High-P-T experimental constraints on the genesis of post-collisional porphyry Cu-Au magmas: A case study from the southeastern Tibetan Plateau - Gong Liu; Xian-Wu Bi; Lei-Luo Xu; Wei Du; Lin-Bo Shang; Jing-Jing Zhu; Ming-Liang Huang; Rui Ma (Lithos, 2026)

Oxidized Late Devonian arc intrusion in the Jiangda area, eastern Tibet: Implication for heterogeneous porphyry Cu-Au fertility of Paleo-Tethyan arc magmas - Rui Ma; Ming-Liang Huang; Lei-Luo Xu; Jing-Jing Zhu; Bao-Di Wang; Hong-Rui Zhang; Rui-Zhong Hu; Xing-Chun Zhang; Xian-Wu Bi (Journal of Asian Earth Sciences, 2026)

Processing sequence alters gelation fate of high-concentration soy protein systems via transglutaminase-mediated crosslinking - Wenzhe Luo; Xueer Yu; Rui Zhang; Xiaokang Na; Ming Du; Chao Wu (Innovative Food Science & Emerging Technologies, 2026)

A lyophilized RPA–CRISPR/Cas13d one-pot platform for rapid detection of porcine circovirus type 3 - Li-Na Shao; Zheng-Luo; Bao-Ling Liu; Tong-Xu; Yuan-Meng Wang; Jia-Qi Duan; Lei-Zhao; You-You Li; Yang-Ming Dai; Qin-Rui Jia; Le-Yi Zhang; Ling-Zhu; et al. (Analytica Chimica Acta, 2026)

One novel sesquiterpenoid from the fungus Pestalotiopsis clavata JSQ 12 isolated from the mushroom Lanmaoa asiatica - Zi-Lin Wang; Shuai-Ming Zhu; Yu Yang; Rui Xu; Xue Zhao; Chen Sun; Qian Luo; You-Zhi Zhang; Guo-Gang Zhang; Chang-Wei Li (Natural Product Research, 2026)

SGDC-UIE: A Semantic Guidance Network with Degradation Consistency for Underwater Image Enhancement - Rui Ming; Jianshan Zhang; Taotao Lai; Haibo Luo; Jiancheng Yang (Journal of Marine Science and Engineering, 2026)
Underwater images often suffer from color distortion, low contrast, and structural blurring caused by wavelength-dependent absorption and scattering, which degrade both visual observation and downstream perception. Existing underwater image enhancement methods usually learn image-level restoration mappings, while the relationships among semantic regions, degradation patterns, and restoration responses are not fully exploited. In this paper, we propose a Semantic Guidance Network with Degradation Consistency for Underwater Image Enhancement (SGDC-UIE). Specifically, SGDC-UIE first extracts dense semantic responses from a frozen DINOv3 prior and converts them into foreground, boundary, and background region gates. These gates are then used to guide pseudo-physical degradation estimation, producing attenuation-like, transmission-like, illumination, structure, and background-light priors for region-aware restoration. These pseudo-physical priors are learned, bounded conditioning variables rather than calibrated estimates of underwater optical parameters. Based on these degradation conditions, a dual-branch restoration network corrects low-frequency color and illumination degradation while recovering high-frequency structural details through semantic-aware wavelet restoration. The color-restored and structure-restored outputs are further integrated by a degradation-consistent fusion gate, which adaptively balances visual fidelity and task-relevant structure preservation. In addition, grouped supervision with quality-anchor replay stabilizes task-aware fine-tuning and reduces visual-quality drift. Extensive experiments on paired and no-reference underwater enhancement benchmarks, semantic segmentation, and underwater object detection show that SGDC-UIE achieves competitive restoration quality and improves the usability of enhanced images for downstream perception.

How to make big data accessible to plant biologists and beyond: Ten years of lessons from TBtools - Junting Feng; Chengjie Chen; Ya Wu; Longjiang Fan; Zhang Zhang; Ming Chen; Haibao Tang; Guangchuang Yu; Jian Ren; Jingchu Luo; Yi Liao; Aiping Luan; et al. (Molecular Plant, 2026)

