Search

Healthcare facilities produce millions of tons of waste annually, with a significant portion consisting of diagnostic plasticware. Here, we introduce a new detection platform that completely replaces traditional assay plates with a piece of membrane, offering a much greener and more sustainable alternative. The membrane, integrated within the portable vortex fluidic device (P-VFD), enables rapid detection of a clinically relevant protein biomarker, urinary p75ECD. This biomarker is utilized to evaluate the prognosis, disease severity, and progression of amyotrophic lateral sclerosis (ALS). This assay has a limit-of-detection (LOD) of 4.03 pg, which is comparable to the plate-based assay (2.24 pg) and has been optimized through a full factorial design of experiments (DOE). P-VFD has great potential in quantifying p75ECD in human biofluids and can significantly reduce the assay time to 5 min compared to the current plate-based p75ECD ELISA assay (3 days), with at least a 4.4-fold reduction in the usage of the detection antibody.

Whitetopping is the concrete overlay used to upgrade distressed asphalt road surface, where the distressed asphalt is milled off and concrete is cast on top as the new wearing course. However, it requires road closure for multiple days, which is not accepted in many urban constructions. Precast concrete pavement (PCP) is the technology that shortens the construction period. However, it is associated with complicated construction procedures due to the brittleness of conventional concrete. In this paper, a brand-new PCP design, i.e., ultra-thin whitetopping (PUTW) is studied and applied in a demonstration project in Singapore. The PUTW is enabled by employing strain-hardening cementitious composites (SHCC) with significantly higher ductility than conventional concrete. The current work includes four parts. First, the desirable combinations of SHCC's tensile and compressive uniaxial behaviors are identified in the analytical model and experimental study to enhance its flexural strength for pavement application. Second, the slab-to-slab joint efficiency in the PUTW, the critical mechanism in the structural design, is examined and validated with a full-scale cyclic wheel test. Third, the structural performance of the entire SHCC-PUTW is examined in a new finite element (FE) model validated against the wheel test. Based on the FE model, parametric studies on the SHCC material properties and PUTW slab dimensions are conducted to optimize the structural design. Fourth, a small-scale field project on the Jurong Island of Singapore, which involves the fabrication and installation of 11 SHCC-PUTW slabs, is reported to assess the feasibility of this new structural design. © 2024 Elsevier Ltd

Pentamethyl cyanine dyes are promising fluorophores for fluorescence sensing and imaging. However, advanced biomedical applications require enhanced control of their excited-state properties. Herein, a synthetic approach for attaching aryl substituents at the C2' position of the thio-pentamethine cyanine (TCy5) dye structure is reported for the first time. C2'-aryl substitution enables the regulation of both the twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET) mechanisms to be regulated in the excited state. Modulation of these mechanisms allows the design of a nitroreductase-activatable TCy5 fluorophore for hypoxic tumor photodynamic therapy and fluorescence imaging. These C2'-aryl TCy5 dyes provide a tunable platform for engineering cyanine dyes tailored to sophisticated biological applications, such as photodynamic therapy. This work reports a promising pentamethyl cyanine platform using C2'-aryl substitutions. It is found that electron-withdrawing groups enhanced twisted intramolecular charge transfer (TICT), resulting in efficient fluorescence quenching. The electron-donating groups make the TICT tendency weaken significantly, while photoinduced electron transfer is enhanced. This change improves both the fluorescence quantum yield and the intersystem crossing (ISC) efficiency.image

Frequency-domain dissipativity of the converter admittance provides an intuitive approach to analyze wideband resonances due to the interactions with the grid. Although the reasons for high- and low-frequency resonances are different, it is found that the proportional capacitor voltage feedforward (CVF) can affect and reshape the converter admittance in a wide frequency range. To enhance wideband dissipativity under a weak/capacitive grid, a proportional-integral (PI)-derivative CVF is proposed in this article. Specifically, high-frequency dissipativity can be guaranteed through the multisampling control (MSC) with proportional-derivative CVF. The low-frequency nondissipative region caused by the phase-locked loop (PLL) and proportional CVF can be compensated through multiorder integrations. In light of grid frequency disturbances, modified integrators are further proposed for the multiorder integrations. The proposed method also applies to the conventional double-sampling control (DSC) with regard to the low-frequency dissipativity enhancement. Finally, experiments validate the proposed control method., QC 20230705

INTRODUCTION: We studied the replication and generalization of previously identified metabolites potentially associated with global cognitive function in multiple race/ethnicities and assessed the contribution of diet to these associations. METHODS: We tested metabolite-cognitive function associations in U.S.A. Hispanic/Latino adults (n = 2222) from the Community Health Study/ Study of Latinos (HCHS/SOL) and in European (n = 1365) and African (n = 478) Americans from the Atherosclerosis Risk In Communities (ARIC) Study. We applied Mendelian Randomization (MR) analyses to assess causal associations between the metabolites and cognitive function and between Mediterranean diet and cognitive function. RESULTS: Six metabolites were consistently associated with lower global cognitive function across all studies. Of these, four were sugar-related (e.g., ribitol). MR analyses provided weak evidence for a potential causal effect of ribitol on cognitive function and bi-directional effects of cognitive performance on diet. DISCUSSION: Several diet-related metabolites were associated with global cognitive function across studies with different race/ethnicities. HIGHLIGHTS: Metabolites associated with cognitive function in Puerto Rican adults were recently identified. We demonstrate the generalizability of these associations across diverse race/ethnicities. Most identified metabolites are related to sugars. Mendelian Randomization (MR) provides weak evidence for a causal effect of ribitol on cognitive function. Beta-cryptoxanthin and other metabolites highlight the importance of a healthy diet.

