) system is an efficient, cost effective and scalable method to produce recombinant adeno-associated virus (rAAV) gene therapy vectors. Most BEV designs emulate the wild type AAV transcriptome and translate the AAV capsid proteins, VP1, VP2 and VP3 from a single mRNA transcript with three overlapping open reading frames (ORFs). Non-canonical translation initiation codons for VP1 and VP2 reduce their abundances relative to VP3. Changing capsid ratios to improve rAAV vector efficacy requires theoretical modification of translational context. We have made a Lac repressor inducible system to empirically regulate expression of VP1 and VP2 proteins relative to VP3 in the context of the BEV. We demonstrate the use of this system to tune the abundance, titer and potency of a neurospecific rAAV9 serotype derivative. VP1:VP2:VP3 ratios of 1:1:8 gave optimal potency for this rAAV. It was discovered that ratios of capsid proteins expressed were different than ratios that ultimately were in purified capsids. Over expressed VP1 did not become incorporated into capsids while over expressed VP2 did. Overabundance of VP2 correlated with reduced rAAV titers. This work demonstrates a novel technology for controlling production of rAAV in the BEV system and shows a new perspective on the biology of rAAV capsid assembly.

Solanesol is a noncyclic terpene alcohol composed of nine isoprene units and it mainly accumulates in solanaceous plants, especially tobacco (Nicotiana tabacum L.). Here, RNA-seq analyses of tobacco leaves, stems, and roots were used to identify solanesol biosynthesis genes. Six 1-deoxy-d-xylulose 5-phosphate synthase, two 1-deoxy-d-xylulose 5-phosphate reductoisomerase, two 2-C-methyl-d-erythritol 4-phosphate cytidylyltransferase, four 4-diphosphocytidyl-2-C-methyl-d-erythritol kinase, two 2-C-methyl-d-erythritol 2,4-cyclodiphosphate synthase, four 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase, two 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate reductase, six isopentenyl diphosphate isomerase, and two solanesyl diphosphate synthase (SPS) genes were identified to be involved in solanesol biosynthesis. Furthermore, the two N. tabacum SPS (NtSPS1 and NtSPS2), which had two conserved aspartate-rich DDxxD domains, were highly homologous with SPS enzymes from other solanaceous plant species. In addition, the solanesol contents of three organs, and leaves from four growing stages, corresponded with the distribution of chlorophyll. Our findings provide a comprehensive evaluation of the correlation between the expression of different biosynthetic genes and the accumulation of solanesol in tobacco.

Delivery systems of the biologically active substance such as proanthocyanidins (PCAN) produced through the electrospinning method were designed using a blend of poly(L-lactide-co-glycolide) and poly[(R,S)-3-hydroxybutyrate]. The research involved the structural and thermal characteristics of the developed electrospun three-dimensional fiber matrix, as well as hydrolytic degradation tests performed on them. Additionally, the release profile of PCAN from the electrospun nonwoven was determined using UV–VIS spectroscopy. Approximately 30% of PCAN was released from the tested electrospun nonwoven during the initial 15-20 days of incubation. The chemical structure of water-soluble oligomers that were formed after the hydrolytic degradation of the developed delivery system was identified through electrospray ionization mass spectrometry. Oligomers of lactic acid and OLAGA oligocopolyester, as well as oligo-3-hydroxybutyrate terminated with hydroxyl and carboxyl end groups, were recognized as degradation products released into the water during the incubation time. It was also demonstrated that variations in the degradation rate of individual mat components influenced the degradation pattern, and the number of formed oligomers. The obtained results suggest that the incorporation of proanthocyanidins into the system slowed down the hydrolytic degradation process of the poly(L-lactide-co-glycolide)/poly[(R,S)-3-hydroxybutyrate] three-dimensional fiber matrix. In addition, in vitro cytotoxicity and antimicrobial studies advocate the use of PCAN for biomedical applications with promising antimicrobial activity.

The framework proposed by Alison Druin about 20 years ago, which defined the roles children play in technology design, has been widely adopted by the Child-Computer Interaction (CCI) community. While some studies have adopted relevant roles as presented in the framework, others have argued for extending the framework to include newly identified roles. Still, other studies argue that Druin's framework failed to account for roles children may take up as they interact with peers or with tools. Consequently, this systematic literature review examines children’s emerging and changing roles in the codesign of new technologies. Specifically, we answered the research question: What new roles emerge in research as children co-design new technologies? Our effort aims at providing evidence of new roles children have adopted in literature, which may help researchers in the CCI community make more informed decisions about participatory design approaches with children.

