Unveiling DNA methylation in Alzheimer’s disease:a review of array-based human brain studies

The intricacies of Alzheimer’s disease pathogenesis are being increasingly illuminated by the exploration of epigenetic mechanisms,particularly DNA methylation.This review comprehensively surveys recent human-centered studies that investigate whole genome DNA methylation in Alzheimer’s disease neuropathology.The examination of various brain regions reveals distinctive DNA methylation patterns that associate with the Braak stage and Alzheimer’s disease progression.The entorhinal cortex emerges as a focal point due to its early histological alterations and subsequent impact on downstream regions like the hippocampus.Notably,ANK1 hypermethylation,a protein implicated in neurofibrillary tangle formation,was recurrently identified in the entorhinal cortex.Further,the middle temporal gyrus and prefrontal cortex were shown to exhibit significant hypermethylation of genes like HOXA3,RHBDF2,and MCF2L,potentially influencing neuroinflammatory processes.The complex role of BIN1 in late-onset Alzheimer’s disease is underscored by its association with altered methylation patterns.Despite the disparities across studies,these findings highlight the intricate interplay between epigenetic modifications and Alzheimer’s disease pathology.Future research efforts should address methodological variations,incorporate diverse cohorts,and consider environmental factors to unravel the nuanced epigenetic landscape underlying Alzheimer’s disease progression.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Pretreatment and analysis techniques development of TKIs in biological samples for pharmacokinetic studies and therapeutic drug monitoring

Tyrosine kinase inhibitors(TKIs)have emerged as the first-line small molecule drugs in many cancer therapies,exerting their effects by impeding aberrant cell growth and proliferation through the modulation of tyrosine kinase-mediated signaling pathways.However,there exists a substantial inter-individual variability in the concentrations of certain TKIs and their metabolites,which may render patients with compromised immune function susceptible to diverse infections despite receiving theoretically efficacious anticancer treatments,alongside other potential side effects or adverse reactions.Therefore,an urgent need exists for an up-to-date review concerning the biological matrices relevant to bioanalysis and the sampling methods,clinical pharmacokinetics,and therapeutic drug monitoring of different TKIs.This paper provides a comprehensive overview of the advancements in pretreatment methods,such as protein precipitation(PPT),liquid-liquid extraction(LLE),solid-phase extraction(SPE),micro-SPE(μ-SPE),magnetic SPE(MSPE),and vortex-assisted dispersive SPE(VA-DSPE)achieved since 2017.It also highlights the latest analysis techniques such as newly developed high performance liquid chromatography(HPLC)and high-resolution mass spectrometry(HRMS)methods,capillary electrophoresis(CE),gas chromatography(GC),supercritical fluid chromatography(SFC)procedures,surface plasmon resonance(SPR)assays as well as novel nanoprobes-based biosensing techniques.In addition,a comparison is made between the advantages and disadvantages of different approaches while presenting critical challenges and prospects in pharmacokinetic studies and therapeutic drug monitoring.

Recent advances and key perspectives of in-situ studies for oxygen evolution reaction in water electrolysis

Electricity-driven water splitting to produce hydrogen is one of the most efficient ways to alleviate energy crisis and environmental pollution problems,in which the anodic oxygen evolution reaction(OER)is the key half-reaction of performance-limiting in water splitting.Given the complicated reaction process and surface reconstruction of the involved catalysts under actual working conditions,unraveling the real active sites,probing multiple reaction intermediates and clarifying catalytic pathways through in-situ characterization techniques and theoretical calculations are essential.In this review,we summarize the recent advancements in understanding the catalytic process,unlocking the water oxidation active phase and elucidating catalytic mechanism of water oxidation by various in-situ characterization techniques.Firstly,we introduce conventionally proposed traditional catalytic mechanisms and novel evolutionary mechanisms of OER,and highlight the significance of optimal catalytic pathways and intrinsic stability.Next,we provide a comprehensive overview of the fundamental working principles,different detection modes,applicable scenarios,and limitations associated with the in-situ characterization techniques.Further,we exemplified the in-situ studies and discussed phase transition detection,visualization of speciation evolution,electronic structure tracking,observation of reaction active intermediates,and monitoring of catalytic products,as well as establishing catalytic structure-activity relationships and catalytic mechanism.Finally,the key challenges and future perspectives for demystifying the water oxidation process are briefly proposed.

