Showing papers by "Hong Kong Baptist University published in 2019"

Topological phases in acoustic and mechanical systems

Abstract: The study of classical wave physics has been reinvigorated by incorporating the concept of the geometric phase, which has its roots in optics, and topological notions that were previously explored in condensed matter physics. Recently, sound waves and a variety of mechanical systems have emerged as excellent platforms that exemplify the universality and diversity of topological phases. In this Review, we introduce the essential physical concepts that underpin various classes of topological phenomena realized in acoustic and mechanical systems: Dirac points, the quantum Hall, quantum spin Hall and valley Hall effects, Floquet topological phases, 3D gapless states and Weyl crystals. This Review describes topological phenomena that can be realized in acoustic and mechanical systems. Methods of symmetry breaking are described, along with the consequences and rich phenomena that emerge.

Theabrownin from Pu-erh tea attenuates hypercholesterolemia via modulation of gut microbiota and bile acid metabolism.

Abstract: Pu-erh tea displays cholesterol-lowering properties, but the underlying mechanism has not been elucidated. Theabrownin is one of the most active and abundant pigments in Pu-erh tea. Here, we show that theabrownin alters the gut microbiota in mice and humans, predominantly suppressing microbes associated with bile-salt hydrolase (BSH) activity. Theabrownin increases the levels of ileal conjugated bile acids (BAs) which, in turn, inhibit the intestinal FXR-FGF15 signaling pathway, resulting in increased hepatic production and fecal excretion of BAs, reduced hepatic cholesterol, and decreased lipogenesis. The inhibition of intestinal FXR-FGF15 signaling is accompanied by increased gene expression of enzymes in the alternative BA synthetic pathway, production of hepatic chenodeoxycholic acid, activation of hepatic FXR, and hepatic lipolysis. Our results shed light into the mechanisms behind the cholesterol- and lipid-lowering effects of Pu-erh tea, and suggest that decreased intestinal BSH microbes and/or decreased FXR-FGF15 signaling may be potential anti-hypercholesterolemia and anti-hyperlipidemia therapies. Pu-erh tea displays cholesterol-lowering properties. Here, Huang et al. show that this is mostly due to the action of a pigment in Pu-erh tea that induces changes in certain gut microbiota and bile acid levels, thus modulating the gut-liver metabolic axis.

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

Abstract: This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories.

Fused Benzothiadiazole: A Building Block for n-Type Organic Acceptor to Achieve High-Performance Organic Solar Cells

Abstract: Narrow bandgap n-type organic semiconductors (n-OS) have attracted great attention in recent years as acceptors in organic solar cells (OSCs), due to their easily tuned absorption and electronic energy levels in comparison with fullerene acceptors. Herein, a new n-OS acceptor, Y5, with an electron-deficient-core-based fused structure is designed and synthesized, which exhibits a strong absorption in the 600-900 nm region with an extinction coefficient of 1.24 × 105 cm-1 , and an electron mobility of 2.11 × 10-4 cm2 V-1 s-1 . By blending Y5 with three types of common medium-bandgap polymers (J61, PBDB-T, and TTFQx-T1) as donors, all devices exhibit high short-circuit current densities over 20 mA cm-2 . As a result, the power conversion efficiency of the Y5-based OSCs with J61, TTFQx-T1, and PBDB-T reaches 11.0%, 13.1%, and 14.1%, respectively. This indicates that Y5 is a universal and highly efficient n-OS acceptor for applications in organic solar cells.

Suicide among physicians and health-care workers: A systematic review and meta-analysis.

