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Radar is a valuable tool for noncontact vital-sign detection. Interference from respiratory harmonics presents a major challenge in radar cardiogram (RCG) extraction—mainly when the frequency of respiratory harmonics is close to or equal to that of the cardiac sub-signals. To address this problem, a respiratory harmonic suppression method employing correlation analysis and an optimized feedback notch filter is proposed, which is based on 7.29 GHz center-frequency impulse-radio ultra-wideband radar. A genetic optimization algorithm is employed to optimize the parameters of the notch filter. Performance comparison analysis is conducted on the conventional notch filter and the feedback notch filter. Contact (ECG) and non-contact (RCG) data from 10 subjects were analyzed. The results verified that the performance of the optimized feedback notch filter is much better than that of the conventional notch filter in overshoot, bandwidth, and notch depth, and the proposed method can effectively locate, identify, and suppress respiratory harmonics from the RCG band while preserving heartbeat components. Consequently, this approach markedly enhances the precision of RCG extraction. The technique shows considerable promise for deployment in diverse practical settings, including non-contact auxiliary monitoring systems in both intelligent medical environments and home healthcare.

Aluminum alloy has the characteristics of light weight, high strength, corrosion resistance, easy forming, etc., and is widely used in manufacturing industry. The extrusion speed of aluminum alloy rim is not uniform, which affects the mechanical properties of the product. In order to solve these 1problems, the finite element analysis model of hot extrusion of aluminum alloy profiles was established in HyperXtrude software. The influences of different bar diameters, hot extrusion velocity, bar preheating temperature, die temperature and extrusion cylinder temperature on velocity difference and maximum extrusion pressure at the exit were analyzed. The results show that the designed hot extrusion rim die meets the requirements of strength and deformation, and the best extrusion process parameters of 7075 aluminum alloy rim profiles in improving profile quality and reducing energy consumption are obtained by Taguchi test gray correlation method.

Abstract Background Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently. Results By leveraging summary statistics of 37 traits from East Asian (N max=254,373) or European (N max=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρ g ) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with $${\widehat{\rho }}_{g}$$ ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs. Conclusions Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.

Abstract Background Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear. Results By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection. Conclusions Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups.

Abstract Background Detecting trans-ethnic common associated genetic loci can offer important insights into shared genetic components underlying complex diseases/traits across diverse continental populations. However, effective statistical methods for such a goal are currently lacking. Methods By leveraging summary statistics available from global-scale genome-wide association studies, we herein proposed a novel genetic overlap detection method called CONTO (COmposite Null hypothesis test for Trans-ethnic genetic Overlap) from the perspective of high-dimensional composite null hypothesis testing. Unlike previous studies which generally analyzed individual genetic variants, CONTO is a gene-centric method which focuses on a set of genetic variants located within a gene simultaneously and assesses their joint significance with the trait of interest. By borrowing the similar principle of joint significance test (JST), CONTO takes the maximum P value of multiple associations as the significance measurement. Results Compared to JST which is often overly conservative, CONTO is improved in two aspects, including the construction of three-component mixture null distribution and the adjustment of trans-ethnic genetic correlation. Consequently, CONTO corrects the conservativeness of JST with well-calibrated P values and is much more powerful validated by extensive simulation studies. We applied CONTO to discover common associated genes for 31 complex diseases/traits between the East Asian and European populations, and identified many shared trait-associated genes that had otherwise been missed by JST. We further revealed that population-common genes were generally more evolutionarily conserved than population-specific or null ones. Conclusion Overall, CONTO represents a powerful method for detecting common associated genes across diverse ancestral groups; our results provide important implications on the transferability of GWAS discoveries in one population to others.

Abstract Background Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. Results We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow. Conclusion In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

Abstract Background Previous studies demonstrated a positive relationship between birthweight and breast cancer; however, inconsistent, sometimes even controversial, observations also emerged, and the nature of such relationship remains unknown. Methods Using summary statistics of birthweight and breast cancer, we assessed the fetal/maternal-specific genetic correlation between them via LDSC and prioritized fetal/maternal-specific pleiotropic genes through MAIUP. Relying on summary statistics we conducted Mendelian randomization (MR) to evaluate the fetal/maternal-specific origin of causal relationship between birthweight, age of menarche, age at menopause and breast cancer. Results With summary statistics we identified a positive genetic correlation between fetal-specific birthweight and breast cancer (r g = 0.123 and P = 0.013) as well as a negative but insignificant correlation between maternal-specific birthweight and breast cancer (r g = − 0.068, P = 0.206); and detected 84 pleiotropic genes shared by fetal-specific birthweight and breast cancer, 49 shared by maternal-specific birthweight and breast cancer. We also revealed fetal-specific birthweight indirectly influenced breast cancer risk in adulthood via the path of age of menarche or age at menopause in terms of MR-based mediation analysis. Conclusion This study reveals that shared genetic foundation and causal mediation commonly drive the connection between the two traits, and that fetal/maternal-specific birthweight plays substantially distinct roles in such relationship. However, our work offers little supportive evidence for the fetal origins hypothesis of breast cancer originating in utero.

