This study presents the consecutive modification of orange peel (OP) by NaOH and sodium dodecyl sulfate (SDS) for simultaneous elimination of basic dyes from the binary system and modeling the adsorption process using an intelligent tool. The natural and modified biosorbents were characterized by variety of analyses such as: field emission scanning electron microscopy with energy dispersive X-ray, N2 physisorption and Fourier transform infrared spectroscopy techniques. The influence of various variables on dye removal like pH, the quantity of biosorbents, dyes concentration, contact time, and temperature in the binary system were investigated and optimized by an artificial neural network (ANN) model as an intelligent tool. The optimum quantity of the sorbent was found to be 0.30 g for orange peel (OP) and 0.25 g for NaOH-treated OP (NOP) and SDS-decorated NOP (SNOP) at pH = 7. The kinetics and thermodynamics investigations showed that the removal of dyes obeyed the pseudo-second-order kinetic model and were spontaneous and exothermic in nature. Moreover, in order to describe the mechanism of sorption process, desorption studies of dyes were carried out. The desorption percentages of methylene blue (MB) in water and HCl were found to be in the range of 1.93%–4.76% and 18.87%–28.76%, respectively; in addition, the desorption percentages of crystal violet (CV) in water and HCl were obtained to be in the range of 4.11%–7.41% and 32.84%–43.00%, respectively; which could be a recommendation ion exchange or electrostatic attachment of dyes onto biosorbents. The ANN predictions matched with the experimental data very well (0.95308 < R2 < 0.99191 and 0.98335 < R2 < 0.99773 for MB and CV, respectively) which indicated high accuracy of the ANN model. In addition, the relative importance of each parameter was calculated by Garson’s equation.

We propose a novel method for salient object detection in different images. Our method integrates spatial features for efficient and robust representation to capture meaningful information about the salient objects. We then train a conditional random field (CRF) using the integrated features. The trained CRF model is then used to detect salient objects during the online testing stage. We perform experiments on two standard datasets and compare the performance of our method with different reference methods. Our experiments show that our method outperforms the compared methods in terms of precision, recall, and F-Measure.

Spontaneous tumours in higher plants can develop depending on plant genotype without any pathogen invasion. Spontaneous tumour formation on the taproots is consistently observed in certain inbred lines of radish (Raphanus sativus var. radicula Pers.). In this work, we have sequenced the genomes of two closely related radish inbred lines that differ in their ability to spontaneously form tumours. We have identified numerous single nucleotide variants (amino acid substitutions, insertions or deletions) that are likely to be associated with the spontaneous tumour formation. Furthermore, we performed the identification of CLE and WOX genes the genomic sequences of the radish inbred lines and, as a result, identified two unique radish CLE genes which probably encode proteins with multiple CLE domains. The results obtained provide a basis for investigating the mechanisms of plant tumour formation and also for future genetic and genomic studies of radish.

Abstract Background: Numerous studies have demonstrated that the amalgamation of populations belonging to the same breed or closely related breeds leads to enhanced accuracies in genomic predictions (GP). Extensive experimentation with diverse Bayesian and Genome-enabled best linear unbiased prediction (GBLUP) models has been conducted to explore multi-breed genomic selection (GS) in livestock, ultimately establishing they as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to examine the efficacy of BayesR and GBLUP model with different weighted genomic relationship matrices (GRM) in making genomic predictions for three distinct beef cattle breeds. Subsequently, we conducted a comparative analysis of the predictive accuracy pertaining to various marker densities and genetic correlations across three distinct beef cattle breeds. This investigation aimed to identify the optimal approach for enhancing the predictive accuracy of multi-breed genomic selection in beef cattle. Results：Genetic relationship matrices revealed moderate similarities between YL and the other breeds, with a striking genetic similarity of 0.87 between WG and HX. In HX cattle, BayesR demonstrated an enhancement in prediction accuracy, achieving 0.52 with HD and 0.46 with WGS, a marked improvement over 0.41 with HD and 0.42 with WGS in GBLUP. In WG and YL breeds, both methods showed comparable accuracies with HD, but BayesR slightly outperformed GBLUP with WGS. Further, multi-breed GS analysis indicated that BayesR consistently surpassed GBLUP in prediction accuracy, particularly with WGS data. For instance, in a combined HX and WG reference population, BayesR achieved a superior accuracy of 0.53 with WGS in HX cattle, a significant enhancement over GBLUP models. The study also underscores the advantage of incorporating multiple breeds in the reference population, which improved prediction accuracy, underscoring the value of broad-based genomic selection strategies. Conclusion: The results show that accuracy of multi-breed genomic predictions was higher with BayesR than with GBLUP, especially for the distantly genetic relationship between reference and validation breeds. Further improvements of multi-breed accuracy of genomic predictions could be achieved by increasing the density of the SNP marker. These findings underscore that BayesR providing a substantial improvement in genomic prediction and the importance of considering genetic relationships in the development of GS strategies for multi-breed cattle populations. Further research is warranted to optimize GRM construction and to explore alternative models for genomic prediction across breeds.

Diseases pose a significant and pressing concern for the sustainable development of the aquaculture sector, particularly as their impact continues to grow due to climatic shifts such as rising water temperatures. While various approaches, ranging from biosecurity measures to vaccines, have been devised to combat infectious diseases, their efficacy is disease- and species-specific and contingent upon a multitude of factors. The field of genetics and genomics offer effective tools to control and prevent disease outbreaks in aquatic animal species. In this study, we present the key findings from our recent research, focusing on the genetic resistance to three specific diseases: White Spot Syndrome Virus WSSV) in white shrimp, Bacterial Necrotic Pancreatitis (BNP) in striped catfish and skin fluke (a parasitic ailment) in yellowtail kingfish. Our investigations reveal that all three species possess substantial heritable genetic components for disease resistant traits, indicating their potential responsiveness to artificial selection in genetic improvement programs tailored to combat these diseases. Also, we observed a high genetic association between disease traits and survival rates. Through selective breeding aimed at enhancing resistance to these pathogens, we achieved substantial genetic gains, averaging 10% per generation. These selection programs also contributed positively to the overall production performance and productivity of these species. Although the effects of selection on immunological traits or immune responses were not significant in white shrimp, they yielded favourable results in striped catfish. Furthermore, our genomic analyses, including shallow genome sequencing of pedigreed populations, enriched our understanding of the genomic architecture underlying disease resistance traits. These traits are primarily governed by a polygenic nature, with numerous genes or genetic variants, each with small effects. Leveraging a range of advanced statistical methods, from mixed models to machine and deep learning, we developed prediction models that demonstrated moderate to high levels of accuracy in forecasting these disease-related traits. In addition to genomics, our RNA-seq experiments identified several genes that undergo upregulation in response to infection or viral loads within the populations. Preliminary microbiome data, while offering limited predictive accuracy for disease traits in one of our studied species, underscore the potential for combining such data with genome sequence information to enhance predictive power for disease traits in our populations. Lastly, this paper briefly discusses the roles of precision agriculture systems, AI algorithms, and outlines the path for future research to expedite the development of disease-resistant genetic lines tailored to our target species. In conclusion, our study underscores the critical role of genetics and genomics in fortifying the aquaculture sector against the threats posed by diseases, paving the way for more sustainable and resilient aquaculture development.

During the COVID-19 pandemic, immunosuppressed patients showed prolonged SARS-CoV-2 infections, with several studies reporting the accumulation of mutations in the viral genome. The weakened immune system present in these individuals, along with the effect of antiviral thera-pies, are thought to create a favourable environment for intra-host viral evolution and have been linked to the emergence of new viral variants, which strongly challenged the containment measures and some therapeutic treatments. To assess whether an impaired immunity could lead to an increased instability of viral genomes, longitudinal nasopharyngeal swabs were collected from eight immunocompromised patients and fourteen non-immunocompromised subjects, all undergoing SARS-CoV-2 infection. Intra-host viral evolution was compared between the two groups through deep sequencing, exploiting a probe-based enrichment method to minimize the possibility of artefactual mutations commonly generated in amplicon-based methods, which heavily rely on PCR amplification. Although, as expected, immunocompromised patients expe-rienced significantly longer infections, the acquisition of novel intra-host viral mutations was similar between the two groups. Moreover, a thorough analysis of viral quasispecies showed that the variability of viral populations in the two groups is comparable not only at the consensus level, but also when considering low frequency mutations. This study suggests that a compro-mised immune system alone does not affect SARS-CoV-2 within-host genomic variability.

This study investigated novel quantitative traits loci (QTLs) associated with the control of grain shape and size as well as grain weight in rice. We employed a joint strategy multiple GAPIT (Genome Association and Prediction Integrated Tool) models [(Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK)), Fixed and random model Circulating Probability Uniform (FarmCPU), Settlement of MLM Under Progressive Exclusive Relationship (SUPER), and General Linear Model (GLM)]–High Density SNP Chip DNA Markers (60,461) to conduct a Genome-Wide Association Study (GWAS). GWAS was performed using genotype and grain-related phenotypes of 143 recombinant inbred lines (RILs). Data show that parental lines (Ilpum and Tung Tin Wan Hein 1, TTWH1, Oryza sativa L., ssp. japonica and indica, respectively) exhibited divergent phenotypes for all analyzed grain traits), which was reflected in their derived population. GWAS results revealed the association between seven SNP Chip makers and quantitative trait loci (QTLs) for grain length, co-detected by all GAPIT models on (Chr) 1–3, 5, 7, and 11), were qGL1-1BFSG (AX-95918134, Chr1: 3820526 bp) explains 65.2%–72.5% of the phenotypic variance explained (PVE). In addition, qGW1-1BFSG (AX-273945773, Chr1: 5623288 bp) for grain width explains 15.5%–18.9% of PVE. Furthermore, BLINK or FarmCPU identified three QTLs for grain thickness independently, and explain 74.9% (qGT1Blink, AX-279261704, Chr1: 18023142 bp) and 54.9% (qGT2-1Farm, AX-154787777, Chr2: AX-154787777 bp) of the observed PVE. For t length-to-width ratio, the qLWR2BFSG (AX-274833045, Chr2: 10000097 bp) explains nearly 15.2%–32% of PVE for LWR. Likewise, the major QTL for thousand-grain weight (TGW) was detected on Chr6 (qTGW6BFSG, AX-115737727, 28484619 bp) and explains 32.8%–54% of PVE. The qTGW6BFSG QTL coincides with qGW6-1Blink for grain width and explained 32.8%–54% of PVE. Putative Candidate genes pooled from major QTLs for each grain traits have interesting annotated functions that require functional studies to elucidate their function in the control of grain size, shape, or weight in rice. Genome selection analysis proposed makers useful for downstream marker-assisted selection based on genetic merit of RILs.

The availability of multiple sequenced genomes from a single species made it possible to explore intra- and inter-specific genomic comparisons at higher resolution and build clade-specific pangenomes of several crops. The pan-genomes of crops constructed from various cultivars/accessions, landraces, and wild ancestral species represent a compendium of genes and structural variations and allow researchers to search for the novel genes and alleles that were inadvertently lost in domesticated crops during the historical process of crop domestication or in the process of extensive plant breeding. Fortunately, many valuable genes and alleles associated with desirable traits like disease resistance, abiotic stress tolerance, plant architecture, and nutrition qualities exist in landraces, ancestral species, and crop wild relatives. The novels genes from the wild ancestors and landraces can be introduced back to high-yielding varieties of modern crops by implementing classical plant breeding, genomic selection, and transgenic/gene editing approaches. Thus, pan-genomic represents a great leap in plant research and offers new avenues for targeted breeding for mitigating the impact of global climate change. Here we summarize the tools used for pangenome assembly and annotations, web-portals hosting plant pangenomes. Furthermore, we highlight a few discoveries made in crops using the pan-genomic approach and its future potential.

