Time-series SAR scattering coefficients over woodland: trend analysis and explainable modelling

In the era of large models, massive amounts of Synthetic Aperture Radar (SAR) scattering data need to be synthesized to meet the demand for interpretation training, which calls for clear temporal patterns of time-series SAR for sequence generation. However, the temporal evolution trends of SAR scattering coefficients have been neither comprehensively studied nor explicitly modelled. To address the issue, this paper takes the long-sequence temperate woodlands as the research object for analysis and explicit modelling, where the trend analysis provides explainable motivations for model design. Using Sentinel-1A ground range detected data with a 12-day revisit cycle, two SAR image sequences are constructed, each consists of VV or VH intensity images of 174 consecutive moments spanning from April 2019 to December 2024. By classifying geographically matched multi-temporal optical images through a fine-grained multi-scale convolutional neural network, the woodland area is identified, and 9.48 million VV/VH scattering coefficient sequences are extracted. The seasonal Mann-Kendall test evaluates the annual changes in scattering intensity, while seasonal-trend decomposition using LOESS provides seasonal patterns. Correlation analysis shows a high correlation between the average temperature and the average scattering intensity. Based on the analysis, a scattering intensity model is constructed using a modified Transformer network, which predicts scattering intensity sequences for woodlands. The evaluation of the synthetic sequence for year 2024 indicates minor deviation of the average intensity prediction, which confirms the effective modelling and the necessary analysis

DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud

Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model performance, but at the cost of increased GPU/CPU/memory usage. Meanwhile, tech companies have built extensive cloud-based services to accelerate training DLRM models at scale. In this paper, we conduct a deep investigation of the DLRM training platforms at AntGroup and reveal two critical challenges: low resource utilization due to suboptimal configurations by users and the tendency to encounter abnormalities due to an unstable cloud environment. To overcome them, we introduce DLRover-RM, an elastic training framework for DLRMs designed to increase resource utilization and handle the instability of a cloud environment. DLRover-RM develops a resource-performance model by considering the unique characteristics of DLRMs and a three-stage heuristic strategy to automatically allocate and dynamically adjust resources for DLRM training jobs for higher resource utilization. Further, DLRover-RM develops multiple mechanisms to ensure efficient and reliable execution of DLRM training jobs. Our extensive evaluation shows that DLRover-RM reduces job completion times by 31%, increases the job completion rate by 6%, enhances CPU usage by 15%, and improves memory utilization by 20%, compared to state-of-the-art resource scheduling frameworks. DLRover-RM has been widely deployed at AntGroup and processes thousands of DLRM training jobs on a daily basis. DLRover-RM is open-sourced and has been adopted by 10+ companies.Comment: Accepted in VLDB'2

Correlation between Vegetable and Fruit Intake and Cognitive Function in Older Adults: A Cross-Sectional Study in Chongqing, China

Objective: To explore the correlation between different types of vegetable and fruit intake and cognitive function among the older adults in Chongqing, China, and to provide a scientific basis for developing efficient lifestyle interventions for the prevention of Mild Cognitive Impairment (MCI). Method: Approximately 728 older adults in urban and rural areas of Chongqing were surveyed using face-to-face questionnaires. Cognitive function was assessed with the Montreal Cognitive Assessment-Basic (MoCA-B) scale, and the vegetable and fruit intake groups were investigated with the Simple Food Frequency Counting Survey Scale. Binary logistic regression was used to explore the effect of the vegetable and fruit intake group on cognitive function. Subgroup analysis was used to demonstrate the robustness of the results. Result: Of the 728 participants in the study, 36.40% were likely to have MCI, which is higher than the national average for this condition. After adjusting for confounders, compared to the Q1 group, fruit and root vegetable intake was a protective factor for MCI, showing a dose–response relationship (p < 0.05). Only lower intake (Q2) of total vegetables, medium intake (Q2, Q3) of solanaceous vegetables, and medium–high intake (Q2, Q4) of fungi and algae was protective against MCI, whereas the leafy vegetables showed no relation to MCI. Apart from this, participants who were older, female, unmarried, non-smoking, and engaged in physical labor, and who had an average monthly income of less than 3000 RMB were more likely to suffer from cognitive impairment. Conclusion: This suggested that the fruit-intake groups and some vegetable-intake groups showed a protective effect on cognitive function, and might behave differently depending on their different intake and demographic characteristics. A sensible, healthy diet can help prevent MCI

