A new study published in Frontiers of Engineering Management presents a novel blockchain-enabled Open BIM data exchange paradigm based on a traceable semantic differential transaction (tSDT) approach. The tSDT approach minimizes data redundancy and enables semantic-level traceability of BIM changes. A Blockchain 3.0 application named openBIMdisk implements the tSDT approach and provides an intuitive and user-friendly interface for BIM practitioners.

Teacher emotion recognition (TER) is crucial for classroom dynamics, yet limited by scarce multimodal data and feature modeling. We present a multimodal TER dataset with 102 lessons and 2,170 video segments, annotated with interaction-based emotional tags. We propose an Emotion Dual-Space Network (EDSN) with a Commonality Space (ECSC) using central moment differences to align modalities, and a Discrimination Space (EDSC) using gradient reversal and orthogonal projection to extract unique features. EDSN achieves 77.0% accuracy and 0.769 F1-score on the dataset, outperforming state-of-the-art models, demonstrating its effectiveness in educational emotion recognition. 

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

DNA double-strand breaks (DSBs) are the most severe form of DNA damage, primarily repaired by non-homologous end joining (NHEJ) pathway. A critical step in this process is DNA synapsis, where the two broken ends are brought together to facilitate timely repair. Deficiencies in NHEJ synapsis can lead to improper DNA end configurations, potentially resulting in chromosomal translocations. NHEJ synapsis is a highly dynamic, multi-protein mediated assembly process. Recent advances in single-molecule techniques have led to significant progress in understanding the molecular mechanisms driving NHEJ synapsis. In this review, we summarize single-molecule methods developed for studying NHEJ synapsis, with a particular focus on the single-molecule fluorescence resonance energy transfer (smFRET) technique. We discuss the various molecular mechanisms of NHEJ synapsis uncovered through these studies and explore the coupling between synapsis and other steps in NHEJ. Additionally, we highlight the strategies, limitations, and future directions for single-molecule studies of NHEJ synapsis.

This review explores how viral infections remodel the host cell’s cytoskeleton and membrane systems to form viral replication factories, facilitating viral replication and assembly. These factories come in various shapes, such as viroplasms, spherules, double-membrane vesicles, and tubes, often derived from host organelles like the endoplasmic reticulum and Golgi apparatus.

In recent years, digital agricultural technology extension services (DATES), leveraging Internet platforms such as WeChat official accounts and mobile applications, have gained popularity, providing a new pathway for agricultural technology dissemination. This service overcomes the temporal and spatial limitations of traditional agricultural technology extension, enabling farmers to conveniently access planting knowledge. Then, can DATES effectively encourage farmers to adopt OMF and contribute to the green transformation of agriculture? Professor Minjuan Zhao from the College of Economics and Management, Northwest A&F University, and her team addressed this question through a survey of farmers in major apple - producing areas in China. The related research has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024590).

Dynamic multi-robot task allocation (MRTA) requires real-time responsiveness and adaptability to rapidly changing con ditions. Existing methods, primarily based on static data and centralized architectures, often fail in dynamic environments that require decentralized, context-aware decisions. To address these challenges, this paper proposes a novel graph reinforce ment learning (GRL) architecture, named Spatial-Temporal Fusing Reinforcement Learning (STFRL), to address real-time distributed target allocation problems in search and rescue scenarios. The proposed policy network includes an encoder, which employs a Temporal-Spatial Fusing Encoder (TSFE) to extract input features and a decoder uses multi-head attention (MHA) to perform distributed allocation based on the encoder’s output and context. The policy network is trained with the REINFORCEalgorithm.Experimentalcomparisonswithstate-of-the-artbaselinesdemonstratethatSTFRLachievessuperior performance in path cost, inference speed, and scalability, highlighting its robustness and efficiency in complex, dynamic environments.

A research team led by Professor Yumei Zhang from the Academy of Global Food Economics and Policy (AGFEP) at China Agricultural University, in collaboration with Dr. Issa Ouedraogo from the Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), has conducted a study that reveals the structural characteristics and emission reduction potential of agrifood system carbon emissions in China and Africa. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025609).

Recently, a research team led by Gengjie Jia at the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, in collaboration with Yu Li at The Chinese University of Hong Kong, Xin Gao at King Abdullah University of Science and Technology, and Andrey Rzhetsky at The University of Chicago, published an article in Quantitative Biology titled “An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.” The study presents an embedding-based approach that encodes medical records into a high-dimensional disease space, providing a versatile framework for uncovering disease associations, facilitating genetic parameter estimation, and enabling data-driven disease classification. The authors also discuss the key challenges and future prospects of this emerging paradigm.