Showing papers by "Xiamen University published in 2019"

Planar perovskite solar cells with long-term stability using ionic liquid additives

Abstract: Solar cells based on metal halide perovskites are one of the most promising photovoltaic technologies(1-4). Over the past few years, the long-term operational stability of such devices has been gre ...

Semiconductor polymeric graphitic carbon nitride photocatalysts: the “holy grail” for the photocatalytic hydrogen evolution reaction under visible light

Abstract: Semiconductor polymeric graphitic carbon nitride (g-C3N4) photocatalysts have attracted dramatically growing attention in the field of the visible-light-induced hydrogen evolution reaction (HER) because of their facile synthesis, easy functionalization, attractive electronic band structure, high physicochemical stability and photocatalytic activity. This review article presents a panorama of the latest advancements in the rational design and development of g-C3N4 and g-C3N4-based composite photocatalysts for HER application. Concretely, the review starts with the development history, synthetic strategy, electronic structure and physicochemical characteristics of g-C3N4 materials, followed by the rational design and engineering of various nanostructured g-C3N4 (e.g. thinner, highly crystalline, doped, and porous g-C3N4) photocatalysts for HER application. Then a series of highly efficient g-C3N4 (e.g., metal/g-C3N4, semiconductor/g-C3N4, metal organic framework/g-C3N4, carbon/g-C3N4, conducting polymer/g-C3N4, sensitizer/g-C3N4) composite photocatalysts are exemplified. Lastly, this review provides a comprehensive summary and outlook on the major challenges, opportunities, and inspiring perspectives for future research in this hot area on the basis of pioneering works. It is believed that the emerging g-C3N4-based photocatalysts will act as the “holy grail” for highly efficient photocatalytic HER under visible-light irradiation.

Catalysis with Two-Dimensional Materials Confining Single Atoms: Concept, Design, and Applications.

Abstract: Two-dimensional materials and single-atom catalysts are two frontier research fields in catalysis. A new category of catalysts with the integration of both aspects has been rapidly developed in recent years, and significant advantages were established to make it an independent research field. In this Review, we will focus on the concept of two-dimensional materials confining single atoms for catalysis. The new electronic states via the integration lead to their mutual benefits in activity, that is, two-dimensional materials with unique geometric and electronic structures can modulate the catalytic performance of the confined single atoms, and in other cases the confined single atoms can in turn affect the intrinsic activity of two-dimensional materials. Three typical two-dimensional materials are mainly involved here, i.e., graphene, g-C3N4, and MoS2, and the confined single atoms include both metal and nonmetal atoms. First, we systematically introduce and discuss the classic synthesis methods, advanced ...

New horizon in C1 chemistry: breaking the selectivity limitation in transformation of syngas and hydrogenation of CO2 into hydrocarbon chemicals and fuels

Abstract: Catalytic transformations of syngas (a mixture of H2 and CO), which is one of the most important C1-chemistry platforms, and CO2, a greenhouse gas released from human industrial activities but also a candidate of abundant carbon feedstock, into chemicals and fuels have attracted much attention in recent years. Fischer-Tropsch (FT) synthesis is a classic route of syngas chemistry, but the product selectivity of FT synthesis is limited by the Anderson-Schulz-Flory (ASF) distribution. The hydrogenation of CO2 into C2+ hydrocarbons involving C-C bond formation encounters similar selectivity limitation. The present article focuses on recent advances in breaking the selectivity limitation by using a reaction coupling strategy for hydrogenation of both CO and CO2 into C2+ hydrocarbons, which include key building-block chemicals, such as lower (C2-C4) olefins and aromatics, and liquid fuels, such as gasoline (C5-C11 hydrocarbons), jet fuel (C8-C16 hydrocarbons) and diesel fuel (C10-C20 hydrocarbons). The design and development of novel bifunctional or multifunctional catalysts, which are composed of metal, metal carbide or metal oxide nanoparticles and zeolites, for hydrogenation of CO and CO2 to C2+ hydrocarbons beyond FT synthesis will be reviewed. The key factors in controlling catalytic performances, such as the catalyst component, the acidity and mesoporosity of the zeolite and the proximity between the metal/metal carbide/metal oxide and zeolite, will be analysed to provide insights for designing efficient bifunctional or multifunctional catalysts. The reaction mechanism, in particular the activation of CO and CO2, the reaction pathway and the reaction intermediate, will be discussed to provide a deep understanding of the chemistry of the new C1 chemistry routes beyond FT synthesis.

