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To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.

This study aimed to determine whether remnant cholesterol (RC) and its variability can predict the onset of metabolic dysfunction-associated steatotic liver disease (MASLD) independently of low-density lipoprotein cholesterol (LDL-C) levels. A longitudinal cohort study involving 43,065 participants who underwent at least two physical examinations was conducted. This study used Cox proportional hazards models to assess the relationships among RC quartile levels (Q1–Q4), visit-to-visit variability, and the risk of MASLD. This variability was quantified using several metrics: standard deviation (SD), logSD, average real variability (ARV), logARV, mean absolute deviation (MAD), and logMAD. Concurrently, this study utilized a combined analysis of RC and LDL-C groups to assess the independent risk of MASLD associated with RC. During a mean visit-to-visit of 3.19 years (SD 2.06 years), 8374 patients (19.45%) developed MASLD. Compared with Q1, Q4 was associated with a significantly greater risk of MASLD (hazard ratio [HR] 1.309, 95% confidence interval [CI] 1.220–1.403, P < 0.001). The fully adjusted Cox model revealed that the HRs of SD, logSD, ARV, logARV, MAD and logMAD were 1.400 (95% CI 1.305–1.502), 1.278 (95% CI 1.188–1.374), 1.152 (95% CI 1.079–1.229), 1.183 (95% CI 1.140–1.227), 1.578 (95% CI 1.433–1.737) and 1.263 (95% CI 1.175–1.358), respectively. In both LDL-C subgroups (≥ 3.4 mmol/L and < 3.4 mmol/L), high baseline RC was associated with elevated MASLD risk (HR 1.208, 95% CI 1.148–1.270, P < 0.001; HR 1.246, 95% CI 1.129–1.374, P < 0.001). RC levels were independently associated with MASLD in healthy individuals, irrespective of LDL-C level. The variability of RC during visit-to-visit periods provides a predictive marker for identifying individuals at heightened risk of MASLD. [ABSTRACT FROM AUTHOR]

Background: To investigate the associations between relative fat mass (RFM) and clinical outcomes in different glucose tolerance statuses and the modified effect of glucose tolerance status. Methods: We analyzed 8,224 participants from a Chinese cohort study, who were classified into normal glucose status (NGT), prediabetes, and diabetes. Outcomes included fatal, nonfatal cardiovascular disease (CVD) events and all-cause mortality. Associations between RFM and outcomes were assessed using Cox regression. The modified effect of glucose tolerance status was investigated using mediation, interaction, and joint analyses. Results: During up to 5 years of follow-up, 154 (1.9%) participants experienced fatal CVD, 153 (1.9%) experienced nonfatal CVD events, and 294 (3.6%) experienced all-cause death. 2,679 participants (32.6%) had NGT, 4,528 (54.8%) had prediabetes, and 1,037 (12.6%) had diabetes. RFM was associated with increased risk of fatal (HR [95% CI], 1.09 [1.06–1.12], p < 0.001), nonfatal CVD events (HR [95% CI], 1.12 [1.09–1.15], p < 0.001), and all-cause mortality (HR [95% CI], 1.10 [1.08–1.12), p < 0.001) in all and those with NGT, prediabetes, and diabetes, and these associations were modified by glucose tolerance status, which included mediating (mediation proportion ranges from 4.74% to 8.69%) and synergistic interactive effects (multiplicative effect ranges from 1.03 to 1.06). The joint analysis identified the subclassification that exhibited the highest HR among 12 subclassifications. Conclusions: RFM was associated with increased risk of fatal, nonfatal CVD events, and all-cause mortality in NGT, prediabetes, and diabetes, and these associations were modified by glucose tolerance status, which could significantly influence how clinicians assess high risk and could lead to more personalized, effective prevention strategies. [ABSTRACT FROM AUTHOR]

This study follows up on previous research conducted by Litavec (J Forensic Sci., 68, 2023, 1780) on sorting commingled sacroiliac joints using deviation analysis. In the present report, the results of this technique are expanded to separating commingled first tarsometatarsal and atlantoaxial joints. Following the methods of Litavec (J Forensic Sci., 68, 2023, 1780), virtual models were created at the University of Tennessee‐Knoxville Donated Skeletal Collection from 69 atlases and axes, 66 first metatarsals, and 64 medial cuneiforms using an EinScan‐Pro 2x+ Handheld Surface Scanner. The shape and congruency of the articular surfaces were analyzed using a deviation analysis in Geomagic Wrap 2021. Receiver operating characteristic curves were conducted on reference samples composed of 200 commingled and non‐commingled bone pairs per joint to identify threshold values for sorting the remains. Validation samples of 225 pairs were subsequently analyzed to identify the efficacy of this method on a sample of unknown individuals. The statistical analyses confirmed that deviation analysis values from commingled first tarsometatarsal and atlantoaxial joint pairs were significantly larger than those from non‐commingled individuals (p < 0.0001). 87%–92% of first tarsometatarsal pairs and 63%–66% of atlantoaxial joint pairs were correctly sorted based on the selected threshold values. This study increases the number of joints able to be sorted by deviation analysis and reemphasizes its value as a technique for resolving commingled human remains. [ABSTRACT FROM AUTHOR]

【Objective】The study aims to elucidate the change patterns of related genes during the color transition of Cymbidium sinense 'Red Sun', in order to provide guidance for the improvement of C. sinense leaf color.【Method】The Illumina HiSeq high-throughput sequencing platform was used to sequence the red, yellow and green leaves of C. sinense 'Red Sun', analyze the enrichment pathway of differentially expressed genes, and conduct a joint analysis of transcriptome and metabolome data. Key differential genes were also verified by fluorescent quantitative PCR.【Result】The results showed that a total of 861 differentially expressed genes (DEGs) were screened during the transition from red to yellow leaves, with 204 up-regulated genes and 657 down-regulated genes; during the transition from yellow to green leaves, a total of 815 DGEs were screened, with 492 up-regulated genes and 323 down-regulated genes. KEGG pathway enrichment analysis revealed that genes related to pigment synthesis and metabolism were significantly enriched during the color transition process of C. sinense 'Red Sun'. During the leaf color transition, the expression levels of transcription factor families such as MYB, NAC, WRKY, and bHLH showed significant differences. Joint analysis with metabolome data indicated that flavonoids, isoflavonoids, flavones, and flavonols, as well as differential genes and metabolites in the metabolites synthesis pathways, were significantly enriched during the color transition process of C. sinense 'Red Sun'. A total of 26 differentially expressed genes in the flavonoid metabolic pathway were identified, which all showed significant down-regulation during the transition from red to yellow leaves. Additionally, the contents of five differential metabolites were significantly decreased during this transition. During the transition from yellow to green leaves, 17 genes and two metabolites (neohesperidin and naringin chalcone) showed significant differences.【Conclusion】The reduction in the content of anthocyanin synthesis precursors and the expression levels of related synthetic enzyme genes leads to the gradual disappearance of the red color in leaves of C. sinense 'Red Sun', and the high expression of photosynthetic pigment synthesis-related genes is the main reason for the leaves of C. sinense 'Red Sun' changing from yellow to green.

