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The gut microbiota is recognized as having major impact in health and disease. Sample storage is an important aspect to obtain reliable results. Mostly recommended is immediate freezing, however, this is not always feasible. Faecal occult blood test (FOBT) papers are an appealing solution in such situations, and most studies find these to be applicable, showing no major changes within 7 days storage at room temperature (RT). As fieldwork often requires RT storage for longer periods, evaluation of this is warranted. We performed 16S rRNA gene sequencing of 19 paired faecal samples immediately frozen or kept five weeks and five months at RT on FOBT papers. Alpha-diversity evaluation revealed no effect of FOBT storage, and evaluation of beta-diversity showed that host explained 65% of community variation, while storage method explained 5%. Evaluation of community dispersion and the Firmicutes/Bacteroidetes ratio revealed a larger effect of storage time for fresh-frozen samples. Single taxa evaluation (order-to-genus level) showed significant alterations of four (of 37) genera after five weeks and five genera after five months. When comparing the two timepoints, alterations were only detectable for fresh-frozen samples. Our findings reveal that long term storage on FOBT papers is an applicable approach for microbiota research. [ABSTRACT FROM AUTHOR]

Prodigiosin, a red pigment produced by Hahella chejuensis, a marine-derived microorganism, has several biological functions, including antimicrobial activity and inflammatory relief. In this study, the antibacterial activity of prodigiosin against skin microorganisms was explored. Paper disc assay on skin bacterial cells revealed that Cutibacterium acnes related to acne vulgaris highly susceptible to prodigiosin. MIC (Minimal Inhibitory Concentration) and MBC (Minimal Bactericidal Concentration) were determined on Cutibacterium species. The RNA-seq analysis of prodigiosin-treated C. acnes cells was performed to understand the antibacterial mechanism of prodigiosin. Among changes in the expression of hundreds of genes, the expression of a stress-responsive sigma factor encoded by sigB increased. Conversely, the gene expression of cell wall biosynthesis and energy metabolism was inhibited by prodigiosin. Specifically, the expression of genes related to the metabolism of porphyrin, a pro-inflammatory metabolite, was significantly reduced. Therefore, prodigiosin could be used to control C. acnes. Our study provided new insights into the antimicrobial mechanism of prodigiosin against C. acnes strains. [ABSTRACT FROM AUTHOR]

For most bioinformatics statistical methods, particularly for gene expression data classification, prognosis, and prediction, a complete dataset is required. The gene sample value can be missing due to hardware failure, software failure, or manual mistakes. The missing data in gene expression research dramatically affects the analysis of the collected data. Consequently, this has become a critical problem that requires an efficient imputation algorithm to resolve the issue. This paper proposed a technique considering the local similarity structure that predicts the missing data using clustering and top K nearest neighbor approaches for imputing the missing value. A similarity-based spectral clustering approach is used that is combined with the K-means. The spectral clustering parameters, cluster size, and weighting factors are optimized, and after that, missing values are predicted. For imputing each cluster's missing value, the top K nearest neighbor approach utilizes the concept of weighted distance. The evaluation is carried out on numerous datasets from a variety of biological areas, with experimentally inserted missing values varying from 5 to 25%. Experimental results prove that the proposed imputation technique makes accurate predictions as compared to other imputation procedures. In this paper, for performing the imputation experiments, microarray gene expression datasets consisting of information of different cancers and tumors are considered. The main contribution of this research states that local similarity-based techniques can be used for imputation even when the dataset has varying dimensionality and characteristics. [ABSTRACT FROM AUTHOR]

This paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV (Respiratory Syncytial Virus). Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments. [ABSTRACT FROM AUTHOR]

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy. [ABSTRACT FROM AUTHOR]

Evaluation, prediction, and measurement of carbon dioxide (CO2) solubility in different polymers are crucial for engineers in various chemical applications, such as extraction and generation of novel materials. In this paper, correlations based on gene expression programming (GEP) were generated to predict the value of carbon dioxide solubility in three polymers. Results showed that the generated correlations could represent an outstanding efficiency and provide predictions for carbon dioxide solubility with satisfactory average absolute relative errors of 9.71%, 5.87%, and 1.63% for polystyrene (PS), polybutylene succinate-co-adipate (PBSA), and polybutylene succinate (PBS), respectively. Trend analysis based on Henry's law illustrated that increasing pressure and decreasing temperature lead to an increase in carbon dioxide solubility. Finally, outlier discovery was applied using the leverage approach to detect the suspected data points. The outlier detection demonstrated the statistical validity of the developed correlations. William's plot of three generated correlations showed that all of the data points are located in the valid zone except one point for PBS polymer and three points for PS polymer. [ABSTRACT FROM AUTHOR]