Chang Tang,1,2,* Lanting Wang,1,2,* Zihua Chen,1,2,* Jin Yang,1,2 Haiqing Gao,1,2 Chenggong Guan,1,2 Qiaozhi Gu,1,2 Shan He,1,2 Fanping Yang,1,2 Shengan Chen,1,2 Li Ma,1,2 Zhen Zhang,1,2 Ying Zhao,1,2 Lin Tang,1,2 Yu Xu,1,2 Yue Hu,3 Xiaoqun Luo1,2 1Department of Allergy & Immunology, Huashan Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China; 2Department of Dermatology, Huashan Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China; 3Department of Clinical Laboratory, Huashan Hospital Affiliated to Fudan University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaoqun Luo, 12 Wulumuqi Zhong Road, Jing’an District, Shanghai, People’s Republic of China, Tel +86 13918825230, Email luoxiaoqun913@126.comPurpose: Several in vivo experiments have shown that molecular hydrogen is a promising therapeutic agent for interstitial lung diseases (ILD). In this study, hydrogen therapy was investigated to determine whether it is superior to N-Acetylcysteine (NAC) for the treatment of patients with early-stage ILD.Patients and Methods: A prospective, single-center, randomized, controlled clinical trial was conducted in 87 patients with early-stage ILD. Hydrogen or NAC therapy was randomly assigned (1:1 ratio) to the eligible patients. The primary endpoint was the change in the high-resolution computed tomography (HRCT) and composite physiologic index (CPI) scores from baseline to week 48. Pulmonary function was evaluated as a secondary endpoint, and adverse events were recorded for safety analysis.Results: The rate of HRCT image improvement from the baseline in the HW group (63.6%) was higher than that in the NAC group (39.5%). A significant decrease in CPI and improvement in DLCO-sb were observed in the hydrogen group compared with those in the control group. Changes in other pulmonary function parameters, including FVC, FEV1, FEV1/FV

Smart materials have gained significant attention in various industries due to their exceptional properties and unique capabilities. Among these materials, smart magnetic materials have emerged as a promising candidate with the ability to sense and respond to environmental changes. This paper presents a novel micro-pump structure, fabricated using 0.18 µm integrated technology, which can be integrated into microfluidic systems and MEMS devices. In this study, the design and manufacturing process of the micro-pump, utilizing magnetic smart materials, are presented. The characteristics of the smart material and the micro-pump's performance are analyzed through simulations. The proposed micro-pump design incorporates one-way valves and has demonstrated improved efficiency and enhanced performance compared to conventional designs. The results of this study provide a foundation for further research and development of magnetic smart material-based micro-pumps and their potential applications in various fields, including lab-on-a-chip devices and biomedical engineering.

Organic dyes in the excited singlet state (S-1) decay via intersystem crossing (ISC) to the triplet state, radiative dissipation as fluorescence, or nonradiative decay for thermal deactivation. Although many studies are conducted to improve ISC and fluorescence efficiency, few have optimized S-1 decay via thermal deactivation, which is crucial for designing photothermal agents. A strategy for inhibiting radiative decay and ISC by introducing electron withdraw groups (EWGs) into the meso position of heptamethine cyanines (Cy7) is reported here, which allows S-1 energy decay via nonradiative relaxation. The decrease in the electron density of Cy7 caused by EWGs improved the photostability, which is important for biological applications because conventional cyanine dyes are easily photobleached. The EWG substitutes acted as efficient rotation groups with low-energy barriers, narrowing the energy gap between S-1 and the ground state and considerably improving the photothermal conversion efficiency (PCE). The PCE of CF(3)cy with the strongest EWG is increased up to 83%. Liposome is used as a carrier to improve the biocompatibility and tumor retention of CF(3)cy, which is combined with the toll-like receptor agonist resiquimod (R848) for synergistic photothermal immunotherapy against both primary and distant tumors and elicits a long-lasting immunological memory effect.

Si-Ting Zhao, Xin-Xin Chen, Xian-Miao Yang, Shan-Chuan He, Fen-Hong Qian Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of ChinaCorrespondence: Fen-Hong Qian, Email zhaoqian604@126.comObjective: This study aims at assessing the potential benefits of observation of monocyte-to-albumin ratio (MAR) and neutrophil percentage-to-hemoglobin ratio (NPHR) in the detection of non-small cell lung cancer (NSCLC).Methods: This study retrospectively involved 195 NSCLC patients and 204 healthy volunteers. The correlations between the clinicopathological characteristics of NSCLC and the two ratios including MAR and NPHR were assessed. The diagnostic efficiency of NSCLC patients by MAR and NPHR, alone or in combination with carcinoembryonic antigen (CEA), was assessed by receiver operating characteristic (ROC) curve. The risk factors for NSCLC were analyzed with binary logistic regression.Results: Compared to healthy controls, the levels of MAR and NPHR in NSCLC patients were elevated. MAR and NPHR were related to clinicopathologic characteristics and increased significantly along with the progression of NSCLC. The area under the curve (AUC) for 95% confidence interval (95% CI) of MAR and NPHR in the diagnosis of NSCLC was 0.812 (0.769– 0.854) and 0.724 (0.675– 0.774), respectively. The combination of MAR, NPHR, and CEA achieved the highest diagnostic utility compared to each individually or combined markers (AUC, 0.86; 95% CI, 0.824– 0.896; sensitivity, 72.8%; specificity, 87.3%). Further analysis showed that MAR combined with NPHR presented the potential to detect early-stage (IA–IIB) NSCLC (AUC, 0.794; 95% CI, 0.743– 0.845; sensitivity, 55.1%; specificity, 87.7%). The result indicated that MAR and NPHR might be risk factors for NSCLC.Conclusion: MAR and NPHR could be novel and effective auxiliary indexes in the detection of NSCLC, especially when combined with CEA.Keywords: diagnosis, non

Shan He,1,* Huangding Wen,1,* Nannan Yao,2 Lu Wang,3 Junqun Huang,4 Zhiqing Li1 1Department of Burns, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 2Department of Neurology, Cangzhou Central Hospital, Cangzhou, 061000, People’s Republic of China; 3Department of Neurology, Hebei General Hospital, Shijiazhuang, 050000, People’s Republic of China; 4Department of Anaesthesia, The Seventh Affiliated Hospital, Southern Medical University, Foshan, 528000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhiqing Li, Department of Burns, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China, Email lizq@smu.edu.cnIntroduction: Drug-resistant bacterial infections and biofilm formation play important roles in the pathogenesis of diabetic refractory wounds. Tea tree oil (TTO) exhibits antimicrobial, antimycotic, and antiviral activities, especially against common clinically resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA), making it a potential natural antimicrobial for the treatment of acute and chronic wounds. However, TTO is insoluble in water, volatile, light-sensitive, and cytotoxic. While previous macroscopic studies have focused on sterilization with TTO, none have sought to alter its structure or combine it with other materials to achieve sustained release.Methods: Electrospun TTO nanoliposomes (TTO-NLs), arranged linearly via high-pressure homogenization, could stabilize the structure and performance of TTO to achieve slow drug release. Herein, we established a composite nano-sustained release system, TTO-NL/polyvinyl alcohol/chitosan (TTO-NL@PCS), using high-voltage electrospinning.Results: Compared with the control, TTO-NL@PCS exhibits higher concentrations of the active TTO drug components, terpinen-4-ol and 1,8-cineole. Owing to its increased stability and slow release, early exp

Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-shared information extraction and domain-private information preservation, following the shared-private framework (SP models), which offers significant advantages over single-domain learning. However, the limited availability of annotated data in each domain considerably hinders the effectiveness of conventional supervised MDL approaches in real-world applications. In this paper, we introduce a novel method called multi-domain contrastive learning (MDCL) to alleviate the impact of insufficient annotations by capturing both semantic and structural information from both labeled and unlabeled data.Specifically, MDCL comprises two modules: inter-domain semantic alignment and intra-domain contrast. The former aims to align annotated instances of the same semantic category from distinct domains within a shared hidden space, while the latter focuses on learning a cluster structure of unlabeled instances in a private hidden space for each domain. MDCL is readily compatible with many SP models, requiring no additional model parameters and allowing for end-to-end training. Experimental results across five textual and image multi-domain datasets demonstrate that MDCL brings noticeable improvement over various SP models.Furthermore, MDCL can further be employed in multi-domain active learning (MDAL) to achieve a superior initialization, eventually leading to better overall performance., Comment: This paper has been accepted to ECAI-23

Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI. However, recent works show that explanations provided by these methods face challenges of being faithful and robust. In this paper, we propose a method with Robustness improvement and Explanation Guided training towards more faithful EXplanations (REGEX) for text classification. First, we improve model robustness by input gradient regularization technique and virtual adversarial training. Secondly, we use salient ranking to mask noisy tokens and maximize the similarity between model attention and feature attribution, which can be seen as a self-training procedure without importing other external information. We conduct extensive experiments on six datasets with five attribution methods, and also evaluate the faithfulness in the out-of-domain setting. The results show that REGEX improves fidelity metrics of explanations in all settings and further achieves consistent gains based on two randomization tests. Moreover, we show that using highlight explanations produced by REGEX to train select-then-predict models results in comparable task performance to the end-to-end method.

In multi-domain learning (MDL) scenarios, high labeling effort is required due to the complexity of collecting data from various domains. Active Learning (AL) presents an encouraging solution to this issue by annotating a smaller number of highly informative instances, thereby reducing the labeling effort. Previous research has relied on conventional AL strategies for MDL scenarios, which underutilize the domain-shared information of each instance during the selection procedure. To mitigate this issue, we propose a novel perturbation-based two-stage multi-domain active learning (P2S-MDAL) method incorporated into the well-regarded ASP-MTL model. Specifically, P2S-MDAL involves allocating budgets for domains and establishing regions for diversity selection, which are further used to select the most cross-domain influential samples in each region. A perturbation metric has been introduced to evaluate the robustness of the shared feature extractor of the model, facilitating the identification of potentially cross-domain influential samples. Experiments are conducted on three real-world datasets, encompassing both texts and images. The superior performance over conventional AL strategies shows the effectiveness of the proposed strategy. Additionally, an ablation study has been carried out to demonstrate the validity of each component. Finally, we outline several intriguing potential directions for future MDAL research, thus catalyzing the field's advancement.

Atmospheric diabatic heating, a major driving force of atmospheric circulation over the tropics, is strongly confined to the tropical western North Pacific (TWNP) region, with the global warmest sea surface temperature (SST). The changes in diabatic heating over the TWNP, which exert great impacts on the global climate system, have recently exhibited a noticeable seasonal dependence with a remarkable increase in boreal spring. In this study, we applied observations, reanalysis data, and numerical experiments to investigate the causes of the seasonality in heating changes. Results show that in boreal spring convection is more sensitive to the TWNP SST, leading to a more significant enhancement of deep convection, although the increase in the SST is nearly the same as that in the other seasons. In the non-spring seasons, the enhanced convection due to increased local SST is suppressed by the anomalous anticyclonic wind shear over the TWNP, generated by the easterly wind anomalies induced by the tropical Indian Ocean (TIO) warming via the Kevin waves. However, the TIO warming does not show any suppressing effect in spring because it is much weaker than that in the other seasons and thus the warming itself cannot induce sufficient convective heating anomalies to excite the Kelvin waves. © 2021, The Author(s).

Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with lack of interpretability. More interpretable visible neural network (VNN) architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. We propose a novel Mutated Pathway VNN or MPVNN architecture, designed using prior signaling pathway knowledge and gene mutation data-based edge randomization simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction results of MPVNN over standard non-NN and other similar sized NN survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that are important in risk prediction for particular cancer types, is reliable., Comment: 11 pages, 2 figures

Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well-defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype-specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype-specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype.

Discussions about the use of blockchain in supply chains often claim a strengthening of trust. In this chapter, we argue that blockchains are not about trust per se, and that supply chains can and do function effectively – with the mediation of blockchain technologies – without the presupposition of trust. Through the phenomenological lens of Heidegger, Merleau-Ponty and Todes, we posit how blockchains redefine trust in the supply chain context with an exploratory field study. Our analysis suggests that blockchains are better understood as sources of information reliability and accountability that make supply chain transactions possible in a zero-trust environment. Overcoming information asymmetry as a basis for enhanced supply chain functionality through undisrupted parallel flows of products, money, and information, is what blockchain can significantly contribute towards. If blockchains are – despite commonly held beliefs – not in-and-of-itself about trust, their technology-mediated features can redefine trust in supply chains. The chapter exemplifies the argument by looking at the rise of social commerce in China, which is a good example to illustrate such possibilities.

Atmospheric diabatic heating, a major driving force of atmospheric circulation over the tropics, is strongly confined to the tropical western North Pacific (TWNP) region, with the global warmest sea surface temperature (SST). The changes in diabatic heating over the TWNP, which exert great impacts on the global climate system, have recently exhibited a noticeable seasonal dependence with a remarkable increase in boreal spring. In this study, we applied observations, reanalysis data, and numerical experiments to investigate the causes of the seasonality in heating changes. Results show that in boreal spring convection is more sensitive to the TWNP SST, leading to a more significant enhancement of deep convection, although the increase in the SST is nearly the same as that in the other seasons. In the non-spring seasons, the enhanced convection due to increased local SST is suppressed by the anomalous anticyclonic wind shear over the TWNP, generated by the easterly wind anomalies induced by the tropical Indian Ocean (TIO) warming via the Kevin waves. However, the TIO warming does not show any suppressing effect in spring because it is much weaker than that in the other seasons and thus the warming itself cannot induce sufficient convective heating anomalies to excite the Kelvin waves. © 2021, The Author(s).

