Zhejiang University of Technology

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.

Since the discovery of mechanically exfoliated graphene in 2004, research on ultrathin two-dimensional (2D) nanomaterials has grown exponentially in the fields of condensed matter physics, material science, chemistry, and nanotechnology. Highlighting their compelling physical, chemical, electronic, and optical properties, as well as their various potential applications, in this Review, we summarize the state-of-art progress on the ultrathin 2D nanomaterials with a particular emphasis on their recent advances. First, we introduce the unique advances on ultrathin 2D nanomaterials, followed by the description of their composition and crystal structures. The assortments of their synthetic methods are then summarized, including insights on their advantages and limitations, alongside some recommendations on suitable characterization techniques. We also discuss in detail the utilization of these ultrathin 2D nanomaterials for wide ranges of potential applications among the electronics/optoelectronics, electrocatalysis, batteries, supercapacitors, solar cells, photocatalysis, and sensing platforms. Finally, the challenges and outlooks in this promising field are featured on the basis of its current development.

New opportunities for the conversion of glycerol into value-added chemicals have emerged in recent years as a result of glycerol's unique structure, properties, bioavailability, and renewability. Glycerol is currently produced in large amounts during the transesterification of fatty acids into biodiesel and as such represents a useful by-product. This paper provides a comprehensive review and critical analysis on the different reaction pathways for catalytic conversion of glycerol into commodity chemicals, including selective oxidation, selective hydrogenolysis, selective dehydration, pyrolysis and gasification, steam reforming, thermal reduction into syngas, selective transesterification, selective etherification, oligomerization and polymerization, and conversion of glycerol into glycerol carbonate.

Nanoscale metals are widely used in many fields such as environment, medicine, and engineering that synthesis of nanoscale metals is a timely topic. At present, nanoscale metals are mainly synthesized by chemical methods that have unintended effects such as environmental pollution, large energy consumption, and potential health problems. In response to these challenges, green synthesis, which uses plant extracts instead of industrial chemical agents to reduce metal ions, has been developed. Green synthesis is more beneficial than traditional chemical synthesis because it costs less, decreases pollution, and improves environmental and human health safety. In this review, current developments in the green synthesis of nanoparticles of gold (Au NPs), silver (Ag NPs), palladium (Pd NPs), copper (Cu NPs), and iron and its oxide (Fe NPs) were evaluated. Major findings reveal the complexity in geographical and seasonal distributions of plants and their compositions that green synthesis is limited by time and place of production as well as issues with low purity and poor yield. However, considering current environmental problems and pollution associated with chemical synthesis, green synthesis offers alternative development prospects and potential applications.

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.

Lignocellulosic biomass is the most abundant and bio-renewable resource with great potential for sustainable production of chemicals and fuels. This critical review provides insights into the state-of the-art accomplishments in the chemocatalytic technologies to generate fuels and value-added chemicals from lignocellulosic biomass, with an emphasis on its major component, cellulose. Catalytic hydrolysis, solvolysis, liquefaction, pyrolysis, gasification, hydrogenolysis and hydrogenation are the major processes presently studied. Regarding catalytic hydrolysis, the acid catalysts cover inorganic or organic acids and various solid acids such as sulfonated carbon, zeolites, heteropolyacids and oxides. Liquefaction and fast pyrolysis of cellulose are primarily conducted over catalysts with proper acidity/basicity. Gasification is typically conducted over supported noble metal catalysts. Reaction conditions, solvents and catalysts are the prime factors that affect the yield and composition of the target products. Most of processes yield a complex mixture, leading to problematic upgrading and separation. An emerging technique is to integrate hydrolysis, liquefaction or pyrolysis with hydrogenation over multifunctional solid catalysts to convert lignocellulosic biomass to value-added fine chemicals and bio-hydrocarbon fuels. And the promising catalysts might be supported transition metal catalysts and zeolite-related materials. There still exist technological barriers that need to be overcome (229 references).

