Showing papers by "Zhen Li published in 2020"

Molecular Packing: Another Key Point for the Performance of Organic and Polymeric Optoelectronic Materials.

Abstract: ConspectusOptoelectronic material properties are governed by the whole collective of organic moieties, and these aggregate states present the characteristic performance of extended assemblies with different molecular packing, not only of single molecules themselves. Thus, controlling molecular packing is an essential issue for obtaining the optimized optical and electronic properties. It is also a great challenge because of the unclear structures and complicated intermolecular interactions, including dispersion forces, electrostatic interactions and hydrogen bonding. Moreover, upon the introduction of some external force as the stimulus source, dynamic optical properties can be achieved with the transformation of molecular packing in some cases, such as the photoinduced room temperature phosphorescence (RTP) effect, mechanochromic luminescence, the thermal treatment-dependent mechanoluminescence effect, and the optimized nonlinear optical (NLO) property achieved after electric poling. Therefore, it is essential to understand the relation between characteristics of molecular packing and the resultant optoelectronic performance at the molecular level, which becomes increasingly demanding for the further development of functional materials for their applications in organic light-emitting diodes (OLEDs), chemo- and biosensors, organic solar cells, data storage, and anticounterfeiting devices.This Account gives a summary of our research on the molecular design of optoelectronic materials, with the consideration of a molecular uniting effect in different aggregated states, such as crystalline states, thin films, and nanoparticles. Through the systematical investigation of structure-packing-performance relationships, some strategies are afforded to partially control the molecular packing via the tunable size, shape, and configuration of aromatic moieties with different electronic and steric effects, together with different types of substituents as functional units to adjust the intermolecular interactions. The utilization of π-π interactions and hydrogen bonding by rational molecular design is considered as the key point to achieve the bright emission of organic materials, including the RTP and mechanoluminescence effects. Also, the dynamic optoelectronic properties are highlighted with different kinds of stimuli, including light irradiation, mechanical force, thermal treatment, and electric field, which are mainly related to the subtle molecular motions under external force and the changeable molecular packing as the metastable state. These selected examples will not only open a window for further development of organic and polymeric optoelectronic materials by the adjustable molecular packing and noncovalent interactions, but also prompt further advances for more interesting and exciting properties.

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling

Abstract: Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively. The key component in our approach is the adaptive sampling (AS) module. It first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. Our AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. To further capture the neighbor and long-range dependencies of the sampled point, we proposed a local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL module enables the learning process insensitive to noise. Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise. Specifically, PointASNL achieves state-of-the-art robust performance for classification and segmentation tasks on all datasets, and significantly outperforms previous methods on real-world outdoor SemanticKITTI dataset with considerate noise. Our code is released through this https URL.

PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling

Abstract: Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively. The key component in our approach is the adaptive sampling (AS) module. It first re-weights the neighbors around the initial sampled points from farthest point sampling (FPS), and then adaptively adjusts the sampled points beyond the entire point cloud. Our AS module can not only benefit the feature learning of point clouds, but also ease the biased effect of outliers. To further capture the neighbor and long-range dependencies of the sampled point, we proposed a local-nonlocal (L-NL) module inspired by the nonlocal operation. Such L-NL module enables the learning process insensitive to noise. Extensive experiments verify the robustness and superiority of our approach in point clouds processing tasks regardless of synthesis data, indoor data, and outdoor data with or without noise. Specifically, PointASNL achieves state-of-the-art robust performance for classification and segmentation tasks on all datasets, and significantly outperforms previous methods on real-world outdoor SemanticKITTI dataset with considerate noise.

Room-Temperature Phosphorescence Resonance Energy Transfer for Construction of Near-Infrared Afterglow Imaging Agents.