Genome-based testing in oncology is a rapidly expanding area of health care which is the basis of the emerging area of precision medicine. Efficient and considered adoption of novel genomic medicine testing is hampered in Canada by the fragmented nature of health care oversight as well as by lack of clear and transparent processes to support rapid evaluation, assessment and implementation of genomic tests. This article provides an overview of some key barriers and proposes approaches to addressing these challenges as a potential pathway to developing a national approach to genomic medicine in oncology.

In recent years, there has been a notable rise in the incidence of breast cancer among young patients, who exhibit worse survival outcomes and distinct characteristics compared to the intermediate and elder patients. Therefore, it’s imperative to identify identify the specific features unique to young patients, which could offer insights into potential therapeutic strategies and improving survival outcomes. In our study, we performed an integrative analysis of bulk transcriptional and genomic data from extensive clinical cohorts to identify prognostic factors. Additionally, we analyzed the single-cell transcriptional data and conducted in vitro experiments. Our work confirmed that young patients exhibited higher grading, worse disease-free survival (DFS), a higher frequency of mutations in TP53 and BRCA1, a lower frequency of mutations in PIK3CA, and upregulation of eight metabolic pathways. Notably, galactose metabolism pathway showed upregulation in young patients and was associated with worse DFS. Further analysis and experiments indicated that galactose metabolism pathway may regulate the stemness of cancer cells and ultimately contribute to worse survival outcomes. In summary, our finding identified distinct clinicopathological, transcriptional, and genomics features and revealed a correlation between galactose metabolism pathway, stemness, and poor disease-free survival of breast cancer in young patients.

The production of plants and crops is influenced by environmental stress, which is a serious scientific issue. Cotton is an essential crop for producing natural fibers that are used to make biofuel and edible oils. Salinity is the main element that influences cotton growth during the beginning of germination. The type of salt and the growth stage affects how sensitive a plant is to salt stress. Developing ways to enhance cotton performance in salty circumstances can be aided by an understanding of the response of cotton to salt, its mechanism of resistance, and its management methods. Osmotic and ionic imbalances originate due to the deposition of soluble salts under salinity stress in the plant's root zone. Soil salinity significantly reduces plant growth due to several factors, such as nutritional ion imbalance, which reduces K+, PO4-, and NO- absorption, excessive salt chloride concentrations, and osmotic stress, which hinders water availability. Research has revealed that compared to subsequent stages, the germination, emergence, and seedling phases are more vulnerable to salinity stress, which ultimately affects the seed cotton yield by delaying blooming, reducing fruiting positions, shedding fewer fruits, and reducing boll weight. The morphology, growth of cotton roots, shoots, yield, and fiber quality are all strongly impacted by salinity stress. It slows down plant feeding, cellular metabolism, and photosynthesis. The soil, water, and climate all affect how the plant responds to salinity stress. During salt stress, excessive exclusion of sodium or its compartmentalization is the key adaptation process in cotton. A major adaptive potential to create cotton types that can withstand salt is provided by the up-regulation of both physicochemical and non-enzymatic antioxidant genes. A successful strategy to increase cotton germination in saline soils is seed priming. Moreover, the transgenic strategy might be a viable choice for improving cotton yield in saline environments. Our review focuses on the impacts of salinity on cotton productivity as well as how plants react to salt stress. It also clarifies recent genetic advancements and molecular breeding for cotton's resistance to soil salt. To create salt-tolerant cultivars, a combination of traditional breeding and novel molecular approaches will be useful.

Noctuidae is known to have high species diversity, although the genomic diversity of Noctuidae species have not been studied extensively. Investigation of transposable elements (TEs) in this family can improve our understanding on the genomic diversity of Noctuidae. In this study, we annotated and characterized genome-wide TEs in ten noctuid species belonging to seven genera. With multiple annotation pipelines, we constructed a consensus sequence library containing 1,038 –2,826 TE consensus. The genome content of TEs showed high variation in the ten Noctuidae genomes, ranging from 11.3% to 45.0%. The relatedness analysis indicated that the TE content, especially the content of LINEs and DNA transposons, are positively correlated with the genome size (r=0.86, p-value=0.001). We identified SINE/B2 as a lineage-specific subfamily in Trichoplusia ni, a species-specific expansion of LTR/Gypsy subfamily in Spodoptera exigua, and a recent expansion of SINE/5S subfamily in Busseola fusca. We further revealed that of the four TE classes, only LINEs showed phylogenetic signal with high confidence. We also examined how the expansion of TEs contributed to the evolution of noctuid genomes. Moreover, we identified a total of 56 horizontal transfer TE (HTT) events among the ten noctuid species and at least three HTT events between the nine Noctuidae species and 11 non-noctuid arthropods. One of HTT events caused by a Gypsy transposon might have caused the recent expansion of Gypsy subfamily in the S.exigua genome. By determining the TE content, dynamics, and HTT events in the Noctuidae genomes, our study emphasized that TE activities and HTT events had substantial impacts on the Noctuidae genome evolution.

Marker-assisted selection (MAS) and genomic selection (GS) have been used to select individuals with desirable traits. MAS used a few markers associated with a specific trait to select individuals with desirable traits, which are determined after a Genome-wide association studies (GWAS). On the contrary, GS uses a large number of markers distributed across the genome to predict the genomic breeding values for a further selection of the individuals. In general, MAS has shown a high prediction accuracy but is not suitable for traits that are controlled for multiple genes, and has another constraint, it is required the phenotypic data; on the contrary, GS has not shown the highest prediction accuracy as MAS but it takes into account the effect of multiple genes controlling a target trait and it can be used without phenotypic data. Including GWAS-selected markers in GS can enhance the reduced prediction accuracy that GS shows in comparison with MAS. Thus, the objective of this study was to compare the prediction accuracy of MAS, and some models of genomic prediction (gBLUP, gBLUP including GWAs-selected markers, and some Bayesian models such as Bayes A, Bayes B, Bayes LASSO and Bayesian Ridge Regression) with GWAS-selected markers incorporated in gBLUP in order to confirm if the incorporation of GWAS in GS increases the prediction accuracy of GS. As a model for this study, it was used data from Sorghum which has shown population structure, to evaluate if the incorporation of GWAs-selected markers into GS improves prediciton accuracy. It was used a sample of 6000 SNPs out of the 265.487 reported in the study conducted by Morris et al (2013), and also it was considered some parameters that affect the efficiency of the selection such as the size of the training population, the heritability, and the number of QTNs. The GWAS-selected SNPs were identified after using the model BLINK. The results showed that the incorporation of GWAS-selected markers enhanced the performance of the genomic selection with similar prediction accuracy as MAS, the number of QTNs and size of the training population affected the accuracy, with higher accuracy with a bigger size of the training population and with a lower number of QTNs, but it seems that the heritability does not have any impact in the model where GWAS-selected SNPs were included in gBLUP.

The unprecedented growth of publicly available SARS-CoV-2 genome sequence data has increased demand for effective and accessible SARS-CoV-2 data analysis and visualization tools. A majority of the currently available tools either require computational expertise to deploy or limit user input to pre-selected subsets of SARS-CoV-2 genomes. To address these limitations, we developed ViralVar, a publicly available, point-and-click webtool that gives users the freedom to investigate and visualize user-selected subsets of SARS-CoV-2 genomes obtained from the GISAID public database. ViralVar has two primary features that enable: 1) visualization of spatiotemporal dynamics of SARS-CoV-2 lineages, and 2) structural/functional analysis of genomic mutations. As proof-of-principle, ViralVar was used to explore the evolution of the SARS-CoV-2 pandemic in the USA in the pediatric, adult, and elderly population (n > 1.7 million genomes). While the spatiotemporal dynamics of variants did not differ between these age groups, several USA-specific sublineages arose relative to the rest of the world. Our development and utilization of ViralVar to provide insights on the evolution of SARS-CoV-2 in the USA demonstrates the importance of developing accessible tools to facilitate and accelerate large-scale surveillance of circulating pathogens. The ViralVar webserver is freely available at http://viralvar.org/.

Abstract: Remarkably, the p53 transcription factor, referred to as “the guardian of the genome”, is not essential for mammalian development. Moreover, efforts to identify p53‑dependent developmental events have produced contradictory conclusions. Given the importance of pluripotent stem cells as models of mammalian development, and their applications in regenerative medicine and disease, resolving these conflicts is essential. Here we attempt to reconcile disparate data into justifiable conclusions predicated on reports that p53‑dependent transcription is first detected in late mouse blastocysts, that p53 activity first becomes potentially lethal during gastrulation, and that apoptosis does not depend on p53. Furthermore, p53 does not regulate expression of genes required for pluripotency in embryonic stem cells (ESCs); it contributes to ESC genomic stability and differentiation. Depending on conditions, p53 accelerates initiation of apoptosis in ESCs in response to DNA damage, but cell cycle arrest as well as the rate and extent of apoptosis in ESCs are p53-independent. In embryonic fibroblasts, p53 induces cell cycle arrest to allow repair of DNA damage, and cell senescence to prevent proliferation of cells with extensive damage.

Genetic disorders are preeminent determinants of infant mortality. The inherited pediatric-onset genetic disorders have consequential stress on child growth and development: several congenital, complex and rare disorders with indistinguishable clinical symptoms where diagnosis always remains a challenging task. Traditional diagnosis methods include biochemical tests followed by chromosomal microarray and sequencing of a single gene or panel of genes. These methods had several limitations, but with the advent of whole-exome sequencing (WES), genetic testing has become cost-effective and transformative. Exome sequencing has been known for its effectiveness, which appropriately elucidates and distinguishes the heterogeneous disorders to avoid misdiagnosis and decode the underlying genetic alterations. WES has led to discovering genes and genomic variants in a broad spectrum of diseases, including autism, epilepsy, congenital heart diseases, neurodevelopmental diseases, cancer, nephrotic disorders, neural tube defects and fetal structural anomalies. WES is significant in producing immense genomic biomarkers that can be made as appropriate pharmacogenomic targets for drug therapy. In this article, we analyze the recent exploration of WES technology to revolutionize not only the process of genetic variation and disease detection but also the convention of preventative and targeted drug discovery.

Crosstalk between the BMP and TGF-β signaling pathways regulates many complex developmental processes from the earliest stages of embryogenesis throughout adult life. In many situations, the two signaling pathways act reciprocally. For example, TGF-β signaling is generally pro-fibrotic whereas BMP signaling is anti-fibrotic and pro-calcific. Sex-specific differences occur in many diseases including cardiovascular pathologies. Differing ratios of fibrosis and calcification in stenotic valves suggests that BMP/TGF-β signaling may vary in men and women. In this review, we focus on the current understanding of the interplay between sex and BMP/TGF-β signaling and pose several unanswered questions.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

Axial resolution is one of the most important characteristics of a microscope. In all microscopes, a high axial resolution is desired in order to discriminate information efficiently along the longitudinal direction. However, when studying thick samples that do not contain laterally overlapping information, a low axial resolution is desirable as information from multiple planes can be recorded simultaneously from a single camera shot instead of plane-by-plane mechanical refocusing. In this study, we increased the focal depth of an infrared microscope non-invasively by introducing a binary axicon fabricated on barium fluoride substrate close to the sample. Preliminary results of imaging thick and sparse silk fibers showed an improved focal depth with a slight decrease in lateral resolution and increase in background noise.

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18\% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crowd density based on Completed Local Binary Pattern (CLBP). We first divide the image into blocks and then re-divide the blocks into cells. For each cell, we compute CLBP and then concatenate them to describe the texture of the corresponding block. We then train a multi-class Support Vector Machine (SVM) classifier, which classifies each block of image into one of four categories, i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS 2009 dataset, and from the experiments, we show to achieve 95% accuracy for the proposed descriptor. We also compare other state-of-the-art texture descriptors and from the experimental results, we show that our proposed method outperforms other state-of-the-art methods.

