Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Beige adipocytes are a distinct type of thermogenic fat cell in mouse and human.

Brown fat generates heat via the mitochondrial uncoupling protein UCP1, defending against hypothermia and obesity. Recent data suggest that there are two distinct types of brown fat: classical brown fat derived from a myf-5 cellular lineage and UCP1-positive cells that emerge in white fat from a non-myf-5 lineage. Here, we report the isolation of "beige" cells from murine white fat depots. Beige cells resemble white fat cells in having extremely low basal expression of UCP1, but, like classical brown fat, they respond to cyclic AMP stimulation with high UCP1 expression and respiration rates. Beige cells have a gene expression pattern distinct from either white or brown fat and are preferentially sensitive to the polypeptide hormone irisin. Finally, we provide evidence that previously identified brown fat deposits in adult humans are composed of beige adipocytes. These data provide a foundation for studying this mammalian cell type with therapeutic potential. PAPERCLIP:

AgeAnnoMO: a knowledgebase of multi-omics annotation for animal aging.

Aging entails gradual functional decline influenced by interconnected factors. Multiple hallmarks proposed as common and conserved underlying denominators of aging on the molecular, cellular and systemic levels across multiple species. Thus, understanding the function of aging hallmarks and their relationships across species can facilitate the translation of anti-aging drug development from model organisms to humans. Here, we built AgeAnnoMO (https://relab.xidian.edu.cn/AgeAnnoMO/#/), a knowledgebase of multi-omics annotation for animal aging. AgeAnnoMO encompasses an extensive collection of 136 datasets from eight modalities, encompassing 8596 samples from 50 representative species, making it a comprehensive resource for aging and longevity research. AgeAnnoMO characterizes multiple aging regulators across species via multi-omics data, comprehensively annotating aging-related genes, proteins, metabolites, mitochondrial genes, microbiotas and age-specific TCR and BCR sequences tied to aging hallmarks for these species and tissues. AgeAnnoMO not only facilitates a deeper and more generalizable understanding of aging mechanisms, but also provides potential insights of the specificity across tissues and species in aging process, which is important to develop the effective anti-aging interventions for diverse populations. We anticipate that AgeAnnoMO will provide a valuable resource for comprehending and integrating the conserved driving hallmarks in aging biology and identifying the targetable biomarkers for aging research.

StemDriver: a knowledgebase of gene functions for hematopoietic stem cell fate determination.

StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.

DRMref: comprehensive reference map of drug resistance mechanisms in human cancer.

Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.

AgeAnno: a knowledgebase of single-cell annotation of aging in human.

Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell level can help to understand the heterogeneous aging process and design targeted anti-aging therapeutics. Here, we built AgeAnno (https://relab.xidian.edu.cn/AgeAnno/#/), a knowledgebase of single cell annotation of aging in human, aiming to provide comprehensive characterizations for aging-related genes across diverse tissue-cell types in human by using single-cell RNA and ATAC sequencing data (scRNA and scATAC). The current version of AgeAnno houses 1 678 610 cells from 28 healthy tissue samples with ages ranging from 0 to 110 years. We collected 5580 aging-related genes from previous resources and performed dynamic functional annotations of the cellular context. For the scRNA data, we performed analyses include differential gene expression, gene variation coefficient, cell communication network, transcription factor (TF) regulatory network, and immune cell proportionc. AgeAnno also provides differential chromatin accessibility analysis, motif/TF enrichment and footprint analysis, and co-accessibility peak analysis for scATAC data. AgeAnno will be a unique resource to systematically characterize aging-related genes across diverse tissue-cell types in human, and it could facilitate antiaging and aging-related disease research.

Hollow UiO-66-NH2 Encapsulated Pd Catalysts for Highly Selective Hydrogenation of Furfural to Furfuryl Alcohol.

