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为促进汉麻仁多肽在功能食品及特医食品领域的应用，利用斑马鱼模型研究汉麻仁多肽的降尿酸作用及可能的机制，分析降尿酸活性肽组分的氨基酸序列。采用10 mmol/L尿酸酶活性抑制剂氧嗪酸钾和50 μmol/L尿酸合成前体黄嘌呤钠盐饲喂斑马鱼20 h，建立斑马鱼高尿酸血症模型。以别嘌呤醇为阳性对照，以不同质量浓度的汉麻仁多肽饲喂斑马鱼，测定斑马鱼的尿酸荧光强度、次黄嘌呤鸟嘌呤磷酸核糖基转移酶（HPRT1）和有机阴离子转运蛋白（OAT1）基因的mRNA表达，利用液相色谱-串联质谱技术对降尿酸活性肽组分氨基酸序列进行分析。结果表明：汉麻仁多肽能够通过上调HPRT1和OAT1基因表达，有效降低高尿酸斑马鱼的尿酸水平；汉麻仁多肽活性组分中丰度较高的8个峰对应的肽段分子质量分别为499.23、578.24、628.27、653.29、715.31、918.43、982.41、1 128.49 Da，其氨基酸序列分别为PGTPE、MPDDV、TTSYTG、TPHWN、NNGDSPL、DDFNPRR、NPHDEFQP、YHSYLCKTD。综上，汉麻仁多肽可作为功能性成分用于降尿酸相关功能食品和保健品的开发。In order to promote the application of hemp kernel polypeptide in functional foods and specialty medical foods, the uric acid-lowering effect and its possible mechanisms of hemp kernel polypeptide in zebrafish model were investigated, and the amino acid sequences of polypeptide components with uric acid-lowering activity were analyzed. The zebrafish hyperuricemia model was established by feeding zebrafish with an uricase inhibitor potassium oxonate at 10 mmol/L and uric acid synthesis precursor xanthine sodium salt at 50 μmol/L for 20 h. Hemp kernel polypeptide with different mass concentrations administered to the zebrafish, with allopurinol as a positive control. The fluorescence intensity of uric acid, and mRNA expression of hypoxanthine guanine-phosphoribosyltransferase(HPRT1) and organic anion transporter(OAT1) gene in zebrafish were measured. The amino acid sequences of the active polypeptides were analyzed by liquid chromatography-mass spectrometry. The results showed that hemp kernel polypeptide effectively reduced uric acid level of zebrafish through up-regulating HPRT1 and OAT1 gene expression. The molecular weight of eight polypeptides with high abundance in hemp kernel polypeptide were 499.23, 578.24, 628.27, 653.29, 715.31, 918.43, 982.41, 1 128.49 Da, and their amino acid sequences were PGTPE, MPDDV, TTSYTG, TPHWN, NNGDSPL, DDFNPRR, NPHDEFQP, YHSYLCKTD, respectively. In conclusion, the hemp kernel polypeptide can be used as functional components in the development of functional food and health care products related to lowering uric acid.

Abstract Background The parathyroid-hormone-related peptide has been shown in earlier studies to be secreted by pancreatic neuroendocrine tumors, although its secretion by gastroenteropancreatic neuroendocrine tumors is very rare. In contrast, a number of solid tumors, such as lung cancer and renal cell carcinoma, have frequently been shown to secrete parathyroid-hormone-related peptide. Case presentation We describe a case report of a 53-year-old Canadian white patient with refractory parathyroid-hormone-related-peptide-mediated hypercalcemia associated with metastatic pancreatic neuroendocrine tumors and review the available research. Our patient had severe hypercalcemia initially refractory to treatment. Computed tomography scan of the abdomen revealed a pancreatic lesion and multiple hepatic metastases. A liver biopsy confirmed metastatic pancreatic neuroendocrine tumor expressing parathyroid-hormone-related peptide. Circulating parathyroid-hormone-related peptide levels were at the upper limit of normal preoperatively and decreased sharply postoperatively following debulking of the tumor. Blood calcium levels eventually normalized on long-term administration of the somatostatin analog lanreotide in combination with denosumab. Conclusions We describe a case with parathyroid-hormone-related-peptide-mediated hypercalcemia in a pancreatic neuroendocrine tumor (parathyroid-hormone-related peptide tumor). Refractory hypercalcemia was likely the result of parathyroid-hormone-related peptide overproduction by the tumor and resolved following normalization of parathyroid-hormone-related peptide levels.

In contemporary society, the rapid progression of urbanization and technological advancements has led to a substantial increase in the number of vehicles, consequently elevating the rate of traffic accidents. To minimize the human and economic losses resulting from these accidents, extensive research has been conducted over the past decades. Recent studies utilizing Grid Maps and time-series methods have shown promising results in identifying factors related to traffic accidents and predicting accident occurrence rates. However, most existing research employs data without thoroughly analyzing its definitive correlation with traffic accidents, focusing solely on an overarching integration process or exclusively considering road conditions, while neglecting environmental factors surrounding the roads. Therefore, this paper focuses on a detailed analysis of factors contributing to traffic accidents in Seoul, the capital city of South Korea, limiting the study period from January 2020 to December 2021. The data encompasses various aspects such as traffic accidents, weather conditions, standard node links, traffic speed, Points of Interest (POI), solar altitude and azimuth, speed bumps, and traffic surveillance cameras. Most of this data is available from national public institutions, with some being computed through specific formulas. Since weather and POI data possess a wide range of features, a Pearson correlation analysis is conducted to extract features relevant to traffic accidents. The extracted features are then compiled and normalized to facilitate the deep learning model’s training, thereby constructing a comprehensive dataset.