Learner corpus collects language data produced by L2 learners, that is second or foreign-language learners. This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction. However, there is little focus on learner corpus for Chinese as Foreign Language (CFL) learners. Therefore, we propose to construct a large-scale, multidimensional annotated Chinese learner corpus. To construct the corpus, we first obtain a large number of topic-rich texts generated by CFL learners. Then we design an annotation scheme including a sentence acceptability score as well as grammatical error and fluency-based corrections. We build a crowdsourcing platform to perform the annotation effectively (https://yaclc.wenmind.net). We name the corpus YACLC (Yet Another Chinese Learner Corpus) and release it as part of the CUGE benchmark (http://cuge.baai.ac.cn). By analyzing the original sentences and annotations in the corpus, we found that YACLC has a considerable size and very high annotation quality. We hope this corpus can further enhance the studies on Chinese International Education and Chinese automatic grammatical error correction., Comment: 4 pages, 3 figures

Glioblastoma multiforme (GBM) is a highly malignant form of cancer that lacks effective treatment options or well-defined strategies for personalized cancer therapy. The disease has been stratified into distinct molecular subtypes; however, the underlying regulatory circuitry that gives rise to such heterogeneity and its implications for therapy remain unclear. We developed a modular computational pipeline, Integrative Modeling of Transcription Regulatory Interactions for Systematic Inference of Susceptibility in Cancer (inTRINSiC), to dissect subtype-specific regulatory programs and predict genetic dependencies in individual patient tumors. Using a multilayer network consisting of 518 transcription factors (TFs), 10,733 target genes, and a signaling layer of 3,132 proteins, we were able to accurately identify differential regulatory activity of TFs that shape subtype-specific expression landscapes. Our models also allowed inference of mechanisms for altered TF behavior in different GBM subtypes. Most importantly, we were able to use the multilayer models to perform an in silico perturbation analysis to infer differential genetic vulnerabilities across GBM subtypes and pinpoint the MYB family member MYBL2 as a drug target specific for the Proneural subtype.

Multi-domain learning (MDL) refers to learning a set of models simultaneously, where each model is specialized to perform a task in a particular domain. Generally, a high labeling effort is required in MDL, as data needs to be labeled by human experts for every domain. Active learning (AL) can be utilized in MDL to reduce the labeling effort by only using the most informative data. The resultant paradigm is termed multi-domain active learning (MDAL). In this work, we provide an exhaustive literature review for MDAL on the relevant fields, including AL, cross-domain information sharing schemes, and cross-domain instance evaluation approaches. It is found that the few studies which have been directly conducted on MDAL cannot serve as off-the-shelf solutions on more general MDAL tasks. To fill this gap, we construct a pipeline of MDAL and present a comprehensive comparative study of thirty different algorithms, which are established by combining six representative MDL models and five commonly used AL strategies. We evaluate the algorithms on six datasets involving textual and visual classification tasks. In most cases, AL brings notable improvements to MDL, and the naive BvSB (best vs. second best) Uncertainty strategy can perform competitively with the state-of-the-art AL strategies. Besides, BvSB with the MAN (multinomial adversarial networks) model can consistently achieve top or above-average performance on all the datasets. Furthermore, we qualitatively analyze the behaviors of the well-performed strategies and models, shedding light on their superior performance in the comparison. Finally, we recommend using BvSB with the MAN model in the application of MDAL due to their good performance in the experiments., Comment: This work has been accepted by IEEE-TETCI

This paper presents a summary of the DFGC 2021 competition. DeepFake technology is developing fast, and realistic face-swaps are increasingly deceiving and hard to detect. At the same time, DeepFake detection methods are also improving. There is a two-party game between DeepFake creators and detectors. This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods. In this paper, we present the organization, results and top solutions of this competition and also share our insights obtained during this event. We also release the DFGC-21 testing dataset collected from our participants to further benefit the research community.

Dynamic Network Embedding (DNE) has recently attracted considerable attention due to the advantage of network embedding in various fields and the dynamic nature of many real-world networks. An input dynamic network to DNE is often assumed to have smooth changes over snapshots, which however would not hold for all real-world scenarios. It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes. To quantify it, an index called Degree of Changes (DoCs) is suggested so that the smaller DoCs indicates the smoother changes. Our comparative study shows several DNE methods are not robust enough to different DoCs even if the corresponding input dynamic networks come from the same dataset, which would make these methods unreliable and hard to use for unknown real-world applications. To propose an effective and more robust DNE method, we follow the notion of ensembles where each base learner adopts an incremental Skip-Gram embedding model. To further boost the performance, a simple yet effective strategy is designed to enhance the diversity among base learners at each timestep by capturing different levels of local-global topology. Extensive experiments demonstrate the superior effectiveness and robustness of the proposed method compared to state-of-the-art DNE methods, as well as the benefits of special designs in the proposed method and its scalability.

Meng-Meng Han,1,* Wen-Shu Zhao,2,* Xin Wang,2,* Shan He,3 Xiao-Rong Xu,2 Cai-Jing Dang,2 Juan Zhang,2 Jia-Mei Liu,2 Mu-Lei Chen,2 Lin Xu,2 Hua Wang2 1Intensive Care Units, Beijing Longfu Hospital, Beijing, 100010, People’s Republic of China; 2Heart Center and Beijing Key Laboratory of Hypertension Research, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, People’s Republic of China; 3Heart Center, Beijing Chaoyang Hospital Jingxi Branch, Capital Medical University, Beijing, 100020, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lin Xu; Hua WangHeart Center and Beijing Key Laboratory of Hypertension Research, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, People’s Republic of ChinaTel +86-10-85231445; +86-10-85231556Email lxdrqw09_dr@163.com; huwsmndr66@yandex.comObjective: This study aimed to find echocardiographic parameters that can predict short- and long-term adverse cardiovascular events in patients with AMI.Methods: A total of 126 patients with AMI admitted to our hospital from July to December 2012 were enrolled in this study. All patients underwent echocardiographic examination within 12 hours after admission and received regular follow-ups until December 2018. The primary endpoint was a composite of the major adverse cardiovascular events (MACEs).Results: In the first year of this study, a primary endpoint occurred in 35 patients and the predictor derived from the echocardiography of 1-year primary endpoint was LVEF< 40% (OR: 9.000, 95% CI 3.242-24.987, p< 0.0001) and the area under the curve (AUC) for the predictor was 0.676 (95% CI 0.561-0.790, p=0.002). For the total 5 years, 57 patients underwent primary endpoint. The results of the 5-year primary endpoint were: E/E’> 15 (OR: 4.094, 95% CI 1.726– 9.710, P=0.001), the wall motion score index was (WMSI)> 1.5 (OR