Abstract: Afterglow imaging that detects photons after cessation of optical excitation avoids tissue autofluorescence and thus possesses higher sensitivity than traditional fluorescence imaging. Purely organic molecules with room-temperature phosphorescence (RTP) have emerged as a new library of benign afterglow agents. However, most RTP luminogens only emit visible light with shallow tissue penetration, constraining their in vivo applications. This study presents an organic RTP nanoprobe (mTPA-N) with emission in the NIR range for in vivo afterglow imaging. Such a probe is composed of RTP molecule (mTPA) as the phosphorescent generator and an NIR-fluorescent dye as the energy acceptor to enable room-temperature phosphorescence resonance energy transfer (RT-PRET), ultimately resulting in redshifted phosphorescent emission at 780 nm. Because of the elimination of background noise and redshifted afterglow luminescence in a biologically transparent window, mTPA-N permits imaging of lymph nodes in living mice with a high signal-to-noise ratio. This study thus opens up a universal approach to develop organic RTP luminogens into NIR afterglow imaging agents via construction of RT-PRET.

Boosting H2O2‐Guided Chemodynamic Therapy of Cancer by Enhancing Reaction Kinetics through Versatile Biomimetic Fenton Nanocatalysts and the Second Near‐Infrared Light Irradiation

Abstract: Fenton reaction–based chemodynamic therapy (CDT) has attracted considerable attention for tumor treatment, because the Fenton reaction can degrade endogenous H2O2 within the tumor to form reactive oxygen species (ROS) to kill cancer cells. The kinetics of the Fenton reaction has significantly influenced its treatment efficacy. It is crucial to enhance the reaction kinetics at the maximum H2O2 concentration to quickly produce vast amounts of ROS to achieve treatment efficacy, which to date, has not been realized. Herein, reported is an efficacious CDT treatment of breast cancer using biomimetic CS-GOD@CM nanocatalysts, which are rationally designed to significantly boost the Fenton reaction through improvement of H2O2 concentration within tumors, and application of the second near-infrared (NIR-II) light irradiation at the maximum concentration, which is monitored by photoacoustic imaging. The biomimetic nanocatalysts are composed of ultra-small Cu2−xSe (CS) nanoparticles, glucose oxidase (GOD), and tumor cell membrane (CM). The nanocatalysts can be retained in tumor for more than two days to oxidize glucose and produce an approximately 2.6-fold increase in H2O2 to enhance the Fenton reaction under the NIR-II irradiation. This work demonstrates for the first time the CDT treatment of cancer enhanced by the NIR-II light.

Highly Selective Reduction of CO2 to C2+ Hydrocarbons at Copper/Polyaniline Interfaces

Abstract: Electrochemical reduction of carbon dioxide (CO2RR) to liquid fuels and valued chemicals is a meaningful approach to decreasing CO2 emissions and alleviating the energy crisis. In particular, the c...

Förster Resonance Energy Transfer: An Efficient Way to Develop Stimulus-Responsive Room-Temperature Phosphorescence Materials and Their Applications

Abstract: Summary In this work, Forster resonance energy transfer (FRET) is utilized to construct a stimulus-responsive room-temperature phosphorescence (RTP) system. Owing to the specificity of the FRET system and its sensitivity to distance, the resultant material shows turn-on persistent RTP emission upon force or heating stimulus. Furthermore, a near-perfect compatibility with existing printing technology is demonstrated for this material, especially for thermal printing. This finding could provide a simple, efficient, and truly commercially viable construction strategy for RTP material.

Heartbeat-Sensing Mechanoluminescent Device Based on a Quantitative Relationship between Pressure and Emissive Intensity

Abstract: Summary The pure organic mechanoluminescent luminogens (tPE-2-Th and tPE-3-Th), with their combination of aggregation-induced emission and the presence of a self-assembly unit, exhibit very bright mechanoluminescence (ML) emission even in daylight. Excitingly, the relationship between the pressure and ML intensity has been successfully established for the first time based on an ML device fabricated by tPE-2-Th. Furthermore, the flexible and wearable ML device demonstrates potential applications in communications, information storage, and health care. In particular, it provides a quick response to the human heartbeat, offering a new approach for the monitoring of force stimuli in daily life. Video Abstract Download : Download video (23MB)

