Nankai University

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is thatthere is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the completeprocess including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increasedautophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in manycases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as forreviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multipleassays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagyrelated protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.

Processable, single-layered graphene oxide (GO) is an intriguing nanomaterial with tremendous potential for electronic applications. We spin-coated GO thin-films on quartz and characterized their sheet resistance and optical transparency using different reduction treatments. A thermal graphitization procedure was most effective, producing films with sheet resistances as low as 10(2) -10(3) Omega/square with 80% transmittance for 550 nm light. Our experiments demonstrate solution-processed GO films have potential as transparent electrodes.

At the atomic-cluster scale, pure boron is markedly similar to carbon, forming simple planar molecules and cage-like fullerenes. Theoretical studies predict that two-dimensional (2D) boron sheets will adopt an atomic configuration similar to that of boron atomic clusters. We synthesized atomically thin, crystalline 2D boron sheets (i.e., borophene) on silver surfaces under ultrahigh-vacuum conditions. Atomic-scale characterization, supported by theoretical calculations, revealed structures reminiscent of fused boron clusters with multiple scales of anisotropic, out-of-plane buckling. Unlike bulk boron allotropes, borophene shows metallic characteristics that are consistent with predictions of a highly anisotropic, 2D metal.

The I-TASSER server (http://zhanglab.ccmb.med.umich.edu/I-TASSER) is an online resource for automated protein structure prediction and structure-based function annotation. In I-TASSER, structural templates are first recognized from the PDB using multiple threading alignment approaches. Full-length structure models are then constructed by iterative fragment assembly simulations. The functional insights are finally derived by matching the predicted structure models with known proteins in the function databases. Although the server has been widely used for various biological and biomedical investigations, numerous comments and suggestions have been reported from the user community. In this article, we summarize recent developments on the I-TASSER server, which were designed to address the requirements from the user community and to increase the accuracy of modeling predictions. Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations. We expect that these new developments will improve the quality of the I-TASSER server and further facilitate its use by the community for high-resolution structure and function prediction.

Because of the remarkably high theoretical energy output, metal-air batteries represent one class of promising power sources for applications in next-generation electronics, electrified transportation and energy storage of smart grids. The most prominent feature of a metal-air battery is the combination of a metal anode with high energy density and an air electrode with open structure to draw cathode active materials (i.e., oxygen) from air. In this critical review, we present the fundamentals and recent advances related to the fields of metal-air batteries, with a focus on the electrochemistry and materials chemistry of air electrodes. The battery electrochemistry and catalytic mechanism of oxygen reduction reactions are discussed on the basis of aqueous and organic electrolytes. Four groups of extensively studied catalysts for the cathode oxygen reduction/evolution are selectively surveyed from materials chemistry to electrode properties and battery application: Pt and Pt-based alloys (e.g., PtAu nanoparticles), carbonaceous materials (e.g., graphene nanosheets), transition-metal oxides (e.g., Mn-based spinels and perovskites), and inorganic-organic composites (e.g., metal macrocycle derivatives). The design and optimization of air-electrode structure are also outlined. Furthermore, remarks on the challenges and perspectives of research directions are proposed for further development of metal-air batteries (219 references).

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

We provide a systematic review of epidemiological surveys of autistic disorder and pervasive developmental disorders (PDDs) worldwide. A secondary aim was to consider the possible impact of geographic, cultural/ethnic, and socioeconomic factors on prevalence estimates and on clinical presentation of PDD. Based on the evidence reviewed, the median of prevalence estimates of autism spectrum disorders was 62/10 000. While existing estimates are variable, the evidence reviewed does not support differences in PDD prevalence by geographic region nor of a strong impact of ethnic/cultural or socioeconomic factors. However, power to detect such effects is seriously limited in existing data sets, particularly in low-income countries. While it is clear that prevalence estimates have increased over time and these vary in different neighboring and distant regions, these findings most likely represent broadening of the diagnostic concets, diagnostic switching from other developmental disabilities to PDD, service availability, and awareness of autistic spectrum disorders in both the lay and professional public. The lack of evidence from the majority of the world's population suggests a critical need for further research and capacity building in low- and middle-income countries.

Graphene materials (GMs) as supercapacitor electrode materials have been investigated. GMs are prepared from graphene oxide sheets, and subsequently suffer a gas-based hydrazine reduction to restore the conducting carbon network. A maximum specific capacitance of 205 F/g with a measured power density of 10 kW/kg at energy density of 28.5 Wh/kg in an aqueous electrolyte solution has been obtained. Meanwhile, the supercapacitor devices exhibit excellent long cycle life along with ∼90% specific capacitance retained after 1200 cycle tests. These remarkable results demonstrate the exciting commercial potential for high performance, environmentally friendly and low-cost electrical energy storage devices based on this new 2D graphene material.

Although organic photovoltaic (OPV) cells have many advantages, their performance still lags far behind that of other photovoltaic platforms. A fundamental reason for their low performance is the low charge mobility of organic materials, leading to a limit on the active-layer thickness and efficient light absorption. In this work, guided by a semi-empirical model analysis and using the tandem cell strategy to overcome such issues, and taking advantage of the high diversity and easily tunable band structure of organic materials, a record and certified 17.29% power conversion efficiency for a two-terminal monolithic solution-processed tandem OPV is achieved.

