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Tomato flavor has changed over the course of long-term domestication and intensive breeding. To understand the genetic control of flavor, we report the meta-analysis of genome-wide association studies (GWAS) using 775 tomato accessions and 2,316,117 SNPs from three GWAS panels. We discover 305 significant associations for the contents of sugars, acids, amino acids, and flavor-related volatiles. We demonstrate that fruit citrate and malate contents have been impacted by selection during domestication and improvement, while sugar content has undergone less stringent selection. We suggest that it may be possible to significantly increase volatiles that positively contribute to consumer preferences while reducing unpleasant volatiles, by selection of the relevant allele combinations. Our results provide genetic insights into the influence of human selection on tomato flavor and demonstrate the benefits obtained from meta-analysis.

Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ∼10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure–activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing. Large-scale in vitro assays may reduce the number of toxicological tests carried out in animals. Here, Huang et al . report a large dataset containing results of in vitro tests of approximately 10,000 chemicals, and use these data to create models that can potentially predict toxicity in humans.

To optimize the irrigation schedule for corn in northern Xinjiang and save water resources while maintaining stable production. Based on the actual water shortage in northern Xinjiang during summer 2024, this study set up different deficit irrigation gradient treatments according to the crop water requirement (ET ) of each growth stage of corn. Combined with the corn growth and yield data of farmers from 2022 to 2024, the model parameters were calibrated and validated through global sensitivity analysis using AquaCrop-OS MATLAB. Then, the Dynamic Reconstruction and Dual Physics-Informed Neural Networks (DR-DPINNs) were integrated with water balance constraints during the corn growth period to optimize the deficit irrigation system for corn in northern Xinjiang. The results showed that in the global sensitivity analysis of the AquaCrop model, the water productivity (wp) and canopy growth coefficient (cgc) parameters had a significant impact on biomass accumulation (STi>0.10), and the canopy senescence parameter (psen) had a marked effect on yield (Si>0.05). The model parameters obtained through sensitivity analysis could meet the application requirements for simulating biomass, canopy cover, soil water content, and yield in the AquaCrop model. After optimization with DR-DPINNs, when the total irrigation amount was 472 mm, the yield increased by 10.8% and the water use efficiency rose by 11.15% compared with the conventional scheme. The DR-DPINNs method, by combining physical mechanisms with dynamic feature extraction, could significantly enhance the solving capability for high-dimensional nonlinear irrigation optimization problems. The optimized spatial and temporal irrigation distribution under a total water volume of 472 mm could achieve a simultaneous increase in yield and water use efficiency. This study can provide theoretical methods with both mechanistic interpretability and decision-making accuracy for the dynamic optimal systems of drip-irrigated corn under water resource constraints in arid regions, and offer theoretical support and technical reference for agricultural water management in arid regions.

The efficient use of text data is very important in investor sentiment research and other fields. Through the sentiment classification of text data containing investor sentiment, we can effectively and accurately identify the sentiment contained in the text. This paper takes the futures market forecast text published by 21 futures companies as the data source and constructs a sentiment classification model of the market forecast text based on BERT (Bidirectional Encoder Representations from Transformers) according to the characteristics of the market forecast text. The sentiment classification of the market forecast text is carried out by using the sentiment classification model of the market forecast text based on BERT and a classification model based on the classical classification algorithm. The classification effects of different models are compared. The results show that the optimized BERT model has the best classification effect. This enriches the research methods of investor sentiment measurement in the financial field and improves the accuracy of this kind of sentiment measurement result.

Stock data have a long memory, that is, changes in stock prices are closely related to historical transaction data. Also, Recurrent Neural Networks have good time series feature extraction capabilities. The paper proposed prediction models based on RNN/LSTM/GRU respectively. The attention mechanism has the ability to select and focus \"key information”. Therefore, based on the conventional Recurrent Neural Network, this paper introduced the attention mechanism and proposed a prediction model based on AT-RNN/AT-LSTM/AT-GRU. And the paper modeled and experimented with it. The results showed that: (1) In the most basic comparison test of RNN-M, LSTM-M, and GRU-M prediction models, the GRU-M and LSTM -M was significantly better than the RNN-M and the GRU-M was slightly better than the LSTM-M; (2) The introduction of the attention mechanism layer was helpful to improve the accuracy of the stock fluctuation prediction model;(3) Deeper neural networks did not necessarily achieve better results.