The microbiota has a profound impact on the host organisms. The interaction between the host and its microbiota has an epigenetic mode of action. In poultry species, gastrointestinal microbiota might be stimulated before hatching. This stimulation with bioactive substances has a broad spectrum and long-term effects. This study aimed to examine the role of miRNA expression stimulated by host-microbiota interaction via administering a bioactive substance at the stage of embryonic development. This paper is a continuation of earlier research in the field of molecular analyzes in immune tissues after in ovo administration of bioactive substances. Eggs of Ross 308 broiler chicken and Polish native breed chicken (Green-legged Partridgelike) were incubated in the commercial hatchery. On day 12 of incubation, eggs were injected: the control group with saline (0.2 mM physiological saline), probiotic—Lactococcus lactis subsp. cremoris, prebiotic—galactooligosaccharides, and synbiotic—mentioned above prebiotic with probiotic. The birds were intended for rearing. miRNA expression analysis was performed using the miRCURY LNA miRNA PCR Assay in the spleen and tonsils of adult chickens. Six miRNAs differed significantly, at least between one pair of treatment groups. The most miRNA changes were observed in the cecal tonsils of Green-legged Partridgelike chickens. At the same time, only miR-1598 and miR-1652 showed significant differences between the treatment groups in the cecal tonsils and spleen of Ross broiler chickens. Only two miRNAs showed significant GeneOntology (GO)enrichment with the ClueGo plug-in. gga-miR-1652 target genes showed only 2 GOs significantly enriched: chondrocyte differentiation and early endosome. gga-miR-1612 target genes, the most significant GO was regulating the RNA metabolic process. The enriched functions were associated with gene expression or protein regulation, the nervous system, and the immune system. Results suggest that early microbiome stimulation in chicken might regulate the miRNA expression in different immune tissues in a genotype-dependent manner. [ABSTRACT FROM AUTHOR]

Cells detect changes in their environment and generate responses, often involving changes in gene expression. In this paper we use information theory and a simple transcription model to analyze whether the resulting gene expression serves to identify extracellular stimuli and assess their intensity when they are encoded in the amplitude, duration or frequency of pulses of a transcription factor's nuclear concentration (or activation state). We find, for all cases, that about three ranges of input strengths can be distinguished and that maximum information transmission occurs for fast and high activation threshold promoters. The three input modulation modes differ in the sensitivity to changes in the promoters parameters. Frequency modulation is the most sensitive and duration modulation, the least. This is key for signal identification: there are promoter parameters that yield a relatively high information transmission for duration or amplitude modulation and a much smaller value for frequency modulation. The reverse situation cannot be found with a single promoter transcription model. Thus, pulses of transcription factors can selectively activate the "frequency-tuned" promoter while prolonged nuclear accumulation would activate promoters of all three modes simultaneously. Frequency modulation is therefore highly selective and better suited than the other encoding modes for signal identification without requiring other mediators of the transduction process. [ABSTRACT FROM AUTHOR]

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future. [ABSTRACT FROM AUTHOR]

In a previous study, we showed that serine/threonine-protein kinase 4 (STK4) is involved in the control on proliferation and migration of endometrial cancer (EC) cells in vitro. In the present paper, we studied STK4 expression in EC tissues from a large cohort of patients to determine whether STK4 can serve as a marker for the aggressiveness and prognosis of EC. Tissue samples from patients with EC were examined for tumor type, grade, and stage. The STK4 protein expression in EC cells was assessed by immunohistochemistry and related to clinicopathological data of patients, such as progression and patient survival rate. The STK4 mRNA levels and its relation to the survival rate were analyzed also in publicly available databases. The STK4 gene expression was low at both, the mRNA and protein levels in EC, especially in serous tumors. Comparison of STK4 expression with the patient survival rate shows that the higher expression is associated with worse prognosis in serous EC, while no such dependence was found in endometrioid EC. Hence, the determination of the SKT4 expression pattern could be used as a putative prognostic marker for serous EC. [ABSTRACT FROM AUTHOR]

To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA–miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA–miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA–miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA. [ABSTRACT FROM AUTHOR]

