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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]

Phenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Analyses of the molecular foundations of phenotypic plasticity are challenging, however, especially in the case of complex social phenotypes. Here we apply a machine learning approach to tackle this challenge by analyzing individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen. We find that caste-associated gene expression profiles respond strongly to queen loss, and that this change is partly explained by attributes such as age but occurs even in individuals that appear phenotypically unaffected. These results demonstrate that large changes in gene expression may occur in the absence of outwardly detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic level but not at the levels of ovarian development or behavior. Connecting genotypes to complex social behaviour is challenging. Taylor et al. use machine learning to show a strong response of caste-associated gene expression to queen loss, wherein individual wasp's expression profiles become intermediate between queen and worker states, even in the absence of behavioural changes. [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]

The penalized Lasso Cox proportional hazards model has been widely used to identify prognosis biomarkers in high-dimension settings. However, this method tends to select many false positives, affecting its interpretability. In order to improve the reproducibility, we develop a knockoff procedure that consists on wrapping the Lasso Cox model with the model-X knockoff, resulting in a powerful tool for variable selection that allows for the control of the false discovery rate in the presence of finite sample guarantees. In this paper, we propose a novel approach to sample valid knockoffs for ordinal and continuous variables whose distributions can be skewed or heavy-tailed, which employs a Latent Mixed Gaussian Copula model to account for the dependence structure between the variables, leading to what we call the Latent Gaussian Copula Knockoff (LGCK) procedure. We then combine the LGCK method with the Lasso coefficient difference (LCD) statistic as the importance metric. To our knowledge, our proposal is the first knockoff framework for jointly considering ordinal and continuous data in a non-Gaussian setting and a survival context. We illustrate the proposed methodology's effectiveness by applying it to a real lung cancer gene expression dataset. [ABSTRACT FROM AUTHOR]

Flatfish undergo a remarkable metamorphosis from symmetrical pelagic larvae to fully asymmetrical benthic juveniles. The most distinctive features of this transformation is the migration of one eye. The molecular role of thyroid hormone in the metamorphosis process in flatfishes is well established. However, the regulatory network that facilitates eye movement remains enigmatic. This paper presents a morphological investigation of the metamorphic process in turbot eyes, using advanced imaging techniques and a global view of gene expression. The study covers migrant and non-migrant eyes and aims to identify the genes that are active during ocular migration. Our transcriptomic analysis shows a significant up-regulation of immune-related genes. The analysis of eye-specific genes reveals distinct patterns during the metamorphic process. Myosin is highlighted in the non-migrant eye, while ependymin is highlighted in the migrant eye, possibly involved in optic nerve regeneration. Furthermore, a potential association between the alx3 gene and cranial restructuring has been identified. Additionally, it confirmed simultaneous adaptation to low light in both eyes, as described by changes in opsins expression during the metamorphic process. The study also revealed that ocular migration activates systems asynchronously in both eyes, providing insight into multifaceted reorganization processes during metamorphosis of flatfish. Flatfish eye migration in metamorphosis study shows up-regulated immune genes. The process involves roles of myosin/ependymin, the link of alx3's to cranial restructuring, changes in opsins adaptation to low light and an asynchronous system activation. [ABSTRACT FROM AUTHOR]

Gene expression profile data have high-dimensionality with a small number of samples. These data characteristics lead to a long training time and low performance in predictive model construction. To address this issue, the paper proposes a feature selection algorithm using non-dominant feature-guide search. The algorithm adopts a filtering framework based on feature sorting and search strategy to overcome the problems of long training time and poor performance. First, the feature pre-selection is completed according to the calculated feature category correlation. Second, a multi-objective optimization feature selection model is constructed. Non-dominant features are defined according to the Pareto dominance theory. Combined with the bidirectional search strategy, the Pareto dominance features under the current category maximum relevance feature are removed one by one. Finally, the optimal feature subset with maximum correlation and minimum redundancy is obtained. Experimental results on six gene expression data sets show that the algorithm is much better than Fisher score, maximum information coefficient, composition of feature relevancy, mini-batch K-means normalized mutual information feature inclusion, and max-Relevance and Min-Redundancy algorithms. Compared to feature selection method based on maximum information coefficient and approximate Markov blanket, the algorithm not only has high computational efficiency but also can obtain better classification capabilities in a smaller dimension. [ABSTRACT FROM AUTHOR]

The article focuses on the Minimum Information About a Microarray Experiment proposed by the Microarray Gene Expression Data Society. The adoption of these guidelines by journals has prompted the submission of microarray data sets to major public repositories such as the ArrayExpress in Europe and the Gene Expression Omnibus in the U.S. ArrayExpress designed submission tools to encourage compliance, while ArrayExpress curators assist submitters in providing the necessary information.

