Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Perspective on 3D Liver Models for Drug Metabolism and Toxicology Studies

The poor predictability of human liver toxicity is still causing high attrition rates of drug candidates in the pharmaceutical industry at the non-clinical, clinical, and post-marketing authorization stages. This is in part caused by animal models that fail to predict various human adverse drug reactions (ADRs), resulting in undetected hepatotoxicity at the non-clinical phase of drug development. In an effort to increase the prediction of human hepatotoxicity, different approaches to enhance the physiological relevance of hepatic in vitro systems are being pursued. Three-dimensional (3D) or microfluidic technologies allow to better recapitulate hepatocyte organization and cell-matrix contacts, to include additional cell types, to incorporate fluid flow and to create gradients of oxygen and nutrients, which have led to improved differentiated cell phenotype and functionality. This comprehensive review addresses the drug-induced hepatotoxicity mechanisms and the currently available 3D liver in vitro models, their characteristics, as well as their advantages and limitations for human hepatotoxicity assessment. In addition, since toxic responses are greatly dependent on the culture model, a comparative analysis of the toxicity studies performed using two-dimensional (2D) and 3D in vitro strategies with recognized hepatotoxic compounds, such as paracetamol, diclofenac, and troglitazone is performed, further highlighting the need for harmonization of the respective characterization methods. Finally, taking a step forward, we propose a roadmap for the assessment of drugs hepatotoxicity based on fully characterized fit-for-purpose in vitro models, taking advantage of the best of each model, which will ultimately contribute to more informed decision-making in the drug development and risk assessment fields.
Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1, and NLRP1) that help predict sepsis severity or mortality. While these genes have been previously associated with sepsis mortality, in this work, we show that these genes are also implicated in complex disease courses, even among survivors. The discovery of eight novel genetic biomarkers related to the overactive innate immune system, including neutrophil function, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.
Contribution of interneuron subtype-specific GABAergic signalling to emergent sensory processing in somatosensory whisker barrel cortex in mouse

Mammalian neocortex is important for conscious processing of sensory information. Fundamental to this function is balanced glutamatergic and GABAergic signalling. Yet little is known about how this interaction arises in the developing forebrain despite increasing insight into early GABAergic interneuron (IN) circuits. To further study this, we assessed the contribution of specific INs to the development of sensory processing in the mouse whisker barrel cortex. Specifically we explored the role of INs in speed coding and sensory adaptation. In wild-type animals, both speed processing and adaptation were present as early as the layer 4 critical period of plasticity, and showed refinement over the period leading to active whisking onset. We then conditionally silenced action-potential-dependent GABA release in either somatostatin (SST) or vasoactive intestinal peptide (VIP) INs. These genetic manipulations influenced both spontaneous and sensory-evoked activity in an age and layer-dependent manner. Silencing SST+ INs reduced early spontaneous activity and abolished facilitation in sensory adaptation observed in control pups. In contrast, VIP+ IN silencing had an effect towards the onset of active whisking. Silencing either IN subtype had no effect on speed coding. Our results reveal how these IN subtypes differentially contribute to early sensory processing over the first few postnatal weeks.
Comparative Transcriptomics and RNA-Seq-Based Bulked Segregant Analysis Reveals Genomic Basis Underlying Cronartium ribicola vcr2 Virulence

