Showing papers by "Hong Kong Baptist University published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide 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.

AutoML: A survey of the state-of-the-art

Abstract: Deep learning (DL) techniques have obtained remarkable achievements on various tasks, such as image recognition, object detection, and language modeling. However, building a high-quality DL system for a specific task highly relies on human expertise, hindering its wide application. Meanwhile, automated machine learning (AutoML) is a promising solution for building a DL system without human assistance and is being extensively studied. This paper presents a comprehensive and up-to-date review of the state-of-the-art (SOTA) in AutoML. According to the DL pipeline, we introduce AutoML methods – covering data preparation, feature engineering, hyperparameter optimization, and neural architecture search (NAS) – with a particular focus on NAS, as it is currently a hot sub-topic of AutoML. We summarize the representative NAS algorithms’ performance on the CIFAR-10 and ImageNet datasets and further discuss the following subjects of NAS methods: one/two-stage NAS, one-shot NAS, joint hyperparameter and architecture optimization, and resource-aware NAS. Finally, we discuss some open problems related to the existing AutoML methods for future research.

An evidence review of face masks against COVID-19.

Abstract: The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people ("source control") with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

Abstract: Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

Agriculture Development, Pesticide Application and Its Impact on the Environment.

Abstract: Pesticides are indispensable in agricultural production. They have been used by farmers to control weeds and insects, and their remarkable increases in agricultural products have been reported. The increase in the world's population in the 20th century could not have been possible without a parallel increase in food production. About one-third of agricultural products are produced depending on the application of pesticides. Without the use of pesticides, there would be a 78% loss of fruit production, a 54% loss of vegetable production, and a 32% loss of cereal production. Therefore, pesticides play a critical role in reducing diseases and increasing crop yields worldwide. Thus, it is essential to discuss the agricultural development process; the historical perspective, types and specific uses of pesticides; and pesticide behavior, its contamination, and adverse effects on the natural environment. The review study indicates that agricultural development has a long history in many places around the world. The history of pesticide use can be divided into three periods of time. Pesticides are classified by different classification terms such as chemical classes, functional groups, modes of action, and toxicity. Pesticides are used to kill pests and control weeds using chemical ingredients; hence, they can also be toxic to other organisms, including birds, fish, beneficial insects, and non-target plants, as well as air, water, soil, and crops. Moreover, pesticide contamination moves away from the target plants, resulting in environmental pollution. Such chemical residues impact human health through environmental and food contamination. In addition, climate change-related factors also impact on pesticide application and result in increased pesticide usage and pesticide pollution. Therefore, this review will provide the scientific information necessary for pesticide application and management in the future.

Mental health consequences of COVID-19 media coverage: the need for effective crisis communication practices.

Abstract: During global pandemics, such as coronavirus disease 2019 (COVID-19), crisis communication is indispensable in dispelling fears, uncertainty, and unifying individuals worldwide in a collective fight against health threats. Inadequate crisis communication can bring dire personal and economic consequences. Mounting research shows that seemingly endless newsfeeds related to COVID-19 infection and death rates could considerably increase the risk of mental health problems. Unfortunately, media reports that include infodemics regarding the influence of COVID-19 on mental health may be a source of the adverse psychological effects on individuals. Owing partially to insufficient crisis communication practices, media and news organizations across the globe have played minimal roles in battling COVID-19 infodemics. Common refrains include raging QAnon conspiracies, a false and misleading “Chinese virus” narrative, and the use of disinfectants to “cure” COVID-19. With the potential to deteriorate mental health, infodemics fueled by a kaleidoscopic range of misinformation can be dangerous. Unfortunately, there is a shortage of research on how to improve crisis communication across media and news organization channels. This paper identifies ways that legacy media reports on COVID-19 and how social media-based infodemics can result in mental health concerns. This paper discusses possible crisis communication solutions that media and news organizations can adopt to mitigate the negative influences of COVID-19 related news on mental health. Emphasizing the need for global media entities to forge a fact-based, person-centered, and collaborative response to COVID-19 reporting, this paper encourages media resources to focus on the core issue of how to slow or stop COVID-19 transmission effectively.

