Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search.

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Detection of uridine diphosphate glucuronosyltransferase 1A1 for pancreatic cancer imaging and treatment via a "turn-on" fluorescent probe.

Uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) is expressed ubiquitously in cancer cells and can metabolize exogenous substances. Studies show higher UGT1A1 levels in pancreatic cancer cells than normal cells. Therefore, we need a method to monitor the activity level of UGT1A1 in pancreatic cancer cells and in vivo. Here, we report a fluorescent probe, BCy-panc, for UGT1A1 imaging in cells and in vivo. Compared with other molecular probes, this probe is readily prepared, with high selectivity and sensitivity for the detection of UGT1A1. Our results show that BCy-panc rapidly detects UGT1A1 in pancreatic cancer. In addition, there is an urgent need for evidence to clarify the relationship between UGT1A1 and pancreatic cancer development. The present investigation found that the increase of UGT1A1 by chrysin was effective in inducing apoptosis in pancreatic cancer cells. These results indicate that the synergistic effect of chrysin and cisplatin at the cellular level is superior to that of cisplatin alone. The UGT1A1 level may be a biomarker for early diagnosis of cancer. Meanwhile, UGT1A1 plays a crucial role in pancreatic cancer, and the combination of chrysin and cisplatin may provide effective ideas for pancreatic cancer treatment.

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets.

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement. Key points: An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

Rapid screening of the novel bioactive peptides with notable α-glucosidase inhibitory activity by UF-LC-MS/MS combined with three-AI-tool from black beans.

This work aimed to propose a rapid method to screen the bioactive peptides with anti-α-glucosidase activity instead of traditional multiple laborious purification and identification procedures. 242 peptides binding to α-glycosidase were quickly screened and identified by bio-affinity ultrafiltration combined with LC-MS/MS from the double enzymatic hydrolysate of black beans. Top three peptides with notable anti-α-glucosidase activity, NNNPFKF, RADLPGVK and FLKEAFGV were further rapidly screened and ranked by the three artificial intelligence tools (three-AI-tool) BIOPEP database, PeptideRanker and molecular docking from the 242 peptides. Their IC50 values were in order as 4.20 ± 0.11 mg/mL, 2.83 ± 0.03 mg/mL, 1.32 ± 0.09 mg/mL, which was opposite to AI ranking, for the hydrophobicity index of the peptides was not included in the screening criteria. According to the kinetics, FT-IR, CD and ITC analyses, the binding of the three peptides to α-glucosidase is a spontaneous and irreversible endothermic reaction that results from hydrogen bonds and hydrophobic interactions, which mainly changes the α-helix structure of α-glucosidase. The peptide-activity can be evaluated vividly by AFM in vitro. In vivo, the screened FLKEAFGV and RADLPGVK can lower blood sugar levels as effectively as acarbose, they are expected to be an alternative to synthetic drugs for the treatment of Type 2 diabetes.

Graph kernel of brain networks considering functional similarity measures.

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods.

Self-standing perylene diimide covalent organic framework membranes for trace TMA sensing at room temperature.

The unprecedented demand for highly selective, real-time monitoring and low-power gas sensors used in food quality control has been driven by the increasing popularity of the Internet of Things (IoT). Herein, the self-standing perylene diimide based covalent organic framework membranes (COFMPDI-THSTZ) were prepared via liquid-liquid interfacial synthesis method. By incorporating the perylene diimide monomer into the COFM through molecular engineering, COFMPDI-THSTZ based sensor demonstrated an outstanding trimethylamine (TMA)-sensing performance at room temperature. Benefited from the TMA-accessible self-standing membrane morphology, π-electron delocalization effect, and extensive surface area with continuous nanochannels, the specific and highly sensitive TMA measurement has been achieved within the range of 0.03-400 ppm, with an exceptional theoretical detection limit as low as 10 ppb. Moreover, the primary internal mechanism of COFMPDI-THSTZ for this efficient TMA detection was investigated through in-situ FT-IR spectra, thereby directly elucidating that the chemisorption interaction of oxygen modulated the depletion layers on sensing material surface, resulting in alterations in sensor resistance upon exposure to the target gas. For practical usage, COFMPDI-THSTZ based sensor exhibited exceptional real-time in-situ sensing capabilities, further confirmed their potential for application in dynamic prediction evaluation of marine fish products and quality monitoring in IoT.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Ozone-mediated cerebral protection: Unraveling the mechanism through ferroptosis and the NRF2/SLC7A11/GPX4 signaling pathway.

