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 studies have been derived from European and East Asian populations. The admixed genome of African American (AA) may provide an opportunity to discovery ancestry-specific loci involved in AD susceptibility. Herein, we present joint analysis of ancestry and genotype effects followed with validation using differential gene expression analysis on AD using 710 AD cases and 1015 non-AD controls from the AA population, genotyped using MEGA followed by imputation using the CAAPA reference panel. The joint analysis identified two novel AD-susceptibility loci, rs2195989 in gene ANGPT1 (8q23.1) and rs62538818 in the intergenic region LURAP1L-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. GWAS identified variants significantly associated with AD in the AA population, including SGK1 (rs113357522, 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.

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.

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.

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.

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.

As an emerging additive manufacturing technology, inkjet printing has been increasingly applied in microelectronics field. However, due to the impacting and rebounding behaviors of conductive ink droplets impinging onto flat substrates, it is challenging to fabricate conductive lines with desired quality, such as suitable line width and line thickness, and matching resistance when it is used for interconnecting multifarious electronic components if there is not a proper configuration of operating parameters. To address this research gap, this article aims to investigate the effect of process parameters on the quality of conductive lines, including the platform temperature, printing speed, number of layers, and delay time (droplet interarrival time), are selected to conduct a full factorial experiment. First, the approximate parameter ranges for ensuring the continuity of conductive lines are determined. Second, this study analyzes the interactive effect among process parameters on line quality. Third, an artificial neural network (ANN) is constructed to predict the quality of printed lines. Results show that the line width does not increase with an increased number of layers, while the line thickness shows an increasing trend. The low resistance and high aspect ratio of printed line are achieved by printing 5 layers with the platform temperature of 70°C, the delay time of 12.2 ms, and the printing speed of 1139.39 mm/min. Moreover, the ANN model can be used to predict line width and line thickness with excellent performance, except for the resistance due to the irregular line edge. This study provides a useful guide for the selection of appropriate printing parameters to realize a diverse range of quality properties for 3D printed conductive lines in integrated circuits.

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.

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.

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.

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.