Abstract Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background. In a Chinese seven-center cohort, data from 347 subjects were analyzed. Our one-step model incorporated normal tissue/organ labels to provide contextual information, offering a solution for tumors with complex backgrounds. To address privacy concerns, we utilized a lightweight deep neural network suitable for hospital deployment. The final model achieved an accuracy of 85.71% for MPNST diagnosis in the validation cohort and 84.75% accuracy in the independent test set, outperforming another classic two-step model. This success suggests potential for AI models in screening other whole-body primary/metastatic tumors.

Growing evidence has linked extreme temperature with neuropsychiatric disorders under climate warming with frequent extreme heat events over the past decades, while cognitive performance in relation to heat exposure remains largely unstudied, particularly in populations at high vulnerability to climate risks (e.g., China). Based on five survey waves of a nationwide dynamic cohort (2011–2020), we analyzed 47,825 cognitive test records from 14,729 respondents aged 45+ years across 126 Chinese cities. Global cognitive performance and its two dimensions (episodic memory and mental status) was measured using standardized questionnaires. Temperature exposure prior to cognitive tests was assessed using both average temperatures and heat days exceeding predefined temperature thresholds. Linear mixed-effects models were utilized to examine the relationship between high temperature exposure and cognitive function. This study revealed consistent evidence for heat-related declines in global cognitive performance and episodic memory across multiple exposure-window analyses, while robust associations were observed solely during prolonged exposure periods (more than 90 d) for mental status. For each 1-°C rise in annual mean temperature within 1 year prior to investigation, cognitive scores declined by 0.058 (95% CI: −0.079, −0.037) points for global performance, 0.033 (95% CI: −0.048, −0.018) points for episodic memory, and 0.025 (95% CI: −0.038, −0.013) points for mental status, respectively. Similar findings were seen in analyses using heat exposure days defined by multiple temperature percentiles, linking per 10-d increase in heat duration to reduced global cognitive scores ranging from −0.142 (95% CI: −0.214, −0.070) to −0.168 (95% CI: −0.254, −0.082). Despite varied evidence by heat exposure metrics and cognitive dimensions, stratified analyses suggested possibly higher susceptibility among females, less-educated, and urban-dwelling residents to heat-related cognitive impairment. These results provided suggestive evidence for the role of exposure to heat in triggering cognitive impairment in middle-aged and older individuals. This finding may be crucial in developing public health strategies for managing climate change risks of neurobehavioral disorders in a healthy aging society.

Abstract Microplastics (MP) in the environment has attracted extensive attention due to their ubiquitous occurrence and potential toxic effects; but less attention has been given to the removal technologies of MP in different environmental media. This review covers the sources, ecotoxicities, and removal technologies of MP in soil and water environments reported in previous studies to derive the future research directions for MP removal technologies. Previous studies reported various sources contribute to MP present in soil and water environments, indicating that the source control may be a better strategy than contaminated media treatment for management of MP contamination. Similarly, different terrestrial and aquatic organisms have been used to determine the toxic effects of MP with different characteristics. Previous ecotoxicity studies of MP on soil organisms cover only a small number of species compared to that of MP on aquatic organisms. Therefore, further studies are necessary to investigate the ecotoxic effects of MP on a broader range of soil organisms. Also, since the characteristics of MP are diverse, more ecotoxicity studies in both water and soil environments are required. The review provides an overview of various removal technologies for MP in soil and water and identifies gaps in existing studies. Although more studies have been conducted for removal of MP in water than in soil, they remain in their early stages. More studies are needed for removal of MP removal in soil than in water. With MP in water, most studies have been carried out on a lab scale with artificial wastewaters, thus, studies with natural waters in a pilot or field scale are required. Overall, this review highlights the need for further studies on MP removal technologies for real world applications.

Abstract Limited whole genome sequencing (WGS) studies in Asian populations result in a lack of representative reference panels, thus hindering the discovery of ancestry-specific variants. Here, we present the South and East Asian reference Database (SEAD) panel ( https://imputationserver.westlake.edu.cn/ ), which integrates WGS data for 11,067 individuals from various sources across 17 Asian countries. The SEAD panel, comprising 22,134 haplotypes and 88,294,957 variants, demonstrates improved imputation accuracy for South Asian populations compared to 1000 Genomes Project, TOPMed, and ChinaMAP panels, with a higher proportion of well-imputed rare variants. For East Asian populations, SEAD shows concordance comparable to ChinaMAP, but outperforming TOPMed. Additionally, we apply the SEAD panel to conduct a genome-wide association study for total hip (Hip) and femoral neck (FN) bone mineral density (BMD) traits in 5369 genotyped Chinese samples. The single-variant test suggests that rare variants near SNTG1 are associated with Hip BMD (rs60103302, MAF = 0.0092, P = 1.67 × 10−7), and variant-set analysis further supports the association (P slide_window = 9.08 × 10−9, P gene_centric = 5.27 × 10−8). This association was not reported previously and can only be detected by using Asian reference panels. Preliminary in vitro experiments for one of the rare variants identified provide evidence that it upregulates SNTG1 expression, which could in turn inhibit the proliferation and differentiation of preosteoblasts.