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Marker-assisted selection(MAS)and genomic selection(GS)breeding have greatly improved the efficiency of rice breeding.Due to the influences of epistasis and gene pleiotropy,ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome.In this study,113 indica rice varieties(V)and their 565 testcross hybrids(TC)were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits.The original traits and general combining ability of the parents,as well as the original traits and midparent heterosis of TC,were subjected to genome-wide association analysis.In total,381 primary significantly associated loci(SAL)and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected.Among these loci,322 candidate genes located within or nearby the SALs were screened,204 of which were cloned genes.A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs.All the SALs were used to construct genetic networks,in which 91 pleiotropic loci were investigated.Additionally,we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs,primary SALs,secondary SALs or epistatic effect SALs as covariates.Although the prediction accuracies of the four models were generally not significantly different in the TC dataset,the incorporation of primary SALs,secondary SALs,and epistatic effect SALs significantly improved the prediction accuracies of 5(26%),3(16%),and 11(58%)traits in the V dataset,respectively.These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines.They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Gut microbiota changes associated with frailty in older adults:A systematic review of observational studies

BACKGROUND Frailty is a complex aging-related syndrome characterized by a cumulative loss of physiological reserve and increased vulnerability to adverse clinical outcomes,including falls,disability,incapacity and death.While an increasing number of studies suggest that the gut microbiota may play a key role in the pathophy-siology of frailty,direct evaluation of the association between gut microbiome alterations and frailty in older adults remains limited.AIM Seven electronic databases(China National Knowledge Infrastructure,VIP,SinoMed,Wanfang,PubMed,Web of Science and EMBASE)were searched for articles published before October 31,2023 to identify observational studies that compared the microbiomes of older adults with and without frailty.The diversity and composition of the gut microbiota were the main outcomes used to analyze the associations of changes in the gut microbiota with frailty in older adults.The quality of the included studies was assessed via the Newcastle-Ottawa Scale and the Agency for Healthcare Research and Quality.RESULTS Eleven observational studies with 912 older adults were included in this review.Consistent results revealed a significant difference in the gut microbiota composition between frail and non-frail older adults,with a significant decrease inαdiversity and a significant increase inβdiversity in frail older adults.The pooled results revealed that at the phylum level,four microbiota(Actinobacteria,Proteo-bacteria,Verrucomicrobia and Synergistetes)were significantly enriched,and two microbiota(Firmicutes and Fusobacteria)were significantly depleted in frail older adults.At the family level,the results consistently revealed that the abundances of 6 families,most of which belong to the Actinobacteria or Proteo-bacteria phylum,were greater in frail than in non-frail older adults.At the genus or species level,consistent results from more than two studies revealed that the abundances of the genera Prevotella,Faecalibacterium,and Roseburia were significantly lower in frail older adults;individual studies revealed that the abundances of some genera or species(e.g.,Megamonas,Blautia,and Megasphaera)were significantly lower,whereas those of other genera or species(e.g.,Bifidobacterium,Oscillospira,Ruminococcus and Pyramidobacter)were significantly greater in frail older adults.CONCLUSION This systematic review suggests that changes in the gut microbiota are associated with frailty in older adults,which is commonly reflected by a reduction in beneficial species and an increase in pathogenic species.However,further studies are needed to confirm these findings.

