Valid inference for machine learning-assisted genome-wide association studies.

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS.

multimedia: Multimodal Mediation Analysis of Microbiome Data.

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software's rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110.

Ultralow-Friction at Cryogenic Temperature Induced by Hydrogen Correlated Quantum Effect.

Temperature is one of the governing factors affecting friction of solids. Undesired high friction state has been generally reported at cryogenic temperatures due to the prohibition of thermally activated processes, following conventional Arrhenius equation. This has brought huge difficulties to lubrication at extremely low temperatures in industry. Here, the study uncovers a hydrogen-correlated sub-Arrhenius friction behavior in hydrogenated amorphous carbon (a-C:H) film at cryogenic temperatures, and a stable ultralow-friction over a wide temperature range (103-348 K) is achieved. This is attributed to hydrogen-transfer-induced mild structural ordering transformation, confirmed by machine-learning-based molecular dynamics simulations. The anomalous sub-Arrhenius temperature dependence of structural ordering transformation rate is well-described by a quantum mechanical tunneling (QMT) modified Arrhenius model, which is correlated with quantum delocalization of hydrogen in tribochemical reactions. This work reveals a hydrogen-correlated friction mechanism overcoming the Arrhenius temperature dependence and provides a new pathway for achieving ultralow friction under cryogenic conditions.

Ecological filtering shapes the impacts of agricultural deforestation on biodiversity.

The biodiversity impacts of agricultural deforestation vary widely across regions. Previous efforts to explain this variation have focused exclusively on the landscape features and management regimes of agricultural systems, neglecting the potentially critical role of ecological filtering in shaping deforestation tolerance of extant species assemblages at large geographical scales via selection for functional traits. Here we provide a large-scale test of this role using a global database of species abundance ratios between matched agricultural and native forest sites that comprises 71 avian assemblages reported in 44 primary studies, and a companion database of 10 functional traits for all 2,647 species involved. Using meta-analytic, phylogenetic and multivariate methods, we show that beyond agricultural features, filtering by the extent of natural environmental variability and the severity of historical anthropogenic deforestation shapes the varying deforestation impacts across species assemblages. For assemblages under greater environmental variability-proxied by drier and more seasonal climates under a greater disturbance regime-and longer deforestation histories, filtering has attenuated the negative impacts of current deforestation by selecting for functional traits linked to stronger deforestation tolerance. Our study provides a previously largely missing piece of knowledge in understanding and managing the biodiversity consequences of deforestation by agricultural deforestation.

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations.

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 µg/m3 to 44.8416 µg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

Multicenter Reproducibility of Liver Iron Quantification with 1.5-T and 3.0-T MRI.

Background MRI is a standard of care tool to measure liver iron concentration (LIC). Compared with regulatory-approved R2 MRI, R2* MRI has superior speed and is available in most MRI scanners; however, the cross-vendor reproducibility of R2*-based LIC estimation remains unknown. Purpose To evaluate the reproducibility of LIC via single-breath-hold R2* MRI at both 1.5 T and 3.0 T with use of a multicenter, multivendor study. Materials and Methods Four academic medical centers using MRI scanners from three different vendors (three 1.5-T scanners, one 2.89-T scanner, and two 3.0-T scanners) participated in this prospective cross-sectional study. Participants with known or suspected liver iron overload were recruited to undergo multiecho gradient-echo MRI for R2* mapping at 1.5 T and 3.0 T (2.89 T or 3.0 T) on the same day. R2* maps were reconstructed from the multiecho images and analyzed at a single center. Reference LIC measurements were obtained with a commercial R2 MRI method performed using standardized 1.5-T spin-echo imaging. R2*-versus-LIC calibrations were generated across centers and field strengths using linear regression and compared using F tests. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic performance of R2* MRI in the detection of clinically relevant LIC thresholds. Results A total of 207 participants (mean age, 38 years ± 20 [SD]; 117 male participants) were evaluated between March 2015 and September 2019. A linear relationship was confirmed between R2* and LIC. All calibrations within the same field strength were highly reproducible, showing no evidence of statistically significant center-specific differences (P > .43 across all comparisons). Calibrations for 1.5 T and 3.0 T were generated, as follows: for 1.5 T, LIC (in milligrams per gram [dry weight]) = -0.16 + 2.603 × 10-2 R2* (in seconds-1); for 2.89 T, LIC (in milligrams per gram) = -0.03 + 1.400 × 10-2 R2* (in seconds-1); for 3.0 T, LIC (in milligrams per gram) = -0.03 + 1.349 × 10-2 R2* (in seconds-1). Liver R2* had high diagnostic performance in the detection of clinically relevant LIC thresholds (area under the ROC curve, >0.98). Conclusion R2* MRI enabled accurate and reproducible quantification of liver iron overload over clinically relevant ranges of liver iron concentration (LIC). The data generated in this study provide the necessary calibrations for broad clinical dissemination of R2*-based LIC quantification. ClinicalTrials.gov registration no.: NCT02025543 © RSNA, 2022 Online supplemental material is available for this article.