Complex carbohydrates of plants are the main food sources of animals and microbes, and serve as promising renewable feedstock for biofuel and biomaterial production. Carbohydrate active enzymes (CAZymes) are the most important enzymes for complex carbohydrate metabolism. With an increasing number of plant and plant-associated microbial genomes and metagenomes being sequenced, there is an urgent need of automatic tools for genomic data mining of CAZymes. We developed the dbCAN web server in 2012 to provide a public service for automated CAZyme annotation for newly sequenced genomes. Here, dbCAN2 (http://cys.bios.niu.edu/dbCAN2) is presented as an updated meta server, which integrates three state-of-the-art tools for CAZome (all CAZymes of a genome) annotation: (i) HMMER search against the dbCAN HMM (hidden Markov model) database; (ii) DIAMOND search against the CAZy pre-annotated CAZyme sequence database and (iii) Hotpep search against the conserved CAZyme short peptide database. Combining the three outputs and removing CAZymes found by only one tool can significantly improve the CAZome annotation accuracy. In addition, dbCAN2 now also accepts nucleotide sequence submission, and offers the service to predict physically linked CAZyme gene clusters (CGCs), which will be a very useful online tool for identifying putative polysaccharide utilization loci (PULs) in microbial genomes or metagenomes.

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.

Density functional theory (DFT) computations were performed to investigate the electronic properties and Li storage capability of Ti(3)C(2), one representative MXene (M represents transition metals, and X is either C or/and N) material, and its fluorinated and hydroxylated derivatives. The Ti(3)C(2) monolayer acts as a magnetic metal, while its derived Ti(3)C(2)F(2) and Ti(3)C(2)(OH)(2) in their stable conformations are semiconductors with small band gaps. Li adsorption forms a strong Coulomb interaction with Ti(3)C(2)-based hosts but well preserves its structural integrity. The bare Ti(3)C(2) monolayer exhibits a low barrier for Li diffusion and high Li storage capacity (up to Ti(3)C(2)Li(2) stoichiometry). The surface functionalization of F and OH blocks Li transport and decreases Li storage capacity, which should be avoided in experiments. The exceptional properties, including good electronic conductivity, fast Li diffusion, low operating voltage, and high theoretical Li storage capacity, make Ti(3)C(2) MXene a promising anode material for Li ion batteries.

The Daya Bay Reactor Neutrino Experiment has measured a nonzero value for the neutrino mixing angle ${\ensuremath{\theta}}_{13}$ with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 $\mathrm{G}{\mathrm{W}}_{\mathrm{th}}$ reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a $43\text{ }000\text{ }\text{ }\mathrm{ton}--\mathrm{G}{\mathrm{W}}_{\mathrm{th}}--\mathrm{day}$ live-time exposure in 55 days, 10 416 (80 376) electron-antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is $R=0.940\ifmmode\pm\else\textpm\fi{}\phantom{\rule{0ex}{0ex}}0.011(\mathrm{stat}.)\ifmmode\pm\else\textpm\fi{}0.004(\mathrm{syst}.)$. A rate-only analysis finds ${sin}^{2}2{\ensuremath{\theta}}_{13}=0.092\ifmmode\pm\else\textpm\fi{}0.016(\mathrm{stat}.)\ifmmode\pm\else\textpm\fi{}0.005(\mathrm{syst}.)$ in a three-neutrino framework.

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

The broadband and tunable high-performance microwave absorption properties of an ultralight and highly compressible graphene foam (GF) are investigated. Simply via physical compression, the microwave absorption performance can be tuned. The qualified bandwidth coverage of 93.8% (60.5 GHz/64.5 GHz) is achieved for the GF under 90% compressive strain (1.0 mm thickness). This mainly because of the 3D conductive network. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people's mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.

Rechargeable aqueous Zn-ion batteries are attractive cheap, safe and green energy storage technologies but are bottlenecked by limitation in high-capacity cathode and compatible electrolyte to achieve satisfactory cyclability. Here we report the application of nonstoichiometric ZnMn 2 O 4 /carbon composite as a new Zn-insertion cathode material in aqueous Zn(CF 3 SO 3 ) 2 electrolyte. In 3 M Zn(CF 3 SO 3 ) 2 solution that enables ∼100% Zn plating/stripping efficiency with long-term stability and suppresses Mn dissolution, the spinel/carbon hybrid exhibits a reversible capacity of 150 mAh g –1 and a capacity retention of 94% over 500 cycles at a high rate of 500 mA g –1 . The remarkable electrode performance results from the facile charge transfer and Zn insertion in the structurally robust spinel featuring small particle size and abundant cation vacancies, as evidenced by combined electrochemical measurements, XRD, Raman, synchrotron X-ray absorption spectroscopy, FTIR, and NMR analysis. The results would enlighten and promote the use of cation-defective spinel compounds and trifluoromethanesulfonic electrolyte to develop high-performance rechargeable zinc batteries.

Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the field of salient object detection where the purpose is to accurately detect and segment the most salient object in a scene. Several widely-used measures such as Area Under the Curve (AUC), Average Precision (AP) and the recently proposed F W/B (Fbw) have been used to evaluate the similarity between a non-binary saliency map (SM) and a ground-truth (GT) map. These measures are based on pixel-wise errors and often ignore the structural similarities. Behavioral vision studies, however, have shown that the human visual system is highly sensitive to structures in scenes. Here, we propose a novel, efficient, and easy to calculate measure known as structural similarity measure (Structure-measure) to evaluate non-binary foreground maps. Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map. We demonstrate superiority of our measure over existing ones using 5 meta-measures on 5 benchmark datasets.