TEAD1/TEAD4基因多态性与非贲门胃癌变关系的研究

目的探讨TEA转录因子1(TEAD1)基因单核苷酸多态性(SNP)rs2304733、TEA转录因子4(TEAD4)SNPrs7135838和rs1990330与非贲门胃癌变发病风险的关系。方法采用酶联免疫吸附测定法(ELISA)检测正常对照组血清样本中抗幽门螺杆菌(Hp)的特异性抗体,根据抗体滴度将470例正常对照组分为Hp感染阴性组(n=223)和阳性组(n=247)。在450例非贲门胃癌病例组和470例对照组中,采用聚合酶链式反应-限制性片段长度多态性(PCR-RFLP)技术对各SNP位点进行基因分型,采用非条件性Logistic回归评估各SNP位点与非贲门胃癌变发病风险的关系。结果TEAD1、TEAD4各SNP位点均与Hp感染没有关联;TEAD1 rs2304733与非贲门胃癌的发病风险相关,与携带TT基因型者相比,携带CT基因型及CC基因型者均增加了非贲门胃癌的发病风险(CTvsTT:OR=2.321,95%CI:1.690~3.188;CCvsTT:OR=5.140,95%CI:1.080~24.463);TEAD4 rs1990330与非贲门胃癌发病风险相关,与携带GG基因型者相比,携带GT基因型者增加了非贲门胃癌的发病风险(OR=2.405,95%CI:1.480~3.908);TEAD4 rs7135838与非贲门胃癌的发病风险无关联;TEAD1rs2304733、TEAD4 rs7135838以及rs1990330对非贲门胃癌发病风险存在交互作用(P<0.05)。结论在包头地区汉族人群中,TEAD1 rs2304733、TEAD4 rs1990330在Hp感染中不起主要作用,在非贲门胃癌发病风险中可能起一定作用;TEAD4 rs7135838在Hp感染以及非贲门胃癌发病风险中可能均不起主要作用;TEAD1 rs2304733、TEAD4 rs1990330对非贲门胃癌发病风险的协同效应最强,为最佳交互模型。

CircFoxo3调节miR-130a-5p/TEAD1轴对心力衰竭大鼠心肌细胞凋亡影响的实验研究

目的探讨环状RNA叉头框蛋白O3(CircFoxo3)调节miR-130a-5p/TEA域转录因子1(TEAD1)轴对心力衰竭(HF)大鼠心肌细胞凋亡的影响。方法将SD大鼠分为对照组、HF组、si-NC组、si-CircFoxo3组、agomir NC组、miR-130a-5p agomir组、si-CircFoxo3+antagomir NC组、si-CircFoxo3+miR-130a-5p antagomir组,每组18只。除对照组外,其他组大鼠均通过腹腔注射盐酸阿霉素的方法构建HF大鼠模型。建模成功后进行药物处理,每3 d给药一次,持续3周。应用反转录实时荧光定量聚合酶链式反应(qRT-PCR)法检测心肌组织中CircFoxo3、miR-130a-5p表达水平。应用彩色多普勒超声仪检测大鼠左室收缩末期内径(LVESD)、左室舒张末期内径(LVEDD)、左室射血分数(LVEF)水平。采用酶联免疫吸附试验(ELISA)法检测大鼠血清人基质裂解素2(ST_(2))、N端脑钠肽前体(NT-pro BNP)水平。应用HE染色、Masson染色检测大鼠心肌组织病理变化和心肌纤维化情况。通过TUNEL染色检测心肌细胞凋亡水平。应用Western blot法检测大鼠心肌组织TEA域转录因子1(TEAD1)、B淋巴细胞瘤-2(Bcl-2)、Bcl-2相关X蛋白(Bax)、裂解的天冬氨酸特异性半胱氨酸蛋白酶-3(cleaved caspase-3)蛋白表达水平。通过双荧光素酶报告基因实验验证CircFoxo3与miR-130a-5p、miR-130a-5p与TEAD1的关系。结果与对照组比较,HF组大鼠心肌组织病理损伤、心肌纤维化严重,LVESD、LVEDD水平上升,心肌组织CircFoxo3及TEAD1、Bax、cleaved caspase-3蛋白表达水平升高,血清ST_(2)、NT-pro BNP水平升高,心肌细胞凋亡率升高,LVEF、miR-130a-5p和Bcl-2蛋白水平降低,差异有统计学意义(P<0.05)。与HF组、si-NC组比较,si-CircFoxo3组大鼠心肌组织病理损伤减轻,LVESD、LVEDD水平降低,心肌组织CircFoxo3及TEAD1、Bax、cleaved caspase-3蛋白表达水平降低,血清ST_(2)、NT-pro BNP水平降低,心肌细胞凋亡率降低,LVEF、miR-130a-5p及Bcl-2蛋白水平升高,差异有统计学意义(P<0.05)。与HF组、agomir NC组比较,miR-130a-5p agomir组大鼠心肌组织病理损伤、心肌纤维化有所改善,LVESD、LVEDD水平降低,心肌组织TEAD1、Bax、cleaved caspase-3蛋白表达水平降低,血清ST_(2)、NT-pro BNP水平降低,心肌细胞凋亡率降低,LVEF、miR-130a-5p及Bcl-2蛋白水平升高,差异有统计学意义(P<0.05)。与si-CircFoxo3组、si-CircFoxo3+antagomir NC组比较,si-CircFoxo3+miR-130a-5p antagomir组大鼠心肌组织病理损伤、心肌纤维化加剧,LVESD、LVEDD水平升高,心肌组织TEAD1、Bax、cleaved caspase-3蛋白表达水平升高,血清ST_(2)、NT-pro BNP水平升高,心肌细胞凋亡率升高,LVEF、miR-130a-5p、Bcl-2蛋白水平降低,差异有统计学意义(P<0.05)。CircFoxo3与miR-130a-5p、miR-130a-5p与TEAD1存在靶向关系。结论沉默CircFoxo3可能通过海绵化上调miR-130a-5p来抑制TEAD1表达,进而抑制HF大鼠心肌细胞凋亡。

Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.

Characterization of mulberry leaf instant tea and evaluation of its hypolipidemia effect via regulation of intestinal microbiota

The purpose of this study was to characterize mulberry leaf instant tea(MLIT)powder prepared from the'Longsang No.1'(Morus abla L.cv.Longsang 1)mulberry leaves in Heilongjiang Province(China)and assess its obesity-preventing/relieving effects.A total of 174 compounds including quercetin,chlorogenic acid,1-deoxyecomycin(1-DNJ)related to antihyperlipidemia effects were identified from the MLIT powder.MLIT treatment reversed the Lee's index,fat coefficient,and serum biochemical parameters in both the obesity relieving and obesity preventing mice fed with high-fat diet.In the obesity relieving experiment,the relative abundance of Desulfovibrio in mouse feces decreased after both 0.5%and 1%MLIT treatments.In obesity preventing experiments,mouse with different amount of MLIT treatments showed increased relative abundance of Akkermansia,Bifidobacterium and Lactobacillus,while Deferribacteres,Desulfobacterota decreased.The beneficial bacteria in the intestinal tract of mice treated with MLIT increased.This study proved that MLIT had antihyperlipidemia potential via modulating intestinal microbiota in mice.

Antibacterial mechanism of kojic acid and tea polyphenols against Escherichia coli O157:H7 through transcriptomic analysis

Escherichia coli O157:H7 is one of the major foodborne pathogenic bacterial that cause infectious diseases in humans.The previous found that a combination of kojic acid and tea polyphenols exhibited better activity against E.coli O157:H7 than using either alone.This study aimed to explore responses underlying the antibacterial mechanisms of kojic acid and tea polyphenols from the gene level.The functional enrichment analysis by comparing kojic acid and tea polyphenols individually or synergistically against E.coli O157:H7 found that acid resistance systems in kojic acid were activated,and the cell membrane and genomic DNA were destructed in the cells,resulting in“oxygen starvation”.The oxidative stress response triggered by tea polyphenols inhibited both sulfur uptake and the synthesis of ATP,which affected the bacteria's life metabolic process.Interestingly,we found that kojic acid combined with tea polyphenols hindered the uptake of iron that played an essential role in the synthesis of DNA,respiration,tricarboxylic acid cycle.The results suggested that the iron uptake pathways may represent a novel approach for kojic acid and tea polyphenols synergistically against E.coli O157:H7 and provided a theoretical basis for bacterial pathogen control in the food industry.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their goods.Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years.The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones.This paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other things.Online reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are dynamic.Then,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave Bayes.The performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics.