Survival and immune response of white shrimp Litopenaeus vannamei following single and concurrent infections with WSSV and Vibrio parahaemolyticus

The survival and immune responses of Litopenaeus vannamei were evaluated during white spot syndrome virus (WSSV) or Vibrio parahaemolyticus single and concurrent infections. The mortality, WSSV load, activities of 4 immune enzymes: acid phosphatase (ACP), alkaline phosphatase (AKP), peroxidase (POD) and superoxide dismutase (SOD), and the transcription of Evolutionarily Conserved Signaling Intermediate in Toll pathways of L.vannamei (LvECSIT) were quantified at 0, 3, 6, 12, 24, 48, 72 and 96 h post-infection (pi). The results showed: (i) the cumulative mortality of the co-infection group (WSSV and V. Parahaemolyticus 83%) was significantly lower than the WSSV infection group (97%) (P < 0.05) at 96 hpi; (ii) copies of WSSV in the co-infection group were significantly lower than that of the single infection group from 24 to 96 hpi (P < 0.05); (iii) ACP, AKP,POD and SOD activity in the gills of the co-infection group was higher than that of the WSSV group at12, 48 and 96 hpi (P < 0.05).The expression of LvECSIT mRNA in the co-infection group was significantly higher than in the WSSV infection group from 12 to 72 hpi (P < 0.05).The results indicate that proliferation of WSSV is inhibited by V.parahaemolyticus infection. In addition, infection with WSSV alone causes a significant reduction in some immune responses of shrimp than co-infection with WSSV and V.parahaemolyticus occurs at 26 °C. Third, LvECSIT, an essential member of TLR signaling pathway might play a crucial role in shrimp defense against WSSV – Vibrio co-infection

Analysis of Microarray-Identified Genes and MicroRNAs Associated with Idiopathic Pulmonary Fibrosis

The aim of this study was to identify potential microRNAs and genes associated with idiopathic pulmonary fibrosis (IPF) through web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32537, GSE53845, and GSE10667 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs)/genes (DEGs) were screened with GEO2R, and their associations with IPF were analyzed by comprehensive bioinformatic analyses. A total of 45 DE-microRNAs were identified between IPF and control tissues, whereas 67 common DEGs were determined to exhibit the same expression trends in all three microarrays. Furthermore, functional analysis indicated that microRNAs in cancer and ECM-receptor interaction were the most significant pathways and were enriched by the 45 DE-miRNAs and 67 common DEGs. Finally, we predicted potential microRNA-target interactions between 17 DE-miRNAs and 17 DEGs by using at least three online programs. A microRNA-mediated regulatory network among the DE-miRNAs and DEGs was constructed that might shed new light on potential biomarkers for the prediction of IPF progression

Characterization of the gut microbiome in Wuhuang pigs and their crossbred offspring

IntroductionAs an indigenous Chinese breed, Wuhuang pigs are valued for their stress resistance, tolerance to coarse feed, and high lean meat yield, while Berkshire pigs serve as ideal sires due to superior meat quality and early maturity. To explore the microbial basis of hybrid vigor in these breeds, we compared the gut microbiota of purebred Wuhuang pigs and Wuhuang–Berkshire hybrids.MethodsMicrobial composition was assessed via 16S rDNA sequencing, and predictive functional profiling was performed using PICRUSt2 analysis.ResultsHybrids exhibited significantly increased microbial α-diversity and altered β-diversity. Notably, hybrid ceca were enriched with probiotic genera involved in fiber degradation and short-chain fatty acid (SCFA) production—such as Prevotella, Ruminococcus, Lachnospiraceae, and Roseburia—accompanied by a higher Firmicutes-to-Bacteroidetes ratio and strengthened microbial network connectivity. Predictive functional profiling further revealed significantly elevated activity in hybrid pigs for key metabolic pathways including tryptophan synthesis, pyridoxal salvage, and galacturonic acid metabolism (FDR &lt; 0.05).DiscussionThese results imply that hybrid animals leverage enriched probiotic consortia to augment nutrient metabolism and immune function, thereby supporting improved stress resilience and feed efficiency. This study provides potential microbial targets for the future genetic improvement of indigenous pig breeds