Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis.

Background: Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system. Results: Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats (Rattus norvegicus) uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis. Conclusions: These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.

Synergistic Passivation With Phenylpropylammonium Bromide for Efficient Inverted Perovskite Solar Cells.

Inverted perovskite solar cells (PSCs) are a promising technology for commercialization due to their reliable operation and scalable fabrication. However, in inverted PSCs, depositing a high-quality perovskite layer comparable to those realized in normal structures still presents some challenges. Defects at grain boundaries and interfaces between the active layer and carrier extraction layer seriously hinder the power conversion efficiency (PCE) and stability of these cells. In this work, it is shown that synergistic bulk doping and surface treatment of triple-cation mixed-halide perovskites with phenylpropylammonium bromine (PPABr) can improve the efficiency and stability of inverted PSCs. The PPABr ligand is effective in eliminating halide vacancy defects and uncoordinated Pb2+ ions at both grain boundaries and interfaces. In addition, a 2D Ruddlesden-Popper (2D-RP) perovskite capping layer is formed on the surface of 3D perovskite by using PPABr post-treatment. This 2D-RP perovskite capping layer possesses a concentrated phase distribution ≈n = 2. This capping layer not only reduces interfacial non-radiative recombination loss and improves carrier extraction ability but also promotes stability and efficiency. As a result, the inverted PSCs achieve a champion PCE of over 23%, with an open-circuit voltage as high as 1.15 V and a fill factor of over 83%.

Removal of nitrate by FeSiBC metallic glasses: high efficiency and superior reusability.

The development of sustainable technologies for efficient nitrate removal has attracted increasing attention, because excessive nitrate emissions can result in serious environmental, economic, and health effects. Herein, we propose to utilize FeSiBC metallic glass (MG) powders as a potential solution for nitrate removal. In terms of removal efficiency and reusability, our results show that the MG powders, as special zero-valent iron carriers, are 2-3 orders of magnitude more efficient in nitrate removal than the previous studies, while maintaining more than 50% nitrate removal efficiency after 9 cycles of reaction. Moreover, the optimal FeSiBC MG dosage, pH value, and temperature for nitrate removal are determined. The mechanism of nitrate removal is also revealed. The present study offers a promising approach to remediate nitrate, one of the world's most widespread water pollutants.

NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution.

Motivation: The interaction between genetic variables is one of the major barriers to characterizing the genetic architecture of complex traits. To consider epistasis, network science approaches are increasingly being used in research to elucidate the genetic architecture of complex diseases. Network science approaches associate genetic variables' disease susceptibility to their topological importance in the network. However, this network only represents genetic interactions and does not describe how these interactions attribute to disease association at the subject-scale. We propose the Network-based Subject Portrait Approach (NSPA) and an accompanying feature transformation method to determine the collective risk impact of multiple genetic interactions for each subject. Results: The feature transformation method converts genetic variants of subjects into new values that capture how genetic variables interact with others to attribute to a subject's disease association. We apply this approach to synthetic and genetic datasets and learn that (1) the disease association can be captured using multiple disjoint sets of genetic interactions and (2) the feature transformation method based on NSPA improves predictive performance comparing with using the original genetic variables. Our findings confirm the role of genetic interaction in complex disease and provide a novel approach for gene-disease association studies to identify genetic architecture in the context of epistasis. Availability and implementation: The codes of NSPA are now available in: https://github.com/MIB-Lab/Network-based-Subject-Portrait-Approach. Contact: ting.hu@queensu.ca. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Uncovering the Inherent Size Dependence of Yield Strength and Failure Mechanism in Micron-Sized Metallic Glass.

The sample size effect on the deformation behavior of metallic glasses (MGs) has recently become research of intense interest. An inverse sample size effect is observed in previous experimental studies; where the yield strength decreases with decreasing sample size, rather than increasing. We propose a theoretical analysis based on the shear banding process to rationalize the inherent size dependence of yield strength, showing an excellent agreement with experimental results. Our model reveals that the anomalous inverse size effect is, in fact, caused by a transition in failure mode; from a rapid shear banding process with a shear band (SB) traversing the entire sample in bulk MGs, to an immature shear banding process with propagated SBs only at the surface in micron-sized MGs. Our results fill the gap in the current understanding of size effects in the strength and failure mechanism of MGs at different length scales.

Atomic vacancies significantly degrade the mechanical properties of phosphorene.

Due to low formation energies, it is very easy to create atomic defects in phosphorene during its fabrication process. How these atomic defects affect its mechanical behavior, however, remain unknown. Here, we report on a systematic study of the effect of atomic vacancies on the mechanical properties and failure behavior of phosphorene using molecular dynamics simulations. It is found that atomic vacancies induce local stress concentration and cause early bond-breaking, leading to a significant degradation of the mechanical properties of the material. More specifically, a 2% concentration of randomly distributed mono-vacancies is able to reduce the fracture strength by ∼40%. An increase in temperature from 10 to 400 K can further deteriorate the fracture strength by ∼60%. The fracture strength of defective phosphorene is also found to be affected by defect distribution. When the defects are patterned in a line, the reduction in fracture strength greatly depends on the tilt angle and the loading direction. Furthermore, we find that di-vacancies cause an even larger reduction in fracture strength than mono-vacancies when the loading is in an armchair direction. These findings provide important guidelines for the structural design of phosphorene in future applications.