Anti-symmetric framework for balanced learning of protein-protein interactions.

MOTIVATION: Protein-protein interactions (PPIs) are essential for the regulation and facilitation of virtually all biological processes. Computational tools, particularly those based on deep learning, are preferred for the efficient prediction of PPIs. Despite recent progress, two challenges remain unresolved: (i) the imbalanced nature of PPI characteristics is often ignored and (ii) there exists a high computational cost associated with capturing long-range dependencies within protein data, typically exhibiting quadratic complexity relative to the length of the protein sequence. RESULT: Here, we propose an anti-symmetric graph learning model, BaPPI, for the balanced prediction of PPIs and extrapolation of the involved patterns in PPI network. In BaPPI, the contextualized information of protein data is efficiently handled by an attention-free mechanism formed by recurrent convolution operator. The anti-symmetric graph convolutional network is employed to model the uneven distribution within PPI networks, aiming to learn a more robust and balanced representation of the relationships between proteins. Ultimately, the model is updated using asymmetric loss. The experimental results on classical baseline datasets demonstrate that BaPPI outperforms four state-of-the-art PPI prediction methods. In terms of Micro-F1, BaPPI exceeds the second-best method by 6.5% on SHS27K and 5.3% on SHS148K. Further analysis of the generalization ability and patterns of predicted PPIs also demonstrates our model's generalizability and robustness to the imbalanced nature of PPI datasets. AVAILABILITY AND IMPLEMENTATION: The source code of this work is publicly available at https://github.com/ttan6729/BaPPI.

Gestational Factors throughout Fetal Neurodevelopment: The Serotonin Link.

Serotonin (5-HT) is a critical player in brain development and neuropsychiatric disorders. Fetal 5-HT levels can be influenced by several gestational factors, such as maternal genotype, diet, stress, medication, and immune activation. In this review, addressing both human and animal studies, we discuss how these gestational factors affect placental and fetal brain 5-HT levels, leading to changes in brain structure and function and behavior. We conclude that gestational factors are able to interact and thereby amplify or counteract each other's impact on the fetal 5-HT-ergic system. We, therefore, argue that beyond the understanding of how single gestational factors affect 5-HT-ergic brain development and behavior in offspring, it is critical to elucidate the consequences of interacting factors. Moreover, we describe how each gestational factor is able to alter the 5-HT-ergic influence on the thalamocortical- and prefrontal-limbic circuitry and the hypothalamo-pituitary-adrenocortical-axis. These alterations have been associated with risks to develop attention deficit hyperactivity disorder, autism spectrum disorders, depression, and/or anxiety. Consequently, the manipulation of gestational factors may be used to combat pregnancy-related risks for neuropsychiatric disorders.

Histamine-4 receptor antagonist JNJ7777120 inhibits pro-inflammatory microglia and prevents the progression of Parkinson-like pathology and behaviour in a rat model.

The activation of microglial cells is presumed to play a key role in the pathogenesis of Parkinson's disease (PD). The activity of microglia is regulated by the histamine-4 receptor (H4R), thus providing a novel target that may prevent the progression of PD. However, this putative mechanism has so far not been validated. In our previous study, we found that mRNA expression of H4R was upregulated in PD patients. In the present study, we validated this possible mechanism using the rotenone-induced PD rat model, in which mRNA expression levels of H4R-, and microglial markers were significantly increased in the ventral midbrain. Inhibition of H4R in rotenone-induced PD rat model by infusion of the specific H4R antagonist JNJ7777120 into the lateral ventricle resulted in blockade of microglial activation. In addition, pharmacological targeting of H4R in rotenone-lesioned rats resulted in reduced apomorphine-induced rotational behaviour, prevention of dopaminergic neuron degeneration and associated decreases in striatal dopamine levels. These changes were accompanied by a reduction of Lewy body-like neuropathology. Our results provide first proof of the efficacy of an H4R antagonist in a commonly used PD rat model, and proposes the H4R as a promising target to clinically tackle microglial activation and thereby the progression of PD.

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.

Mendelian randomization to evaluate the effect of folic acid supplement on the risk of Alzheimer disease.

We conducted a study to evaluate the impact of folic acid supplementation on the risk of Alzheimer disease (AD). A Mendelian randomization (MR) analysis model assessed the causal effects of folic acid supplementation on AD, utilizing data from recent genome-wide association studies. Effect estimates were scrutinized using various methods: inverse-variance weighted (IVW), simple mode, weighted mode, simple median, weighted median, penalized weighted median, and the MR-Egger method. The sensitivity analysis assessed heterogeneity and pleiotropy of individual single nucleotide polymorphisms (SNPs) using the IVW method with Cochran Q statistics and MR Egger intercept, respectively. Additionally, a leave-one-out sensitivity analysis determined potential SNP-driven associations. Both fixed-effect and random-effect IVW models in the MR analysis revealed a reduced risk of AD associated with folic acid supplementation (odds ratio, 0.930; 95% CI, 0.903-0.958, P < .001; odds ratio, 0.930; 95% CI, 0.910-0.950, P < .001) based on 7 SNPs as instrumental variables. The reverse MR analysis indicated no causal association between AD and folic acid supplementation. This study, utilizing genetic data, suggests that folic acid supplementation may potentially reduce the risk of AD and provides novel insights into its etiology and preventive measures.