BACKGROUND: The pathogenesis of brain ischemic/reperfusion (I/R) insult is characterized by neuronal loss due to excessive oxidative stress responses. Ferroptosis, a form of oxidative cell death, can be triggered when the balance between antioxidants and pro-oxidants in cells is disrupted. Ozone, a natural bioactive molecule with antioxidant/anti-apoptotic and pro-autophagic properties, has been shown to enhance the antioxidant system's capacity and ameliorate oxidative stress. However, its role in neuronal ferroptosis remains unclear. Therefore, we investigated the functions and possible mechanisms of ozone in cerebral I/R-induced ferroptotic neuronal death. METHODS: A cerebral ischemia-reperfusion injury model was induced in Sprague-Dawley (SD) rats pre-treated with ozone. Intraperitoneal administration of the NRF2 inhibitor ML385, the SLC7A11 inhibitor Erastin, and the GPX4 inhibitor RSL3 was performed one hour prior to model establishment. RESULTS: Our results showed that ozone preconditioning mitigated neuronal damage caused by cerebral I/R, reduced the severity of neurological deficits, lowered cerebral infarct volume in middle cerebral artery occlusion (MCAO) rats, and decreased the volume of cerebral infarcts. Transmission electron microscopy, immunofluorescence, and Western blotting indicated ferroptosis following MCAO-induced brain damage. MCAO resulted in morphological damage to neuronal mitochondria, increased lipid peroxidation accumulation, and elevated malondialdehyde (MDA) production. Furthermore, MCAO decreased levels of FTH1 and GPX4 (negative regulators of ferroptosis) and increased ACSL4 levels (a positive regulator of ferroptosis). Ozone preconditioning demonstrated a neuroprotective effect by increasing NRF2 nuclear translocation and the expression of SLC7A11 and GPX4. Treatment with ML385, Erastin, and RSL3 significantly reversed ozone preconditioning's protective effect on neuronal ferroptosis. CONCLUSION: Our findings demonstrated that ozone treatment attenuates ferroptosis in a cerebral ischemia/reperfusion injury rat model via the NRF2/SLC7A11/GPX4 pathway, providing a theoretical basis for ozone's potential use as a therapy to prevent ischemic stroke.

MetaNorm: incorporating meta-analytic priors into normalization of NanoString nCounter data.

MOTIVATION: Non-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency. RESULTS: We propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency. AVAILABILITY AND IMPLEMENTATION: R Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.

Mapping Cellular Interactions from Spatially Resolved Transcriptomics Data.

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between γδ T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.

Toxoplasma rhoptry proteins that affect encephalitis outcome.

Toxoplasma gondii, a widespread obligate intracellular parasite, can infect almost all warm-blooded animals, including humans. The cellular barrier of the central nervous system (CNS) is generally able to protect the brain parenchyma from infectious damage. However, T. gondii typically causes latent brain infections in humans and other vertebrates. Here, we discuss how T. gondii rhoptry proteins (ROPs) affect signaling pathways in host cells and speculate how this might affect the outcome of Toxoplasma encephalitis.

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding.

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αß T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

Cytomulate: accurate and efficient simulation of CyTOF data.

Recently, many analysis tools have been devised to offer insights into data generated via cytometry by time-of-flight (CyTOF). However, objective evaluations of these methods remain absent as most evaluations are conducted against real data where the ground truth is generally unknown. In this paper, we develop Cytomulate, a reproducible and accurate simulation algorithm of CyTOF data, which could serve as a foundation for future method development and evaluation. We demonstrate that Cytomulate can capture various characteristics of CyTOF data and is superior in learning overall data distributions than single-cell RNA-seq-oriented methods such as scDesign2, Splatter, and generative models like LAMBDA.

Azo-Linkage Redox Metal-Organic Framework Incorporating Carbon Nanotubes for High-Performance Aqueous Energy Storage.

The design of well-defined hierarchical free-standing electrodes for robust high-performance energy storage is challenging. We report herein that azo-linkage redox metal-organic frameworks (MOFs) incorporate single-walled carbon nanotubes (CNTs) as flexible electrodes. The in situ-guided growth, crystallinity and morphology of UiO-66-NO2 MOFs were finely controlled in the presence of CNTs. The MOFs' covalent anchoring to CNTs and solvothermal grafting anthraquinone (AQ) pendants endow the hybrid (denoted as CNT@UiO-66-AQ) with greatly improved conductivity, charge storage pathways and electrochemical dynamics. The flexible CNT@UiO-66-AQ displays a highest areal specific capacitance of 302.3 mF cm-2 (at 1 mA cm-2) in -0.4~0.9 V potential window, together with 100% capacitance retention over 5000 cycles at 5 mA cm-2. Its assembled symmetrical supercapacitor (SSC) achieves a maximum energy density of 0.037 mWh cm-2 and a maximum power density of 10.4 mW cm-2, outperforming many MOFs-hybrids-based SSCs in the literature. Our work may open a new avenue for preparing azo-coupled redox MOFs hybrids with carbaneous substrates for high-performance robust aqueous energy storage.

