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Objective: To study the composition of non-volatile small molecules in shrimp pastes from different geographical origins. Methods: Metabolomics based on ultra-high performance liquid chromatography-Q Exactive high field-mass spectrometry was used to detect metabolites in shrimp pastes from Shandong, Guangdong and Hebei. The characteristic metabolites of shrimp paste from each region were identified by orthogonal partial least square-discriminant analysis (OPLS-DA). Results: Shrimp paste was rich in amino acid and fatty acid metabolites, forming a large number of N-acyl compounds, with cinnamic acid, 11’-carboxy-α-chromanol and β-leucine being the major ones. It was found that shrimp paste also contained active neurotransmitters such as dopamine, norepinephrine, 6-hydroxymelatonin, and γ-glutamine-5-hydroxytryptophan. The major characteristic metabolites of Shandong shrimp paste were cinnamic acid and β-leucine. 12-Hete-T and N-oleyl taurine were the major characteristic metabolites in Guangdong shrimp paste. The major metabolites of Hebei shrimp paste were eicosapntemacnioc acid (EPA) and cyanidin-3-O-(6’-malonyl-arabinoside). Conclusion: Shrimp paste is rich in small molecules such as β-leucine, glutamic acid, proline-valine and leucine-glycine as well as bioactive ingredients such as taurine, norepinephrine and dopamine. The composition of metabolites in shrimp paste is region specific.

Abstract Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictors of treatment response poses great challenges for personalized treatment. The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings of pharmacological treatment response in ADHD. Based on genotype data of medication-naïve patients with ADHD who received pharmacological treatments for 12 weeks, the current study performed GWAS using the percentage changes in ADHD-RS score as phenotype. Then, DL models were constructed to predict percentage changes in symptom scores using genetic variants selected based on four different genome-wide P thresholds (E-02, E-03, E-04, E-05) as inputs. The current GWAS results identified two significant loci (rs10880574, P = 2.39E-09; rs2000900, P = 3.31E-09) which implicated two genes, TMEM117 and MYO5B, that were primarily associated with both brain- and gut-related disorders. The convolutional neural network (CNN) model, using variants with genome-wide P values less than E-02 (5516 SNPs), demonstrated the best performance with mean squared error (MSE) equals 0.012 (Accuracy = 0.83; Sensitivity = 0.90; Specificity = 0.75) in the validation dataset, 0.081 in an independent test dataset (Acc = 0.61, Sensitivity = 0.81; Specificity = 0.26). Notably, the variant that contributed most to the CNN model was NKAIN2, an ADHD-related gene, which is also associated with metabolic processes. To conclude, the integration of GWAS and DL methods revealed new genes contribute to ADHD pharmacological treatment responses, and underscored the interplay between neural systems and metabolic processes, potentially providing critical insights into precision treatment. Furthermore, our CNN model exhibited good performance in an independent dataset, encouraged future studies and implied potential clinical applications.

Abstract Background The fat mass and obesity-associated protein (FTO) has been showed to be involved in the pathogenesis and progression of coronary artery disease (CAD). However, the effects of FTO variants on CAD risk remain poorly understood. We herein genotyped three SNPs (rs1121980, rs72803657, and rs4783818) in FTO to investigate the influence of FTO polymorphisms on individual susceptibility to CAD. Methods Genotyping for the three SNPs (rs1121980, rs72803657, and rs4783818) was conducted in a cohort of 712 CAD cases with 349 myocardial infarction (MI) cases and 701 control participants, utilizing the polymerase chain reaction-ligation detection reaction (PCR-LDR) technique. The associations of these SNPs with CAD were analyzed using multivariate logistic regression, and the associations with lipid profiles were assessed by the Kruskal-Wallis or Wilcoxon-Mann-Whitney tests. Results The A allele (OR = 1.26, 95% CI = 1.01–1.57, and P = 0.044) and the AA genotype (OR = 3.13, 95% CI = 1.53–6.38, and P = 0.002) of FTO rs1121980 were significantly associated with an elevated risk of CAD. Similarly, the A allele (OR = 1.54, 95% CI = 1.18–2.02, and P = 0.002) and the AA genotype (OR = 5.61, 95% CI = 2.57–12.27, and P

This study investigated the combined application of visible/near-infrared (Vis-NIR) spectroscopy with linear discriminant analysis (LDA) and machine learning (ML) for the classification of apples with different degrees of damage. The Vis-NIR spectral data of apples with different degrees of damage were collected, and the effect of different spectral preprocessing methods on the support vector machine (SVM) classification model was analyzed. LDA was used to reduce the dimensionality of the preprocessed spectral data, and five machine learning models including SVM, random forest (RF), K-nearest neighbor (KNN), decision tree (DT) and extreme gradient boosting (XGBoost) were constructed and compared for the classification of apple damage. The results showed that the SVM model based on preprocessed spectra with Savitzky-Golay (SG) smoothing had the best classification performance, with an accuracy of 87.3%. After dimensionality reduction using LDA, the classification accuracy of all the models was significantly improved, with the highest increase of 16% being observed in the DT model. The KNN model showed the best classification performance, with an accuracy of 96.0% and a precision of 96.4%. This study provides a basis for efficient and accurate assessment of the degree of mechanical damage in apples.

Leontopodium nyingchiense, a new species of Asteraceae from the Xizang (Tibet) Autonomous Region of China, is described and illustrated here. Morphologically, it is most similar to L. lingianum but can be distinguished by the combination of monoecious and dioecious individuals, involucral bracts arranged in 3–5 series (with outer series herbaceous and middle to inner series membranous), fimbriate apices on female florets, rough-edged lobes on male florets, and achenes lacking costae. Phylogenetic analyses further support the separation of this new species from related taxa. Finally, we characterize this new species through both morphological comparisons and molecular analyses.