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Summary: Phenotypic associations have been reported between heart failure (HF) and blood lipids (BLs), blood pressure (BP), and blood glucose (BG). However, the shared genetic etiology underlying these associations remains incompletely understood. Conducting a large-scale multi-trait association study for HF with these traits, we discovered 143 previously unreported genomic risk loci for HF. Results showed that 46, 35, and 14 colocalized loci were shared by HF with BLs, BP, and BG, respectively. Notably, the loci shared by HF with these traits rarely overlapped, indicating distinct mechanisms. The combination of gene-mapping, gene-based, and transcriptome-wide association analyses prioritized noteworthy candidate genes (such as lipoprotein lipase [LPL], G protein-coupled receptor kinase 5 [GRK5], and troponin C1, slow skeletal and cardiac type [TNNC1]) for HF. Enrichment analysis revealed that HF exhibited comparable characteristics to cardiovascular traits and metabolic traits correlated to BLs, BP, and BG. Finally, we reported drug repurposing candidates and plasma protein targets for HF. These results provide biological insights into the pathogenesis of these comorbidities of HF.

In object detection algorithms, the bounding box regression (BBR) loss directly influences the model’s accuracy in predicting object positions. Initially, we conducted simulation experiments on the proposed intersection over union-based loss. Through in-depth analysis, we discovered that the width and height components of the distance term could hinder BBR. To address this, we proposed the AIoU-v1 loss, which decouples the width and height components of the distance term, thereby preventing the suppression of BBR loss by the distance term. Additionally, issues such as the imbalance between sample quantity and sample quality in the dataset, as well as labeling errors, can adversely affect BBR. To tackle these dataset problems, we designed an adaptive dynamic non-monotonic focusing mechanism with strong robustness and wide applicability. Finally, we proposed a post-processing algorithm that combines fusion and non-maximum suppression, resulting in more accurate bounding boxes during the post-processing stage. Our source code and data are available at https://github.com/Wenhua-Zheng/AIOU.

Abstract In the field of data analysis, it is often faced with a large number of missing values, especially in metabolomics data, this problem is more prominent. Data imputation is a common method to deal with missing metabolomics data, while traditional data imputation methods usually ignore the differences in missing types, and thus the results of data imputation are not satisfactory. In order to discriminate the missing types of metabolomics data, a missing data classification model (PX-MDC) based on particle swarm algorithm and XGBoost is proposed in this paper. First, the missing values in a given missing data set are obtained by panning the missing values to obtain the largest subset of complete data, and then the particle swarm algorithm is used to search for the concentration threshold of missing data and the proportion of low concentration deletions as a percentage of overall deletions. Next, the missing data are simulated based on the search results. Finally, the training data are trained using the XGBoost model using the feature set proposed in this paper in order to build a classifier for the missing data. The experimental results show that the particle swarm algorithm is able to match the traditional enumeration method in terms of accuracy and significantly reduce the search time in concentration threshold search. Compared with the current mainstream methods, the PX-MDC model designed in this paper exhibits higher accuracy and is able to distinguish different deletion types for the same metabolite. This study is expected to make an important breakthrough in metabolomics data imputation and provide strong support for research in related fields.

Real-time monitoring and timely warning of air quality are vital components of building livable cities and implementing the “Healthy China” strategy. Real-time, efficient, and accurate detection of air quality anomalies holds great significance. However, almost all existing methods for air quality anomaly detection often overlook the imbalanced distribution of data. In addition, many traditional methods cannot learn both pointwise representation and pairwise association, so they cannot solve complex features. This study proposes an anomaly detection method for air quality monitoring based on Deep Smooth Random Sampling and Association Attention in Transformer (DSRS-AAT). Firstly, based on the third geographical law, the more similar the geographical environment, the closer the geographical target features are. We cluster sites according to the surrounding geographic features to fully explore latent feature associations. Then, we employ Deep Smooth Random Sampling to rebalance the air quality datasets. Meanwhile, the Transformer with association attention considers both prior associations and series associations to distinguish anomaly patterns. Experiments are carried out with real data from 95 monitoring stations in Haikou City, China. Final results demonstrate that the proposed DSRS-AAT improves the effectiveness of anomaly detection and provides interpretability analysis for traceability, owing to a significant improvement with the baselines (OmniAnomaly, THOC, etc.). The proposed method effectively enhances the effectiveness of air quality anomaly detection and provides a reference value for real-time monitoring and early warning of urban air quality.