The tropical marine cyanobacterium Moorena bouillonii occupies a large geographic range across the Indian and Western Tropical Pacific Oceans and is a prolific producer of structurally unique and biologically active natural products. An ensemble of computational approaches, including the creation of the ORCA (Objective Relational Comparative Analysis) pipeline for flexible MS1 feature detection and multivariate analyses, were used to analyze various M. bouillonii samples. The observed chemogeographic patterns suggested the production of regionally specific natural products by M. bouillonii. Analyzing the drivers of these chemogeographic patterns allowed for the identification, targeted isolation, and structure elucidation of a regionally specific natural product, doscadenamide A (1). Analyses of MS2 fragmentation patterns further revealed this natural product to be part of an extensive family of herein annotated, proposed natural structural analogs (doscadenamides B-J, 2-10); the ensemble of structures reflect a combinatorial biosynthesis using nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) components. Compound 1 displayed synergistic in vitro cancer cell cytotoxicity when administered with lipopolysaccharide (LPS). These discoveries illustrate the utility in leveraging chemogeographic patterns for prioritizing natural product discovery efforts.

The paper reports some of the essential works done on the utilization of sensors for the detection of food intake. Due to the health problems currently faced by the modern world, the detection of food intake has become one of the essential sectors where the researchers are getting invested. Because of the alteration in food quality, the imbalance caused in life due to personal and professional pressure, and transmission of new diseases as epidemics, the knowledge about the amount of food intake and time taken to consume the same has become essential to maintain a healthy life. Wearable and non-wearable sensors hold a pivotal role in monitoring the activities associated with food intake due to their quick response, simple operating principle, and the ability to provide instant feedback to the individual. This paper exhibits certain experiments done by the researchers using sensing prototypes to monitor individuals eating food under controlled conditions. It compares certain prototypes attached to the different parts of the body, as well as explains some of the challenges related to the current sensors. Finally, the current and future market survey is also outlined in the paper.

Wanrui Ma,1,2 Shan He,3 Yanping Xu,4 Guoxue Qi,3 Huiyan Ma,3 John J Bang,5 P Andy Li2 1Department of General Medicine, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 2Department of Pharmaceutical Sciences, Biomanufacturing Research Institute and Technological Enterprise (BRITE), North Carolina Central University, Durham, NC, USA; 3Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 4Unit of Echocardiography, Division of Functional Examination in Heart Center, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 5Department of Environmental, Earth and Geospatial Sciences, North Carolina Central University, Durham, NC, USACorrespondence: P Andy LiDepartment of Pharmaceutical Sciences, Biomanufacturing Research Institute and Technological Enterprise (BRITE), North Carolina Central University, 1801 Fayetteville Street, Durham, NC 27707, USATel +1 919 530 6872Fax +1 919 530 6600Email pli@nccu.eduBackground: The application of silver nanoparticles (AgNPs) is growing exponentially, and its potential damage to the cardiac remains to be elucidated. The purpose of this study was to investigate the ameliorative effect of sodium selenite on silver nanoparticles-induced myocardiocyte structural alterations in rats.Materials and Methods: Forty male Sprague-Dawley (SD) rats were randomly divided into four groups: control group, AgNPs group, Se control group, and AgNPs + Se group. SD rats were administered AgNPs through a single intratracheal instillation, and sodium selenite was given by intraperitoneal injection for seven days. Cardiac function was determined by echocardiography and hemodynamic, ultrastructural changes by transmission electron microscopy examination. Mitochondrial fission and autophagy markers were measured by Western blotting.Results: AgNPs caused a significant decrease in cardiac contraction, diastolic dysfunction, fragm

Wanrui Ma, 1–3 Shan He, 4 Huiyan Ma, 4 Haifeng Jiang, 5 Ning Yan, 6 Lili Zhu, 6 John J Bang, 3 P Andy Li, 2 Shaobin Jia 6 1Department of General Medicine, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 2Department of Pharmaceutical Sciences, Biomanufacturing Research Institute and Technological Enterprise (BRITE), North Carolina Central University, Durham, NC, USA; 3Department of Environmental, Earth and Geospatial Sciences, North Carolina Central University, Durham, NC, USA; 4School of Clinical Medicine, Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of hina; 5Department of Pathology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of China; 6Heart Centre, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, People’s Republic of ChinaCorrespondence: P Andy LiDepartment of Pharmaceutical Sciences, Biomanufacturing Research Institute and Technological Enterprise (BRITE), North Carolina Central University, 1801 Fayetteville Street, Durham, NC 27707, USATel +1 919 530 6872Fax +1 919 530 6600Email pli@nccu.eduShaobin JiaHeart Centre, General Hospital of Ningxia Medical University, 804 Shengli South Street, Xingqing District, Yinchuan 750004, People’s Republic of ChinaTel +86 139 9507 8969Email jsbxn@163.comBackground: With the increased application of Silver nanoparticles (AgNP), its potential concerns to the health of human beings remain to be defined. This study aims to explore the harmful effects of AgNP on lung tissue in animals and to examine the mechanisms of protection achieved by sodium selenite.Methods: Sprague-Dawley(SD) rats were exposed to AgNP (200 μL,1mg/mL) through a single intratracheal instillation. Sodium selenite (0.2mg/kg) was i.p. injected. Malondialdehyde (MDA) and glutathione (GSH) were measured using a spectrophotometer. Histological outcomes and ultrastructural cha