Stable and low-photovoltage-loss perovskite solar cells by multifunctional passivation

Abstract: Metal halide perovskite solar cells have demonstrated a high power conversion efficiency (PCE), and further enhancement of the PCE requires a reduction of the bandgap-voltage offset (WOC) and the non-radiative recombination photovoltage loss (ΔVOC,nr). Here, we report an effective approach for reducing the photovoltage loss through the simultaneous passivation of internal bulk defects and dimensionally graded two-dimensional perovskite interface defects. Through this dimensionally graded perovskite formation approach, an open-circuit voltage (VOC) of 1.24 V was obtained with a champion PCE of 21.54% in a 1.63 eV perovskite system (maximum VOC = 1.25 V, WOC = 0.38 V and ΔVOC,nr = 0.10 V); we further decreased the WOC to 0.326 V in a 1.53 eV perovskite system with a VOC of 1.21 V and a PCE of 23.78% (certified 23.09%). This approach is equally effective in achieving a low WOC (ΔVOC,nr) in 1.56 eV and 1.73 eV perovskite solar cell systems, and further leads to the substantially improved operational stability of perovskite solar cells. The use of a dimensionally graded 2D perovskite interface and passivation results in perovskite solar cells with very low photovoltage loss.

Interpenetrating interfaces for efficient perovskite solar cells with high operational stability and mechanical robustness.

Abstract: The perovskite solar cell has emerged rapidly in the field of photovoltaics as it combines the merits of low cost, high efficiency, and excellent mechanical flexibility for versatile applications However, there are significant concerns regarding its operational stability and mechanical robustness Most of the previously reported approaches to address these concerns entail separate engineering of perovskite and charge-transporting layers Herein we present a holistic design of perovskite and charge-transporting layers by synthesizing an interpenetrating perovskite/electron-transporting-layer interface This interface is reaction-formed between a tin dioxide layer containing excess organic halide and a perovskite layer containing excess lead halide Perovskite solar cells with such interfaces deliver efficiencies up to 222% and 201% for rigid and flexible versions, respectively Long-term (1000 h) operational stability is demonstrated and the flexible devices show high endurance against mechanical-bending (2500 cycles) fatigue Mechanistic insights into the relationship between the interpenetrating interface structure and performance enhancement are provided based on comprehensive, advanced, microscopic characterizations This study highlights interface integrity as an important factor for designing efficient, operationally-stable, and mechanically-robust solar cells Operational stability and mechanical robustness remain as engineering bottlenecks in perovskite solar cells technology Here, Dong et al introduce an interpenetrating perovskite at the electron-transporting-layer interface that enables a 1000-hour stable operation and high endurance against bending fatigue over 2500 cycles

Geometry-aware Instance-reweighted Adversarial Training

Abstract: In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy. However, the other direction, whether we can keep the accuracy while improving the robustness, is conceptually and practically more interesting, since robust accuracy should be lower than standard accuracy for any model. In this paper, we show this direction is also promising. Firstly, we find even over-parameterized deep networks may still have insufficient model capacity, because adversarial training has an overwhelming smoothing effect. Secondly, given limited model capacity, we argue adversarial data should have unequal importance: geometrically speaking, a natural data point closer to/farther from the class boundary is less/more robust, and the corresponding adversarial data point should be assigned with larger/smaller weight. Finally, to implement the idea, we propose geometry-aware instance-reweighted adversarial training, where the weights are based on how difficult it is to attack a natural data point. Experiments show that our proposal boosts the robustness of standard adversarial training; combining two directions, we improve both robustness and accuracy of standard adversarial training.

Graph Spectral Image Processing

Abstract: Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2-D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this paper, we overview recent graph spectral techniques in GSP specifically for image/video processing. The topics covered include image compression, image restoration, image filtering, and image segmentation.

