Zero-shot stance detection based on multi-expert collaboration.

Zero-shot stance detection is pivotal for autonomously discerning user stances on novel emerging topics. This task hinges on effective feature alignment transfer from known to unseen targets. To address this, we introduce a zero-shot stance detection framework utilizing multi-expert cooperative learning. This framework comprises two core components: a multi-expert feature extraction module and a gating mechanism for stance feature selection. Our approach involves a unique learning strategy tailored to decompose complex semantic features. This strategy harnesses the expertise of multiple specialists to unravel and learn diverse, intrinsic textual features, enhancing transferability. Furthermore, we employ a gating-based mechanism to selectively filter and fuse these intricate features, optimizing them for stance classification. Extensive experiments on standard benchmark datasets demonstrate that our model significantly surpasses existing baseline models in performance.

Exploring the potential mechanism of Chinese herbal medicine Fuzi on aplastic anemia based on UHPLC-MS/MS method combined with network pharmacology and molecular docking.

To reveal the potential mechanism of the effect of Chinese Herbal Medicine Fuzi on Aplastic anaemia (AA) according to the network pharmacology approach and molecular docking. According to Ultra High Performance Liquid Chromatography Mass Spectrometry (UHPLC-MS/MS), 146 chemical ingredients of Fuzi were obtained. By SwissADME online system analysis, a total of 55 compounds such as Magnoflorine, Scutellarein, Luteolin and Gingerol may be the main active components of Fuzi and 145 common targets related to AA were predicted. 17 targets such as MAPK1, AKT1 and GRB2 were considered as hub targets. KEGG and GO enrichment analysis obtained 122 signalling pathways and 950 remarkable results. These results suggested that Fuzi exerted pharmacological effects on AA mainly by regulating PI3K-Akt, MAPK and JAK-STAT signalling pathways and epithelial cell proliferation, cell differentiation, regulate energy production and other biological processes. Meanwhile, molecular docking results showed that the hub targets had good binding ability with the main active ingredients.

Sugarcane disease recognition through visible and near-infrared spectroscopy using deep learning assisted continuous wavelet transform-based spectrogram.

Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.

Transformative skeletal motion analysis: optimization of exercise training and injury prevention through graph neural networks.

Introduction: Exercise is pivotal for maintaining physical health in contemporary society. However, improper postures and movements during exercise can result in sports injuries, underscoring the significance of skeletal motion analysis. This research aims to leverage advanced technologies such as Transformer, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs) to optimize sports training and mitigate the risk of injuries. Methods: The study begins by employing a Transformer network to model skeletal motion sequences, facilitating the capture of global correlation information. Subsequently, a Graph Neural Network is utilized to delve into local motion features, enabling a deeper understanding of joint relationships. To enhance the model's robustness and adaptability, a Generative Adversarial Network is introduced, utilizing adversarial training to generate more realistic and diverse motion sequences. Results: In the experimental phase, skeletal motion datasets from various cohorts, including professional athletes and fitness enthusiasts, are utilized for validation. Comparative analysis against traditional methods demonstrates significant enhancements in specificity, accuracy, recall, and F1-score. Notably, specificity increases by ~5%, accuracy reaches around 90%, recall improves to around 91%, and the F1-score exceeds 89%. Discussion: The proposed skeletal motion analysis method, leveraging Transformer and Graph Neural Networks, proves successful in optimizing exercise training and preventing injuries. By effectively amalgamating global and local information and integrating Generative Adversarial Networks, the method excels in capturing motion features and enhancing precision and adaptability. Future research endeavors will focus on further advancing this methodology to provide more robust technological support for healthy exercise practices.

A Prospective Observational Study of Factors Affecting the Change in Quality of Life in Patients With Primary Aldosteronism After Treatment.