Abstract The Nuclear factor erythroid 2–related factor (Nrf2)/ Heme oxygenase-1 (HO-1) pathway, known for its significant role in regulating innate antioxidant defense mechanisms, is increasingly being recognized for its potential in neuroprotection studies. Derived from bee venom, melittin's neuroprotective effects have raised interest. This study confirmed that melittin specificity upregulated the weakened Nrf2/HO-1 signaling in mice brain. Interestingly, we also revealed melittin’s efficient tactic, as it was suggested to first restore redox balance and then gradually stabilized other regulations of the mouse hippocampus. Using a neuro-stress-induced scopolamine model, chromatography and mass spectrometry analysis revealed that melittin crossed the compromised blood–brain barrier and accumulated in the hippocampus, which provided the chance to interact directly to weakened neurons. A wide range of improvements of melittin action were observed from various tests from behavior Morris water maze, Y maze test to immune florescent staining, western blots. As we need to find out what is the focus of melittin action, we conducted a careful observation in mice which showed that: the first signs of changes, in the hippocampus, within 5 h after melittin administration were the restoration of the Nrf2/HO-1 system and suppression of oxidative stress. After this event, from 7 to 12.5 h after administration, a diversity of conditions was all ameliorated: inflammation, apoptosis, neurotrophic factors, cholinergic function, and tissue ATP level. This chain reaction underscores that melittin focus was on redox balance's role, which revived multiple neuronal functions. Evidence of enhancement in the mouse hippocampus led to further exploration with hippocampal cell line HT22 model. Immunofluorescence analysis showed melittin-induced Nrf2 translocation to the nucleus, which would initiating the translation of antioxidant genes like HO-1. Pathway inhibitors pinpointed melittin's direct influence on the Nrf2/HO-1 pathway. 3D docking models and pull-down assays suggested melittin's direct interaction with Keap1, the regulator of the Nrf2/HO-1 pathway. Overall, this study not only highlighted melittin specifically effect on Nrf2/HO-1, thus rebalancing cellular redox, and also showed that this is an effective multi-faceted therapeutic strategy against neurodegeneration.

Abstract Early and precise diagnosis of craniosynostosis (CSO), which involves premature fusion of cranial sutures in infants, is crucial for effective treatment. Although computed topography offers detailed imaging, its high radiation poses risks, especially to children. Therefore, we propose a deep-learning model for CSO and suture-line classification using 2D cranial X-rays that minimises radiation-exposure risks and offers reliable diagnoses. We used data comprising 1,047 normal and 277 CSO cases from 2006 to 2023. Our approach integrates X-ray-marker removal, head-pose standardisation, skull-cropping, and fine-tuning modules for CSO and suture-line classification using convolution neural networks (CNNs). It enhances the diagnostic accuracy and efficiency of identifying CSO from X-ray images, offering a promising alternative to traditional methods. Four CNN backbones exhibited robust performance, with F1-scores exceeding 0.96 and sensitivity and specificity exceeding 0.9, proving the potential for clinical applications. Additionally, preprocessing strategies further enhanced the accuracy, demonstrating the highest F1-scores, precision, and specificity. A qualitative analysis using gradient-weighted class activation mapping illustrated the focal points of the models. Furthermore, the suture-line classification model distinguishes five suture lines with an accuracy of > 0.9. Thus, the proposed approach can significantly reduce the time and labour required for CSO diagnosis, streamlining its management in clinical settings.

Abstract Background Previous research has shown that the use of renin-angiotensin system (RAS) blockers is linked to a lower prevalence of posttraumatic stress disorder (PTSD), but longitudinal studies are scarce. We aimed to estimate the association between the use of RAS blockers and the risk of PTSD among individuals taking antihypertensive medications. Methods This longitudinal study included participants aged 40–69 from the UK Biobank. Exposure data were obtained from the initial assessment (2006–10), while outcome data were obtained from the online mental health questionnaire administered 6–11 years later (2016–17). We included participants who were under antihypertensive treatment and did not have a prior diagnosis of PTSD before the initial assessment. Use of RAS blockers was defined as self-reported regular use, at the initial assessment, of angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB). Among participants who experienced adverse life experiences, cases of probable PTSD were defined with the six-item PTSD Checklist-Civilian version score ≥ 14. Logistic regression with inverse probability of treatment weighting was used to estimate the odds ratios (ORs) and 95% confidence interval (CI) for the association between RAS blocker use and the risk of probable PTSD. Results Of the 15,954 participants (mean age = 59.9 years; 42.6% women) under antihypertensive treatment with no prior history of PTSD at the initial assessment, 64.5% were taking RAS blockers. After a mean follow-up of 7.5 years, 1,249 (7.8%) were newly identified with probable PTSD. RAS blocker users had a lower risk of probable PTSD than RAS blocker non-users (OR = 0.84 [95% CI: 0.75–0.94]), whereas the use of other antihypertensive medications showed no such association (users vs. non-users; calcium channel blockers, OR = 0.99 [95% CI: 0.88–1.11]; beta-blockers, 1.20 [1.08–1.34]; and thiazide-related diuretics, 1.15 [1.03–1.29]). The association between probable PTSD risk and the use of ACEi vs. ARB showed no significant difference (p = 0.96). Conclusions Among individuals under antihypertensive treatment, the use of RAS blockers was associated with a decreased risk of probable PTSD. This added benefit of RAS blockers should be considered in the selection of antihypertensive medications.

