Search

Water quality and hypoxia in lakes and reservoirs are strongly associated with water renewal. Yet vertical water exchange is still not fully understood and challenging to evaluate in highly dynamic systems. Here, we applied a scaling approach using the vertical timescale, vertical water age (VWA), defined as time since a water parcel last touched the water surface. We established a 3D hydrodynamic-based age model to quantify the vertical water renewal in Xiangxi Bay, a tributary bay of the Three Gorges Dam. The integrated effects of hydrodynamic processes like stratification, intruding density currents from the mainstream, and upstream inflow on the vertical renewal were accounted for. Results indicated that the spatial–temporal distribution of VWA in Xiangxi Bay depended on stratification and forms of intruding density currents. Age was large in spring and summer, and small in autumn and winter, reaching a maximum of 25 days in April. The vertical water renewal was faster during bottom intrusions from the mainstream than during middle and surface intrusions. At times, the epilimnion contained old water due to circulations, and the hypolimnion contained young water due to upstream flushing. In contrast to natural lakes, the bottom water was often younger than overlying intermediate waters. This demonstrated that mixed layer depth was insufficient to fully capture the vertical exchange in riverine systems with significant surface/bottom intrusion. The findings suggested VWA as a quantitative measure of vertical water transport in highly dynamic systems and its usability for environmental water managemen

Most gait recognition methods exploit spatial temporal representations from static appearances and dynamic walking patterns However we observe that many part based methods neglect representations at boundaries In addition the phenomenon of overfitting on training data is relatively common in gait recognition which is perhaps due to insufficient data and low informative gait silhouettes Motivated by these observations we propose a novel mask based regularization method named ReverseMask By injecting perturbation on the feature map the proposed regularization method helps convolutional architecture learn the discriminative representations and enhances generalization Also we design an Inception like ReverseMask Block which has three branches composed of a global branch a feature dropping branch and a feature scaling branch Precisely the dropping branch can extract fine grained representations when partial activations are zero outed Meanwhile the scaling branch randomly scales the feature map keeping structural information of activations and preventing overfitting The plug and play Inception like ReverseMask block is simple and effective improving the performance of many state of the art methods Extensive experiments demonstrate that the ReverseMask regularization help baseline achieves higher accuracy and better generalization Moreover the base line with Inception like Block significantly outperforms state of the art methods on the two most popular datasets CASIA B and OUMVLP

Biosimilars are biological medicines highly similar to a previously licensed reference product and their licensing is expected to improve access to biological therapies. This study aims to present an overview of biosimilars approval by thirteen regulatory authorities (RA). The study is a cross-national comparison of regulatory decisions involving biosimilars in Argentina, Australia, Brazil, Chile, Canada, Colombia, Europe, Hungary, Guatemala, Italy, Mexico, Peru and United States. We examined publicly available documents containing information regarding the approval of biosimilars and investigated the publication of public assessment reports for registration applications, guidelines for biosimilars licensing, and products approved. Data extraction was conducted by a network of researchers and regulatory experts. All the RA had issued guidance documents establishing the requirements for the licensing of biosimilars. However, only three RA had published public assessment reports for registration applications. In total, the investigated jurisdictions had from 19 to 78 biosimilars approved, most of them licensed from 2018 to 2020. In spite of the advance in the number of products in recent years, some challenges still persist. Limited access to information regarding the assessment of biosimilars by RA can affect confidence, which may ultimately impact adoption of these products in practice.

Antimicrobial peptides (AMPs) are potentially powerful alternatives to conventional antibiotics in combating multidrug resistance, given their broad spectrum of activity. They mainly interact with cell membranes through surface electrostatic potentials and the formation of secondary structures, resulting in permeability and destruction of target microorganism membranes. Our earlier work showed that two leading AMPs, MSI‐78 (4–20) and pardaxin (1–22), had potent antimicrobial activity against a range of bacteria. It is known that the attachment of moderate‐length lipid carbon chains to cationic peptides can further improve the functionality of these peptides through enhanced interactions with the membrane lipid bilayer, inducing membrane curvature, destabilization, and potential leakage. Thus, in this work, we aimed to investigate the antimicrobial activity, oligomerization propensity, and lipid‐membrane binding interactions of a range of N‐terminal lipidated analogs of MSI‐78 (4–20) and pardaxin (1–22). Molecular modeling results suggest that aggregation of the N‐lipidated AMPs may impart greater structural stability to the peptides in solution and a greater depth of lipid bilayer insertion for the N‐lipidated AMPs over the parental peptide. Our experimental and computational findings provide insights into how N‐terminal lipidation of AMPs may alter their conformations, with subsequent effects on their functional properties in regard to their self‐aggregation behavior, membrane interactions, and antimicrobial activity.