OBJECTIVE: To explore the changes in the health-related quality of life (HRQoL) in patients with primary aldosteronism (PA) after standardized treatment and determine the effects of different variables on the change in the HRQoL of patients. METHODS: A total of 116 patients with PA were prospectively included from November 2020 to March 2022. Data were collected at their initial diagnosis and the follow-up after 12 months of treatment, including demographic and clinical data and the scores of the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). The scores of each dimension of SF-36 of patients before and after treatment were compared, and the factors affecting their change in the quality of life were analyzed using multiple linear regression. RESULTS: After standardized treatment, the aldosterone-to-renin ratio (Z = -4.967, P < .001), systolic blood pressure (t = 8.985, P < .001), and diastolic blood pressure (t = 7.233, P < .001) of patients with PA decreased compared with baseline, and hypokalemia was effectively corrected (χ2 = 69.014, P < .001). In terms of quality of life, 6 of 8 dimensions of SF-36 and the total score of SF-36 significantly improved at 1-year follow-up compared with baseline (all P < .05). The results of multiple linear regression showed that the improvement in the HRQoL in patients with PA after standardized treatment was correlated with the change in the blood potassium level (P = .007) and systolic blood pressure (P = .003). CONCLUSION: Correction of hypokalemia and control of diastolic blood pressure are essential factors contributing to the improvement in the HRQoL in patients with PA regardless of the standardized treatment received.

Low plasma renin activity is associated with "Apparently" idiopathic atrial fibrillation.

Background: Previous studies have reported the direct or indirect relationship between the renin-angiotensin-aldosterone system (RAAS) and atrial fibrillation (AF). However, in patients with "apparently" idiopathic AF without possible external influence, whether RAAS is dysregulated at an early stage of AF and its relationship with the recurrence of AF after ablation have not been studied. Methods: This single-center, prospective, case-control study included apparently healthy individuals with AF (the case group) or paroxysmal supraventricular tachycardia (PSVT, the control group) referred for catheter ablation at the same period. The primary outcome was RAAS activation in these two groups. The secondary outcome was the 1-year recurrence of AF after ablation. Results: This study included 51 "apparently" idiopathic AF and 91 patients with PSVT. A greater proportion of patients in the case group had plasma renin activity (PRA) levels < 1 ng/ml/h compared to the control group (25.5 % vs. 7.7 %, P = 0.003). PRA < 1 ng/ml/h was the only factor found to be associated with the diagnose of AF in both the univariate model (odds ratio [OR] 4.11, 95 % confidence interval [CI] 1.52-11.11, P = 0.005) and the model adjusted for age and sex (OR 3.98, 95 % CI 1.20-13.25, P = 0.024). A similar pattern was seen with paroxysmal AF. No significant difference in the components of RAAS was observed between 11 patients with the recurrence of AF and 40 without the recurrence at the 1-year follow-up. Conclusions: This observational study revealed an association between low renin activity and the diagnosis of "apparently" idiopathic AF, particularly paroxysmal AF.

Detecting fa leptin receptor mutation in Zucker rats with tetra-primer amplification-refractory mutation system (ARMS)-PCR.

Due to the genetic mutation (fa) in the gene encoding for leptin receptor, homozygous Zucker rats (fa-/-) develop excessive adiposity and become an experimental animal model in obesity and metabolic-related diseases research. Based on tetra-primer amplification refractory mutation system-polymerase chain reaction (ARMS-PCR), we developed a method to quickly genotype Zucker rats with a mutated fa allele from their wildtype littermates. The three genotypes are clearly discriminated on 2.0% agarose gel. Our method can be used as a reliable tool to set up and maintain the breeding colony in animal facilities as well as assign animals to control and treatment groups based on their genotypes for animal studies.

Characteristic wavelength optimization for partial least squares regression using improved flower pollination algorithm.

Wavelength selection is crucial to the success of near-infrared (NIR) spectroscopy analysis as it considerably improves the generalization of the multivariate model and reduces model complexity. This study proposes a new wavelength selection method, interval flower pollination algorithm (iFPA), for spectral variable selection in the partial least squares regression (PLSR) model. The proposed iFPA consists of three phases. First, the flower pollination algorithm is applied to search for informative spectral variables, followed by variable elimination. Subsequently, the iFPA performs a local search to determine the best continuous interval spectral variables. The interpretability of the selected variables is assessed on three public NIR datasets (corn, diesel and soil datasets). Performance comparison with other competing wavelength selection methods shows that the iFPA used in conjunction with the PLSR model gives better prediction performance, with the root mean square error of prediction values of 0.0096-0.0727, 0.0015-3.9717 and 1.3388-29.1144 are obtained for various responses in corn, diesel and soil datasets, respectively.