The image attributes of sports star athletes demonstrate positive effects on sports fans' psychological factors. In the context of golf, fans are also influenced by the players they support, experiencing a range of emotions. Therefore, this study aimed to examine the effects of the image attributes of golf star athletes—supported by adult amateur golfers—on the desire for sports participation and the intention to continue. This study involved 356 adult amateur golfers in Korea (253 male, 103 female). Questionnaires were used to measure participants' perceptions of image attributes, their desire for sports participation, and their intention to continue participating in the sport. The data were analyzed using descriptive statistics, validity and reliability, and multiple regression analyses. First, the image attributes factors (physical appearance, performance ability, and moral image) all had a significant effect on both the desire for sports participation and the intention to continue participating in the sport. However, performance ability did not impact affective desire. Second, while cognitive and behavioral desires significantly influenced the intention to continue participating, affective desire did not have an effect. In conclusion, fans identify diverse athletes' images (e.g., physical appearance, performance, and personality) through attending or watching sports games, and positive sports star image attributes enhance sports fandom, participation desire, and continuation intention. The results of this study indicate a close relationship between golf fans' perceptions of players' image attributes and their psychological factors. These findings could help improve fans' quality of life and provide valuable insights for understanding their fandom.

Physical, chemical, and biological processes within sewers significantly alter sewage composition during conveyance. This leads to the formation of sulfide and methane—compounds that contribute to sewer corrosion and greenhouse gas emissions. Reliable modeling of these compounds is essential for effective sewer management, but the development of machine learning (ML) models is hindered by differences in data accessibility and sampling frequencies of water quality variables. Here we present a mechanistically enhanced hybrid (ME-Hybrid) model that combines mechanistic modeling with data-driven approaches. This model harmonizes datasets with varying sampling frequencies and generates synthetic samples for ML training, thereby enhancing the monitoring of methane and sulfide in sewers. The optimal ME-Hybrid model integrates the backpropagation neural network with mechanistic frequency harmonization. We demonstrate that the ME-Hybrid model outperforms pure ML and linear interpolation in capturing fluctuating trends and extremes of sulfide concentrations, achieving a coefficient of determination (R2) of 0.94. Synthetic samples generated through mechanistic augmentation closely approximate real samples in modeling performance, statistical distribution, and data structure. This enables the model to maintain high predictive accuracy (R2 > 0.76) for sulfide even when trained on only 50% of the dataset. Additionally, the ME-Hybrid model successfully assesses sewer methane concentrations with an R2 of 0.94, validating its applicability and generalization ability. Our results provide a reliable methodological framework for modeling and prediction under data scarcity. By facilitating better monitoring and management of sewer systems, the ME-Hybrid model aids in the development of strategies that minimize environmental impacts, enhance urban resilience, and ultimately lead to sustainable urban water systems.

Oral levothyroxine (LT4) is prescribed worldwide for hypothyroidism. Bariatric surgery for patients with obesity has shown a substantial, long-term weight loss and considerable improvement of obesity-related diseases. LT4 malabsorption represents a significant cause of refractory hypothyroidism, well known after malabsorptive bariatric surgery such as Roux-en-Y gastric bypass. However, few studies have shown an increase in oral LT4 needed after sleeve gastrectomy. We present a 47-year-old woman with class III obesity and a history of papillary thyroid cancer, with an excellent biochemical and structural response after total thyroidectomy and radioactive iodine. She underwent sleeve gastrectomy 3 years later and developed refractory hypothyroidism despite taking high doses of oral LT4 and ensuring compliance. The T4 absorption test confirmed gastrointestinal LT4 malabsorption. She was initiated on intramuscular LT4 and oral T3 (liothyronine) with improving symptoms and levels of thyroid-stimulating hormone.

The involvement of jasmonic acid (JA) in the rice’s response to cadmium (Cd) stress is well recognized, though the underlying mechanisms remain unclear. In this study, exposure to Cd stress rapidly elevated endogenous JA concentrations in rice roots, meanwhile, a mutant coleoptile photomorphogenesis 2 (cpm2) which produces less JA, was more sensitive to Cd stress than its wild type (WT). JA mitigated Cd toxicity by decreasing Cd absorption in root cell wall and shoot, which was achieved by up-regulating the expression of the Cd-chelation and efflux-related genes such as OsHMA3, OsABCG36 and OsCAL1; down-regulating the transcript level of the Cd uptake and translocation-related genes, including OsHMA2, OsCCX2, OsNRAMP1/5, and OsZIP5/7. Additionally, a reduction in hemicellulose content was observed in the root cell wall. Further analysis indicated that the mitigation effect of JA on Cd accumulation was dependent on the inhibition of nitric oxide (NO) synthesis, as the NO donor SNP could diminish this effect. In summary, JA effectively reduced Cd content in rice by modulating the cell wall’s capacity for Cd uptake, potentially through reducing the production of NO.