The problem of minimum-time, low-thrust, Earth-to-Mars interplanetary orbital trajectory optimization is considered. The minimum-time orbital transfer problem is modeled as a four-phase optimal control problem where the four phases correspond to planetary alignment, Earth escape, heliocentric transfer, and Mars capture. The four-phase optimal control problem is then solved using a direct collocation adaptive Gaussian quadrature collocation method. The following three models are used in the study: (1) circular planetary motion; (2) elliptic planetary motion; and (3) elliptic planetary motion with gravity perturbations, where the transfer begins in a geostationary orbit and terminates in a Mars-stationary orbit. Results for all three cases are provided, and one particular case is studied in detail to show the key features of the optimal solutions. Using the particular value thrust specific force of $0.00098\times 10^{-4}~\textrm{m}\cdot\textrm{s}^{-2}$, it was found that the minimum times for cases (1), (2), and (3) are, respectively, 215 d, 196 d, and 198 d with departure dates, respectively, of 1 July 2020, 30 June 2020, and 28 June 2020. Finally, the problem formulation developed in this study is compared against prior work on an Earth-to-Mars interplanetary orbit transfer where it is found that the results of this research show significant improvement in transfer time relative to the prior work., Comment: 41 pages, 8 figures, 7 tables

Learning low-dimensional topological representation of a network in dynamic environments is attracting much attention due to the time-evolving nature of many real-world networks. The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step. The idea of most existing DNE methods is to capture the topological changes at or around the most affected nodes (instead of all nodes) and accordingly update node embeddings. Unfortunately, this kind of approximation, although can improve efficiency, cannot effectively preserve the global topology of a dynamic network at each time step, due to not considering the inactive sub-networks that receive accumulated topological changes propagated via the high-order proximity. To tackle this challenge, we propose a novel node selecting strategy to diversely select the representative nodes over a network, which is coordinated with a new incremental learning paradigm of Skip-Gram based embedding approach. The extensive experiments show GloDyNE, with a small fraction of nodes being selected, can already achieve the superior or comparable performance w.r.t. the state-of-the-art DNE methods in three typical downstream tasks. Particularly, GloDyNE significantly outperforms other methods in the graph reconstruction task, which demonstrates its ability of global topology preservation. The source code is available at https://github.com/houchengbin/GloDyNE, Comment: Accepted by IEEE-TKDE 2020

Software-intensive organizations rely on large numbers of software assets of different types, e.g., source-code files, tables in the data warehouse, and software configurations. Who is the most suitable owner of a given asset changes over time, e.g., due to reorganization and individual function changes. New forms of automation can help suggest more suitable owners for any given asset at a given point in time. By such efforts on ownership health, accountability of ownership is increased. The problem of finding the most suitable owners for an asset is essentially a program comprehension problem: how do we automatically determine who would be best placed to understand, maintain, evolve (and thereby assume ownership of) a given asset. This paper introduces the Facebook Ownesty system, which uses a combination of ultra large scale data mining and machine learning and has been deployed at Facebook as part of the company's ownership management approach. Ownesty processes many millions of software assets (e.g., source-code files) and it takes into account workflow and organizational aspects. The paper sets out open problems and challenges on ownership for the research community with advances expected from the fields of software engineering, programming languages, and machine learning., Comment: Author order is alphabetical. Contact author: Ralf L\"ammel (rlaemmel@acm.org). The subject of the paper is covered by the contact author's keynote at the same conference

Unlike the constraint frontal face condition, faces in the wild have various unconstrained interference factors, such as complex illumination, changing perspective and various occlusions. Facial expressions recognition (FER) in the wild is a challenging task and existing methods can't perform well. However, for occluded faces (containing occlusion caused by other objects and self-occlusion caused by head posture changes), the attention mechanism has the ability to focus on the non-occluded regions automatically. In this paper, we propose a Lossless Attention Model (LLAM) for convolutional neural networks (CNN) to extract attention-aware features from faces. Our module avoids decay information in the process of generating attention maps by using the information of the previous layer and not reducing the dimensionality. Sequentially, we adaptively refine the feature responses by fusing the attention map with the feature map. We participate in the seven basic expression classification sub-challenges of FG-2020 Affective Behavior Analysis in-the-wild Challenge. And we validate our method on the Aff-Wild2 datasets released by the Challenge. The total accuracy (Accuracy) and the unweighted mean (F1) of our method on the validation set are 0.49 and 0.38 respectively, and the final result is 0.42 (0.67 F1-Score + 0.33 Accuracy)., Comment: 5 pages,4 figures

This paper discusses the relationship between the spatial structure of the built environment and people’s memory of the city as derived from their perceptual knowledge. We explore how spatial comprehension is influenced by the spatial layout pattern in urban settings and individuals’ daily activities. In doing so, we seek to determine whether better spatial knowledge is a function of the legibility of the city and of temporal factors, particularly the amount of time spent in a place. For this purpose, we created a web-based visual survey in the form of a geo-guessing game. The participants were asked to guess the locations of random street views within a familiar neighborhood by placing a pin on a map. This system enabled us to measure how well they remember different urban images on the basis of two indicators of spatial familiarity: location identification and visual recognition. Thus, the resulting datasets are quantitatively different from those collected manually by traditional techniques. By analyzing the combination of the quantitative and qualitative datasets, our proposed methodology can clarify previously unknown aspects of the cognitive role in exploring the built environment and on hidden patterns embedded in the relationship between the city’s spatial elements and people’s mental maps.

This study examines autogenous healing of interface between micro polymeric fiber and hydraulic cement matrix. Experiments were carried out to evaluate the healing-induced recovery of fiber/matrix interface bond strength and to characterize the morphology and chemical compositions of the healing products. Results show that interface frictional bond strength is significantly recovered after eight cycles of water/air conditioning. The healing products are mainly CaCO3 which has a crystal-like morphology with a particle size of 0.5–2 μm. Fiber/matrix interface healing results in significant recovery of fiber-bridging strength of pre-cracked FRCC. Furthermore, inclusion of fly ash in the mix composition remarkably enhances the autogenous healing of fiber/matrix interface as the healing degree of interface frictional bond strength increases from 63% to 94% and that of the fiber-bridging strength improves from 82% to 127%.

Fiber-reinforced cementitious composites (FRCC) represent a large group highperformance concrete for buildings and constructions. In this paper, the long ignored autogenous healing at debonded micro fiber/cement matrix interface (~1 μm crack) is demonstrated for the first time. Class F fly ash, a common Portland cement replacement that is rich in silicate, is engaged to promote interfacial healing. Single micro fiber pullout tests and microstructural characterization show that the presumed fiber/matrix healing indeed takes place and causes significant increase of the mechanical bond between fiber and matrix; additional silicate-rich fly ash greatly improves the healing efficiency. Uniaxial tensile test to the fiber-bridging indicates that the fiber/matrix interfacial healing can enhance the tensile strength of FRCC.