Adaptive Context Selection for Polyp Segmentation

Abstract: Accurate polyp segmentation is of great significance for the diagnosis and treatment of colorectal cancer. However, it has always been very challenging due to the diverse shape and size of polyp. In recent years, state-of-the-art methods have achieved significant breakthroughs in this task with the help of deep convolutional neural networks. However, few algorithms explicitly consider the impact of the size and shape of the polyp and the complex spatial context on the segmentation performance, which results in the algorithms still being powerless for complex samples. In fact, segmentation of polyps of different sizes relies on different local and global contextual information for regional contrast reasoning. To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM). Specifically, LCA modules deliver local context features from encoder layers to decoder layers, enhancing the attention to the hard region which is determined by the prediction map of previous layer. GCM aims to further explore the global context features and send to the decoder layers. ASM is used for adaptive selection and aggregation of context features through channel-wise attention. Our proposed approach is evaluated on the EndoScene and Kvasir-SEG Datasets, and shows outstanding performance compared with other state-of-the-art methods. The code is available at https://github.com/ReaFly/ACSNet.

Advanced functional polymer materials

Abstract: The research on advanced functional polymers is being driven by the fast-growing demand for new functional materials that can be used in revolutionary technologies. Polymers can be endowed with functions by using certain special preparation methods or by introducing functional groups or fillers into materials. These functions are either intrinsically possessed by materials or actuated by external stimuli. In this review, we present an overview of the recent developments made in the research hotspots of functional polymers, encompassing polymerization methodologies, luminescent polymers, photovoltaic polymers, other electronic and optical polymers (including low-k polyimides and second-order nonlinear optical polymers), biorelated polymers (particularly those for biomedical applications), supramolecular polymers, stimuli-responsive polymers, shape-memory polymers, separation polymer membranes, energy storage polymers, and covalent organic framework polymers. The concepts, design strategies at the molecular level, preparation methods, classifications, properties, potential applications, and recent progress made in such polymers are summarized. Challenges and future perspectives of each type of functional polymers are also addressed, including research efforts regarding the design and fabrication of functional polymers for serving the increasing demand for new materials.

Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multi-Center Study During the COVID-19 Outbreak

Abstract: The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 is needed. We performed a multicenter study including 105 patients with cancer and 536 age-matched noncancer patients confi rmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematologic cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Patients with nonmetastatic cancer experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, whereas patients who underwent only radiotherapy did not demonstrate signifi cant differences in severe events when compared with patients without cancer. These fi ndings indicate that patients with cancer appear more vulnerable to SARS-CoV-2 outbreak. SIGnIFICAnCE: Because this is the fi rst large cohort study on this topic, our report will provide muchneeded information that will benefi t patients with cancer globally. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients. 1 Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China. 2 Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, China. 3 Hubei Cancer Clinical Study Center, Wuhan, China. 4 Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts. 5 Department of Pediatrics, Harvard Medical School, Boston, Massachusetts. 6 Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 7 Department of Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China. 8 Cancer Center, Union Hospital affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 9 Department of Emergency, The Central Hospital of Wuhan affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 10 Department of Gynecology, The Central Hospital of Huanggang, Huanggang, Hubei, China 11 Affi liated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, Guangdong Province, China. 12 Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China. 13 Department of Infectious Disease, Zhongnan Hospital of Wuhan University, Wuhan, China. 14 Cancer Center, Tongji Hospital affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 15 Department of Oncology, The Central Hospital of Wuhan affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 16 Department of Radiology, Hubei Cancer Hospital, Wuhan, Hubei, China. 17 Department of Thoracic Surgery, Hubei Cancer Hospital, Wuhan, Hubei, China. 18 Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China. 19 Department of Oncology, Wuhan Puren Hospital, Wuhan, Hubei, China. 20 Department of Obstetrics and Gynecology, The Central Hospital of Xianning, Xianning, Hubei, China. 21 Department of Oncology, The Central Hospital of Xiaogan, Xiaogan, China. 22 Department of Obstetrics and Gynecology, The People's Hospital of Huangmei, Huangmei, Hubei, China. 23 Department of Obstetrics and Gynecology, Xiangyang First People's Hospital affi liated to Hubei University of Medicine, Xiangyang, Hubei, China. 24 Department of Obstetrics and Gynecology, The People's Hospital of Shiyan, Shiyan, Hubei, China. 25 Harvard Stem Cell Institute, Harvard Medical School, Boston, Massachusetts. 26 Cancer Science Institute of Singapore, National University of Singapore, Singapore. 27 Harvard T.H. Chan School of Public Health, Dana-Farber, Harvard Cancer Center, Boston, Massachusetts. note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/). Cancer Research. on January 10, 2021. © 2020 American Association for cancerdiscovery.aacrjournals.org Downloaded from Published OnlineFirst April 28, 2020; DOI: 10.1158/2159-8290.CD-20-0422

