Guangzhou University

PANTHER (Protein Analysis Through Evolutionary Relationships, http://pantherdb.org) is a resource for the evolutionary and functional classification of genes from organisms across the tree of life. We report the improvements we have made to the resource during the past two years. For evolutionary classifications, we have added more prokaryotic and plant genomes to the phylogenetic gene trees, expanding the representation of gene evolution in these lineages. We have refined many protein family boundaries, and have aligned PANTHER with the MEROPS resource for protease and protease inhibitor families. For functional classifications, we have developed an entirely new PANTHER GO-slim, containing over four times as many Gene Ontology terms as our previous GO-slim, as well as curated associations of genes to these terms. Lastly, we have made substantial improvements to the enrichment analysis tools available on the PANTHER website: users can now analyze over 900 different genomes, using updated statistical tests with false discovery rate corrections for multiple testing. The overrepresentation test is also available as a web service, for easy addition to third-party sites.

We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regres- sors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formula- tion which capitalizes on recent advances in Deep Learn- ing. We present a detailed empirical analysis with state-of- art or better performance on four academic benchmarks of diverse real-world images.

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of the client through the auditing of whether his data stored in the cloud are indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion, and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public auditability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for the seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the existing proof of storage models by manipulating the classic Merkle Hash Tree construction for block tag authentication. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multiuser setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis show that the proposed schemes are highly efficient and provably secure.

In recent years, nanocrystals of metal sulfide materials have attracted scientific research interest for renewable energy applications due to the abundant choice of materials with easily tunable electronic, optical, physical and chemical properties. Metal sulfides are semiconducting compounds where sulfur is an anion associated with a metal cation; and the metal ions may be in mono-, bi- or multi-form. The diverse range of available metal sulfide materials offers a unique platform to construct a large number of potential materials that demonstrate exotic chemical, physical and electronic phenomena and novel functional properties and applications. To fully exploit the potential of these fascinating materials, scalable methods for the preparation of low-cost metal sulfides, heterostructures, and hybrids of high quality must be developed. This comprehensive review indicates approaches for the controlled fabrication of metal sulfides and subsequently delivers an overview of recent progress in tuning the chemical, physical, optical and nano- and micro-structural properties of metal sulfide nanocrystals using a range of material fabrication methods. For hydrogen energy production, three major approaches are discussed in detail: electrocatalytic hydrogen generation, powder photocatalytic hydrogen generation and photoelectrochemical water splitting. A variety of strategies such as structural tuning, composition control, doping, hybrid structures, heterostructures, defect control, temperature effects and porosity effects on metal sulfide nanocrystals are discussed and how they are exploited to enhance performance and develop future energy materials. From this literature survey, energy conversion currently relies on a limited range of metal sulfides and their composites, and several metal sulfides are immature in terms of their dissolution, photocorrosion and long-term durability in electrolytes during water splitting. Future research directions for innovative metal sulfides should be closely allied to energy and environmental issues, along with their advanced characterization, and developing new classes of metal sulfide materials with well-defined fabrication methods.

This paper employs the Auto-Encoding Variational Bayes (AEVB) estimator based on Stochastic Gradient Variational Bayes (SGVB), designed to optimize recognition models for challenging posterior distributions and large-scale datasets. It has been applied to the mnist dataset and extended to form a Dynamic Bayesian Network (DBN) in the context of time series. The paper delves into Bayesian inference, variational methods, and the fusion of Variational Autoencoders (VAEs) and variational techniques. Emphasis is placed on reparameterization for achieving efficient optimization. AEVB employs VAEs as an approximation for intricate posterior distributions.

Development of easy‐to‐make, highly active, and stable bifunctional electrocatalysts for water splitting is important for future renewable energy systems. Three‐dimension (3D) porous Ni/Ni 8 P 3 and Ni/Ni 9 S 8 electrodes are prepared by sequential treatment of commercial Ni‐foam with acid activation, followed by phosphorization or sulfurization. The resultant materials can act as self‐supported bifunctional electrocatalytic electrodes for direct water splitting with excellent activity toward oxygen evolution reaction and hydrogen evolution reaction in alkaline media. Stable performance can be maintained for at least 24 h, illustrating their versatile and practical nature for clean energy generation. Furthermore, an advanced water electrolyzer through exploiting Ni/Ni 8 P 3 as both anode and cathode is fabricated, which requires a cell voltage of 1.61 V to deliver a 10 mA cm −2 water splitting current density in 1.0 m KOH solution. This performance is significantly better than that of the noble metal benchmark—integrated Ni/IrO 2 and Ni/Pt–C electrodes. Therefore, these bifunctional electrodes have significant potential for realistic large‐scale production of hydrogen as a replacement clean fuel to polluting and limited fossil‐fuels.

Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet. Finally, we discuss some issues that still need to be resolved before deep residual learning can be applied on more complex problems.

Rational design and exploration of robust and low‐cost bifunctional oxygen reduction/evolution electrocatalysts are greatly desired for metal–air batteries. Herein, a novel high‐performance oxygen electrode catalyst is developed based on bimetal FeCo nanoparticles encapsulated in in situ grown nitrogen‐doped graphitic carbon nanotubes with bamboo‐like structure. The obtained catalyst exhibits a positive half‐wave potential of 0.92 V (vs the reversible hydrogen electrode, RHE) for oxygen reduction reaction, and a low operating potential of 1.73 V to achieve a 10 mA cm −2 current density for oxygen evolution reaction. The reversible oxygen electrode index is 0.81 V, surpassing that of most highly active bifunctional catalysts reported to date. By combining experimental and simulation studies, a strong synergetic coupling between FeCo alloy and N‐doped carbon nanotubes is proposed in producing a favorable local coordination environment and electronic structure, which affords the pyridinic N‐rich catalyst surface promoting the reversible oxygen reactions. Impressively, the assembled zinc–air batteries using liquid electrolytes and the all‐solid‐state batteries with the synthesized bifunctional catalyst as the air electrode demonstrate superior charging–discharging performance, long lifetime, and high flexibility, holding great potential in practical implementation of new‐generation powerful rechargeable batteries with portable or even wearable characteristic.

Abstract Herein, we demonstrate the use of heterostructures comprised of Co/β‐Mo 2 C@N‐CNT hybrids for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in an alkaline electrolyte. The Co can not only create a well‐defined heterointerface with β‐Mo 2 C but also overcomes the poor OER activity of β‐Mo 2 C, thus leading to enhanced electrocatalytic activity for HER and OER. DFT calculations further proved that cooperation between the N‐CNTs, Co, and β‐Mo 2 C results in lower energy barriers of intermediates and thus greatly enhances the HER and OER performance. This study not only provides a simple strategy for the construction of heterostructures with nonprecious metals, but also provides in‐depth insight into the HER and OER mechanism in alkaline solution.

ZnCo2 O4 quantum dots anchored on nitrogen-doped carbon nanotubes (N-CNT) retain the high catalytic activity of ZnCo2 O4 to oxidize water while enabling an efficient oxygen reduction performance thereby combining these desirable features. These advantages realize a bifunctional catalytic activity for ZnCo2 O4 /N-CNT that can be used in rechargeable zinc-air batteries.

Intrinsically conducting polymers (ICP) and conductive fillers incorporated conductive polymer-based composites (CPC) greatly facilitate the research in electromagnetic interference (EMI) shielding because they not only provide excellent EMI shielding but also have advantages of electromagnetic wave absorption rather than reflection. In this review, the latest developments in ICP and CPC based EMI shielding materials are highlighted. In particular, existing methods for adjusting the morphological structure, electric and magnetic properties of EMI shielding materials are discussed along with the future opportunities and challenges in developing ICP and CPC for EMI shielding applications.

Abstract The oxygen evolution reaction (OER) generally exists in electrochemistry‐enabled applications that are coupled with cathodic reactions like hydrogen evolution, carbon dioxide reduction, ammonia synthesis, and electrocatalytic hydrogenation. The OER heavily impacts the overall energy efficiency of these devices because the sluggish OER kinetics result in a huge overpotential, thus, a large amount of efficient catalysts are needed. The benchmark iridium and ruthenium (Ir/Ru)‐based materials (mostly used in acid media) are, however, significantly limited by their scarcity. Non‐precious metal‐based catalysts (NPMCs) have emerged as the most promising alternatives; however, they tend to degrade quickly under the harsh operating conditions of typical OER devices. Another challenge is the unsatisfying performance of OER catalysts when integrated in real‐world devices. Herein, the OER active sites for three mainstream types of NPMCs including non‐precious transition metal oxides/(oxy)hydroxides, metal‐free carbon materials, and hybrid non‐precious metal and carbon composites are reviewed. In addition, possible degradation mechanisms for active sites and mitigation strategies are discussed in detail. This review also provides insights into the gaps between R&D of NPMCs for the OER and their applications in practical devices.

Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human effort. In this paper, we propose a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner. Our approach advances the existing AL methods in two aspects. First, we incorporate deep convolutional neural networks into AL. Through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. Second, we present a cost-effective sample selection strategy to improve the classification performance with less manual annotations. Unlike traditional methods focusing on only the uncertain samples of low prediction confidence, we especially discover the large amount of high-confidence samples from the unlabeled set for feature learning. Specifically, these high-confidence samples are automatically selected and iteratively assigned pseudolabels. We thus call our framework cost-effective AL (CEAL) standing for the two advantages. Extensive experiments demonstrate that the proposed CEAL framework can achieve promising results on two challenging image classification data sets, i.e., face recognition on the cross-age celebrity face recognition data set database and object categorization on Caltech-256.

A nonradical oxidation process via metal-free peroxymonosulfate (PMS) activation has recently attracted considerable attention for organic pollutant degradation; however, the origin of singlet oxygen (1O2) generation still remains controversial. In this study, nitrogen-doped carbon nanosheets (NCN-900) derived from graphitic carbon nitride were developed for activation of PMS and elucidation of 1O2 production. With a large specific surface area (1218.7 m2 g–1) and high nitrogen content (14.5 at %), NCN-900 exhibits superior catalytic activity in PMS activation, as evidenced by complete degradation of bisphenol A within 2 min using 0.1 g L–1 NCN-900 and 2 mM PMS. Moreover, the reaction rate constant fitted by pseudo-first-order kinetics for NCN-900 reaches an impressive value of 3.1 min–1. Electron paramagnetic resonance measurements and quenching tests verified 1O2 as the primary reactive oxygen species in the NCN-900/PMS system. Based on X-ray photoelectron spectroscopy analysis and theoretical calculations, an unexpected generation pathway of 1O2 involving PMS oxidation over the electron-deficient carbon atoms neighboring graphitic N in NCN-900 was unraveled. Besides, the NCN-900/PMS system is also applicable for remediation of actual industrial wastewater. This work highlights the important role of electron-deficient carbon atoms in 1O2 generation from PMS oxidation and furnishes theoretical support for further relevant studies.

Oxygen-doped graphitic carbon nitride (O–CN) was fabricated via a facile thermal polymerization method using urea and oxalic acid dihydrate as the graphitic carbon nitride precursor and oxygen source, respectively. Experimental and theoretical results revealed that oxygen doping preferentially occurred on the two-coordinated nitrogen positions, which create the formation of low and high electron density areas resulting in the electronic structure modulation of O–CN. As a result, the resultant O–CN exhibits enhanced catalytic activity and excellent long-term stability for peroxymonosulfate (PMS) activation toward the degradation of organic pollutants. The O–CN with modulated electronic structure enables PMS oxidation over the electron-deficient C atoms for the generation of singlet oxygen (1O2) and PMS reduction around the electron-rich O dopants for the formation of hydroxyl radical (•OH) and sulfate radical (SO4•–), in which 1O2 is the major reactive oxygen species, contributing to the selective reactivity of the O–CN/PMS system. Our findings not only propose a novel PMS activation mechanism in terms of simultaneous PMS oxidation and reduction for the production of nonradical and radical species but also provide a valuable insight for the development of efficient metal-free catalysts through nonmetal doping toward the persulfate-based environmental cleanup.

Abstract We present a microkinetic model for CO (2) reduction (CO (2) R) on Cu(211) towards C 2 products, based on energetics estimated from an explicit solvent model. We show that the differences in both Tafel slopes and pH dependence for C 1 vs C 2 activity arise from differences in their multi-step mechanisms. We find the depletion in C 2 products observed at high overpotential and high pH to arise from the 2 nd order dependence of C-C coupling on CO coverage, which decreases due to competition from the C 1 pathway. We further demonstrate that CO (2) reduction at a fixed pH yield similar activities, due to the facile kinetics for CO 2 reduction to CO on Cu, which suggests C 2 products to be favored for CO 2 R under alkaline conditions. The mechanistic insights of this work elucidate how reaction conditions can lead to significant enhancements in selectivity and activity towards higher value C 2 products.