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]

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data. RGStraP is a computational pipeline to estimate RNA-seq-based genetic principal components and assess whether these can be used to control for population structure in gene expression analyses. [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]

Robust group lasso regression plays an important role in high-dimensional regression modeling such as biological data analysis for disease diagnosis and gene expression. However, most existing methods are optimized with prior variable structure knowledge under the traditional empirical risk minimization (ERM) framework, in which the estimators are excessively dependent on prior structure information and sensitive to outliers. To address this issue, we propose a new robust variable structure discovery method for group lasso based on a convergent bilevel optimization framework. In this paper, we adopt tilted empirical risk minimization (TERM) as the target function to improve the robustness of the estimator by assigning less weight to the outliers. Moreover, we modify the TERM objective function to calculate its Fenchel conjugate while maintaining its robust property, which is proven theoretically and empirically. Experimental results on both synthetic and real-world datasets show that the proposed method can improve the robust performance on prediction and variable structure discovery compared to the existing techniques. [ABSTRACT FROM AUTHOR]

In recent years, evolutionary algorithms have shown great advantages in the field of feature selection because of their simplicity and potential global search capability. However, most of the existing feature selection algorithms based on evolutionary computation are wrapper methods, which are computationally expensive, especially for high-dimensional biomedical data. To significantly reduce the computational cost, it is essential to study an effective evaluation method. In this paper, a two-stage improved gray wolf optimization (IGWO) algorithm for feature selection on high-dimensional data is proposed. In the first stage, a multilayer perceptron (MLP) network with group lasso regularization terms is first trained to construct an integer optimization problem using the proposed algorithm for pre-selection of features and optimization of the hidden layer structure. The dataset is compressed using the feature subset obtained in the first stage. In the second stage, a multilayer perceptron network with group lasso regularization terms is retrained using the compressed dataset, and the proposed algorithm is employed to construct the discrete optimization problem for feature selection. Meanwhile, a rapid evaluation strategy is constructed to mitigate the evaluation cost and improve the evaluation efficiency in the feature selection process. The effectiveness of the algorithm was analyzed on ten gene expression datasets. The experimental results show that the proposed algorithm not only removes almost more than 95.7% of the features in all datasets, but also has better classification accuracy on the test set. In addition, the advantages of the proposed algorithm in terms of time consumption, classification accuracy and feature subset size become more and more prominent as the dimensionality of the feature selection problem increases. This indicates that the proposed algorithm is particularly suitable for solving high-dimensional feature selection problems. [ABSTRACT FROM AUTHOR]

The article focuses on a brute-force approach studied by Len Pannacchio and colleagues for testing a large number of DNA fragments for enhancer activity. The researchers fused each putative enhancer fragment to a gene engineered to turn blue when expressed and then injected the fragments into a fertilized mouse eggs. The team will spend the next five years testing about 2,000 additional elements to add to the resource, with the goal of finding enhancers for all human genes.

The article focuses on the collaboration between scientists Antonio Iavarone and Anna Lasorella in exploring the role of a family of proteins in brain cancer. Their study has led them from gene expression to basic cell-division processes, and onto neurobiology. They discovered that a family of proteins essential to the basic processes of cell proliferation and cancer also exerts influence beyond cell division in nervous-system development.

The article discusses a study conducted by Kazuki Horikawa and colleagues from the University of Tokyo in Japan that examines how cells synchronize their clocks so that they all tick in unison. The scientists put genetically engineered cells from the pre-somitic mesoderm into zebrafish embryos to monitor the impacts of different molecular changes on segment formation and gene expression. The team has demonstrated that the process of cell division changes the timing of clocks in individual cells.

The article focuses on gene expression in cells. A study was conducted at Sunny Xie laboratory at Harvard University to determine the reasons behind the different personalities of persons who have the same set of genes, commonly called as identical twins. Xie proposed the use of β-galactosidase, a bacterial enzyme to monitor the expression of a single protein in a living cell.

Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models have also been used to extract molecular features from whole slide images. Although molecular tests carry rich information, they are often expensive, time-consuming, and require additional tissue to sample. In this paper, we propose tRNAsformer, an attention-based topology that can learn both to predict the bulk RNA-seq from an image and represent the whole slide image of a glass slide simultaneously. The tRNAsformer uses multiple instance learning to solve a weakly supervised problem while the pixel-level annotation is not available for an image. We conducted several experiments and achieved better performance and faster convergence in comparison to the state-of-the-art algorithms. The proposed tRNAsformer can assist as a computational pathology tool to facilitate a new generation of search and classification methods by combining the tissue morphology and the molecular fingerprint of the biopsy samples. tRNAsformer enables prediction of bulk RNA-seq from histological slides using machine learning approaches. [ABSTRACT FROM AUTHOR]

With the development of data mining, artificial intelligence, neural network, expert system and machine learning, information system (i-system) becomes more and more important. If the objects, attributes and information values in an i-system are replaced by cells, genes and gene expression values, respectively, then the i-system is said to be a gene space. Because gene expression data is characterized by small samples, high dimension and noise, there is considerable uncertainty in a gene space. Traditional machine learning and statistical methods are often powerless to a gene space. Granular computing (GrC) can effectively deal with various uncertainties. This paper studies the uncertainty measurement of gene space based on the class-consistent technology and discusses its application in gene selection from the perspective of GrC. A class-consistent relation between cells in a gene space is first established by the gene expression values of cells on the basis of class-consistent technology. Then, the information granules (i-granules) are obtained from a gene space by using the class-consistent relation. Next, two metrics (information granularity and information entropy) to measure the uncertainty of gene space are defined and their properties are also investigated. The results of numerical experiments and statistical tests verify their effectiveness. Furthermore, as their application to gene space, two gene selection algorithms are proposed. Finally, the clustering experiments and statistical tests on 16 gene spaces show that the designed gene selection algorithms outperform some state-of-the-art feature selection algorithms in terms of three clustering performance indicators. [ABSTRACT FROM AUTHOR]

A reply by the authors to a letter to the editor on their article "MAQC papers over the cracks," is presented.

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]

In this paper, the cgt gene encoding cyclodextrin glycosyltransferase (CGTase) from Bacillus stearothermophilus was cloned into pWB980 plasmid for extracellular expression in Bacillus subtilis SCK6. Through adding a six-histidine affinity tag fused to the C-terminus, the recombinant CGTase could be purified by nickel ion affinity chromatography, and its molecular weight was approximately 76 kDa on SDS-PAGE. Then, the enzymatic properties were determined, and results were as follows: the optimum temperature and pH were identified as 40 ℃ and pH 5.0, respectively. CGTase had good tolerance to metal ions of Mn2+, Ca2+, and Mg2+. The enzyme activity was activated by Na+, Al3+, Fe3+, and Ni+, and it was remarkably inhibited by Cu2+ and Zn2+. To improve the aqueous solubility of rutin, CGTase was used to catalyze the transglycosylation reaction, and the conversion rate could reach as high as 80.13% under optimal conditions. Furthermore, the reaction mixture was treated with glucoamylase and microporous adsorbent resin. The yield of glycosyl-rutin was 56.1%, and its purity was 74.3%, which further improved the value of the product. [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]

In life-science research isogenic B-lymphoblastoid cell lines (LCLs) are widely known and preferred for their genetic stability – they are often used for studying mutations for example, where genetic stability is crucial. We have shown previously that phenotypic variability can be observed in isogenic B-lymphoblastoid cell lines. Isogenic LCLs present well-defined phenotypic differences on various levels, for example on the gene expression level or the chromatin level. Based on our investigations, the phenotypic variability of the isogenic LCLs is accompanied by certain genetic variation too. We have developed a compendium of LCL datasets that present the phenotypic and genetic variability of five isogenic LCLs from a multiomic perspective. In this paper, we present additional datasets generated with Next Generation Sequencing techniques to provide genomic and transcriptomic profiles (WGS, RNA-seq, single cell RNA-seq), protein-DNA interactions (ChIP-seq), together with mass spectrometry and flow cytometry datasets to monitor the changes in the proteome. We are sharing these datasets with the scientific community according to the FAIR principles for further investigations. [ABSTRACT FROM AUTHOR]