A Research Conception on Indigenous Chinese Leadership:Based on Comparative Case Studies on Political Elites in Ancient China

Due to the complexity of leadership and its variability across situations,it is suggested that research on leadership should focus on attempts to understand leadership in specific situations or roles,and develop local knowledge about leadership in each situation or role.This paper validates the possibility and importance of building indigenous leadership theories from historical perspective.We demonstrated how to carry out research on indigenous Chinese leadership from a historical perspective by comparative case studies using historical data.Based on the principles of typicalness,representativeness,and comparativeness,we selected six counterparts of political elites in ancient China as comparative cases,which includes Guan Zhong&Shang Yang(organizational reformer),Liu Bang&Xiang Yu(entrepreneur),Li Shimin&Qin Shihuang(intergenerational transition successor),Zhuge Liang&Zeng Guofan(professional manager),Lu Su&Zhang Juzheng(mess-to-order maker),and Zuo Zongtang&Wang Anshi(national dream driver).The paper aims to develop indigenous leadership theory specific to Chinese mental states and culture.

Exploring the Path of Shantytown Renewal Based on the Theory of Environment-behavior Studies: A Case Study of the Northwest Area of Saihantala in Baotou City

The application of environment-behavior studies plays an important role in studying the renewal of shantytowns.One of the most humane ways of renewal is designing and constructing the necessary urban spatial environment from the perspective of human daily behavior.Most of the shantytown renovation projects currently carried out in China are mainly focused on demolition and reconstruction,which not only incurs high costs but also damages the original social structure and economic model.During the process of shantytown renewal,it should improve the urban landscape,living environment,and life quality of residents,and ensure the daily life of shantytown residents without increasing their economic burden,and preserve the traces of urban development.In order to achieve this goal,it should explore the renewal strategies of shantytowns from the perspective of environment-behavior studies through behavior maps,on-site research,and literature review.Updating and design from a human perspective often achieve faster and better development;the renewal of shantytowns should be a bottom-up approach and start from a grassroots perspective,completing the renovation design through the composition of the population and behavioral patterns.

Genetic Parameters and Genome-Wide Association Studies for Body Size Traits of Shuxuan Cattle in China

In domestic cattle,the body size traits have important implications in terms of breed characteristics and production performance.Shuxuan cattle is a dual-purpose breed mainly raised in Sichuan province,China,for which we have known less about the genetic parameters and underlying candidate genes in relation to the body size traits.In this study,we obtained the genome-wide single nucleotide polymorphisms(SNPs)using the Illumina Bovine BeadChip in 275 Shuxuan cattle.These SNPs were first used for estimating genetic parameters for the withers height(WH)and diagonal body length(BL).Using the bivariate animal model,the estimates(±standard error)of heritabilities were 0.71±0.22 and 0.49±0.29 for BL,and their genetic correlation was 0.64±0.37.Second,the genome-wide association study(GWAS)was performed.However,these did not result into genome-wide significant SNPs for both WH and BL traits.According to a less stringent suggestive significance,some positional candidate genes were found,and some of them(such as FAM110B,TAS1R2,PAX3,and FHIT)were previously reported in literature to be associated with body size traits in cattle.In conclusion,we estimated the genetic parameters in Shuxuan cattle using genomic information for the first time,which are required for implementing the genomic selection programs in the future.

Genome-wide and candidate gene association studies identify BnPAP17 as conferring the utilization of organic phosphorus in oilseed rape

Phosphorus(P)is essential for living plants,and P deficiency is one of the key factors limiting the yield in rapeseed production worldwide.As the most important organ for plants,root morphology traits(RMTs)play a key role in P absorption.To investigate the genetic variability of RMT under low P availability,we dissected the genetic structure of RMTs by genome-wide association studies(GWAS),linkage mapping and candidate gene association studies(CGAS).A total of 52 suggestive loci were associated with RMTs under P stress conditions in 405 oilseed rape accessions.The purple acid phosphatase gene BnPAP17 was found to control the lateral root number(LRN)and root dry weight(RDW)under low P stress.The expression of BnPAP17 was increased in shoot tissue in P-efficient cultivars compared to root tissue and P-inefficient cultivars in response to low P stress.Moreover,the haplotype of BnPAP17^(Hap3)was detected for the selective breeding of P efficiency in oilseed rape.Over-expression of the BnPAP17^(Hap3)could promote the shoot and root growth with enhanced tolerance to low P stress and organic phosphorus(Po)utilization in oilseed rape.Collectively,these findings increase our understanding of the mechanisms underlying BnPAP17-mediated low P stress tolerance in oilseed rape.