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.

Learning topological representation of a network in dynamic environments has recently attracted considerable attention due to the time-evolving nature of many real-world networks i.e. nodes/links might be added/removed as time goes on. Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network. For seen nodes, the existing methods either treat them equally important or focus on the $k$ most affected nodes at each time step. However, the former solution is time-consuming, and the later solution that relies on incoming changes may lose the global topology---an important feature for downstream tasks. To address these challenges, we propose a dynamic network embedding method called DynWalks, which includes two key components: 1) An online network embedding framework that can dynamically and efficiently learn embeddings based on the selected nodes; 2) A novel online node selecting scheme that offers the flexible choices to balance global topology and recent changes, as well as to fulfill the real-time constraint if needed. The empirical studies on six real-world dynamic networks under three different slicing ways show that DynWalks significantly outperforms the state-of-the-art methods in graph reconstruction tasks, and obtains comparable results in link prediction tasks. Furthermore, the wall-clock time and complexity analysis demonstrate its excellent time and space efficiency. The source code of DynWalks is available at https://github.com/houchengbin/DynWalks, Comment: 14 pages, 7 figures, 5 tables

Finding a low dimensional representation of hierarchical, structured data described by a network remains a challenging problem in the machine learning community. An emerging approach is embedding these networks into hyperbolic space because it can naturally represent a network's hierarchical structure. However, existing hyperbolic embedding approaches cannot deal with attributed networks, in which nodes are annotated with additional attributes. These attributes might provide additional proximity information to constrain the representations of the nodes, which is important to learn high quality hyperbolic embeddings. To fill this gap, we introduce HEAT (Hyperbolic Embedding of ATributed networks), the first method for embedding attributed networks to a hyperbolic space. HEAT consists of 1) a modified random walk algorithm to obtain training samples that capture both topological and attribute similarity; and 2) a learning algorithm for learning hyperboloid embeddings from the obtained training samples. We show that by leveraging node attributes, HEAT can outperform a state-of-the-art Hyperbolic embedding algorithm on several downstream tasks. As a general embedding method, HEAT opens the door to hyperbolic manifold learning on a wide range of attributed and unattributed networks., Comment: 15 pages, 4 figures

Many countries have been constructing modern ground transportation projects. This raises questions about the impacts of such projects on development of impervious surfaces, yet there have been few attempts to systematically analyze these impacts. This paper attempts to narrow this information gap using the Hangzhou Bay Bridge project, China, as an exploratory case study. Using remotely sensed data, we developed a framework based on statistical techniques, wavelet multi-resolution analysis and Theil-Sen slope analysis to measure the changes in impervious surfaces. The derived changes were then linked to the bridge project with respect to socio-economic factors and land use development activities. The findings highlight that the analytical framework could reliably quantify the area, pattern and form of new urban area and urban intensification. Change detection analysis showed that urban area, GDP and the length of highways increased moderately in the pre-Hangzhou Bay Bridge period (1995–2002) while all of these variables increased more substantially during (2002–2009) and after (2009–2013) the bridge construction. The results indicate that the development of impervious surfaces due to new urban area came at the expense of permeable surfaces in the urban fringe and within rural regions, while urban intensification occurred mainly in the form of the redevelopment of older structures to modern high-rise buildings within existing urban regions. In the context of improved transportation infrastructure, our findings suggest that new urban area and urban intensification can be attributed to consecutive events which act like a chain reaction: construction of improved transportation projects, their impacts on land use development policies, effects of both systems on socio-economic variables, and finally all these changes influence new urban area and urban intensification. However, more research is needed to better understand this sequential process and to examine the broader a

Natural habitats are exposed to an increasing number of environmental stressors that cause important ecological consequences. However, the multifarious nature of environmental change, the strength and the relative timing of each stressor largely limit our understanding of biological responses to environmental change. In particular, early response to unpredictable environmental change, critical to survival and fitness in later life stages, is largely uncharacterized. Here, we characterize the early transcriptional response of the keystone species Daphnia magna to twelve environmental perturbations, including biotic and abiotic stressors. We first perform a differential expression analysis aimed at identifying differential regulation of individual genes in response to stress. This preliminary analysis revealed that a few individual genes were responsive to environmental perturbations and they were modulated in a stressor and genotype-specific manner. Given the limited number of differentially regulated genes, we were unable to identify pathways involved in stress response. Hence, to gain a better understanding of the genetic and functional foundation of tolerance to multiple environmental stressors, we leveraged the correlative nature of networks and performed a weighted gene co-expression network analysis. We discovered that approximately one-third of the Daphnia genes, enriched for metabolism, cell signalling and general stress response, drives transcriptional early response to environmental stress and it is shared among genetic backgrounds. This initial response is followed by a genotype- and/or condition-specific transcriptional response with a strong genotype-by-environment interaction. Intriguingly, genotype- and condition-specific transcriptional response is found in genes not conserved beyond crustaceans, suggesting niche-specific adaptation.

Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e.g. a social network with user profiles. Attributed Network Embedding (ANE) has recently attracted considerable attention, which aims to learn unified low dimensional node embeddings while preserving both structural and attribute information. The resulting node embeddings can then facilitate various network downstream tasks e.g. link prediction. Although there are several ANE methods, most of them cannot deal with incomplete attributed networks with missing links and/or missing node attributes, which often occur in real-world scenarios. To address this issue, we propose a robust ANE method, the general idea of which is to reconstruct a unified denser network by fusing two sources of information for information enhancement, and then employ a random walks based network embedding method for learning node embeddings. The experiments of link prediction, node classification, visualization, and parameter sensitivity analysis on six real-world datasets validate the effectiveness of our method to incomplete attributed networks., Comment: 7 pages, 5 figures

There has been an interdecadal shift towards a less humid state in Sahel summer rainfall since the 1960s. The decreased Sahel summer rainfall was associated with enhanced summer latent heating over the South Asian and western Pacific summer monsoon region and anomalous zonal-vertical cell of the Asian summer monsoon circulation, indicating that the latent heating plays a significant role in the change in Sahel rainfall. The effects of the latent heating over different monsoon domains on the Sahel rainfall are investigated through several model experiments. Results show that the remote monsoon heating mainly affects Sahel rainfall by generating changes in the zonal-vertical atmospheric circulation. © 2017 The Author(s).