Blood is an important component for maintaining animal lives and synthesizing sugars, lipids, and proteins in organs. Revealing the relationship between genes and metabolite expression and milk somatic cell count (SCC), milk fat percentage, milk protein percentage, and lactose percentage in blood is helpful for understanding the molecular regulation mechanism of milk formation. Therefore, we separated the buffy coat and plasma from the blood of Xinjiang Brown cattle (XJBC) and Chinese Simmental cattle (CSC), which exhibit high and low SCC/milk fat percentage/milk protein percentage/lactose percentages, respectively. The expression of genes in blood and the metabolites in plasma was detected via RNA-Seq and LC-MS/MS, respectively. Based on the weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis of differentially expressed genes (DEGs), we further found that the expression of genes in the blood mainly affected the SCC and milk fat percentage. Immune or inflammatory-response-related pathways were involved in the regulation of SCC, milk fat percentage, milk protein percentage, and lactose percentage. The joint analysis of the metabolome and transcriptome further indicated that, in blood, the metabolism pathways of purine, glutathione, glycerophospholipid, glycine, arginine, and proline are also associated with SCC, while lipid metabolism and amino-acid-related metabolism pathways are associated with milk fat percentage and milk protein percentage, respectively. Finally, related SCC, milk fat percentage, and milk protein percentage DEGs and DEMs were mainly identified in the blood. [ABSTRACT FROM AUTHOR]

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Introduction: Maize photosensitivity and the control of flowering not only are important for reproduction, but also play pivotal roles in the processes of domestication and environmental adaptation, especially involving the utilization strategy of tropical maize in high-latitude regions. Methods: In this study, we used a linkage mapping population and an inbred association panel with the photoperiod sensitivity index (PSI) phenotyped under different environments and performed transcriptome analysis of T32 and QR273 between long-day and short-day conditions. Results: The results showed that PSIs of days to tasseling (DTT), days to pollen shedding (DTP), and days to silking (DTS) indicated efficacious interactions with photoperiod sensitivity for maize latitude adaptation. A total of 48 quantitative trait loci (QTLs) and 252 quantitative trait nucleotides (QTNs) were detected using the linkage population and the inbred association panel. Thirteen candidate genes were identified by combining the genome-wide association study (GWAS) approach, linkage analysis, and transcriptome analysis, wherein five critical candidate genes, MYB163, bif1, burp8, CADR3, and Zm00001d050238, were significantly associated with photoperiod sensitivity. Discussion: These results would provide much more abundant theoretical proofs to reveal the genetic basis of photoperiod sensitivity, which would be helpful to understand the genetic changes during domestication and improvement and contribute to reducing the barriers to use of tropical germplasm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tea Science is the property of Journal of Tea Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Because number of matured muscle fibers in poultry does not increase after birth, the meat yield is mainly determined during embryogenesis. We previously indicated breast muscle grew rapidly from 18th day after hatching (E18) to E27, and almost stopped from E27 to E34 of Jiaji ducks, while the mechanism is unclear. This study utilized RNA-seq to explore the related genes of muscle development and their relationship with small molecule metabolites at E18, E27 and E34 of Jiaji ducks. Several thousand differentially expressed genes (DEGs) were detected among E18, E27 and E34. DEGs expression profiles included 8 trend maps, among which trend 1 was opposite to and trend 6 was consistent with breast muscle development trend of Jiaji ducks. Through joint analysis between trend 1 of DEGs and trend 1 of differential metabolites (DEMs), protein digestion and absorption pathway stood out. The decrease of COL8A2 gene expression will lead to the decrease of arginine content, which will inhibit the development of breast muscle in embryonic Jiaji duck. Similarly, joint analysis between trend 6 of DEGs and trend 6 of DEMs indicated the increase of GAMT gene expression will cause the increase of proline content, and then promote the development of breast muscle of Jiaji duck in embryonic period. These results will be helpful for further understanding the mechanism of muscle yields of Jiaji ducks.

• The study presents the first report of ATM :c.1564_1565del variant segregating with breast cancer and Ataxia-Telangiectasia (A-T) and in the same family. • This study highlights the importance of a comprehensive genomic investigation and appropriate cancer risk management of heterozygote carriers of ATM in families with A-T. • Given the age-related or moderate penetrance of ATM in breast cancer, ATM -positive family members of an affected individual with A-T are likely asymptomatic for breast or any other epithelial cancer. ATM gene is implicated in the development of breast cancer in the heterozygous state, and Ataxia-telangiectasia (A-T) in a homozygous or compound heterozygous state. Ataxia-telangiectasia (A-T) is a rare cerebellar ataxia syndrome presenting with progressive neurologic impairment, telangiectasia, and an increased risk of leukemia and lymphoma. Although the role of ATM, separately, in association with A-T and breast cancer is well documented, there is a limited number of studies investigating ATM variants when segregating with both phenotypes in the same family. Here, using joint analysis and whole genome sequencing, we investigated ATM c.1564_1565del in a family with one homozygous member presenting with A-T (OMIM # 208900) and three heterozygous members, of whom one had breast cancer (OMIM # 114480). To our knowledge, this is the first study of ATM c.1564_1565del segregation with both A-T and breast cancer phenotypes within the same kindred. This study highlights the need for a comprehensive genomic approach in the appropriate cancer risk management of heterozygote carriers of ATM in families with A-T. [ABSTRACT FROM AUTHOR]