Abstract This study was conducted to provide evidence, using in vitro and in silico testing methods, regarding the adverse effects of iprodione, a representative dichlorophenyl dicarboxamide fungicide, on the endocrine system. In the present study, we used the HeLa9903 stably transfected transactivation assay (OECD TG 455), 22Rv1/MMTV_GR‒KO androgen receptor transcriptional activation assay (OECD TG 458), and toxicity prediction using VEGA QSAR. Our results showed that iprodione had no estrogen receptor antagonistic or androgen receptor agonistic effects; however, iprodione was determined to be an estrogen receptor agonist (log PC10 value is less than − 9) and androgen receptor antagonist (log IC30 value is − 4.58) without intrinsic toxicity against the human cell lines used in this study. VEGA QSAR was used to evaluate five substances with structures similar to that of iprodione. Among them, four chemicals were found to have positive androgen receptor and aromatase activities and have been observed to be developmental toxicants. These results suggest that iprodione regulates steroid hormone receptor interactions and is a potential reproductive toxicant.

Abstract The search of the new physics (NP) beyond the Standard Model is one of the most important topics in current high energy physics. With the increasing luminosities at the colliders, the search for NP signals requires the analysis of more and more data, and the efficiency in data processing becomes particularly important. As a machine learning algorithm, support vector machine (SVM) is expected to to be useful in the search of NP. Meanwhile, the quantum computing has the potential to offer huge advantages when dealing with large amounts of data, which suggests that quantum SVM (QSVM) is a potential tool in future phenomenological studies of the NP. How to use SVM and QSVM to optimize event selection strategies to search for NP signals are studied in this paper. Taking the tri-photon process at a muon collider as an example, it can be shown that the event selection strategies optimized by the SVM and QSVM are effective in the search of the dimension-8 operators contributing to the anomalous quartic gauge couplings.

Abstract Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.

Abstract Background Rickettsia and related diseases have been identified as significant global public health threats. This study involved comprehensive field and systematic investigations of various rickettsial organisms in Yunnan Province. Methods Between May 18, 2011 and November 23, 2020, field investigations were conducted across 42 counties in Yunnan Province, China, encompassing small mammals, livestock, and ticks. Preliminary screenings for Rickettsiales involved amplifying the 16S rRNA genes, along with additional genus- or species-specific genes, which were subsequently confirmed through sequencing results. Sequence comparisons were carried out using the Basic Local Alignment Search Tool (BLAST). Phylogenetic relationships were analyzed using the default parameters in the Molecular Evolutionary Genetics Analysis (MEGA) program. The chi-squared test was used to assess the diversities and component ratios of rickettsial agents across various parameters. Results A total of 7964 samples were collected from small mammals, livestock, and ticks through Yunnan Province and submitted for screening for rickettsial organisms. Sixteen rickettsial species from the genera Rickettsia, Anaplasma, Ehrlichia, Neoehrlichia, and Wolbachia were detected, with an overall prevalence of 14.72%. Among these, 11 species were identified as pathogens or potential pathogens to humans and livestock. Specifically, 10 rickettsial organisms were widely found in 42.11% (24 out of 57) of small mammal species. High prevalence was observed in Dremomys samples at 5.60%, in samples from regions with latitudes above 4000 m or alpine meadows, and in those obtained from Yuanmou County. Anaplasma phagocytophilum and Candidatus Neoehrlichia mikurensis were broadly infecting multiple genera of animal hosts. In contrast, the small mammal genera Neodon, Dremomys, Ochotona, Anourosorex, and Mus were carrying individually specific rickettsial agents, indicating host tropism. There were 13 rickettsial species detected in 57.14% (8 out of 14) of tick species, with the highest prevalence (37.07%) observed in the genus Rhipicephalus. Eight rickettsial species were identified in 2375 livestock samples. Notably, six new Rickettsiales variants/strains were discovered, and Candidatus Rickettsia longicornii was unambiguously identified. Conclusions This large-scale survey provided further insight into the high genetic diversity and overall prevalence of emerging Rickettsiales within endemic hotspots in Yunnan Province. The potential threats posed by these emerging tick-borne Rickettsiales to public health warrant attention, underscoring the need for effective strategies to guide the prevention and control of emerging zoonotic diseases in China. Graphical Abstract

Abstract The behavior of quark matter with both external electric field and chiral chemical potential is theoretically and experimentally interesting to consider. In this paper, the case of simultaneous presence of imaginary electric field and chiral chemical potential is investigated using the lattice QCD approach with $$N_f=1+1$$ N f = 1 + 1 dynamical staggered fermions. We find that overall both the imaginary electric field and the chiral chemical potential can exacerbate chiral symmetry breaking, which is consistent with theoretical predictions. However we also find a non-monotonic behavior of chiral condensation at specific electric field strengths and chiral chemical potentials. The behavior of Polyakov loop in the complex plane is not significantly affected by chiral chemical potential in the region of the parameters considered.