Allergic rhinitis and asthma are the most common causes of chronic inflammation of the upper and lower airways in childhood. However, a nasal biomarker that can link to pulmonary inflammation is yet to be found. The present paper aims to investigate the possible role in inflammation of two inducible 70-kDa Heat Shock Proteins (HSP70) members, HSPA1A/B and HSPA6, in nasal mucosa cells of allergic children through their mRNA expression analysis, and their correlation to both spirometric and FeNO values. The relationship between FeNO in lower airways and ∆Cts of HSPA1A/B in nasal mucosa seems to be influenced by clinical symptoms regardless of age, sex, and sensitization patterns. Therefore, HSP70 expression, as well as FeNO levels, could have a predictive capability to identify lower airways inflammation and thus to recognize rhinitic children having a potential risk of asthma development. [ABSTRACT FROM AUTHOR]

Meniscal degeneration is defined by semi-quantitative assessment of multiple histological findings and has been implicated in biomechanical dysfunction, yet little is known about its relationship with biological properties. This paper aimed to quantitatively evaluate degenerative findings in human meniscus to examine their relationship with gene expression and biomechanical properties, and to extract histological findings that reflect biological properties like gene expression and cytokine secretion. This study included lateral menisci of 29 patients who underwent total knee arthroplasty. The menisci were divided into six samples. For each sample, Pauli's histological evaluation and corresponding quantitative assessment (surface roughness, DNA content, collagen orientation, and GAG content) were performed, with surface roughness showing the highest correlation with the histological evaluation in a single correlation analysis (r = 0.66, p < 0.0001) and multivariate analysis (p < 0.0001). Furthermore, surface roughness was associated with gene expression related to meniscal degeneration and with tangent modulus which decreases with increasing degeneration (r = − 0.49, p = 0.0002). When meniscal tissue was classified by surface integrity, inflammatory cytokine secretion tended to be higher in severe degenerated menisci. These results suggest that the evaluation of meniscal surface texture could predict the degree of degeneration and inflammatory cytokine secretion. [ABSTRACT FROM AUTHOR]

Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to identify gene signatures that are related to a certain disease from high-dimensional gene expression data. The expression of genes may have grouping structures, for example, a group of co-regulated genes that have similar biological functions tend to have similar expressions. Thus it is preferable to take the grouping structure into consideration to select features. In this paper, we propose a Bayesian Robit regression method with Hyper-LASSO priors (shortened by BayesHL) for feature selection in high dimensional genomic data with grouping structure. The main features of BayesHL include that it discards more aggressively unrelated features than LASSO, and it makes feature selection within groups automatically without a pre-specified grouping structure. We apply BayesHL in gene expression analysis to identify subsets of genes that contribute to the 5-year survival outcome of endometrial cancer (EC) patients. Results show that BayesHL outperforms alternative methods (including LASSO, group LASSO, supervised group LASSO, penalized logistic regression, random forest, neural network, XGBoost and knockoff) in terms of predictive power, sparsity and the ability to uncover grouping structure, and provides insight into the mechanisms of multiple genetic pathways leading to differentiated EC survival outcome. [ABSTRACT FROM AUTHOR]

Environmental perturbations impact multiple cellular traits, including gene expression. Bacteria respond to these stressful situations through complex gene interaction networks, thereby inducing stress tolerance and survival of cells. In this paper, we study the response mechanisms of E. coli when exposed to different environmental stressors via differential expression and co-expression analysis. Gene co-expression networks were generated and analyzed via Weighted Gene Co-expression Network Analysis (WGCNA). Based on the gene co-expression networks, genes with similar expression profiles were clustered into modules. The modules were analysed for identification of hub genes, enrichment of biological processes and transcription factors. In addition, we also studied the link between transcription factors and their differentially regulated targets to understand the regulatory mechanisms involved. These networks validate known gene interactions and provide new insights into genes mediating transcriptional regulation in specific stress environments, thus allowing for in silico hypothesis generation. [ABSTRACT FROM AUTHOR]

Estrogen related receptors are orphan members of the nuclear receptor superfamily acting as transcription factors (TFs). In contrast to classical nuclear receptors, the activities of the ERRs are not controlled by a natural ligand. Regulation of their activities thus relies on availability of transcriptional co-regulators. In this paper, we focus on ERRα, whose involvement in cancer progression has been broadly demonstrated. We propose a new approach to identify potential co-activators, starting from previously identified ERRα-activated genes in a breast cancer (BC) cell line. Considering mRNA gene expression from two sets of human BC cells as major endpoint, we used sparse partial least squares modeling to uncover new transcriptional regulators associated with ERRα. Among them, DDX21, MYBBP1A, NFKB1, and SETD7 are functionally relevant in MDA-MB-231 cells, specifically activating the expression of subsets of ERRα-activated genes. We studied SET7 in more details and showed its co-localization with ERRα and its ERRα-dependent transcriptional and phenotypic effects. Our results thus demonstrate the ability of a modeling approach to identify new transcriptional partners from gene expression. Finally, experimental results show that ERRα cooperates with distinct co-regulators to control the expression of distinct sets of target genes, thus reinforcing the combinatorial specificity of transcription. [ABSTRACT FROM AUTHOR]