Cellular-Connected UAV: Potential, Challenges, and Promising Technologies

Abstract: Enabling high-rate, low-latency and ultra-reliable wireless communications between UAVs and their associated ground pilots/users is of paramount importance to realize their large-scale usage in the future. To achieve this goal, cellular- connected UAV, whereby UAVs for various applications are integrated into the cellular network as new aerial users, is a promising technology that has drawn significant attention recently. Compared to conventional cellular communication with terrestrial users, cellular-connected UAV communication possesses substantially different characteristics that present new research challenges as well as opportunities. In this article, we provide an overview of this emerging technology, by first discussing its potential benefits, unique communication and spectrum requirements, as well as new design considerations. We then introduce promising technologies to enable the future generation of 3D heterogeneous wireless networks with coexisting aerial and ground users. Last, we present simulation results to corroborate our discussions and highlight key directions for future research.

Hypergraph Neural Networks

Abstract: In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.

Chemistry with Electrochemically Generated N-Centered Radicals

Abstract: N-centered radicals are versatile reaction intermediates that can react with various π systems to construct C-N bonds. Current methods for generating N-centered radicals usually involve the cleavage of an N-heteroatom bond; however, similar strategies that are applicable to N-H bonds prove to be more challenging to develop and therefore are attracting increasing attention. In this Account, we summarize our recent efforts in the development of electrochemical methods for the generation and synthetic utilization of N-centered radicals. In our studies, N-aryl amidyl radical, amidinyl radical and iminyl radical cation intermediates are generated from N-H precursors through direct electrolysis or indirect electrolysis assisted by a redox catalyst. In addition, an electrocatalytic method that converts oximes to iminoxyl radicals has also been developed. The electrophilic amidyl radical intermediates can participate in 5-exo or 6-exo cyclization with alkenes and alkynes to afford C-centered radicals, which can then undergo various transformations such as H atom abstraction, single-electron transfer oxidation to a carbocation, cyclization, or aromatic substitution, leading to a diverse range of N-heterocyclic products. Furthermore, amidinyl radicals, iminyl radical cations, and iminoxyl radicals can undergo intramolecular aromatic substitution to afford various N-heteroaromatic compounds. Importantly, the electrochemical reaction can be channeled toward a specific product despite the presence of other competing pathways. For a successful electrosynthesis, it is important to take into consideration of both the electron transfer steps associated with the electrode and the nonelectrode related processes. A unique feature of electrochemistry is the simultaneous occurrence of anodic oxidation and cathodic reduction, which, as this Account demonstrates, allows the dehydrogenative transformations to proceed through H2 evolution without the need for chemical oxidants. In addition, cathodic solvent reduction can continuously generate a low concentration of base, which facilitates anodic substrate oxidation. Such a mechanistic paradigm obviates the need for stoichiometric strong bases and avoids base-promoted decomposition of sensitive substrates or products. Furthermore, electrode materials can also be adjusted to control the reaction outcome, as demonstrated by the synthesis of N-heteroaromatics and the corresponding N-oxides from biaryl ketoximes.

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification

Abstract: This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional methods are mainly to reduce feature distribution gap between the source and target domains. However, these studies largely neglect the intra-domain variations in the target domain, which contain critical factors influencing the testing performance on the target domain. In this work, we comprehensively investigate into the intra-domain variations of the target domain and propose to generalize the re-ID model w.r.t three types of the underlying invariance, i.e., exemplar-invariance, camera-invariance and neighborhood-invariance. To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties. The memory allows us to enforce the invariance constraints over global training batch without significantly increasing computation cost. Experiment demonstrates that the three invariance properties and the proposed memory are indispensable towards an effective domain adaptation system. Results on three re-ID domains show that our domain adaptation accuracy outperforms the state of the art by a large margin. Code is available at: https://github.com/zhunzhong07/ECN

Scalable synthesis of ant-nest-like bulk porous silicon for high-performance lithium-ion battery anodes

Abstract: Although silicon is a promising anode material for lithium-ion batteries, scalable synthesis of silicon anodes with good cyclability and low electrode swelling remains a significant challenge. Herein, we report a scalable top-down technique to produce ant-nest-like porous silicon from magnesium-silicon alloy. The ant-nest-like porous silicon comprising three-dimensional interconnected silicon nanoligaments and bicontinuous nanopores can prevent pulverization and accommodate volume expansion during cycling resulting in negligible particle-level outward expansion. The carbon-coated porous silicon anode delivers a high capacity of 1,271 mAh g−1 at 2,100 mA g−1 with 90% capacity retention after 1,000 cycles and has a low electrode swelling of 17.8% at a high areal capacity of 5.1 mAh cm−2. The full cell with the prelithiated silicon anode and Li(Ni1/3Co1/3Mn1/3)O2 cathode boasts a high energy density of 502 Wh Kg−1 and 84% capacity retention after 400 cycles. This work provides insights into the rational design of alloy anodes for high-energy batteries. Silicon is a promising anode material for lithium-ion batteries but experiences large volume changes during cycling. Here the authors report a scalable method to synthesize porous ant-nest-like silicons. The unique structure of this anode solves the swelling problem and enables impressive performance.