Abstract: Background : Medical-related professions are at high suicide risk. However, data are contradictory and comparisons were not made between gender, occupation and specialties, epochs of times. Thus, we conducted a systematic review and meta-analysis on suicide risk among healthcare workers. Method : The PubMed, Cochrane Library, Science Direct and Embase databases were searched without language restriction on April 2019, with the following keywords: suicide* AND (« health care worker* » OR physician* OR nurse*). When possible, we stratified results by gender, countries, time, and specialties. Estimates were pooled using random-effect metaanalysis. Differences by study-level characteristics were estimated using stratified metaanalysis and meta-regression. Suicides, suicidal attempts, and suicidal ideation were retrieved from national or local specific registers or case records. In addition, suicide attempts and suicidal ideation were also retrieved from questionnaires (paper or internet). Results : The overall SMR for suicide in physicians was 1.44 (95CI 1.16, 1.72) with an important heterogeneity (I2 = 93.9%, p<0.001). Female were at higher risk (SMR = 1.9; 95CI 1.49, 2.58; and ES = 0.67; 95CI 0.19, 1.14; p<0.001 compared to male). US physicians were at higher risk (ES = 1.34; 95CI 1.28, 1.55; p <0.001 vs Rest of the world). Suicide decreased over time, especially in Europe (ES = -0.18; 95CI -0.37, -0.01; p = 0.044). Some specialties might be at higher risk such as anesthesiologists, psychiatrists, general practitioners and general surgeons. There were 1.0% (95CI 1.0, 2.0; p<0.001) of suicide attempts and 17% (95CI 12, 21; p<0.001) of suicidal ideation in physicians. Insufficient data precluded meta-analysis on other health-care workers. Conclusion : Physicians are an at-risk profession of suicide, with women particularly at risk. The rate of suicide in physicians decreased over time, especially in Europe. The high prevalence of physicians who committed suicide attempt as well as those with suicidal ideation should benefits for preventive strategies at the workplace. Finally, the lack of data on other health-care workers suggest to implement studies investigating those occupations.

Reinforcement Learning in Healthcare: A Survey

Abstract: As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey discusses the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in healthcare domains ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection

Abstract: Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring

Abstract: Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis largely owing to inefficient diagnosis and tenacious drug resistance. Activation of pancreatic stellate cells (PSCs) and consequent development of dense stroma are prominent features accounting for this aggressive biology1,2. The reciprocal interplay between PSCs and pancreatic cancer cells (PCCs) not only enhances tumour progression and metastasis but also sustains their own activation, facilitating a vicious cycle to exacerbate tumorigenesis and drug resistance3-7. Furthermore, PSC activation occurs very early during PDAC tumorigenesis8-10, and activated PSCs comprise a substantial fraction of the tumour mass, providing a rich source of readily detectable factors. Therefore, we hypothesized that the communication between PSCs and PCCs could be an exploitable target to develop effective strategies for PDAC therapy and diagnosis. Here, starting with a systematic proteomic investigation of secreted disease mediators and underlying molecular mechanisms, we reveal that leukaemia inhibitory factor (LIF) is a key paracrine factor from activated PSCs acting on cancer cells. Both pharmacologic LIF blockade and genetic Lifr deletion markedly slow tumour progression and augment the efficacy of chemotherapy to prolong survival of PDAC mouse models, mainly by modulating cancer cell differentiation and epithelial-mesenchymal transition status. Moreover, in both mouse models and human PDAC, aberrant production of LIF in the pancreas is restricted to pathological conditions and correlates with PDAC pathogenesis, and changes in the levels of circulating LIF correlate well with tumour response to therapy. Collectively, these findings reveal a function of LIF in PDAC tumorigenesis, and suggest its translational potential as an attractive therapeutic target and circulating marker. Our studies underscore how a better understanding of cell-cell communication within the tumour microenvironment can suggest novel strategies for cancer therapy.

A meta-analysis of the health action process approach.