Effect of a Commercial Extract of Green Tea and a Pure Catechin on Two Veillonella strains

The catechin Epigallocatechin-3-O-Gallate (EGCG) which is found in of Green Tea extracts (GTE), displays a variety of microbicidal properties. It is largely believed that EGCG inhibits the growth of cariogenic and periodontopathic bacteria. Objective: In this paper we compared the inhibitory activity of EGCG and a commercial GTE on the growth of Veillonella parvula. Chlorhexidine was used as positive control. Methodology: V. parvula ATCC 10790 and a clinical isolate obtained from a periodontal disease patient were cultured in the presence of EGCG or a commercial GTE, and the measurements of bacterial growth inhibition were compared to the values obtained with 0.12 and 0.2% chlorhexidine. Results: Chlorhexidine inhibited bacterial growth, however in contrast to a previous report, neither EGCG nor the GTE showed any effect on bacterial growth. Conclusions: The data show and confirm that chlorhexidine is a growth inhibitor of V. parvula while EGCG and GTE do not display such effect.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

Duplicated chalcone synthase(CHS)genes modulate flavonoid production in tea plants in response to light stress

In tea plants,the abundant flavonoid compounds are responsible for the health benefits for the human body and define the astringent flavor profile.While the downstream mechanisms of flavonoid biosynthesis have been extensively studied,the role of chalcone synthase(CHS)in this secondary metabolic process in tea plants remains less clear.In this study,we compared the evolutionary profile of the flavonoid metabolism pathway and discovered that gene duplication of CHS occurred in tea plants.We identified three CsCHS genes,along with a CsCHS-like gene,as potential candidates for further functional investigation.Unlike the CsCHS-like gene,the CsCHS genes effectively restored flavonoid production in Arabidopsis chs-mutants.Additionally,CsCHS transgenic tobacco plants exhibited higher flavonoid compound accumulation compared to their wild-type counterparts.Most notably,our examination of promoter and gene expression levels for the selected CHS genes revealed distinct responses to UV-B stress in tea plants.Our findings suggest that environmental factors such as UV-B exposure could have been the key drivers behind the gene duplication events in CHS.

Carbonic anhydrase 2 mediates anti-obesity effects of black tea as thermogenic activator

Obesity is a metabolic disorder due to over-accumulation of adipose tissue and ultimately becomes a“disease”.Brown adipose tissue(BAT)thermogenesis and white adipose tissue(WAT)browning emerge as a potential strategy of anti-obesity by dissipating energy as heat.However,drugs based on adipose tissue thermogenesis have not been successfully approved yet.In current study,we found that black tea extract(BTE)obtained by patentauthorized manufacturing process prevented body weight gain as novel thermogenic activator with reduction of adiposity,improvement of adipose distribution,and glucose metabolism improvement in diet-induced obesity mice.Mechanismly,anti-obesity effect of BTE depends on promoting BAT thermogenesis and WAT browning with upregulation of uncoupling protein 1(UCP1),especially visceral adipose tissue(VAT)with browning resistance.Specifically,utilizing in silico approach of network pharmacology and molecular docking,we identified carbonic anhydrase 2(CA2)in nitrogen metabolism as anti-obesity target of BTE and further elucidated that protein kinase B(AKT)signaling pathway linked CA2 and UCP1.Meanwhile gut microbiota regulation may prompt the CA2-dependent thermogenesis activation.Our findings demonstrated anti-obesity effect of BTE as thermogenic activator through CA2-mediated BAT thermogenesis and WAT browning via CA2-AKT-UCP1 signaling pathway,which could be developed as promising anti-obesity agent with good safety and efficacy.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Correlation and Pathway Analysis of the Carbon,Nitrogen,and Phosphorus in Soil-Microorganism-Plant with Main Quality Components of Tea(Camellia sinensis)

