The association between antiphospholipid syndrome and atrial fibrillation: a single center retrospective case-control study.

Antiphospholipid syndrome (APS) is a systemic autoimmune syndrome characterized by arterial or venous thrombosis, pregnancy complications and thrombocytopenia. The aim of this study is to investigate the association between APS and atrial fibrillation (AF) among patients in Peking University People's Hospital. A single center retrospective study was conducted. Cases were hospitalized patients diagnosed with AF by a cardiologist while the control group patients did not exhibit cardiac diseases. The results of the study revealed that in multivariable logistic regression, APS, anticardiolipin antibody (aCL) positivity and anti-beta-2-glycoprotein antibody (anti-ß2GPI) positivity are independent risk factors of AF. APS, aCL positivity and anti-ß 2GPI positivity are statistically different between AF patients and non-AF patients. Forthcoming studies are needed to clarify the potential link between APS and AF.

[Effects of Rosa roxburghii Pomace Biochar on Yield and Quality of Chinese Cabbage and Soil Properties].

In order to explore the effect of Rosa roxburghii pomace biochar on the yield and quality of Chinese cabbage and soil properties and realize the resource utilization of R. roxburghii pomace, a pot experiment was conducted to study the effect of R. roxburghii pomace biochar on the yield and quality of Chinese cabbage and soil properties by setting five biochar application rates of 0 % (CK), 1 % (T1), 3 % (T2), 5 % (T3), and 7 % (T4). The results showed that:① The application of R. roxburghii pomace biochar could significantly improve the yield and quality of Chinese cabbage, and the effect was the best at a 5 % biochar application rate. The yield, soluble solids, soluble sugar, vitamin C, total nitrogen, total phosphorus, and total potassium content of Chinese cabbage increased by 71.51 %, 40.14 %, 33.65 %, 38.08 %, 9.03 %, 28.85 %, and 35.38 %, respectively, compared with those in CK. ② The application of biochar from R. roxburghii pomace could significantly improve soil properties and increase soil nutrient content and availability. The effect was better at a 5 % biochar application rate. The soil pH, organic matter, total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, and available potassium content increased by 41.06 %, 134.84 %, 157.48 %, 140.79 %, 341.75 %, and 627.13 %, respectively, compared with those in CK. The contents of available Fe, Mn, Cu, and Zn and exchangeable Ca and Mg increased by 37.68 %, 61.69 %, 400.00 %, 4 648.84 %, 617.17 %, and 351.42 %, respectively, compared with those in CK. ③ The application of biochar from R. roxburghii pomace could significantly enhance soil enzyme activity. Compared with those in the CK treatment, soil urease, acid phosphatase, catalase, and sucrase increased by 51.43 %-362.86 %, 90.63 %-134.14 %, 21.40 %-85.12 %, and 82.92 %-218.43 %, respectively. ④ Redundancy analysis showed that soil AK; exchangeable Ca, SOM, and AP; and available Zn were the main factors affecting the yield and quality of Chinese cabbage, and there was a significant positive correlation between them. In summary, the application of R. roxburghii pomace biochar can significantly increase the yield and quality of Chinese cabbage and improve soil properties. The preparation of R. roxburghii pomace into biochar can provide a theoretical reference for the rational utilization of R. roxburghii pomace resources.

Co-training based prediction of multi-label protein-protein interactions.

Prediction of protein-protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-Label prediction of Protein-Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain.

HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules.

Self-assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self-assembling peptides from scratch. This model explores the self-assembly properties by molecular structure, leveraging 1,377 self-assembling non-peptidal small molecules to navigate chemical space and improve structural diversity. Utilizing HydrogelFinder, 111 peptide candidates are generated and synthesized 17 peptides, subsequently experimentally validating the self-assembly and biophysical characteristics of nine peptides ranging from 1-10 amino acids-all achieved within a 19-day workflow. Notably, the two de novo-designed self-assembling peptides demonstrated low cytotoxicity and biocompatibility, as confirmed by live/dead assays. This work highlights the capacity of HydrogelFinder to diversify the design of self-assembling peptides through non-peptidal small molecules, offering a powerful toolkit and paradigm for future peptide discovery endeavors.

In situ profiling reveals spatially metabolic injury in the initiation of polystyrene nanoplastic-derived intestinal epithelial injury in mice.