Abstract: Objective The health action process approach (HAPA) is a social-cognitive model specifying motivational and volitional determinants of health behavior. A meta-analysis of studies applying the HAPA in health behavior contexts was conducted to estimate the size and variability of correlations among model constructs, test model predictions, and test effects of past behavior and moderators (behavior type, sample type, measurement lag, study quality) on model relations. Method A literature search identified 95 studies meeting inclusion criteria with 108 independent samples. Averaged corrected correlations among HAPA constructs and multivariate tests of model predictions were computed using conventional meta-analysis and meta-analytic structural equation modeling, with separate models estimated in each moderator group. Results Action and maintenance self-efficacy and outcome expectancies had small-to-medium sized effects on health behavior, with effects of outcome expectancies and action self-efficacy mediated by intentions, and action and coping planning. Effects of risk perceptions and recovery self-efficacy were small by comparison. Past behavior attenuated the intention-behavior relationship. Few variations in model effects were observed across moderator groups. Effects of action self-efficacy on intentions and behavior were larger in studies on physical activity compared with studies on dietary behaviors, whereas effects of volitional self-efficacy on behavior were larger in studies on dietary behaviors. Conclusions Findings highlight the importance of self-efficacy in predicting health behavior in motivational and volitional action phases. The analysis is expected to catalyze future research including experimental studies targeting change in individual HAPA constructs, and longitudinal research to examine change and reciprocal effects among constructs in the model. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Industrial potential of carotenoid pigments from microalgae: Current trends and future prospects

Abstract: Microalgae are rich source of various bioactive molecules such as carotenoids, lipids, fatty acids, hydrocarbons, proteins, carbohydrates, amino acids, etc. and in recent Years carotenoids from algae gained commercial recognition in the global market for food and cosmeceutical applications. However, the production of carotenoids from algae is not yet fully cost effective to compete with synthetic ones. In this context the present review examines the technologies/methods in relation to mass production of algae, cell harvesting for extraction of carotenoids, optimizing extraction methods etc. Research studies from different microalgal species such as Spirulina platensis, Haematococcus pluvialis, Dunaliella salina, Chlorella sps., Nannochloropsis sps., Scenedesmus sps., Chlorococcum sps., Botryococcus braunii and Diatoms in relation to carotenoid content, chemical structure, extraction and processing of carotenoids are discussed. Further these carotenoid pigments, are useful in various health applications and their use in food, feed, nutraceutical, pharmaceutical and cosmeceutical industries was briefly touched upon. The commercial value of algal carotenoids has also been discussed in this review. Possible recommendations for future research studies are proposed.

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature.

Abstract: This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories.

Busting the “Princelings”: The Campaign Against Corruption in China’s Primary Land Market

Abstract: Using data on over a million land transactions during 2004–2016 where local governments are the sole seller, we find that firms linked to members of China's supreme political elites—the Politburo—obtained a price discount ranging from 55.4% to 59.9% compared with those without the same connections. These firms also purchased slightly more land. In return, the provincial party secretaries who provided the discount to these “princeling” firms are 23.4% more likely to be promoted to positions of national leadership. To curb corruption, President Xi Jinping stepped up investigations and strengthened personnel control at the province level. Using a spatially matched sample (e.g., within a 500-meter radius), we find a reduction in corruption of between 42.6% and 31.5% in the provinces either targeted by the central inspection teams or whose party secretary was replaced by one appointed by Xi. Accordingly, this crackdown on corruption has also significantly reduced the promotional prospects of those local officials who rely on supplying a discount to get ahead.

Ligand design strategies to increase stability of gadolinium-based magnetic resonance imaging contrast agents

Abstract: Gadolinium(III) complexes have been widely utilised as magnetic resonance imaging (MRI) contrast agents for decades. In recent years however, concerns have developed about their toxicity, believed to derive from demetallation of the complexes in vivo, and the relatively large quantities of compound required for a successful scan. Recent efforts have sought to enhance the relaxivity of trivalent gadolinium complexes without sacrificing their stability. This review aims to examine the strategic design of ligands synthesised for this purpose, provide an overview of recent successes in gadolinium-based contrast agent development and assess the requirements for clinical translation.

