Search

Abstract Multidimensional host and viral factors determine the clinical course of COVID-19. While the virology of the disease is well studied, investigating host-related factors, including genome, transcriptome, metabolome, and exposome, can provide valuable insights into the underlying pathophysiology. We conducted integrative omics analyses to explore their intricate interplay in COVID-19. We used data from the UK Biobank (UKB), and employed single-omics, pairwise-omics, and multi-omics models to illustrate the effects of different omics layers. The dataset included COVID-19 phenotypic data as well as genome, imputed-transcriptome, metabolome and exposome data. We examined the main, interaction effects and correlations between omics layers underlying COVID-19. Single-omics analyses showed that the transcriptome (derived from the coronary artery tissue) and exposome captured 3–4% of the variation in COVID-19 susceptibility, while the genome and metabolome contributed 2–2.5% of the phenotypic variation. In the omics-exposome model, where individual omics layers were simultaneously fitted with exposome data, the contributions of genome and metabolome were diminished and considered negligible, whereas the effects of the transcriptome showed minimal change. Through mediation analysis, the findings revealed that exposomic factors mediated about 60% of the genome and metabolome’s effects, while having a relatively minor impact on the transcriptome, mediating only 7% of its effects. In conclusion, our integrative-omics analyses shed light on the contribution of omics layers to the variance of COVID-19.

Abstract Background Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis‐free phenome‐wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC. Methods We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype‐specific genetic risk scores (OC‐GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture. Results The OC‐GRS was associated with 10 diseases, and the clear cell OC‐GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10−4). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of “secondary malignant neoplasm of digestive systems” (OR 1.64, 95% CI 1.33, 2.02), “ascites” (1.48, 95% CI 1.17, 1.86), “chronic airway obstruction” (1.17, 95% CI 1.07, 1.29), and “abnormal findings on examination of the lung” (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10−8). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis. Conclusions OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions.

Background We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high‐sensitivity cardiac troponin T, N‐terminal pro‐B‐type natriuretic peptide, high‐sensitivity C‐reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long‐term mortality risk. Methods and Results BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high‐frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker‐based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all‐cause death were evaluated using accelerated failure time models (median follow‐up, 9.1 years; 141 deaths). Three biomarker‐based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long‐term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44–0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39–1.32; P=0.281) compared with patients with a repeat ACS. Conclusions Patients with subphenotypes of post‐ACS with different all‐cause mortality risks during long‐term follow‐up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

Cross-ancestry genetic correlation can reveal differences in the genetic basis of traits between populations, but methods can be biased by ancestry-specific genetic architecture. Here, the authors present a method to for estimating cross-ancestry genetic correlations, accounting for ancestry-specific genetic architecture.

A possible route to scalability of trapped-ion-based quantum computing platforms is to connect multiple modules where ions can be shuttled across different registers. Here, the authors demonstrate fast and low-loss transfer of trapped ions between two microchip modules.

Surfacing Eroding Thermocouples (SETC) have been used to provide high spatial and temporal resolution heat flux measurement in DIII-D. SETCs were first tested in the lower divertor of DIII-D using the Divertor Material Evaluation System (DiMES), then an array of SETCS was permanently installed in the upper SAS-VW divertor for studying the heat flux mitigation in closed divertor geometry. SETCs proved that inducing divertor detachment with various methods is an effective way to reduce the peak heat flux at the divertor targets. In the investigated discharges the heat flux is reduced to less than 30% under detachment compared to attached conditions. A new method of using the combination of recessed SETC and flush SETC was demonstrated to be able to measure the heat flux from volumetric radiation and the charge exchange neutrals. It was observed that the magnitude of the heat flux from radiation and charge exchange neutrals increased during the process of detachment. While 30% of the total incident heat flux is attributable to the volumetric radiation and the charge exchange neutrals in the fully detached divertor condition with B×∇B drift into the divertor, it could reach more than 60% with B×∇B drift away from the divertor.

