Caixia Yu

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.

Abstract The real-time detection and analysis of seismic signals is crucial in geophysics research, especially when it comes to monitoring catastrophic events. We present an evolutionary deep learning method that yields a model named MCU-Quake. This model encodes the discrimination process as a single numerical value, offering interpretability with only 2693 parameters. Trained on raw seismic waveforms from Utah, USA, MCU-Quake demonstrates its generalization capability across a global natural earthquake dataset. Notably, the model effectively identifies typical explosions during the Russia-Ukraine war in Europe. The knowledge to discriminate between ambient noise, explosions and natural earthquakes can be represented by values of −5.01 (std: 1.14), 1.96 (std: 0.36), 1.01 (std: 0.49), respectively. The model can be deployed on Internet of Things (IoT) devices, including most microcontrollers, which are constrained by limited computational resources (kilo-bytes of memory) and energy consumption (micro-Watts). The results indicate the prospect of on-site missions of artificial intelligent sensors.

This study presents a mathematical framework to evaluate the value of ecological services in areas targeted for land use projects. It first employs a composite coefficient set to establish the baseline ecological status of the land, taking into account services across air, water, and soil. This set provides a quantitative basis to assess the land's initial ecological value. The framework then uses a logistic growth model to predict how the project's implementation may impact the land's ecological services over time. The model captures the non-linear nature of ecological changes and identifies a “tipping point” where the project either maximizes ecological benefits or risks causing degradation. This information helps planners make more informed decisions on project scale and timing. The framework was applied to three cases: Kubuqi Desert greening, China Baowu Steel Group's steel production, and Wolong National Nature Reserve. In the Kubuqi Desert, greening efforts shifted the land's ecological value from negative to positive, with the model identifying a tipping point for maximizing benefits. For the Baowu Steel Group, the model indicated that increased industrial activity progressively worsened ecological conditions due to pollution, highlighting the need for mitigation strategies. In the Wolong Nature Reserve, the model showed minimal impact on the original ecological value, aligning with the reserve's conservation goals. Overall, this framework provides a versatile tool for measuring and predicting ecological impacts across different land use projects. By combining a composite coefficient set and a logistic growth model, it offers valuable insights into optimizing land use strategies, balancing development with environmental sustainability, and guiding policy decisions based on ecological service value.

Abstract Elastic full-waveform inversion (FWI) methods are expected to construct high-resolution subsurface elastic properties, which are of great importance for accurately characterizing and delineating hydrocarbon reservoirs. However, elastic FWI for land seismic data is challenging due to low signal-to-noise ratio data, complex near-surface environments, unknown source parameters, etc. In this study, elastic FWI is applied to multicomponent land walkaway vertical seismic profile data acquired in the Sichuan Basin of Southwest Oil and Gas Field in China. A series of methods and strategies are used to overcome the difficulties for elastic FWI. For example, the spectral element method with irregular mesh is used for forward modeling and inversion with complex topographic variations. The source wavelets are estimated shot by shot using the direct P waves. Vertical transverse isotropy wave-equation traveltime tomography with a velocity-based model parameterization is first conducted to invert for the low-wavenumber velocity and anisotropy structures. This step helps reduce the cycle-skipping problem and multiparameter coupling effects. Finally, reflected P waves and P-S converted waves are extracted from the shot gathers for constructing the detailed elastic impedance profiles, which provide valuable information for identifying the potential reservoir zones.

The Genome Warehouse (GWH), accessible at https://ngdc.cncb.ac.cn/gwh, is an extensively utilized public repository dedicated to the deposition, management and sharing of genome assembly sequences, annotations, and metadata. This paper highlights noteworthy enhancements to the GWH since the 2021 version, emphasizing substantial advancements in web interfaces for data submission, database functionality updates, and resource integration. Key updates include the reannotation of released prokaryotic genomes, mirroring of genome resources from National Center for Biotechnology Information (NCBI) GenBank and RefSeq, integration of Poxviridae sequences, implementation of an online batch submission system, enhancements to the quality control system, advanced search capabilities, and the introduction of a controlled-access mechanism for human genome data. These improvements collectively augment the ease and security of data submission and access as well as genome data value, thereby fostering heightened convenience and utility for researchers in the genomic field.