Purpose: Young transgender and non-binary are exposed to situations of discrimination and have a greater risk of violence. The purpose of this study is to analyze which protective, violence and health and well-being factors have more influence on transgender and non-binary people compared to cisgender people. Method: The sample comprised 856 people between 14 and 25 years old. A survey including questions about sociodemographic information and protective, violence and health and well-being factors was designed ad hoc for this study. Results: The results show non-binary group received the least support from family and friends, higher risk of suffering cyberbullying and a higher feel isolated and unhappy. Non-binary and transgender have suffered more verbal attacks both inside and outside their school and physical attacks at school than cisgender young. Conclusions: These results are important and may contribute to promote public policies and clinical interventions to favor the integration of non-binary and transgender people in our society.

A simple model has been used to investigate the nature of chemical order in Na-Pb and Na-Hg liquid binary alloy at 700K and 673K respectively. The energy parameter obtained from the model was used to calculate the concentration dependent mixing properties such as Gibb’s free energy of mixing, Concentration fluctuations in the long wavelength limit and the Warren-Cowley chemical short range order parameter. Results obtained showed that both alloys are hetero-coordinated throughout the entire concentration and there is tendency for segregation and demixing to take place in the liquid alloys. We observed that Na-Hg liquid alloy is more strongly interacting binary alloy and chemically ordered than Na-Pb liquid alloy.

Background Homologous recombination deficiency (HRD) evolved into a major diagnostic marker in high grade ovarian cancer (HGOC) predicting the response to poly (Adenosine diphosphate-ribose) polymerase inhibitors (PARPi) and also platinum-based therapy. In addition to HRD, the type of peritoneal tumor spread influences treatment response and patient survival; miliary type tumor spread has poorer predicted outcome than non-miliary type tumor spread. Methods Known methods for HRD assessment were adapted for our technical requirements and the Predictive-value Integrated Genomic Instability Score (PIGIS) for HRD assessment evolved as an outcome. PIGIS was validated in HGOC samples from 122 patients. We used PIGIS to analyze whether the type of tumor spread correlated with HRD status and whether this had an impact on survival. Results We demonstrated that PIGIS can discriminate HRD positive from HRD negative samples. Tumors with miliary tumor spread are HRD negative and have a very bad prognosis with a progression free survival (PFS) of 15.6 months and an overall survival (OS) of 3.9 years. However, HRD negative non-miliary spreading tumors in our cohort had a much better prognosis (PFS 35.4 months, OS 8.9 years), similar to HRD positive tumors (PFS 34.7 months, OS 8.9 years). Conclusions Our results indicate that in a predominantly PARPi naïve cohort the type of tumor spread might be a better predictor of response to platinum sensitivity than HRD. It remains to be determined, whether this also applies for sensitivity to PARPi.

Chronic Myeloid Leukemia, resulting due to chromosomal aberration t(9;22) through formation of oncogenic BCR-ABL fusion oncogene. Modern BCR-ABL inhibitors, called TKIs, have revolutionized CML treatment. CML has three stages: chronic, accelerated, and blast crisis. TKIs work well in CP-CML, where patients survive as long as the normal population, but they don't work in AP- and BC-CML. Even with advances in treatment, BC-CML has an average overall survival of less than a year, giving oncologists little time to clinically intervene. Oncologists can delay or prevent CML advancement by detecting patients at risk of disease progression early and making timely treatment decisions, especially with third and fourth generation TKIs. However, no universal molecular biomarkers exist to diagnose CML patient groups at risk of disease progression. A recent study found that all BC-CML patients have mutant FANCD2. Our study was designed to detect mutant FANCD2 in AP-CML (early progression phase) to investigate its potential as a novel biomarker of early CML progression from chronic phase to accelerated phase due to the urgent need for such a biomarker. Our study comprised of 123 CP-CML (control group) and 60 AP-CML patients (as experimental group) from Hayatabad Medical Complex, Peshawar, Pakistan, from Jan 2020 to July 2023. DNA was extracted from the patients and FANCD2 gene was sequenced using Illumina next generation sequencer (NGS) Illumina MiSeq sequencer. NGS analysis revealed a unique splice site mutation in FANCD2 gene (c. 2022-5C>T). This mutation was detected in all CP-CML patients but in none of CP-CML. The mutation was confirmed by Sanger sequencing. FANCD2 is member of Fanconi anemia (FA-) pathway gene involved in DNA repair and genomic instability. Therefore, our studies show that FANCD2 (c. 2022-5C>T) mutation as a very specific molecular biomarker for early CML progression. We recommend to clinical validate this biomarker is prospective clinical trials.

Heterosis plays a significant role in enhancing variety, boosting yield, and raising economic value, but the molecular mechanism is still unclear. We analyzed the transcriptomes and 3D genomes of hybrid (F1) and its parents (w30 and 082). Analysis of the expression revealed a total of 485 specially expressed genes (SEGs), 173 differentially expressed genes (DEGs) above parental expression level, more actively expressed genes and up-regulated DEGs in F1. Further study revealed that the DEGs detected in F1 and its parents were mainly involved in response to auxin, plant hormone signal transduction, DNA metabolic process, Purine metabolism, starch and sucrose metabolism, which suggested that these biological processes may play a crucial role in the heterosis of B. rapa. The analysis of 3D genome data revealed that hybrid F1 tend to contain more transcriptionally active A compartments after hybridization. Additionally, F1 had a smaller TAD (topologically associated domain) genome length, but the number was the highest, and the expression change of activated TAD was higher than repressed TAD. More specific TAD boundaries were detected between parents and F1. Subsequently, 140 DEGs with genomic structural variants were selected as potential candidate genes. Among them, we found two DEGs with consistent expression changes in A/B compartments and TADs. Our findings suggest that genomic structural variants, such as TADs and A/B compartments, may affect gene expression and contribute to heterosis in B. rapa. This study provides further insight into the molecular mechanism of heterosis in B. rapa.

Fat has a high energy density and excessive fatness has been recognized as a problem for egg production and the welfare of chickens. The identification of a genetic polymorphism controlling fat deposition would be helpful to select against excessive fatness in the laying hen. This study aimed to estimate genomic heritability and identify the genetic architecture of abdominal fat deposition in a population of chickens from a Dongxiang blue-shelled local breed crossbred with the White Leghorn. A genome-wide association study wase conducted on abdominal fat percentage, egg production and body weights using a sample of 1534 hens genotyped with a 600K Chicken Genotyping Array. The analysis yielded a heritability estimate of 0.19 ± 0.04 for abdominal fat percentage, 0.56 ± 0.04 for body weight at 72 weeks, 0.11 ± 0.03 for egg production and 0.24 ± 0.04 for body weight gain. The genetic correlation of abdominal fat percentage with egg production between 60 and 72 weeks of age was 0.35 ± 0.18. This implies a potential trade-off between these two traits related to allocation of resource. Strong positive genetic correlations were found between fat deposition and weight traits. A promising locus close to COL12A1 on chromosome 3, associated with abdominal fat percent, was found in the present study. The other region located around HTR2A on chromosome 1, where allele substitution was predicted to be associated with body weight gain, accounted for 2.9% of phenotypic variance. Another region located on chromosome 1, but close to SOX5, was associated with egg production. These results may be used to influence the balanced genetic selection for laying hens.

Ovarian tissue cryopreservation (OTC) or testicular tissue cryopreservation (TTC) are effective and often the only options for fertility preservation in female or male patients due to oncological, medical, or social aspects. While TTC and resumption of spermatogenesis, either in vivo or in vitro, has still be considered an experimental approach in humans, OTC and autotransplantation has been applied increasingly to preserve fertility with more than 200 live births worldwide. However, the cryopreservation of reproductive cells followed by the resumption of gametogenesis, either in vivo or in vitro, may interfere with sensitive and highly regulated cellular processes. In particular, the epigenetic profile, which includes not just reversible modifications of the DNA itself but also post-translational histone modifications, small non-coding RNAs, gene expression and availability, and storage of related proteins or transcripts, have to be considered in this context. Due to complex reprogramming and maintenance mechanisms of the epigenome in germ cells, growing embryos, and offspring, OTC and TTC are carried out at very critical moments early in the life cycle. Given this background, the safety of OTC and TTC taking into account the epigenetic profile has to be clarified. Cryopreservation of mature germ cells (including Metaphase II oocytes and mature spermatozoa collected via ejaculation or more invasively after testicular biopsy) or embryos has been used successfully for many years in Medically Assisted Reproduction (MAR). However, tissue freezing followed by in vitro or in vivo gametogenesis has become more attractive in the past, while few human studies have analysed the epigenetic effects, with most data deriving from animal studies. In this review, we highlight the potential influence of the cryopreservation of immature germ cells and subsequent in vivo or in vitro growth and differentiation on the epigenetic profile in humans and animals.

The experiments described in this research article were designed to test the effect of rare variants into genomic prediction in dairy cattle. Common polymorphisms are able to explain only a small proportion of the underlying genetic variation of complex phenotypes. Variants representing functional mutations with large effects on complex phenotypes are expected to be rare due to natural (humans) or artificial (livestock) selection pressure. Therefore, it is important to check whether the use of rare variants could increase the accuracy of ranking of animals by providing the tool for more precise differentiation among the bulls with high additive genetic merit. The goal of our study was to verify whether including rare variants in a genomic selection model allows for a more accurate description of the additive genetic background of traits under selection in dairy cattle. We used the linear mixed model for comparison SNP estimates for Holstein-Friesian cattle of the two data sets – a set containing only single nucleotide polymorphisms defined by minor allele frequency ≥ 0.01, which is routinely used in the Polish genomic evaluation system (46,216 SNPs), and a set containing SNPs selected based only on the call rate (54,378 SNPs). Based on the SNP estimates we also calculated DGV and GEBV and compared them between both data sets. In all the analyses we used production, fertility, conformation and udder health traits. We also assessed the time required for the two most computationally demanding components of genomic selection: preparing genotype data, and estimation of SNP effects between those two data sets. The results of our study indicated that the analysis including rare variants resulted in changes in the individual ranking of the top 100 male and female candidates, but had no effect on the outcome of the quality of EBV prediction as expressed by the Interbull validation test.

Ralstonia solanacearum is the causative agent of bacterial wilt, one of the most destructive plant diseases. While chemical control has an environmental impact, biological control strategies can allow sustainable agrosystems. Three lytic bacteriophages (phages) of R. solanacearum with biocontrol capacity in environmental water and plant were isolated from river water in Europe but not fully characterized, their genomic characterization being fundamental to understand their biology. In this work, the phage genomes were sequenced and subjected to bioinformatic analysis. The morphology was also observed by electron microscopy. Phylogenetic analyses were performed with a selection of phages able to infect R. solanacearum and the closely related phytopathogenic species R. pseudosolanacearum. The results indicated that the genomes of vRsoP-WF2, vRsoP-WM2 and vRsoP-WR2 range from 40,688 to 41,158 bp with almost 59% GC-contents, 52 ORFs in vRsoP-WF2 and vRsoP-WM2, and 53 in vRsoP-WR2 but, with only 22 or 23 predicted proteins with functional homologs in databases. Among them, two lysins and one exopolysaccharide (EPS) depolymerase, this type of depolymerase being identified in R. solanacearum phages for the first time. These three European phages belong to the same novel species within the Gyeongsanvirus, Autographiviridae family (formerly Podoviridae). These genomic data will contribute to a better understanding of the abilities of these phages to damage host cells and, consequently, to an improvement in the biological control of R. solanacearum.

A signal analysis of the genoma sequenced of coronavirus variants: B.1.1.7, B.1.135, B.1.429-B.1.427, B.1.525 and P1 is presented. We deal with a certain type of finite alternating sum series having independently distributed terms associated with binary (0,1) indicators for the nucleotide bases A,C,G,T. This method provides additional information to conventional Similarity comparisons and Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of genomic sequences according to its progression along the nucleotide bases position. Hence, the method could be useful for survelliance of genoma variants.