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events. Investigating alternative splicing could provide new insight into the genetic etiology of breast cancer. By a multi-tissue, joint splicing-based transcriptome-wide association study (TWAS) that integrates signals of multiple excised introns in a gene, the authors identified 88 breast cancer susceptibility genes that have not been reported in previous TWASs. [ABSTRACT FROM AUTHOR]

Because number of matured muscle fibers in poultry does not increase after birth, the meat yield is mainly determined during embryogenesis. We previously indicated breast muscle grew rapidly from 18th day after hatching (E18) to E27, and almost stopped from E27 to E34 of Jiaji ducks, while the mechanism is unclear. This study utilized RNA-seq to explore the related genes of muscle development and their relationship with small molecule metabolites at E18, E27 and E34 of Jiaji ducks. Several thousand differentially expressed genes (DEGs) were detected among E18, E27 and E34. DEGs expression profiles included 8 trend maps, among which trend 1 was opposite to and trend 6 was consistent with breast muscle development trend of Jiaji ducks. Through joint analysis between trend 1 of DEGs and trend 1 of differential metabolites (DEMs), protein digestion and absorption pathway stood out. The decrease of COL8A2 gene expression will lead to the decrease of arginine content, which will inhibit the development of breast muscle in embryonic Jiaji duck. Similarly, joint analysis between trend 6 of DEGs and trend 6 of DEMs indicated the increase of GAMT gene expression will cause the increase of proline content, and then promote the development of breast muscle of Jiaji duck in embryonic period. These results will be helpful for further understanding the mechanism of muscle yields of Jiaji ducks. [ABSTRACT FROM AUTHOR]

This study numerically examines the evolution of the stress and displacement fields of rock sur-rounding an oblique normal fault in the longwall working face of a mine. The aim is to determine the law of activation of an abnormal geological body containing coal and rock mass and to provide timely warnings of geological hazards, such as coal and gas bursts, rock bursts, and flooding during coal mining. A high-precision microseismic monitoring system was used to monitor the activation of and damage to the rock surrounding the mine. The results of numerical simulations coupled with those of microseismic monitoring showed that the coal and rock mass near the fault and goaf was situated in an area of high stress, with a high frequency and energy of microseismic events, wherein changes in the displacement of the roof were more significant when the working face was pushed to the fault layer. The combined analysis provides information on the activation of rock surrounding the coal mine and can be used to provide early warnings of mining accidents. The results provide a reference for the safe mining of the working face and technical guidance for the design parameters of pressure relief holes. [ABSTRACT FROM AUTHOR]

Comparisons between randomized trial analyses and observational analyses that attempt to address similar research questions have generated many controversies in epidemiology and the social sciences. There has been little consensus on when such comparisons are reasonable, what their implications are for the validity of observational analyses, or whether trial and observational analyses can be integrated to address effectiveness questions. Here, we consider methods for using observational analyses to complement trial analyses when assessing treatment effectiveness. First, we review the framework for designing observational analyses that emulate target trials and present an evidence map of its recent applications. We then review approaches for estimating the average treatment effect in the target population underlying the emulation, using observational analyses of the emulation data alone and using transportability analyses to extend inferences from a trial to the target population. We explain how comparing treatment effect estimates from the emulation against those from the trial can provide evidence on whether observational analyses can be trusted to deliver valid estimates of effectiveness—a process we refer to as benchmarking—and, in some cases, allow the joint analysis of the trial and observational data. We illustrate different approaches using a simplified example of a pragmatic trial and its emulation in registry data. We conclude that synthesizing trial and observational data—in transportability, benchmarking, or joint analyses—can leverage their complementary strengths to enhance learning about comparative effectiveness, through a process combining quantitative methods and epidemiologic judgments. [ABSTRACT FROM AUTHOR]

Purpose: Estimating the joint probability of occurrence and joint return period of suspended sediment load (SSL) is necessary for the design and operation of hydraulic structures. The amount of SSL is closely related to the amount of runoff, which is highly dependent on the amount of rainfall. Therefore, the purpose of this study is to estimate the joint probability of occurrence and the joint return period of SSL values given rainfall and river discharge values. Methods: The vine copula family was used in this study to trivariate frequency analyses of SSL associated with rainfall and stream flow in Allah Basin, Iran. We used daily data of 140 events recorded at Jokanak station during 1975–2020. To create the probabilistic model, the structures of D-vine, C-vine, and R-vine copula functions, as well as rotational, Gaussian, and independent modes, were examined based on log-likelihood, AIC, and BIC criteria. Results: The results of examining the marginal distributions showed that the generalized Pareto (RMSE=3.91, NSE=0.98, MAPE=3.91), log-normal (RMSE=2.69, NSE=0.99, MAPE=7.69), and GEV (RMSE=2.16, NSE=0.99, MAPE=5.43) are the best-fitted distributions on rainfall, river flow, and SSL data, respectively. By examining the various vine structures, the D-vine was chosen as a suitable copula (AIC=−727.8, BIC=−714.3, and log-likelihood=366.9) for modeling the dependency structure among rainfall (R), river flow (Q), and SSL. Examining the tree structure of considered copulas revealed that the D-vine copula maintains the dependency of the pairs of variables by selecting the best edges until the last tree. Frank and Clayton's 180-degree copulas were chosen as the best internal copula functions. The conditional return period of suspended sediment load in the study area was calculated and presented as contour curves based on rainfall and corresponding discharge. The return period provided is given by the occurrence of runoff and rainfall values. Compared to the univariate method, the presented return period includes a range of data. For example, in the 2-year return period, changes in SSL values provide a range of 0.46 to 1,395,579 tons per day, given the corresponding rainfall and runoff values with probabilities of 0.0031 to 0.50. Conclusions: Using this model, SSL values can be estimated by having the amounts of rainfall and river flow at different probability levels. The results showed that using the rotated copulas can describe the correlation in all directions, and therefore provide more reliable results. [ABSTRACT FROM AUTHOR]

Maize is a versatile crop, which allows from the production of whole plant silage to the harvest of grain maize, but it is necessary to verify the genotypes with these potentials according to the region of cultivation. Thus, the objective of this work was to identify the aptitude of maize for biomass for silage and grain in two years of second season cultivation in Dourados-MS. The experiment was conducted at Embrapa Western Agriculture, Dourados, MS, Brazil under field conditions in the experimental design was in randomized complete block in a 6x2 factorial scheme be six maize genotypes (BRS1010, KWS9606, BRS3046, 1P2224, 1Q2383, CAPO) and two growing years (2021 and 2022) with five replications. The agronomic traits of plant biomass for silage (plant and ear heigh, stem diameter number of leaves, green mass yield plant, leaf, steam more tassel and ear, dry matter yield in the plant and efficiency of land) and maize grain (ear diameter ear length, number of grain rows per ear, number of grains per row in ears, hundred seed weight and grain yield) were evaluated. There was an increase in the values of the traits for whole maize plant in the year 2022 compared to the year 2021. The maize genotypes indicated for biomass for silage were BRS3046, KWS9606 and 1Q2383, as for maize grain, the experimental genotypes that are under development, 1P2224 and 1Q2383, are promising options for the region.