Recently intraductal carcinoma of the prostate (IDCP) has attracted increasing interest owing to its unfavorable prognoses. To effectively identify the IDCP-specific gene expression profile, we took a novel approach of characterizing a typical IDCP case using spatial gene expression analysis. A formalin-fixed, paraffin-embedded sample was subjected to Visium CytAssist Spatial Gene Expression analysis. IDCP within invasive prostate cancer sites was recognized as a distinct cluster separate from other invasive cancer clusters. Highly expressed genes defining the IDCP cluster, such as MUC6, MYO16, NPY, and KLK12, reflected the aggressive nature of high-grade prostate cancer. IDCP sites also showed increased hypoxia markers HIF1A, BNIP3L, PDK1, and POGLUT1; decreased fibroblast markers COL1A2, DCN, and LUM; and decreased immune cell markers CCR5 and FCGR3A. Overall, these findings indicate that the hypoxic tumor microenvironment and reduced accessibility to immune cells, which reflect the pathological characteristics of IDCP, may influence the aggressiveness of high-grade prostate cancer.

Ocean big data is the scientific practice of using big data technology in the marine field. Data from satellites, manned spacecraft, space stations, airship, unmanned aerial vehicles, shore-based radar and observation stations, exploration platforms, buoys, underwater gliders, submersibles, and submarine observation networks are seamlessly combined into the ocean’s big data. Increasing numbers of scholars have tried to fully analyze the ocean’s big data. To explore the key research technology knowledge graphs related to ocean big data, articles between 1990 and 2020 were collected from the “Web of Science”. By comparing bibliometric software and using the visualization software Cite-Space, the pivotal literature related to ocean big data, as well as countries, institutions, categories, and keywords, were visualized and recognized. Journal co-citation analysis networks can help determine the national distribution of core journals. Co-citation analysis networks for documents show authors who are influential at key technical levels. Key co-occurrence analysis network keywords can determine research hot spots and research frontiers. The three supporting elements of marine big data research are shown in the co-citation network. These elements are author, institution, and country. By examining the co-occurrence of keywords, the key technology research directions for future marine big data were determined.

The severe respiratory distress syndrome (SRDS) has been a significant clinical issue in modern respiratory medicine. Its role in COVID-19 pandemic is not well defined. Outside-the-hospital SRDS is usually community-acquired septic pneumonia; however, in-hospitals incidences are complicated by co-infections. While SRDS is genetically selected in European and African origins, these are not clear in Middle East, particularly in COVID-19 backgrounds. There is a severe paucity in high quality data on correlations between COVID-19, ARDS, co-infectome, and patient demographics. We have conducted a comprehensive investigation on 298 patients for associations of SRDS, coinfections, and patient demographics on COVID-19 patients’ outcomes. Of these, 9.4% (n=28) had SRDS, and the rest (90.6%) had not. 54% of those with SRDS died while 84% survived; SRDS fatality was highly significant (Chi-square test P-value = 0.00000246). Irrespective of gender, the age of patients was significantly associated with SRDS (72.9 +/-8.9) compared to those without it (56.2 +/-15.1). However, there was no significant difference neither in the age of admitted patients before COVID-19 (58.5 +/-15.3) and during COVID-19 (57.2 +/-15.5) nor in the gender and COVID-19 fatality (Fisher Exact test 2-sided 1.000, 1-sided .546) ruling out that SRDS age-specificity were selected by virus susceptibility. A 100% of SRDS patients without bacterial co-infections survived while only 25% of those with co- infectome did not; this association was highly significant (P value= 0.00041). Co-infections alone without underlying SRDS was also associated with high fatality among patients (P value= 0.00000000076). Almost all COVID-19 patients without co-infectome (99.2%) survived while 28% of those with bacterial co-infection died. The major bacterial pathogens that potentially predisposed to SRDS, were Acinetobacter baumannii, and Escherichia coli either alone or in a mixed infection with Klebsiella pneumoniae were predominant species identified during SRDS attack. Thus, Gram-negative co-infectome potentially induced fatal SRDS aggravating COVID-19 outcome. These findings have significant clinical implications in specific differential diagnosis of SRDS syndromes for subsequent empiric therapy and patient management strategies. Future vertical investigation for similar mechanisms of cytokine-induced SRDS by Gram negative pathogens is imperative since hypervirulent strains are rapidly circulating in the region. The study is limited by a single center study confined to Ha’il hospitals; large scale investigation in major national hospitals would gain more insights.

Leishmania parasites present astonishing adaptative abilities that represent a matter of life or death within disparate environments during the heteroxenous parasite life cycle. From an evolutionary perspective, organisms develop methods of overcoming such challenges. Strategies that extend beyond the genetic diversity have been discussed and include variability between parasite cells during the infections of their hosts. The occurrence of Leishmania subpopulation fluctuations with variable structural genomic contents demonstrates that a single strain might shelter the variability required to overcome inconsistent environments. Such intrastrain variability provides parasites with an extraordinary ability to adapt and thus survive and propagate. However, different perspectives on this evolution have been proposed. Strains or species living in the same environment can cooperate but also compete. These interactions might increase the replication rate of some parasites but cause the loss of more aggressive competitors for others. Adaptive responses to intra- and interspecific competition can evolve as a fixed strategy (replication is adapted to the average genetic complexity of infections) or an optional strategy (replication varies according to the genetic complexity of the current infection). This review highlights the complexity of interspecies and intrastrain interactions among Leishmania parasites as well as the different factors that influence this interplay.