The contents of carbon(C),nitrogen(N),and phosphorus(P)in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea,such as tea polyphenols,amino acids,and caffeine.However,few studies have quantified the effects of these factors on the main quality components of tea.The study aimed to explore the interactions of C,N,and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method.The results indicated that(1)The contents of C,N,and P in soil,microorganisms,and tea plants were highly correlated and collinear,and showed significant correlations with the main quality components of tea.(2)Optimal regression equations were established to esti-mate tea polyphenol,amino acid,catechin,caffeine,and water extract content based on C,N,and P contents in soil,microorganisms,and tea plants(R^(2)=0.923,0.726,0.954,0.848,and 0.883,respectively).(3)Pathway analysis showed that microbial biomass phosphorus(MBP),root phosphorus,branch nitrogen,and microbial biomass carbon(MBC)were the largest direct impact factors on tea polyphenol,catechin,water extracts,amino acid,and caffeine content,respectively.Leaf carbon,root phosphorus,and leaf nitrogen were the largest indirect impact factors on tea polyphenol,catechin,and water extract content,respectively.Leaf carbon indirectly affected tea polyphenol content mainly by altering MBP content.Root phosphorus indirectly affected catechin content mainly by altering soil organic carbon content.Leaf nitrogen indirectly affected water extract content mainly by altering branch nitrogen content.The research results provide the scientific basis for reasonable fertilization in tea gardens and tea quality improvement.

Pattern of Eye Diseases among Tea Workers

Background: Bangladesh’s tea industry is essential to the country’s economic expansion. Since tea workers in Bangladesh are marginalized within our community, they have limited access to comprehensive eye care services. Productivity and well-being are cornerstones of comprehensive health care strategy. Ocular disorders are influenced by life expectancy, sociodemographic status, and the epidemiological transition. In this context, the state of ocular health and the many eye illnesses remain to be significantly addressed. Purpose: To evaluate the pattern of eye diseases among tea workers in a tea estate of Bangladesh. Methods: This cross-sectional observational study was carried out in Halda Valley Tea Estate, Nazirhat, Fatickchari, Chattogram, Bangladesh, under the supervision of the Department of Community Ophthalmology, BSMMU, following ethical clearance and approval by the IRB board of BSMMU. With informed written consent and approval from the authority of the tea estate, a total of 110 tea workers were recruited. Socio-demographic characteristics, ocular findings, and patterns of eye diseases were determined and recorded. Results: The mean age of the study participants was 39.60 ± 11.63 years. The maximum (58.1%) study participants were 31 - 50 years old, female (64.5%), illiterate (82.7%), and tribal (71.8%) indigenes. Eye diseases were found in 94.5% of workers. Presbyopia (28.2%), cataracts (27.3%), and refractive error (26.4%) were the most common. Tea workers with eye diseases were significantly older than those who did not have any eye diseases (40.20 ± 11.57 vs. 29.17 ± 7.31 years, p Conclusion: A significant number of tea workers had eye diseases, of which presbyopia, cataracts, and refractive error were the most common.

Junshanyinzhen tea extract prevents obesity by regulating gut microbiota and metabolic endotoxemia in high-fat diet fed rats

Obesity is associated with gut dysbiosis and metabolic endotoxin.Junshanyinzhen tea extract(JSTE)reduced fat accumulation and body weight in obese mice.However,the effects and mechanism of JSTE in preventing obesity were unclear.Therefore,we used different doses of JSTE(75,150 and 300 mg/(kg·day))to evaluate the effect on high-fat diet(HFD)-induced rats under 8 weeks of intervention.Here,our results showed that JSTE could significantly reduce body weight gain,blood lipid levels and fat accumulation,improve fatty damage in liver tissue(P<0.05).In addition,JSTE increased the expression of intestinal tight junction proteins(P<0.05),relieved metabolic endotoxemia(P<0.05)and chronic low-grade inflammation in HFD rats.Sequencing of fecal samples showed that JSTE could effectively reverse the microbial diversity and the ratio of Firmicutes to Bacteroidetes to normal levels in HFD-fed rats.Desulfovibrioceae and Erysipelotrichaceae,which are positively related to obesity,were decreased by JSTE intervention(P<0.05).while Bifidobacteriaceae,Bacteroidaceae,Akkermansia,and Clostridium,which are negatively related to obesity,were increased.Together,these results suggested that JSTE might effectively prevent obesity by modulating gut microbiota dysbiosis,intestinal barrier dysfunction,metabolic endotoxemia and chronic low-grade infl ammation in HFD-induced rats.

