Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46 %, 92.15/5.13 %, 97.60/1.68 %(valence), and 97.48/1.42 %(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.

SUMMARY: Cosmetics form an important part of our daily lives, and it is therefore important to understand the basic physicochemical properties, metabolic pathways, and toxicological and safe concentrations of these cosmetics molecules. Therefore, comprehensive cosmetic ingredients bioinformatics platform (CCIBP) was developed here, which is a unique comprehensive cosmetic database providing information on regulations, physicochemical properties, and human metabolic pathways for cosmetic molecules from major regions of the world, whilst also correlating plant information in natural products. CCIBP supports formulation analysis, efficacy component analysis, and also combines knowledge of synthetic biology to facilitate access to natural molecules and biosynthetic production. CCIBP, empowered with chemoinformatics, bioinformatics, and synthetic biology data and tools, presents a very helpful platform for cosmetic research and development of ingredients. AVAILABILITY AND IMPLEMENTATION: CCIBP is available at: http://design.rxnfinder.org/cosing/.

BACKGROUND: The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS: We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS: SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.

In this research, we aimed to design a architecture of brain-computer interaction (BCI) in routing nodes routing and forwarding mechanism by exploring the memristive neuron networks (MNN) among heterogeneous multi-agent system (HMAS). In recent years, field programmable gate array (FPGA) has become a popular choice to construct a heterogeneous network system for routing and forwarding mechanism due to its ability to describe a hardware information system in a software defined method. However, most BCI approaches focus only on capturing the dynamics of EEG signal by simply receiving all wave of individual or making up them as a single system. Such methods neglect the existing problems of traditional BCI mode. To this moment, we propose a novel Heterogeneous Multi-Agent System (HMAS) to model the MNN among a group of logic units for realizing the multi-agent system in BCI approach. Specifically, we first feed each agent's non-linear feature into a routing node to model the routing and forwarding functions. Subsequently, at one time step, the outputs of all testing routers are fed into a heterogeneous network space, which mainly consists of multiple sub-agents, a new forward port, and some memory memristive neuron which build a non-linear network system to set up a multiple agent in MNN environment. In the FPGA logic unit, each sub-memory unit stores individual motion information, while these programmable units selectively integrates and stores a huge number of motion information between multiple interacting persons from multiple sub-memory units via the forwarding ports and MNN storage unit, respectively. According to the extensive experimental results on several experimental datasets validate the effectiveness of the proposed HMAS-BCI by comparing against a optimization method of the experimental scene.

SUMMARY: The field of synthetic biology lacks a comprehensive knowledgebase for selecting synthetic target molecules according to their functions, economic applications and known biosynthetic pathways. We implemented ChemHub, a knowledgebase containing >90 000 chemicals and their functions, along with related biosynthesis information for these chemicals that was manually extracted from >600 000 published studies by more than 100 people over the past 10 years. AVAILABILITY AND IMPLEMENTATION: Multiple algorithms were implemented to enable biosynthetic pathway design and precursor discovery, which can support investigation of the biosynthetic potential of these functional chemicals. ChemHub is freely available at: http://www.rxnfinder.org/chemhub/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Living cell strains have important applications in synthesizing their native compounds and potential for use in studies exploring the universal chemical space. Here, we present a web server named as Cell2Chem which accelerates the search for explored compounds in organisms, facilitating investigations of biosynthesis in unexplored chemical spaces. Cell2Chem uses co-occurrence networks and natural language processing to provide a systematic method for linking living organisms to biosynthesized compounds and the processes that produce these compounds. The Cell2Chem platform comprises 40 370 species and 125 212 compounds. Using reaction pathway and enzyme function in silico prediction methods, Cell2Chem reveals possible biosynthetic pathways of compounds and catalytic functions of proteins to expand unexplored biosynthetic chemical spaces. Cell2Chem can help improve biosynthesis research and enhance the efficiency of synthetic biology. AVAILABILITY AND IMPLEMENTATION: Cell2Chem is available at: http://www.rxnfinder.org/cell2chem/.

MOTIVATION: The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. RESULTS: We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. AVAILABILITYAND IMPLEMENTATION: SARS2020 is available at http://design.rxnfinder.org/sars2020/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.