Fragile X syndrome (FXS) is a neurodevelopmental disorder associated with a wide range of cognitive, behavioral and medical problems. It arises from the silencing of the fragile X mental retardation 1 (FMR1) gene, and consequently, in the absence of its encoded protein, FMRP (Fragile X Mental Retardation Protein). FMRP is a ubiquitously expressed and multifunctional RNA-binding protein, primarily considered as a translational regulator. Pre-clinical studies of the past two decades have therefore focus on this function to relate FMRP’s absence to the molecular mechanisms underlying FXS physiopathology. Based on these data, successful pharmacological strategies were developed to rescue fragile X phenotype in animal models. Unfortunately, these results did not translate into human, as clinical trials using same therapeutic approaches did not reach the expected outcomes. These failures highlight the need to put into perspectives the different functions of FMRP in order to get a more comprehensive understanding of FXS pathophysiology. In this review, FMRP’s involvement on noteworthy molecular mechanisms are pointed out; ultimately contributing to various biochemicals alterations composing the fragile X phenotype.

Upon inbreeding, the architecture of the inbreeding load shifts as selection purges strongly deleterious recessive mutations and drift fixes many milder ones. Most small inbred populations show limited genetic variation while crosses between such populations commonly express pronounced heterosis, confirming fixation. In contrast, purging appears to be limited in that inbred populations often retain substantial inbreeding depression. In addition we have the enigma Darwin noted: purely selfing taxa are unknown. Because both purging and fixation reduce inbreeding depression and load, another mechanism must exist to sustain these. Background selection and the associations that develop among alleles in small inbred populations will shift the architecture of the load potentially creating blocks of recessive mutations linked in repulsion. This would generate pseudo-overdominance that could sustain these “PODs” and inbreeding load. Recombination and crosses between lineages could erode PODs. Crosses between populations fixed for different mutations would generate high pseudo-overdominance, enhancing heterosis and potentially POD formation. New recessive mutations arising within PODs would reinforce overdominance. PODs should generate clear genetic signatures including genomic hotspots of heterozygosity and linkage disequilibrium containing alleles at intermediate frequency generating segregating load. Results from several simulation and empirical studies match these predictions. Further simulations and comparative genomic analyses are needed to rigorously test whether PODs exist in sufficient strength and number to generate persistent inbreeding depression and load in inbred lineages.

Genomic medicine is moving from research to the clinic. There is a lack of evidence about the impact of genomic medicine interventions on health outcomes. This is due in part to a lack of standardized outcome measures that can be used across different programs to evaluate the impact of interventions targeted to specific genetic conditions. The eMERGE Outcomes working group (OWG) developed measures to collect information on outcomes following the return of genomic results to participants for several genetic disorders. These outcomes were compared to outcome intervention pairs for genetic disorders developed independently by the ClinGen Actionability working group (AWG). In general, there was concordance between the defined outcomes between the two groups. The ClinGen outcomes tended to be higher level and the AWG scored outcomes represented a subset of outcomes referenced in the accompanying AWG evidence review. eMERGE OWG outcomes were more detailed and discrete, facilitating collection of relevant information from health records. This paper demonstrates that common outcomes for genomic medicine interventions can be identified. Further work is needed to standardize outcomes across genomic medicine implementation projects and make these publicly available to enhance dissemination and assist in making precision public health a reality.

κ Draconis is a binary system with a classical Be star as the primary component. Its emission-line spectrum consists of hydrogen lines, notably the Hα line with a peak intensity ratio (V/R) variations phase-locked with the orbital period P = 61.55 days. Among binaries demonstrating the Be phenomenon, κ Dra stands out as one of a few systems with a discernible mass of its secondary component. Based on more than 200 spectra obtained in 2014 − 2023, we verified the physical parameters and constructed the mass function. We used part of these data obtained in 2014 − 2021 to investigate regions in the circumstellar disk of the primary component that emit the Hα line using the Doppler tomography method. The results show that the disk has a non-uniform density distribution with a prominent enhancement at Vy≈ 99 km s−1 and Vx≈−6 km s−1 that corresponds to a cloud-like source of the double-peaked Hα line profile. We argue that this enhancement’s motion is responsible for the periodic variations of the Hα V/R ratio, which is synchronised in orbital phase with the radial velocity (RV) of absorption lines from the atmosphere of the primary component.

During geomagnetic storms, which are a result of solar wind interaction with the Earth’s magnetosphere, geomagnetically induced currents (GICs) begin to flow in the long-distance high-voltage power grids on the Earth’s surface. It causes a number of negative phenomena that affect the normal operation of the entire electric power system. To investigate the nature of the phenomenon and its possible effects on transformers, a GIC monitoring system was created in 2011, the devices of which were installed at five substations of the Kola-Karelian power transit in northwestern Russia. Over 12 years of operating the system a large amount of data has been accumulated, which cannot be analyzed manually within a reasonable amount of time. To analyze the constantly growing volume of recorded data effectively, a method for automatic classification of GIC in autotransformer neutral was proposed. The method is based on a continuous wavelet transform of the neutral current data combined with a convolutional neural network (CNN) to classify the obtained scalogram images. As the result of comparing four CNNs with different architectures, a model that showed excellent GIC classification performance on the validation set (100.00% accuracy and loss of 0.0115) was chosen.

The quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts that has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy ‘flips’ r binary variables to get a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, very high-quality solutions within a short CPU time.

In Los Angeles, at one point, the Covid-19 testing positivity rate was 6.25%, or one in sixteen. This translates to, on average, one in sixteen specimens testing positive and the vast majority testing negative. Usually, we run sixteen tests on sixteen specimens to identify the positive one(s). This process can be time consuming and expensive. Since a group of negative specimens pooled together for testing will produce a negative result, one single test could potentially eliminate many specimens. Only when the pooled specimen tests positive do we need further testing to identify the positive one(s). Based on this concept, we designed a strategy that will identify the positive specimen(s) efficiently. Assuming one in sixteen specimens is positive, we find that only four tests are needed. Furthermore, we can run them simultaneously, saving both resources and time. Although, in the real world, we cannot make the assumption of only one positive specimen, the same strategy works with slight modification and proves to be much more efficient than the conventional testing. Our strategy returns an answer 48% of the time in four tests and one time cycle. Overall, the average number of tests is seven or eight depending on the follow-up testing, and the average time cycle is about one and a half.

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

Efficient alternatives in energy production and consumption are constantly investigated by increasingly strict policies. In this way, the pollutant emissions that contribute to the greenhouse effect reduce and sustainability of the electricity sector increase. With more than a third of the world's energy consumption, buildings have great potential to contribute these sustainability goals. Additionally, with growing incentives in the Distributed Generation (DG) and Electric Vehicle (EV) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this work, an energy management system in SBs are developed to reduce the power demanded of a residential building. In order to balance the demand and power provided by the grid, microgrids such as Battery Energy Storage System (BESS), EVs and Photovoltaic Generation panels (PV) are used. Here, a Mixed Binary Linear Programming formulation (MBLP) is proposed to optimize the charge and discharge scheduling of EVs and also BESS. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis is considered. The results point a 65% reduction in peak load consumption supplied by grid and a 28.4% reduction in electricity consumption costs.

Impressing discoveries in the field of the genetic code have been described by its researchers by means of the terminology borrowed from linguistics and the theory of communications. Leading experts on structural linguistics believe for a long time already that languages of human dialogue were formed not from an empty place, but they are continuation of genetic language or, anyhow, are closely connected with it, confirming the idea of information commonality of organisms. The aticle continues the theme about a connection of linquistic languages with the genetic language. It describes results of comparative study of long Russian literary texts (novels by L.Tolstoy, F.Dostoevsky, A.Pushkin, etc.) and long sequences of hydrogen bonds in double helixes of DNA of different organisms. Formalisms of quantum informatics are used in modeling some of these results taking into account thoughts of many researches about possible using principles of quantum informatics in organisation of living bodies.

Background: Alfalfa, the most economically important forage legume worldwide, features modest genetic progress due to long selection cycles and large extent of non-additive genetic variance associated with its autotetraploid genome. Methods: To improve the efficiency of genomic selection in alfalfa, we explored the effects of genome parametrization (as autotetraploid and diploid dosage, plus allele ratios) and SNP marker subsetting (all available SNPs; only genic regions; only non-genic regions) on genomic regressions, together with various levels of filtering on reading depth and missing rates. We used Genotyping by Sequencing-generated data and focused on traits of different genetic complexity, i.e., dry biomass yield in moisture-favorable (FE) and drought stress (SE) environments, leaf size, and onset of flowering, assessed on 143 genotyped plants from a genetically-broad European reference population and their phenotyped half-sib progenies. Results: On average, allele ratio improved the predictive ability compared with other genome parametrizations (+7.9% vs. tetraploid dosage, +12.6% vs. diploid dosage), SNP subsetting offered an advantage compared with any tested subsettings (+3.7% vs. genic regions, +7.6% vs. non-genic regions). However, when focusing on specific traits, different combinations of genome parametrization and subsetting reached better performances. We also released Legpipe2, a SNP calling pipeline tailored for reduced representation (GBS, RAD) medium-sized genotyping experiments.

Background: The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of a first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern. Results: We developed the CoVigator tool with three components: 1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; 2) a comprehensive variant calling pipeline; 3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequenc-ing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of the variant calling pipeline are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de. Conclusion: With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of up-to-date list of mutations, which can be incorporated into global efforts to sustainably prevent or treat infections.

Cytosine (C) to uracil (U) RNA editing is one of the most important post-transcriptional processes, however exploring C-to-U editing events efficiently within the crop mitochondrial genome remains a challenge. An improving predictive RNA editor for crop mitochondrial genomes, iPReditor-CMG, was proposed, which was based on SVM, three common crop mitochondrial genomes and self-sequenced tobacco mitochondrial ATPase. After multi-combination feature extracting, high-dimension feature screening and multi-test independent predicting, the results showed that the average accuracy of intraspecific prediction was 0.85, and the highest value even up to 0.91, which outperformed the previous reference models. While the prediction accuracies were 0.78 between dicotyledons and no more than 0.56 between dicotyledons and monocotyledons, implying a possible similarity in C-to-U editing mechanisms among close relatives. The best model was finally identified with an independent test accuracy of 0.91 and an area under the curve of 0.88, and further suggested that five unreported feature sequences TGACA, ACAAC, GTAGA, CCGTT and TAACA were closely associated with the editing phenomenon. Multiple evaluation findings supported that the iPReditor-CMG could be effectively applied to predict crop mitochondrial editing sites, which may contribute to insight into their recognition mechanisms and even other post-transcriptional events in crop mitochondria.

The mechanisms by which bacteriophage T4 converts the metabolism of its E. coli host to one dedicated to progeny phage production was the subject of decades of intense research in many labs from the 1950’s through the 1980’s. At this point, a wide range of phages are starting to be used therapeutically and in many other applications and also the range of available phage sequence data is skyrocketing. It is thus important to re-explore the extensive available data about the intricacies of the T4 infection process as summarized here, expand it to looking much more broadly at other genera of phages, and explore phage infections using newly-available modern techniques and a range of appropriate environmental conditions.

Recently, some authors have claimed that we may live in one of a pair of universes in which one is expanding receding from the Big Bang and its partner is contracting and moving towards the Big Bang. We show that near a black hole binary system, this event may be occurring. If we suppose that there is a N dimensional brane which contains many smaller k dimensional branes and black holes, there is a possibility that the curved space times near black holes act like mirrors. This is because these space-times contain two regions such that the branes in the one region have opposite quantum numbers with respect to the branes in the other region. Also, if branes in one region are expanding and go away from a point state and the Big Bang, anti-branes in the other region are contracting and moving towards the point state and the Big Bang. If black holes join and form a binary system, then their states become similar to two mirrors that are facing each other and producing many images themselves of smaller branes and even black holes in the binary system. Summing over all branes and images give the CPT system. Dark energy could be a result of the images of observed matter. Comparing the masses of the observed objects and dark energy, the angle between cosmic mirrors like black hole binary systems could be determined.