A novel clinical-imaging nomogram for predicting primary aldosteronism in patients with hypertension.

This study aimed to develop and validate the accuracy of a clinical-imaging index nomogram in predicting primary aldosteronism (PA) in patients with hypertension. This case-control study enrolled 404 hypertension patients in the First Affiliated Hospital of Nanjing Medical University, China, from April 2017 to September 2021. The patients were randomly divided into the training set (n = 283, 70%) and the validation set (n = 121, 30%). Univariate and multivariate logistic regression analyses were performed to identify independent predictors of PA, which were then used construct a nomogram. The receiver operating characteristic (ROC) curve and calibration plot were drawn to assess the predictive value. The accuracies of our nomogram and other known prediction models were compared using decision curve analyses (DCA). Four significant variables (history of hypokalemia [OR = 2.684, 95% CI: 1.281-5.623, P < 0.001], typical imaging feature [OR = 2.316, 95% CI: 1.166-4.601, P = 0.003], 24 h urine potassium [OR = 0.956, 95% CI: 0.932-0.980, P < 0.001], plasma renin activity [PRA] [OR = 1.423, 95% CI: 1.161-1.744, P < 0.001]) in the multivariate logistic regression analysis were sifted out, and used to build the nomogram. The predictive nomogram yielded an AUC of 0.890 (95% CI, 0.853-0.927) in the training set and 0.860 (95% CI, 0.793-0.927) in the validation set. Predicted and actual probability of PA matched well in the nomogram. Moreover, the DCA showed that the nomogram gained a net benefit in clinical practice in predicting PA when the threshold value was set between 0.1 and 1.0. Our four-variable nomogram was accurate in predicting PA patients and might be introduced into clinical management.

Robust Weak Antilocalization Effect Up to ∼120 K in the van der Waals Crystal Fe5-xGeTe2 with Near-Room-Temperature Ferromagnetism.

The van der Waals Fe5-xGeTe2 is a 3d ferromagnetic metal with a high Curie temperature of 275 K. We report herein the observation of an exceptional weak antilocalization (WAL) effect that can persist up to 120 K in an Fe5-xGeTe2 nanoflake, indicating the dual nature with both itinerant and localized magnetism of 3d electrons. The WAL behavior is characterized by the magnetoconductance peak around zero magnetic field and is supported by the calculated localized nondispersive flat band around the Fermi level. The peak to dip crossover starting around 60 K in magnetoconductance is visible, which could be ascribed to temperature-induced changes in Fe magnetic moments and the coupled electronic band structure as revealed by angle-resolved photoemission spectroscopy and first-principles calculations. Our findings would be instructive for understanding the magnetic exchanges in transition metal magnets as well as for the design of next-generation room-temperature spintronic devices.

New approach for sugarcane disease recognition through visible and near-infrared spectroscopy and a modified wavelength selection method using machine learning models.

The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.

MARCKS is a New Prognostic Biomarker in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most common type of cancers, but there is still a lack of known biomarkers for the effective diagnosis or prognosis of HCC. Myristoylated alanine-rich C-kinase substrate (MARCKS) is a substrate of protein kinase C, which was located in the cell plasma membrane. The purpose of our study was to evaluate the role of MARCKS in HCC. Methods: The role of MARCKS in HCC was explored by bioinformatics and experiment. Results: We demonstrated that MARCKS expression was significantly elevated in HCC datasets of TCGA. MARCKS was up-regulated in tumor sample in HCC. Functional enrichment indicated that MARCKS-related differentially expressed genes (DEGs) were mainly enriched in cell junction tissue, response to growth factors and cell population proliferation. Tumor and ECM-receptor interactions related pathways were enriched by the KEGG. MARCKS expression in HCC patients was higher in females, younger individuals, and those at worse clinical stages. Cox regression analysis showed that MARCKS expression was a risk factor for overall survival and disease-specific survival of patients. Conclusion: MARCKS was up-regulated in HCC, may play a crucial role in HCCs, and has prognostic value for clinical outcomes.

The biological functions of protein S-sulfhydration in eukaryotes and the ever-increasing understanding of its effects on bacteria.