Guangxi is one of the provinces having the highest notification rate of tuberculosis in China. However, spatial and temporal patterns and the association between environmental diversity and tuberculosis notification are still unclear. To detect the spatiotemporal pattern of tuberculosis notification rates from 2010 to 2016 and its potential association with ecological environmental factors in Guangxi Zhuang autonomous region, China. We performed a spatiotemporal analysis with prediction using time series analysis, Moran's I global and local spatial autocorrelation statistics, and space-time scan statistics to detect temporal and spatial clusters of tuberculosis notifications in Guangxi between 2010 and 2016. Spatial panel models were employed to identify potential associating factors. The number of reported cases peaked in spring and summer and decreased in autumn and winter. The predicted number of reported cases was 49,946 in 2017. Moran's I global statistics were greater than 0 (0.363-0.536) during the study period. The most significant hot spots were mainly located in the central area. The eastern area exhibited a low-low relation. By the space-time scanning, the clusters identified were similar to those of the local autocorrelation statistics, and were clustered toward the early part of 2016. Duration of sunshine, per capita gross domestic product, the treatment success rate of tuberculosis and participation rate of the new cooperative medical care insurance scheme in rural areas had a significant negative association with tuberculosis notification rates. The notification rate of tuberculosis in Guangxi remains high, with the highest notification cluster located in the central region. The notification rate is associated with economic level, treatment success rate and participation in the new cooperative medical care insurance scheme.

Background Ovarian follicle development plays an important role in determination of poultry egg production. The follicles at the various developmental stages possess their own distinct molecular genetic characteristics and have different biological roles in chicken ovary development and function. In the each stage, several genes of follicle-specific expression and biological pathways are involved in the vary-sized follicular development and physiological events. Identification of the pivotal genes and signaling pathways that control the follicular development is helpful for understanding their exact regulatory functions and molecular mechanisms underlying egg-laying traits of laying hens. Results The comparative mRNA transcriptomic analysis of ovarian follicles at three key developmental stages including slow growing white follicles (GWF), small yellow follicles (SYF) of recruitment into the hierarchy, and differentiated large yellow follicles (LYF), was accomplished in the layers with lower and higher egg production. Totally, 137, 447, and 229 of up-regulated differentially expressed genes (DEGs), and 99, 97, and 157 of down-regulated DEGs in the GWF, SYF and LYF follicles, including VIPR1 , VIPR2 , ADRB2 , and HSD17B1 were identified, respectively. Moreover, NDUFAB1 and GABRA1 genes, two most promising candidates potentially associated with egg-laying performance were screened out from the 13 co-expressed DEGs in the GWF, SYF and LYF samples. We further investigated the biological effects of NDUFAB1 and GABRA1 on ovarian follicular development and found that NDUFAB1 promotes follicle development by stimulating granulosa cell (GC) proliferation and decreasing cell apoptosis, increases the expression of CCND1 and BCL-2 but attenuates the expression of caspase-3, and facilitates steroidogenesis by enhancing the expression of STAR and CYP11A1. In contrast, GABRA1 inhibits GC proliferation and stimulates cell apoptosis, decreases the expression of CCND1, BCL-2, STAR, and CYP11A1 but elevates the expression of caspase-3. Furthermore, the three crucial signaling pathways such as PPAR signaling pathway, cAMP signaling pathway and neuroactive ligand-receptor interaction were significantly enriched, which may play essential roles in ovarian follicle growth, differentiation, follicle selection, and maturation. Conclusions The current study provided new molecular data for insight into the regulatory mechanism underlying ovarian follicle development associated with egg production in chicken.

