Identification and validation of supervariants reveal novel loci associated with human white matter microstructure.

As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.

An evolutionarily conserved pathway mediated by neuroparsin-A regulates reproductive plasticity in ants.

Phenotypic plasticity displayed by an animal in response to different environmental conditions is supposedly crucial for its survival and reproduction. The female adults of some ant lineages display phenotypic plasticity related to reproductive role. In pharaoh ant queens, insemination induces substantial physiological/behavioral changes and implicates remarkable gene regulatory network (GRN) shift in the brain. Here, we report a neuropeptide neuroparsin A (NPA) showing a conserved expression pattern associated with reproductive activity across ant species. Knock-down of NPA in unmated queen enhances ovary activity, whereas injection of NPA peptide in fertilized queen suppresses ovary activity. We found that NPA mainly affected the downstream gene JHBP in the ovary, which is positively regulated by NPA and suppression of which induces elevated ovary activity, and shadow which is negatively regulated by NPA. Furthermore, we show that NPA was also employed into the brain-ovary axis in regulating the worker reproductive changes in other distantly related species, such as Harpegnathos venator ants.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction.

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.

The correct prediction of disease-associated miRNAs plays an essential role in disease prevention and treatment. Current computational methods to predict disease-associated miRNAs construct different miRNA views and disease views based on various miRNA properties and disease properties and then integrate the multiviews to predict the relationship between miRNAs and diseases. However, most existing methods ignore the information interaction among the views and the consistency of miRNA features (disease features) across multiple views. This study proposes a computational method based on multiple hypergraph contrastive learning (MHCLMDA) to predict miRNA-disease associations. MHCLMDA first constructs multiple miRNA hypergraphs and disease hypergraphs based on various miRNA similarities and disease similarities and performs hypergraph convolution on each hypergraph to capture higher order interactions between nodes, followed by hypergraph contrastive learning to learn the consistent miRNA feature representation and disease feature representation under different views. Then, a variational auto-encoder is employed to extract the miRNA and disease features in known miRNA-disease association relationships. Finally, MHCLMDA fuses the miRNA and disease features from different views to predict miRNA-disease associations. The parameters of the model are optimized in an end-to-end way. We applied MHCLMDA to the prediction of human miRNA-disease association. The experimental results show that our method performs better than several other state-of-the-art methods in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve.

Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks.

Predicting the therapeutic effect of anti-cancer drugs on tumors based on the characteristics of tumors and patients is one of the important contents of precision oncology. Existing computational methods regard the drug response prediction problem as a classification or regression task. However, few of them consider leveraging the relationship between the two tasks. In this work, we propose a Multi-task Interaction Graph Convolutional Network (MTIGCN) for anti-cancer drug response prediction. MTIGCN first utilizes an graph convolutional network-based model to produce embeddings for both cell lines and drugs. After that, the model employs multi-task learning to predict anti-cancer drug response, which involves training the model on three different tasks simultaneously: the main task of the drug sensitive or resistant classification task and the two auxiliary tasks of regression prediction and similarity network reconstruction. By sharing parameters and optimizing the losses of different tasks simultaneously, MTIGCN enhances the feature representation and reduces overfitting. The results of the experiments on two in vitro datasets demonstrated that MTIGCN outperformed seven state-of-the-art baseline methods. Moreover, the well-trained model on the in vitro dataset GDSC exhibited good performance when applied to predict drug responses in in vivo datasets PDX and TCGA. The case study confirmed the model's ability to discover unknown drug responses in cell lines.

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank.

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

Unraveling the extracellular vesicle network: insights into ovarian cancer metastasis and chemoresistance.

Ovarian cancer (OC) is one of the most prevalent and lethal gynecological malignancies, with high mortality primarily due to its aggressive nature, frequent metastasis, and resistance to standard therapies. Recent research has highlighted the critical role of extracellular vesicles (EVs) in these processes. EVs, secreted by living organisms and carrying versatile and bioactive cargoes, play a vital role in intercellular communication. Functionally, the transfer of cargoes orchestrates multiple processes that actively affect not only the primary tumor but also local and distant pre-metastatic niche. Furthermore, their unique biological properties position EVs as novel therapeutic targets and promising drug delivery systems, with potential profound implications for cancer patients.This review summarizes recent progress in EV biology, delving into the intricate mechanisms by which EVs contribute to OC metastasis and drug resistance. It also explores the latest advances and therapeutic potential of EVs in the clinical context of OC. Despite the progress made, EV research in OC remains in its nascent stages. Consequently, this review presents existing research limitations and suggests avenues for future investigation. Altogether, the review aims to elucidate the critical roles of EVs in OC and spotlight their promising potential in this field.

Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets.

Metastasis remains the principal cause of cancer-related lethality despite advancements in cancer treatment. Dysfunctional epigenetic alterations are crucial in the metastatic cascade. Among these, super-enhancers (SEs), emerging as new epigenetic regulators, consist of large clusters of regulatory elements that drive the high-level expression of genes essential for the oncogenic process, upon which cancer cells develop a profound dependency. These SE-driven oncogenes play an important role in regulating various facets of metastasis, including the promotion of tumor proliferation in primary and distal metastatic organs, facilitating cellular migration and invasion into the vasculature, triggering epithelial-mesenchymal transition, enhancing cancer stem cell-like properties, circumventing immune detection, and adapting to the heterogeneity of metastatic niches. This heavy reliance on SE-mediated transcription delineates a vulnerable target for therapeutic intervention in cancer cells. In this article, we review current insights into the characteristics, identification methodologies, formation, and activation mechanisms of SEs. We also elaborate the oncogenic roles and regulatory functions of SEs in the context of cancer metastasis. Ultimately, we discuss the potential of SEs as novel therapeutic targets and their implications in clinical oncology, offering insights into future directions for innovative cancer treatment strategies.

Roles of leukemia inhibitory factor receptor in cancer.

Leukemia inhibitory factor receptor (LIFR), in complex with glycoprotein 130 (gp130) as the receptor for leukemia inhibitory factor (LIF), can bind to a variety of cytokines and subsequently activate a variety of signaling pathways, including Janus kinase/signal transducer and activator of transcription 3. LIF, the most multifunctional cytokines of the interleukin-6 family acts as both a growth factor and a growth inhibitor in different types of tumors. LIF/LIFR signaling regulates a broad array of tumor-related processes including proliferation, apoptosis, migration, invasion. However, due to the activation of different signaling pathways, opposite regulatory effects are observed in certain tumor cells. Therefore, the role of LIFR in human cancers varies across different tumor and tissue, despite their recognized value in tumor treatment and prognosis observation is affirmed. Given its aberrant expression in numerous tumor cells and crucial regulatory function in tumorigenesis and progression, LIFR is considered as a promising targeted therapeutic agent. This review provides an overview of LIFR's initiating signaling pathway function as a cytokine receptor and summarize the current literature on the role of LIFR in cancer and its possible use in therapy.

Global epidemiology of type 2 diabetes in patients with NAFLD or MAFLD: a systematic review and meta-analysis.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) and metabolic-associated fatty liver disease (MAFLD) shares common pathophysiological mechanisms with type 2 diabetes, making them significant risk factors for type 2 diabetes. The present study aimed to assess the epidemiological feature of type 2 diabetes in patients with NAFLD or MAFLD at global levels. METHODS: Published studies were searched for terms that included type 2 diabetes, and NAFLD or MAFLD using PubMed, EMBASE, MEDLINE, and Web of Science databases from their inception to December 2022. The pooled global and regional prevalence and incidence density of type 2 diabetes in patients with NAFLD or MAFLD were evaluated using random-effects meta-analysis. Potential sources of heterogeneity were investigated using stratified meta-analysis and meta-regression. RESULTS: A total of 395 studies (6,878,568 participants with NAFLD; 1,172,637 participants with MAFLD) from 40 countries or areas were included in the meta-analysis. The pooled prevalence of type 2 diabetes among NAFLD or MAFLD patients was 28.3% (95% confidence interval 25.2-31.6%) and 26.2% (23.9-28.6%) globally. The incidence density of type 2 diabetes in NAFLD or MAFLD patients was 24.6 per 1000-person year (20.7 to 29.2) and 26.9 per 1000-person year (7.3 to 44.4), respectively. CONCLUSIONS: The present study describes the global prevalence and incidence of type 2 diabetes in patients with NAFLD or MAFLD. The study findings serve as a valuable resource to assess the global clinical and economic impact of type 2 diabetes in patients with NAFLD or MAFLD.

