Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements:Developments and Validation Using Double Edge-Notched Tensile Specimen

To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is developed.The procedure includes(a)conversion of the finite element data into a triangular mesh,(b)selection of a common coordinate system,(c)determination of the rigid body transformation to place both measurements and FEA data in the same system and(d)interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates.For an aluminum Al-6061 double edge notched tensile specimen,FEA results are obtained using both the von Mises isotropic yield criterion and Hill’s quadratic anisotropic yield criterion,with the unknown Hill model parameters determined using full-field specimen strain measurements for the nominally plane stress specimen.Using Hill’s quadratic anisotropic yield criterion,the point-by-point comparison of experimentally based full-field strains and stresses to finite element predictions are shown to be in excellent agreement,confirming the effectiveness of the field comparison process.

Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process prediction.Therefore,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion.Firstly,the first layer BERT network learns the correlations between different category attribute data.Then,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events.Next,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual predictions.Finally,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

Screening and immune infiltration analysis of ferroptosis-related genes in pancreatic cancer with predictions for traditional Chinese medicine treatments

Background:This study aims to explore the involvement of ferroptosis-related genes and pathogenesis in pancreatic cancer and predict potential therapeutic interventions using Traditional Chinese Medicine(TCM).Methods:We utilized gene expression datasets,ferroptosis upregulated genes and applied machine learning algorithms,including LASSO and SVM-RFE,to identify key ferroptosis-related genes in pancreatic cancer.Perform Gene Ontology,Kyoto Encyclopedia of Genes and Genomes,and Disease Ontology enrichment analysis,immune infiltration analysis and correlation analysis between immune infiltrating cells and characteristic genes on differentially expressed genes using the R software package.Retrieve potential traditional Chinese medicine for targeted ferroptosis gene therapy for pancreatic cancer through Coremine and Herb databases.Results:Seventeen feature genes were identified,with significant implications for immune cell infiltration in pancreatic cancer.The results of immune cell infiltration analysis showed that B cells naive,B cells memory,T cells regulatory,and M0 macrophages were significantly upregulated in pancreatic cancer patients;Mast cells resting were significantly downregulated.Chinese herbal medicines such as ginkgo,turmeric,ginseng,Codonopsis pilosula,Zedoary turmeric,deer tendons,senna leaves,Guanmu Tong,Huangqi,and Banzhilian are potential drugs for targeted ferroptosis gene therapy for pancreatic cancer.Conclusion:TIMP1 emerged as a key gene,with several TCM herbs predicted to modulate its expression,offering new avenues for treatment.

Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies

Epilepsy is a severe,relapsing,and multifactorial neurological disorder.Studies regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy.The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota composition.Satisfactory results are lacking for conventional treatments for epilepsy.Surgical resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy.Non-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research focus.This review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of epilepsy.It also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation.Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures.Additionally,many new technologies for the diagnosis and treatment of epilepsy are being explored.However,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.

Development of a nomogram for overall survival in patients with esophageal carcinoma:A prospective cohort study in China

BACKGROUND Esophageal carcinoma(EC)presents a significant public health issue in China,with its prognosis impacted by myriad factors.The creation of a reliable prog-nostic model for the overall survival(OS)of EC patients promises to greatly advance the customization of treatment approaches.AIM To create a more systematic and practical model that incorporates clinically significant indicators to support decision-making in clinical settings.METHODS This study utilized data from a prospective longitudinal cohort of 3127 EC patients treated at Chongqing University Cancer Hospital between January 1,2018,and December 12,2020.Utilizing the least absolute shrinkage and selection operator regression alongside multivariate Cox regression analyses helped pinpoint pertinent variables for constructing the model.Its efficacy was assessed by concordance index(C-index),area under the receiver operating characteristic curve(AUC),calibration curves,and decision curve analysis(DCA).RESULTS Nine variables were determined to be significant predictors of OS in EC patients:Body mass index(BMI),Karnofsky performance status,TNM stage,surgery,radiotherapy,chemotherapy,immunotherapy,platelet-to-lymphocyte ratio,and albumin-to-globulin ratio(ALB/GLB).The model demonstrated a C-index of 0.715(95%CI:0.701-0.729)in the training cohort and 0.711(95%CI:0.689-0.732)in the validation cohort.In the training cohort,AUCs for 1-year,3-year,and 5-year OS predictions were 0.773,0.787,and 0.750,respectively;in the validation cohort,they were 0.772,0.768,and 0.723,respectively,illustrating the model's precision.Calibration curves and DCA verified the model's predictive accuracy and net benefit.CONCLUSION A novel prognostic model for determining the OS of EC patients was successfully developed and validated to help clinicians in devising individualized treatment schemes for EC patients.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Intricacies during pregnancy with gestational diabetes mellitus