BackgroundThe cardiac-brain connection has been identified as the basis for multiple cardio-cerebral diseases. However, effective therapeutic targets for these diseases are still limited. Therefore, this study aimed to identify pleiotropic and specific therapeutic targets for cardio-cerebral diseases using Mendelian randomization (MR) and colocalization analyses.MethodsThis study included two large protein quantitative trait loci studies with over 4,000 plasma proteins were included in the discovery and replication cohorts, respectively. We initially used MR to estimate the associations between protein and 20 cardio-cerebral diseases. Subsequently, Colocalization analysis was employed to enhance the credibility of the results. Protein target prioritization was based solely on including highly robust significant results from both the discovery and replication phases. Lastly, the Drug-Gene Interaction Database was utilized to investigate protein-gene-drug interactions further.ResultsA total of 46 target proteins for cardio-cerebral diseases were identified as robust in the discovery and replication phases by MR, comprising 7 pleiotropic therapeutic proteins and 39 specific target proteins. Followed by colocalization analysis and prioritization of evidence grades for target protein, 6 of these protein-disease pairs have achieved the highly recommended level. For instance, the PILRA protein presents a pleiotropic effect on sick sinus syndrome and Alzheimer's disease, whereas GRN exerts specific effects on the latter. APOL3, LRP4, and F11, on the other hand, have specific effects on cardiomyopathy and ischemic stroke, respectively.ConclusionsThis study successfully identified important therapeutic targets for cardio-cerebral diseases, which benefits the development of preventive or therapeutic drugs.

The cowpea chlorotic mottle virus (CCMV) is a plant virus explored as a nanotechnological platform. The robust self-assembly mechanism of its capsid protein allows for drug encapsulation and targeted delivery. Additionally, the capsid nanoparticle can be used as a programmable platform to display different molecular moieties. In view of future applications, efficient production and purification of plant viruses are key steps. In established protocols, the need for ultracentrifugation is a significant limitation due to cost, difficult scalability, and safety issues. In addition, the purity of the final virus isolate often remains unclear. Here, an advanced protocol for the purification of the CCMV from infected plant tissue was developed, focusing on efficiency, economy, and final purity. The protocol involves precipitation with PEG 8000, followed by affinity extraction using a novel peptide aptamer. The efficiency of the protocol was validated using size exclusion chromatography, MALDI-TOF mass spectrometry, reversed-phase HPLC, and sandwich immunoassay. Furthermore, it was demonstrated that the final eluate of the affinity column is of exceptional purity (98.4%) determined by HPLC and detection at 220 nm. The scale-up of our proposed method seems to be straightforward, which opens the way to the large-scale production of such nanomaterials. This highly improved protocol may facilitate the use and implementation of plant viruses as nanotechnological platforms for in vitro and in vivo applications.

This study was conducted to investigate the effects of different additives on the fermentation quality, nutrient composition, bacterial communities, and metabolic profiles of the silage of hybrid Pennisetum. The experiment was conducted using five treatments, i.e., CK, control group, MA, 1% malic acid of fresh matter (FM) basis, GL, 1% glucose of FM basis, CE, 100 U/g FM cellulase, and BS, 106 cfu/g FM Bacillus subtilis, with six replicates each treatment. After a 120-day fermentation, 30 silage packages were opened for subsequent determination. As a result, all four additives had positive effects on the fermentation quality and nutrient composition of the silage of hybrid Pennisetum. The high-throughput sequencing of V3–V4 regions in 16S rRNA was performed, and results showed that Firmicutes and Proteobacteria were the dominant phyla and that Aquabacterium and Bacillus were the dominant genera. MA, GL, CE, and BS treatment resulted in 129, 21, 25, and 40 differential bacteria, respectively. The four additives upregulated Bacillus smithii but downregulated Lactobacillus rossiae. Metabolic profiles were determined by UHPLC-Q/TOF-MS technology and the differential metabolites caused by the four additives were 47, 13, 47, and 18, respectively. These metabolites played antioxidant, antibacterial, and anti-inflammatory functions and involved in pathways, such as the citrate cycle, carbon fixation in photosynthetic organisms, and glyoxylate and dicarboxylate metabolism. In conclusion, silage additives promoted fermentation quality and nutrient composition by altering bacterial communities and metabolic profiles. This study provided potential biomarkers for the improvement of silage quality.