Abstract Background Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. Methods We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. Results We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. Conclusions Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision.

Element geochemistry, Sr and Nd isotope, and LA-ICP-MS zircon U-Pb isotope data have been obtained for the granitoids of Dazesan pluton in the Jiaodong Peninsula, East China, and their intermediate microgranular dark enclaves so as to reveal their petrogenesis and tectonic implications. These granitoids have high SiO2 (68.25–71.56 wt.%), K2O (3.44–5.50 wt.%), total alkalis (K2O + Na2O = 7.29–9.00 wt.%), Sr (451–638 ppm), Ba (1157–2842 ppm) and light rare earth elements (LREEs) (131.57–210.08), with strong depletion both in heavy rare earth element (HREE) and high field strength element (HFSE) concentrations as well as unclearly Eu anomalies, showing typical signatures of high Ba-Sr granitoids. They possess high (La/Yb)N (32–50) and Sr/Y (50–79) values and low MgO (0.76–1.11 wt.%), Cr (9.9–19.6 ppm) and Ni (4.51–7.04 ppm) concentrations. All the above geochemical compositions are similar to those of late Early Cretaceous granitoids, in combination with zircon LA-ICP-MS U-Pb ages of 119.6 ± 1.3 to 120 ± 1 Ma for these granitoids obtained in this study, indicating c. 120 Ma probably represents the lower limit of ages when late Early Cretaceous granitoids emplaced in the Jiaodong Peninsula. The microgranular dark enclaves, forming a linear trend with their host granitoids on the oxide against SiO2 plots, display higher MgO contents of 3.05–4.39 wt.% at lower SiO2 concentrations of 54.25–56.84 wt.% and possess a zircon LA-ICP-MS U-Pb age of 119 ± 2 Ma, identical to those of these granitoids, indicating the acid magma and intermediate magma were coeval. Furthermore, dark enclaves and their host granitoids have indistinguishable (87Sr/86Sr)i values of 0.709523–0.70972 and 0.709361–0.709858, respectively, and plot within a two-liquid immiscible field on the Greig pseudoternary phase diagram. In addition, they have markedly parallel REE patterns, with the dark enclaves having much greater REE and HFSE abundances than those of their host granitoids. Therefore, it is suggested that liquid immiscibility is a viable model to explain the chemical compositional variations between the Dazeshan granitoids and their dark enclaves. Based on the element geochemistry, geochronology and Sr- and Nd-isotope of the Dazeshan granitoids and their dark enclaves, it is envisaged the crust-derived acid melts due to partial melting of ancient continental lower crust in the Jiaodong Peninsula (mainly Neoarchean-Palaeoproterozoic basement in the Jiaobei terrane) containing a subduction-related material, resulting from the addition of the enriched subcontinental lithospheric mantle-derived melts, assimilated the lithospheric mantle-derived basic melts and formed the homogeneous magma chamber at the crust base, then split into two immiscible liquids, with one granitic liquid producing the Dazeshan granitoids and the other intermediate one forming the dark enclave during its ascent. Combined with previous studies, the identification of a lithospheric mantle-derived material in the Dazeshan granitoids suggests a catastrophic lithospheric thinning at c. 120 Ma, reflecting an abrupt change in the direction of Palaeo-Pacific plate subducting and the corresponding regional tectonic transition from E–W extension to NW–SE extension.

Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs.Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways.Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as “urea cycle” (P = 0.036), “arginine biosynthesis” (P = 0.004) on AD and “phenylalanine, tyrosine and tryptophan biosynthesis” (P = 0.046) on ALS.Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

Cardiovascular diseases (CVDs) remain the main cause of morbidity and mortality worldwide. The pathological mechanism and underlying biological processes of these diseases with metabolites remain unclear. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of metabolites on these diseases by making full use of the latest GWAS summary statistics for 486 metabolites and six major CVDs. Extensive sensitivity analyses were implemented to validate our MR results. We also conducted linkage disequilibrium score regression (LDSC) and colocalization analysis to investigate whether MR findings were driven by genetic similarity or hybridization between LD and disease-associated gene loci. We identified a total of 310 suggestive associations across all metabolites and CVDs, and finally obtained four significant associations, including bradykinin, des-arg(9) (odds ratio [OR] = 1.160, 95% confidence intervals [CIs]: 1.080–1.246, false discovery rate [FDR] = 0.022) on ischemic stroke, N-acetylglycine (OR = 0.946, 95%CIs: 0.920–0.973, FDR = 0.023), X-09026 (OR = 0.845, 95%CIs: 0.779–0.916, FDR = 0.021) and X-14473 (OR = 0.938, 95%CIs = 0.907–0.971, FDR = 0.040) on hypertension. Sensitivity analyses showed that these causal associations were robust, the LDSC and colocalization analyses demonstrated that the identified associations were unlikely confused by LD. Moreover, we identified 15 important metabolic pathways might be involved in the pathogenesis of CVDs. Overall, our work identifies several metabolites that have a causal relationship with CVDs, and improves our understanding of the pathogenesis and treatment strategies for these diseases.