Facial expression has lots of applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on Action Unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. Sparse representation based approach is then proposed to detect the active AUs of testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for Jaffe and CK+ databases, respectively. Better cross-database performance has also been observed.

New setting is introduced to study types of coloring numbers, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Different types of procedures including neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are proposed in this way, some results are obtained. General classes of neutrosophic hypergraphs are used to obtain chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Using colors to assign to the vertices of neutrosophic hypergraphs and characterizing representatives of the colors are applied in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. Some questions and problems are posed concerning ways to do further studies on this topic. Using different ways of study on neutrosophic hypergraphs to get new results about number, degree and co-degree in the way that some number, degree and co-degree get understandable perspective. Neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are studied to investigate about the notions, coloring, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. In this way, sets of representatives of colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges have key points to get new results but in some cases, there are usages of sets and numbers instead of optimal ones. Simultaneously, notions chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges are applied into neutrosophic hypergraphs, especially, neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs to get sensible results about their structures. Basic familiarities with neutrosophic hypergraphs theory and hypergraph theory are proposed for this article.

At Mae-Moh power plant, Thailand, superheater tubes, which are exposed in fly ash environ-ment, often degrade due to solid particle erosion. To extend the service lifetime of the superheater tubes, the high velocity oxy-fuel (HVOF) thermal spray technique is used to deposit a protective coating on the material, SA213-T22 steel. In this work, the solid particle erosion of the Co-28Cr and WC-12Co coatings was investigated using erodent particle impingement at the angles of 30o and 90o with an average particle size of 60 μm. The erosion behavior of SA213-T22 with and without Co-28Cr and WC-12Co coatings was explained using ductile and brittle erosion modes. The erosion test resulted in the brittle mode for both Co-28Cr and WC-12Co coatings, while SA213-T22 without coating indicated the ductile mode. On the investigation of surface morphology, the Co-28Cr coating showed mostly microcracks in contrast with the WC-12Co coating, fracture, de-bonding and deep cavities were also observed. Erosion resistance of the Co-28Cr coating was significantly higher than the WC-12Co coating, due to high hardness, low porosity and high density of the coating.

Mutations that provide environment dependent selective advantages drive adaptive divergence among species. Many phenotypic differences among related species are more likely to result from gene expression divergence rather than from non-synonymous mutations. In this regard, cis-regulatory mutations play an important part in generating functionally significant variation. Some proposed mechanisms that explore the role of cis-regulatory mutations in gene expression divergence involve microsatellites. Microsatellites exhibit high mutation rates and are abundant in both coding and non-coding regions and could influence gene function and products. Here we tested the hypothesis that microsatellites contribute to gene expression divergence among species with 50 individuals from nine closely related Helianthus species using an RNA-seq approach. Differential expression analyses of the transcriptomes revealed that genes containing microsatellites in non-coding regions (UTRs and introns) are more likely to be differentially expressed among species when compared to genes with microsatellites in the coding regions and transcripts lacking microsatellites. We detected a greater proportion of shared microsatellites in 5’UTRs and coding regions compared to 3’UTRs and non-coding transcripts among Helianthus spp. Further, allele frequency differences measured by pairwise FST at single nucleotide polymorphisms (SNPs), indicate greater genetic divergence in transcripts containing microsatellites compared to those lacking microsatellites. A gene ontology (GO) analysis revealed that microsatellite-containing differentially expressed genes are significantly enriched for GO terms associated with regulation of transcription and transcription factor activity. Collectively, our study provides compelling evidence to support the role of microsatellites in gene expression divergence.

Molecular studies regarding regulatory elements such as small ncRNAs and their mechanisms are poorly understood in infectious diseases. Tuberculosis is one of the oldest infectious diseases of humanity, and it is still a challenge to prevent and treat it. The control of the infection as well as its diagnosis are still complex, and treatments used are linked to several side effects. This study aimed to investigate miRNA’s expression profile to identify possible biomarkers for tuberculosis. We applied NGS techniques to investigate miRNA’s global expression profile from blood samples of infected patients with tuberculosis, their respective healthy physicians, and external healthy individuals as controls. Samples from 22 individuals run through a differential expression, target genes, gene set enrichment, and miRNA-gene network analysis. We observed 153 altered miRNAs, among which, only three DEmiRNAs (hsa-let-7g-5p, hsa-miR-486-3p and hsa-miR-4732-5p) were found between the investigated patients and their respective physicians. These DEmiRNAs are suggested to play an important role in granuloma regulation and their immune physiopathology. Our results propose that miRNAs may be involved in immune modulation, regulating the repertoire of genes expressed in the immune system’s cells. Our findings encourage the application of miRNAs as potential biomarkers for tuberculosis.