As a critical endogenous signaling molecule, hydrogen sulfide may induce reversible post-translational modifications on cysteine residues of proteins, generating a persulfide bond known as S-sulfhydration. A systemic overview of the biofunctions of S-sulfhydration will equip us better to characterize its regulatory roles in antioxidant defense, inflammatory response, and cell fate, as well as its pathological mechanisms related to cardiovascular, neurological, and multiple organ diseases, etc. Nevertheless, the understanding of S-sulfhydration is mostly built on mammalian cells and animal models. We subsequently summarized the mediation effects of this specific post-transcriptional modification on physiological processes and virulence in bacteria. The high-sensitivity and high-throughput detection technologies are required for studying the signal transduction mechanism of H2S and protein S-sulfhydration modification. Herein, we reviewed the establishment and development of different approaches to assess S-sulfhydration, including the biotin-switch method, modified biotin-switch method, alkylation-based cysteine-labelled assay, and Tag-switch method. Finally, we discussed the limitations of the impacts of S-sulfhydration in pathogens-host interactions and envisaged the challenges to design drugs and antibiotics targeting the S-sulfhydrated proteins in the host or pathogens.

Neutrophil extracellular traps: A novel target for the treatment of stroke.

Stroke is a threatening cerebrovascular disease caused by thrombus with high morbidity and mortality rates. Neutrophils are the first to be recruited in the brain after stroke, which aggravate brain injury through multiple mechanisms. Neutrophil extracellular traps (NETs), as a novel regulatory mechanism of neutrophils, can trap bacteria and secret antimicrobial molecules, thereby degrading pathogenic factors and killing bacteria. However, NETs also exacerbate certain non-infectious diseases by activating autoimmune or inflammatory responses. NETs have been found to play important roles in the pathological process of stroke in recent years. In this review, the mechanisms of NETs formation, the physiological roles of NETs, and the dynamic changes of NETs after stroke are summarized. NETs participate in stroke through various mechanisms. NETs promote the coagulation cascade and interact with platelets to induce thrombosis. tPA induces the degranulation of neutrophils to form NETs, leading to hemorrhagic transformation and thrombolytic resistance. NETs aggravate stroke by mediating inflammation, atherosclerosis and vascular injury. In addition, the regulation of NETs in stroke, the potential of NETs as biomarker and the treatment of stroke targeting NETs are discussed. The increasing evidences suggest that NETs may be a potential target for stroke treatment. Inhibition of NETs formation or promotion of NETs degradation plays protective effects in stroke. However, how to avoid the adverse effects of NETs-targeted therapy deserves further study. In summary, this review provides a reference for the pathogenesis, drug targets, biomarkers and drug development of NETs in stroke.

Light-Induced Tunable Ferroelectric Polarization in Dipole-Embedded Metal-Organic Framework.

Reversible regulation of ferroelectric polarization possesses great potentials recently in bionic neural networks. Photoinduced cis-trans isomers have changeable dipole moments, but they cannot be directed to some specific orientation. Here, we construct a host-guest composite structure which consists of a porous ferroelectric metal (Ni)-organic framework [Ni(DPA)2] as host and photoisomer, azobenzene (AZB), as guest molecules. When AZB molecules are embedded in the nanopores of Ni(DPA)2 in the form of a single molecule, polarization strength tunable regulation is realized after ultraviolet irradiation of 365 and 405 nm via cis-trans isomerism transformation of AZB. An intrinsic built-in field originating from the distorted {NiN2O4} octahedra in Ni(DPA)2 directs the dipole moments of AZB to the applied electric field. As a result, the overlapped ferroelectric polarization strength changes with content of cis-AZB after ultraviolet and visible irradiation. Such a connection of ferroelectric Ni(DPA)2 structure with cis-trans isomers provides an important strategy for regulating the ferroelectric polarization strength.

The Influence of Repeated Drop Jump Training on Countermovement Jump Performance.