On-site rapid detection of multiple pesticide residues in tea leaves by lateral flow immunoassay

The application of pesticides (mostly insecticides and fungicides) during the tea-planting process will undoubtedly increase the dietary risk associated with drinking tea. Thus, it is necessary to ascertain whether pesticide residues in tea products exceed the maximum residue limits. However, the complex matrices present in tea samples comprise a major challenge in the analytical detection of pesticide residues. In this study, nine types of lateral flow immunochromatographic strips (LFICSs) were developed to detect the pesticides of interest (fenpropathrin, chlorpyrifos, imidacloprid, thiamethoxam, acetamiprid, carbendazim, chlorothalonil, pyraclostrobin, and iprodione). To reduce the interference of tea substrates on the assay sensitivity, the pretreatment conditions for tea samples, including the extraction solvent, extraction time, and purification agent, were optimized for the simultaneous detection of these pesticides. The entire testing procedure (including pretreatment and detection) could be completed within 30 min. The detected results of authentic tea samples were confirmed by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), which suggest that the LFICS coupled with sample rapid pretreatment can be used for on-site rapid screening of the target pesticide in tea products prior to their market release.

The modulatory effect of oolong tea polyphenols on intestinal flora and hypothalamus gene expression in a circadian rhythm disturbance mouse model

The interaction between host circadian rhythm and gut microbes through the gut-brain axis provides new clues for tea polyphenols to improve host health.Our present research showed that oolong tea polyphenols(OTP)improved the structural disorder of the intestinal flora caused by continuous darkness,thereby modulating the production of metabolites related to pyruvate metabolism,glycolysis/gluconeogenesis,and tryptophan metabolism to alleviate the steady-state imbalance.After fecal microbiota transplantation from the OTP group,the single-cell transcriptomic analysis revealed that OTP significantly increased the number of hypothalamus cell clusters,up-regulated the number of astrocytes and fibroblasts,and enhanced the expression of circadian rhythm genes Cry2,Per3,Bhlhe41,Nr1d1,Nr1d2,Dbp and Rorb in hypothalamic cells.Our results confirmed that OTP can actively improve the intestinal environmental state as well as internal/peripheral circadian rhythm disorders and cognitive impairment,with potential prebiotic functional characteristics to notably contribute to host health.

Integration of 1H-NMR-Based Metabolomics Approach with Traditional Chinese Pulse Diagnosis to Evaluate the Qi-Enhancing Activity of Herbal Tea

Human metabolism is intricately linked to an individual’s health status. Regardless of living habits, it will be reflected in the metabolic characteristics of urine. The utilization of the 1H NMR-based metabolomics method has enabled examine the metabolomic changes in urine under various physiology conditions, providing valuable insights into metabolites. In this particular study, volunteers were divided into two groups based on the strength of their spleen pulses, using the pulse diagnosis method employed in traditional Chinese medicine. Subsequently, their urine samples were analyzed, revealing notable variances in urea, creatinine, citric acid, succinic acid, trimethylamine-N-oxide (TMAO), alanine, hippuric acid, and glycine between the two groups. Interestingly, individuals with weak spleen pulses showed significant improvements after consuming herbal tea. Furthermore, we conducted LC-MS analysis on herbal tea and performed adenosine triphosphate (ATP) activity tests on the C2C12 mouse skeletal muscle cell line. The results indicated that within a reasonable concentration range, exposure to herbal tea led to an increase in the mitochondrial ATP production capacity of C2C12 cells. These findings shed light on the relationship between traditional Chinese medicine pulse diagnosis and urine metabolites, highlighting their potential as non-invasive and straightforward health assessment indicators. They can aid in the preliminary determination of necessary dietary and lifestyle changes to enhance overall bodily health.

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

Tea Production Response to Seasonal Rainfall Variability: Evidence from Rwanda

Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.