Pseudomagnetic suppression of non-Hermitian skin effect.

It has recently been shown that the non-Hermitian skin effect can be suppressed by magnetic fields. In this work, using a two-dimensional tight-binding lattice, we demonstrate that a pseudomagnetic field can also lead to the suppression of the non-Hermitian skin effect. With an increasing pseudomagnetic field, the skin modes are found to be pushed into the bulk, accompanied by the reduction of skin topological area and the restoration of Landau level energies. Our results provide a time-reversal invariant route to localization control and could be useful in various classical wave devices that are able to host the non-Hermitian skin effect but inert to magnetic fields.

Multiple Brillouin Zone Winding of Topological Chiral Edge States for Slow Light Applications.

Photonic Chern insulators are known for their topological chiral edge states (CESs), whose absolute existence is determined by the bulk band topology, but concrete dispersion can be engineered to exhibit various properties. For example, the previous theory suggested that the edge dispersion can wind many times around the Brillouin zone to slow down light, which can potentially overcome fundamental limitations in conventional slow-light devices: narrow bandwidth and keen sensitivity to fabrication imperfection. Here, we report the first experimental demonstration of this idea, achieved by coupling CESs with resonance-induced nearly flat bands. We show that the backscattering-immune hybridized CESs are significantly slowed down over a relatively broad bandwidth. Our work thus paves an avenue to broadband topological slow-light devices.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Three-dimensional flat Landau levels in an inhomogeneous acoustic crystal.

When electrons moving in two dimensions (2D) are subjected to a strong uniform magnetic field, they form flat bands called Landau levels (LLs). LLs can also arise from pseudomagnetic fields (PMFs) induced by lattice distortions. In three-dimensional (3D) systems, there has been no experimental demonstration of LLs  as a type of flat band thus far. Here, we report the experimental realization of a flat 3D LL in an acoustic crystal. Starting from a lattice whose bandstructure exhibits a nodal ring, we design an inhomogeneous distortion corresponding to a specific pseudomagnetic vector potential (PVP). This distortion causes the nodal ring states to break up into LLs, including a zeroth LL that is flat along all three directions. These findings suggest the possibility of using nodal ring materials to generate 3D flat bands, allowing access to strong interactions and other attractive physical regimes in 3D.

Genetic correlation and causal associations between circulating C-reactive protein levels and lung cancer risk.

PURPOSE: We aimed to characterize genetic correlations and causal associations between circulating C-reactive protein (CRP) levels and the risk of lung cancer (LC). METHODS: Leveraging summary statistics from genome-wide association studies of circulating CRP levels among 575,531 individuals of European ancestry, and LC risk among 29,266 cases and 56,450 controls, we investigated genetic associations of circulating CRP levels with the risk of overall lung cancer and its histological subtypes, by using linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analyses. RESULTS: Significant positive genetic correlations between circulating CRP levels and the risk of LC and its histological subtypes were identified from LDSC regression, with correlation coefficients ranging from 0.12 to 0.26, and all false discovery adjusted p < 0.05. Univariable MR demonstrated a nominal association between CRP levels and an increased risk of lung squamous cell carcinoma (SCC) (inverse variance-weighted OR = 1.15, 95% CI 1.01-1.30). However, this association disappeared when multivariable MR included cigarettes per day and/or body mass index. By using our recently developed constrained maximum likelihood-based MR method, we identified significant associations of CRP levels with the risk of overall LC (OR 1.06, 95% CI 1.03-1.09), SCC (OR 1.06, 95% CI 1.02-1.09), and small cell lung cancer (SCLC, OR 1.09, 95% CI 1.03-1.15). Moreover, most univariable and multivariable MR analyses also revealed consistent CRP-SCLC associations. CONCLUSION: There may be a genetic and causal association between circulating CRP levels and the risk of SCLC, which is in line with previous population-based observational studies.

MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data.

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added or edited in the package since version 0.5.0, when we last described the package and its contents. The main additions to the package since that time are: 1) new robust methods for performing Mendelian randomization, particularly in the cases of bias from weak instruments and/or winner's curse, and pleiotropic variants, 2) methods for performing Mendelian randomization with correlated variants using dimension reduction to summarize large numbers of highly correlated variants into a limited set of principal components, 3) functions for calculating first-stage F statistics, representing instrument strength, in both univariable and multivariable contexts, and with uncorrelated and correlated genetic variants. We also discuss some pragmatic issues relating to the use of correlated variants in Mendelian randomization.

A machine learning approach to graduate admissions and the role of letters of recommendation.