A Lepidoptera Synthesis Phylogeny of 11,001 Species and Its Application in Phylogenetic Profiling of Herbivore DNA Barcodes: Towards an Integrative Phylogenetic Framework for Insects - Douglas Chesters; Ming‐Qiang Wang; Perttu Anttonen; Jing‐Ting Chen; Rui Cheng; Yi Li; Arong Luo; Michael C. Orr; Tingting Xie; Qingsong Zhou; Chao‐Dong Zhu (Zoologica Scripta, 2026)
We herein constructed a phylogeny of Lepidoptera synthesizing multiple information types, a phylogenomics backbone, a multigene supermatrix with species‐comprehensive DNA barcodes, and topological information from a compiled database of standardized machine‐readable trees from over 100 previous studies. We then applied the new phylogenetic synthesis as a profiling framework for a large DNA barcode dataset of caterpillars from a tree diversity experiment. The synthesis phylogeny comprised 11,001 species. Phylogenetic information content was taxonomically skewed towards butterflies (i.e., Nymphalidae, Papilionidae and Pieridae), and parts of Macroheterocera (Geometridae, Sphingidae, Saturniidae and Bombycidae), while undersampling of Lepidoptera species diversity was present in Erebidae, Noctuidae, Gelechioidea and Pyraloidea. Caterpillar Phylogenetic Diversity (PD) increased with tree richness in the diversity experiment regardless of processing choices in calculating caterpillar PD, including whether calculated on plot OTU alone, plot OTU placed onto a simple reference phylogeny or plot OTU placed to the comprehensive reference phylogeny. Stronger correlations between tree diversity and uncorrected Faith's PD of caterpillars were observed where OTU were placed onto the full reference tree, and where long branch OTU were removed prior to PD calculation, though no significant differences in the strength of the tree diversity effect across context and processing choice were observed for standardized PD. In calculation of plot‐level PD, focus should remain on community sample size, with the results herein supporting the use of any available reference phylogeny as context, though correspondence of PD indices to environmental gradients might be stronger where plot OTU can be placed to a comprehensive reference phylogeny. The phylogeny presented herein enables inference of varied evolutionary information for Lepidoptera DNA barcode sets.

Metabolic inflexibility across heart failure phenotypes: mechanisms and type-specific therapeutic implications - Li Chen; Xin-Rui Yang; Yan Jiang; Shi-Qi Cheng; Zhen-Xun Wan; Jin-Wen Wu; Ming-Tai Chen; Yuan-Yuan Li; Gang Luo; Meng-Nan Liu (Apoptosis, 2026)

Dynamic error elimination method of strapdown gravimeter based on artificial neural network - Shao-kun Cai; Yang Yu; Rui-hang Yu; Zhi-ming Xiong; Ju-liang Cao; Yan Guo; Bai-nan Yang; Kai-xin Luo; Mei-ping Wu (Applied Geophysics, 2026)

The light side of the microbiome in trauma: Mechanism and applications - Han-Yue Luo; Lei-Ming Cao; Kan Zhou; Yi-Fu Yu; Yao Xiao; Guang-Rui Wang; Bing Liu; Qiu-Ji Wu; Xiao-Long Wang; Zhi-Yong Song; Lin-Lin Bu (Chemical Engineering Journal, 2026)

Accelerating Sugarcane Breeding with Machine Learning: A Concurrent Multi-trait Predictive Approach - Haichuan Fan; Jing Chao; Xiaoyan Liu; Pengcheng Ma; Guanghu Zhu; Ming Li; Rui Yan; Xinyi Li; Yan Jing; Fengbing Li; Ting Luo; Prakash Lakshmanan (Sugar Tech, 2026)
A large and highly polyploid genome, long breeding cycles, low narrow-sense heritability of yield and its components, and persisting yield stagnation continue to challenge sugarcane variety improvement through both conventional and molecular breedings. To address this, this study developed an efficient phenotype-based concurrent multi-trait prediction framework using artificial intelligence, leveraging 2700 parent combinations and their offspring field performance data from the Sugarcane Research Institute of Guangxi Academy of Agricultural Sciences. Five machine learning paradigms were selected based on data characteristics: tree-based models (GBDT, XGBoost) for structured tabular data and nonlinear trait interactions; attention-based tabular models (TabTransformer, TabNet) for subtle cross-trait dependencies; and an MLP as a baseline. All models were optimized via 80/20 stratified train–test split, class-weighted loss, and cross-validated grid search. GBDT achieved the best overall performance with a macro F 1 score of 0.7316, Hamming loss of 0.2052, and sample accuracy of 0.5630, excelling in predicting sucrose content, cane yield, and smut resistance. XGBoost performed comparably, while TabTransformer offered complementary advantages with a higher F 1 score (0.6829) on overall agronomic performance. These results indicate the potential of using artificial intelligence (AI), particularly machine learning (ML), to increase the predictive power and robustness through integrated AI approaches in the future. This phenotype-based framework shows the value of ML as an efficient decision support tool that can help breeders prioritize cross combinations and optimize resource use, thereby reducing reliance on extensive field trials. While the potential of AI in accelerating variety development is evident, its routine application in real-world commercial breeding requires validation.

tRNAGlu−CTC-1-1-derived small RNAs inhibit the proliferation of immature porcine Sertoli cells - Guo Ming; Cha Ding-rui; Yang Jin; Luo Jun; Zeng Wen-xian; Chen Xiao-xu (Theriogenology, 2026)

Mechanisms of Action of FUNDC1 in Cerebral Ischemia-Reperfusion Injury - Xin-Yi Zou; Luo-Yang Cai; Jin Zhang; Ying Yuan; Jie Song; Zhao-Duan Hu; Rui Peng; Xiao-Feng Ruan; Xiao-Ming Zhang (Molecular Neurobiology, 2026)