In blue-ice areas (BIAs), deep ice is directly exposed at the surface, allowing for the cost-effective collection of large-sized old-ice samples. However, chronostratigraphic studies on blue-ice areas are challenging owing to fold and fault structures. Here, we report on a surface transect of ice with an undisturbed horizontal stratigraphy from the Larsen BIA, northern Victoria Land, East Antarctica. Ice layers defined by dust bands and ground-penetrating radar (GPR) surveys indicate a monotonic increase in age along the ice flow direction on the downstream side, while the upstream ice exhibits a potential repetition of ages on scales of tens of meters, which result from a complicated fold structure. Stable water isotopes (δ18Oice and δ2Hice) and components of the occluded air (i.e., CO2, N2O, CH4, δ15N–N2, δ18Oatm (=δ18O-O2), δO2/N2, δAr/N2​​​​​​​, 81Kr, and 85Kr) are analyzed for surface ice and shallow ice core samples. Correlating δ18Oice, δ18Oatm, and CH4 records from the Larsen BIA with ice from previously drilled ice cores indicates that the gas age at various shallow vertical coring sites ranges between 9.2–23.4 kyr BP, while the ice age sampled from the surface ranges from 5.6 to 24.7 kyr BP. Absolute radiometric 81Kr dating for the two vertical cores confirms ages within acceptable levels of analytical uncertainty. A tentative climate reconstruction suggests a large deglacial warming of 15 ± 5 ∘C (1σ) and an increase in snow accumulation by a factor of 1.7–4.6 (from 24.3 to 10.6 kyr BP). Our study demonstrates that BIAs in northern Victoria Land may help to obtain high-quality records for paleoclimate and atmospheric greenhouse gas compositions through the last deglaciation, although in general climatic interpretation is complicated by the need for upstream flow corrections, evidence for strong surface sublimation during the last glacial period, and potential errors in the estimated gas age–ice age difference.

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter

Summary: Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Intro: Since November 2021, gradual changes in non-pharmaceutical interventions (NPI) had begun in consideration of the socio-economic cost and the high rate of COVID-19 vaccination. As an effort to monitor the COVID-19 situation and the effect of NPIs, regular risk assessment for COVID-19 has been conducted based on the input from the external advisory committee. This paper examines the correlation between the risk assessment results and other indicators, such as the incidence of COVID-19, public perception, community mobility, and the government policy from November 2021 to May 2022. Methods: For weekly regular risk assessment of COVID-19, the data were collected from the National Infectious Disease Surveillance System website, which included incidence, mortality, and % of severe cases. Other indicators were from regular surveys on perceived risk among the public, community mobility from the Google website, and the government's response policy from The Oxford Stringency Index. The level of risk was classified into five levels from very low to very high. The level of COVID-19 risk and its correlation with the COVID-19 incidence, risk perception, community mobility, and government policy were examined. Findings: The result of the correlation analysis showed the highest positive correlation between the risk level and risk perception (r=0.86, CI 0.72-0.94). This indicates that the actual risk level and the risk perceived by the public are similar. Among the incidence-related indicators, the number of new severe cases of COVID-19 per week showed the highest correlation with the risk level (r=0.62, CI 0.33-0.80). Conclusion: The high correlation between the weekly severe cases and the overall COVID-19 risk level suggests that Korea's COVID-19 policy priority was mainly at protecting the high-risk population

The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25∘; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.

Abstract Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

Genetic studies on complex traits have revealed a substantial role for common, small-effect polygenic burden and large-effect variants. Here, the authors investigate how these types of variation can inform disease risk stratification and prediction.

Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

Correlated errors coming from leakage out of the computational subspace are an obstacle to fault-tolerant superconducting circuits. Here, the authors use a multi-level reset protocol to improve the performances of a bit-flip error correcting code by reducing the magnitude of correlations.

Linear mixed models have bias due to the assumed independence between random effects. Here, the authors describe a genome-based restricted maximum likelihood, CORE GREML, which estimates covariance between random effects. Application to UK Biobank data highlights this as an important parameter for multi-omics analyses of phenotypic variance.

In order to operate autonomous vehicles and unmanned delivery vehicle, it is important to accurately acquire location of the device itself. However, since these devices are mainly operated in urban areas, there is a limit in obtaining location information based on GNSS. Therefore, it is necessary to utilize a method of calibrating its own location information by measuring the reference point provided by the existing high-precision map of the region. Point cloud based multi-dimensional high-precision maps are acquired in advance using high-performance LiDAR and GNSS devices for infrastructure such as roads, and provide a reference point for autonomous driving or map updating. Since such high-performance surveying equipment requires high cost, it is difficult to attach to autonomous vehicles or unmanned vehicle for commercialization. Therefore, autonomous vehicles or unmanned delivery vehicle are operated with relatively low performance LiDAR and GNSS, so it is often impossible to accurately measure the reference point, which directly leads to a decrease in the accuracy of the location information of the device. To compensate for this, this study proposes a point interpolation method to extract GCP information from sparse point cloud maps acquired with low performance LiDAR. The proposed method uses calibration parameters between point data and the image data acquired from the device. In general, images provide higher resolution than point clouds, even when using low-end cameras, so that the position of point coordinates relative to a reference point can be measured relatively accurately from the image and projection data of the point cloud. The data acquisition vehicle is an MMS vehicle that provides a panoramic image using four DSLRs and a point cloud with Velodyne VLP 16. The researchers first conducted a reference point survey on features such as road signs. The panorama image including the road sign was transformed into a bird eye’s view, and point projection was performed on the bird eye’s view image. The reference point coordinates, which were not acquired by the point cloud, were obtained from the shape of the road sign in the bird eye’s view image, and the accuracy was compared with the measured data.