The graduate admissions process is time-consuming, subjective, and complicated by the need to combine information from diverse data sources. Letters of recommendation (LORs) are particularly difficult to evaluate and it is unclear how much impact they have on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information extracted from structured application forms (e.g., undergraduate GPA, standardized test scores, etc.), applicants' resumes, and LORs. A particular challenge in our study is the different modalities of application data (i.e., text vs. structured forms). To address this issue, we converted the textual LORs into features using a commercial natural language processing product and a manual rating process that we developed. By analyzing the predictive performance of the models using different subsets of features, we show that LORs alone provide only modest, but useful, predictive signals to admission decisions; the best model for predicting admissions decisions utilized both LOR and non-LOR data and achieved 89% accuracy. Our experiments demonstrate promising results in the utility of automated systems for assisting with graduate admission decisions. The findings confirm the value of LORs and the effectiveness of our feature engineering methods from LOR text. This study also assesses the significance of individual features using the SHAP method, thereby providing insight into key factors affecting graduate admission decisions.

Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data.

Transcriptome-wide association studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our motivating and target application, we'd like to identify causal genes for low-density lipoprotein cholesterol (LDL), which is crucial for developing new treatments for hyperlipidemia and cardiovascular diseases. The statistical principle underlying TWAS is (two-sample) two-stage least squares (2SLS) using multiple correlated SNPs as instrumental variables (IVs); it is closely related to typical (two-sample) Mendelian randomization (MR) using independent SNPs as IVs, which is expected to be impractical and lower-powered for TWAS (and some other) applications. However, often some of the SNPs used may not be valid IVs, e.g. due to the widespread pleiotropy of their direct effects on the outcome not mediated through the gene of interest, leading to false conclusions by TWAS (or MR). Building on recent advances in sparse regression, we propose a robust and efficient inferential method to account for both hidden confounding and some invalid IVs via two-stage constrained maximum likelihood (2ScML), an extension of 2SLS. We first develop the proposed method with individual-level data, then extend it both theoretically and computationally to GWAS summary data for the most popular two-sample TWAS design, to which almost all existing robust IV regression methods are however not applicable. We show that the proposed method achieves asymptotically valid statistical inference on causal effects, demonstrating its wider applicability and superior finite-sample performance over the standard 2SLS/TWAS (and MR). We apply the methods to identify putative causal genes for LDL by integrating large-scale lipid GWAS summary data with eQTL data.

Haplotype-resolved genome assembly provides insights into the evolution of S-locus supergene in distylous Nymphoides indica.

Distyly has evolved independently in numerous animal-pollinated angiosperm lineages. Understanding of its molecular basis has been restricted to a few species, primarily Primula. Here, we investigate the genetic architecture of the single diallelic locus (S-locus) supergene, a linkage group of functionally associated genes, and explore how it may have evolved in distylous Nymphoides indica, a lineage of flowering plants not previously investigated. We assembled haplotype-resolved genomes, used read-coverage-based genome-wide association study (rb-GWAS) to locate the S-locus supergene, co-expression network analysis to explore gene networks underpinning the development of distyly, and comparative genomic analyses to investigate the origins of the S-locus supergene. We identified three linked candidate S-locus genes - NinBAS1, NinKHZ2, and NinS1 - that were only evident in the short-styled morph and were hemizygous. Co-expression network analysis suggested that brassinosteroids contribute to dimorphic sex organs in the short-styled morph. Comparative genomic analyses indicated that the S-locus supergene likely evolved via stepwise duplications and has been affected by transposable element activities. Our study provides novel insight into the structure, regulation, and evolution of the supergene governing distyly in N. indica. It also provides high-quality genomic resources for future research on the molecular mechanisms underlying the striking evolutionary convergence in form and function across heterostylous taxa.

Stiefel-Whitney topological charges in a three-dimensional acoustic nodal-line crystal.

Band topology of materials describes the extent Bloch wavefunctions are twisted in momentum space. Such descriptions rely on a set of topological invariants, generally referred to as topological charges, which form a characteristic class in the mathematical structure of fiber bundles associated with the Bloch wavefunctions. For example, the celebrated Chern number and its variants belong to the Chern class, characterizing topological charges for complex Bloch wavefunctions. Nevertheless, under the space-time inversion symmetry, Bloch wavefunctions can be purely real in the entire momentum space; consequently, their topological classification does not fall into the Chern class, but requires another characteristic class known as the Stiefel-Whitney class. Here, in a three-dimensional acoustic crystal, we demonstrate a topological nodal-line semimetal that is characterized by a doublet of topological charges, the first and second Stiefel-Whitney numbers, simultaneously. Such a doubly charged nodal line gives rise to a doubled bulk-boundary correspondence-while the first Stiefel-Whitney number induces ordinary drumhead states of the nodal line, the second Stiefel-Whitney number supports hinge Fermi arc states at odd inversion-related pairs of hinges. These results experimentally validate the two Stiefel-Whitney topological charges and demonstrate their unique bulk-boundary correspondence in a physical system.