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the LBP-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce Local Binary Patterns (LBP) into Generative Adversarial Networks (GANs), which effectively utilize the texture features of the source images, so that the network has stronger feature extraction ability and anti-interference ability. In the feature extraction stage, we introduce a pseudo-siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN through the fusion of RGB and infrared images on the RoadScene dataset. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain rich detailed information.

In this paper after introducing a model of binary data matrix (BDM) for physical parameters of an evolving system (of particles), we develop a Hilbert space as an ambient space to derive induced metric tensor on embedded parametric manifold identified by associated joint probabilities of particles observables (parameters). Parameter manifold assumed as space-like hypersurface evolving along time axis, an approach that resembles 3+1 formalism of ADM and numerical relativity. We show the relation of endowed metric with related density matrix. Identification of system density matrix by this metric tensor, leads to the equivalence of quantum Liouville equation and metric compatibility condition while covariant derivative of metric tensor has been calculated respect to Wick rotated time or spatial coordinates. After deriving a formula for expected energy per particles, we prove the equality of this expected energy with local scalar curvature of related manifold. We show the compatibility of BDM model with Hamilton-Jacobi formalism and canonical forms. On the basis of the model, I derive the Ricci flow like dynamics as the governing dynamics and subsequently derive the action of BDM model and Einstein field equations. Given examples clarify the compatibility of the results with well-known principles such as equipartition energy principle and Landauer’s principle. This model provides a background for geometrization of quantum mechanics compatible with curved manifolds and information geometry. Finally, we conclude a “bit density principle” which predicts the Planck equation, De Broglie wave particle relation, , Beckenstein bound and Bremermann limit.

Background: Atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. Methods: We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Results: The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. Conclusions: In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

Understanding the genetic architecture of a disease is crucial for development of valid diagnostic and therapeutic interventions. The analysis of genomic variations associated with pathological conditions is the starting point for uncovering disease-causing pathways (candidate processes). However, the complexity of intergenic and genetic-environmental interactions hinders the identification of pathogenic values of genomic changes. Furthermore, heredity, epigenetics and somatic mosaicism make the interpretation of genomic data even more sophisticated. To succeed, a variety of bioinformatic techniques are applied. Here, reviewing own and literature data, knowledge-based prioritization of genomic variations is described. Theoretical basis of the knowledge-based prioritization is given with a special regard to gene ontology, heuristics, hermeneutics (genomic hermeneutics) and analytics. Practical and methodological issues of prioritization using ontology- or pathway-based systems analysis are considered in the light of optimistic and realistic scenarios of cumulative phenotypic effects of the variome (the whole set of genomic variations in an individual or specific set of genomic variations for a phenotypic outcome). In the present communication, copy number variants (CNVs) in children with neurodevelopmental diseases are used as a practical foundation for the prioritization, inasmuch as these genome variations are systematically overlooked in the so-called NGS era. Nonetheless, it is highly likely that the prioritization is applicable to almost all types of genomic variations (e.g. chromosome abnormalities, gene mutations, functional synonymous variants etc.). The present methodology seems to be a valuable addition to current biomedical science widening the opportunities for medical genomics and genetics.

The Influenza A virus (IAV) is a highly infectious virus that poses a significant threat to global public health and food supplies. The current subtype of avian influenza virus (AIV), H5N1, is being closely monitored worldwide due to its unprecedented spread from Europe to North America and now to Central and South America. This review summarizes recent updates on the evolution of the different IAV subtypes in birds and mammals including humans, in Chile. The distribution and spread of AIV H5N1 in Chile indicated a complex interplay between ecological and human factors in that it was negatively correlated with distance to the closest urban center and precipitation and temperature seasonality. It is evident that highly pathogenic avian influenza (HPAI) H5N1 in Chile was introduced from North America via the Atlantic migratory flyways as opposed to local transmission from other countries in South America. The presence of these viruses in Chile underscores the need for increased biosecurity on poultry farms and continuous genomic surveillance approaches to understand and control AIVs in both wild and domestic bird populations in Chile.

Background: After its initial detection in Wuhan, China, in December 2019, SARS-CoV-2 has spread rapidly, causing successive epidemic waves worldwide. This study aims to provide a genomic epidemiology of SARS-CoV-2 in Burkina Faso. Methods: Three hundred and seventy-seven SARS-CoV-2 genomes obtained from PCR-positive nasopharyngeal samples (PCR cycle threshold score <35) collected between May 5, 2020, and January 31, 2022 were analysed. Genomic sequences were assigned to phylogenetic clades using NextClade and to Pango lineages using pangolin. Phylogenetic and phylogeographic analyses were performed to determine the geographical sources and time of virus introduction in Burkina Faso. Results: The analyzed SARS-CoV-2 genomes could be assigned to 10 phylogenetic clades and 27 Pango lineages already described worldwide. Our analyses revealed the important role of cross-border human mobility in the successive SARS-CoV-2 introductions in Burkina Faso from neighboring countries. Conclusion: This study provides additional insights into the genomic epidemiology of SARS-CoV-2 in West Africa. It highlights the importance of land travel in the spread of the virus and the need to rapidly implement preventive policies. Regional cross-border collaborations and the adherence of the general population to government policies are key to prevent new epidemic waves.

Heavy metals are compounds that can be hazardous and impair growth of living organisms. Bacteria have evolved the capability not only to cope with heavy metals but also to detoxify polluted environments. Three heavy metal-resistant strains of Mucilaginibacer rubeus and one of Mucilaginibacter kameinonensis were isolated from the gold/copper Zijin mining site, Longyan, Fujian, China. These strains were shown to exhibit high resistance to heavy metals with minimal inhibitory concentration reaching up to 3.5 mM Cu(II), 21 mM Zn(II), 1.2 mM Cd(II), and 10.0 mM As(III). Genomes of the four strains were sequenced by Illumina. Sequence analyses revealed the presence of a high abundance of heavy metal resistance (HMR) determinants. One of the strain, M. rubeus P2, carried genes encoding 6 putative P1B-1-ATPase, 5 putative P1B-3-ATPase and 4 putative Zn(II)/Cd(II) P1B-4 type ATPase, and 16 putative RND-type metal transporter systems. Moreover, the four genomes carry a high abundance of genes coding for putative metal binding chaperones. Analysis of the close vicinity of these HMR determinants uncovered the presence of clusters of genes potentially associated with mobile genetic elements. These loci include genes coding for tyrosine recombinases (integrases) and subunits of mating pore (type 4 secretion system) respectively allowing integration/excision and conjugative transfer of numerous genomic islands. Further in silico analyses revealed that their genetic organization and gene products resemble the Bacteroides integrative and conjugative element CTnDOT. These results highlight the pivotal role of genomic islands in the acquisition and dissemination of adaptive traits, allowing for rapid adaption of bacteria and colonization of hostile environments.

The time required to reach a correct diagnosis is one of the most important problems for rare disease (RD) patients. Diagnostic delay can be intolerably long, to the point that it is usually described as a “diagnostic odyssey” and, sometimes, a diagnosis might never occur. The International Rare Disease Research Consortium proposed, as ultimate goal for 2017-2027, to enable all people with a suspected RD to be diagnosed within one year if the disorder is known, and to enter a globally coordinated diagnostic and research pipeline for the unsolved cases. In-depth analysis of the genotype through next generation sequencing, together with a standardized in-depth phenotype description and sophisticated high-throughput approaches, have been applied as diagnostic tools to increase the chance of a timely and accurate diagnosis. This approach is very fruitful as, according to the Orphanet database, from 2010 to March 2017 more than 600 new RDs have been described and about 3600 genes linked to RDs have been identified. However, combination of -omics and phenotype data and international sharing of this information raise ethical concerns. Values to be assessed include not only patient autonomy but also family implications, beneficence, non-maleficence, justice, solidarity and reciprocity, which must be respected and promoted and, at the same time, balanced among each other. In this work we suggest that, to maximise patients involvement in the search for a diagnosis and identification of new causative genes, undiagnosed patients should have the possibility to: 1) actively participate in the description of their phenotype; 2) choose the level of visibility of their profile in matchmaking databases; 3) express their preferences regarding return of new findings, in particular which level of Variant of Unknown Significance (VUS) significance should be considered relevant to them. The quality of the relation between individual patients and physicians, and between the patient community and the scientific community is critically important for making the best use of available data and combining efforts in the search for matches with similar cases worldwide that will help to solve unsolved cases. The contribution of patients to collecting and coding data comprehensively is critical for efficient use of data downstream of data collection.

The port environments have some structures that still need regular control. However, the nature of these environments presents a number of challenges in determining an accurate vehicle position and consequently performing successful image similarity detection. These scenarios, however, can have poor visibility conditions and are highly dynamic, making place recognition difficult because the appearance of a place can change over time. Under these operational conditions, imaging sonars are a promising solution. There are some factors that affect the quality of the captured images, but the fact that they do not suffer from haze is a plus. Therefore, in this paper, a purely acoustic similarity detection method based on forward-looking sonar (FLS) data is proposed to overcome the inherent perceptual problems in a port scenario. Considering the lack of available data in this context and to facilitate the variation of environmental parameters and sensor configurations, a port scenario was simulated using the Stonefish simulator. Experiments were conducted on predefined trajectories near structures. The place recognition approach performs better compared to the results obtained with optical imagery. In general, it provides a good compromise in terms of distinctiveness and achieves a performance of 87.5% when appropriate constraints and assumptions are made.

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants in topics surrounding politics, climate, the economy and other areas where an agreement is required. There are multiple approaches to investigating the scenarios in which polarization occurs and given that polarization is not a new phenomenon but that its virality may be supported by the low cost and latency messaging offered by online social media platforms; an investigation into the intrinsic dynamics of online opinion evolution is presented for complete networks. Extending a model which utilizes the Binary Voter Model (BVM) to examine the effect of the degree of freedom for selecting contacts based upon homophily, simulations show that different opinions are reinforced for a period of time when users have a greater range of choice for association. The facility of discussion threads and groups formed upon common views further delays the rate in which a consensus can form between all members of the network. This can temporarily incubate members from interacting with those who can present an alternative opinion where a voter model would then proceed to produce a homogeneous opinion based upon pairwise interactions.

The article is devoted to algebraic modeling of universal rules of stochastic organization of genomic DNA of higher and lower organisms, previously published by the author. The proposed algebraic apparatus, which uses formalisms of quantum mechanics and quantum informatics and which is based on the so-called tensor-unitary transformations of vectors that generate families of interrelated stochastic-deterministic vectors of increased dimensions. The features of the vectors' interconnections in these families model the stochastic-deterministic properties of the named phenomenological rules. There are new approaches to modeling of developing multi-parameter biosystems, whose number of parameters increases in the course of step-by-step development. In the light of the presented materials, the issues of fractal-like organization in genetically inherited biosystems are considered. The development of the theory of stochastic determinism as an antipode of deterministic chaos is discussed.