Genetic data from large cohorts of unrelated individuals can be used to create polygenic risk scores, which could be used to predict individual risk of developing a specific disease. Here the authors show that smaller cohorts of related individuals can provide similarly powerful predictive ability.

In the field of cultural heritage, replication has been performed for preservation, exhibition, and education purposes. In particular, due to advancement in computer technology, replication which combines the three-dimensional (3D) scanning and printing has widely performed. These technologies have been able to ensure morphological similarity as well as to avoid damaging artifacts in a contactless manner. In this study, a design mock-up for producing replacements was made for the purpose of preserving original forms, usability, and mass production for ritual utensils used in ancestral memorial rites annually. 3D precision scanner was used to obtain external information of ritual utensils and shape information of pattern parts. The measurements on height, width, and thickness of the body, and two handles and three feet showed fine shape differences, respectively. Therefore, representative models were selected and reconstructed. In addition, the upper and lower parts of the body, handles, and feet were separately manufactured for mass production by using sand casting. A model manufactured during the reverse design like above was completed by considering average shrinkage (4%) for the casting of copper-tin alloys. A model was completed and 3D-printed with a material extrusion technique, and a design mock-up for replication was created. In this study, a 3D printing technology was applied to ritual utensils and presented a replication methodology applicable to used artifacts. For this purpose, a model suitable for the replication method was produced based on the data obtained by 3D scanning of ritual utensils. A design mock-up, which is 3D-printed with a material extrusion technique, has enhanced design completeness by performing continuous design and dimensional inspection.

Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include individual self-management of environmental risk factors, such as improving diet quality, increasing physical activity, and reducing smoking and alcohol consumption, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the individual level. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 16 GxE interactions. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure) and by alcohol and smoking for three (BMI, glucose, waist–hip ratio and BMI and diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on individual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.

Trace elements Co, Cr were added to investigate their influence on the microstructure and physical properties of Al-Si extruded alloy. The Co, Cr elements were randomly distributed in the matrix, forms intermetallic phase and their existence were confirmed by XRD, EDS and SEM analysis. With addition of trace elements, the microstructure was modified, Si particle size was reduced and the growth rate of β-(Al5FeSi) phase limited. Compared to parent alloy, hardness and tensile strength were enhanced while the linear coefficient of thermal expansion (CTE) was significantly reduced by 42.4% and 16.05% with Co and Cr addition respectively. It is considered that the low CTE occurs with addition of Co was due to the formation of intermetallic compound having low coefficient of thermal expansion. The results suggested that Co acts as an effective element in improving the mechanical properties of Al-Si alloy.

Nanostructured thermoelectric materials receiving great attention for its high thermoelectric performance. In this research, nanostructured n-type Bi2Te2.7Se0.3 alloys have prepared using high energy ball milling and followed by spark plasma sintering. Also, we have varied ball milling time to investigate milling time parameter on the thermoelectric properties of n-type Bi2Te2.7Se0.3 powder. The powders were discrete at 10 min milling and later particles tend to agglomerate at higher milling time due to cold welding. The bulk fracture surface display multi-scale grains where small grains intersperse in between large grains. The maximum Seebeck coefficient value was obtained at 20-min milling time due to their lower carrier density. The κ values were decreased with increasing milling time due to the decreasing trend observed in their κL values. The highest ZT of 0.7 at 350 K was observed for 30-min milling time which was ascribed to its lower thermal conductivity. The Vickers hardness values also greatly improved due to their fine microstructure.

Complex traits are often influenced by genetic and non-genetic factors (such as environmental exposures), which are themselves interconnected. Here, the authors develop a method for disentangling genotype–covariate correlation and interaction, and investigate their effects on estimating statistical genetic parameters.