The alarming growth rate of malicious apps has become a serious issue that sets back the prosperous mobile ecosystem. A recent report indicates that a new malicious app for Android is introduced every 10 s. To combat this serious malware campaign, we need a scalable malware detection approach that can effectively and efficiently identify malware apps. Numerous malware detection tools have been developed, including system-level and network-level approaches. However, scaling the detection for a large bundle of apps remains a challenging task. In this paper, we introduce Significant Permission IDentification (SigPID), a malware detection system based on permission usage analysis to cope with the rapid increase in the number of Android malware. Instead of extracting and analyzing all Android permissions, we develop three levels of pruning by mining the permission data to identify the most significant permissions that can be effective in distinguishing between benign and malicious apps. SigPID then utilizes machine-learning-based classification methods to classify different families of malware and benign apps. Our evaluation finds that only 22 permissions are significant. We then compare the performance of our approach, using only 22 permissions, against a baseline approach that analyzes all permissions. The results indicate that when a support vector machine is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which are about the same as those produced by the baseline approach while incurring the analysis times that are 4-32 times less than those of using all permissions. Compared against other state-of-the-art approaches, SigPID is more effective by detecting 93.62% of malware in the dataset and 91.4% unknown/new malware samples.

It is extensively verified that continued oxidative stress and oxidative damage may lead to chronic inflammation, which in turn can mediate most chronic diseases including cancer, diabetes, cardiovascular, neurological, inflammatory bowel disease and pulmonary diseases. Curcumin, a yellow coloring agent extracted from turmeric, shows strong anti-oxidative and anti-inflammatory activities when used as a remedy for the prevention and treatment of chronic diseases. How oxidative stress activates inflammatory pathways leading to the progression of chronic diseases is the focus of this review. Thus, research to date suggests that chronic inflammation, oxidative stress, and most chronic diseases are closely linked, and the antioxidant properties of curcumin can play a key role in the prevention and treatment of chronic inflammation diseases.

Mitochondria play a pivotal role in bioenergetics and respiratory functions, which are essential for the numerous biochemical processes underpinning cell viability. Mitochondrial morphology changes rapidly in response to external insults and changes in metabolic status via fission and fusion processes (so-called mitochondrial dynamics) that maintain mitochondrial quality and homeostasis. Damaged mitochondria are removed by a process known as mitophagy, which involves their degradation by a specific autophagosomal pathway. Over the last few years, remarkable efforts have been made to investigate the impact on the pathogenesis of Alzheimer’s disease (AD) of various forms of mitochondrial dysfunction, such as excessive reactive oxygen species (ROS) production, mitochondrial Ca 2+ dyshomeostasis, loss of ATP, and defects in mitochondrial dynamics and transport, and mitophagy. Recent research suggests that restoration of mitochondrial function by physical exercise, an antioxidant diet, or therapeutic approaches can delay the onset and slow the progression of AD. In this review, we focus on recent progress that highlights the crucial role of alterations in mitochondrial function and oxidative stress in the pathogenesis of AD, emphasizing a framework of existing and potential therapeutic approaches.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. Promising as it is, an arising challenge is to perform secure deduplication in cloud storage. Although convergent encryption has been extensively adopted for secure deduplication, a critical issue of making convergent encryption practical is to efficiently and reliably manage a huge number of convergent keys. This paper makes the first attempt to formally address the problem of achieving efficient and reliable key management in secure deduplication. We first introduce a baseline approach in which each user holds an independent master key for encrypting the convergent keys and outsourcing them to the cloud. However, such a baseline key management scheme generates an enormous number of keys with the increasing number of users and requires users to dedicatedly protect the master keys. To this end, we propose Dekey , a new construction in which users do not need to manage any keys on their own but instead securely distribute the convergent key shares across multiple servers. Security analysis demonstrates that Dekey is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement Dekey using the Ramp secret sharing scheme and demonstrate that Dekey incurs limited overhead in realistic environments.