Water-Soluble Se-Containing Proteins from Chicken Alleviate DSS-Induced Ulcerative Colitis in Mice via Inhibiting TLR4/MyD88 Pathway and Protecting the Goblet Cell Pathway.

The influence of water-soluble selenium-containing proteins (WSSeP) in chicken on ulcerative colitis (UC) is not known. This work aims to investigate the effect of two WSSeP including h-Se with 1.78 µg Se/g and l-Se with 1.04 µg Se/g on mice UC induced by dextran sodium sulfate (DSS) versus 5-aminosalicylic acid (5-ASA). Seventy C57BL/6 mice were randomly divided into seven groups: groups 1 and 7 were given normal saline. Group 2 to group 4 were administrated orally 500, 1500, and 3000 mg/kg/day h-Se, respectively. Group 5 was given 1500 mg/kg/day l-Se as the control of group 3. From day 14 to day 21, groups 2 to 7 were fed with 3% DSS. Synchronously, group 6 was fed with 150 mg/kg/day 5-ASA. On day 21, the disease activity index, colon length, the histopathological changes, the expressions of claudin-1, occludin, ZO-1, TLR4, and MyD88 in colons, the levels of inflammatory cytokines (IFN-γ, IL-1ß, IL-6, TNF-α), and antioxidant markers (LPS, GSH-Px, SOD, MDA) in serum were determined. WSSeP can effectively improve the damages of DSS to the colon, thymus, and spleen, which present protein and Se dose-dependent. 1.50 g h-Se dose can significantly promote the expression levels of claudin-1, occludin, and ZO-1, to surround crypt gland and goblet and epithelial cells and inhibit the attack of DSS, suppress TLR4/MyD88 pathway, decrease the levels of IL-1ß, IL-6, TNF-α, IFN-γ, LPS, and MDA, and increase the activities of GSH-Px and SOD, which are better than those of 5-ASA. Therefore, WSSeP would be a natural and potential anti-inflammatory agent for UC.

Goodness-of-fit testing for meta-analysis of rare binary events.

Random-effects (RE) meta-analysis is a crucial approach for combining results from multiple independent studies that exhibit heterogeneity. Recently, two frequentist goodness-of-fit (GOF) tests were proposed to assess the fit of RE model. However, they tend to perform poorly when assessing rare binary events. Under a general binomial-normal framework, we propose a novel GOF test for the meta-analysis of rare events. Our method is based on pivotal quantities that play an important role in Bayesian model assessment. It further adopts the Cauchy combination idea proposed in a 2019 JASA paper, to combine dependent p-values computed using posterior samples from Markov Chain Monte Carlo. The advantages of our method include clear conception and interpretation, incorporation of all data including double zeros without the need for artificial correction, well-controlled Type I error, and generally improved ability in detecting model misfits compared to previous GOF methods. We illustrate the proposed method via simulation and three real data applications.

An imine-rich polymer with enlarged π-conjugated planes for aqueous zinc-ion batteries.

Two imine-rich polymers (planar P3Q and linear TDB) were synthesized through a facile method. P3Q demonstrates an enlarged π-conjugated structure, increasing the coordination and conversion of Zn2+. As a result, for aqueous zinc-ion batteries, P3Q exhibits more excellent electrochemical performance than TDB with a discharge capacity of 226 mA h g-1 at 0.3 A g-1 and relatively high capacity retention of 70% after 100 cycles at 0.5 A g-1. The redox energy storage mechanism is also explored by ex situ characterization and density functional theory calculation.

Unravelling Metabolic Heterogeneity of Chinese Baijiu Fermentation in Age-Gradient Vessels.

Fermentation vessels affect the characteristics of food fermentation; however, we lack an approach to identify the biomarkers indicating fermentation. In this study, we applied metabolomics and high-throughput sequencing analysis to reveal the dynamic of metabolites and microbial communities in age-gradient fermentation vessels for baijiu production. Furthermore, we identified 64 metabolites during fermentation, and 19 metabolites significantly varied among the three vessels (p < 0.05). Moreover, the formation of these 19 metabolites were positively correlated with the core microbiota (including Aspergillus, Saccharomyces, Lactobacillus, and Bacillus). In addition, ethyl lactate or ethyl acetate were identified as the biomarkers for indicating the metabolism among age-gradient fermentation vessels by BP-ANN (R2 > 0.40). Therefore, this study combined the biological analysis and predictive model to identify the biomarkers indicating metabolism in different fermentation vessels, and it also provides a potential approach to assess the profiling of food fermentations.

Draft genome sequence of yeast Vanrija sp. strain TS01, isolated from leukemia patient's urine.

Vanrija sp. strain TS01 was isolated from urine sample from a leukemia patient in Nanjing, China. The closest known relative strain is Vanrija humicola with average nucleotide identity value of 93.1. The draft genome comprises 22.0 Mb in 52 contigs, with G + C content of 62.57%.