Survey data describing land cover information such as type and diversity over several decades are scarce. Therefore, our capacity to reconstruct historical land cover using field data and archived remotely sensed data over large areas and long periods of time is somewhat limited. This study explores the relationship between CORONA texture—a surrogate for actual land cover type and complexity—with spectral vegetation indices and texture variables derived from Landsat MSS under the Spectral Variation Hypothesis (SVH) such as to reconstruct historical continuous land cover type and complexity. Image texture of CORONA was calculated using a mean occurrence measure while image textures of Landsat MSS were calculated by occurrence and co-occurrence measures. The relationship between these variables was evaluated using correlation and regression techniques. The reconstruction procedure was undertaken through regression kriging. The results showed that, as expected, texture based on the visible bands and corresponding indices indicated larger correlation with CORONA texture, a surrogate of land cover (correlation >0.65). In terms of prediction, the combination of the first-order mean of band green, second-order measure of tasseled cap brightness, second-order mean of Normalized Visible Index (NVI) and second-order entropy of NIR yielded the best model with respect to Akaike’s Information Criterion (AIC), r-square, and variance inflation factors (VIF). The regression model was then used in regression kriging to map historical continuous land cover. The resultant maps indicated the type and degree of complexity in land cover. Moreover, the proposed methodology minimized the impacts of topographic shadow in the region. The performance of this approach was compared with two conventional classification methods: hard classifiers and continuous classifiers. In contrast to conventional techniques, the technique could clearly quantify land cover complexity and type. Future applicati

Yueju (Cantonese opera), which originated in Southern China, has a long history in the genre of Chinese opera. This study examines how KSK Art Crew sustains this genre in Malaysia and examines the changes in its transmission processes. Past literatures reveal a number of published research monographs and articles on yueju in mainland China and the diasporic Chinese communities in the United States. This study focuses on KSK Art Crew, led by Kam Sin Kiew, and its innovative teaching methods and continuous effort in the sustainability of Cantonese opera in Malaysia. Findings reveal how Kam, the artistic director and founder of KSK Art Crew led new approaches in the teaching of yueju incorporating technological advances, M-learning approach, and pre-recorded music accompaniment that led to aesthetic and pedagogical changes. In addition, how performances were re-contextualized in the form of a dinner-show to adapt to the modern Malaysian audience is discussed.

Weak measurement [1,19] combined with quantum delayed-choice experiment that use quantum beam splitter instead of the beam splitter give rise to a surprising amplification effect, i.e., counterintuitive negative amplification effect. We show that this effect is caused by the wave and particle behaviours of the system to be and can't be explained by a semiclassical wave theory, due to the entanglement of the system and the ancilla in quantum beam splitter. The amplification mechanism about wave-particle duality in quantum mechanics lead us to a scheme for implementation of weak measurement in optomechanical system.

Bacteriosis has become a major global economic problem in freshwater farmed whiteleg shrimp Penaeus vannamei. However, limited information is available on the incidence of Aeromonas schubertii infections in freshwater cultured P. vannamei. Red body disease, an epidemic frequently associated with P. vannamei freshwater farming, occurred in a P. vannamei farm in Shanghai China in June 2013. A pathogenic strain of A. schubertii (isolate HS1) was isolated from diseased freshwater cultured P. vannamei suffering from red body disease, and identified through phylogenetic analysis and phenotypic characteristics. A phylogenetic tree was constructed to examine the relatedness of isolate HS1 with other A. schubertii isolates. In addition, isolate HS1 showed no signs developing antibiotic resistance when screened against a range of common antibiotics used in aquaculture. To the best of our knowledge, this is the first report of A. schubertii infection in freshwater farmed P. vannamei.

Whiteleg shrimp Penaeus vannamei is an important commercial shrimp species cultivated in China and many other countries worldwide. Bacteriosis is a major economic problem that inhibits the farming of this species in fresh water. White feces syndrome is an emerging epidemic in freshwater cultured P. vannamei and has caused significant economic damage. Only scarce information is available on Vibrio cholerae as a possible causal agent for this disease. In this study, a virulent strain BB31 was isolated from diseased P. vannamei suffering from white feces syndrome, and identified as a V. cholerae isolate through phylogenetic analysis and phenotypic characteristics. A phylogenetic tree was constructed to examine the relationship of isolate BB31 to other V. cholerae isolates. Three genes encoding hemolysin, outer membrane protein, and cholera toxin transcriptional activator were present in the BB31 isolate confirming its potential pathogenicity. In addition, isolate BB31 is known to have developed resistance to penicillin, sulfonamides and cephalosporin antibiotics. This was demonstrated when screened against a range of common antibiotics for aquaculture and veterinary use. To the best of our knowledge, this is the first report of white feces syndrome caused by V. cholerae in freshwater farmed P. vannamei.

Thesis: S.M., Massachusetts Institute of Technology, Department of Architecture, 2014., Cataloged from PDF version of thesis., Includes bibliographical references (pages [49]-50)., How do we remember urban space? How can we measure what is remembered? This thesis presents a new approach to study urban spatial perception in an efficient, automated, and scalable way. It explores the use of novel tools developed for online surveys and data visualization. Previous studies in human spatial perception have used methods such as face-to-face interviews, questionnaires, recognition tasks and surveys that ask subjects to draw sketch maps. Those conventional methods produced significant urban studies such as the one by Kevin Lynch (1960), but they are laborious, sensitive to the individuality of subjects, prone to examiners' biases and conducted with a limited number of subjects. Their results are also difficult to quantify. In contrast, the method developed here uses geo-tagged street views and a web-based visual survey. An online experiment conducted in this thesis collected 394 participants in 20 days who were asked to guess the locations of street views from a familiar neighborhood. Results are presented in the form of interactive visualizations. Analysis revealed that memory for exact location of place improves with degree of interaction and proximity to center, rather than number of encounters; memory for one location may vary dramatically between different viewpoints. The results also suggest that the irregularity of urban structure doesn't prevent the forming of strong mental images. While this new method cannot completely replace face-to-face interviews, it demonstrates the possibility of using available technology to scale visual surveys to hundreds or even thousands of people and rapidly visualize the resulting data. It thus opens up new possibilities for large-scale, fine-grained studies in urban perception., by Shan He., S.M.