IntroductionFunctional traits of desert plants exhibit remarkable responsiveness, adaptability and plasticity to environmental heterogeneity.MethodsIn this study, we measured six crucial plant functional traits (leaf carbon, leaf nitrogen, leaf phosphorus, leaf thickness, chlorophyll concentration, and plant height) and employed exemplar analysis to elucidate the effects of soil environmental heterogeneity on intraspecific traits variation in the high-moisture-salinity and low-moisture-salinity habitats of the Ebinur LakeWetland National Nature Reserve.ResultsThe results showed that (1) The soil moisture and electrical conductivity heterogeneity showed significant differences between the two moisture-salinity habitats. Moreover, soil nutrient in high moisture-salinity habitat exhibited higher heterogeneity than in low moisture-salinity habitat. The order of intraspecific trait variation among different life forms was herbs > shrubs > trees in both the soil moisture-salinity habitats. (2) At the community level, intraspecific variation of leaf carbon, nitrogen, plant height and chlorophyll content in high moisture-salinity habitat was higher than that in low moisture-salinity habitat, while the opposite was true for leaf thickness and leaf phosphorus content. (3) Our findings revealed a positive impact of soil heterogeneity on intraspecific traits variation. In high moisture-salinity habitat, the heterogeneity of soil organic carbon had the highest explanatory power for intraspecific traits variation, reaching up to 20.22%, followed by soil total nitrogen (9.55%) and soil total phosphorus (3.49%). By comparison, in low-moisture-salinity habitat, the heterogeneity of soil moisture alone contributes the highest explanatory power for intraspecific traits variation in community-level, reaching up to 13.89%, followed by the heterogeneity of soil total nitrogen (3.76%).DiscussionThis study emphasizes the differences in soil heterogeneity and intraspecific trait variation among plant life forms under various soil moisture-salinity habitats and confirms the significant promoting effect of soil heterogeneity on intraspecific trait variation of desert plant. Our findings provide valuable theoretical basis and reference for predicting plant adaptation strategies under environmental change scenarios.

Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.

Lithium-ion batteries have notable benefits in their elevated energy power, density, and efficiency. However, the deterioration of capacity remains a prominent concern throughout their usage. Specifically, calculating the remaining capacity is essential for guaranteeing safe operations, which has prompted the creation of accurate capacity estimate models. Battery capacity estimation is one of the critical functions in the Battery Management System (BMS), and battery capacity indicates a battery's maximum storage capability, which is vital for the battery State of Charge (SOC) estimation and lifespan management. In order to increase the accuracy of battery capacity prediction, we provide in this work an improved Extreme Gradient Boosting (XGBoost) model that has been tuned by Random Search hyperparameter tweaking. Our proposed method achieved a R2 value of 0.9931%, a Mean Squared Error (MSE) of 0.0068, and a Root Mean Squared Error (RMSE) of 0.0825 when applied to an extensive dataset. These outcomes indicate significant improvements compared to traditional regression models. Moreover, the suggested technique has yielded superior accuracy and resilience in estimating lithium-ion battery capacity.

Abstract Chiral light emission plays a key role in sensing, tomography, quantum communication, among others. Whereas, achieving highly pure, tunable chirality emission across a broad spectrum currently presents significant challenges. Free‐electron radiation emerges as a promising solution to surpass these barriers, especially in hard‐to‐reach regimes. Here, chiral free‐electron radiation is presented by exploiting the spin‐momentum locking (SML) property of spoof surface plasmons (SSPs). When the phase velocity of free electrons matches that of the SSPs, the SSPs can be excited. By implementing wavenumber compensation through perturbations, the confined SSPs are transformed into free‐space free‐electron radiation. Owing to the law of angular momentum conservation, this process converts the transverse spin angular momentum of SSPs into the longitudinal spin angular momentum of free‐electron radiation during the process, producing pure, tunable, and chiral free‐electron radiation across a broad spectrum. This method achieves an optimal degree of circular polarization approaching −1. The innovative methodology can be adapted to SML‐enabled guided states or silicon photonics platforms, offering new avenues for achieving chiral emission.

Abstract Background Extracellular adenosine triphosphate (ATP) is an important signal molecule. In previous studies, intensive research had revealed the crucial roles of family with sequence similarity 3 member A (FAM3A) in controlling hepatic glucolipid metabolism, islet β cell function, adipocyte differentiation, blood pressure, and other biological and pathophysiological processes. Although mitochondrial protein FAM3A plays crucial roles in the regulation of glucolipid metabolism via stimulating ATP release to activate P2 receptor pathways, its mechanism in promoting ATP release in hepatocytes remains unrevealed. Methods db/db, high-fat diet (HFD)-fed, and global pannexin 1 (PANX1) knockout mice, as well as liver sections of individuals, were used in this study. Adenoviruses and adeno-associated viruses were utilized for in vivo gene overexpression or inhibition. To evaluate the metabolic status in mice, oral glucose tolerance test (OGTT), pyruvate tolerance test (PTT), insulin tolerance test (ITT), and magnetic resonance imaging (MRI) were conducted. Protein–protein interactions were determined by coimmunoprecipitation with mass spectrometry (MS) assays. Results In livers of individuals and mice with steatosis, the expression of ATP-permeable channel PANX1 was increased (P