To discover epigenetic changes that may underly neuroblastoma pathogenesis, we identified differentially methylated genes in neuroblastoma cells compared to neural crest cells, the presumptive precursors cells for neuroblastoma, by using genome-wide DNA methylation analysis. We previously described genes that were hypermethylated in neuroblastoma; in this paper we report on 67 hypomethylated genes, which were filtered to select genes that showed transcriptional over-expression and an association with poor prognosis in neuroblastoma, highlighting GATA3 for detailed studies. Specific methylation assays confirmed the hypomethylation of GATA3 in neuroblastoma, which correlated with high expression at both the RNA and protein level. Demethylation with azacytidine in cultured sympathetic ganglia cells led to increased GATA3 expression, suggesting a mechanistic link between GATA3 expression and DNA methylation. Neuroblastomas that had completely absent GATA3 methylation and/or very high levels of protein expression, were associated with poor prognosis. Knock-down of GATA3 in neuroblastoma cells lines inhibited cell proliferation and increased apoptosis but had no effect on cellular differentiation. These results identify GATA3 as an epigenetically regulated component of the neuroblastoma transcriptional control network, that is essential for neuroblastoma proliferation. This suggests that the GATA3 transcriptional network is a promising target for novel neuroblastoma therapies. [ABSTRACT FROM AUTHOR]

One disadvantage of the JCR impact factor, the most commonly used assessment tool for ranking and evaluating scientific journals, is its inability in distinguishing among different shapes of citation distribution curves, leading to unfair evaluation of journals in some cases. This paper aims to put forward an alternative impact factor (IF') that can properly reflect citation distributions. The two impact factors are linearly and positively correlated, and have roughly the same order of magnitude. Because of the ability of IF9 in distinguishing among different shapes of citation distribution curves, IF' may properly reflect the academic performance of a scientific journal in a way that is different from the JCR impact factor with some unique features that reward journals with highly cited papers. Therefore, it is suggested that IF9 could be used to complement the JCR impact factor. [ABSTRACT FROM AUTHOR]

The forced swim test (FST) is widely used to screen for potential antidepressant drugs and treatments. Despite this, the nature of stillness during FST and whether it resembles "depressive-like behavior" are widely debated issues. Furthermore, despite being widely used as a behavioral assay, the effects of the FST on the brain transcriptome are rarely investigated. Therefore, in this study we have investigated changes in the transcriptome of the rat hippocampus 20 min and 24 h after FST exposure. RNA-Seq is performed on the hippocampus tissues of rats 20 min and 24 h after an FST. Differentially expressed genes (DEGs) were identified using limma and used to construct gene interaction networks. Fourteen differentially expressed genes (DEGs) were identified only in the 20-m group. No DEGs were identified 24 h after the FST. These genes were used for Gene Ontology term enrichment and gene-network construction. Based on the constructed gene-interaction networks, we identified a group of DEGs (Dusp1, Fos, Klf2, Ccn1, and Zfp36) that appeared significant based on multiple methods of downstream analysis. Dusp1 appears especially important, as its role in the pathogenesis of depression has been demonstrated both in various animal models of depression and in patients with depressive disorders. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

This paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infected with HRV, RSV, H1N1, and H3N2. We address the problem of identifying discriminatory biomarkers between controls and eventual shedders in the first 32 h post-infection. Our exploratory analysis shows that the most discriminatory biomarkers exhibit a strong dependence on time over the course of the human response to infection. We visualize the feature sets to provide evidence of the rapid evolution of the gene expression profiles. To quantify this observation, we partition the data in the first 32 h into four equal time windows of 8 h each and identify all discriminatory biomarkers using sparsity-promoting classifiers and Iterated Feature Removal. We then perform a comparative machine learning classification analysis using linear support vector machines, artificial neural networks and Centroid-Encoder. We present a range of experiments on different groupings of the diseases to demonstrate the robustness of the resulting models. [ABSTRACT FROM AUTHOR]