Lithium-sulfur batteries have attracted attention due to their six-fold specific energy compared with conventional lithium-ion batteries. Dissolution of lithium polysulfides, volume expansion of sulfur and uncontrollable deposition of lithium sulfide are three of the main challenges for this technology. State-of-the-art sulfur cathodes based on metal-oxide nanostructures can suppress the shuttle-effect and enable controlled lithium sulfide deposition. However, a clear mechanistic understanding and corresponding selection criteria for the oxides are still lacking. Herein, various nonconductive metal-oxide nanoparticle-decorated carbon flakes are synthesized via a facile biotemplating method. The cathodes based on magnesium oxide, cerium oxide and lanthanum oxide show enhanced cycling performance. Adsorption experiments and theoretical calculations reveal that polysulfide capture by the oxides is via monolayered chemisorption. Moreover, we show that better surface diffusion leads to higher deposition efficiency of sulfide species on electrodes. Hence, oxide selection is proposed to balance optimization between sulfide-adsorption and diffusion on the oxides.

Type 2 diabetes is a serious and common chronic disease resulting from a complex inheritance-environment interaction along with other risk factors such as obesity and sedentary lifestyle. Type 2 diabetes and its complications constitute a major worldwide public health problem, affecting almost all populations in both developed and developing countries with high rates of diabetes-related morbidity and mortality. The prevalence of type 2 diabetes has been increasing exponentially, and a high prevalence rate has been observed in developing countries and in populations undergoing "westernization" or modernization. Multiple risk factors of diabetes, delayed diagnosis until micro- and macro-vascular complications arise, life-threatening complications, failure of the current therapies, and financial costs for the treatment of this disease, make it necessary to develop new efficient therapy strategies and appropriate prevention measures for the control of type 2 diabetes. Herein, we summarize our current understanding about the epidemiology of type 2 diabetes, the roles of genes, lifestyle and other factors contributing to rapid increase in the incidence of type 2 diabetes. The core aims are to bring forward the new therapy strategies and cost-effective intervention trials of type 2 diabetes.

DESI (Dark Energy Spectroscopic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations (BAO) and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. To trace the underlying dark matter distribution, spectroscopic targets will be selected in four classes from imaging data. We will measure luminous red galaxies up to $z=1.0$. To probe the Universe out to even higher redshift, DESI will target bright [O II] emission line galaxies up to $z=1.7$. Quasars will be targeted both as direct tracers of the underlying dark matter distribution and, at higher redshifts ($ 2.1 < z < 3.5$), for the Ly-$α$ forest absorption features in their spectra, which will be used to trace the distribution of neutral hydrogen. When moonlight prevents efficient observations of the faint targets of the baseline survey, DESI will conduct a magnitude-limited Bright Galaxy Survey comprising approximately 10 million galaxies with a median $z\approx 0.2$. In total, more than 30 million galaxy and quasar redshifts will be obtained to measure the BAO feature and determine the matter power spectrum, including redshift space distortions.

In this technical note, we consider adaptive control of single input uncertain nonlinear systems in the presence of input saturation and unknown external disturbance. By using backstepping approaches, two new robust adaptive control algorithms are developed by introducing a well defined smooth function and using a Nussbaum function. The Nussbaum function is introduced to compensate for the nonlinear term arising from the input saturation. Unlike some existing control schemes for systems with input saturation, the developed controllers do not require assumptions on the uncertain parameters within a known compact set and a priori knowledge on the bound of the external disturbance. Besides showing global stability, transient performance is also established and can be adjusted by tuning certain design parameters.

Size effect has been regularly utilized to tune the catalytic activity and selectivity of metal nanoparticles (NPs). Yet, there is a lack of understanding of the size effect in the electrocatalytic reduction of CO2, an important reaction that couples with intermittent renewable energy storage and carbon cycle utilization. We report here a prominent size-dependent activity/selectivity in the electrocatalytic reduction of CO2 over differently sized Pd NPs, ranging from 2.4 to 10.3 nm. The Faradaic efficiency for CO production varies from 5.8% at -0.89 V (vs reversible hydrogen electrode) over 10.3 nm NPs to 91.2% over 3.7 nm NPs, along with an 18.4-fold increase in current density. Based on the Gibbs free energy diagrams from density functional theory calculations, the adsorption of CO2 and the formation of key reaction intermediate COOH* are much easier on edge and corner sites than on terrace sites of Pd NPs. In contrast, the formation of H* for competitive hydrogen evolution reaction is similar on all three sites. A volcano-like curve of the turnover frequency for CO production within the size range suggests that CO2 adsorption, COOH* formation, and CO* removal during CO2 reduction can be tuned by varying the size of Pd NPs due to the changing ratio of corner, edge, and terrace sites.

In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99 594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30 863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms.