The study by Cao et al aimed to identify early second-trimester biomarkers that could predict gestational diabetes mellitus(GDM)development using advanced proteomic techniques,such as Isobaric tags for relative and absolute quantitation isobaric tags for relative and absolute quantitation and liquid chromatography-mass spectrometry liquid chromatography-mass spectrometry.Their analysis revealed 47 differentially expressed proteins in the GDM group,with retinol-binding protein 4 and angiopoietin-like 8 showing significantly elevated serum levels compared to controls.Although these findings are promising,the study is limited by its small sample size(n=4 per group)and lacks essential details on the reproducibility and reliability of the protein quantification methods used.Furthermore,the absence of experimental validation weakens the interpretation of the protein-protein interaction network identified through bioinformatics analysis.The study's focus on second-trimester biomarkers raises concerns about whether this is a sufficiently early period to implement preventive interventions for GDM.Predicting GDM risk during the first trimester or pre-conceptional period may offer more clinical relevance.Despite its limitations,the study presents valuable insights into potential GDM biomarkers,but larger,well-validated studies are needed to establish their predictive utility and generalizability.

Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC) Part Ⅱ:The Experimental Seasonal Prediction

A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.

Changing trends of disease burden of gastric cancer in China from 1990 to 2019 and its predictions:Findings from Global Burden of Disease Study

Objective:China is one of the countries with the heaviest burden of gastric cancer(GC)in the world.Understanding the epidemiological trends and patterns of GC in China can contribute to formulating effective prevention strategies.Methods:The data on incidence,mortality,and disability-adjusted life-years(DALYs)of GC in China from1990 to 2019 were obtained from the Global Burden of Disease Study(2019).The estimated annual percentage change(EAPC)was calculated to evaluate the temporal trends of disease burden of GC,and the package Nordpred in the R program was used to perform an age-period-cohort analysis to predict the numbers and rates of incidence and mortality in the next 25 years.Results:The number of incident cases of GC increased from 317.34 thousand in 1990 to 612.82 thousand in2019,while the age-standardized incidence rate(ASIR)of GC decreased from 37.56 per 100,000 in 1990 to 30.64 per 100,000 in 2019,with an EAPC of-0.41[95%confidence interval(95%CI):-0.77,-0.06].Pronounced temporal trends in mortality and DALYs of GC were observed.In the next 25 years,the numbers of new GC cases and deaths are expected to increase to 738.79 thousand and 454.80 thousand,respectively,while the rates of incidence and deaths should steadily decrease.The deaths and DALYs attributable to smoking were different for males and females.Conclusions:In China,despite the fact that the rates of GC have decreased during the past three decades,the numbers of new GC cases and deaths increased,and will continue to increase in the next 25 years.Additional strategies are needed to reduce the burden of GC,such as screening and early detection,novel treatments,and the prevention of risk factors.

The Impact of Horizontal Resolution on the CNOP and on Its Identified Sensitive Areas for Tropical Cyclone Predictions

In this study, the ilnpacts of horizontal resolution on the conditional nonlinear optimal perturbation （CNOP） and on its identified sensitive areas were investigated for tropical cyclone predictions. Three resolutions, 30 km, 60 km, and 120 kin, were studied for three tropical cyclones, TC Mindulle （2004）, TC Meari （2004）, and TC Matsa （2005）. Results show that CNOP may present different structures with different resolutions, and the major parts of CNOP become increasingly localized with increased horizontal resolution. CNOP produces spiral and baroclinic structures, which partially account for its rapid amplification. The differences in CNOP structures result in different sensitive areas, but there are common areas for the CNOP-identified sensitive areas at various resolutions, and the size of the common areas is different from case to case. Generally, the forecasts benefit more from the reduction of the initial errors in the sensitive areas identified using higher resolutions than those using lower resolutions. However, the largest improvement of the forecast can be obtained at the resolution that is not the highest for some cases. In addition, the sensitive areas identified at lower resolutions are also helpful for improving the forecast with a finer resolution, but the sensitive areas identified at the same resolution as the forecast would be the most beneficial.

Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model

The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.

Contrasting the Skills and Biases of Deterministic Predictions for the Two Types of El Nio

The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Nio and how much is common to both this type and the conventional Eastern Pacific(EP)-type El Nio. In this study, the deterministic performance of an El Nio–Southern Oscillation(ENSO) ensemble prediction system is examined for the two types of El Nio. Ensemble hindcasts are run for the nine EP El Nio events and twelve CP El Nio events that have occurred since 1950. The results show that(1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times;(2) the systematic forecast biases come mostly from the prediction of the CP events; and(3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Nio. Further improvements to coupled atmosphere–ocean models in terms of CP El Nio prediction should be recognized as a key and high-priority task for the climate prediction community.

Analyses and predictions of rock cuttabilities under different confining stresses and rock properties based on rock indentation tests by conical pick

The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength.Based on the test results,the regression analyses,support vector machine(SVM)and generalized regression neural network(GRNN)were used to find the relationship among rock cuttability,uniaxial confining stress applied to rock,uniaxial compressive strength(UCS)and tensile strength of rock material.It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability,which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material.Based on prediction models for revealing the optimal stress condition and determining the cutting parameters,the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.