We propose a Rayleigh Scattering and adaptive color compensation method. It capitalizes on the brightness and color differentials between the regions where DCP has failed within images for effective regional segmentation. First, we added B-channel compensation to the atmospheric illumination, made a simple evaluation of the B channel through the atmospheric illumination of the R channel and the G channel. It repeatedly iterated to obtain and repaired the atmospheric illumination of the B channel, which eliminates the color dilution. Secondly, we obtained the dark channel image and the bright channel image, and jointly evaluated the failure point of the dark channel prior method to select the area with inaccurate transmission. This can select the areas which need re-estimate the transmission. This step improves the image quality of the area and repairs the image details. Finally, we validated the effectiveness and resilience of the proposed method through comprehensive experiments. It is conducted across diverse scenarios, involving the adjustment of various parameters.

Objective To investigate the potential links between soluble transferrin receptor (sTfR), the monocyte/HDL cholesterol ratio (MHR), and heart failure with preserved ejection fraction (HFpEF), in order to provide new biomarkers for clinical evaluation of HFpEF and new ideas for disease treatment. Method 66 patients diagnosed with HFpEF who visited the cardiology department of Cangzhou Central Hospital from January 2023 to October 2023 were selected as the study group, and 70 healthy participants from concurrent physical examinations at the hospital’s examination center were designated as controls. Record demographic data, hematological/biochemical parameters (including sTfR, MHR), and echocardiographic measures of cardiac structure and function. Compare these indices between groups to assess for statistically significant differences. Conduct a multi-factor analysis of the risk factors obtained from the single factor analysis above to explore the independent risk factors of HFpEF. Conduct subgroup analysis on the research group to explore the correlation between sTfR and cardiac structure, function, and activity tolerance in HFpEF patients. Follow up with the patients in the research group for 1 year and analyze their prognosis. Result Female representation (69.7% vs 51.4%), left atrial diameter (37.52 ± 3.57 mm vs 35.04 ± 2.83 mm), and sTfR [3.29 (2.76, 3.57) mg/L vs 2.43 (2.08, 2.78) mg/L] were significantly greater in the study group compared to the control group across both cohorts ( P  < 0.05); There were no significant intergroup differences in terms of age, demographic and clinical histories (smoking, alcohol use, hypertension, diabetes), blood lipid profile, hepatic and renal function, other biochemical parameters, or MHR ( P  > 0.05 for all). Multivariable analysis identified sTfR (OR 1.293, P  = 0.012) and LAD (OR 15.229, P < 0.01) as independent risk factors for HFpEF. Their predictive performance, assessed by ROC curve analysis, yielded AUC values of 0.835 for sTfR and 0.609 for LAD. The corresponding optimal diagnostic thresholds for predicting HFpEF were 3.05 mg/L and 37.5 mm, respectively. Subgroup analysis revealed significantly higher BNP and LAD, but lower 6MWT, in patients with high versus low sTfR expression (all P  < 0.05). Over the 1-year follow-up, cumulative event-free survival did not differ significantly between patients with high versus low sTfR expression (median 11.17 vs. 11.65 months; Log-Rank χ 2  = 0.174, P  = 0.676). Conclusion Serum sTfR correlates with HFpEF severity and prognosis, offering a potential biomarker for disease assessment and outcome prediction.

Follicular development is closely related to poultry egg production. Granulosa cells (GCs) are important components of follicles and vital for follicular selection and hierarchical development. Previous studies demonstrated that miRNAs play a key role in controlling follicular development and atresia. However, biological effects and mechanism of action of miR-726-3p on GC proliferation and differentiation of hen ovaries remain poorly understood. In this study, we compared miRNA profiles in larger white follicles (LWF), along with large (LYF) and small yellow follicles (SYF), to identify target genes related to follicular development. Nine cDNA libraries were constructed, in which 61 miRNA-linked genes were significantly differentially expressed (DE). MiR-726-3p was the most crucial co-DE miRNA and, therefore, was chosen for further evaluation. Using bioinformatics and the luciferase reporter assay, we identified CYB5A as the target of miR-726-3p. Over-expression of miR-726-3p significantly negatively regulated GC proliferation, differentiation and knocking down CYB5A expression supported miR-726-3p overexpression. Overall, miR-726-3p suppresses GC proliferation and differentiation by targeting CYB5A , thus providing new insight into the mechanism that underlies hen follicle development.