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This study provides an innovative and novel method for sorting commingled human remains at the sacroiliac joint using deviation analyses. Virtual models were created at the University of Tennessee‐Knoxville Donated Skeletal Collection from 69 os coxae and 66 sacra using an EinScan‐Pro 2× + Handheld Surface Scanner. The shape of the auricular surfaces was analyzed in Geomagic Wrap 2017, and the congruency of the two auricular surfaces was measured using a deviation analysis. ROC curves were performed on a reference sample composed of 200 commingled and non‐commingled joint pairs to identify threshold values that could help sort the commingled remains. A validation sample of 225 pairs was subsequently analyzed to demonstrate the efficacy of this new method on a sample of unknown individuals. Statistical analyses demonstrated that the deviation analysis values from sacroiliac joints of commingled pairs were significantly larger than those from non‐commingled individuals (p < 0.0001). Based on the selected threshold values, 98%–100% of pairs were correctly sorted and reassociated. This novel and objective technique improves upon previously subjective strategies for sorting commingled remains and, in the future, will be applied to additional joint surfaces. [ABSTRACT FROM AUTHOR]

This article considers the possibility of using probabilistic models to analyze the maximal flow discharges of rivers in order to obtain reliable calculated statistical characteristics for basins with poorly studied hydrological features. The research was performed by the example of Cisbaikalia, which is characterized by a flood regime of river flow. It is found that floods in the study area most often occur in summer (July−August), are associated with the climatic characteristics of the region, and are often destructive. The analysis of the maximal flow of rivers is based on data from the Roshydromet observation network. The series of maximal water discharges are checked for homogeneity and, in general, no disturbances in the steady state of runoff caused by climate changes are detected. A generalized distribution of extremes is proposed as the main probabilistic model; it is recommended to determine its parameters on the basis of the group analysis. The integrated approach has been applied for the first time; it combines conventional methods of hydrological calculations, which are most often used to refine the characteristics obtained for the runoff in the zone of extreme values: the apparatus for truncation of distributions; joint analysis of data; a reduction formula with the reduction of the drain modulus value not only to the area of 200 km2, but also to the mean height of basins in the region; and the frequency probability method for estimation of obtained results. These methods are recommended by regulatory documents for discharge calculations and are most often individually used. The comprehensive approach described by the authors enables us to take into account the features of the runoff formation in the zone of extreme values and obtain more accurate values of characteristic quantiles of a given probability of excess for use in design on poorly studied rivers of the region. [ABSTRACT FROM AUTHOR]

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Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: In recent years, due to the increasing scale of pasture, the incidence of hoof disease is increasing and hoof deformation is one of the common hoof diseases in dairy cows. In order to explore the metabolic and ionic changes of hoof-deformed dairy cows, LC-MS/MS and ICP-OES/MS techniques were used to analyze the serum samples of dairy cows. As a result, 127 metabolites were screened by metabolomics, and 28 metabolic pathways were enriched by KEGG metabolic pathway enrichment analysis, including nine metabolic pathways. The results of ion omics showed that 13 kinds of ions such as K, Li and Pb were up-regulated, while 18 kinds of ions such as Al, Cu and Sb were down-regulated. The integrated analysis of metabolomics and ion omics found that potassium ions were positively correlated with L-tyrosine and L-proline, positively correlated with thiamine and negatively correlated with L-valine. Sodium ions were positively correlated with L-valine and negatively correlated with α-D-glucose. The aim of this study was to investigate the ionic and metabolic changes in the organism after hoof deformation in dairy cows. In order to explore the metabolic and ionic changes of hoof-deformed cows, the serum samples of 10 healthy cows (group C) and 10 hoof-deformed cows (group T) were analyzed by LC-MS/MS and ICP-OES/MS. The pathway enrichment of differential metabolites was analyzed by screening and identifying differential metabolites and ions and using a bioinformatics method. The integration of metabolomics and ionics was analyzed with ggplot2 software in R language, and verified by MRM target metabolomics. The results showed that 127 metabolites were screened by metabolomics, of which 81 were up-regulated (p < 0.05) and 46 were down-regulated (p < 0.05). The results of ICP-OES/MS showed that 13 kinds of ions such as K, Li, and Pb in serum of dairy cows were up-regulated, while 18 kinds of ions such as Al, Cu and Sb were down-regulated. The integrated analysis of metabolomics and ionics found that potassium ions were positively correlated with L-tyrosine, L-proline, thiamine and L-valine. Sodium ions were positively correlated with L-valine and negatively correlated with α-D-glucose. The results of high-throughput target metabolomics showed that the contents of L-proline, L-phenylalanine and L-tryptophan in serum of dairy cows increased significantly, which was consistent with the results of non-target metabolomics. In a word, the metabolism and ion changes in dairy cows with hoof deformation were revealed by metabolomics and ionics. [ABSTRACT FROM AUTHOR]

Abstract Investigating the interaction of water resources such as rainfall, river flow and groundwater level can be useful to know the behavior of water balance in a basin. In this study, using the rainfall, river flow and groundwater level deficiency signatures for a 60-day duration, accuracy of vine copulas was investigated by joint frequency analysis. First, while investigating correlation of pair-variables, tree sequences of C-, D- and R-vine copulas were investigated. The results were evaluated using AIC, Log likelihood and BIC statistics. Finally, according to the physics of the problem and evaluation criteria, D-vine copula was selected as the best copula and the relevant tree sequence was introduced. Kendall’s tau test was used to evaluate the correlation of pair-signatures. The results of the Kendall’s tau test showed that pair-signatures studied have a good correlation. Using D-vine copula and its conditional structure, the joint return period of groundwater deficiency signature affected by rainfall and river flow deficiency signatures was investigated. The results showed that the main changes in the groundwater level deficiency is between 0.3 and 2 m, which due to the rainfall and the corresponding river flow deficiency, return periods will be less than 5 years. Copula-based simulations were used to investigate the best copula accuracy in joint frequency analysis of the studied signatures. Using copula data of the studied signatures, the groundwater deficiency signature was simulated using D-vine copula and a selected tree sequence. The results showed acceptable accuracy of D-vine copula in simulating the copula values of the groundwater deficiency signature. After confirming the accuracy of D-vine copula, the probability of occurrence of groundwater deficiency signature was obtained from the joint probability of occurrence of other signatures. This method can be used as a general drought monitoring system for better water resources management in the basin.