Chronic obstructive pulmonary disease (COPD) is a respiratory disease associated with airways inflammation and lung parenchyma fibrosis. The primary goals of COPD treatment are to re-duce symptoms and risk of exacerbations, therefore pulmonary rehabilitation is considered the key component of managing COPD patients. Oxidative airway damage, inflammation and re-duction of endogenous antioxidant enzymes are known to play a crucial role in the pathogenesis of COPD. Natural antioxidants have also recently been considered as they play an important role in metabolism, DNA repair and fighting the effects of oxidative stress. In this paper we evaluated the response of 105 elderly COPD patients to pulmonary rehabilitation (PR), based on high or low vegetable consumption, by analyzing clinical parameters and biological measure-ments at baseline and after completion of the three weeks PR. We found that high vegetable in-take in normal diet, without any specific intervention, can increase the probability to success-fully respond to rehabilitation (65.4% of responders ate vegetables daily vs. 40.0% of Non-Responders, p=0.033). Three weeks of pulmonary rehabilitation are probably too short to reveal a reduction of the oxidative stress and DNA damage, but are enough to show an im-provement in the patient's inflammatory state.

Chronic obstructive pulmonary disease (COPD) is a respiratory disease associated with airways inflammation and lung parenchyma fibrosis. The primary goals of COPD treatment are to re-duce symptoms and risk of exacerbations, therefore pulmonary rehabilitation is considered the key component of managing COPD patients. Oxidative airway damage, inflammation and re-duction of endogenous antioxidant enzymes are known to play a crucial role in the pathogenesis of COPD. Natural antioxidants have also recently been considered as they play an important role in metabolism, DNA repair and fighting the effects of oxidative stress. In this paper we evaluated the response of 105 elderly COPD patients to pulmonary rehabilitation (PR), based on high or low vegetable consumption, by analyzing clinical parameters and biological measure-ments at baseline and after completion of the three weeks PR. We found that high vegetable in-take in normal diet, without any specific intervention, can increase the probability to success-fully respond to rehabilitation (65.4% of responders ate vegetables daily vs. 40.0% of Non-Responders, p=0.033). Three weeks of pulmonary rehabilitation are probably too short to reveal a reduction of the oxidative stress and DNA damage, but are enough to show an im-provement in the patient's inflammatory state.

Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma (ccRCC) samples were re-analyzed from the Genomic Fabric Perspective that considers the transcriptome a multi-dimensional mathematical object, constrained by a dynamic set of expression correlations among genes. Every gene in the chest wall metastasis (MET), two primary tumors (PTA, PTB) and the surrounding normal tissue (NOR) of the right kidney was characterized by three independent measures: average expression level (AVE), relative expression variation (REV) and expression correlation (COR) with each other gene. AVE was used to determine the regulation of the genomic fabrics of ccRCC, apoptosis, chemokine and VEGF signaling pathways. REV quantified the alteration of the transcripts’ abundances control, while COR determined the remodeling of the transcriptomic networks of chemokine signaling and oxidative phosphorylation genes. The gene hierarchy was established in based on Gene Commanding Height and the Gene Master Regulators (GMR) TASOR (PTA), FAM27C (PTB) and ALG13 (MET) and DAPK3 (NOR) were identified in each profiled region. We predict that TASOR overexpression would block transcription in PTA but not in PTB, while slightly stimulating it in NOR. Silencing of ALG3 would slow-down the cell-cycle in all three cancer regions with practically no effect in NOR.

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings.Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

Cancer cell lines are widely used as in vitro models of tumorigenesis, facilitating fundamental discoveries in cancer biology and translational medicine. Currently, there are few options for glioblastoma (GBM) treatment and limited in vitro models with accurate genomic and transcriptomic characterization. Here, a detailed characterization of a new GBM cell line, namely AHOL1, was conducted in order to fully characterize its molecular composition based on its copy number alteration (CNA) and transcriptome profiling, followed by the validation of key elements associated with GBM tumorigenesis. Large numbers of CNAs and differentially expressed genes (DEGs) were identified. CNAs were distributed throughout the genome, including gains at Xq11.1-q28, Xp22.33-p11.1, Xq21.1-q21.33, 4p15.1-p14, 8q23.2-q23.3 and losses at Yq11.21-q12, Yp11.31-p11.2 and 12p13.31 positions. Nine druggable genes were identified, including HCRTR2, ETV1, PTPRD, PRKX, STS, RPS6KA6, ZFY, USP9Y and KDM5D. By integrating DEGs and CNAs, we identified 57 overlapping genes enriched in fourteen pathways. Altered expression of several cancer-related candidates found in the DEGs-CNA dataset was confirmed by RT-qPCR. Taken together, this first comprehensive genomic and transcriptomic landscape of AHOL1 provides unique resources for further studies and identifies several druggable targets that may be useful for therapeutics and biologic and molecular investigation of GBM.

The purpose of this study is to analyze the causes of cost variation in natural disaster prevention projects (NDPP) in Gyeongsangbuk-do and to develop effective cost and risk management strategies. The study collected data by conducting an online survey of 244 stakeholders. Binary logistic regression was used to analyze the impact of various variables on cost deviation, in-cluding project management risk (PMR), project costing and execution risk (PCER), project exe-cution strategy risk (PESR), construction project risk (CPR), project cost and schedule risk (PCSR), project management challenges (PMC), and construction project subcontractor and safety man-agement (CPSSM). The results showed that PMR (OR=3.744, 95% C.I. [1.657, 8.457]), PCER (OR=5.068, 95% C.I. [2.236, 11.484]), PESR (OR=3.447, 95% C.I. [1. 853, 6.413]), CPR (OR=2.292, 95% C.I. [1.020, 5.151]), PCSR (OR=4.817, 95% C.I. [2.250, 10.317]), PMC (OR=2.954, 95% C.I. [1.452, 6. 013]), and CPSSM (OR=2.419, 95% C.I. [1.297, 4.515]) had statistically significant effects on cost deviation. In particular, PMR, PCER, and PESR were identified as the main causes, and the effects of additional factors such as CPSSM were also important. The developed predictive model plays an important role in helping stakeholders of NDPP to manage risks in advance and take appro-priate preventive measures to ensure the success of the project. This study empirically supports the theoretical hypotheses of previous studies and provides a clear understanding of the causes of cost deviation in NDPP, which provides important insights for strategic decision-making and provides a new perspective on risk management.

Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

In the article, we are trying to find a general law that describes the physical properties of a complex compound through the same physical properties of compound elements. For metal alloys operated under extreme conditions - neutron and proton irradiations and thermal heating, the mechanical properties of these alloys and their elements have been studied. It turned out that the ultimate tensile strength of tantalum alloys used in the design of space reactors can be expressed in terms of the weighted sum of the ultimate tensile strengths of its elements in the range of neutron doses: (0 - 0.96) displacement per atom (dpa) for Ta–10W alloy and (1.4 - 1.7) dpa for T–111 alloy with a neutron energy of more than 0.1 MeV. For tungsten binary alloys used in the design of fusion reactors, the Vickers hardness can also be expressed in terms of the weighted sum of the Vickers hardnesses of the alloy elements in the range of proton irradiation doses: (0 - 0.05) dpa for W-1%Re alloy, (0.05 - 0.2) dpa for W-3%Re alloy and (0.05 - 1) dpa for W-1%Ta with a proton energy of 1 MeV.

In any region of a space, the gravitational field cannot be eliminated. The speed of light in a vacuum has never been observed and cannot be observed with current technology. Till now, only the speed of light in a gravitational field has been observed. It results in that all the observed speed of light are always being varied by the gravity. Here, it is presented that light could be dispersion in a gravitational field analogous to the dispersion of light in the Newtonian prism experiment. The relativistic mass density on the surface of a neutron star is on the level of 10¹⁷kg/m³ while on the surface of the Earth is only 10^-7kg/m³, the effect of the gravitational field acting on the speed of light by a neutron star is much larger than that by the Earth. Therefore, the dispersion of light in strong gravitational field could be generally observed from the image of a star and it should have been observed through the double-image gravitational lensing and the spectroscopic binary system. And, it should affect the observed numbers, spectrogram, size and distance of the celestial objects.

Starting from a very simple economic scenario, we build on it a game and then we introduce a general strategy able to reduce a regression problem to an equivalent binary classification problem. This reduction scheme (that we call adaptive reduction or also dynamic reduction) can be also used to derive a new boosting algorithm for regression problems named bOOst^d. The bOOst^d algorithm is very simple to implement, and it can use any learning algorithm with no priori assumptions. We present a conjecture for bOOst^d performances, which ensures a little error on training set. More important we can also provide a very good theoretical upper bound for the generalization error. We give a set of preliminary experimental results that seems to confirm our conjecture for bOOst^d performances on training set and the theoretical assumptions for the generalization error. We also provide a possible justification of why boosting often does not overfit. Finally, we leave some open problems and argue that in the future an adaptive single boosting (with an unique code) algorithm for binary, multi class and regression problems can be derived.

Recent advancements in molecular diagnostics have highlighted the potential of circulating cell-free DNA (ccfDNA) as a biomarker in various diseases. This study aims to explore the role of ccfDNA in the early diagnosis, prognosis, and therapeutic monitoring of diseases, with a focus on oncology. We conducted a comprehensive review and analysis of current literature, combined with experimental approaches involving advanced genomic sequencing and bioinformatics techniques. Our research systematically characterizes the molecular composition of ccfDNA, including its origins, size distribution, and methylation patterns, across different disease states. Furthermore, we investigate the dynamics of ccfDNA release and clearance in the bloodstream, elucidating mechanisms that could influence its diagnostic accuracy. In the context of cancer, we analyze the correlation between tumor-derived ccfDNA and disease progression, response to therapy, and overall survival. Our findings indicate that ccfDNA holds promise as a non-invasive biomarker for early disease detection, real-time monitoring of treatment efficacy, and potentially, predicting relapse. This study contributes to the growing field of liquid biopsies and opens new avenues for personalized medicine, emphasizing the need for standardized protocols in ccfDNA analysis to maximize its clinical utility. The implications of our findings extend beyond oncology, suggesting potential applications of ccfDNA in other pathological conditions.

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson's Disease (PD) heterogeneity among individuals is a proof that disease subtypes exist, and assigning individuals to subgroups is necessary for a better understanding of disease mechanisms and designing precise treatment approaches. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. 210 post mortem brain RNA-Seq samples from PD (n = 115) and Normal Controls (NC, n = 95) were obtained with a systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and Network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p.adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p &lt; 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat the COVID-19 disease. Several approaches are being used by scientists for investigation including 1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease and 2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition; thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, new B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries such as the Netherlands, Switzerland and France.

Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient’s variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this Review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.

In recent, Chae found that, at weak gravitational acceleration g_N≲10⁻⁹ms⁻², the observed orbit of the wide binary stars is not accordant with current theory of binary star. Here, we present, this finding should be an observational evidence for that Newtonian theory of orbit perturbation is still valid in galactic scale.

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

Local binary pattern is one of the visual descriptors and can be used as a powerful feature extractor for texture classification. In this paper, a novel representation for face recognition is proposed, called it Bilateral Line Local Binary Patterns (BL-LBP). This scheme is an extension of Line Local Binary Patterns descriptors in the statistical learning subspace. The present bilateral descriptors are fused with an ensemble learning of calibrated SVM models. The performance of this scheme is evaluated using 5 standard face databases. It is found that it is robust against illumination variation, diverse facial expressions and head pose variations and its recognition accuracy reaches 98 percent, running on a mobile device with a processing speed of 63 ms per face. Results suggest that our proposed method can be very useful for the vision systems that have limited resources where the computational cost is critical.

In this paper, we observe that if X is a set and (Bin(X), □) is the semigroup of binary systems on X, then its center ZBin(X) consists of the locally-zero-semigroups and that these can be modeled as (simple) graphs and conversely. Using this device we show that we may obtain many results of interest concerning groupoids by reinterpreting graph theoretical properties and at the same time results on graphs G may be obtained by considering them as elements of centers of the semigroups of binary systems (Bin(X), □) where X = V(G), the vertex set of G.

This paper presents novel feature descriptors and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC) stained slides. The first descriptor is called a characteristic curve which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is an LBP feature curve which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation, and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and comparative analysis.