Improving cancer driver gene identification using multi-task learning on graph convolutional network.

Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves. The MTGCN is freely available via https://github.com/weiba/MTGCN.

Early Postoperative Patient-Reported Outcomes of Sarcopenia Versus Nonsarcopenia in Patients Undergoing Video-Assisted Thoracoscopic Surgery for Lung Cancer.

BACKGROUND: To compare early postoperative patient-reported outcomes between sarcopenic and nonsarcopenic patients undergoing video-assisted thoracoscopic surgery (VATS) for lung cancer. METHODS: The data used in this study were acquired from a longitudinal prospective study (CN-PRO-Lung 1) between November 2017 and January 2020. Skeletal muscle index was measured at L3 vertebral level on preoperative computed tomography to identify sarcopenia based on an established threshold. Symptoms severity and status of functional impairments were reported as proportions of patients with clinically relevant moderate-to-severe scores on 0-10 scales, which were measured by using the MD Anderson Symptom Inventory-Lung Cancer at baseline, daily postoperative hospitalization, and weekly after discharge up to 4 weeks. Symptom severity, functional status, and postoperative clinical outcomes were compared between the sarcopenia and nonsarcopenia groups. RESULTS: This study included 125 patients undergoing VATS for lung cancer. Sarcopenia was identified in 34 (27.2%) patients. Sarcopenic patients reported more moderate-to-severe pain (P = 0.002) at discharge and more moderate-to-severe fatigue (P = 0.027) during the 4 weeks after discharge. Besides, sarcopenic patients had a longer recovery time from both pain (P = 0.002) and fatigue (P = 0.007) than nonsarcopenic patients. Meanwhile, no significant between-group difference was found in the postoperative clinical outcomes (all P > 0.05). CONCLUSIONS: Sarcopenic patients undergoing VATS for lung cancer may have more pain and fatigue, as well as longer symptoms recovery time than nonsarcopenic patients during the early postoperative period.

Chemoproteomic Approaches to Studying RNA Modification-Associated Proteins.

The function of cellular RNA is modulated by a host of post-transcriptional chemical modifications installed by dedicated RNA-modifying enzymes. RNA modifications are widespread in biology, occurring in all kingdoms of life and in all classes of RNA molecules. They regulate RNA structure, folding, and protein-RNA interactions, and have important roles in fundamental gene expression processes involving mRNA, tRNA, rRNA, and other types of RNA species. Our understanding of RNA modifications has advanced considerably; however, there are still many outstanding questions regarding the distribution of modifications across all RNA transcripts and their biological function. One of the major challenges in the study of RNA modifications is the lack of sequencing methods for the transcriptome-wide mapping of different RNA-modification structures. Furthermore, we lack general strategies to characterize RNA-modifying enzymes and RNA-modification reader proteins. Therefore, there is a need for new approaches to enable integrated studies of RNA-modification chemistry and biology.In this Account, we describe our development and application of chemoproteomic strategies for the study of RNA-modification-associated proteins. We present two orthogonal methods based on nucleoside and oligonucleotide chemical probes: 1) RNA-mediated activity-based protein profiling (RNABPP), a metabolic labeling strategy based on reactive modified nucleoside probes to profile RNA-modifying enzymes in cells and 2) photo-cross-linkable diazirine-containing synthetic oligonucleotide probes for identifying RNA-modification reader proteins.We use RNABPP with C5-modified cytidine and uridine nucleosides to capture diverse RNA-pyrimidine-modifying enzymes including methyltransferases, dihydrouridine synthases, and RNA dioxygenase enzymes. Metabolic labeling facilitates the mechanism-based cross-linking of RNA-modifying enzymes with their native RNA substrates in cells. Covalent RNA-protein complexes are then isolated by denaturing oligo(dT) pulldown, and cross-linked proteins are identified by quantitative proteomics. Once suitable modified nucleosides have been identified as mechanism-based proteomic probes, they can be further deployed in transcriptome-wide sequencing experiments to profile the substrates of RNA-modifying enzymes at nucleotide resolution. Using 5-fluorouridine-mediated RNA-protein cross-linking and sequencing, we analyzed the substrates of human dihydrouridine synthase DUS3L. 5-Ethynylcytidine-mediated cross-linking enabled the investigation of ALKBH1 substrates. We also characterized the functions of these RNA-modifying enzymes in human cells by using genetic knockouts and protein translation reporters.We profiled RNA readers for N6-methyladenosine (m6A) and N1-methyladenosine (m1A) using a comparative proteomic workflow based on diazirine-containing modified oligonucleotide probes. Our approach enables quantitative proteome-wide analysis of the preference of RNA-binding proteins for modified nucleotides across a range of affinities. Interestingly, we found that YTH-domain proteins YTHDF1/2 can bind to both m6A and m1A to mediate transcript destabilization. Furthermore, m6A also inhibits stress granule proteins from binding to RNA.Taken together, we demonstrate the application of chemical probing strategies, together with proteomic and transcriptomic workflows, to reveal new insights into the biological roles of RNA modifications and their associated proteins.

Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study.

OBJECTIVE: Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine. METHODS: We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results. RESULTS: The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86. INTERPRETATION: Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.

Large transmittance contrast via 90-degree sharp bends in square lattice glide-symmetric photonic crystal waveguides.

We demonstrate an intriguing transmittance contrast in a glide-symmetric square-lattice photonic crystal waveguide with a 90-degree sharp bend. The glide-symmetry gives rise to a degeneracy point in the band structure and separates a high-frequency and a low-frequency band. Previously, a similar large transmittance contrast between these two bands has been observed in glide-symmetric triangular- or honeycomb-lattice photonic crystals without inversion symmetry, and this phenomenon has been attributed to the valley-photonic effect. In this study, we demonstrate the first example of this phenomenon in square-lattice photonic crystals, which do not possess the valley effect. Our result sheds new light onto unexplored properties of glide-symmetric waveguides. We show that this phenomenon is related to the spatial distribution of circular polarization singularities in glide-symmetric waveguides. This work expands the possible designs of low-loss photonic circuits and provides a new understanding of light transmission via sharp bends in photonic crystal waveguides.

Heavy metals, oxidative stress, and the role of AhR signaling.

The Aryl Hydrocarbon Receptor (AhR) is a ligand-activated transcriptional factor pivotal in responding to environmental stress and maintaining cellular homeostasis. Exposure to specific xenobiotics or industrial compounds in the environment activates AhR and its subsequent signaling, inducing oxidative stress and related toxicity. Past research has also identified and characterized several classes of endogenous ligands, particularly some tryptophan (Trp) metabolic/catabolic products, that act as AhR agonists, influencing a variety of physiological and pathological states, including the modulation of immune responses and cell death. Heavy metals, being non-essential elements in the human body, are generally perceived as toxic and hazardous, originating either naturally or from industrial activities. Emerging evidence indicates that heavy metals significantly influence AhR activation and its downstream signaling. This review consolidates current knowledge on the modulation of the AhR signaling pathway by heavy metals, explores the consequences of co-exposure to AhR ligands and heavy metals, and investigates the interplay between oxidative stress and AhR activation, focusing on the regulation of immune responses and ferroptosis.

AhR signaling modulates Ferroptosis by regulating SLC7A11 expression.

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that is pivotal in development, metabolic homeostasis, and immune responses. While recent research has highlighted AhR's significant role in modulating oxidative stress responses, its mechanistic relationship with ferroptosis-an iron-dependent, non-apoptotic cell death-remains to be fully elucidated. In our study, we discovered that AhR plays a crucial role in ferroptosis, in part by transcriptionally regulating the expression of the solute carrier family 7 member 11 (SLC7A11). Our findings indicate that both pharmacological inactivation and genetic ablation of AhR markedly enhance erastin-induced ferroptosis. This enhancement is achieved by suppressing SLC7A11, leading to increased lipid peroxidation. We also obtained evidence of post-translational modifications of SLC7A11 during ferroptosis. Additionally, we observed that indole 3-pyruvate (I3P), an endogenous ligand of AhR, protects cells from ferroptosis through an AhR-dependent mechanism. Based on these insights, we propose that AhR transcriptionally regulates the expression of SLC family genes, which in turn play a pivotal role in mediating ferroptosis. This underscores AhR's essential role in suppressing lipid oxidation and ensuring cell survival under oxidative stress.