Copyright of China Forest Products Industry is the property of China Forest Products Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ABSTRACT Besides rubber production, one of the main goals of the genetic improvement of rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] is to develop vigorous clones. The objective of the present study was to evaluate the vigor of 34 elite juvenile rubber clones. To this end, three experiments were conducted in the municipality of Pontes Gestal, in the northwest region of the São Paulo State. Each experiment consisted of 10 treatments, and 2 controls in common to the 3 experiments. The experiments were arranged in the randomized block design, with three replications and ten plants per plot, in the 8 m x 2.5 m spacing. Individual and joint analyses of variance were performed. The means were compared using the Dunnett test at 5%. Significant effect of treatments was found in the three experiments and in the joint analysis. The most vigorous clones were not different from the controls in the comparison of means by the individual and joint analyses. The less vigorous clones were statistically different from the others and from the controls, reflecting this difference in the genetic variability observed. The breeding program must consider the lack of difference between the most vigorous clones and the controls when recombining superior clones, since genetic gain depends on variability.

The stability of tunnel site depends upon the deformation behaviors of the surrounding rocks, including joint density, mineral composition, micro cracks, and grain boundaries. The aim of this study was to analyze the mechanical properties of the Besham Complex in Northern Pakistan in terms of petrography and joint analysis. The Besham complex is prone to deformation as it is bounded by the Main Mantle Thrust (MMT) in the north, the Thakot fault (Tf) in the east, and the Swat block in the west. In the study area, the deformation was observed at the mesoscopic scale, while alteration in grains and fractures were determined at a microscopic scale. Detailed petrographic study of the representative samples from each station revealed that the area is dominantly composed of granite, quartz syenite, quartz monzonite, granodiorite, quartz-rich granitoid, monzogranite, alkali feldspar granite, and quartz monzodiorite. Extensive field work was performed to determine the joint volume (JV), in which the number of joints per cubic meter was recorded for each joint set. The rock quality designation (RQD) was calculated through JV analysis. An RQD value

Large-scale comprehensive single-cell experiments are often resource- intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data that might occur due to different technology platforms, reagent lots, or handling personnel. Such technical differences confound biological variations of interest and need to be corrected during the data integration process. Data integration is a challenging task due to the overlapping of biological and technical factors, which makes it difficult to distinguish their individual contribution to the overall observed effect. Moreover, the choice of integration method may impact the downstream analyses, including searching for differentially expressed genes. From the existing data integration methods, we selected only those that return the full expression matrix. We evaluated six methods in terms of their influence on the performance of differential gene expression analysis in two single-cell datasets with the same biological study design that differ only in the way the measurement was done: one dataset manifests strong batch effects due to the measurements of each sample at a different time. Integrated data were visualized using the UMAP method. The evaluation was done both on individual gene level using parametric and non-parametric approaches for finding differentially expressed genes and on gene set level using gene set enrichment analysis. As an evaluation metric, we used two correlation coefficients, Pearson and Spearman, of the obtained test statistics between reference, test, and corrected studies. Visual comparison of UMAP plots highlighted ComBat-seq, limma, and MNN, which reduced batch effects and preserved differences between biological conditions. Most of the tested methods changed the data distribution after integration, which negatively impacts the use of parametric methods for the analysis. Two algorithms, MNN and Scanorama, gave very poor results in terms of differential analysis on gene and gene set levels. Finally, we highlight ComBat-seq as it led to the highest correlation of test statistics between reference and corrected dataset among others. Moreover, it does not distort the original distribution of gene expression data, so it can be used in all types of downstream analyses. [ABSTRACT FROM AUTHOR]

We describe a regularized regression model for the selection of gene–environment (G × E) interactions. The model focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure. We propose an efficient fitting algorithm and screening rules that can discard large numbers of irrelevant predictors with high accuracy. We present simulation results showing that the model outperforms existing joint selection methods for (G × E) interactions in terms of selection performance, scalability and speed, and provide a real data application. Our implementation is available in the gesso R package. for this article are available online. [ABSTRACT FROM AUTHOR]

Given the dynamically diverse outcomes of biological, behavioral, and environmental factors, the heterogeneity of the subjective well-being of older adults must be considered in the context of aged societies. This study conducted a joint analysis of older adults' subjective indicators of satisfaction with living arrangements (SLA) and life satisfaction (LS). The bivariate generalized ordered probit model was employed to study 2045 urban and rural older adults. The findings show that family relationships and depression were critical sources of heterogeneity for SLA and LS when controlled for an older adults' area of residence (urban or rural). Better family relationships and lack of depression were significant in prompting positive attitudes toward SLA and LS in older adults. In particular, there was significant heterogeneity among urban older adults who self-reported health effects on their cognitive perception of LS, but rural older adults did not show this effect. Simultaneously, urban seniors cared more about their spouses' health and its effect on their SLA and LS. Moreover, being a widow/widower significantly affected the SLA of rural seniors. This study marks a breakthrough regarding sample heterogeneity in bivariate ordered probit models. It demonstrates that urban and rural older adults have different satisfaction levels, even in the homogeneous areas of residence. The findings of this study reveal subjective cognitive discrepancies between urban and rural older adults' SLA and LS, thereby highlighting that the heterogeneous outcomes experienced by older adults through environmental interactions should be of greater concern in an aged society. [ABSTRACT FROM AUTHOR]