Polarization-Orthogonal Nondegenerate Plasmonic Higher-Order Topological States.

Photonic topological states, providing light-manipulation approaches in robust manners, have attracted intense attention. Connecting photonic topological states with far-field degrees of freedom (d.o.f.) has given rise to fruitful phenomena. Recently emerged higher-order topological insulators (HOTIs), hosting boundary states two or more dimensions lower than those of bulk, offer new paradigms to localize or transport light topologically in extended dimensionalities. However, photonic HOTIs have not been related to d.o.f. of radiation fields yet. Here, we report the observation of polarization-orthogonal second-order topological corner states at different frequencies on a designer-plasmonic kagome metasurface in the far field. Such phenomenon stands on two mechanisms, i.e., projecting the far-field polarizations to the intrinsic parity d.o.f. of lattice modes and the parity splitting of the plasmonic corner states in spectra. We theoretically and numerically show that the parity splitting originates from the underlying interorbital coupling. Both near-field and far-field experiments verify the polarization-orthogonal nondegenerate second-order topological corner states. These results promise applications in robust optical single photon emitters and multiplexed photonic devices.

Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data.

Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.

Blidingia sp. extracts improve intestinal health and reduce diarrhoea in weanling piglets.

Blidingia sp. is a prominent fouling green macroalga and we previously found that extracts from Blidingia sp. alleviated intestinal inflammation in mice challenged with lipopolysaccharides. However, whether these extracts are effective in weanling piglets remains unknown. In the present study, Blidingia sp. extracts were supplemented in the diet and their effects on growth performance, incidence of diarrhoea and intestinal function in weanling piglets were explored. The results showed that diets supplemented with 0.1% or 0.5% Blidingia sp. extract significantly increased average daily body weight gain and feed intake in weanling piglets. Meanwhile, piglets supplemented with 0.5% Blidingia sp. extract showed decreased incidence of diarrhoea as well as reduced fecal water and Na+ content. Furthermore, the diet supplemented with 0.5% Blidingia sp. extracts improved intestinal morphology, as indicated by the results of hematoxylin and eosin staining. Diet supplemented with 0.5% Blidingia sp. extracts also improved tight junction function, as indicated by increased expression of Occludin, Claudin-1 and Zonula occludens-1, and alleviated the inflammatory response, as indicated by decreased tumor necrosis factor-α and interleukin-6 (IL6) contents and increased IL10 levels. Taken together, our results showed that Blidingia sp. extracts had beneficial effects in weanling piglets and we suggest that Blidingia sp. extracts could be potentially used as an additive for piglets.

Robust multivariable Mendelian randomization based on constrained maximum likelihood.

Mendelian randomization (MR) is a powerful tool for causal inference with observational genome-wide association study (GWAS) summary data. Compared to the more commonly used univariable MR (UVMR), multivariable MR (MVMR) not only is more robust to the notorious problem of genetic (horizontal) pleiotropy but also estimates the direct effect of each exposure on the outcome after accounting for possible mediating effects of other exposures. Despite promising applications, there is a lack of studies on MVMR's theoretical properties and robustness in applications. In this work, we propose an efficient and robust MVMR method based on constrained maximum likelihood (cML), called MVMR-cML, with strong theoretical support. Extensive simulations demonstrate that MVMR-cML performs better than other existing MVMR methods while possessing the above two advantages over its univariable counterpart. An application to several large-scale GWAS summary datasets to infer causal relationships between eight cardiometabolic risk factors and coronary artery disease (CAD) highlights the usefulness and some advantages of the proposed method. For example, after accounting for possible pleiotropic and mediating effects, triglyceride (TG), low-density lipoprotein cholesterol (LDL), and systolic blood pressure (SBP) had direct effects on CAD; in contrast, the effects of high-density lipoprotein cholesterol (HDL), diastolic blood pressure (DBP), and body height diminished after accounting for other risk factors.

Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease.

The prognosis and treatment of patients suffering from Alzheimer's disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.

Continuum of Bound States in a Non-Hermitian Model.

In a Hermitian system, bound states must have quantized energies, whereas free states can form a continuum. We demonstrate how this principle fails for non-Hermitian systems, by analyzing non-Hermitian continuous Hamiltonians with an imaginary momentum and Landau-type vector potential. The eigenstates, which we call "continuum Landau modes" (CLMs), have Gaussian spatial envelopes and form a continuum filling the complex energy plane. We present experimentally realizable 1D and 2D lattice models that host CLMs; the lattice eigenstates are localized and have other features matching the continuous model. One of these lattices can serve as a rainbow trap, whereby the response to an excitation is concentrated at a position proportional to the frequency. Another lattice can act a wave funnel, concentrating an input excitation onto a boundary over a wide frequency bandwidth. Unlike recent funneling schemes based on the non-Hermitian skin effect, this requires a simple lattice design with reciprocal couplings.