On the Reconstruction of Face Images from Deep Face Templates

Abstract: State-of-the-art face recognition systems are based on deep (convolutional) neural networks. Therefore, it is imperative to determine to what extent face templates derived from deep networks can be inverted to obtain the original face image. In this paper, we study the vulnerabilities of a state-of-the-art face recognition system based on template reconstruction attack. We propose a neighborly de-convolutional neural network ( NbNet ) to reconstruct face images from their deep templates. In our experiments, we assumed that no knowledge about the target subject and the deep network are available. To train the NbNet reconstruction models, we augmented two benchmark face datasets (VGG-Face and Multi-PIE) with a large collection of images synthesized using a face generator. The proposed reconstruction was evaluated using type-I (comparing the reconstructed images against the original face images used to generate the deep template) and type-II (comparing the reconstructed images against a different face image of the same subject) attacks. Given the images reconstructed from NbNets , we show that for verification, we achieve TAR of 95.20 percent (58.05 percent) on LFW under type-I (type-II) attacks @ FAR of 0.1 percent. Besides, 96.58 percent (92.84 percent) of the images reconstructed from templates of partition fa ( fb ) can be identified from partition fa in color FERET. Our study demonstrates the need to secure deep templates in face recognition systems.

Balancing mTOR Signaling and Autophagy in the Treatment of Parkinson’s Disease

Abstract: The mammalian target of rapamycin (mTOR) signaling pathway plays a critical role in regulating cell growth, proliferation, and life span. mTOR signaling is a central regulator of autophagy by modulating multiple aspects of the autophagy process, such as initiation, process, and termination through controlling the activity of the unc51-like kinase 1 (ULK1) complex and vacuolar protein sorting 34 (VPS34) complex, and the intracellular distribution of TFEB/TFE3 and proto-lysosome tubule reformation. Parkinson’s disease (PD) is a serious, common neurodegenerative disease characterized by dopaminergic neuron loss in the substantia nigra pars compacta (SNpc) and the accumulation of Lewy bodies. An increasing amount of evidence indicates that mTOR and autophagy are critical for the pathogenesis of PD. In this review, we will summarize recent advances regarding the roles of mTOR and autophagy in PD pathogenesis and treatment. Further characterizing the dysregulation of mTOR pathway and the clinical translation of mTOR modulators in PD may offer exciting new avenues for future drug development.

SGD: General Analysis and Improved Rates

Abstract: We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form mini-batches. This is the first time such an analysis is performed, and most of our variants of SGD were never explicitly considered in the literature before. Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on the variance of the stochastic gradients. By specializing our theorem to different mini-batching strategies, such as sampling with replacement and independent sampling, we derive exact expressions for the stepsize as a function of the mini-batch size. With this we can also determine the mini-batch size that optimizes the total complexity, and show explicitly that as the variance of the stochastic gradient evaluated at the minimum grows, so does the optimal mini-batch size. For zero variance, the optimal mini-batch size is one. Moreover, we prove insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime.

Selectivity control of CO versus HCOO− production in the visible-light-driven catalytic reduction of CO2 with two cooperative metal sites

Abstract: It is highly desirable to discover molecular catalysts with controlled selectivity for visible-light-driven CO2 reduction to fuels. In the design of catalysts employing earth-abundant metals, progress has been made for CO production, but formate generation has been observed more rarely. Here, we report a binuclear Co complex bearing a bi-quaterpyridine ligand that can selectively reduce CO2 to HCOO− or CO under visible light irradiation. Selective formate production (maximum of 97%) was obtained with a turnover number of up to 821 in basic acetonitrile solution. Conversely, in the presence of a weak acid, CO2 reduction affords CO with high selectivity (maximum of 99%) and a maximum turnover number of 829. The catalytic process is controlled by the two Co atoms acting synergistically, and the selectivity can be steered towards the desired product by simply changing the acid co-substrate. Performing photocatalytic CO2 reduction in a selective fashion with molecular catalysts represents a considerable challenge. Here, a binuclear cobalt complex featuring a bi-quaterpyridine ligand is developed that can selectively afford either carbon monoxide or formate by selection of the reaction medium acidity.