Breeding programs of five-needle pines have documented both major gene resistance (MGR) and quantitative disease resistance (QDR) to Cronartium ribicola (Cri), a non-native, invasive fungal pathogen causing white pine blister rust (WPBR). WPBR is one of the most deadly forest diseases in North America. However, Cri virulent pathotypes have evolved and can successfully infect and kill trees carrying resistance (R) genes, including vcr2 that overcomes MGR conferred by the western white pine (WWP, Pinus monticola) R gene (Cr2). In the absence of a reference genome, the present study generated a vcr2 reference transcriptome, consisting of about 20,000 transcripts with 1,014 being predicted to encode secreted proteins (SPs). Comparative profiling of transcriptomes and secretomes revealed vcr2 was significantly enriched for several gene ontology (GO) terms relating to oxidation-reduction processes and detoxification, suggesting that multiple molecular mechanisms contribute to pathogenicity of the vcr2 pathotype for its overcoming Cr2. RNA-seq-based bulked segregant analysis (BSR-Seq) revealed genome-wide DNA variations, including about 65,617 single nucleotide polymorphism (SNP) loci in 7,749 polymorphic genes shared by vcr2 and avirulent (Avcr2) pathotypes. An examination of the distribution of minor allele frequency (MAF) uncovered a high level of genomic divergence between vcr2 and Avcr2 pathotypes. By integration of extreme-phenotypic genome-wide association (XP-GWAS) analysis and allele frequency directional difference (AFDD) mapping, we identified a set of vcr2-associated SNPs within functional genes, involved in fungal virulence and other molecular functions. These included six SPs that were top candidate effectors with putative activities of reticuline oxidase, proteins with common in several fungal extracellular membrane (CFEM) domain or ferritin-like domain, polysaccharide lyase, rds1p-like stress responsive protein, and two Cri-specific proteins without annotation. Candidate effectors and vcr2-associated genes provide valuable resources for further deciphering molecular mechanisms of virulence and pathogenicity by functional analysis and the subsequent development of diagnostic tools for monitoring the virulence landscape in the WPBR pathosystems.
Supplemental Xylooligosaccharide Modulates Intestinal Mucosal Barrier and Cecal Microbiota in Laying Hens Fed Oxidized Fish Oil

Our previous study indicated that dietary xylooligosaccharide (XOS) supplementation improved feed efficiency, ileal morphology, and nutrient digestibility in laying hens. The objective of this study was to evaluate the mitigative effects of XOS on intestinal mucosal barrier impairment and microbiota dysbiosis induced by oxidized fish oil (OFO) in laying hens. A total of 384 Hy-Line Brown layers at 50 weeks of age were randomly divided into four dietary treatments, including the diets supplemented with 20 g/kg of fresh fish oil (FFO group) or 20 g/kg of oxidized fish oil (OFO group), and the OFO diets with XOS addition at 200 mg/kg (OFO/XOS200 group) or 400 mg/kg (OFO/XOS400 group). Each treatment had eight replicates with 12 birds each. The OFO treatment decreased (P < 0.05) the production performance of birds from 7 to 12 weeks of the experiment, reduced (P < 0.05) ileal mucosal secretory immunoglobulin A (sIgA) content, and increased (P < 0.05) serum endotoxin concentration, as well as downregulated (P < 0.05) mRNA expression of claudin-1 (CLDN1) and claudin-5 (CLDN5) in the ileal mucosa at the end of the experiment. Dietary XOS addition (400 mg/kg) recovered (P < 0.05) these changes and further improved (P < 0.05) ileal villus height (VH) and the villus height-to-crypt depth ratio (VCR). In addition, OFO treatment altered cecal microbial composition of layers, and these alterations were probably involved in OFO-induced ileal mucosal impairment as causes or consequences. Supplemental XOS remodeled cecal microbiota of layers fed the OFO diet, characterized by an elevation in microbial richness and changes in microbial composition, including increases in Firmicutes, Ruminococcaceae, Verrucomicrobia (Akkermansia), Paraprevotella, Prevotella_9, and Oscillospira, along with a decrease in Erysipelatoclostridium. The increased abundance of Verrucomicrobia (Akkermansia) had positive correlations with the improved ileal VH and ileal mucosal expression of CLDN1. The abundance of Erysipelatoclostridium decreased by XOS addition was negatively associated with ileal VH, VCR, ileal mucosal sIgA content, and the relative expression of zonula occludens-2, CLDN1, and CLDN5. Collectively, supplemental XOS alleviated OFO-induced intestinal mucosal barrier dysfunction and performance impairment in laying hens, which could be at least partially attributed to the modulation of gut microbiota.
Variability in scRNA-Seq analysis is mitigated by regularized or supervised approaches

Transcriptional dynamics of evolutionary processes through time are highly complex and require single-cell resolution datasets. This is especially important in cancer during the evolution of resistance, where stochasticity can lead to selection for divergent transcriptional mechanisms. Statistical methods developed to address various questions in single-cell datasets are prone to variability and require careful adjustments of multiple parameter space. To assess the impact of this variation, we utilized commonly used single-cell RNA-Seq analysis tools in a combinatorial fashion to evaluate how repeatable the results are when different methods are combined. In the context of clustering and trajectory estimation, we benchmark the combinatorial space and highlight ares and methods that are sensitive to parameter changes. We have observed that utilizing temporal information in a supervised framework or regularization in latent modeling reduces variability leading to improved overlap when different parameters/methods are used. We hope that future studies can benefit from the results presented here as use of scRNA-Seq analysis tools as out of the box is becoming a standard approach in cancer research.
Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