Simultaneously Dual Modification of Ni-Rich Layered Oxide Cathode for High-Energy Lithium-Ion Batteries

Abstract: A critical challenge in the commercialization of layer-structured Ni-rich materials is the fast capacity drop and voltage fading due to the interfacial instability and bulk structural degradation of the cathodes during battery operation. Herein, with the guidance of theoretical calculations of migration energy difference between La and Ti from the surface to the inside of LiNi0.8Co0.1Mn0.1O2, for the first time, Ti-doped and La4NiLiO8-coated LiNi0.8Co0.1Mn0.1O2 cathodes are rationally designed and prepared, via a simple and convenient dual-modification strategy of synchronous synthesis and in situ modification. Impressively, the dual modified materials show remarkably improved electrochemical performance and largely suppressed voltage fading, even under exertive operational conditions at elevated temperature and under extended cutoff voltage. Further studies reveal that the nanoscale structural degradation on material surfaces and the appearance of intergranular cracks associated with the inconsistent evolution of structural degradation at the particle level can be effectively suppressed by the synergetic effect of the conductive La4NiLiO8 coating layer and the strong TiO bond. The present work demonstrates that our strategy can simultaneously address the two issues with respect to interfacial instability and bulk structural degradation, and it represents a significant progress in the development of advanced cathode materials for high-performance lithium-ion batteries.

Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

Abstract: Structured pruning of filters or neurons has received increased focus for compressing convolutional neural networks. Most existing methods rely on multi-stage optimizations in a layer-wise manner for iteratively pruning and retraining which may not be optimal and may be computation intensive. Besides, these methods are designed for pruning a specific structure, such as filter or block structures without jointly pruning heterogeneous structures. In this paper, we propose an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner. To accomplish this, we first introduce a soft mask to scale the output of these structures by defining a new objective function with sparsity regularization to align the output of baseline and network with this mask. We then effectively solve the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end-to-end manner. By forcing more scale factors in the soft mask to zero, the fast iterative shrinkage-thresholding algorithm (FISTA) can be leveraged to fast and reliably remove the corresponding structures. Extensive experiments demonstrate the effectiveness of GAL on different datasets, including MNIST, CIFAR-10 and ImageNet ILSVRC 2012. For example, on ImageNet ILSVRC 2012, the pruned ResNet-50 achieves 10.88% Top-5 error and results in a factor of 3.7x speedup. This significantly outperforms state-of-the-art methods.

Metal-Organic Framework-Based Stimuli-Responsive Systems for Drug Delivery

Abstract: With the rapid development of nanotechnology, stimuli-responsive nanomaterials have provided an alternative for designing controllable drug delivery systems due to their spatiotemporally controllable properties. As a new type of porous material, metal-organic frameworks (MOFs) have been widely used in biomedical applications, especially drug delivery systems, owing to their tunable pore size, high surface area and pore volume, and easy surface modification. Here, recent progress in MOF-based stimuli-responsive systems is presented, including pH-, magnetic-, ion-, temperature-, pressure-, light-, humidity-, redox-, and multiple stimuli-responsive systems for the delivery of anticancer drugs. The remaining challenges and suggestions for future directions for the rational design of MOF-based nanomedicines are also discussed.

Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

Abstract: Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we present a deep RL-based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency, and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL-based offloading scheme reduces the energy consumption, computation latency, and task drop rate, and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes.

In situ Raman spectroscopic evidence for oxygen reduction reaction intermediates at platinum single-crystal surfaces

Abstract: Developing an understanding of structure–activity relationships and reaction mechanisms of catalytic processes is critical to the successful design of highly efficient catalysts. As a fundamental reaction in fuel cells, elucidation of the oxygen reduction reaction (ORR) mechanism at Pt(hkl) surfaces has remained a significant challenge for researchers. Here, we employ in situ electrochemical surface-enhanced Raman spectroscopy (SERS) and density functional theory (DFT) calculation techniques to examine the ORR process at Pt(hkl) surfaces. Direct spectroscopic evidence for ORR intermediates indicates that, under acidic conditions, the pathway of ORR at Pt(111) occurs through the formation of HO2*, whereas at Pt(110) and Pt(100) it occurs via the generation of OH*. However, we propose that the pathway of the ORR under alkaline conditions at Pt(hkl) surfaces mainly occurs through the formation of O2−. Notably, these results demonstrate that the SERS technique offers an effective and reliable way for real-time investigation of catalytic processes at atomically flat surfaces not normally amenable to study with Raman spectroscopy. The oxygen reduction reaction, catalysed by platinum, is a crucial process in the operation of fuel cells, but the mechanistic pathways through which it occurs remain a matter for debate. Here, the authors use in situ Raman spectroscopy to identify key intermediates for this reaction at different atomically flat platinum surfaces, shedding light on the mechanism.