The selective hydrogenation of furfural (FFA) to furfuryl alcohol (FA) is regarded as attractive transformation to achieve the sustainable synthesis of value-added chemicals from biomass resources. However, the conventional supported catalysts are significantly restricted by their narrow pore size, ununiform dispersion and easy leaching or aggregation of catalytic sites. Herein, we designed hollow UiO-66-NH2 as the support to encapsulate Pd nanoparticles (Pd@H-UiO-66-NH2) to achieve the highly active and selective conversion of FFA to FA. Benefiting from the void-confinement effect and substrate enrichment of hollow structure, as well as the surface wrinkles, the as-prepared catalyst Pd@H-UiO-66-NH2 exhibited 96.8 % conversion of FFA with satisfactory selectivity reaching up to 92.4 % at 80 °C, 0.5 MPa H2 in isopropanol solvent within 6 h. More importantly, as-prepared Pd@H-UiO-66-NH2 catalyst exhibited excellent long-term stability, as well as good universality toward a series of hydrogenation of unsaturated hydrocarbons.

Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression.

Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.

Cortical thickness differences are associated with cellular component morphogenesis of astrocytes and excitatory neurons in nonsuicidal self-injuring youth.

Nonsuicidal self-injury (NSSI) generally occurs in youth and probably progresses to suicide. An examination of cortical thickness differences (ΔCT) between NSSI individuals and controls is crucial to investigate potential neurobiological correlates. Notably, ΔCT are influenced by specific genetic factors, and a large proportion of cortical thinning is associated with the expression of genes that overlap in astrocytes and pyramidal cells. However, in NSSI youth, the mechanisms underlying the relations between the genetic and cell type-specific transcriptional signatures to ΔCT are unclear. Here, we studied the genetic association of ΔCT in NSSI youth by performing a partial least-squares regression (PLSR) analysis of gene expression data and 3D-T1 brain images of 45 NSSI youth and 75 controls. We extracted the top-10 Gene Ontology terms for the enrichment results of upregulated PLS component 1 genes related to ΔCT to conduct the cell-type classification and enrichment analysis. Enrichment of cell type-specific genes shows that cellular component morphogenesis of astrocytes and excitatory neurons accounts for the observed NSSI-specific ΔCT. We validated the main results in independent datasets to verify the robustness and specificity. We concluded that the brain ΔCT is associated with cellular component morphogenesis of astrocytes and excitatory neurons in NSSI youth.

Debunking Mental Health Misperceptions in Short-Form Social Media Videos: An Experimental Test of Scientific Credibility Cues.

In recent years, short-form social media videos have emerged as an important source of health-related advice. In this study, we investigate whether experts or ordinary users in such videos are more effective in debunking the common misperception that talking about suicide should be avoided. We also explore a new trend on TikTok and other platforms, in which users attempt to back up their arguments by displaying scientific articles in the background of their videos. To test the effect of source type (expert vs. ordinary user) and scientific references (present or absent), we conducted a 2 × 2 between-subject plus control group experiment (n = 956). In each condition, participants were shown a TikTok video that was approximately 30 seconds long. Our findings show that in all four treatment groups, participants reduced their misperceptions on the topic. The expert was rated as being more authoritative on the topic compared to the ordinary user. However, the expert was also rated as being less credible compared to the ordinary user. The inclusion of a scientific reference did not make a difference. Thus, both experts and ordinary users may be similarly persuasive in a short-form video environment.

Effectiveness of integrated care for older adults-based interventions on depressive symptoms: A systematic review and meta-analysis.