The phenotype of carcass traits in beef cattle are affected by random genetic and non-genetic effects, which both can be modulated by an environmental variable such as Temperature-Humidity Index (THI), a key environmental factor in cattle production. In this study, a multivariate reaction norm model (MRNM) was used to assess if the random genetic and non-genetic (i.e., residual) effects of carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) were modulated by THI, using 9,318 Hanwoo steers (N = 8,964) and cows (N = 354) that were genotyped on the Illumina Bovine SNP50 BeadChip (50K). THI was measured based on the period of 15–45 days before slaughter. Both the correlation and the interaction between THI and random genetic and non-genetic effects were accounted for in the model. In the analyses, it was shown that the genetic effects of EMA and the non-genetic effects of CW and MS were significantly modulated by THI. No significant THI modulation of such effects was found for BFT. These results highlight the relevance of THI changes for the genetic and non-genetic variation of CW, EMA, and MS in Hanwoo beef cattle. Importantly, heritability estimates for CW, EMA, and MS from additive models without considering THI interactions were underestimated. Moreover, the significance of interaction can be biased if not properly accounting for the correlation between THI and genetic and non-genetic effects. Thus, we argue that the estimation of genetic parameters should be based on appropriate models to avoid any potential bias of estimates. Our finding should serve as a basis for future studies aiming at revealing genotype by environment interaction in estimation and genomic prediction of breeding values.

Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups in populations stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of information. Here, we introduce a novel concept of genotype-by-population (G × P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G × P interactions across diverse populations including white British and other white Europeans from the UK Biobank (N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioral traits such as age at first sexual intercourse and academic qualification. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations.

Background Both genetic and nongenetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention. Methods and Results We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For example, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified individuals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance. Conclusions Lifestyle changes are relevant to cardiovascular disease prevention. Individual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.

Past climate changes in Greenland ice were accompanied by large aerosol concentration changes. Here, the authors show that by correcting for transport effects, reliable source changes for biogenic aerosol from North America, sea salt aerosol from the North Atlantic, and dust from East Asian deserts can be derived.

The logistics industry plays a very important role in the operation of modern cities. Meanwhile, the development of logistics industry has derived various problems that are urgent to be solved, such as the safety of logistics products. This paper combines the study of logistics industry traceability and logistics centre environment safety supervision with virtual reality technology, creates an interactive logistics centre information integration system. The proposed system utilizes the immerse characteristic of virtual reality, to simulate the real logistics centre scene distinctly, which can make operation staff conduct safety supervision training at any time without regional restrictions. On the one hand, a large number of sensor data can be used to simulate a variety of disaster emergency situations. On the other hand, collecting personnel operation data, to analyse the improper operation, which can improve the training efficiency greatly.

Abstract We estimated genotype by environment interaction (G × E) on later cognitive performance and educational attainment across four unique environments, i.e. 1) breastfed without maternal smoking, 2) breastfed with maternal smoking, 3) non-breastfed without maternal smoking and 4) non-breastfed with maternal smoking, using a novel design and statistical approach that was facilitated by the availability of datasets with the genome-wide single nucleotide polymorphisms (SNPs). There was significant G × E for both fluid intelligence (p-value = 1.0E-03) and educational attainment (p-value = 8.3E-05) when comparing genetic effects in the group of individuals who were breastfed without maternal smoking with those not breastfed without maternal smoking. There was also significant G × E for fluid intelligence (p-value = 3.9E-05) when comparing the group of individuals who were breastfed with maternal smoking with those not breastfed without maternal smoking. Genome-wide significant SNPs were different between different environmental groups. Genomic prediction accuracies were significantly higher when using the target and discovery sample from the same environmental group than when using those from the different environmental groups. This finding demonstrates G × E has important implications for future studies on the genetic architecture, genome-wide association studies and genomic predictions.

Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2–4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes.

This study examined college calculus instructors' preferences in solving two calculus tasks to examine college calculus instructors' use of important cognitive roots in understanding derivatives of function. Our results showed that only one instructor consistently uses cognitive roots while other instructors either focus on algebraic methods or expressing the constraints of students' knowledge, demonstrating their orientations and conflicts. These results potentially show what students experience when differentiation of function is taught.

This study explores calculus students' opportunities to learn the concepts of integral by examining one mathematician's videotaped lessons and the textbook. Results show that both lessons and the textbook introduce important cognitive resources briefly and focus on other units of knowledge. Implications to these results are also discussed.