Over 17% Efficiency Binary Organic Solar Cells with Photoresponses Reaching 1000 nm Enabled by Selenophene-Fused Nonfullerene Acceptors

Abstract: Nonfullerene acceptors (NFAs) have played an important role in the development of organic solar cells. However, the optical absorption of most NFAs is limited within 600–900 nm, prohibiting further...

A secure authenticated and key exchange scheme for fog computing

Abstract: Fog computing architecture is used in various environments such as smart manufacturing, vehicular ad hoc networks. However, as an extension of cloud computing, inheriting security challenges of clo...

Tin Halide Perovskite Solar Cells: An Emerging Thin-Film Photovoltaic Technology

Abstract: ConspectusPerovskite semiconductors are regarded as next-generation photovoltaic materials owing to their superb optoelectronic properties, including an excellent carrier diffusion length, strong l...

Online Health Information Seeking: A Review and Meta-Analysis

Abstract: Online health information, as an emerging field in health communication research, has attracted close attention from researchers. To identify major determinants of why individuals seek health information online, we conducted a meta-analysis that systematically accumulates the existing research findings. To that end, by integrating three theories or models for examining information-seeking behavior, we developed a theoretical framework for the current meta-analysis that emphasizing psychosocial, instrumental, contextual, and demographic factors. By analyzing the effect sizes from 44 articles representing 54 empirical samples, we found that the quality, trustworthiness, and utility of online health information were the dominant predictors of seeking it and that instrumental factors were more important than psychological ones in determining whether individuals did so. Moreover, the development of information and communication technology, the sampling method, and the type of information sought significantly moderated pairwise relationships between determinants and seeking behavior, whereas culture did not. Herein, we discuss the theoretical implications of our findings as well as directions for future research.

A critical review on various feedstocks as sustainable substrates for biosurfactants production: a way towards cleaner production.

Abstract: The quest for a chemical surfactant substitute has been fuelled by increased environmental awareness. The benefits that biosurfactants present like biodegradability, and biocompatibility over their chemical and synthetic counterparts has contributed immensely to their popularity and use in various industries such as petrochemicals, mining, metallurgy, agrochemicals, fertilizers, beverages, cosmetics, etc. With the growing demand for biosurfactants, researchers are looking for low-cost waste materials to use them as substrates, which will lower the manufacturing costs while providing waste management services as an add-on benefit. The use of low-cost substrates will significantly reduce the cost of producing biosurfactants. This paper discusses the use of various feedstocks in the production of biosurfactants, which not only reduces the cost of waste treatment but also provides an opportunity to profit from the sale of the biosurfactant. Furthermore, it includes state-of-the-art information about employing municipal solid waste as a sustainable feedstock for biosurfactant production, which has not been simultaneously covered in many published literatures on biosurfactant production from different feedstocks. It also addresses the myriad of other issues associated with the processing of biosurfactants, as well as the methods used to address these issues and perspectives, which will move society towards cleaner production.

Recent advances and challenges of inter-disciplinary biomass valorization by integrating hydrothermal and biological techniques

Abstract: Biomass valorization for the production of various value-added biochemicals and biofuels plays a significant role in modern biorefineries/bioenergy towards a climate neutrality future. Among the developed valorization techniques for biorefining, hydrothermal and biological treatments have been demonstrated to valorize raw biomass materials or upgrade biorefinery intermediate products to afford respectably desired product yields. From the perspective of both green chemistry and circular bioeconomy, whether the inter-disciplinary approach could achieve complete biomass valorization with balanced energy and extra profits remains questionable. This review is presented to provide an overview of the opportunities combining both hydrothermal and biological techniques for biomass valorization. Combinations including (1) hydrothermal pretreatment followed by biological valorization (such as enzymatic hydrolysis, fermentation, anaerobic digestion, or composting) and (2) hydrothermal valorization of substrates generated from biological techniques (such as enzymatic hydrolysis pretreated food waste, or anaerobic digestion produced digestate) for the production of biochar, biocrude or syngas are comprehensively reviewed. The recent advances regarding treatment conditions, synergies between hydrothermal and biological techniques, and optimal performances are summarized and compared. Assessment of the energy balance and economic feasibility of different integrated options reported in previous studies is also compared. Finally, challenges and perspectives for advancing integrated hydrothermal and biological techniques toward complete biomass valorization are concluded.