Cancer tumor classification based on morphological characteristics alone has been shown to have serious limitations. Breast, lung, colorectal, thyroid, and ovarian are the most commonly diagnosed cancers among women. Precise classification of cancers into their types is considered a vital problem for cancer diagnosis and therapy. In this paper, we proposed a stacking ensemble deep learning model based on one-dimensional convolutional neural network (1D-CNN) to perform a multi-class classification on the five common cancers among women based on RNASeq data. The RNASeq gene expression data was downloaded from Pan-Cancer Atlas using GDCquery function of the TCGAbiolinks package in the R software. We used least absolute shrinkage and selection operator (LASSO) as feature selection method. We compared the results of the new proposed model with and without LASSO with the results of the single 1D-CNN and machine learning methods which include support vector machines with radial basis function, linear, and polynomial kernels; artificial neural networks; k-nearest neighbors; bagging trees. The results show that the proposed model with and without LASSO has a better performance compared to other classifiers. Also, the results show that the machine learning methods (SVM-R, SVM-L, SVM-P, ANN, KNN, and bagging trees) with under-sampling have better performance than with over-sampling techniques. This is supported by the statistical significance test of accuracy where the p-values for differences between the SVM-R and SVM-P, SVM-R and ANN, SVM-R and KNN are found to be p = 0.003, p = < 0.001, and p = < 0.001, respectively. Also, SVM-L had a significant difference compared to ANN p = 0.009. Moreover, SVM-P and ANN, SVM-P and KNN are found to be significantly different with p-values p = < 0.001 and p = < 0.001, respectively. In addition, ANN and bagging trees, ANN and KNN were found to be significantly different with p-values p = < 0.001 and p = 0.004, respectively. Thus, the proposed model can help in the early detection and diagnosis of cancer in women, and hence aid in designing early treatment strategies to improve survival. [ABSTRACT FROM AUTHOR]

The polychaete Boccardia wellingtonensis is a poecilogonous species that produces different larval types. Females may lay Type I capsules, in which only planktotrophic larvae are present, or Type III capsules that contain planktotrophic and adelphophagic larvae as well as nurse eggs. While planktotrophic larvae do not feed during encapsulation, adelphophagic larvae develop by feeding on nurse eggs and on other larvae inside the capsules and hatch at the juvenile stage. Previous works have not found differences in the morphology between the two larval types; thus, the factors explaining contrasting feeding abilities in larvae of this species are still unknown. In this paper, we use a transcriptomic approach to study the cellular and genetic mechanisms underlying the different larval trophic modes of B. wellingtonensis. By using approximately 624 million high-quality reads, we assemble the de novo transcriptome with 133,314 contigs, coding 32,390 putative proteins. We identify 5221 genes that are up-regulated in larval stages compared to their expression in adult individuals. The genetic expression profile differed between larval trophic modes, with genes involved in lipid metabolism and chaetogenesis over expressed in planktotrophic larvae. In contrast, up-regulated genes in adelphophagic larvae were associated with DNA replication and mRNA synthesis. [ABSTRACT FROM AUTHOR]

Bromus sterilis is an annual weedy grass, causing high yield losses in winter cereals. Frequent use of herbicides had led to the evolution of herbicide resistance in this species. Mechanisms underlying herbicide resistance in B. sterilis must be uncovered because this problem is becoming a global threat. qRT-PCR and the next-generation sequencing technologies can elucidate the resistance mechanisms. Although qRT-PCR can calculate precise fold changes, its preciseness depends on the expression of reference genes. Regardless of stable expression in any given condition, no gene can act as a universal reference gene. Hence, it is necessary to identify the suitable reference gene for each species. To our knowledge, there are no reports on the suitable reference gene in any brome species so far. Thus, in this paper, the stability of eight genes was evaluated using qRT-PCR experiments followed by expression stability ranking via five most commonly used software for reference gene selection. Our findings suggest using a combination of 18S rRNA and ACCase to normalise the qRT-PCR data in B. sterilis. Besides, reference genes are also recommended for different experimental conditions. The present study outcomes will facilitate future molecular work in B. sterilis and other related grass species. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

The stress response is adaptive and aims to guarantee survival. However, the persistence of a stressor can culminate in pathology. Catecholamines released as part of the stress response over activate beta adrenoceptors (β-AR) in the heart. Whether and how stress affects the expression of components of the intracellular environment in the heart is still, however, unknown. This paper used microarray to analyze the gene expression in the left ventricle wall of rats submitted to foot shock stress, treated or not treated with the selective β2-AR antagonist ICI118,551 (ICI), compared to those of non-stressed rats also treated or not with ICI, respectively. The main findings were that stress induces changes in gene expression in the heart and that β2-AR plays a role in this process. The vast majority of genes disregulated by stress were exclusive for only one of the comparisons, indicating that, in the same stressful situation, the profile of gene expression in the heart is substantially different when the β2-AR is active or when it is blocked. Stress induced alterations in the expression of such a large number of genes seems to be part of stress-induced adaptive mechanism. [ABSTRACT FROM AUTHOR]