Hybrid Pennisetum (HP) is a perennial herb with a high yield and high quality, which makes it valuable for research as feed for herbivores. In order to make better use of hybrid Pennisetum as feed, this study studied the effects of cellulase (CE), Lactobacillus plantarum (LP), sucrose (SU), and their mixtures on fermentation parameters, chemical composition, and the bacterial community of hybrid Pennisetum silage. The experiment was divided into 7 treatments, silage treatment, and its abbreviation: CON (control group), CE (100 U/g FM cellulase), LP (1 × 106 cfu/g FM Lactobacillus plantarum), SU (1% FM sucrose), CE+LP (100 U/g FM cellulase + 1 × 106 cfu/g FM Lactobacillus plantarum), CE+SU (100 U/g FM cellulase + 1% FM sucrose), and LP+SU (1 × 106 cfu/g FM Lactobacillus plantarum + 1% FM sucrose). The silage bag was opened on the 60th day of ensilage for subsequent determination. The addition of CE and LP increased lactic acid content (p > 0.05). The pH and acetic acid of CE and LP were lower than CON (p < 0.05), and the crude protein content of CE was higher than CON. Cellulase and Lactobacillus plantarum can improve the quality of hybrid Pennisetum silage. Compared with Lactobacillus plantarum and sucrose, cellulase has better nutrition preservation and the ability to inhibit protein hydrolysis. 16S rRNA analysis showed that the dominant phyla were Fimicutes and Proteobacteria, and the dominant genera were Lactobacillus and Weissella. The changes in fermentation parameters and chemical components of hybrid Pennisetum silage caused by cellulase, Lactobacillus plantarum, sucrose, and their mixture may be the result of bacterial community changes.

The purpose of the experiment was to study the effects of different concentrations of Lactobacillus plantarum (LP) and Bacillus licheniformis (BL) on the quality of hybrid Pennisetum (HP) silage. The experiment consisted of five treatment groups. The control group did not use additives, and the experimental groups were added with LP or BL of 1 × 105 cfu/g fresh weight (FW) and 1 × 107 cfu/g FW, respectively. The results showed that LP and BL could increase the in vitro fermentation gas production and reduce the ammonia nitrogen (AN) content in HP silage. Water-soluble carbohydrates (WSC), lactic acid (LA) content, and gas production in the LP group were positively correlated with LP addition, and acetic acid (AA) was negatively correlated with addition. The content of WSC and LA in the LP7 group was significantly higher than that in the control group (p < 0.05), and AA was lower than that in the control group (p > 0.05). Dry matter (DM), crude protein (CP), and gas production were negatively correlated with the addition of BL, while acid detergent fiber (ADF) content was positively correlated with the addition of BL. Furthermore, in the above indicators, the BL5 group reached a significant level with the control group (p < 0.05). The results of 16sRNA showed that the use of LP and BL could increase the relative abundance of Lactobacillus and decrease the relative abundance of Weissella in HP silage compared with the control group. In conclusion, LP and BL can significantly improve the quality of HP silage. The LP7 group and the BL5 group have the best silage effect. From the perspective of gas production in in vitro fermentation, the LP7 group had stronger fermentability and higher nutritional value.

Objectives: China's 2009 national essential medicine system (NEMS) was designed to reduce prices through a zero-markup policy and a centralized bidding system. To analyze NEMS's short-term impact on drug prices, we estimated the retail and wholesale prices before and after the reform at health institutions in rural Jiangxi Province. Materials and Methods: We undertook two cross-sectional surveys of prices of 39 medicines in November 2008 and May 2010, calculated inflation adjusted prices, and used the Wilcoxon signed-rank and rank-sum tests to examine price changes at different health institutions. Results: Retail prices at pilot (P < 0.01) and nonpilot (P < 0.01) township health centers decreased significantly, whereas the declines at retail pharmacies (P = 0.57) and village clinics (P = 0.29) were insignificant. The decline at pilot township health centers was the largest, compared with other kinds of health institutions (P < 0.01). Retail prices of essential and non-essential medicines declined significantly at pilot facilities (P < 0.05); price drops for non-essential medicines occurred only at pilot facilities (P < 0.05). No significant decline of wholesale prices were found at pilot (P = 0.86) and nonpilot units (P = 0.18), retail pharmacies (P = 0.18), and village clinics (P = 0.20). The wholesale prices changes at pilot units before and after the reform were higher than at nonpilot public units (P < 0.05), retail pharmacies (P < 0.05), and village clinics (P < 0.05). Conclusion: While the NEMS zero-markup policy significantly reduced retail prices at pilot health institutions, the centralized bidding system was insufficient to lower wholesale prices. A drug price management system should be constructed to control medicine prices and a long-term price information system is needed to monitor price changes. [ABSTRACT FROM AUTHOR]