Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in disease brain are limited. Here, we integrate genotype and RNA-sequencing in brain samples from 1695 subjects with autism, schizophrenia, bipolar disorder and controls. Over 25% of the transcriptome exhibits differential splicing or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. co-expression networks isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon response modules defining novel neural-immune mechanisms. We prioritize disease loci likely mediated by cis-effects on brain expression via transcriptome-wide association analysis. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for mechanistic insight and therapeutic development.

SignificanceNonapoptotic cell death-induced tissue damage has been implicated in a variety of diseases, including neurodegenerative disorder, inflammation, and stroke. In this study, we demonstrate that ferroptosis, a newly defined iron-dependent cell death, mediates both chemotherapy- and ischemia/reperfusion-induced cardiomyopathy. RNA-sequencing analysis revealed up-regulation of heme oxygenase 1 by doxorubicin as a major mechanism of ferroptotic cardiomyopathy. As a result, heme oxygenase 1 degrades heme and releases free iron in cardiomyocytes, which in turn leads to generation of oxidized lipids in the mitochondria membrane. Most importantly, both iron chelation therapy and pharmacologically blocking ferroptosis could significantly alleviate cardiomyopathy in mice. These findings suggest targeting ferroptosis as a strategy for treating deadly heart disease.

Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other algorithms such as belief propagation can do so. Here, we present a class of spectral algorithms based on a nonbacktracking walk on the directed edges of the graph. The spectrum of this operator is much betterbehaved than that of the adjacency matrix or other commonly used matrices, maintaining a strong separation between the bulk eigenvalues and the eigenvalues relevant to community structure even in the sparse case. We show that our algorithm is optimal for graphs generated by the stochastic block model, detecting communities all of the way down to the theoretical limit. We also show the spectrum of the nonbacktracking operator for some real-world networks, illustrating its advantages over traditional spectral clustering.D etecting communities or modules is a central task in the study of social, biological, and technological networks. Two of the most popular approaches are statistical inference, where we fix a generative model such as the stochastic block model to the network (1, 2); and spectral methods, where we classify vertices according to the eigenvectors of a matrix associated with the network such as its adjacency matrix or Laplacian (3).Both statistical inference and spectral methods have been shown to work well in networks that are sufficiently dense, or when the graph is regular (4-8). However, for sparse networks with widely varying degrees, the community detection problem is harder. Indeed, it was recently shown (9-11) that there is a phase transition below which communities present in the underlying block model are impossible for any algorithm to detect. Whereas standard spectral algorithms succeed down to this transition when the network is sufficiently dense, with an average degree growing as a function of network size (8), in the case where the average degree is constant these methods fail significantly above the transition (12). Thus, there is a large regime in which statistical inference succeeds in detecting communities, but where current spectral algorithms fail.It was conjectured in ref. 11 that this gap is artificial and that there exists a spectral algorithm that succeeds all of the way to the detectability transition even in the sparse case. Here, we propose an algorithm based on a linear operator considerably different from the adjacency matrix or its variants: namely, a matrix that represents a walk on the directed edges of the network, with backtracking prohibited. We give strong evidence that this algorithm indeed closes the gap.The fact that this operator has better spectral properties than, for instance, the standard random walk operator, has been used in the past in the context of random matrices and random graphs (13-15). In the theory of zeta functions of graphs, it is known as the edge adjacency operator, or the Hashimoto matrix...

Maize kernel oil is a valuable source of nutrition. Here we extensively examine the genetic architecture of maize oil biosynthesis in a genome-wide association study using 1.03 million SNPs characterized in 368 maize inbred lines, including 'high-oil' lines. We identified 74 loci significantly associated with kernel oil concentration and fatty acid composition (P < 1.8 × 10(-6)), which we subsequently examined using expression quantitative trait loci (QTL) mapping, linkage mapping and coexpression analysis. More than half of the identified loci localized in mapped QTL intervals, and one-third of the candidate genes were annotated as enzymes in the oil metabolic pathway. The 26 loci associated with oil concentration could explain up to 83% of the phenotypic variation using a simple additive model. Our results provide insights into the genetic basis of oil biosynthesis in maize kernels and may facilitate marker-based breeding for oil quantity and quality.

Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum physics, we propose a generative model using matrix product states, which is a tensor network originally proposed for describing (particularly one-dimensional) entangled quantum states. Our model enjoys efficient learning analogous to the density matrix renormalization group method, which allows dynamically adjusting dimensions of the tensors and offers an efficient direct sampling approach for generative tasks. We apply our method to generative modeling of several standard data sets including the Bars and Stripes random binary patterns and the MNIST handwritten digits to illustrate the abilities, features, and drawbacks of our model over popular generative models such as the Hopfield model, Boltzmann machines, and generative adversarial networks. Our work sheds light on many interesting directions of future exploration in the development of quantum-inspired algorithms for unsupervised machine learning, which are promisingly possible to realize on quantum devices.