Correction for 'Hybrid micro-/nano-structures derived from metal-organic frameworks: preparation and applications in energy storage and conversion' by Xiehong Cao et al., Chem. Soc. Rev., 2017, 46, 2660-2677.

Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.

Data-based process monitoring has become a key technology in process industries for safety, quality, and operation efficiency enhancement. This paper provides a timely update review on this topic. First, the natures of different industrial processes are revealed with their data characteristics analyzed. Second, detailed terminologies of the data-based process monitoring method are illustrated. Third, based on each of the main data characteristics that exhibits in the process, a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods. Finally, the relevant research perspectives and several promising issues are highlighted for future work.

By decomposing the visual tracking task into two subproblems as classification for pixel category and regression for object bounding box at this pixel, we propose a novel fully convolutional Siamese network to solve visual tracking end-to-end in a per-pixel manner. The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction. Different from state-of-the-art trackers like Siamese-RPN, SiamRPN++ and SPM, which are based on region proposal, the proposed framework is both proposal and anchor free. Consequently, we are able to avoid the tricky hyper-parameter tuning of anchors and reduce human intervention. The proposed framework is simple, neat and effective. Extensive experiments and comparisons with state-of-the-art trackers are conducted on challenging benchmarks including GOT-10K, LaSOT, UAV123 and OTB-50. Without bells and whistles, our SiamCAR achieves the leading performance with a considerable real-time speed. The code is available at https://github.com/ohhhyeahhh/SiamCAR.

Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health in the last decade. Many genes can confer resistance, but evaluating the relative health risks of ARGs is complex. Factors such as the abundance, propensity for lateral transmission and ability of ARGs to be expressed in pathogens are all important. Here, an analysis at the metagenomic level from various habitats (6 types of habitats, 4572 samples) detects 2561 ARGs that collectively conferred resistance to 24 classes of antibiotics. We quantitatively evaluate the health risk to humans, defined as the risk that ARGs will confound the clinical treatment for pathogens, of these 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. Our results demonstrate that 23.78% of the ARGs pose a health risk, especially those which confer multidrug resistance. We also calculate the antibiotic resistance risks of all samples in four main habitats, and with machine learning, successfully map the antibiotic resistance threats in global marine habitats with over 75% accuracy. Our novel method for quantitatively surveilling the health risk of ARGs will help to manage one of the most important threats to human and animal health.

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

High–energy density lithium (Li) metal batteries (LMBs) are promising for energy storage applications but suffer from uncontrollable electrolyte degradation and the consequently formed unstable solid-electrolyte interphase (SEI). In this study, we designed self-assembled monolayers (SAMs) with high-density and long-range–ordered polar carboxyl groups linked to an aluminum oxide–coated separator to provide strong dipole moments, thus offering excess electrons to accelerate the degradation dynamics of carbon-fluorine bond cleavage in Li bis(trifluoromethanesulfonyl)imide. Hence, an SEI with enriched lithium fluoride (LiF) nanocrystals is generated, facilitating rapid Li + transfer and suppressing dendritic Li growth. In particular, the SAMs endow the full cells with substantially enhanced cyclability under high cathode loading, limited Li excess, and lean electrolyte conditions. As such, our work extends the long-established SAMs technology into a platform to control electrolyte degradation and SEI formation toward LMBs with ultralong life spans.

High Resolution Image Download MS PowerPoint Slide Two-dimensional transition-metal carbide materials (termed MXene) have attracted huge attention in the field of electrochemical energy storage due to their excellent electrical conductivity, high volumetric capacity, etc. Herein, with inspiration from the interesting structure of pillared interlayered clays, we attempt to fabricate pillared Ti 3 C 2 MXene (CTAB–Sn(IV)@Ti 3 C 2 ) via a facile liquid-phase cetyltrimethylammonium bromide (CTAB) prepillaring and Sn 4+ pillaring method. The interlayer spacing of Ti 3 C 2 MXene can be controlled according to the size of the intercalated prepillaring agent (cationic surfactant) and can reach 2.708 nm with 177% increase compared with the original spacing of 0.977 nm, which is currently the maximum value according to our knowledge. Because of the pillar effect, the assembled LIC exhibits a superior energy density of 239.50 Wh kg –1 based on the weight of CTAB–Sn(IV)@Ti 3 C 2 even under higher power density of 10.8 kW kg –1 . When CTAB–Sn(IV)@Ti 3 C 2 anode couples with commercial AC cathode, LIC reveals higher energy density and power density compared with conventional MXene materials.