ShanghaiTech University

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.

Conversion of carbon dioxide (CO2) to carbon monoxide (CO) and other value-added carbon products is an important challenge for clean energy research. Here we report modular optimization of covalent organic frameworks (COFs), in which the building units are cobalt porphyrin catalysts linked by organic struts through imine bonds, to prepare a catalytic material for aqueous electrochemical reduction of CO2 to CO. The catalysts exhibit high Faradaic efficiency (90%) and turnover numbers (up to 290,000, with initial turnover frequency of 9400 hour(-1)) at pH 7 with an overpotential of -0.55 volts, equivalent to a 26-fold improvement in activity compared with the molecular cobalt complex, with no degradation over 24 hours. X-ray absorption data reveal the influence of the COF environment on the electronic structure of the catalytic cobalt centers.

This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.

The fourth generation wireless communication systems have been deployed or are soon to be deployed in many countries. However, with an explosion of wireless mobile devices and services, there are still some challenges that cannot be accommodated even by 4G, such as the spectrum crisis and high energy consumption. Wireless system designers have been facing the continuously increasing demand for high data rates and mobility required by new wireless applications and therefore have started research on fifth generation wireless systems that are expected to be deployed beyond 2020. In this article, we propose a potential cellular architecture that separates indoor and outdoor scenarios, and discuss various promising technologies for 5G wireless communication systems, such as massive MIMO, energy-efficient communications, cognitive radio networks, and visible light communications. Future challenges facing these potential technologies are also discussed.

Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor, and a feature decoder module. We use the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation, and retinal optical coherence tomography layer segmentation.

A recently developed adenine base editor (ABE) efficiently converts A to G and is potentially useful for clinical applications. However, its precision and efficiency in vivo remains to be addressed. Here we achieve A-to-G conversion in vivo at frequencies up to 100% by microinjection of ABE mRNA together with sgRNAs. We then generate mouse models harboring clinically relevant mutations at Ar and Hoxd13, which recapitulates respective clinical defects. Furthermore, we achieve both C-to-T and A-to-G base editing by using a combination of ABE and SaBE3, thus creating mouse model harboring multiple mutations. We also demonstrate the specificity of ABE by deep sequencing and whole-genome sequencing (WGS). Taken together, ABE is highly efficient and precise in vivo, making it feasible to model and potentially cure relevant genetic diseases.

Unlike linear RNAs terminated with 5' caps and 3' tails, circular RNAs are characterized by covalently closed loop structures with neither 5' to 3' polarity nor polyadenylated tail. This intrinsic characteristic has led to the general under-estimation of the existence of circular RNAs in previous polyadenylated transcriptome analyses. With the advent of specific biochemical and computational approaches, a large number of circular RNAs from back-spliced exons (circRNAs) have been identified in various cell lines and across different species. Recent studies have uncovered that back-splicing requires canonical spliceosomal machinery and can be facilitated by both complementary sequences and specific protein factors. In this review, we highlight our current understanding of the regulation of circRNA biogenesis, including both the competition between splicing and back-splicing and the previously under-appreciated alternative circularization.

The recent upsurge of diversified mobile applications, especially those supported by AI, is spurring heated discussions on the future evolution of wireless communications. While 5G is being deployed around the world, efforts from industry and academia have started to look beyond 5G and conceptualize 6G. We envision 6G to undergo an unprecedented transformation that will make it substantially different from the previous generations of wireless cellular systems. In particular, 6G will go beyond mobile Internet and will be required to support ubiquitous AI services from the core to the end devices of the network. Meanwhile, AI will play a critical role in designing and optimizing 6G architectures, protocols, and operations. In this article, we discuss potential technologies for 6G to enable mobile AI applications, as well as AI-enabled methodologies for 6G network design and optimization. Key trends in the evolution to 6G will also be discussed.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Isothermal amplification of nucleic acids is a simple process that rapidly and efficiently accumulates nucleic acid sequences at constant temperature. Since the early 1990s, various isothermal amplification techniques have been developed as alternatives to polymerase chain reaction (PCR). These isothermal amplification methods have been used for biosensing targets such as DNA, RNA, cells, proteins, small molecules, and ions. The applications of these techniques for in situ or intracellular bioimaging and sequencing have been amply demonstrated. Amplicons produced by isothermal amplification methods have also been utilized to construct versatile nucleic acid nanomaterials for promising applications in biomedicine, bioimaging, and biosensing. The integration of isothermal amplification into microsystems or portable devices improves nucleic acid-based on-site assays and confers high sensitivity. Single-cell and single-molecule analyses have also been implemented based on integrated microfluidic systems. In this review, we provide a comprehensive overview of the isothermal amplification of nucleic acids encompassing work published in the past two decades. First, different isothermal amplification techniques are classified into three types based on reaction kinetics. Then, we summarize the applications of isothermal amplification in bioanalysis, diagnostics, nanotechnology, materials science, and device integration. Finally, several challenges and perspectives in the field are discussed.