Agmatine, an endogenous polyamine and putative neuromodulator, is known to have neuroprotective effects on various neurons in the central nervous system. We determined whether or not topically administered agmatine could reduce ischemic retinal injury. Transient ocular ischemia was achieved by intraluminal occlusion of the middle cerebral artery of ddY mice (30-35 g) for 2 h, which is known to also induce occlusion of the ophthalmic artery. In the agmatine group (N = 6), a 1.0 mM agmatine-containing ophthalmic solution was administered four times daily for 2 weeks before occlusion. In the control group (N = 6), a 0.1% hyaluronic acid ophthalmic solution was instilled at the same times. At 22 h after reperfusion, the eyeballs were enucleated and the retinal sections were stained by terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL). Transient ocular ischemia induced apoptosis of retinal cells in the entire retinal layer, and topically administered agmatine can significantly reduce this ischemic retinal injury. The proportion of apoptotic cells was definitely decreased (P < 0.001; Kruskal-Wallis test). Overall, we determined that topical agmatine application effectively decreases retinal damage in an in vivo ocular ischemic injury model. This implies that agmatine is a good candidate as a direct neuroprotective agent for eyes with ocular ischemic diseases.

In this study a model of simulated vascular injury in vitro was used to study the characterization of bone-marrow-derived mesenchymal stem cells (BMSCs) morphology and to investigate the differentiation and migration of BMSCs in the presence of adventitial fibroblasts. BMSCs from rats were indirectly cocultured with adventitial fibroblasts in a transwell chamber apparatus for 7 days, and clonogenic assays demonstrated that BMSCs could be differentiated into smooth muscle-like cells with this process, including smooth muscle α-actin (α-SMA) expression by immunofluorescence staining. Cell morphology of BMSCs was assessed by inverted microscope, while cell proliferation was assessed by MTT assay. The expressions of TGF-β1, MMP-1, and NF-κB were detected by immunofluorescence staining and Smad3 mRNA was measured by reverse transcription PCR. Migration ability of BMSCs with DAPI-labeled nuclei was measured by laser confocal microscopy. Our results demonstrate that indirect interactions with adventitial fibroblasts can induce proliferation, differentiation, and migration of BMSCs that can actively participate in neointimal formation. Our results indicate that the pathogenesis of vascular remodeling might perform via TGF-β1/Smad3 signal transduction pathways.

Agmatine has neuroprotective effects on retinal ganglion cells (RGCs) as well as cortical and spinal neurons. It protects RGCs from oxidative stress even when it is not present at the time of injury. As agmatine has high affinity for various cellular receptors, we assessed protective mechanisms of agmatine using transformed RGCs (RGC-5 cell line). Differentiated RGC-5 cells were pretreated with 100 μM agmatine and consecutively exposed to 1.0 mM hydrogen peroxide (H2O2). Cell viability was determined by measuring lactate dehydrogenase (LDH), and the effects of selective alpha 2-adrenergic receptor antagonist yohimbine (0-500 nM) and N-methyl-D-aspartic acid (NMDA) receptor agonist NMDA (0-100 µM) were evaluated. Agmatine’s protective effect was compared to a selective NMDA receptor antagonist MK-801. After a 16-h exposure to H2O2, the LDH assay showed cell loss greater than 50%, which was reduced to about 30% when agmatine was pretreated before injury. Yohimbine almost completely inhibited agmatine’s protective effect, but NMDA did not. In addition, MK-801 (0-100 µM) did not significantly attenuate the H2O2-induced cytotoxicity. Our results suggest that neuroprotective effects of agmatine on RGCs under oxidative stress may be mainly attributed to the alpha 2-adrenergic receptor signaling pathway.

The Intermediate Frequency (IF) amplifier is one of the most important electronic components of a Radar guider, and its reliability estimation is a key issue in radar steady work. Limited by technology and time etc., it is very difficult to model, analyse, and estimate the reliability of an Intermediate Frequency (IF) amplifier by means of traditional methods, since Intermediate Frequency (IF) amplifiers are characterized by small sample size and long life, and it is hard to get large samples of the failure criteria in a short time. However, the failure criteria of the product are created by combining the Physics of Failure (PoF) analysis and the information from historic data about failed products. Based on the degradation trends and the failure criteria, the storage lifetime of the product can be predicted. Degradation of GaAs microwave device nearly related to stability of device’s metallization which includes gate contact and ohmic contact. This article establishes a performance degradation model based on failure physics with uncertain activation energy. High temperature accelerant strass life test was performed on GaAs device to calculation the activation energy, and then uses the performance data and scanty life data to estimate the storage lifetime based on the new model. The instance analysis proves that the failure-physics-analysis-based method for the Intermediate Frequency (IF) amplifier is more effective in life forecast and consistent with engineering facts than the fake-life-based method.