The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful understanding of DNA replication mechanisms and the regulatory mechanisms of genetic expressions. For eukaryotes in particular, multiple ORIs exist in each of their gene sequences to complete the replication in a reasonable period of time. To simplify the identification process of eukaryote's ORIs, most of existing methods are developed by traditional machine learning algorithms, and target to the gene sequences with a fixed length. Consequently, the identification results are not satisfying, i.e. there is still great room for improvement. To break through the limitations in previous studies, this paper develops sequence segmentation methods, and employs the word embedding technique, 'Word2vec', to convert gene sequences into word vectors, thereby grasping the inner correlations of gene sequences with different lengths. Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew's correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis. [ABSTRACT FROM AUTHOR]

The identification of prognostic biomarkers for predicting cancer progression is an important problem for two reasons. First, such biomarkers find practical application in a clinical context for the treatment of patients. Second, interrogation of the biomarkers themselves is assumed to lead to novel insights of disease mechanisms and the underlying molecular processes that cause the pathological behavior. For breast cancer, many signatures based on gene expression values have been reported to be associated with overall survival. Consequently, such signatures have been used for suggesting biological explanations of breast cancer and drug mechanisms. In this paper, we demonstrate for a large number of breast cancer signatures that such an implication is not justified. Our approach eliminates systematically all traces of biological meaning of signature genes and shows that among the remaining genes, surrogate gene sets can be formed with indistinguishable prognostic prediction capabilities and opposite biological meaning. Hence, our results demonstrate that none of the studied signatures has a sensible biological interpretation or meaning with respect to disease etiology. Overall, this shows that prognostic signatures are black-box models with sensible predictions of breast cancer outcome but no value for revealing causal connections. Furthermore, we show that the number of such surrogate gene sets is not small but very large. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

In this paper, a metasurface composed of 3-bit coding linear polarization conversion elements and its application to RCS reduction of the patch antenna is intensively studied. At first, 3-bit coding metasurface are constructed by a sequence of eight coded unit cells, which have a similar cross-polarized reflected amplitude response and gradient reflected phase responses covering 0–2π, respectively. Equivalent circuit models of these unit cells are created to describe their electrical behavior for the two linear incident polarizations at the same time. Then, a patch antenna is integrated on the 3-bit metasurface, of which the elements are placed with a 2-dimensional linear coding sequence. The metal square ring is set around the patch antenna to protect it from the disturbance of metasurface. Both the simulation and experiment results demonstrate that the designed metasurface can primarily reduce the antenna RCS at a broadband, while the antenna performances are not degraded significantly. [ABSTRACT FROM AUTHOR]

This paper presents a digital PCR system based on a novel thermal cycled chip, which wraps microchannels on a trapezoidal structure made of polydimethylsiloxane (PDMS) in a multi-helix manner for the first time. It is found that compared to the single helix chip commonly used in previous reports, this kind of novel multi-helix chip can make the surface temperature in the renaturation zone more uniform, and even in the case of rapid fluid flow, it can improve the efficiency of the polymerase chain reaction. What's more, the winding method of multi helix (such as double helix, six helix and eight helix) can obtain better temperature uniformity than the winding of odd helix (such as single helix and three helix). As a proof of concept, the temperature-optimized double-helical chip structure is applied to continuous-flow digital PCR and there is no need to add any surfactant to both the oil phase and reagent. In addition, we successfully analyzed the fluorescence signal of continuous-flow digital PCR by using CMOS camera. Finally, this method is applied for the absolute quantification of the clinical serum sample infected by HBV. The accuracy of the test results has been confirmed by commercial instruments. [ABSTRACT FROM AUTHOR]