The species Niphon spinosus (Cuvier, 1829) is the only representative of the family Niphonidae and the genus Niphon, and its taxonomic history is complicated; it is still unclear in a phylogenetic sense. In this study, we report the complete mitochondrial genome of N. spinosus (OP391482), which was determined to be 16,503 bp long with biased A + T contents (53.8%) using next-generation technology. The typical set of 13 protein-coding genes (PCGs), 2 rRNA genes, 22 tRNA genes, and one control region (D-loop) are included in the mitochondrial genome. The H-strand encoded 28 genes (14 tRNA, 2 rRNA, and 12 PCGs), and D-loop, whereas the L-strand encoded the remaining 9 genes (8 tRNA and ND6). Its nucleotide composition, gene arrangement, codon usage patterns, and tRNA secondary structures are identical with other members of the Percoidei suborder. Furthermore, we reconstructed phylogenetic trees based on the 13 PCGs. The resulting phylogenetic trees showed N. spinosus placing as a separate lineage within the family Niphonidae, its close relationship to Trachinus draco (Trachinidae), and the clustering of major subfamilies like Luciopercinae and Percinae of the Percoidei suborder. These findings will contribute to future studies on the evolutionary history, population genetics, molecular taxonomy, and phylogeny of N. spinosus and related species.

Lysosomal transmembrane protein 5 (LAPTM5) is a lysosomal-associated protein that interacts with surface receptors on various immune cells, including B cells, T cells, macrophages and dendritic cells. Dysregulated expression of LAPTM5 is implicated in the development of multiple immune system-related diseases. In the context of tumors, elevated LAPTM5 levels in immune cells are associated with decreased cell membrane levels of T cell receptors (TCR) or B cell receptors (BCR), leading to impaired antigen presentation and immune escape, thereby promoting tumor progression. Besides, LAPTM5 is critical for inducing non-apoptotic cell death in tumor parenchymal cells since its downregulation leads to inhibition of the cell death pathway in the tumor parenchyma and subsequent enhanced tumorigenesis. LAPTM5 also affects the cell cycle as the elevated LAPTM5 expression in solid tumors causes its inability to block the G0/G1 stage. In non-solid tumors, abnormal LAPTM5 expression disrupts blood cell development and causes irregular proliferation. Furthermore, in the nervous system, aberrant LAPTM5 expression in microglia is correlated with Alzheimer's disease severity. In this context, further preclinical research is essential to validate LAPTM5 as a potential target for diagnosis, therapy, and prognosis in immune-related disorders and tumors. This review summarized the current insights into LAPTM5's role in tumors and immune-related deficits, highlighting its potential as a valuable biomarker and therapeutic target.

Remediating soil contaminated with polycyclic aromatic hydrocarbons (PAHs) presents a significant environmental challenge due to their toxic and carcinogenic properties. Traditional PAHs remediation methods—chemical, thermal, and bioremediation—along with conventional soil-washing agents like surfactants and cyclodextrins face challenges of cost, ecological harm, and inefficiency. Here we show an effective and environmentally friendly calixarene derivative for PAHs removal through soil washing. Thiacalix[4]arene tetrasulfonate (TCAS) has a unique molecular structure of a sulfonate group and a sulfur atom, which enhances its solubility and facilitates selective binding with PAHs. It forms host-guest complexes with PAHs through π-π stacking, OH-π interactions, hydrogen bonding, van der Waals forces, and electrostatic interactions. These interactions enable partial encapsulation of PAH molecules, aiding their desorption from the soil matrix. Our results show that a 0.7% solution of TCAS can extract approximately 50% of PAHs from contaminated soil while preserving soil nutrients and minimizing adverse environmental effects. This research unveils the pioneering application of TCAS in removing PAHs from contaminated soil, marking a transformative advancement in resource-efficient and sustainable soil remediation strategies.

BackgroundPancreatic cancer (PC), sometimes referred to as pancreatic ductal adenocarcinoma (PDAC), is a major cause of global mortality from cancer. Pancreatic cancer is a very aggressive and devastating kind of cancer, characterized by limited options for therapy and low possibilities of survival. Sulforaphane (SFN), a naturally occurring sulfur-containing compound, is believed to possess anti-inflammatory, anti-obesity, and anti-cancer characteristics.ObjectiveHowever, efficient preventative and treatment measures are essential and SFN has been studied for its ability to suppress pancreatic cancer cell proliferation and induce apoptosis.MethodsHere, SFN induced cytotoxicity and apoptosis in PDAC cell lines such as MIA PaCa-2 and PANC-1 cells, as evaluated by cytotoxicity, colony formation, western blot analysis, fluorescence-activated cell sorting (FACS), reactive oxygen species (ROS) detection, caspase-3 activity assay, immunofluorescence assay, and mitochondrial membrane potential assay.ResultsIn MIA PaCa-2 and PANC-1 cells, SFN inhibited cell survival and proliferation in a dose-dependent manner. The activation of caspase zymogens results in cleaved PARP and cleaved caspase-3, which is associated with an accumulation in the sub G1 phase. Furthermore, SFN increased ROS level and γH2A.X expression while decreasing mitochondrial membrane potential (ΔΨm). Notably, the ROS scavenger N-Acetyl-L-cysteine (NAC) was shown to reverse SFN-induced cytotoxicity and ROS level. Subsequently, SFN-induced cell cycle arrest and apoptosis induction as a Trojan horse to eliminate pancreatic cancer cells via ROS-mediated pathways were used to inhibit pancreatic cancer cells.ConclusionCollectively, our data demonstrates that SFN-induced cell death follows the apoptosis pathway, making it a viable target for therapeutic interventions against pancreatic cancer.