Preliminary Evaluations of FGOALS-g2 for Decadal Predictions

The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2） for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature （SAT）, particularly in that of Nifio3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate （the control） by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 （LICOM2）. The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.

Predictions of ENSO with a Coupled Atmosphere-Ocean General Circulation Model

Predictions of ENSO are described by using a coupled atmosphere-ocean general circulation model. The initial conditions are created by forcing the coupled system using SST anomalies in the tropical Pacific at the background of the coupled model climatology. A series of 24-month hindcasts for the period from November 1981 to December 1997 are carried out to validate the performance of the coupled system. Correlations of SST anomalies in the Nino3 region exceed 0.54 up to 15 months in advance and the rms errors are less than 0.9℃. The system is more skillful in predicting SST anomalies in the 1980s and less in the 1990s. The model skills are also seasonal-dependent, which are lower for the predictions starting from late autumn to winter and higher for those from spring to autumn in a year-time forecast length. The prediction, beginning from March, persists & months long with the correlation skill exceeding 0.6, which is important in predictions of summer rainfall in China. The predictions are succesful in many aspects for the 1997-2000 ENSO events.

Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations

Background:Genotyping by sequencing(GBS)still has problems with missing genotypes.Imputation is important for using GBS for genomic predictions,especially for low depths,due to the large number of missing genotypes.Minor allele frequency(MAF)is widely used as a marker data editing criteria for genomic predictions.In this study,three imputation methods(Beagle,IMPUTE2 and FImpute software)based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions,based on simulated data of livestock population.Results:Four MAFs(no MAF limit,MAF≥0.001,MAF≥0.01 and MAF≥0.03)were used for editing marker data before imputation.Beagle,IMPUTE2 and FImpute software were applied to impute the original GBS.Additionally,IMPUTE2 also imputed the expected genotype dosage after genotype correction(GcIM).The reliability of genomic predictions was calculated using GBS and imputed GBS data.The results showed that imputation accuracies were the same for the three imputation methods,except for the data of sequencing read depth(depth)=2,where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2.GcIM was observed to be the best for all of the imputations at depth=4,5 and 10,but the worst for depth=2.For genomic prediction,retaining more SNPs with no MAF limit resulted in higher reliability.As the depth increased to 10,the prediction reliabilities approached those using true genotypes in the GBS loci.Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points,and FImpute gained 3 percentage points at depth=2.The best prediction was observed at depth=4,5 and 10 using GcIM,but the worst prediction was also observed using GcIM at depth=2.Conclusions:The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths.Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths.These results suggest that the application of IMPUTE2,based on a corrected GBS(GcIM)to improve genomic predictions for higher depths,and FImpute software could be a good alternative for routine imputation.

Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations

Background: Genotyping by sequencing(GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes,dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations.Results: Chip array(Chip) and four depths of GBS data was simulated. After quality control(call rate ≥ 0.8 and MAF ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS(GBSc), true genotypes for the GBS loci(GBSr) and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively.Conclusions: The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths.

Time trends and gender disparities of Chinese cataract burden and their predictions

AIM:To evaluate the trends and changes in the number and rates of disability-adjusted life years(DALYs)and prevalence of cataract in China between 1990 and 2019,and to predict the trends of cataract burden from 2020 to 2030.METHODS:The Global Burden of Diseases(GBD)database was employed to collect the data on DALYs and the prevalence of cataract in China,which was distinguished by age and sex during the past three decades from 1990 to 2019,and then changes in the number and rates of cataract from 2020 to 2030 were predicted.All data were analyzed by the R program(version 4.2.2)and GraphPad Prism 9.0 statistics software.RESULTS:The number of DALYs of cataract increased from 449322.84 in 1990 to 1087987.61 in 2019,number of cataract cases increased from 5607600.94 in 1990 to 18142568.96 in 2019.The age-standardized DALY rates(ASDR)generally increased slightly[estimated annual percentage change(EAPC=0.1;95%CI:-0.24 to 0.45)],age-standardized prevalence rates(ASPR)also increased(EAPC=0.88;95%CI:0.6 to 1.15).Cataract burden increased with age and female gender.Among the causes of cataract,air pollution was the most important,followed by smoking,high fasting plasma glucose,and high body mass index(BMI).The burden of cataract is predicted to grow persistently from 2020 to 2030,the number of DALYs and prevalence for cataract will rise to 2336431 and 43698620 respectively by 2030,the ASDR is predicted to be 85/100000 and ASPR will be 1586/100000 in 2030,females will still be at greater risk of suffering from cataract than males.CONCLUSION:The burden of cataract in China kept rising from 1990 to 2019.Increasing age and female gender are risk factors for cataract.Air pollution,smoking,high fasting plasma glucose,and high BMI are associated with cataract.The burden of cataract in China will gradually increase from 2020 to 2030,the elderly women in particular need attention.Our results may be of help for providing reference strategies to reduce cataract burden in the near future.