Ecological, health and environmental concerns are driving the need for bio-resourced foams for the building industry. In this paper, we examine foams made from polylactic acid (PLA) and micro cellulose fibrils (MCF). To ensure no volatile organic compounds in the foam, supercritical CO2 (sc-CO2) physical foaming of melt mixed systems was conducted. Mechanical and thermal conductivity properties were determined and applied to a net zero energy model house. The results showed that MCF had a concentration dependent impact on the foams. First structurally, the presence of MCF led to an initial increase followed by a decrease of open porosity, higher bulk density, lower expansion ratios and cell size. Differential Scanning Calorimetry and Scanning Electron Microscopy revealed that MCF decreased the glass transition of PLA allowing for a decrease in cell wall thickness when MCF was added. The mechanical performance initially increased with MCF and then decreased. This trend was mimicked by thermal insulation which initially improved. Biodegradation tests showed that the presence of cellulose in PLA improved the compostability of the foams. A maximum comparative mineralization of 95% was obtained for the PLA foam with 3 wt.% MCF when expressed as a fractional percentage of the pure cellulose reference. Energy simulations run on a model house showed that relative to an insulation of polyurethane, the bio-resourced foams led to no more than a 12% increase in heating and cooling. The energy efficiency of the foams was best at low MCF fractions. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about cell differentiation. In this paper, we explore unsupervised generative neural methods, based on the variational autoencoder, that can model cell differentiation by building meaningful representations from the high dimensional and complex gene expression data. We use disentanglement methods based on information theory to improve the data representation and achieve better separation of the biological factors of variation in the gene expression data. In addition, we use a graph autoencoder consisting of graph convolutional layers to predict relationships between single-cells. Based on these models, we develop a computational framework that consists of methods for identifying the cell types in the dataset, finding driver genes for the differentiation process and obtaining a better understanding of relationships between cells. We illustrate our methods on datasets from multiple species and also from different sequencing technologies. [ABSTRACT FROM AUTHOR]

This paper examines the effect of TET1 expression on survival in glioma patients using open-access data from the Genomic Data Commons. A neural network-based survival model was built on expression data from a selection of genes most affected by TET1 knockdown with a median cross-validated survival concordance of 82.5%. A synthetic experiment was then conducted that linked two separately trained neural networks: a multitask model estimating cancer hallmark gene expression from TET1 expression, and a survival neural network. This experiment quantified the mediation of the TET1 survival effect through eight cancer hallmarks: apoptosis, cell cycle, cell death, cell motility, DNA repair, immune response, two phosphorylation pathways, and a randomized gene sets. Immune response, DNA repair, and apoptosis displayed greater mediation than the randomized gene set. Cell motility was inversely associated with only 12.5% mediated concordance. We propose the neural network linkage mediation experiment as an approach to collecting evidence of hazard mediation relationships with prognostic capacity useful for designing interventions. [ABSTRACT FROM AUTHOR]

The Special AT-rich sequence binding protein 1 (SATB1) is a genome organizer protein that controls gene expression of numerous genes by regulating chromatin architecture and targeting chromatin-remodeling/-modifying enzymes onto specific chromatin regions. SATB1 is overexpressed in various tumors. In head and neck squamous cell carcinoma (HNSCC), SATB1 upregulation is correlated with TNM classification, metastasis, poor prognosis and reduced overall survival. In this paper, we comprehensively analyze cellular and molecular effects of SATB1 in a large set of primary cell lines from primary HNSCC or metastases, using RNAi-mediated knockdown in vitro and, therapeutically, in tumor xenograft mouse models in vivo. In a series of 15 cell lines, major differences in SATB1 levels are observed. In various 2-D and 3-D assays, growth inhibition upon efficient siRNA-mediated SATB1 knockdown depends on the cell line rather than initial SATB1 levels. Inhibitory effects are found to be based on cell cycle deceleration, apoptosis induction, decreased HER3 and Heregulin A&B expression, and effects on EMT genes. In vivo, systemic treatment of tumor xenograft-bearing mice with siRNAs formulated in polymeric nanoparticles inhibits tumor growth of two HNSCC xenograft models, resulting from therapeutic SATB1 reduction and concomitant decrease of proliferation and induction of apoptosis. In conclusion, SATB1 represents a promising target in HNSCC, affecting crucial cellular processes and molecular pathways. [ABSTRACT FROM AUTHOR]

Since the world population is ageing, dementia is going to be a growing concern. Alzheimer's disease is the most common form of dementia. The pathogenesis of Alzheimer's disease is extensively studied, yet unknown remains. Therefore, we aimed to extract new knowledge from existing data. We analysed about 2700 upregulated genes and 2200 downregulated genes from three studies on the CA1 of the hippocampus of brains with Alzheimer's disease. We found that only the calcium signalling pathway enriched by 48 downregulated genes was consistent between all three studies. We predicted miR-129 to target nine out of 48 genes. Then, we validated miR-129 to regulate six out of nine genes in HEK cells. We noticed that four out of six genes play a role in synaptic plasticity. Finally, we confirmed the upregulation of miR-129 in the hippocampus of brains of rats with scopolamine-induced amnesia as a model of Alzheimer's disease. We suggest that future research should investigate the possible role of miR-129 in synaptic plasticity and Alzheimer's disease. This paper presents a novel framework to gain insight into potential biomarkers and targets for diagnosis and treatment of diseases. [ABSTRACT FROM AUTHOR]