Existing feature selection methods easily neglect the distribution of data, and require most of the neighborhood radius in neighborhood rough sets (NRS) to be selected artificially. These limitations result in the misclassification of samples. To address these drawbacks, this paper presents a mixed measure-based feature selection method using the Fisher score and an NRS model. First, the variation coefficient of the features in different decision classes is defined to depict the dispersion degree of different features, based on which, the neighborhood class is described to develop a novel NRS model. The concepts of dependency degree, neighborhood knowledge granularity, and average neighborhood entropy are defined, and then a mixed measure combining the information and algebra views is proposed to measure the uncertainty in neighborhood decision systems. Second, the average correlation degree of the feature subset is computed to assess the redundancy of the reduced feature subset. By combining the classification accuracy of the selected features, the reduction rate of the classification result, and the average correlation degree of the reduced feature set, we can construct an adaptive neighborhood radius function to avoid the artificial selection of the optimal neighborhood radius. Then, an optimal feature subset can be obtained according to the internal and external significance of the features. Third, the variation coefficient of the samples in different decision classes in each feature is defined to compute the dispersion degree of the samples, and the average of all samples in each feature is added to the between-class scatter to eliminate the effect of the different measurement dimensions of the features; then, the Fisher score model is improved to eliminate the noise of the high-dimensional data. Finally, a heuristic feature selection algorithm with the Fisher score based on the new NRS model is designed to select an optimal feature subset. Experimental results applied to five low-dimensional UCI datasets and nine high-dimensional gene expression datasets showed that the developed algorithm is effective and can select an optimal reduced subset with high classification accuracy when compared with some of the latest algorithms. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death throughout the world, with constant increasing morbidity and mortality. Existing studies suggest that immunotherapy is beneficial to the treatment of advanced HCC. At present, it is imperative to identify biomarkers suitable for HCC immunotherapy. In this paper, the mRNA expression data of HCC were downloaded from The Cancer Genome Atlas, and Stromal Score, Immune Score and ESTIMATE Score of each sample were calculated. Weighted gene co-expression network analysis clustered the pretreated genes into eight modules. The brown module that was remarkably associated with Immune Score was identified by module eigengene-immune trait analysis, in which genes were mainly enriched in immune-related pathways. Four hub genes (CCR5, CD53, ITGB2, and TYROBP) related to tumor immunity, were screened out by intramodular gene connectivity combined with protein–protein interaction network topology analysis. Kaplan–Meier survival analysis presented a correlation between high expression of CCR5 and CD53, and better prognoses of HCC patients. TIMER analysis revealed a positive correlation between expression of each hub gene and immune cell infiltration level, and a positive correlation between the expression of each hub gene and the expression of immunosuppressive factors CTLA4 and PDCD1. Gene set enrichment analysis displayed that there was an evident difference in the activation of cytokines and the activation of immune signal transduction-related pathways between high- and low-expression groups of each hub gene. In conclusion, this study identified four potential genetic markers associated with HCC immunity and assessed their association with HCC patient's prognosis and immune microenvironment. The study results are expected to provide theoretical guidance for immunotherapy of HCC patients. [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]

Feature Selection (FS) algorithms are applied in bioinformatics applications to identify the disease causing genes. Performance of such algorithms is measured in terms of accuracy of the model and stability of FS algorithms. Stability evaluates the identical replication of feature sets obtained after every execution. Recently research has shown that a stability measure must satisfy set of properties like, fully defined, monotonicity, boundedness, deterministic maximum stability, and correction for chance. Among the existing stability measures, only Nogueira's frequency based stability measure satisfies all the required properties. However, frequency based stability measures fail to discriminate among the cases when overall frequency of features are same. In order to address this issue, the paper proposes a hybrid similarity based stability measure which satisfies all the desirable properties, as mentioned earlier. The proposed stability measure is unique as it is the first similarity based stability measure that satisfies all the required properties. Also, all these essential properties are mathematically established. Further, the paper also proposes a combination of frequency based and similarity based measure which preserves all the aspects of both the approaches. The work presented also analyzes the stability performance of LASSO and Elastic Net, using synthetic and microarray gene expression datasets. Elastic Net depicts higher stability and selection of relevant features. [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]

Cellular signaling generates calcium (Ca2+) ions, which are ubiquitous secondary messengers decoded by calcium-dependent protein kinases, calcineurins, calreticulin, calmodulins (CAMs), and CAM-binding proteins. Previous studies in the model plant Arabidopsis thaliana have shown the critical roles of the CAM-BINDING PROTEIN 60 (CBP60) protein family in plant growth, stress responses, and immunity. Certain CBP60 factors can regulate plant immune responses, like pattern-triggered immunity, effector-triggered immunity, and synthesis of major plant immune-activating metabolites salicylic acid (SA) and N-hydroxypipecolic acid (NHP). Although homologous CBP60 sequences have been identified in the plant kingdom, their function and regulation in most species remain unclear. In this paper, we specifically characterized 11 members of the CBP60 family in the agriculturally important crop tomato (Solanum lycopersicum). Protein sequence analyses revealed that three CBP60 homologs have the closest amino acid identity to Arabidopsis CBP60g and SARD1, master transcription factors involved in plant immunity. Strikingly, AlphaFold deep learning–assisted prediction of protein structures highlighted close structural similarity between these tomato and Arabidopsis CBP60 homologs. Conserved domain analyses revealed that they possess CAM-binding domains and DNA-binding domains, reflecting their potential involvement in linking Ca2+ signaling and transcriptional regulation in tomato plants. In terms of their gene expression profiles under biotic (Pseudomonas syringae pv. tomato DC3000 pathogen infection) and/or abiotic stress (warming temperatures), five tomato CBP60 genes were pathogen-responsive and temperature-sensitive, reminiscent of Arabidopsis CBP60g and SARD1. Overall, we present a genome-wide identification of the CBP60 gene/protein family in tomato plants, and we provide evidence on their regulation and potential function as Ca2+-sensing transcriptional regulators. [ABSTRACT FROM AUTHOR]