AIM: To pool existing studies to assess the overall effectiveness of integrated care for older adults (ICOPE)-based interventions in improving depressive symptoms in older adults. DESIGN: A systematic review and meta-analysis. DATA SOURCES: Ten databases were systematically searched from inception to 15 July 2023 and the search was last updated on 2 September 2023. METHODS: Standardized mean difference (SMD) was calculated using random effects models. RoB 2 and GRADEpro GDT were used to assess the methodological quality and confidence in the cumulative evidence. Funnel plots, egger's test and begg's test were used to analyse publication bias. Sensitivity, subgroup and meta-regression analyses were performed to explore potential sources of heterogeneity. RESULTS: The results of 18 studies showed ICOPE-based interventions had a significant effect on improving depressive symptoms (SMD = -.84; 95% CI, -1.20 to -.3647; p < .001; 18 RCTs, 5010 participants; very low-quality evidence). Subgroup analysis showed the intervention group was characterized by mean age (70-80 years old), intervention duration between 6 to 12 months, gender (female <50%), non-frail older adults, depressed older adults and mixed integration appeared to be more effective. Sensitivity analysis found the results to be robust. CONCLUSION: ICOPE-based interventions may be a potentially effective alternative approach to reduce depressive symptoms in the older adults. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Healthcare professionals are expected to use ICOPE as one of the interventions for depressive symptoms in older adults, and this ICOPE could provide more comprehensive care services for older adults to reduce depressive symptoms. IMPACT: ICOPE-based interventions may be a potentially effective alternative approach to reduce depressive symptoms in the older adults. ICOPE-based interventions had a significant effect on reducing depressive symptoms in the older adults. The intervention group characterized by mean age of older adults, intervention duration, gender ratio, health condition and integration types may influence the effect size. REPORTING METHOD: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision.

The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.

Integrated Omnidirectional Design of Non-volatile Solid Additive Enables Binary Organic Solar Cells with Efficiency Exceeding 19.5.

Solid additives have drawn great attention due to their numerous appealing benefits in enhancing the power conversion efficiencies (PCEs) of organic solar cells (OSCs). To date, various strategies have been reported for the selection or design of non-volatile solid additives. However, the lack of a general design/evaluation principles for developing non-volatile solid additives often results in individual solid additives offering only one or two efficiency-boosting attributes. In this work, we propose an integrated omnidirectional strategy for designing non-volatile solid additives. By validating the method on the 4,5,9,10-pyrene diimide (PyDI) system, a novel non-volatile solid additive named PyMC5 was designed. PyMC5 is capable of enhancing device performance by establishing synergistic dual charge transfer channels, forming appropriate interactions with active layer materials, reducing non-radiative voltage loss and optimizing film morphology. Notably, the binary device (PM6:L8-BO) treated by PyMC5 achieved a PCE over 19.5%, ranking among the highest reported to date. In addition, the integration of PyMC5 mitigated the degradation process of the devices under photo- and thermal-stress conditions. This work demonstrates an efficient integrated omnidirectional approach for designing non-volatile solid additives, offering a promising avenue for further advancements in OSC development.

Insulin desensitization and cell senescence induced by heat stress in pig testicular cell model.

Thermostatic animals need to maintain a stable body temperature. A high-temperature environment can cause body temperature to exceed the range of tolerance of the organism, resulting in a heat stress response. The reproductive organs (such as the testes) are more sensitive to temperature due to their special anatomical location. However, to date, the effect of heat stress on the biological function of insulin in testicular cells has not been revealed. Therefore, the current study established a testis cell model to study the effect of heat stress on the biological activity of insulin. The results showed significant alterations in the insulin-induced intracellular signaling under heat stress conditions. Moreover, the IR-mediated intracellular signaling pathway was significantly downregulated under heat stress conditions. Further studies demonstrated that heat stress led to senescence of testicular cells by Sa-ß-gal staining. Furthermore, the expression of senescence markers (p16 and p21) was increased under heat stress. In addition, heat stress was found to cause oxidative stress in testicular cells, which may be the underlying molecular mechanism by which heat stress changes the signaling properties of insulin. Collectively, the current study showed that heat stress caused alterations in insulin-induced intracellular signaling. Heat stress also induced testicular cell senescence.

SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.

MOTIVATION: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. RESULTS: To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. AVAILABILITY AND IMPLEMENTATION: The code is available in Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MolTrans: Molecular Interaction Transformer for drug-target interaction prediction.

