Core Viewpoint - The 21st Beijing Conference on Analytical Testing (BCEIA 2025) will be held from September 10 to 12, 2025, at the China International Exhibition Center in Beijing, focusing on the theme "Forty Glorious Years, A New Chapter" to commemorate the 40th anniversary of BCEIA [2][15]. Group 1: Event Overview - BCEIA 2025 will feature nearly a thousand academic reports and over 4,000 domestic and international experts and scholars in attendance [2][5]. - The event aims to showcase the latest achievements in analytical science and instrumentation, focusing on industry hot topics and technological trends [2][5]. Group 2: Academic and Technical Highlights - The conference will include a keynote speech by Academician Jiang Guibin, reviewing the 40-year development of BCEIA and exploring future directions in analytical testing science [5]. - Topics of interest will include genomics, single-cell sequencing, and artificial intelligence, with 18 specialized academic sub-sessions covering key areas such as mass spectrometry, chromatography, and environmental analysis [5][6]. Group 3: Exhibition Details - The exhibition will cover an area of 53,400 square meters, showcasing nearly 10,000 products and attracting over 30,000 professional visitors [10]. - More than 700 renowned companies from home and abroad are expected to participate, displaying cutting-edge analytical instruments, new materials, and innovative applications across various sectors including healthcare, environmental protection, and advanced manufacturing [12][14]. Group 4: Networking and Collaboration Opportunities - The event will host a series of networking activities, including a forum for young scientists and meetings with editors of prestigious journals, aimed at fostering cross-disciplinary innovation and international collaboration [6][12]. - BCEIA 2025 serves as a platform for the integration of analysis testing with key fields such as intelligent manufacturing and biomedicine, emphasizing the importance of machine learning and artificial intelligence in the industry [15][16].

Core Viewpoint - The research highlights the development of a high-throughput and ultra-sensitive single-nucleus ATAC sequencing technology (UUATAC-seq) and a deep learning model (NvwaCE) for predicting regulatory sequences in vertebrates, providing valuable resources for understanding the regulatory language of vertebrate genomes [5][15]. Group 1: Technology Development - The UUATAC-seq technology allows for the efficient construction of chromatin accessibility maps within a single day for a given species [8]. - The NvwaCE model is designed to interpret the "grammar" of cis-regulatory elements (cCRE) and can directly predict cCRE landscapes from genomic sequences with high accuracy [11]. Group 2: Research Findings - The study found that the conservation of regulatory grammar is significantly stronger than that of nucleotide sequences, revealing the sequence basis for cell-type-specific gene expression [6]. - The analysis indicated that differences in genome size among species affect the number of cCREs but not their size [10]. Group 3: Practical Applications - The NvwaCE model accurately predicts the impact of synthetic mutations on lineage-specific cCRE functionality, aligning with quantitative trait loci (QTL) and genome editing results [13]. - A specific gene mutation site (HBG1-68:A>G) was predicted to have curative potential for sickle cell disease, marking the first instance of an AI-designed functional site being validated in human cells [14].

Core Viewpoint - Google DeepMind has introduced a groundbreaking biological model, AlphaGenome, which can accurately predict genomic sequence variations in just one second, marking a significant advancement in the field of genomics [3][2]. Group 1: Model Capabilities - AlphaGenome can predict thousands of functional genomic features from DNA sequences up to 1 million base pairs long, assessing variation effects with single-base resolution [4][5]. - The model outperforms existing models across various tasks, providing a powerful tool for deciphering genomic regulatory codes [5][8]. - It is described as a milestone in biology, being the first unified model that integrates a wide range of genomic tasks with high accuracy and performance [7][10]. Group 2: Model Architecture - The architecture of AlphaGenome is inspired by U-Net, processing 1 million base pairs of DNA input sequences through downsampling to generate two types of sequence representations [13]. - It employs convolutional layers for local sequence pattern modeling and Transformer blocks for modeling longer-range dependencies, achieving high-resolution training of complete base pairs [13]. - The model outputs 11 modalities, covering 5,930 human or 1,128 mouse genomic tracks, demonstrating its comprehensive predictive capabilities [13]. Group 3: Training and Performance - AlphaGenome is trained through a two-phase process involving pre-training and distillation, achieving inference times under one second on NVIDIA H100 GPUs [15][16]. - In evaluations across 24 genomic tracks, AlphaGenome maintained a leading position in 22 tasks, showing a 17.4% relative improvement in cell-type-specific LFC predictions compared to existing models [19]. - The model achieved significant enhancements in various tasks, such as a 25.5% improvement in expression QTL direction predictions compared to Borzoi3 [21]. Group 4: Clinical Applications - AlphaGenome can aid researchers in understanding the underlying causes of diseases and discovering new therapeutic targets, exemplified by its application in T-cell acute lymphoblastic leukemia research [29]. - The model's capabilities extend to predicting synthetic DNA designs and assisting in fundamental DNA research, with potential for broader species coverage and improved prediction accuracy in the future [29]. Group 5: Availability - A preview version of AlphaGenome is currently available, with plans for a formal release, inviting users to experience its capabilities [30].