Use of SAMe-TT2R2 in a racially diverse anticoagulation clinic: prediction of optimal anticoagulation.

The SAMe-TT2R2 score predicts optimal long-term oral Vitamin K Antagonist (VKA) anticoagulation for homogenous Caucasian and homogenous Asian populations for non-valvular atrial fibrillation but at different score thresholds. The score that predicts optimal VKA anticoagulation in significantly diverse populations for multiple indications for systemic anticoagulation has not been reported. We determined whether optimal VKA anticoagulation is predicted by SAMe-TT2R2 score in a diverse inner-city population for non-valvular atrial fibrillation (NVAF), unprovoked venous and pulmonary thromboembolic disease (VTE), mechanical prosthetic heart valves and all other indications. All patients on long term VKA's that attended an inner-city anticoagulation clinic between February 2016 and October 2017 were included in this study. Eligible patients were grouped according to oral anticoagulation indication: (1) NVAF, (2) VTE, (3) prosthetic valves and (4) other indications. Each patient's SAMe-TT2R2 score and percent time of INR in the therapeutic range (TTR) was calculated with optimal international normalized ratio (INR) control defined as TTR ≥ 65%. The correlation between SAMe-TT2R2 score and TTR was determined by logistic regression for each oral anticoagulant indication. Receiver operating characteristic curves were then used to identify the best cutoff for prediction of ≥ 65% TTR. Of 316 patients meeting study criteria, 54% were non-Caucasian and there was a significant negative correlation between the SAMe-TT2R2 score and TTR (coefficient - 0.35, P < 0.0001) for all patients. A SAMe-TT2R2 score < 4 was identified as the best threshold for predicting optimal TTR (Youden's J-statistics = 0.238) with accuracy and positive likelihood ratio of 63.4% and 1.73, respectively. The SAMe-TT2R2 score predicts optimal VKA anticoagulation for systemic anticoagulation for multiple indications in a diverse urban population at a higher score than the original report for non-valvular atrial fibrillation of a cohort where < 10% non-Caucasians.

Erratum: Observation of π/2 Modes in an Acoustic Floquet System [Phys. Rev. Lett. 129, 254301 (2022)].

This corrects the article DOI: 10.1103/PhysRevLett.129.254301.

Transient non-Hermitian skin effect.

The discovery of non-Hermitian skin effect (NHSE) has opened an exciting direction for unveiling unusual physics and phenomena in non-Hermitian system. Despite notable theoretical breakthroughs, actual observation of NHSE's whole evolvement, however, relies mainly on gain medium to provide amplified mode. It typically impedes the development of simple, robust system. Here, we show that a passive system is fully capable of supporting the observation of the complete evolution picture of NHSE, without the need of any gain medium. With a simple lattice model and acoustic ring resonators, we use complex-frequency excitation to create virtual gain effect, and experimentally demonstrate that exact NHSE can persist in a totally passive system during a quasi-stationary stage. This results in the transient NHSE: passive construction of NHSE in a short time window. Despite the general energy decay, the localization character of skin modes can still be clearly witnessed and successfully exploited. Our findings unveil the importance of excitation in realizing NHSE and paves the way towards studying the peculiar features of non-Hermitian physics with diverse passive platforms.

Genetic variation and clonal diversity in floating aquatic plants: Comparative genomic analysis of water hyacinth species in their native range.

Many eukaryotic organisms reproduce by sexual and asexual reproduction. Genetic diversity in populations can be strongly dependent on the relative importance of these two reproductive modes. Here, we compare the amounts and patterns of genetic diversity in related water hyacinths that differ in their propensity for clonal propagation - highly clonal Eichhornia crassipes and moderately clonal E. azurea (Pontederiaceae). Our comparisons involved genotype-by-sequencing (GBS) of 137 E. crassipes ramets from 60 locations (193,495 nucleotide sites) and 118 E. azurea ramets from 53 locations (198,343 nucleotide sites) among six hydrological basins in central South America, the native range of both species. We predicted that because of more prolific clonal propagation, E. crassipes would exhibit lower clonal diversity than E. azurea. This prediction was supported by all measures of clonal diversity that we examined. Eichhornia crassipes also had a larger excess of heterozygotes at variant sites, another signature of clonality. However, genome-wide heterozygosity was not significantly different between the species. Eichhornia crassipes had weaker spatial genetic structure and lower levels of differentiation among hydrological basins than E. azurea, probably because of higher clonality and more extensive dispersal of its free-floating life form. Our findings for E. crassipes contrast with earlier studies from the invasive range which have reported very low levels of clonal diversity and extensive geographic areas of genetic uniformity.