The Islamic Studies in China:Research in the Field of History

A history of the Islamic studies in China,not only includes studies on world Islam,it also includes research on Islam and Muslims in China.Before the Reform and Opening Up,China did not achieve much in this field,since then,the research field of Islam and Muslim in China has been expanding and the results have been published,forming a new trend.In this field,scholars such as Jin Yijiu,Li Xinghua,Qin Huibin,Zhou Guoli,Sha Qiuzhen,Zhou Xiefan,Wu Yungui,and Yang Huaizhong have made the most outstanding achievements.Especially in the collation of reference books and historical materials,in the past 20 years,The Encyclopedia of the history of Islam,The Chinese Encyclopedia of Islam have emerged,and the Chinese and World Encyclopedia of Islamic Law and other important historical books have been published.This paper focuses on the field of historical research and divided into academic research history at home and abroad,which is discussed from several fields such as general history,special history,national history,and regional history.

Exploration of Interdisciplinary Integration in Teaching Reform of Legal Studies and Auditing

This paper attempts to explore interdisciplinary integration from four aspects:the necessity of interdisciplinary integration between legal studies and auditing,the difficulties and challenges encountered in this integration,the ideas for teaching reform in the context of interdisciplinary integration,and the expected outcomes.The aim is to achieve an organic integration of legal studies and auditing through systematic teaching reforms,thereby providing students with comprehensive and integrated knowledge and skills training,ensuring the quality of talent cultivation,and adapting to the needs of social development.

Longitudinal Studies on Illness Perceptions in Cancer Patients: A Scoping Review

Objective:To conduct a scoping review of longitudinal studies related to disease perception in cancer patients and to provide a reference for the improvement of disease perception in cancer patients.Methods:Using the scoping method,PubMed,Web of Science,The Cochrane Library,Medline,CNKI,Wanfang Database,VIP Chinese Scientific and Technical Journals Database,and China Biomedical Literature Database were searched up to July 2023 for analysis and discussion of the included literature.Results:A total of 19 articles were included,distributed across 9 countries including China,the Netherlands,and the United Kingdom,with 8 articles published in China.The study subjects included patients with breast cancer,colorectal cancer,laryngeal cancer,lung cancer,esophageal cancer,and bladder cancer.The content covered dynamic changes in disease perception,the impact of disease perception,and the influencing factors of disease perception.Conclusion:The number of longitudinal studies on disease perception in cancer patients is gradually increasing and becoming more diverse.However,issues such as a small total number of studies,single research methods,and short observation periods persist.In the future,it is necessary to conduct in-depth longitudinal research on disease perception in cancer patients,expand the scope of research fields,and provide more comprehensive theories to guide clinical treatment and nursing practices,ultimately improving the treatment outcomes and quality of life for cancer patients.

In Vivo Studies and Flow Cytometric Investigation on Anticancer Potential of Selenium Nanoparticles Synthesized via Aqueous Extract of Clerodendron phlomidis