ECOOP 2023, Contains fulltext : 294796.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext, ICSME 2023

Item does not contain fulltext, 21 november 2023

This work aims to locate a query isolated sign in a continuous sign video. In this task, the domain gap between the isolated and continuous sign videos often handicaps the localization performance. To address this issue, we propose a parallel multi-modal sign spotting framework. In a nutshell, our framework firstly takes advantage of multi-modal information (including RGB frames, 2D key-points and 3D key-points) to achieve representative sign features. The multi-modal features are employed to complement each other and thus compensate for the deficiency of a single modality, thus leading to informative representations for sign spotting. Moreover, we introduce a testing time top-k transferring technique into our framework to reduce the aforementioned domain gap. Concretely, we first compare the query sign with extracted sign video clips, and then update the feature of the query sign with the features of the top-k best matching video clips. In this manner, the updated query feature will exhibit a smaller domain gap with respect to continuous signs, facilitating feature matching in the following iterations. Experiments on the challenging OSLWL-Test-Set benchmark demonstrate that our method achieves superior performance (0.559 F1-score) compared to the baseline (0.395 F1-score). Our code is available at https://github.com/bb12346/OpenSLR.

As China’s population ages rapidly, the health risks associated with a changing climate are becoming more threatening. The 2022 China report of the Lancet Countdown, led by Tsinghua University with the contributions of 73 experts from 23 leading global institutions, tracks progress in climate change and health in China through 27 indicators across five domains: (1) Climate change impacts, exposure, and vulnerability; (2) adaptation, planning, and resilience for health; (3) mitigation actions and health co-benefits; (4) economics and finance; and (5) public and political engagement. This report is the third China Lancet Countdown report, paying particular attention to the impacts on the elderly and highlighting the urgency of taking action. We selected the most urgent and relevant indicators to complete a policy brief that provides a better understanding of recent progress on climate change and health in China. We found heat-related health impacts increased from 2020 to 2021, increasing heat-related mortality, reducing labour capacity, and undermining the capacity to partake in physical activity due to rising temperature. In addition, exposure to wildfire, extreme drought, and extreme rainfall also increased in different regions across China. In 2021, compared with the 1986–2005 average, people in China had an average of 7.85 more heatwave days (which led to an extra 13185 heatwave-related deaths), and a loss of 0.67 more hours of safe outdoor physical exercise per day. The rising temperature also caused the annual average exposure to wildfire to increase by 60.0% between 2017–2021 compared with the 2001–2005 average. Meanwhile, the engagement on health and climate issues from individuals, scholars, and public sectors continues to grow rapidly. From 2020 to 2021, the number of climate-related articles and documents on the official websites of four Chinese Government departments grew by 1.83 times, and the number of climate-and-health-related articles and documents gr

Catalytic ceramic nanofiltration (NF) is a promising technology for direct wastewater reclamation, given its high separation selectivity and reactive surfaces for oxidative removal of fouling. A better understanding of the relation between fouling types and oxidative cleaning efficacy under high organic loading conditions is of practical importance for realizing stable filtration/cleaning performance in long-term water reclamation operations. In this work, Fenton cleaning, using a hydrogen peroxide solution and an iron oxychloride catalyst pre-coat layer on top of commercially available ceramic NF membranes, was studied with respect to high organic loaded fouling, simulated by a concentrated sodium alginate solution in the presence of calcium. Adsorption (in the absence of a permeate flow) and constant-pressure filtration (with a permeate flow) experiments were performed to distinguish between permeance decreases as a result of either adsorptive or cake layer fouling. The results show that the flux evolution could be divided into an initial sharp flux decline, due to rapid adsorption of the foulants, and a subsequent gradual flux decrease, resulting from progressive cake build-up on the membrane. The two-stage flux decrease was enhanced during the constant-pressure filtration experiments, because they start at a high flux with a high fouling rate, while the flux gradually decreases as fouling proceeds. During multiple adsorption/cake filtration/Fenton cleaning cycles, the cake layer fouling was sufficiently removed by Fenton cleaning in contrast to the adsorptive fouling. However, the total permeate production during ceramic NF was not influenced by the remaining adsorptive fouling (after cleaning), since the adsorptive fouling always only occurs at the beginning of each cycle. The findings provide new insights into the criteria for evaluating and optimizing the efficacy of oxidative (Fenton) cleaning during ceramic NF in water treatment., Sanitary Engineering