Large-scale comprehensive single-cell experiments are often resource-intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data that might occur due to different technology platforms, reagent lots, or handling personnel. Such technical differences confound biological variations of interest and need to be corrected during the data integration process. Data integration is a challenging task due to the overlapping of biological and technical factors, which makes it difficult to distinguish their individual contribution to the overall observed effect. Moreover, the choice of integration method may impact the downstream analyses, including searching for differentially expressed genes. From the existing data integration methods, we selected only those that return the full expression matrix. We evaluated six methods in terms of their influence on the performance of differential gene expression analysis in two single-cell datasets with the same biological study design that differ only in the way the measurement was done: one dataset manifests strong batch effects due to the measurements of each sample at a different time. Integrated data were visualized using the UMAP method. The evaluation was done both on individual gene level using parametric and non-parametric approaches for finding differentially expressed genes and on gene set level using gene set enrichment analysis. As an evaluation metric, we used two correlation coefficients, Pearson and Spearman, of the obtained test statistics between reference, test, and corrected studies. Visual comparison of UMAP plots highlighted ComBat-seq, limma, and MNN, which reduced batch effects and preserved differences between biological conditions. Most of the tested methods changed the data distribution after integration, which negatively impacts the use of parametric methods for the analysis. Two algorithms, MNN and Scanorama, gave very poor results in terms of differential analysis on gene and gene set levels. Finally, we highlight ComBat-seq as it led to the highest correlation of test statistics between reference and corrected dataset among others. Moreover, it does not distort the original distribution of gene expression data, so it can be used in all types of downstream analyses.

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Actinidia arguta Sieb.Zucc. is a fruit that is rich in flavonoids. Nevertheless, details of flavonoid formation and the potential mechanism behind flavonoid biosynthesis have not previously been reported. In order to explore the biosynthetic regulation mechanism of flavonoids in A. arguta Sieb.Zucc., we conducted a combination of extensive targeted metabolite analysis and analyzed transcriptomes to determine the flavonoids present and the genes bound up with flavonoid biosynthesis in the two main cultivated varieties of A. arguta Sieb.Zucc. in Northern China. The maturity period is from August to September. A total of 118 flavonoids were found in fruits. Among them, 39 flavonoids were accumulated at significant levels after fruit ripening. Transcriptome analysis indicated that most flavonoid biosynthesis structural genes and certain regulatory genes exhibited differential expression between the two varieties. Correlation analysis of transcriptome and metabolite profiles showed that the ways of expression of 21 differentially expressed genes related to structure and regulation between the 2 varieties were more highly correlated with 7 flavonoids after fruit ripening. These results contribute to the development of A. arguta Sieb.Zucc. as a food and drug homologous functional food. [ABSTRACT FROM AUTHOR]

Actinidia arguta Sieb.Zucc. is a fruit that is rich in flavonoids. Nevertheless, details of flavonoid formation and the potential mechanism behind flavonoid biosynthesis have not previously been reported. In order to explore the biosynthetic regulation mechanism of flavonoids in A. arguta Sieb.Zucc., we conducted a combination of extensive targeted metabolite analysis and analyzed transcriptomes to determine the flavonoids present and the genes bound up with flavonoid biosynthesis in the two main cultivated varieties of A. arguta Sieb.Zucc. in Northern China. The maturity period is from August to September. A total of 118 flavonoids were found in fruits. Among them, 39 flavonoids were accumulated at significant levels after fruit ripening. Transcriptome analysis indicated that most flavonoid biosynthesis structural genes and certain regulatory genes exhibited differential expression between the two varieties. Correlation analysis of transcriptome and metabolite profiles showed that the ways of expression of 21 differentially expressed genes related to structure and regulation between the 2 varieties were more highly correlated with 7 flavonoids after fruit ripening. These results contribute to the development of A. arguta Sieb.Zucc. as a food and drug homologous functional food.

Abstract Background In the last decade, Genome-wide Association studies (GWASs) have contributed to decoding the human genome by uncovering many genetic variations associated with various diseases. Many follow-up investigations involve joint analysis of multiple independently generated GWAS data sets. While most of the computational approaches developed for joint analysis are based on summary statistics, the joint analysis based on individual-level data with consideration of confounding factors remains to be a challenge. Results In this study, we propose a method, called Coupled Mixed Model (CMM), that enables a joint GWAS analysis on two independently collected sets of GWAS data with different phenotypes. The CMM method does not require the data sets to have the same phenotypes as it aims to infer the unknown phenotypes using a set of multivariate sparse mixed models. Moreover, CMM addresses the confounding variables due to population stratification, family structures, and cryptic relatedness, as well as those arising during data collection such as batch effects that frequently appear in joint genetic studies. We evaluate the performance of CMM using simulation experiments. In real data analysis, we illustrate the utility of CMM by an application to evaluating common genetic associations for Alzheimer’s disease and substance use disorder using datasets independently collected for the two complex human disorders. Comparison of the results with those from previous experiments and analyses supports the utility of our method and provides new insights into the diseases. The software is available at https://github.com/HaohanWang/CMM .

Investigating the interaction of water resources such as rainfall, river flow and groundwater level can be useful to know the behavior of water balance in a basin. In this study, using the rainfall, river flow and groundwater level deficiency signatures for a 60-day duration, accuracy of vine copulas was investigated by joint frequency analysis. First, while investigating correlation of pair-variables, tree sequences of C-, D- and R-vine copulas were investigated. The results were evaluated using AIC, Log likelihood and BIC statistics. Finally, according to the physics of the problem and evaluation criteria, D-vine copula was selected as the best copula and the relevant tree sequence was introduced. Kendall's tau test was used to evaluate the correlation of pair-signatures. The results of the Kendall's tau test showed that pair-signatures studied have a good correlation. Using D-vine copula and its conditional structure, the joint return period of groundwater deficiency signature affected by rainfall and river flow deficiency signatures was investigated. The results showed that the main changes in the groundwater level deficiency is between 0.3 and 2 m, which due to the rainfall and the corresponding river flow deficiency, return periods will be less than 5 years. Copula-based simulations were used to investigate the best copula accuracy in joint frequency analysis of the studied signatures. Using copula data of the studied signatures, the groundwater deficiency signature was simulated using D-vine copula and a selected tree sequence. The results showed acceptable accuracy of D-vine copula in simulating the copula values of the groundwater deficiency signature. After confirming the accuracy of D-vine copula, the probability of occurrence of groundwater deficiency signature was obtained from the joint probability of occurrence of other signatures. This method can be used as a general drought monitoring system for better water resources management in the basin. [ABSTRACT FROM AUTHOR]