Abstract The search for new physics beyond the Standard Model is one of the central problems of current high energy physics interest. As the luminosities of current and near-future colliders continue to increase, the search for new physics has increased the requirements for processing large amounts of data. Meanwhile, quantum computing which is rapidly evolving, has great potential to become a powerful tool to help search for new physics signals. Since the k-means algorithm is known to be able to be accelerated with the help of quantum computing, we investigate and propose an event selection strategy based on k-means algorithm to search for new physics signals. Taking the case of tri-photon processes at the muon colliders as an example, the event selection strategy is shown to be effective in helping to search for the signals of dimension-8 operators contributing to anomalous quartic gauge couplings. Compared with traditional event selection strategy, the expected constraints are generally tighter.

Background Long-term oxygen therapy (LTOT) improves the survival of patients with hypoxemia due to chronic respiratory diseases. The clinical outcomes of LTOT are strongly associated with patient adherence. To improve the adherence of patients, physicians have focused on the efficacy of LTOT. However, poor adherence may stem from patients’ perceptions of LTOT. Herein we evaluated patients’ perceptions of LTOT affecting adherence. Methods We conducted a cross-sectional survey study using descriptive, open, and closed-ended questionnaire. Patients using oxygen therapy (OT) or requiring it but avoiding OT responded to the questionnaires at three university hospitals. Results Seventy-nine patients responded to the questionnaires. The number of patients using home and portable OT was 69 (93%) and 37 (46.3%), respectively. Patients with good adherence were 22 (30.1%). Among patients with good adherence, 90.9% used oxygen according to physicians’ prescriptions whereas only 37.3% of those with poor adherence followed physicians’ prescriptions (p

Coronary atherosclerosis (or coronary heart disease [CHD]) is a common cardiovascular disease that seriously damages human health. Percutaneous coronary stent implantation represents the primary treatment option for severe CHD in clinical practice; meanwhile, dual antiplatelet therapy (DAPT) is widely used to reduce the risk of postoperative thrombosis. Although the mechanisms of action of the two most commonly used antiplatelet drugs, aspirin and clopidogrel, remain unclear, clinical studies have shown that some patients are susceptible to stent thrombosis—antiplatelet resistance (high on-treatment platelet reactivity [HTPR])—despite using these drugs. Therefore, screening for HTPR and formulating personalized antiplatelet therapies is necessary. Ticagrelor, indobufen, and rivaroxaban are the most common and safe antiplatelet drugs used in clinical practice, with broad application prospects. This review summarizes the mechanisms of action of existing antiplatelet drugs, reasons for personalized treatment, screening of antiplatelet reactions, and development of novel antiplatelet drugs.

The worldwide spread of carbapenemase-producing Enterobacterales (CPE) represents a significant threat owing to the high mortality and morbidity rates. Traditional diagnostic methods are often too slow and complex for rapid point-of-care testing. Therefore, we developed a recombinase polymerase amplification (RPA)-coupled CRISPR/Cas12a system (RCCS), a rapid, accurate, and simple diagnostic platform for detecting antimicrobial-resistant genes. The RCCS detected carbapenemase genes (blaKPC and blaNDM) within 50 min, including 10 min for DNA extraction and 30–40 min for RCCS reaction (a 20 min RPA reaction with a 10–20-min CRISPR/Cas12a assay). Fluorescence signals obtained from the RCCS platform were visualized using lateral-flow test strips (LFSs) and real-time and endpoint fluorescence. The LFS clearly displayed test lines while detecting carbapenemase genes. Furthermore, the RCCS platform demonstrated high sensitivity by successfully detecting blaKPC and blaNDM at the attomolar and picomolar levels, respectively. The accuracy of the RCCS platform was validated with clinical isolates of Klebsiella pneumoniae and Escherichia coli; a 100% detection accuracy was achieved, which has not been reported when using conventional PCR. Overall, these findings indicate that the RCCS platform is a powerful tool for rapid and reliable detection of carbapenemase-encoding genes, with significant potential for implementation in point-of-care settings and resource-limited environments.

ABSTRACTThis study presents a comparison of prediction performances by an artificial neural network (ANN), well-known deep convolutional neural network (D-CNN) models, and four proposed shallow convolutional neural network (S-CNN) models to forecast three key physicochemical properties (PCPs): salinity, °Brix, and moisture content of kimchi sauce (KS). The S-CNN models effectively minimized underfitting issues found in D-CNN models, predicting PCPs with a low error rate even with small image datasets. Furthermore, the ANN model using color values also allowed for competitive predictions. We used two nondestructive prediction strategies: (i) using color values with ANNs for immediate application in small-scale enterprises and (ii) using photographs as input for S-CNN models, allowing for faster and more accurate quality prediction. These results highlight the potential for image-based quality prediction in food science, possibly enhancing the efficiency and accuracy of real-time quality control. Future enhancements could incorporate additional data sources for improved predictive performance.