The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensionality and high noise, the application of biostatistics and machine learning methods to analyze gene expression data is a challenging task, such as the low reproducibility of important biomarkers in different studies. Meta-analysis is an effective approach to deal with these problems, but the current methods have some limitations. In this paper, we propose the meta-analysis based on three nonconvex regularization methods, which are L1/2 regularization (meta-Half), Minimax Concave Penalty regularization (meta-MCP) and Smoothly Clipped Absolute Deviation regularization (meta-SCAD). The three nonconvex regularization methods are effective approaches for variable selection developed in recent years. Through the hierarchical decomposition of coefficients, our methods not only maintain the flexibility of variable selection and improve the efficiency of selecting important biomarkers, but also summarize and synthesize scientific evidence from multiple studies to consider the relationship between different datasets. We give the efficient algorithms and the theoretical property for our methods. Furthermore, we apply our methods to the simulation data and three publicly available lung cancer gene expression datasets, and compare the performance with state-of-the-art methods. Our methods have good performance in simulation studies, and the analysis results on the three publicly available lung cancer gene expression datasets are clinically meaningful. Our methods can also be extended to other areas where datasets are heterogeneous. [ABSTRACT FROM AUTHOR]

Neural stem cells (NSCs) generate neurons of the cerebral cortex with distinct morphologies and functions. How specific neuron production, differentiation and migration are orchestrated is unclear. Hippo signaling regulates gene expression through Tead transcription factors (TFs). We show that Hippo transcriptional coactivators Yap1/Taz and the Teads have distinct functions during cortical development. Yap1/Taz promote NSC maintenance and Satb2+ neuron production at the expense of Tbr1+ neuron generation. However, Teads have moderate effects on NSC maintenance and do not affect Satb2+ neuron differentiation. Conversely, whereas Tead2 blocks Tbr1+ neuron formation, Tead1 and Tead3 promote this early fate. In addition, we found that Hippo effectors regulate neuronal migration to the cortical plate (CP) in a reciprocal fashion, that ApoE, Dab2 and Cyr61 are Tead targets, and these contribute to neuronal fate determination and migration. Our results indicate that multifaceted Hippo signaling is pivotal in different aspects of cortical development. [ABSTRACT FROM AUTHOR]

We recently identified the CDC25A phosphatase as a key actor in proliferation and differentiation in acute myeloid leukemia expressing the FLT3-ITD mutation. In this paper we demonstrate that CDC25A level is controlled by a complex STAT5/miR-16 transcription and translation pathway working downstream of this receptor. First, we established by CHIP analysis that STAT5 is directly involved in FLT3-ITD-dependent CDC25A gene transcription. In addition, we determined that miR-16 expression is repressed by FLT3-ITD activity, and that STAT5 participates in this repression. In accordance with these results, miR-16 expression was significantly reduced in a panel of AML primary samples carrying the FLT3-ITD mutation when compared with FLT3wt cells. The expression of a miR-16 mimic reduced CDC25A protein and mRNA levels, and RNA interference-mediated down modulation of miR-16 restored CDC25A expression in response to FLT3-ITD inhibition. Finally, decreasing miR-16 expression partially restored the proliferation of cells treated with the FLT3 inhibitor AC220, while the expression of miR-16 mimic stopped this proliferation and induced monocytic differentiation of AML cells. In summary, we identified a FLT3-ITD/STAT5/miR-16/CDC25A axis essential for AML cell proliferation and differentiation. [ABSTRACT FROM AUTHOR]

In the field of computational bioinformatics, identifying a set of genes which are responsible for a particular cellular mechanism, is very much essential for tasks such as medical diagnosis or disease gene identification. Accurately grouping (clustering) the genes is one of the important tasks in understanding the functionalities of the disease genes. In this regard, ensemble clustering becomes a promising approach to combine different clustering solutions to generate almost accurate gene partitioning. Recently, researchers have used generative model as a smart ensemble method to produce the right consensus solution. In the current paper, we develop a protein-protein interaction-based generative model that can efficiently perform a gene clustering. Utilizing protein interaction information as the generative model's latent variable enables enhance the generative model's efficiency in inferring final probabilistic labels. The proposed generative model utilizes different weak supervision sources rather utilizing any ground truth information. For weak supervision sources, we use a multi-objective optimization based clustering technique together with the world's largest gene ontology based knowledge-base named Gene Ontology Consortium(GOC). These weakly supervised labels are supplied to a generative model that eventually assigns all genes to probabilistic labels. The comparative study with respect to silhouette score, Biological Homogeneity Index (BHI) and Biological Stability Index (BSI) proves that the proposed generative model outperforms than other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]