Speckle noise reduction is an important area of research in the field of ultrasound image processing. Several algorithms for speckle noise characterization and analysis have been recently proposed in the area. Synthetic ultrasound images can play a key role in noise evaluation methods as they can be used to generate a variety of speckle noise models under different interpolation and sampling schemes, and can also provide valuable ground truth data for estimating the accuracy of the chosen methods. However, not much work has been done in the area of modelling synthetic ultrasound images, and in simulating speckle noise generation to get images that are as close as possible to real ultrasound images. An important aspect of simulated synthetic ultrasound images is the requirement for extensive quality assessment for ensuring that they have the texture characteristics and gray-tone features of real images. This paper presents texture feature analysis of synthetic ultrasound images using local binary patterns (LBP) and demonstrates the usefulness of a set of LBP features for image quality assessment. Experimental results presented in the paper clearly show how these features could provide an accurate quality metric that correlates very well with subjective evaluations performed by clinical experts.

The article continues the author's publications about the matrix-tensor study of universal rules of stochastic (probabilistic) organization of long single-stranded DNA sequences in eukaryotic and prokaryotic genomes. The author reveals that corresponding matrices of probabilities of n-plets in n-textual representations of each genomic DNA are numerically interrelated each with other in such algebraic form, which has analogies with formalisms of the known tensor-matrix theory of digital antenna arrays. These arrays combine many separate antennas into a single coordinated ensemble with unique emergent properties, due to which antenna arrays are widely used in devices of medicine, astrophysics, avionics, etc. The noted analogies allow putting forward the author's hypothesis that the stochastic organization of genomic DNAs is connected with bio-antenna arrays. From the point of view of this hypothesis, many known facts about using principles of antenna arrays in inherited physiological phenomena are collected in a single grouping with genomic DNAs. This new topic about the biological meaning of profitable properties of antenna arrays includes problems of biological evolution, the origin of the genetic code, regenerative medicine, and the development of algebraic biology. These issues are discussed jointly with the author's results of quantum information analysis of stochastic features of genomic DNAs.

The performance of the presurgical test (preSurg) for temporal lobe epilepsy (TLE) was evaluated via video-electroencephalography (VEEG), electroencephalography (EEG), 99mTc-HmPAO single-photon emission tomography (SPECT) and 1.5 T magnetic resonance imaging (MRI) in a group of 112 men (37.0 ± 1.1 years) and 106 women (39.7 ± 1.1 years) operated on for TLE. The epileptic zone (EZ) was adequately identified to determine whether the patient reached an Engel I grade (EI) at least one year postop. Accuracy was evaluated by the coefficient α, ranging from 3 (when result = EZ) to 2 (result in the same hemisphere as the EZ), 1 (noninformative result) or 0 (EZ in the contralateral hemisphere). The simplicity of diagnosis was defined as the number of preSurg surgeries needed to identify the EZ. EI was obtained in 85.8% of patients even though 42.2% of patients had noninformative MRI results. For preSurg α was (mean ± SEM) = VEEG (2.83 ± 0.04) &gt; MRI (2.14 ± 0.08) &gt; EEG (1.87 ± 0.09) &gt; SPECT (1.71 ± 0.09). The accuracy (combination of sensitivity and specificity) was calculated as follows: VEEG (0.797) &gt; MRI (0.518) &gt; EEG (0.446) &gt; SPECT (0.360). The likelihood positive ratio was greater, and the likelihood negative ratio was lower for VEEG. The most relevant factor for the regression model was VEEG (3.106) &gt; MRI (2.558) &gt; EEG (1.905), and the factors were not significantly related to SPECT. EZs in EI patients with low simplicity were identified mainly by VEEG. A very good postoperative outcome can be obtained even in TLE patients with no lesions on MRI. The VEEG is the most reliable preSurg test and may be the only reliable test for patients with very low simplicity (i.e., difficult).single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

We consider boundary value problems for a nonlinear mass transfer model, which generalizes the classical Boussinesq approximation, under inhomogeneous Dirichlet boundary conditions for the velocity and the substance’s concentration. It is assumed that the leading coefficients of viscosity and diffusion and the buoyancy force in the model equations depend on concentration. We develop mathematical apparatus of studying inhomogeneous boundary value problems under consideration. It is based on using of a weak solution of the boundary value problem and construction of liftings of the inhomogeneous boundary data. They remove the inhomogeneity of the data and reduce original problems to equivalent homogeneous boundary value problems. Based on this apparatus we will prove the theorem of the global existence of a weak solution to boundary value problem under study and establish important properties of the solution. In particular, we will prove the validity of the maximum principle for the substance’s concentration. We will also establish sufficient conditions for the problem data, ensuring the local uniqueness of weak solutions.

In 2011, by 1.3 mm wavelength VLBI radio wave observations of the SgrA*, Fish, V. L. et al showed that the emissions tightly related to the formation of a black hole shadow have a remarkably large time-varying feature within a region of less than 50 μas. The present paper suggests that the origin of the time variation in the observed emission is due to effects of the orbital motion of the existing super-massive black hole binary orbiting at SgrA* with a period of 2150±2.5 s. This suggestion is based on observations of decameter radio wave pulses from SgrA*. We show a good correlation between the time variation in the coherent flux density of the VLBI results and the time variation model of estimated emission intensities based on the periodic motion of the super-massive black hole binary by applying parameters deduced from the decameter radio wave pulse observation model (DRWP-Model). With further confirmation by Fourier analyses of the potential periodicity of the VLBI data that show the same periods of DRWP Model, we conclude that the time variation detected by the 1.3 mm wavelength radio wave VLBI is evidence of an existing super-massive black hole at Sgr A*.

Mass plays a strange multiple role in classical physics. This results in a difference between gravity and all other forces - a difference that stood at the beginning of Einstein's development of his general theory of relativity. The equivalence principle (EP) deals with homogenious gravitational fields, which, in fact, don‘t exist in nature. There is only an approximation for this field in Einstein-Lift possible for local situations. Nevertheless this EP can be true, because in classical formal binary logic there can be formulated a true conclusion from a wrong premise or assumption.

Influenza virus transcription is catalyzed by the viral RNA-polymerase (FluPol) through a cap-snatching activity. The snatching of the cap of cellular mRNA by FluPol is preceded by its binding to the flexible C-terminal domain (CTD) of the RPB1 subunit of RNA-polymerase II (Pol II). To better understand how FluPol brings the 3’-end of the genomic RNAs in close proximity to the host-derived primer, we hypothesized that FluPol may recognize additional Pol II subunits/domains to ensure cap-snatching. Using binary complementation assays between the Pol II and FluPol subunits and their structural domains, we revealed an interaction between the N-third domain of PB2 and RPB4. This interaction was confirmed by a co-immunoprecipitation assay and found to occur with the homologous domains of influenza B and C FluPols. Residues [1-72] of RPB4 were found critical in this interaction. Numerous punctual mutants generated at conserved positions between influenza A, B and C FluPols in the N-third domain of PB2 exhibited strong transcriptional activity defect. These results suggest that FluPol interacts with several domains/subunits of Pol II, the CTD to bind Pol II initiating host transcription and a second on RPB4 to locate FluPol at the proximity of the 5’-end of nascent host mRNA.

The storage of biochemical information, which is a prerequisite for the development of the first cell, is an unsolved problem affecting all concepts of the origin of life. However, if the protected environment in the continental crust is taken into account, completely new possibilities emerge for identifying processes that may have been crucial for the formation of the first cell. Under this background, we can hypothesize that, before cellular life began, a self-sustaining cycle of molecular reaction steps with information storage in RNA existed outside of a cell. This cycle was made possible in an open system bound to gas-permeable tectonic fracture zones with a high proportion of CO2 and N2. The formation of peptides and vesicles in supercritical CO2 and the chemical evolution of peptides have already been proven for the upper continental crust. Further considerations include the interactions of vesicles with catalytic peptides and the emergence of proto-tRNA. In combination with the formation of proto-tRNA synthetases, which consist of only two amino acid species and associated proto-tRNAs, the first RNA as an information storage system could have been formed with the information of proto-enzymes.

The article presents the author's results of studying hidden rules of structural organizations of long DNA sequences in eukaryotic and prokaryotic genomes. The results concern some rules of percentages (or probabilities) of n-plets in genomes. To reveal such rules, the author considers genomic DNA nucleotide sequences as multilayers sequences of n-plets and studies the percentage contents of n-plets in different layers. Unexpected rules of invariance of total sums of percentages in certain tetra-groupings of n-plets in different layers of genomic DNA sequences are revealed. These discovered rules are candidates for the role of universal genomic rules. A tensor family of matrix representations of interrelated DNA-alphabets of 4 nucleotides, 16 doublets, 64 triplets, and 256 tetraplets is used in the study. This matrix approach allows revealing algebraic properties of the mentioned genetic rules of probabilities, which are useful for developing algebraic and quantum biology. Some analogies of the discovered genetic phenomena with phenomena of Gestalt psychology are noted and discussed. The author connects the received results about the genomic percentages rules with a supposition of P. Jordan, who is one of the creators of quantum mechanics and quantum biology, that life's missing laws are the rules of chance and probability of the quantum world. Additional attention is paid to the algebraic features of the system of structured DNA alphabets and their relationship with the methods of algebraic holography, known in the technique of processing discrete signals. The concept of algebraic-holographic genetics is being developed for the understanding of inherited holographic properties of organisms.

This paper presents a discrete compartmental Susceptible-Exposed-Infected-Recovered/Dead (SEIR/D) model to address the expansion of Covid-19 pandemic. When time passes, the status of the cells is determined by binary rules that update following both a neighbourhood and a delay pattern. The model assumes the environmental parameters have a crucial impact on the expansion of the disease so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids. This proposal shows how the grid architecture, along with an update rule and a neighbourhood pattern is a valuable tool to model the pandemic expansion. This model has already been analyzed in previous works and compared with the corresponding continuous models solved by ordinary differential equations (ODE), coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between Susceptible and Asymptomatic, Asymptomatic and Infected, Infected and Recovered/Dead) may have a crucial impact on both the peak of Infected and the Recovery/Death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and in the case of plant pests.

Motivated by diffraction experiments on the $\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$ reconstructed Si(111) due to deposition of rare earth elements (Dy, Tb) and silicide formation we analyse the splitting and non-splitting of superstructure spots. For this purpose, we model diffraction patterns for one dimensional structures generated by the binary surface technique and use supercell models to keep the analysis simple. Diffraction pattern are calculated in the framework of the kinematical diffraction theory and they are analyzed as a function of the domains and domain boundaries. Basic properties of the diffraction pattern are analyzed for model systems of a two-fold and a three-fold periodicity. The rules derived from these calculations are applied to the "real-world" system of Si(111)-($\left(2\sqrt{3}\times \sqrt{3}\right)\mathrm{R}{30}^{\circ }$)-RESi$2_{}$ (RE = Dy or Tb). Depending on the combination of domains and domain boundaries of different types a plethora of different features are observed in the diffraction patterns. These are analyzed to determine the sizes of both domain boundaries and domains from experimentally observed splitting of specific superstructure spots.

Legionella pneumophila can cause a large panel of symptoms beside the classic pneumonia presentation. We present here a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of legionnaires’ disease in the same ward. While the first one was easily assumed as nosocomial based on the date of symptoms onset, the other case needed to rely on clear typing results to be either assigned as nosocomial and related to the same environmental source as the first one, or community acquired. To untangle this specific question, we applied Core-genome MultiLocus Typing (MLST), whole-genome Single Nucleotide Polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large scale epidemiological investigations, NGS typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in environment.