Persistent organic room temperature phosphorescence: what is the role of molecular dimers?

Abstract: Molecular dimers have been frequently found to play an important role in room temperature phosphorescence (RTP), but its inherent working mechanism has remained unclear. Herein a series of unique characteristics, including singlet excimer emission and thermally activated delayed fluorescence, were successfully integrated into a new RTP luminogen of CS-2COOCH3 to clearly reveal the excited-state process of RTP and the special role of molecular dimers in persistent RTP emission.

SspABCD-SspE is a phosphorothioation-sensing bacterial defence system with broad anti-phage activities

Abstract: Bacteria have evolved diverse mechanisms to fend off predation by bacteriophages. We previously identified the Dnd system, which uses DndABCDE to insert sulfur into the DNA backbone as a double-stranded phosphorothioate (PT) modification, and DndFGH, a restriction component. Here, we describe an unusual SspABCD-SspE PT system in Vibrio cyclitrophicus, Escherichia coli and Streptomyces yokosukanensis, which has distinct genetic organization, biochemical functions and phenotypic behaviour. SspABCD confers single-stranded and high-frequency PTs with SspB acting as a nickase and possibly introducing nicks to facilitate sulfur incorporation. Strikingly, SspABCD coupled with SspE provides protection against phages in unusual ways: (1) SspE senses sequence-specific PTs by virtue of its PT-stimulated NTPase activity to exert its anti-phage activity, and (2) SspE inhibits phage propagation by introducing nicking damage to impair phage DNA replication. These results not only expand our knowledge about the diversity and functions of DNA PT modification but also enhance our understanding of the known arsenal of defence systems.

The development of mechanoluminescence from organic compounds: breakthrough and deep insight

Abstract: Organic mechanoluminescence (ML) compounds have experienced breakthrough developments in recent years, with ML being discovered in many kinds of organic compounds. Accordingly, the ML composition is becoming more complicated; complications can be observed from the initial stages of nitrogen discharge to fluorescence, dual emission of fluorescence and phosphorescence and the more novel persistent room-temperature phosphorescence (RTP) on to photo-sensitive ML, presenting unique luminous properties with more perspectives. Since ML is found to be highly correlated with the molecular configuration and the intermolecular packing style in the aggregate state, the photophysical properties should be analyzed carefully with the consideration of MUSIC (Molecular Uniting Set Identified Characteristic). In this review paper, we summarized the recently reported organic ML compounds, which were classified according to their luminous and chemical structure features. The investigation of organic ML can promote the development of “aggregated state chemistry” and the luminous theory in the aggregate state.