MOTIVATION: Drug-target interaction (DTI) prediction is a foundational task for in-silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (i) existing molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain and (ii) existing methods focus on limited labeled data while ignoring the value of massive unlabeled molecular data. RESULTS: We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (i) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction and (ii) an augmented transformer encoder to better extract and capture the semantic relations among sub-structures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real-world data and show it improved DTI prediction performance compared to state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: The model scripts are available at https://github.com/kexinhuang12345/moltrans. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepPurpose: a deep learning library for drug-target interaction prediction.

SUMMARY: Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/kexinhuang12345/DeepPurpose. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Electrochemical fabrication of Co(OH)2 nanoparticles decorated carbon cloth for non-enzymatic glucose and uric acid detection.

Cobalt hydroxide nanoparticles (Co(OH)2 NPs) were uniformly deposited on flexible carbon cloth substrate (Co(OH)2@CC) rapidly by a facile one-step electrodeposition, which can act as an enzyme-free glucose and uric acid sensor in an alkaline electrolyte. Compositional and morphological characterization were examined by X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS), which confirmed the deposited nanospheres were Co(OH)2 nanoparticles (NPs). The electrochemical oxidation of glucose and uric acid at Co(OH)2@CC electrode was investigated by electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), differential pulse voltammetry (DPV), and chronoamperometry methods. The results revealed a remarkable electrocatalytic activity toward the single and simultaneous determination of glucose and uric acid at about 0.6 V and 0.3 V (vs. Ag/AgCl), respectively, which is attributed to a noticeable synergy effect between Co(OH)2 NPs and CC with good repeatability, satisfactory reproducibility, considerable long-term stability, superior selectivity, outstanding sensitivity, and wide linear detection range from 1 uM to 2 mM and 25 nM to 1.5 uM for glucose and UA, respectively. The detection limits were 0.36 nM for UA and 0.24 µM for glucose (S/N = 3). Finally, the Co(OH)2@CC electrode was utilized for glucose and uric acid determination in human blood samples and satisfying results were obtained. The relative standard derivations (RSDs) for glucose and UA were in the range 6 to 14% and 0 to 3%, respectively. The recovery ranges for glucose an UA were 97 to 103% and 95 and 101%, respectively. These features make the novel Co(OH)2@CC sensor developed by a low-cost, efficient, and eco-friendly preparation method a potentially practical candidate for application to biosensors.

Comparative Transcriptome Analysis of Two Peach Rootstocks Uncovers the Effect of Gene Differential Expression on Nitrogen Use Efficiency.

Nitrogen is an important nutrient element that limits plant growth and yield formation, but excessive nitrogen has negative effects on plants and the environment. It is important to reveal the molecular mechanism of high NUE (nitrogen use efficiency) for breeding peach rootstock and variety with high NUE. In this study, two peach rootstocks, Shannong-1 (S) and Maotao (M), with different NUE were used as materials and treated with 0.1 mM KNO3 for transcriptome sequencing together with the control group. From the results of comparison between groups, we found that the two rootstocks had different responses to KNO3, and 2151 (KCL_S vs. KCL_M), 327 (KNO3_S vs. KCL_S), 2200 (KNO3_S vs. KNO3_M) and 146 (KNO3_M vs. KCL_M) differentially expressed genes (DEGs) were identified, respectively, which included multiple transcription factor families. These DEGs were enriched in many biological processes and signal transduction pathways, including nitrogen metabolism and plant hormone signal transduction. The function of PpNRT2.1, which showed up-regulated expression under KNO3 treatment, was verified by heterologous expression in Arabidopsis. The plant height, SPAD (soil and plant analyzer development) of leaf and primary root length of the transgenic plants were increased compared with those of WT, indicating the roles of PpNRT2.1 in nitrogen metabolism. The study uncovered for the first time the different molecular regulatory pathways involved in nitrogen metabolism between two peach rootstocks and provided gene reserve for studying the molecular mechanism of nitrogen metabolism and theoretical basis for screening peach rootstock or variety with high NUE.