Hyperspectral Image Classification Based on 3-D Octave Convolution With Spatial–Spectral Attention Network

Abstract: In recent years, with the development of deep learning (DL), the hyperspectral image (HSI) classification methods based on DL have shown superior performance. Although these DL-based methods have great successes, there is still room to improve their ability to explore spatial–spectral information. In this article, we propose a 3-D octave convolution with the spatial–spectral attention network (3DOC-SSAN) to capture discriminative spatial–spectral features for the classification of HSIs. Especially, we first extend the octave convolution model using 3-D convolution, namely, a 3-D octave convolution model (3D-OCM), in which four 3-D octave convolution blocks are combined to capture spatial–spectral features from HSIs. Not only the spatial information can be mined deeply from the high- and low-frequency aspects but also the spectral information can be taken into account by our 3D-OCM. Second, we introduce two attention models from spatial and spectral dimensions to highlight the important spatial areas and specific spectral bands that consist of significant information for the classification tasks. Finally, in order to integrate spatial and spectral information, we design an information complement model to transmit important information between spatial and spectral attention features. Through the information complement model, the beneficial parts of spatial and spectral attention features for the classification tasks can be fully utilized. Comparing with several existing popular classifiers, our proposed method can achieve competitive performance on four benchmark data sets.

The design of responsive luminescent lanthanide probes and sensors.

Abstract: The principles of the design of responsive luminescent probes and sensors based on lanthanide emission are summarised, based on a mechanistic understanding of their mode of action. Competing kinetic pathways for deactivation of the excited states that occur are described, highlighting the need to consider each of the salient quenching processes. Such an analysis dictates the choice of both the ligand and its integral sensitising moiety for the particular application. The key aspects of quenching involving electron transfer and vibrational and electronic energy transfer are highlighted and exemplified. Responsive systems for pH, pM, pX and pO2 and selected biochemical analytes are distinguished, according to the nature of the optical signal observed. Signal changes include both simple and ratiometric intensity measurements, emission lifetime variations and the unique features associated with the observation of circularly polarised luminescence (CPL) for chiral systems. A classification of responsive lanthanide probes is introduced. Examples of the operation of probes for reactive oxygen species, citrate, bicarbonate, α1-AGP and pH are used to illustrate reversible and irreversible transformations of the ligand constitution, as well as the reversible changes to the metal primary and secondary coordination sphere that sensitively perturb the ligand field. Finally, systems that function by modulation of dynamic quenching of the ligand or metal excited states are described, including real time observation of endosomal acidification in living cells, rapid urate analysis in serum, accurate temperature assessment in confined compartments and high throughput screening of drug binding to G-protein coupled receptors.

An Agenda for Open Science in Communication

Abstract: In the last 10 years, many canonical findings in the social sciences appear unreliable. This so-called “replication crisis” has spurred calls for open science practices, which aim to increase the reproducibility, replicability, and generalizability of findings. Communication research is subject to many of the same challenges that have caused low replicability in other fields. As a result, we propose an agenda for adopting open science practices in Communication, which includes the following seven suggestions: (1) publish materials, data, and code; (2) preregister studies and submit registered reports; (3) conduct replications; (4) collaborate; (5) foster open science skills; (6) implement Transparency and Openness Promotion Guidelines; and (7) incentivize open science practices. Although in our agenda we focus mostly on quantitative research, we also reflect on open science practices relevant to qualitative research. We conclude by discussing potential objections and concerns associated with open science practices.

Artificial Intelligence: A Powerful Paradigm for Scientific Research

Abstract: Artificial Intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day to day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes performs a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The goal of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Shareholder Litigation and Corporate Innovation

Abstract: We investigate whether and to what extent shareholder litigation shapes corporate innovation by examining the staggered adoption of universal demand laws in 23 states from 1989 to 2005. These laws ...