The Ministry of Finance released the Synergy Program (Joint Program) in 2017, where one of the subprograms is a Joint Analysis between the Directorate General of Tax and Customs. Joint Analysis is intended to optimize state revenue, tax compliance and can be developed by other subprograms. The implementation of Joint Analysis can encompass non-compliant importers and exporters and close the tax avoidance gap by shifting the location of activities or company domiciles between regions of Indonesia. The purpose of this study is to analyze the enabling factors in implementing a joint analysis policy between the Directorate General of Tax and Customs. This study adopts the positivism paradigm with qualitative methods. Primary data sources were obtained through in-depth interviews with selected informants. The results showed that political support and commitment from the leadership were quite good, but equal treatment at the Regional Office level was still a chore to complete. The legal regulations are complete and clear enough to accommodate the implementation of the program even though the technical operational standards at the Regional Office level are not yet rigid. Resources and governance are quite good although it requires time for the joint team to learn about business processes. Understanding between parties is considered quite good with the growing development of trust and joint commitment to secure state revenue. Communication between joint teams is done practically and flexibly, data exchange can be by email or WhatsApp group. The use of data analytics applications is also quite effective even though it has not yet reached all Regional Offices.

Atopic dermatitis (AD) is a chronic itchy inflammatory disease of the skin. Genetic studies have identified multiple risk factors linked to the disease; however, most of the studies have been derived from European and East Asian populations. The admixed African American (AA) genome may provide an opportunity to discovery ancestry-specific loci involved in AD susceptibility. Herein, we present joint analysis of ancestry and genotype effects followed by validation using differential gene expression analysis on AD using 726 AD-affected individuals and 999 non-AD control individuals from the AA population, genotyped using Multi-Ethnic Global Array (MEGA) followed by imputation using the Consortium on Asthma among African Ancestry Populations in the Americas (CAAPA) reference panel. The joint analysis identified two novel AD-susceptibility loci, rs2195989 in gene ANGPT1 (8q23.1) and rs62538818 in the intergenic region between genes LURAP1L and MPDZ (9p23). Admixture mapping (AM) results showed potential genomic inflation, and we implemented genomic control and identified five ancestry-of-origin loci with European ancestry effects. The multi-omics functional prioritization of variants in AM signals prioritized the loci SLAIN2, RNF39, and FOXA2. Genome-wide association study (GWAS) identified variants significantly associated with AD in the AA population, including SGK1 (rs113357522, odds ratio [OR] = 2.81), EFR3A (rs16904552, OR = 1.725), and MMP14 (rs911912, OR = 1.791). GWAS variants were common in the AA but rare in the European population, which suggests an African-ancestry-specific risk of AD. Four genes (ANGPT1, LURAP1L, EFR3A, and SGK1) were further validated using qPCR from AD and healthy skin. This study highlighted the importance of genetic studies on admixed populations, as well as local ancestry and genotype-ancestry joint effects to identify risk loci for AD., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Besides rubber production, one of the main goals of the genetic improvement of rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] is to develop vigorous clones. The objective of the present study was to evaluate the vigor of 34 elite juvenile rubber clones. To this end, three experiments were conducted in the municipality of Pontes Gestal, in the northwest region of the São Paulo State. Each experiment consisted of 10 treatments, and 2 controls in common to the 3 experiments. The experiments were arranged in the randomized block design, with three replications and ten plants per plot, in the 8 m x 2.5 m spacing. Individual and joint analyses of variance were performed. The means were compared using the Dunnett test at 5%. Significant effect of treatments was found in the three experiments and in the joint analysis. The most vigorous clones were not different from the controls in the comparison of means by the individual and joint analyses. The less vigorous clones were statistically different from the others and from the controls, reflecting this difference in the genetic variability observed. The breeding program must consider the lack of difference between the most vigorous clones and the controls when recombining superior clones, since genetic gain depends on variability. [ABSTRACT FROM AUTHOR]

Dynamic prediction methods incorporate longitudinal biomarker information to produce updated, more accurate predictions of conditional survival probability. There are two approaches for obtaining dynamic predictions: (1) a joint model of the longitudinal marker and survival process, and (2) an approximate approach that specifies a model for a specific component of the joint distribution. In the case of a binary marker, an illness‐death model is an example of a joint modeling approach that is unified and produces consistent predictions. However, previous literature has shown that approximate approaches, such as landmarking, with additional flexibility can have good predictive performance. One such approach proposes using a Gaussian copula to model the joint distribution of conditional continuous marker and survival distributions. It has the advantage of specifying established, flexible models for the marginals for which goodness‐of‐fit can be assessed, and has easy estimation that can be implemented in standard software. In this article, we provide a Gaussian copula approach for dynamic prediction to accommodate a binary marker using a continuous latent variable formulation. We compare the predictive performance of this approach to joint modeling and landmarking using simulations and demonstrate its use for obtaining dynamic predictions in an application to a prostate cancer study. [ABSTRACT FROM AUTHOR]

In microbiome research, metagenomic sequencing generates enormous amounts of data. These data are typically classified into taxa for taxonomy analysis, or into genes for functional analysis. However, a joint analysis where the reads are classified into taxa-specific genes is often overlooked. To enable the analysis of this biologically meaningful feature, we developed a novel bioinformatic toolkit, MetaPrism, which can analyze sequence reads for a set of joint taxa/gene analyses to: 1) classify sequence reads and estimate the abundances for taxa-specific genes; 2) tabularize and visualize taxa-specific gene abundances; 3) compare the abundances between groups; and 4) build prediction models for clinical outcome. We illustrated these functions using a published microbiome metagenomics dataset from patients treated with immune checkpoint inhibitor therapy and showed the joint features can serve as potential biomarkers to predict therapeutic responses. MetaPrism is a toolkit for joint taxa and gene analysis. It offers biological insights on the taxa-specific genes on top of the taxa-alone or gene-alone analysis. [ABSTRACT FROM AUTHOR]