The computational role of the abundant feedback connections in the ventral visual stream (VVS) is unclear, enabling humans and non-human primates to effortlessly recognize objects across a multitude of viewing conditions. Prior studies have augmented feedforward convolutional neural networks (CNNs) with recurrent connections to study their role in visual processing; however, often these recurrent networks are optimized directly on neural data or the comparative metrics used are undefined for standard feedforward networks that lack these connections. In this work, we develop task-optimized convolutional recurrent (ConvRNN) network models that more correctly mimic the timing and gross neuroanatomy of the ventral pathway. Properly chosen intermediate-depth ConvRNN circuit architectures, which incorporate mechanisms of feedforward bypassing and recurrent gating, can achieve high performance on a core recognition task, comparable to that of much deeper feedforward networks. We then develop methods that allow us to compare both CNNs and ConvRNNs to fine-grained measurements of primate categorization behavior and neural response trajectories across thousands of stimuli. We find that high performing ConvRNNs provide a better match to this data than feedforward networks of any depth, predicting the precise timings at which each stimulus is behaviorally decoded from neural activation patterns. Moreover, these ConvRNN circuits consistently produce quantitatively accurate predictions of neural dynamics from V4 and IT across the entire stimulus presentation. In fact, we find that the highest performing ConvRNNs, which best match neural and behavioral data, also achieve a strong Pareto-tradeoff between task performance and overall network size. Taken together, our results suggest the functional purpose of recurrence in the ventral pathway is to fit a high performing network in cortex, attaining computational power through temporal rather than spatial complexity.
Integument transcriptome profile of the European sea cucumber Holothuria forskali (Holothuroidea, Echinodermata)

In non-model organisms, Next Generation Sequencing (NGS) technology improve our ability to analyze gene expression and identify new genes or transcripts of interest. In this research, paired-end Illumina HiSeq sequencing has been used to describe a composite transcriptome based on two libraries generated from dorsal and ventral integuments of the European sea cucumber Holothuria forskali (Holothuroidea, Echinodermata). A total of 43,044,977 million HQ reads were initially generated. After de novo assembly, a total of 111,194 unigenes were predicted. On all predicted unigenes, 32,569 show significant matches with genes/proteins present in the reference databases. Around 50% of annotated unigenes were significantly similar to sequences from the purple sea urchin Strongylocentrotus purpuratus genome. Annotation analyses were performed on predicted unigenes using public reference databases. These RNA-seq data provide an interesting resource for researchers with a broad interest in sea cucumber biology.
Epigenetic modulation in the pathogenesis and treatment of inherited aortic aneurysm conditions

Shprintzen-Goldberg syndrome (SGS) is a rare systemic connective tissue disorder characterized by craniofacial, skeletal, neurodevelopmental, cutaneous, and cardiovascular manifestations, including aortic root aneurysm. It has significant phenotypic overlap with both Marfan syndrome (MFS) and Loeys-Dietz syndrome (LDS). We previously reported that SGS is caused by heterozygous mutations in the Sloan-Kettering Institute proto-oncogene (SKI), which encodes a potent suppressor of transforming growth factor beta (TGFβ) target gene expression. Herein, we show that mouse lines harboring orthologous amino acid substitutions in Ski recapitulate multiple human SGS phenotypic manifestations, including skin collagen deposition, skeletal kyphosis, behavioral hypoactivity, and aortic root aneurysm. Furthermore, aortic root aneurysm in SGS mice is associated with both increased acetylation of histone H3 at lysine-27 (H3K27) and TGFβ target gene expression, all of which can be ameliorated by pharmacological CBP/P300 inhibition in vivo; similar findings were seen in cultured dermal fibroblast from SGS patients. Aortic root growth is also abrogated in a mouse model of MFS by selective CBP/P300 inhibition in association with blunted expression of TGFβ target genes. These data document excessive H3K27 acetylation and hence TGFβ target gene expression in the pathogenesis of inherited presentations of aortic root aneurysm and the therapeutic potential of pharmacological epigenetic modulation.
Breaching the Bacterial Envelope: The Pivotal Role of Perforin-2 (MPEG1) Within Phagocytes