Nowadays,doctors and nutritionists recommend individuals incorporate selenium-rich foods such as nuts,cereals,and mushrooms into their regular diet to maintain fitness and overall health.Selenium nanoparticles(SeNPs)exhibit strong chemopreventive capabilities.The anticipations for SeNPs with enhanced and tunable bioactive activities have led to a keen interest in phytofabrication.In this study,the aqueous extract of Clerodendron phlomidis plant leaves was utilized for the synthesis of SeNPs.In traditional Indian medicine,this plant extract is recognized as a significant anti-diabetic agent.The flavonoids tetrahydroxylflavone,7-hydroxyflavanone,and 6,4’-dimethyl-7-acetoxy-scutellarein present in this plant leaf extract demonstrate excellent anticancer activity.These secondary metabolites exhibit the ability to reduce sodium selenite into SeNPs.At a concentration of 13μg/mL,the synthesized SeNPs effectively inhibited the proliferation of the HepG2 cell line.The results suggest that the SeNPs possess promising anti-cancer potential against liver cancer and can be considered as a therapeutic agent for liver cancer treatment.Additionally,the cell cycle arrest induced by SeNPs was further confirmed by the fluorescence-activated cell sorting(FACS)method,indicating that SeNPs could efficiently differentiate cancer cells from normal cells.Notably,it showed a significant improvement in diethylnitrosamine(DEN)-induced Swiss Wistar rat groups.This scientific investigation highlights the high anti-cancer potential of SeNPs,positioning them as a promising therapeutic agent for liver cancer treatment.

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.

Meteorological factors, ambient air pollution, and daily hospital admissions for depressive disorder in Harbin: A time-series study

BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.

“Blind men and an elephant”:The need for animals in research,drug safety studies,and understanding civilizational diseases

Animal-based research and drug safety studies are essential to understanding the mysteries of nature and the long-term survival of humans.Due to the rapid increase in the global human population,conflict-and economically driven human migration,tourism-related activities,densely populated metropolitan areas,and local policies,humans will be affected by a multitude of novel disease-causing microorganisms and civilizational diseases.Despite disparities among countries,recent and planned changes in regulations concerning animal research and drug safety studies could have detrimental effects on both the animal research community and nations lacking sufficient social support systems.Based on existing scientific literature,I argue that we need animal research encompassing aspects such as animal development,behavior,drug safety studies,and for the understanding of future civilizational diseases.Depending on the nature of the research questions and local challenges,a suitable animal model organism should be made mandatory.

Genome-wide association studies reveal the genetic basis of amino acid content variation in tea plants

Tea is one of the most popular non-alcoholic beverages in the world,and free amino acids,especially theanine,make a major contribution to the umami taste of tea.However,the genetic basis of the variation in amino acid content in tea plants remains largely unknown.Here,we measured the free amino acid content in fresh leaves of 174 tea accessions over two years using a targeted metabolomics approach and obtained genotype data via RNA sequencing.Genome-wide association studies were conducted to investigate loci affecting the content of free amino acids.A total of 69 quantitative trait loci(–log10(P-value)>5)were identified.Functional annotation revealed that branched-chain amino acid aminotransferase,glutamine synthetase,nitrate transporter,and glutamate decarboxylase might be important for amino acid metabolism.Two significant loci,glutamine synthetase(Glu1,P=3.71×10^(−4);Arg1,P=4.61×10^(−5))and branched-chain amino acid aminotransferase(Val1,P=4.67×10^(−5);I_Leu1,P=3.56×10^(−6)),were identified,respectively.Based on the genotyping result,two alleles of CsGS(CsGS-L and CsGS-H)and CsBCAT(CsBCAT-L and CsBCAT-H)were selected to perform function verification.Overexpression of CsGS-L and CsGS-H enhanced the contents of glutamate and arginine in transgenic plants,and overexpression of CsBCAT-L and CsBCAT-H promoted the accumulation of valine,isoleucine and leucine.Enzyme activity assay uncovered that SNP1054 is important for CsGS catalyzing glutamate into glutamine.Furthermore,CsGS-L and CsGS-H differentially regulated the accumulation of glutamine,and CsBCAT-L and CsBCAT-H differentially regulated the accumulation of branched-chain amino acids.In summary,the findings in our study would provide new insights into the genetic basis of amino acids contents variation in tea plants and facilitate the identification of elite genes to enhance amino acids content.