Recent advances in micro-electromechanical systems technology have enabled the evolution of miniature, low-power, and high-performance inertial motion sensors that are commonly found in most present-day smart gadgets. Furthermore, high-speed and power-efficient communication and computing technologies may enable these sensors to potentially pave the way for home-based remote monitoring and assessment of human health in the imminent age of new technologies such as Smart Home, internet-of-things, and internet-of-everything. Continuous monitoring of lower-limb joints in a wearable platform is such an application that may potentially enable the tele-rehabilitation of patients with motor impairment, gait abnormalities, and joint injuries through quantitative rather than observational analysis of gait health. In this work, we designed, implemented, and validated a two-stage sensor fusion algorithm to estimate lower-limb joint angles in real-time. The drift in the cumulatively integrated gyroscope data was estimated in real-time using a gradient descent approach that was subsequently used to correct the inclination of the sensors. The roll and pitch angles thus obtained for each sensor mounted above and below the joint were then fused in the second stage to obtain a real-time estimate of joint angle by exploiting a gradient descent method. Since the joint angles were estimated primarily from the gyroscope data and without incorporating any magnetic field measurement, the joint angles thus obtained were least affected by the external acceleration and are insensitive to magnetic disturbances. The performance of the proposed algorithm was validated with a publicly available dataset and in the presence of simulated external acceleration. [ABSTRACT FROM AUTHOR]

In this paper, we propose a joint modeling approach to analyze dependency in parallel response data. We define two types of dependency: higher-level dependency and within-item conditional dependency. While higher-level dependency can be estimated with common latent variable modeling approaches, within-item conditional dependency is a unique kind of information that is often not captured with extant methods, despite its potential to shed new insights into the relationship between the two types of response data. We differentiate three ways of modeling within-item conditional dependency by conditioning on raw values, expected values, or residual values of the response data, which have different implications in terms of response processes. The proposed approach is illustrated with the example of analyzing parallel data on response accuracy and brain activations from a Theory of Mind assessment. The consequence of ignoring within-item conditional dependency is investigated with empirical and simulation studies in comparison to conventional dependency analysis that focuses exclusively on relationships between latent variables. [ABSTRACT FROM AUTHOR]

Objective. In recent years, artificial intelligence-based electrocardiogram (ECG) methods have been massively applied to myocardial infarction (MI). However, the joint analysis of static and dynamic features to achieve accurate and interpretable MI detection has not been comprehensively addressed. Approach. This paper proposes a simplified ensemble tree method with a joint analysis of static and dynamic features to solve this issue for MI detection. Initially, the dynamic features are extracted by modeling the intrinsic dynamics of ECG via dynamic learning in addition to extracting classical static features. Secondly, a two-stage feature selection strategy is designed to identify a few significant features, which substitute the original variables that are employed in constructing the ensemble tree. This approach enhances the discriminative ability by selecting significant static and dynamic features. Subsequently, this paper presents an interpretable classification method named StackTree by introducing a stacked ensemble scheme to modify the ensemble tree simplification algorithm. The representative rules of the raw ensemble trees are selected as the intermediate training data that is used to retrain a decision tree with performance close to that of the source ensemble model. Using this scheme, the significant precision and interpretability of MI detection are thus comprehensively addressed. Main results. The effectiveness of our method in detecting MI is evaluated using the Physikalisch-Technische Bundesanstalt (PTB) and clinical database. The findings suggest that our algorithm outperforms the traditional methods based on a single type of feature. Additionally, it is comparable to the conventional random forest, achieving 97.1% accuracy under the inter-patient framework on the PTB database. Furthermore, feature subsets trained on PTB are validated using the clinical database, resulting in an accuracy of 84.5%. The chosen important features demonstrate that both static and dynamic information have crucial roles in MI detection. Crucially, the proposed method provides clear internal workings in an easy-to-understand visual manner., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Aiming to identify more genomic loci associated with bone mineral density (BMD), we conducted a joint association analysis of 2 genome-wide association study (GWAS) by the integrative association method multi-trait analysis of GWAS (MTAG). The first one is the single GWAS of estimated heel BMD (eBMD) in the UK biobank (UKB) cohort (N = 426,824), and the second one is the GWAS meta-analysis of total body BMD (TB-BMD) in 66,628 participants from 30 studies. Approximate conditional association analysis was performed in the identified novel loci to identify secondary association signal. Statistical fine-mapping was conducted to prioritize plausible credible risk variants (CRVs). Candidate genes were prioritized based on the analyses of cis- expression quantitative trait locus (cis-eQTL) and cis-protein QTL (cis-pQTL) information as well as the functional category of the SNP. By integrating the information carried in over 490,000 participants, this largest joint analysis of BMD GWAS identified 12 novel genomic loci at the genome-wide significance level (GWS, p = 5.0 × 10−8), nine of which were for eBMD and four were for TB-BMD, explaining an additional 0.11 and 0.23% heritability for the two traits, respectively. These loci include 1p33 (lead SNP rs10493130, peBMD = 3.19 × 10−8), 5q13.2 (rs4703589, peBMD = 4.78 × 10−8), 5q31.3 (rs9324887, pTB−BMD = 1.36 × 10−9), 6p21.32 (rs6905837, peBMD = 3.32 × 10−8), 6q14.1 (rs10806234, peBMD = 2.63 × 10−8), 7q21.11 (rs10806234, pTB−BMD = 3.37 × 10−8), 8q24.12 (rs11995866, peBMD = 6.72 × 10−9), 12p13.31 (rs1639122, peBMD = 4.43 × 10−8), 12p12.1 (rs58489179, peBMD = 4.74 × 10−8), 12q24.23 (rs75499226, peBMD = 1.44 × 10−8), 19q13.31 (rs7255083, pTB−BMD = 2.18 × 10−8) and 22q11.23 (rs13056137, pTB−BMD = 2.54 × 10−8). All lead SNPs in these 12 loci are nominally significant in both original studies as well as consistent in effect direction between them, providing solid evidence of replication. Approximate conditional analysis identified one secondary signal in 5q13.2 (rs11738874, pconditional = 5.06 × 10−9). Statistical fine-mapping analysis prioritized 269 CRVs. A total of 65 candidate genes were prioritized, including those with known biological function to bone development (such as FGF1, COL11A2 and DEPTOR). Our findings provide novel insights into a better understanding of the genetic mechanism underlying bone development as well as candidate genes for future functional investigation.