Background: Chronic Myeloid Leukemia (CML) is initiated in the bone marrow due to the chromosomal translocation t(9;22), resulting in the fusion oncogene BCR-ABL. Tyrosine kinase inhibitors (TKIs) targeting BCR-ABL have transformed fatal CML into an almost curable disease. However, TKIs lose efficacy during disease progression, and the mechanism of CML progression remains to be fully understood. Additionally, common molecular biomarkers for CML progression are lacking. Our studies previously detected ANKRD36 (c.1183_1184 delGC and c.1187_1188 dupTT) associated exclusively with advanced phase CML. However, clinical validation of this finding was pending. Therefore, this study aimed to clinically validate mutated ANKRD36 as a novel biomarker of CML progression. Materials and Methods: The study enrolled 124 patients in all phases of CML, recruited from Mayo Hospital and Hameed Latif Hospital in Lahore, Punjab, between January 2020 and September 2023. All response criteria were adopted from the European LeukemiaNet guideline 2020. Informed consent was obtained from all study subjects. The study was approved byscientific and ethical review committees of all participating centers. Sanger sequencing was employed to detect ANKRD36mutations in CML patients in accelerated phase (AP) (n=11) and blast crisis (BC) (n=10), with chronic-phase CML (CP-CML) patients as controls (n=103). Samples were processed using Big Dye Terminator Cycle Sequencing Ready Reaction kits and sequenced using ABI Prism 3730 Genetic Analyzer, and sequencing using forward and reverse primers for ANKRD36.Results: During our study, 17% of CML patients progressed to advanced phases AP-CML n= 11 (8.9%) and BC-CML n=10(8.1%). The chronic- and advanced-phase patients showed significant difference with respect to male-to-female ratio, hemoglobin level, WBC count, and platelet count. Sanger sequencing detected ANKRD36 mutations c.1183_1184 delGC and c.1187_1185 dupTT exclusively in all AP- and BC-CML patients but in none of the CP-CML patients. Nevertheless, mutations status was not associated with male-to-female ratio, hemoglobin level, WBC count, and platelet count, which makes ANKRD32 as an independent predictor of early and terminal disease progression in CML. Conclusions: The study confirms ANKRD36 as a novelgenomic biomarker for early and late CML progression. Further prospective studies should be carried out in this regard. ANKRD36, although fully uncharacterized in humans, shows the highest expression in bone marrow, particularly myeloid cells. Functional integrated genomic studies are recommended to further explore the role of ANKRD36 in the biology and pathogenesis of CML.

The life cycle of cancer follows the life cycle of the common ancestor of amoebozoans, metazoans, and fungi (AMF) and its systemic germline, which serves as a blueprint for all germlines capable of asymmetric cell division (ACD) and stem cell differentiation. Consequently, the oxygen sensitivity of the ancient non-gametogenic germline (Urgermline) was inherited by all germ and stem cell lines including the cancer germline. They all respond to Ugermline’s hyperoxia with loss of stemness and ACD ability and a dysregulated phenotype with irreparable DNA defects and defective symmetric cell divisions (DSCD). In protists, defective DSCD cells undergo an ancient MGRS repair program involving cell and nuclear fusion and hyperploid giant nuclei that restores the damaged genome to its former pre-DSCD state, with ACD potential and stemness. Human and metazoan DSCD use the same MGRS repair program inherited from the AMF ancestor. Ectopic DSCDs and DSCD-like phenotypes can survive in humans for many years in suitable niches. Under favorable environmental conditions, they also have access to the ancient MGRS repair mechanism including the ancient gene regulatory network (aGRN) and all other AMF genes. The aGRN takes control of cancer’s hybrid genome and represses human genes. It installs a G+S cancer life cycle of AMF imprinting that shapes the differentiation of naïve cancer stem cells (CSCs). CSCs are deeply homologous to the aGSCs of the AMF ancestor. Reprogramming of the DSCD genome by MGRS paves the way for oncogenesis. In this light, cancer is not a mutational or genetic disease, but a non-mutational genome-altering disease.

Papillary thyroid carcinoma (PTC) has two main histologic variants: classical-PTC (CL-PTC) and follicular variant PTC (FV-PTC). Recently, due to its similar features to benign lesions, the encapsulated FV-PTC variant was reclassified as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Nonetheless, specific molecular signatures are not yet available. It is well known that telomere-related genome instability is caused by inappropriate DNA repair of dysfunctional telomeres and that mechanisms involved in the damaged telomere repair processing may led to detrimental outcomes, altering the 3D nuclear telomere and genome organization in cancer cells. This pilot study aimed to evaluate whether a specific nuclear telomere architecture might characterize NIFTP, potentially distinguishing it from other PTC histologic variants. Our findings demonstrate that 3D telomere profiles of CL-PTC and FV-PTC were different from NIFTP and that NIFTP more closely resembles follicular thyroid adenoma (FTA). NIFTP has longer telomeres than CL-PTC and FV-PTC samples and telomere length overlaps in NIFTP and FTA. There was no association between BRAF expression and telomere length in all tested samples. Our data showing that 3D nuclear telomere organization is altered differently in thyroid cancer variants, suggest that this parameter might guide clinical management of NIFTP. Although further investigations in a larger cohort of patients are necessary to corroborate our observations, telomere-related genomic instability might be of value in the diagnosis of NIFTP and allow for a more appropriate selection of the correct treatment.

For the majority of lipophilic compounds adipose tissue is traditionally considered as storage depot and only rarely as a target organ. Meanwhile, abnormalities in adipose tissue physiology induced by chemical exposures may contribute to the current epidemic of obesity and metabolic diseases. Polybrominated diphenyl ethers (PBDEs) is a group of lipophilic flame retardants found in majority of human samples in North America. Their ability to alter physiology of adipose tissue is unknown. We exposed pregnant mice to 0.2 mg/kg body weight/day of BDE-47 perinatally. Transcriptomic changes in gonadal adipose tissue were analyzed in male offspring using RNA-seq approach with subsequent bioinformatic analysis. Genes of coagulation and complement cascade, de novo lipogenesis, and xenobiotic metabolism were altered in expression in response to BDE-47 exposure. The affected molecular network included the following hubs: PPARα, HNF1A and HNF4. These findings suggest that adipose tissue should be considered a target tissue for BDE-47, in addition to its role as a storage depot. This study also builds a background for a targeted search of sensitive phenotypic endpoints of BDE-47 exposure, including lipid profile parameters and coagulation factors in circulation. Additional studies are needed to investigate the role of PBDEs as an obesogen.

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

Free-living amoebae (FLA) are protozoa widely distributed in the environment, found in a great diversity of terrestrial biomes. However, few genera of FLA are linked to human infections. Within these genera, Acanthamoeba spp., classified by genotypes (T1-T23), being T1, T2, T4, T5, T10, T12, and T18 as capable of causing granulomatous amoebic encephalitis (GAE) in immunocompromised patients mostly and Acanthamoeba keratitis related to genotypes T2, T3, T4, T5, T6, T10, T11, T12 and T15 in apparently healthy patients. Meanwhile, Naegleria fowleri is the causative agent of an acute infection called primary amoebic meningoencephalitis (PAM), while Balamuthia mandrillaris, like some Acanthamoeba genotypes, causes GAE, differing from the latter in the description of numerous cases in patients immunocompetent. Finally, other FLA related to the pathologies mentioned above have been reported; Sappinia pedata is responsible for one case of amoebic encephalitis; Vermamoeba vermiformis has been found in cases of ocular damage, and its extraordinary capacity as endo-cytobiont for microorganisms of public health importance such as Legionella pneumophila, Bacillus anthracis, Pseudomonas aeruginosa, among others. In this review, issues related to the epidemiology of each one are addressed, updating their geographic distribution and cases reported in recent years for pathogenic FLA.

Due to the rapid developments in aquaculture industry, Artemia franciscana, originally an American species, has been intentionally introduced to the Eurasia, Africa and Australia. In the present study, we used a partial sequence of the mitochondrial DNA Cytochrome Oxidase subunit I (mt-DNA COI) gene and genomic fingerprinting by Inter-Simple Sequence Repeats (ISSRs) to determine the genetic variability and population structure of Artemia populations (indigenous and introduced) from 14 different geographical locations in Western Asia. Based on the haplotype spanning network, Artemia urmiana has exhibited higher genetic variation than native parthenogenetic populations. Although A. urmiana represented a completely private haplotype distribution, no apparent genetic structure was recognized among the native parthenogenetic and invasive A. franciscana populations. Our ISSR findings have documented that despite invasive populations have lower variation than source population in Great Salt Lake (Utah, USA), they have significantly revealed higher genetic variability compare to the native populations in Western Asia. According to the ISSR results, the native populations were not fully differentiated by the PCoA analysis, but the exotic A. franciscana populations were geographically divided in four genetic groups. We believe that during the colonization, invasive populations have experienced substantial genetic divergences, under new ecological conditions in the non-indigenous regions.

The saturation properties of symmetric and asymmetric nuclear matter have been computed using the finite range simple effective interaction having Yukawa form-factor. The results of higher order derivatives of the energy per particle and the symmetry energy computed at saturation, namely, Q0, Ksym, Kτ, Qsym, are compared with the corresponding range of values extracted from studies involving theory, experiment and astrophysical observations. The ability of the equations of state computed with this simple effective interaction in predicting the threshold mass for prompt collapse in binary neutron star merger and gravitational redshift have been examined in terms of the compactness of the neutron star and the incompressibility at the central density of the maximum mass star. The correlations existing between neutron star properties with the nuclear matter saturation properties have been analyzed and compared with the predictions of other model calculations.

Modern medicine adopted four presumptions when it evolved from ancient experienced-based mind-body medicine. To understand its failure in finding cures for chronic diseases, we examined four presumptions, and found that statistical population of health properties does not exist for most research purposes, mathematical models are misused to model intensive properties, synthetic drugs are inherently more dangerous than nature-made medicines under their respective application conditions, and reductionist treatments are inferior and inherently dangerous. We found that clinical trials are valid only for research where treatment effect is much stronger than the total effects of all interfering or co-causal factors or errors introduced by misused mathematical models can be tolerated. In all other situations, clinical trials introduce excessive errors and fail to detect treatment effects, or produce biased, incorrect or wrong results. We further found that chronic diseases are manifestation of small departures in multiple process attributes in distinctive personal biological pathways networks, that modern medicine lacks required accuracy for accurately characterizing chronic diseases, and that reductionist treatments are good at controlling symptoms and safe for short term uses. For all stated reasons, as long as modern medicine continues relying on the flawed presumptions, it can never find predictable cures for chronic diseases. By implication, predictable cures to chronic diseases are adjustments to lifestyle, dietary, emotional, and environmental factors to slowly correct departures in process attributes responsible for chronic diseases.

Leukemia encompasses diverse blood cancers marked by the unchecked growth and developmental blockage of myeloid or lymphoid precursor cells within the bone marrow and the bloodstream. Treatment traditionally involves intensive chemotherapy or hematopoietic stem cell transplantation, albeit these approaches often yield severe side effects and encounter resistance from leukemia cells. Hence, the quest for novel therapies targeting leukemic cells selectively is paramount to enhancing leukemia outcomes. Exploring natural products is a promising avenue for pursuing effective chemotherapy and preventive measures against leukemia. These compounds serve as a crucial wellspring for drug development and offer a source of biologically active elements for potential therapeutic interventions in leukemia. The enzyme telomerase plays a vital role in stabilizing chromosomes by adding DNA sections to the end of chromosomes during the mitotic cycle. Notably, hTERT mRNA expression strongly correlates with telomerase activity, making it a potential target for therapeutic intervention without adverse systemic effects. Telomerase inhibition is a promising target in cancer therapy, especially in combination with natural ingredients, demonstrating less cytotoxicity than chemotherapies.

The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called ‘the clones of high risk’ often drive the spread of resistance within particular species. Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. In this dataset, we present the results of a comprehensive genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen Acinetobacter baumannii. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic blaOXA-51-like gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. These data will be useful for researchers in the field of A. baumannii genomic epidemiology, resistance analysis and prevention measure development.