Porous scaffolds fabricated from biocompatible and biodegradable polymers play vital roles in tissue engineering and regenerative medicine. Among various scaffold matrix materials, poly(lactide-co-glycolide) (PLGA) is a very popular and an important biodegradable polyester owing to its tunable degradation rates, good mechanical properties and processibility, etc. This review highlights the progress on PLGA scaffolds. In the latest decade, some facile fabrication approaches at room temperature were put forward; more appropriate pore structures were designed and achieved; the mechanical properties were investigated both for dry and wet scaffolds; a long time biodegradation of the PLGA scaffold was observed and a three-stage model was established; even the effects of pore size and porosity on in vitro biodegradation were revealed; the PLGA scaffolds have also been implanted into animals, and some tissues have been regenerated in vivo after loading cells including stem cells.

Rationale: Maintaining iron homeostasis is essential for proper cardiac function. Both iron deficiency and iron overload are associated with cardiomyopathy and heart failure via complex mechanisms. Although ferritin plays a central role in iron metabolism by storing excess cellular iron, the molecular function of ferritin in cardiomyocytes remains unknown. Objective: To characterize the functional role of ferritin H (Fth) in mediating cardiac iron homeostasis and heart disease. Methods and Results: Mice expressing a conditional Fth knockout allele were crossed with two distinct Cre recombinase-expressing mouse lines, resulting in offspring that lack Fth expression specifically in myocytes (MCK-Cre) or cardiomyocytes (Myh6-Cre). Mice lacking Fth in cardiomyocytes had decreased cardiac iron levels and increased oxidative stress, resulting in mild cardiac injury upon aging. However, feeding these mice a high-iron diet caused severe cardiac injury and hypertrophic cardiomyopathy, with molecular features typical of ferroptosis, including reduced glutathione (GSH) levels and increased lipid peroxidation. Ferrostatin-1, a specific inhibitor of ferroptosis, rescued this phenotype, supporting the notion that ferroptosis plays a pathophysiological role in the heart. Finally, we found that Fth-deficient cardiomyocytes have reduced expression of the ferroptosis regulator Slc7a11, and overexpressing Slc7a11 selectively in cardiomyocytes increased GSH levels and prevented cardiac ferroptosis. Conclusions: Our findings provide compelling evidence that ferritin plays a major role in protecting against cardiac ferroptosis and subsequent heart failure, thereby providing a possible new therapeutic target for patients at risk of developing cardiomyopathy.

We propose a general framework for solving statistical mechanics of systems with finite size. The approach extends the celebrated variational mean-field approaches using autoregressive neural networks, which support direct sampling and exact calculation of normalized probability of configurations. It computes variational free energy, estimates physical quantities such as entropy, magnetizations and correlations, and generates uncorrelated samples all at once. Training of the network employs the policy gradient approach in reinforcement learning, which unbiasedly estimates the gradient of variational parameters. We apply our approach to several classic systems, including 2D Ising models, the Hopfield model, the Sherrington-Kirkpatrick model, and the inverse Ising model, for demonstrating its advantages over existing variational mean-field methods. Our approach sheds light on solving statistical physics problems using modern deep generative neural networks.Consider a statistical physics model such as the celebrated Ising model, the joint probability of spins s ∈ {±1} N follows the Boltzmann distributionwhere β = 1/T is the inverse temperature and Z is the partition function. Given a problem instance, statistical mechanics problems concern about how to estimate the free energy F = − 1 β ln Z of the instance, how to compute macroscopic properties of the system such as magnetizations and correlations, and how to sample from the Boltzmann distribution efficiently. Solving these problems are not only relevant to physics, but also find broad applications in fields like Bayesian inference where the Boltzmann distribution naturally acts as posterior distribution, and in combinatorial optimizations where the task is equivalent to study zero temperature phase of a spin-glass model.When the system has finite size, computing exactly the free energy belongs to the class of #P-hard problems, hence is in general intractable. Therefore, usually one employs approximate algorithms such as variational approaches. The variational approach adopts an ansatz for the joint distribution q θ (s) parametrized by variational parameters θ, and adjusts them so that q θ (s) is as close as possible to the Boltzmann distribution p(s). The closeness between two distributions is measured bywhereis the variational free energy corresponding to distribution q θ (s). Since the KL divergence is non-negative, minimizing the KL divergence is equivalent to minimizing the variational free energy F q , an upper bound to the true free energy F.One of the most popular variational approaches, namely the variational mean-field method, assumes a factorized vari-is the marginal probability of the ith spin. In such parametrization, the variational free energy F q can be expressed as an analytical function of parameters q i (s i ), as well as its derivative with respect to q i (s i ). By setting the derivatives to zero, one obtains a set of iterative equations, known as the naïve mean-field (NMF) equations. Despite its simplicity, NMF has been used in various applica...