High-consistency proton exchange membrane fuel cells enabled by oxygen-electron mixed-pathway electrodes via digitalization design.

Proton exchange membrane (PEM) fuel cell has been regarded as a promising approach to the decarbonization and diversification of energy sources. In recent years, durability and cost issues of PEM fuel cells are increasingly significant with the rapid increase of power density. However, the failure to maintain the cell consistency, as one major cause of the above issue, has attracted little attention. Therefore, this study intends to figure out the underlying cause of cell inconsistency and provide solutions to it from the perspective of multi-physics transport coupled with electrochemical reactions. The PEM fuel cells with electrodes under two compression modes are firstly discussed to fully explain the relationship of cell performance and consistency to electrode structure and multi-physics transport. The result indicates that one main cause of cell inconsistency is the intrinsic conflict between the separated transport and cooperated consumption of oxygen and electron throughout the active area. Then, a mixed-pathway electrode design is proposed to reduce the cell inconsistency by enhancing the mixed transport of oxygen and electron in the electrode. It is found that the mixing of pathways in electrodes at under-rib region is more effective than that at the under-channel region, and can achieve an up to 40% reduction of the cell inconsistency with little (3.3%) sacrificed performance. In addition, all the investigations are implemented based on a self-developed digitalization platform that reconstructs the complex physical-chemical system of PEM fuel cells. The fully observable physical information of the digitalized cells provides strong support to the related analysis.

Open-Source CFD Elucidating Mechanism of 3D Pillar Electrode in Improving All-Solid-State Battery Performance.

All-solid-state batteries (ASSBs) have become an important technology because of their high performance and low-risk operation. However, the high interface resistance and low ionic conductivity of ASSBs hinder their application. In this study, a self-developed electrochemical model based on an open-source computational fluid dynamics platform is presented. The effect of contact area reduction at the electrode/solid-state electrolyte interface is investigated. Then, a new conceptual 3D structure is introduced to circumvent the existing barriers. The results demonstrate that the discharge time is shortened by over 20% when the area contact ratio reduces from 1.0 to 0.8 at 1 C-rate, owing to the increased overpotential. By adopting the new 3D pillar design, the energy density of ASSBs can be improved. However, it is only when a 3D current collector is contained in the cathode that the battery energy/power density, capacity, and material utilization can be greatly enhanced without being limited by pillar height issues. Therefore, this work provides important insight into the enhanced performance of 3D structures.

Matching biomedical ontologies based on formal concept analysis.

BACKGROUND: The goal of ontology matching is to identify correspondences between entities from different yet overlapping ontologies so as to facilitate semantic integration, reuse and interoperability. As a well developed mathematical model for analyzing individuals and structuring concepts, Formal Concept Analysis (FCA) has been applied to ontology matching (OM) tasks since the beginning of OM research, whereas ontological knowledge exploited in FCA-based methods is limited. This motivates the study in this paper, i.e., to empower FCA with as much as ontological knowledge as possible for identifying mappings across ontologies. METHODS: We propose a method based on Formal Concept Analysis to identify and validate mappings across ontologies, including one-to-one mappings, complex mappings and correspondences between object properties. Our method, called FCA-Map, incrementally generates a total of five types of formal contexts and extracts mappings from the lattices derived. First, the token-based formal context describes how class names, labels and synonyms share lexical tokens, leading to lexical mappings (anchors) across ontologies. Second, the relation-based formal context describes how classes are in taxonomic, partonomic and disjoint relationships with the anchors, leading to positive and negative structural evidence for validating the lexical matching. Third, the positive relation-based context can be used to discover structural mappings. Afterwards, the property-based formal context describes how object properties are used in axioms to connect anchor classes across ontologies, leading to property mappings. Last, the restriction-based formal context describes co-occurrence of classes across ontologies in anonymous ancestors of anchors, from which extended structural mappings and complex mappings can be identified. RESULTS: Evaluation on the Anatomy, the Large Biomedical Ontologies, and the Disease and Phenotype track of the 2016 Ontology Alignment Evaluation Initiative campaign demonstrates the effectiveness of FCA-Map and its competitiveness with the top-ranked systems. FCA-Map can achieve a better balance between precision and recall for large-scale domain ontologies through constructing multiple FCA structures, whereas it performs unsatisfactorily for smaller-sized ontologies with less lexical and semantic expressions. CONCLUSIONS: Compared with other FCA-based OM systems, the study in this paper is more comprehensive as an attempt to push the envelope of the Formal Concept Analysis formalism in ontology matching tasks. Five types of formal contexts are constructed incrementally, and their derived concept lattices are used to cluster the commonalities among classes at lexical and structural level, respectively. Experiments on large, real-world domain ontologies show promising results and reveal the power of FCA.