The membrane attack complex (MAC) of the complement system and Perforin-1 are well characterized innate immune effectors. MAC is composed of C9 and other complement proteins that target the envelope of gram-negative bacteria. Perforin-1 is deployed when killer lymphocytes degranulate to destroy virally infected or cancerous cells. These molecules polymerize with MAC-perforin/cholesterol-dependent cytolysin (MACPF/CDC) domains of each monomer deploying amphipathic β-strands to form pores through target lipid bilayers. In this review we discuss one of the most recently discovered members of this family; Perforin-2, the product of the Mpeg1 gene. Since their initial description more than 100 years ago, innumerable studies have made macrophages and other phagocytes some of the best understood cells of the immune system. Yet remarkably it was only recently revealed that Perforin-2 underpins a pivotal function of phagocytes; the destruction of phagocytosed microbes. Several studies have established that phagocytosed bacteria persist and in some cases flourish within phagocytes that lack Perforin-2. When challenged with either gram-negative or gram-positive pathogens Mpeg1 knockout mice succumb to infectious doses that the majority of wild-type mice survive. As expected by their immunocompromised phenotype, bacterial pathogens replicate and disseminate to deeper tissues of Mpeg1 knockout mice. Thus, this evolutionarily ancient gene endows phagocytes with potent bactericidal capability across taxa spanning sponges to humans. The recently elucidated structures of mammalian Perforin-2 reveal it to be a homopolymer that depends upon low pH, such as within phagosomes, to transition to its membrane-spanning pore conformation. Clinical manifestations of Mpeg1 missense mutations further highlight the pivotal role of Perforin-2 within phagocytes. Controversies and gaps within the field of Perforin-2 research are also discussed as well as animal models that may be used to resolve the outstanding issues. Our review concludes with a discussion of bacterial counter measures against Perforin-2.
Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability

There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.
Selective auditory attention detection using dynamic learning systems: The study of RNN and reinforcement learning

The cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. In the proposed dynamic system, after preprocessing of the input signals, the probabilistic state space of the system is formed. Then, in the learning stage, different dynamic learning methods, including recurrent neural network (RNN) and reinforcement learning (Markov decision process (MDP) and deep Q-learning) are applied to make the final decision as to the attended speech. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach (MDP+RNN) provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.
Identification and Validation of a Prognostic Gene Signature for Diffuse Large B-Cell Lymphoma Based on Tumor Microenvironment-Related Genes

Diffuse large B-cell lymphoma (DLBCL) is an extremely heterogeneous tumor entity, which makes prognostic prediction challenging. The tumor microenvironment (TME) has a crucial role in fostering and restraining tumor development. Consequently, we performed a systematic investigation of the TME and genetic factors associated with DLBCL to identify prognostic biomarkers for DLBCL. Data for a total of 1,084 DLBCL patients from the Gene Expression Omnibus database were included in this study, and patients were divided into a training group, an internal validation group, and two external validation groups. We calculated the abundance of immune–stromal components of DLBCL and found that they were related to tumor prognosis and progression. Then, differentially expressed genes were obtained based on immune and stromal scores, and prognostic TME‐related genes were further identified using a protein–protein interaction network and univariate Cox regression analysis. These genes were analyzed by the least absolute shrinkage and selection operator Cox regression model to establish a seven-gene signature, comprising TIMP2, QKI, LCP2, LAMP2, ITGAM, CSF3R, and AAK1. The signature was shown to have critical prognostic value in the training and validation sets and was also confirmed to be an independent prognostic factor. Subgroup analysis also indicated the robust prognostic ability of the signature. A nomogram integrating the seven-gene signature and components of the International Prognostic Index was shown to have value for prognostic prediction. Gene set enrichment analysis between risk groups demonstrated that immune-related pathways were enriched in the low-risk group. In conclusion, a novel and reliable TME relevant gene signature was proposed and shown to be capable of predicting the survival of DLBCL patients at high risk of poor survival.