A novel coronavirus [severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2)] outbreak has caused a global coronavirus disease 2019 (COVID-19) pandemic, resulting in tens of thousands of infections and thousands of deaths worldwide. The RNA-dependent RNA polymerase [(RdRp), also named nsp12] is the central component of coronaviral replication and transcription machinery, and it appears to be a primary target for the antiviral drug remdesivir. We report the cryo-electron microscopy structure of COVID-19 virus full-length nsp12 in complex with cofactors nsp7 and nsp8 at 2.9-angstrom resolution. In addition to the conserved architecture of the polymerase core of the viral polymerase family, nsp12 possesses a newly identified β-hairpin domain at its N terminus. A comparative analysis model shows how remdesivir binds to this polymerase. The structure provides a basis for the design of new antiviral therapeutics that target viral RdRp.

Phenylpropanoid metabolism is one of the most important metabolisms in plants, yielding more than 8,000 metabolites contributing to plant development and plant-environment interplay. Phenylpropanoid metabolism materialized during the evolution of early freshwater algae that were initiating terrestrialization and land plants have evolved multiple branches of this pathway, which give rise to metabolites including lignin, flavonoids, lignans, phenylpropanoid esters, hydroxycinnamic acid amides, and sporopollenin. Recent studies have revealed that many factors participate in the regulation of phenylpropanoid metabolism, and modulate phenylpropanoid homeostasis when plants undergo successive developmental processes and are subjected to stressful environments. In this review, we summarize recent progress on elucidating the contribution of phenylpropanoid metabolism to the coordination of plant development and plant-environment interaction, and metabolic flux redirection among diverse metabolic routes. In addition, our review focuses on the regulation of phenylpropanoid metabolism at the transcriptional, post-transcriptional, post-translational, and epigenetic levels, and in response to phytohormones and biotic and abiotic stresses.

Promising antiviral protease inhibitors With no vaccine or proven effective drug against the virus that causes coronavirus disease 2019 (COVID-19), scientists are racing to find clinical antiviral treatments. A promising drug target is the viral main protease M pro , which plays a key role in viral replication and transcription. Dai et al. designed two inhibitors, 11a and 11b, based on analyzing the structure of the M pro active site. Both strongly inhibited the activity of M pro and showed good antiviral activity in cell culture. Compound 11a had better pharmacokinetic properties and low toxicity when tested in mice and dogs, suggesting that this compound is a promising drug candidate. Science , this issue p. 1331

Whereas standard transmission electron microscopy studies are unable to preserve the native state of chemically reactive and beam-sensitive battery materials after operation, such materials remain pristine at cryogenic conditions. It is then possible to atomically resolve individual lithium metal atoms and their interface with the solid electrolyte interphase (SEI). We observe that dendrites in carbonate-based electrolytes grow along the <111> (preferred), <110>, or <211> directions as faceted, single-crystalline nanowires. These growth directions can change at kinks with no observable crystallographic defect. Furthermore, we reveal distinct SEI nanostructures formed in different electrolytes.

Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to tackle the anomaly detection problem within a video prediction framework. To the best of our knowledge, this is the first work that leverages the difference between a predicted future frame and its ground truth to detect an abnormal event. To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task. Such spatial and motion constraints facilitate the future frame prediction for normal events, and consequently facilitate to identify those abnormal events that do not conform the expectation. Extensive experiments on both a toy dataset and some publicly available datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events. All codes are released in https://github.com/StevenLiuWen/ano_pred_cvpr2018.

Immune checkpoint blockade therapy has become a major weapon in fighting cancer. Antibody drugs, such as anti-PD-1 and anti-PD-L1, demonstrate obvious advantages such as broad applicability across cancer types and durable clinical response when treatment is effective. However, the overall response rates are still unsatisfying, especially for cancers with low mutational burden. Moreover, adverse effects, such as autoimmune symptoms and tumor hyperprogression, present a significant downside in some clinical applications. These challenges reflect the urgent need to fully understand the basic biology of immune checkpoints. In this review, we discuss regulation of immune checkpoint signaling at multiple levels to provide an overview of our current understanding of checkpoint biology. Topics include the regulation of surface expression levels for known immune checkpoint proteins via surface delivery, internalization, recycling, and degradation. Upon reaching the surface, checkpoints engage in both conventional trans and also cis interactions with ligands to induce signaling and regulate immune responses. Novel therapeutic strategies targeting these pathways in addition to classical checkpoint blockade have recently emerged and been tested in preclinical models, providing new avenues for developing next-generation immunotherapies.

Organic-inorganic hybrid perovskites, which have proved to be promising semiconductor materials for photovoltaic applications, have been made into atomically thin two-dimensional (2D) sheets. We report the solution-phase growth of single- and few-unit-cell-thick single-crystalline 2D hybrid perovskites of (C4H9NH3)2PbBr4 with well-defined square shape and large size. In contrast to other 2D materials, the hybrid perovskite sheets exhibit an unusual structural relaxation, and this structural change leads to a band gap shift as compared to the bulk crystal. The high-quality 2D crystals exhibit efficient photoluminescence, and color tuning could be achieved by changing sheet thickness as well as composition via the synthesis of related materials.

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Abstract Tumor cells metabolize in distinct pathways compared with most normal tissue cells. The resulting tumor microenvironment would provide characteristic physiochemical conditions for selective tumor modalities. Here we introduce a concept of sequential catalytic nanomedicine for efficient tumor therapy by designing and delivering biocompatible nanocatalysts into tumor sites. Natural glucose oxidase (GOD, enzyme catalyst) and ultrasmall Fe 3 O 4 nanoparticles (inorganic nanozyme, Fenton reaction catalyst) have been integrated into the large pore-sized and biodegradable dendritic silica nanoparticles to fabricate the sequential nanocatalyst. GOD in sequential nanocatalyst could effectively deplete glucose in tumor cells, and meanwhile produce a considerable amount of H 2 O 2 for subsequent Fenton-like reaction catalyzed by Fe 3 O 4 nanoparticles in response to mild acidic tumor microenvironment. Highly toxic hydroxyl radicals are generated through these sequential catalytic reactions to trigger the apoptosis and death of tumor cells. The current work manifests a proof of concept of catalytic nanomedicine by approaching selectivity and efficiency concurrently for tumor therapeutics.

N6-methyladenosine (m6A) is the most abundant internal modification of eukaryotic messenger RNA (mRNA) and plays critical roles in RNA biology. The function of this modification is mediated by m6A-selective ‘reader’ proteins of the YTH family, which incorporate m6A-modified mRNAs into pathways of RNA metabolism. Here, we show that the m6A-binding protein YTHDC1 mediates export of methylated mRNA from the nucleus to the cytoplasm in HeLa cells. Knockdown of YTHDC1 results in an extended residence time for nuclear m6A-containing mRNA, with an accumulation of transcripts in the nucleus and accompanying depletion within the cytoplasm. YTHDC1 interacts with the splicing factor and nuclear export adaptor protein SRSF3, and facilitates RNA binding to both SRSF3 and NXF1. This role for YTHDC1 expands the potential utility of chemical modification of mRNA, and supports an emerging paradigm of m6A as a distinct biochemical entity for selective processing and metabolism of mammalian mRNAs.