Promoting electrocatalytic CO2 reduction to formate via sulfur-boosting water activation on indium surfaces.

Abstract: Electrocatalytic reduction of CO2 to fuels and chemicals is one of the most attractive routes for CO2 utilization. Current catalysts suffer from low faradaic efficiency of a CO2-reduction product at high current density (or reaction rate). Here, we report that a sulfur-doped indium catalyst exhibits high faradaic efficiency of formate (>85%) in a broad range of current density (25–100 mA cm−2) for electrocatalytic CO2 reduction in aqueous media. The formation rate of formate reaches 1449 μmol h−1 cm−2 with 93% faradaic efficiency, the highest value reported to date. Our studies suggest that sulfur accelerates CO2 reduction by a unique mechanism. Sulfur enhances the activation of water, forming hydrogen species that can readily react with CO2 to produce formate. The promoting effect of chalcogen modifiers can be extended to other metal catalysts. This work offers a simple and useful strategy for designing both active and selective electrocatalysts for CO2 reduction. CO2 conversion to liquid fuels provides an appealing means to remove the greenhouse gas, although it is challenging to find materials that are both active and selective. Here, authors show sulfur-doped indium to be a highly active and selective electrocatalyst that transforms CO2 into formate.

GMAN: A Graph Multi-Attention Network for Traffic Prediction

Abstract: Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at this https URL.

Progressive Feature Alignment for Unsupervised Domain Adaptation

Abstract: Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets.

21-Gene Recurrence Score Assay and Outcomes of Adjuvant Radiotherapy in Elderly Women With Early-Stage Breast Cancer After Breast-Conserving Surgery.

Abstract: Introduction: To assess the role of the 21-gene recurrence score (RS) assay on decision-making of postoperative radiotherapy (RT) following breast-conserving surgery (BCS) in elderly women with early-stage breast cancer. Methods: The 21-gene RS for elderly (≥65 years) women with stage T1–2N0M0 estrogen receptor-positive breast cancer who underwent BCS from 2004 to 2015 was obtained from the Surveillance, Epidemiology, and End Results program. We estimated the association of 21-gene RS and adjuvant RT related to breast cancer-specific survival (BCSS) using propensity score matching (PSM). Results: We identified 18,456 patients, of which 15,326 (83.0%) received postoperative RT. Of identified patients, 58.9%, 34.0%, and 7.1% of patients had a low-, intermediate-, and high-risk RS, respectively. Receipt of postoperative RT was not related to the year of diagnosis according to the 21-gene RS groups. Multivariate analysis suggested that receipt of postoperative RT was an independent predictor of better BCSS before (hazard ratio [HR] 0.587, 95% confidence interval [CI] 0.426–0.809, P = 0.001) and after (HR 0.613, 95%CI 0.390–0.963, P = 0.034) PSM. However, subgroups analyses indicated that receipt of postoperative RT was related to better BCSS in women with intermediate-risk RS before (HR 0.467, 95%CI 0.283–0.772, P = 0.003) and after (HR 0.389, 95%CI 0.179–0.846, P = 0.017) PSM, but not in women with low- and high-risk RS groups before and after PSM. Conclusions: Although causation cannot be implied, adjuvant RT in elderly women was associated with a greater effect size in patients with an intermediate-risk RS.

Toward Flexible Surface-Enhanced Raman Scattering (SERS) Sensors for Point-of-Care Diagnostics

Abstract: Surface-enhanced Raman scattering (SERS) spectroscopy provides a noninvasive and highly sensitive route for fingerprint and label-free detection of a wide range of molecules. Recently, flexible SERS has attracted increasingly tremendous research interest due to its unique advantages compared to rigid substrate-based SERS. Here, the latest advances in flexible substrate-based SERS diagnostic devices are investigated in-depth. First, the intriguing prospect of point-of-care diagnostics is briefly described, followed by an introduction to the cutting-edge SERS technique. Then, the focus is moved from conventional rigid substrate-based SERS to the emerging flexible SERS technique. The main part of this report highlights the recent three categories of flexible SERS substrates, including actively tunable SERS, swab-sampling strategy, and the in situ SERS detection approach. Furthermore, other promising means of flexible SERS are also introduced. The flexible SERS substrates with low-cost, batch-fabrication, and easy-to-operate characteristics can be integrated into portable Raman spectroscopes for point-of-care diagnostics, which are conceivable to penetrate global markets and households as next-generation wearable sensors in the near future.

ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network

Abstract: The classification of electrocardiogram (ECG) signals is very important for the automatic diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of feature extraction and the step of pattern classification. Owing to recent advances in artificial intelligence, it has been demonstrated that deep neural network, which trained on a huge amount of data, can carry out the task of feature extraction directly from the data and recognize cardiac arrhythmias better than professional cardiologists. This paper proposes an ECG arrhythmia classification method using two-dimensional (2D) deep convolutional neural network (CNN). The time domain signals of ECG, belonging to five heart beat types including normal beat (NOR), left bundle branch block beat (LBB), right bundle branch block beat (RBB), premature ventricular contraction beat (PVC), and atrial premature contraction beat (APC), were first transformed into time-frequency spectrograms by short-time Fourier transform. Subsequently, the spectrograms of the five arrhythmia types were utilized as input to the 2D-CNN such that the ECG arrhythmia types were identified and classified finally. Using ECG recordings from the MIT-BIH arrhythmia database as the training and testing data, the classification results show that the proposed 2D-CNN model can reach an averaged accuracy of 99.00%. On the other hand, in order to achieve optimal classification performances, the model parameter optimization was investigated. It was found when the learning rate is 0.001 and the batch size parameter is 2500, the classifier achieved the highest accuracy and the lowest loss. We also compared the proposed 2D-CNN model with a conventional one-dimensional CNN model. Comparison results show that the 1D-CNN classifier can achieve an averaged accuracy of 90.93%. Therefore, it is validated that the proposed CNN classifier using ECG spectrograms as input can achieve improved classification accuracy without additional manual pre-processing of the ECG signals.

Penaeid shrimp genome provides insights into benthic adaptation and frequent molting

Abstract: Crustacea, the subphylum of Arthropoda which dominates the aquatic environment, is of major importance in ecology and fisheries. Here we report the genome sequence of the Pacific white shrimp Litopenaeus vannamei, covering ~1.66 Gb (scaffold N50 605.56 Kb) with 25,596 protein-coding genes and a high proportion of simple sequence repeats (>23.93%). The expansion of genes related to vision and locomotion is probably central to its benthic adaptation. Frequent molting of the shrimp may be explained by an intensified ecdysone signal pathway through gene expansion and positive selection. As an important aquaculture organism, L. vannamei has been subjected to high selection pressure during the past 30 years of breeding, and this has had a considerable impact on its genome. Decoding the L. vannamei genome not only provides an insight into the genetic underpinnings of specific biological processes, but also provides valuable information for enhancing crustacean aquaculture. The Pacific white shrimp Litopenaeus vannamei is an important aquaculture species and a promising model for crustacean biology. Here, the authors provide a reference genome assembly, and show that gene expansion is involved in the regulation of frequent molting as well as benthic adaptation of the shrimp.

deepDR: a network-based deep learning approach to in silico drug repositioning.

Abstract: Motivation Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. Results In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug-disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug-disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer's disease (e.g. risperidone and aripiprazole) and Parkinson's disease (e.g. methylphenidate and pergolide). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR. Supplementary information Supplementary data are available online at Bioinformatics.

METTL3 and ALKBH5 oppositely regulate m6A modification of TFEB mRNA, which dictates the fate of hypoxia/reoxygenation-treated cardiomyocytes

Abstract: N6-methyladenosine (m6A) mRNA modifications play critical roles in various biological processes. However, no study addresses the role of m6A in macroautophagy/autophagy. Here, we show that m6A modifications are increased in H/R-treated cardiomyocytes and ischemia/reperfusion (I/R)-treated mice heart. We found that METTL3 (methyltransferase like 3) is the primary factor involved in aberrant m6A modification. Silencing METTL3 enhances autophagic flux and inhibits apoptosis in H/R-treated cardiomyocytes. However, overexpression of METTL3 or inhibition of the RNA demethylase ALKBH5 has an opposite effect, suggesting that METTL3 is a negative regulator of autophagy. Mechanistically, METTL3 methylates TFEB, a master regulator of lysosomal biogenesis and autophagy genes, at two m6A residues in the 3ʹ-UTR, which promotes the association of the RNA-binding protein HNRNPD with TFEB pre-mRNA and subsequently decreases the expression levels of TFEB. Further experiments show that autophagic flux enhanced by ME...