Countermovement jump (CMJ) is used to assess athletic performance of the lower limbs. Drop jump (DJ) is an effect training method that can improve athlete's jumping performance. The main purpose of this study is to explore the effects of different drop jump heights (DJH)30, DJH40, and DJH50 cm for 250 drop jumps (DJs250) on CMJ. Eighteen male athletes were selected as subjects. After the 50th, 100th, 150th, 200th, and 250th DJs, perform 5 groups of CMJ (the average of 3 times for each group) and record them as the 50th, 100th, 150th, 200th, and 250th CMJ jumps (CMJs50, CMJs100, CMJs150, CMJs200, and CMJs250). The BTS motion capture system and two force plates are used to record data. The MATLAB software was used to analyze data through one-way ANOVA repeated measures. If there is a significant difference, the LSD method is used for post hoc comparison. Jump height (JH), contact time (CT), reaction intensity index (RSI), average rate of force development (ARFD), left average rate of force development (LARFD), and right average rate of force development (RARFD) of CMJs50, CMJs100, CMJs150, and CMJs200 at DJH50 were greater than those at DJH40 and DJH30 (all p < 0.05). DJH50 height and DJs200 training times can improve SSC mechanism and improve athlete CMJ performance.

Long-term administration of salvianolic acid A promotes endogenous neurogenesis in ischemic stroke rats through activating Wnt3a/GSK3ß/ß-catenin signaling pathway.

Stroke is the major cause of death and disability worldwide. Most stroke patients who survive in the acute phase of ischemia display various extents of neurological deficits. In order to improve the prognosis of ischemic stroke, promoting endogenous neurogenesis has attracted great attention. Salvianolic acid A (SAA) has shown neuroprotective effects against ischemic diseases. In the present study, we investigated the neurogenesis effects of SAA in ischemic stroke rats, and explored the underlying mechanisms. An autologous thrombus stroke model was established by electrocoagulation. The rats were administered SAA (10 mg/kg, ig) or a positive drug edaravone (5 mg/kg, iv) once a day for 14 days. We showed that SAA administration significantly decreased infarction volume and vascular embolism, and ameliorated pathological injury in the hippocampus and striatum as well as the neurological deficits as compared with the model rats. Furthermore, we found that SAA administration significantly promoted neural stem/progenitor cells (NSPCs) proliferation, migration and differentiation into neurons, enhanced axonal regeneration and diminished neuronal apoptosis around the ipsilateral subventricular zone (SVZ), resulting in restored neural density and reconstructed neural circuits in the ischemic striatum. Moreover, we revealed that SAA-induced neurogenesis was associated to activating Wnt3a/GSK3ß/ß-catenin signaling pathway and downstream target genes in the hippocampus and striatum. Edaravone exerted equivalent inhibition on neuronal apoptosis in the SVZ, as SAA, but edaravone-induced neurogenesis was weaker than that of SAA. Taken together, our results demonstrate that long-term administration of SAA improves neurological function through enhancing endogenous neurogenesis and inhibiting neuronal apoptosis in ischemic stroke rats via activating Wnt3a/GSK3ß/ß-catenin signaling pathway. SAA may be a potential therapeutic drug to promote neurogenesis after stroke.

Prognostic value of miR-1181 in non-small cell lung cancer and its regulatory effect on tumor progression.

Non-small cell lung cancer (NSCLC) is one of the most important causes of cancer-related death. miR-1181 has been reported to have roles in various cancer types and its function in the progression of NSCLC was investigated in the present study. A total of 118 patients with NSCLC were recruited and their tumor tissues were collected. The expression of miR-1181 in NSCLC tissues and cells was detected by reverse transcription-quantitative PCR. The prognostic value of miR-1181 was evaluated by Kaplan-Meier and Cox regression analysis and the roles of miR-1181 in cell proliferation, migration and invasion of NSCLC were analyzed by Cell Counting Kit-8 and Transwell assays. miR-1181 was indicated to be upregulated in NSCLC tissues and cell lines, and to be associated with lymph node metastasis and the TNM stage of patients. Patients with high miR-1181 expression had a poorer prognosis than those with low miR-1181 expression. miR-1181 levels and TNM stage were determined to be two independent prognostic factors for NSCLC. In addition, overexpression of miR-1181 exerted enhancing effects on cell proliferation, migration and invasion of NSCLC, while its knockdown inhibited these cellular processes. In conclusion, upregulation of miR-1181 in NSCLC was associated with lymph node metastasis and TNM stage of patients and was indicative of poor prognosis. miR-1181 was indicated to exert promoting effects on cell proliferation, migration and invasion of NSCLC cells and to be involved in tumor progression, providing novel insight for the development of biomarkers and therapies for NSCLC.