Miracles of molecular uniting

Abstract: 有机材料的宏观性质是分子聚集效应的客观体现, 不仅取决于单个分子的结构, 而且与整个分子的聚集形式密切相关. 通过对分子聚集态行为的有效调控, 科学家们发现了一些完全不同于单个分子特性的聚集态发光现象, 包括发光强度、颜色、形式以及激发过程的差异. 本文对这些有趣的聚集态发光材料进行了简要的综述, 系统分析了分子聚集模式和分子间相互作用对材料发光性能的影响, 并介绍了“MUSIC”的理念, 以音乐创作形象化材料设计, 强调分子聚集态行为的重要性.

Targeting Microglia for Therapy of Parkinson's Disease by Using Biomimetic Ultrasmall Nanoparticles.

Abstract: Microglia as an important type of innate immune cell in the brain have been considered as an effective therapeutic target for the treatment of central nervous degenerative diseases. Herein, we report cell membrane coated novel biomimetic Cu2-xSe-PVP-Qe nanoparticles (denoted as CSPQ@CM nanoparticles, where PVP is poly(vinylpyrrolidone), Qe is quercetin, and CM is the cell membrane of neuron cells) for effectively targeting and modulating microglia to treat Parkinson's disease (PD). The CSPQ nanoparticles exhibit multienzyme activities and could effectively scavenge the reactive oxygen species and promote the polarization of microglia into the anti-inflammatory M2-like phenotype to relieve neuroinflammation. We reveal that biomimetic CSPQ@CM nanoparticles targeted microglia through the specific interactions between the membrane surface vascular cells adhering to molecule-1 and α4β1 integrin expressed by microglia. They could significantly improve the symptoms of PD mice to result in an excellent therapeutic efficacy, as evidenced by the recovery of their dopamine level in cerebrospinal fluid, tyrosine hydroxylase, and ionized calcium binding adapter protein 1 to normal levels. Our work demonstrates the great potential of these robust biomimetic nanoparticles in the targeted treatment of PD and other central nervous degenerative diseases.

Photothermal effect-enhanced photoelectrochemical water splitting of a BiVO4 photoanode modified with dual-functional polyaniline

Abstract: Both bulk charge separation and surface reaction kinetics are highly important to semiconductor-based water splitting photoanodes. Polyaniline (PANI) with dual functions of hole transport and photothermal conversion is introduced to construct PANI/BiVO4 composite photoanodes. On the one hand, bulk charge separation is enhanced due to the formation of a p–n heterojunction. On the other hand, the charge separation efficiency is further improved because of the temperature elevation induced by the photothermal effect of PANI. In addition, cobalt phosphate (Co-Pi) as a cocatalyst is introduced to accelerate the surface water oxidation reaction, which is also further promoted via the photothermal effect of PANI. Assisted by NIR light, the resultant Co-Pi/PANI/BiVO4 photoanode achieves a remarkable water oxidation photocurrent of 4.05 mA cm−2 at 1.23 VRHE (V vs. the reversible hydrogen electrode), over 300% higher than that of the pristine BiVO4 photoanode. The introduction of the photothermal effect can be extended to improve the PEC performance of other photoelectrodes, such as Fe2O3, WO3 and TiO2.

Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion

Abstract: LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep LiDAR point cloud, the accurate semantic segmentation is non-trivial to achieve. In this paper, we propose a novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors. In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input. By merging multiple frames in the LiDAR sequence as supervision, the optimized SSC module has learned the contextual shape priors from sequential LiDAR data, completing the sparse single sweep point cloud to the dense one. Thus, it inherently improves SS optimization through fully end-to-end training. Besides, a Point-Voxel Interaction (PVI) module is proposed to further enhance the knowledge fusion between SS and SSC tasks, i.e., promoting the interaction of incomplete local geometry of point cloud and complete voxel-wise global structure. Furthermore, the auxiliary SSC and PVI modules can be discarded during inference without extra burden for SS. Extensive experiments confirm that our JS3C-Net achieves superior performance on both SemanticKITTI and SemanticPOSS benchmarks, i.e., 4% and 3% improvement correspondingly.