Chronic Heat Stress Weakened the Innate Immunity and Increased the Virulence of Highly Pathogenic Avian Influenza Virus H5N1 in Mice

Chronic heat stress (CHS) can negatively affect immune response in animals. In this study we assessed the effects of CHS on host innate immunity and avian influenza virus H5N1 infection in mice. Mice were divided into two groups: CHS and thermally neutral (TN). The CHS treatment group exhibited reduced local immunity in the respiratory tract, including the number of pulmonary alveolar macrophages and lesions in the nasal mucosa, trachea, and lungs. Meanwhile, CHS retarded dendritic cells (DCs) maturation and reduced the mRNA levels of IL-6 and IFN-β significantly (P < .05). After the CHS treatment, mice were infected with H5N1 virus. The mortality rate and viral load in the lungs of CHS group were higher than those of TN group. The results suggest that the CHS treatment could suppress local immunity in the respiratory tract and innate host immunity in mice significantly and moderately increased the virulence in H5N1-infected mice

P3^3OVD: Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection

Inspired by the success of visual-language methods (VLMs) in zero-shot classification, recent works attempt to extend this line of work into object detection by leveraging the localization ability of pre-trained VLMs and generating pseudo labels for unseen classes in a self-training manner. However, since the current VLMs are usually pre-trained with aligning sentence embedding with global image embedding, the direct use of them lacks fine-grained alignment for object instances, which is the core of detection. In this paper, we propose a simple but effective Pretrain-adaPt-Pseudo labeling paradigm for Open-Vocabulary Detection (P$^3$OVD) that introduces a fine-grained visual-text prompt adapting stage to enhance the current self-training paradigm with a more powerful fine-grained alignment. During the adapting stage, we enable VLM to obtain fine-grained alignment by using learnable text prompts to resolve an auxiliary dense pixel-wise prediction task. Furthermore, we propose a visual prompt module to provide the prior task information (i.e., the categories need to be predicted) for the vision branch to better adapt the pretrained VLM to the downstream tasks. Experiments show that our method achieves the state-of-the-art performance for open-vocabulary object detection, e.g., 31.5% mAP on unseen classes of COCO

Population genetics analysis of the black rockfish <em>Sebastes schlegelii</em> in Northern China based on 2b-RAD simplified genome sequencing

The black rockfish Sebastes schlegelii is an important fishery species in Japan, South Korea, and China. Overfishing has severely depleted the natural resources of S. schlegelii in recent years, leading to the initiation of programs aimed at enhancing fish stock. However, the genetic structure of northern populations remains elusive, posing challenges in collecting and preserving germplasm resources. In this study, a total of 191 S. schlegelii individuals from seven populations, including one cultured population (Changdao: CDYZ) and six wild populations (Lianyungang: LYG; Qingdao: QD; Weihai: WH; Changdao: CDYS; Beidaihe: BDH) sequenced by 2b-RAD method and their population genetics was analyzed using 27,064 SNPs obtained. The results indicated low genetic diversity in both wild and cultured populations (PIC Ho: 0.174-0.273, He: 0.173-0.234), with the cultured population exhibiting higher diversity than the wild ones. Moderate genetic differentiation existed between the cultured population and six wild populations (0.05 Fst Fst Nm > 1). This study provides a theoretical basis for conserving and rationalizing germplasm resources for S. schlegelii

Evaluation of protein extraction methodologies on bacterial proteomic profiling: a comparative analysis

Bacterial proteomics is a pivotal tool for elucidating microbial physiology and pathogenicity. The efficiency and reliability of proteomic analyses are highly dependent on the protein extraction methodology, which directly influences the detectable proteome. In this study, we systematically compared four protein extraction protocols—SDT lysis buffer with boiling (SDT-B), SDT lysis buffer with ultrasonication (SDT-U/S), a combination of boiling and ultrasonication (SDT-B-U/S), and SDT lysis buffer with liquid nitrogen grinding followed by ultrasonication (SDT-LNG-U/S)—to evaluate their effects on peptide and protein identification, distribution, and reproducibility in Escherichia coli and Staphylococcus aureus. Both data-dependent acquisition (DDA) and data-independent acquisition (DIA) strategies were employed for comprehensive proteomic profiling. DDA analysis identified 23,912 unique peptides corresponding to 2,141 proteins in E. coli and 13,150 unique peptides corresponding to 1,511 proteins in S. aureus. DIA analysis yielded slightly fewer peptides (21,027 for E. coli and 7,707 for S. aureus) but demonstrated superior reproducibility. Among the tested protocols, SDT-B-U/S outperformed the others, identifying 16,560 peptides for E. coli and 10,575 peptides for S. aureus in DDA mode. It also exhibited the highest technical replicate correlation in DIA analysis (R2 = 0.92). This method enhanced the extraction of proteins within key molecular weight ranges (20–30 kDa for E. coli; 10–40 kDa for S. aureus) and was particularly effective for recovering membrane proteins (e.g., OmpC). Additionally, ultrasonication-based protocols outperformed the liquid nitrogen grinding approach in extracting the S. aureus proteome. These findings underscore the significant impact of protein extraction methods on bacterial proteomics. The SDT-B-U/S protocol—thermal denaturation followed by ultrasonication—proved most effective, enhancing protein recovery and reproducibility across both Gram-negative and Gram-positive bacteria. This work offers key guidance for optimizing microbial proteomic workflows

Impact of COVID-19 on the Spatio-temporal Distribution of CO

CO2 is the determining factor of global warming, affecting the intensity and rate of global warming. Although the outbreak of COVID-19 deeply affected the emission of global carbon, the impact on the temporal variation and spatial distribution of CO2 emission rate (ECO2 ) is not yet conclusive. This study systematically analyzed the spatial-temporal distribution of EC02 from 2019 to 2021 based on one latest near real-time CO2 dataset named GRACED. Studies show that COVID-19 has no significant impact on the spatial distribution of CO2 in the world, but significantly reduce the values. From the perspective of the seasonal cycle, the outbreak of COVID-19 caused a shift in the minimum ECO2 in 2020 from the Northern Hemisphere summer (JJA) to the Northern Hemisphere winter (MAM), reflecting the impact of the COVID-19 outbreak on global ECO2. As for the temporal variation, the impact of the COVID-19 outbreak on the monthly cycle mainly occurred in 2020, especially from March to June of that year. By 2021, the global mean values of E-C02 had largely recovered to 2019 levels as the impact of COVID-19 faded

Retraction: Development of functional hydrogels for heart failure

Retraction for ‘Development of functional hydrogels for heart failure’ by Yanxin Han et al., J. Mater. Chem. B, 2019, 7, 1563–1580, DOI: 10.1039/C8TB02591F.</p

Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition

Accelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations. With rapid advancements in computer vision and deep learning, research into tracking the motion of individual pixels with vision cameras has increased. The recently developed CoTracker, a transformer-based model, has demonstrated excellence in motion tracking, yet its performance in measuring structural vibrations has not been fully explored. This paper investigates the efficacy of the CoTracker model in extracting full-field structural vibrations using cameras. It is initially applied to capture the dense point movements in video sequences of a cantilever beam recorded using a high-speed camera. Subsequently, modal analysis using delay-embedding dynamic mode decomposition (DMD) is conducted to extract modal parameters including natural frequencies, damping ratios, and mode shapes. The results, benchmarked against those from a reference accelerometer and the Finite Element Method (FEM) result, demonstrate CoTracker’s high potential for general applicability in structural vibration measurements