The biodiversity and ecosystem service contributions and trade-offs of forest restoration approaches.

Forest restoration is being scaled up globally to deliver critical ecosystem services and biodiversity benefits; however, there is a lack of rigorous comparison of cobenefit delivery across different restoration approaches. Through global synthesis, we used 25,950 matched data pairs from 264 studies in 53 countries to assess how delivery of climate, soil, water, and wood production services, in addition to biodiversity, compares across a range of tree plantations and native forests. Benefits of aboveground carbon storage, water provisioning, and especially soil erosion control and biodiversity are better delivered by native forests, with compositionally simpler, younger plantations in drier regions performing particularly poorly. However, plantations exhibit an advantage in wood production. These results underscore important trade-offs among environmental and production goals that policy-makers must navigate in meeting forest restoration commitments.

Analysis of Land Use/Cover Change and Driving Forces in the Selenga River Basin.

The Selenga River basin is an important section of the Sino-Mongolian Economic Corridor. It is an important connecting piece of the Eurasian Continental Bridge and an important part of Northeast Asia. Against the background of the evolution of the geopolitical pattern since the disintegration of the Soviet Union and global warming, based on the land cover data in the Selenga River basin from 1992, 2000, 2009, and 2015, this paper describes the dynamic changes in land use in the basin. Through a logistic model, the driving factors of land cover change were revealed, and the CA-Markov model was used to predict the land cover pattern of 2027. The results showed that (1) from 1992 to 2015, the agricultural population in the Selenga River basin continued to decrease, which led to a reduction in agricultural sown area. The intensification of climate warming and drying had a significant impact on the spatial distribution of crops. Grassland expansion mostly occurred in areas with relatively abundant rainfall, low temperature, and low human activity. (2) The simulation results showed that, according to the current development trend, the construction land area of the Selenga River basin will be slightly expanded in 2027, the area of arable land and grassland will be slightly reduced, and the areas of forest, water/wetland, and bare land will remain stable. In the future, human activities in the basin will increase in the process of the construction of the China-Mongolia-Russia economic corridor. Coupled with global warming, the land/cover of the basin will be affected by both man-made and natural disturbances, and attention should be paid to the possible risk of vegetation degradation.

Sample size formula for general win ratio analysis.

Originally proposed for the analysis of prioritized composite endpoints, the win ratio has now expanded into a broad class of methodology based on general pairwise comparisons. Complicated by the non-i.i.d. structure of the test statistic, however, sample size estimation for the win ratio has lagged behind. In this article, we develop general and easy-to-use formulas to calculate sample size for win ratio analysis of different outcome types. In a nonparametric setting, the null variance of the test statistic is derived using U-statistic theory in terms of a dispersion parameter called the standard rank deviation, an intrinsic characteristic of the null outcome distribution and the user-defined rule of comparison. The effect size can be hypothesized either on the original scale of the population win ratio, or on the scale of a "usual" effect size suited to the outcome type. The latter approach allows one to measure the effect size by, for example, odds/continuation ratio for totally/partially ordered outcomes and hazard ratios for composite time-to-event outcomes. Simulation studies show that the derived formulas provide accurate estimates for the required sample size across different settings. As illustration, real data from two clinical studies of hepatic and cardiovascular diseases are used as pilot data to calculate sample sizes for future trials.