Despite increasing concerns regarding the harmful effects of plastic-induced gut injury, mechanisms underlying the initiation of plastic-derived intestinal toxicity remain unelucidated. Here, mice were subjected to long-term exposure to polystyrene nanoplastics (PS-NPs) of varying sizes (80, 200, and 1000 nm) at doses relevant to human dietary exposure. PS-NPs exposure did not induce a significant inflammatory response, histopathological damage, or intestinal epithelial dysfunction in mice at a dosage of 0.5 mg/kg/day for 28 days. However, PS-NPs were detected in the mouse intestine, coupled with observed microstructural changes in enterocytes, including mild villous lodging, mitochondrial membrane rupture, and endoplasmic reticulum (ER) dysfunction, suggesting that intestinal-accumulating PS-NPs resulted in the onset of intestinal epithelial injury in mice. Mechanistically, intragastric PS-NPs induced gut microbiota dysbiosis and specific bacteria alterations, accompanied by abnormal metabolic fingerprinting in the plasma. Furthermore, integrated data from mass spectrometry imaging-based spatial metabolomics and metallomics revealed that PS-NPs exposure led to gut dysbiosis-associated host metabolic reprogramming and initiated intestinal injury. These findings provide novel insights into the critical gut microbial-host metabolic remodeling events vital to nanoplastic-derived-initiated intestinal injury.

scCAN: Clustering With Adaptive Neighbor-Based Imputation Method for Single-Cell RNA-Seq Data.

Single-cell RNA sequencing (scRNA-seq) is widely used to study cellular heterogeneity in different samples. However, due to technical deficiencies, dropout events often result in zero gene expression values in the gene expression matrix. In this paper, we propose a new imputation method called scCAN, based on adaptive neighborhood clustering, to estimate the zero value of dropouts. Our method continuously updates cell-cell similarity information by simultaneously learning similarity relationships, clustering structures, and imposing new rank constraints on the Laplacian matrix of the similarity matrix, improving the imputation of dropout zero values. To evaluate the performance of this method, we used four simulated and eight real scRNA-seq data for downstream analyses, including cell clustering, recovered gene expression, and reconstructed cell trajectories. Our method improves the performance of the downstream analysis and is better than other imputation methods.

PEB-DDI: A Task-Specific Dual-View Substructural Learning Framework for Drug-Drug Interaction Prediction.

Adverse drug-drug interactions (DDIs) pose potential risks in polypharmacy due to unknown physicochemical incompatibilities between co-administered drugs. Recent studies have utilized multi-layer graph neural network architectures to model hierarchical molecular substructures of drugs, achieving excellent DDI prediction performance. While extant substructural frameworks effectively encode interactions from atom-level features, they overlook valuable chemical bond representations within molecular graphs. More critically, given the multifaceted nature of DDI prediction tasks involving both known and novel drug combinations, previous methods lack tailored strategies to address these distinct scenarios. The resulting lack of adaptability impedes further improvements to model performance. To tackle these challenges, we propose PEB-DDI, a DDI prediction learning framework with enhanced substructure extraction. First, the information of chemical bonds is integrated and synchronously updated with the atomic nodes. Then, different dual-view strategies are selected based on whether novel drugs are present in the prediction task. Particularly, we constructed Molecular fingerprint-Molecular graph view for transductive task, and Bipartite graph-Molecular graph view for inductive task. Rigorous evaluations on benchmark datasets underscore PEB-DDI's superior performance. Notably, on DrugBank, it achieves an outstanding accuracy rate of 98.18% when predicting previously unknown interactions among approved drugs. Even when faced with novel drugs, PEB-DDI consistently exhibits outstanding generalization capabilities with an accuracy rate of 88.06%, attributing to the proper migrating of molecular basic structure learning.

Individual ambient ozone exposure during pregnancy and adverse birth outcomes: Exploration of the potentially vulnerable windows.

BACKGROUND: Ambient ozone (O3) exposure during pregnancy might be associated with preterm birth (PTB) and low birth weight (LBW); however, existing evidence remains inconclusive. It is necessary to explore the relationships and potential susceptible periods further. METHODS: To explore the relationship between O3 exposure and adverse birth outcomes, a study using records of 34,122 singleton live births in Beijing between 2016 and 2019 was conducted. The O3 exposure in each gestational week of pregnant women was estimated, and Cox proportional hazard models were used to estimate the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Distributed lag nonlinear model (DLNM) incorporated in Cox proportional hazard models were used to explore potential critical windows. RESULTS: An increase of 10 µg/m3 in O3 exposure was associated with a 3.9% (95%CI: 0.6-7.3%) higher risk of PTB. Additionally, this increase in O3 exposure was positively linked to PTB during the 2nd - 7th, 22nd - 29th, and 37th gestational weeks, and LBW during the 2nd - 7th, 24th - 29th, and 37th gestational weeks. CONCLUSIONS: This study demonstrates a positive correlation between O3 exposure and PTB, and identified specific sensitive periods during pregnancy when the risk was higher.

Insights into lignocellulose degradation: comparative genomics of anaerobic and cellulolytic Ruminiclostridium-type species.