SGD: General Analysis and Improved Rates

Abstract: We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form mini-batches. This is the first time such an analysis is performed, and most of our variants of SGD were never explicitly considered in the literature before. Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on the variance of the stochastic gradients. By specializing our theorem to different mini-batching strategies, such as sampling with replacement and independent sampling, we derive exact expressions for the stepsize as a function of the mini-batch size. With this we can also determine the mini-batch size that optimizes the total complexity, and show explicitly that as the variance of the stochastic gradient evaluated at the minimum grows, so does the optimal mini-batch size. For zero variance, the optimal mini-batch size is one. Moreover, we prove insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime.

Review of Current Strategies for Delivering Alzheimer's Disease Drugs across the Blood-Brain Barrier.

Abstract: Effective therapy for Alzheimer’s disease is a major challenge in the pharmaceutical sciences. There are six FDA approved drugs (e.g., donepezil, memantine) that show some effectiveness; however, they only relieve symptoms. Two factors hamper research. First, the cause of Alzheimer’s disease is not fully understood. Second, the blood-brain barrier restricts drug efficacy. This review summarized current knowledge relevant to both of these factors. First, we reviewed the pathophysiology of Alzheimer’s disease. Next, we reviewed the structural and biological properties of the blood-brain barrier. We then described the most promising drug delivery systems that have been developed in recent years; these include polymeric nanoparticles, liposomes, metallic nanoparticles and cyclodextrins. Overall, we aim to provide ideas and clues to design effective drug delivery systems for penetrating the blood-brain barrier to treat Alzheimer’s disease.

Mass spectrometry-based metabolomics: Targeting the crosstalk between gut microbiota and brain in neurodegenerative disorders.

Abstract: Metabolomics seeks to take a "snapshot" in a time of the levels, activities, regulation and interactions of all small molecule metabolites in response to a biological system with genetic or environmental changes. The emerging development in mass spectrometry technologies has shown promise in the discovery and quantitation of neuroactive small molecule metabolites associated with gut microbiota and brain. Significant progress has been made recently in the characterization of intermediate role of small molecule metabolites linked to neural development and neurodegenerative disorder, showing its potential in understanding the crosstalk between gut microbiota and the host brain. More evidence reveals that small molecule metabolites may play a critical role in mediating microbial effects on neurotransmission and disease development. Mass spectrometry-based metabolomics is uniquely suitable for obtaining the metabolic signals in bidirectional communication between gut microbiota and brain. In this review, we summarized major mass spectrometry technologies including liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and imaging mass spectrometry for metabolomics studies of neurodegenerative disorders. We also reviewed the recent advances in the identification of new metabolites by mass spectrometry and metabolic pathways involved in the connection of intestinal microbiota and brain. These metabolic pathways allowed the microbiota to impact the regular function of the brain, which can in turn affect the composition of microbiota via the neurotransmitter substances. The dysfunctional interaction of this crosstalk connects neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease and Huntington's disease. The mass spectrometry-based metabolomics analysis provides information for targeting dysfunctional pathways of small molecule metabolites in the development of the neurodegenerative diseases, which may be valuable for the investigation of underlying mechanism of therapeutic strategies.

A Concise Review on the Molecular Structure and Function Relationship of β-Glucan

Abstract: β-glucan is a non-starch soluble polysaccharide widely present in yeast, mushrooms, bacteria, algae, barley, and oat. β-Glucan is regarded as a functional food ingredient due to its various health benefits. The high molecular weight (Mw) and high viscosity of β-glucan are responsible for its hypocholesterolemic and hypoglycemic properties. Thus, β-glucan is also used in the food industry for the production of functional food products. The inherent gel-forming property and high viscosity of β-glucan lead to the production of low-fat foods with improved textural properties. Various studies have reported the relationship between the molecular structure of β-glucan and its functionality. The structural characteristics of β-glucan, including specific glycosidic linkages, monosaccharide compositions, Mw, and chain conformation, were reported to affect its physiochemical and biological properties. Researchers have also reported some chemical, physical, and enzymatic treatments can successfully alter the molecular structure and functionalities of β-glucan. This review article attempts to review the available literature on the relationship of the molecular structure of β-glucan with its functionalities, and future perspectives in this area.