Erasure Detection of a Dual-Rail Qubit Encoded in a Double-Post Superconducting Cavity.

Qubits with predominantly erasure errors present distinctive advantages for quantum error correction (QEC) and fault-tolerant quantum computing. Logical qubits based on dual-rail encoding that exploit erasure detection have been recently proposed in superconducting circuit architectures, with either coupled transmons or cavities. Here, we implement a dual-rail qubit encoded in a compact, double-post superconducting cavity. Using an auxiliary transmon, we perform erasure detection on the dual-rail subspace. We characterize the behavior of the code space by a novel method to perform joint-Wigner tomography. This is based on modifying the cross-Kerr interaction between the cavity modes and the transmon. We measure an erasure rate of 3.981±0.003 (ms)^{-1} and a residual, postselected dephasing error rate up to 0.17 (ms)^{-1} within the code space. This strong hierarchy of error rates, together with the compact and hardware-efficient nature of this novel architecture, holds promise in realizing QEC schemes with enhanced thresholds and improved scaling.

Bilayer t-J-J_{⊥} Model and Magnetically Mediated Pairing in the Pressurized Nickelate La_{3}Ni_{2}O_{7}.

The recently discovered nickelate superconductor La_{3}Ni_{2}O_{7} has a high transition temperature near 80 K under pressure, providing an additional avenue for exploring unconventional superconductivity. Here, with state-of-the-art tensor-network methods, we study a bilayer t-J-J_{⊥} model for La_{3}Ni_{2}O_{7} and find a robust s-wave superconductive (SC) order mediated by interlayer magnetic couplings. Large-scale density matrix renormalization group calculations find algebraic pairing correlations with Luttinger parameter K_{SC}≲1. Infinite projected entangled-pair state method obtains a nonzero SC order directly in the thermodynamic limit, and estimates a strong pairing strength Δ[over ¯]_{z}∼O(0.1). Tangent-space tensor renormalization group simulations elucidate the temperature evolution of SC pairing and further determine a high SC temperature T_{c}^{*}/J∼O(0.1). Because of the intriguing orbital selective behaviors and strong Hund's rule coupling in the compound, t-J-J_{⊥} model has strong interlayer spin exchange (while negligible interlayer hopping), which greatly enhances the SC pairing in the bilayer system. Such a magnetically mediated pairing has also been observed recently in the optical lattice of ultracold atoms. Our accurate and comprehensive tensor-network calculations reveal a robust SC order in the bilayer t-J-J_{⊥} model and shed light on the pairing mechanism of the high-T_{c} nickelate superconductor.

Combining genome-wide association study and linkage mapping in the genetic dissection of amylose content in maize (Zea mays L.).

KEY MESSAGE: Integrated linkage and association analysis revealed genetic basis across multiple environments. The genes Zm00001d003102 and Zm00001d015905 were further verified to influence amylose content using gene-based association study. Maize kernel amylose is an important source of human food and industrial raw material. However, the genetic basis underlying maize amylose content is still obscure. Herein, we used an intermated B73 × Mo17 (IBM) Syn10 doubled haploid population composed of 222 lines and a germplasm set including 305 inbred lines to uncover the genetic control for amylose content under four environments. Linkage mapping detected 16 unique QTL, among which four were individually repeatedly identified across multiple environments. Genome-wide association study revealed 17 significant (P = 2.24E-06) single-nucleotide polymorphisms, of which two (SYN19568 and PZE-105090500) were located in the intervals of the mapped QTL (qAC2 and qAC5-3), respectively. According to the two population co-localized loci, 20 genes were confirmed as the candidate genes for amylose content. Gene-based association analysis indicated that the variants in Zm00001d003102 (Beta-16-galactosyltransferase GALT29A) and Zm00001d015905 (Sugar transporter 4a) affected amylose content across multi-environment. Tissue expression analysis showed that the two genes were specifically highly expressed in the ear and stem, respectively, suggesting that they might participate in sugar transport from source to sink organs. Our study provides valuable genetic information for breeding maize varieties with high amylose.