A novel cost-effective and widely applicable pH indicator was developed using anthocyanins extracted from the purple subtype of Ocimum sanctum L. and common lab filter paper. This pH indicator was successfully tested to monitor the pH of a wide range of buffers, solutions, irrigation water, and soil solution. Upon testing, the indicator displayed specific colors at corresponding pH ranges. Sucrose showed a stabilizing effect for the color of the extracted anthocyanins. Further, molecular analysis indicated that the leaves from the purple subtypes showed higher transcripts abundance for chalcone synthase, chalcone isomerase, anthocyanidin synthase, and dihydroflavonol 4-reductase than that of the green subtype. Similarly, transcription factors HY5 and a bHLH putatively involved in the biosynthesis of anthocyanins showed up-regulation in the purple subtype of O. sanctum. [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]

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments. [ABSTRACT FROM AUTHOR]

In this paper, the bovine serum albumin protected gold nanozymes (BSA-Au nanozymes) were utilized as a novel nanodrug for treatment of acute T-type lymphoblastic leukemia (Jurkat) by production of excessive ROS and effect on the expression of anti-apoptotic genes. The effect of BSA-Au nanozymes on the Bcl-2 expression and survivin in the Jurkat cell line was checked. The results showed that the expression of anti-apoptotic genes was significantly reduced after treatment of the Jurkat cell line with the BSA-Au nanozymes (p-value of 0.001) as the potential nanodrug while their expression in the normal PBMC was not affected by the nanodrug. Moreover, the cytotoxic effect of the developed nanodrug on the Jurkat cell line was evaluated which illustrated that survival rate in the studied cell line reaches its minimum value (100% lethality, 0.0% survival) after treatment for 48 h. The IC50 for the nanodrug was calculated at 0.05 mM of the developed nanodrug. Overall, the BSA-Au nanozymes can be used as the nanodrug for treatment of T-type lymphoblastic leukemia via reducing the expression of anti-apoptotic genes, increasing the effect of common anticancer drugs such as Adriamycin and ara-C, and consequently increasing the survival of patients with leukemia. [ABSTRACT FROM AUTHOR]

This paper considers the use of machine learning for diagnosis of diseases that is based on the analysis of a complete gene expression profile. This distinguishes our study from other approaches that require a preliminary step of finding a limited number of relevant genes (tens or hundreds of genes). We conducted experiments with complete genetic expression profiles (20 531 genes) that we obtained after processing transcriptomes of 801 patients with known oncologic diagnoses (oncology of the lung, kidneys, breast, prostate, and colon). Using the indextron (instant learning index system) for a new purpose, i.e., for complete expression profile processing, provided diagnostic accuracy that is 99.75% in agreement with the results of histological verification. [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]

This paper presents the form of the equations of state of molecular crystals of nitro compounds constructed on the basis of dividing Helmholtz free energy into an intramolecular and an intermolecular components. This division enabled us to determine the form of the Grüneisen equation for molecular crystals, the volume dependence of the Grüneisen coefficient, and the temperature dependence on the volumetric expansion coefficient. Comparative analysis of the calculated and experimental values of volumes of elementary cells of triaminotrinitrobenzene crystals depending on the temperature has shown their fair agreement. [ABSTRACT FROM AUTHOR]

Seeding is a technique used to leverage population diversity in genetic algorithms. This paper presents a quick survey of different seeding approaches, and evaluates one of the promising ones called the Seeding Genetic Algorithm. The Seeding GA does not include mutation, and it has been shown to work well on some GA-hard problems with binary representation, such as the Hierarchical If-and-Only-If or Deceptive Trap. This paper investigates the effectiveness of the Seeding GA on two problems with more complex non-binary representations: capacitated lot-sizing and single-machine scheduling. The results show, with statistical significance, that the new GA is consistently outperformed by the conventional GA, and that not including mutation is the main reason why. A detailed analysis of the results is presented and suggestions are made to enhance and improve the method. [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]