Mesophilic, anaerobic, and cellulolytic Ruminiclostridium-type bacterial species can secrete an extracellular, multi-enzyme machinery cellulosome, which efficiently degrades cellulose. In this study, we first reported the complete genome of Ruminiclostridium papyrosolvens DSM2782, a single circular 5,027,861-bp chromosome with 37.1% G + C content, and compared it with other Ruminiclostridium-type species. Pan-genome analysis showed that Ruminiclostridium-type species share a large number of core genes to conserve basic functions, although they have a high level of intraspecific genetic diversity. Especially, KEGG mapping revealed that Ruminiclostridium-type species mainly use ABC transporters regulated by two-component systems (TCSs) to absorb extracellular sugars but not phosphotransferase systems (PTSs) that are employed by solventogenic clostridia, such as Clostridium acetobutylicum. Furthermore, we performed comparative analyses of the species-specific repertoire of CAZymes for each of the Ruminiclostridium-type species. The high similarity of their cohesins suggests a common ancestor and potential cross-species recognition. Additionally, both differences between the C-terminal cohesins and other cohesins of scaffoldins and between the dockerins linking with cellulases and other catalytic domains indicate a preference for the location of cellulosomal catalytic subunits at scaffoldins. The information gained in this study may be utilized directly or developed further by genetic engineering and optimizing enzyme systems or cell factories for enhanced biotechnological biomass deconstruction and biofuel production.

Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning.

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

Sequence Alignment/Map format: a comprehensive review of approaches and applications.

The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

Integration of hybrid and self-correction method improves the quality of long-read sequencing data.

Third-generation sequencing (TGS) technologies have revolutionized genome science in the past decade. However, the long-read data produced by TGS platforms suffer from a much higher error rate than that of the previous technologies, thus complicating the downstream analysis. Several error correction tools for long-read data have been developed; these tools can be categorized into hybrid and self-correction tools. So far, these two types of tools are separately investigated, and their interplay remains understudied. Here, we integrate hybrid and self-correction methods for high-quality error correction. Our procedure leverages the inter-similarity between long-read data and high-accuracy information from short reads. We compare the performance of our method and state-of-the-art error correction tools on Escherichia coli and Arabidopsis thaliana datasets. The result shows that the integration approach outperformed the existing error correction methods and holds promise for improving the quality of downstream analyses in genomic research.

Organic Cathode with Dual-Type Multielectron Reaction Centers for High-Energy-Density Lithium Primary Batteries.

Organic electrode materials are composed of abundant elements, have diverse and designable molecular structures, and are relatively easily synthesized, promising a bright future for low-cost and large-scale energy storage. However, they are facing low specific capacity and low energy density. Herein, we report a high-energy-density organic electrode material, 1,5-dinitroanthraquinone, which is composed of two kinds of electrochemically active sites of nitro and carbonyl groups. They experience six- and four-electron reduction and are transformed into amine and methylene groups, respectively, in the presence of fluoroethylene carbonate (FEC) in the electrolyte. Drastically increased specific capacity and energy density are demonstrated with an ultrahigh specific capacity of 1321 mAh g-1 and a high voltage of ∼2.62 V, corresponding to a high energy density of 3400 Wh kg-1. This surpasses the electrode materials in commercial lithium batteries. Our findings provide an effective strategy to design high-energy-density and novel lithium primary battery systems.

In Situ Fabricated Non-Flammable Quasi-Solid Electrolytes for Li-Metal Batteries.

Lithium metal batteries (LMBs) are viewed as one of the most promising high energy density battery systems, but their practical application is hindered by significant fire hazards and fast performance degradation due to the lack of a safe and compatible configuration. Herein, nonflammable quasi-solid electrolytes (NQSEs) are designed and fabricated by using the in situ polymerization method, in which 1,3,2-dioxathiolan-2,2-oxide is used as both initiator to trigger the in situ polymerization of solvents and interphase formation agent to construct robust interface layers to protect the electrodes, and triethyl phosphate as a fire-retardant agent. The NQSEs show a high ionic conductivity of 0.38 mS cm-1 at room temperature and enable intimate solid-electrolyte interphases, and demonstrate excellent performance with stable plating/striping of Li metal anode, and high voltage (4.5 V) and high temperature (>60 °C) survivability. The findings provide an effective strategy to build high-temperature, high-energy density, and safe quasi-solid LMBs.

An fMRI study of visual geometric shapes processing.

Cross-modal correspondence has been consistently evidenced between shapes and other sensory attributes. Especially, the curvature of shapes may arouse the affective account, which may contribute to understanding the mechanism of cross-modal integration. Hence, the current study used the functional magnetic resonance imaging (fMRI) technique to examine brain activity's specificity when people view circular and angular shapes. The circular shapes consisted of a circle and an ellipse, while the angular shapes consisted of a triangle and a star. Results show that the brain areas activated by circular shapes mainly involved the sub-occipital lobe, fusiform gyrus, sub and middle occipital gyrus, and cerebellar VI. The brain areas activated by angular shapes mainly involve the cuneus, middle occipital gyrus, lingual gyrus, and calcarine gyrus. The brain activation patterns of circular shapes did not differ significantly from those of angular shapes. Such a null finding was unexpected when previous cross-modal correspondence of shape curvature was considered. The different brain regions detected by circular and angular shapes and the potential explanations were discussed in the paper.

Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder.

Single-cell RNA sequencing (scRNA-seq) is a revolutionary breakthrough that determines the precise gene expressions on individual cells and deciphers cell heterogeneity and subpopulations. However, scRNA-seq data are much noisier than traditional high-throughput RNA-seq data because of technical limitations, leading to many scRNA-seq data studies about dimensionality reduction and visualization remaining at the basic data-stacking stage. In this study, we propose an improved variational autoencoder model (termed DREAM) for dimensionality reduction and a visual analysis of scRNA-seq data. Here, DREAM combines the variational autoencoder and Gaussian mixture model for cell type identification, meanwhile explicitly solving 'dropout' events by introducing the zero-inflated layer to obtain the low-dimensional representation that describes the changes in the original scRNA-seq dataset. Benchmarking comparisons across nine scRNA-seq datasets show that DREAM outperforms four state-of-the-art methods on average. Moreover, we prove that DREAM can accurately capture the expression dynamics of human preimplantation embryonic development. DREAM is implemented in Python, freely available via the GitHub website, https://github.com/Crystal-JJ/DREAM.

Machine learning on protein-protein interaction prediction: models, challenges and trends.

Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilitating PPI detection. In recent years, benefiting from the enormous amount of protein data produced by advanced high-throughput technologies, machine learning models have been well developed in the field of PPI prediction. In this paper, we present a comprehensive survey of the recently proposed machine learning-based prediction methods. The machine learning models applied in these methods and details of protein data representation are also outlined. To understand the potential improvements in PPI prediction, we discuss the trend in the development of machine learning-based methods. Finally, we highlight potential directions in PPI prediction, such as the use of computationally predicted protein structures to extend the data source for machine learning models. This review is supposed to serve as a companion for further improvements in this field.

Amplitude of low-frequency fluctuation after taste exposure revealed by resting-state fMRI.

Taste perception has been deeply explored from the behavioural level to delineating neural mechanisms. However, most previous studies about the neural underpinnings of taste perception have focused on task-related brain activation. Notably, evidence indicates that task-induced brain activation often involves interference from irrelevant task materials and only accounts for a small fraction of the brain's energy consumption. Investigation of the resting-state spontaneous brain activity would bring us a comprehensive understanding of the neural mechanism of taste perception. Here we acquired resting-state functional magnetic resonance imaging (rs-fMRI) data from twenty-two participants immediately after they received sweet, sour and tasteless gustatory stimulation. Our results showed that, in contrast to the tasteless condition, the sour exposure induced decreased amplitude of low-frequency fluctuation (ALFF) in the somatosensory cortex in the left post-central gyrus, and the sweet exposure led to increased ALFF in the bilateral putamen involved in reward processing. Moreover, in contrast to the sweet stimulation condition, the sour stimulation condition showed increased ALFF in the right superior frontal gyrus, which has been linked to functioning in high-order cognitive control. Altogether, our data indicate that taste exposure may affect the spontaneous functional activity in brain regions, including the somatosensory areas, reward processing areas and high-order cognitive functioning areas. Our findings may contribute to a further understanding the neural network and mechanisms after taste exposure.

Ultrafast Non-Equilibrium Synthesis of Cathode Materials for Li-Ion Batteries.

The synthesis of cathode materials plays an important role in determining the production efficiency, cost, and performance of lithium-ion batteries. However, conventional synthesis methods always experience a slow heating rate and involve a complicated multistep reaction process and sluggish reaction dynamics, leading to high energy and long time consumption. Herein, a high-temperature shock (HTS) strategy is reported for the ultrafast synthesis of cathode materials in seconds. The HTS process experiences an ultrahigh heating rate, leading to a non-equilibrium reaction and fast reaction kinetics, and avoids high energy and long time consumption. Mainstream cathode materials (such as LiMn2 O4 , LiCoO2 , LiFePO4 , and Li-rich layered oxide/NiO heterostructured material) are successfully synthesized with pure phases, oxygen vacancies, ultrasmall particle sizes, and good electrochemical performance. The HTS process not only provides an efficient synthesis approach for cathode materials, but also can be extended beyond lithium-ion batteries.