Many gait recognition methods first partition the human gait into N-parts and then combine them to establish part-based feature representations. Their gait recognition performance is often affected by partitioning strategies, which are empirically chosen in different datasets. However, we observe that strips as the basic component of parts are agnostic against different partitioning strategies. Motivated by this observation, we present a strip-based multi-level gait recognition network, named GaitStrip, to extract comprehensive gait information at different levels. To be specific, our high-level branch explores the context of gait sequences and our low-level one focuses on detailed posture changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the strip-based feature representations by directly taking each strip of the human body as the basic unit. Moreover, we propose a novel multi-branch structure, called Enhanced Convolution Module (ECM), to extract different representations of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network. Specifically, ST aims to extract spatial-temporal features of gait sequences, while FL is used to generate the feature representation of each frame. Second, the parameters of the ECM can be reduced in test by introducing a structural re-parameterization technique. Extensive experimental results demonstrate that our GaitStrip achieves state-of-the-art performance in both normal walking and complex conditions. The source code is published at https://github.com/M-Candy77/GaitStrip.

Biosimilars are biological medicines highly similar to a previously licensed reference product and their licensing is expected to improve access to biological therapies. This study aims to present an overview of biosimilars approval by thirteen regulatory authorities (RA). The study is a cross-national comparison of regulatory decisions involving biosimilars in Argentina, Australia, Brazil, Chile, Canada, Colombia, Europe, Hungary, Guatemala, Italy, Mexico, Peru and United States. We examined publicly available documents containing information regarding the approval of biosimilars and investigated the publication of public assessment reports for registration applications, guidelines for biosimilars licensing, and products approved. Data extraction was conducted by a network of researchers and regulatory experts. All the RA had issued guidance documents establishing the requirements for the licensing of biosimilars. However, only three RA had published public assessment reports for registration applications. In total, the investigated jurisdictions had from 19 to 78 biosimilars approved, most of them licensed from 2018 to 2020. In spite of the advance in the number of products in recent years, some challenges still persist. Limited access to information regarding the assessment of biosimilars by RA can affect confidence, which may ultimately impact adoption of these products in practice.

A precise understanding of the mechanisms causing phytoplankton blooms in reservoirs is still lacking, especially in large riverine reservoirs. To better understand these blooms, the role of the complex hydrodynamics caused by dam operation must be quantified. Here we examine how synergistic hydrodynamic processes, rather than individual metrics, trigger blooms in Xiangxi Bay, a typical tributary bay of the Three Gorges Reservoir, China. We used a 3D ecological-hydrodynamic model, which integrated hydrodynamics with the abiotic factors that limit phytoplankton growth to simulate one whole year (2010). By implementing a scaling criterion, we quantified the contribution of local phytoplankton growth and hydrodynamic processes, including advection transport and vertical mixing, on bloom dynamics. Results indicated vertical mixing was the main process inhibiting blooms in colder months (from October to February) but horizontal advection, which flushed and diluted blooms, was dominant in warmer months (from May to July) when stratification was intense and nutrients were replete. Accordingly, blooms occurred when both vertical mixing and horizontal advection were low. We suggested a potential dam operation strategy to mitigate blooms during stratification, which involves withdrawing the warm surface water from upstream reservoirs to increase horizontal flows in the surface layer. Extending the application of critical turbulence model, our study shows how vertical mixing and horizontal advection rate interact with phytoplankton growth rate to drive blooms in highly dynamic riverine systems.

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have com

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have com

Over the past decades, direct nanofiltration (NF) without pre-treatment has been widely recognized as an alternative for conventional membrane technologies in both drinking water and wastewater treatment, owing to its advantages in energy saving, low chemical usage and high permeate purity. As an alternative, ceramic NF has received growing attention in recent years, given its good robustness and stable separation capabilities as compared to polymeric NF membranes. Organic fouling of ceramic NF membranes remains the key problem affecting the performance of the membranes in water treatment. However, conventional forward flush with pure water is not effective for removing the organic fouling due to its sticky nature. Backwash with pure water has to be applied at high pressures, thereby having a risk of damaging the structure of the membranes. Therefore, chemical forward flush with strong acids, bases or chlorine is frequently required as a substitute for backwash and conventional forward flush, leading to more consumption of the chemicals. To apply innovative ceramic NF in direct surface water treatment, an eco-friendly cleaning strategy of using Fenton-based oxidation was studied in relation to the fouling characteristic of the membrane. A literature study was done to review the present knowledge on using oxidation methods for fouling mitigation of ceramic membranes. It was found that existing studies predominantly were focused on direct oxidation of organic substances in feed water of ceramic membrane filtration. This kind of oxidation strategies could mainly reduce cake layer fouling of the membranes, while in many cases aggravating pore clogging due to an oxidation-induced conversion of large-sized organic molecules into smaller ones. Additionally, there is a risk of secondary pollution by using oxidation in the feed water, since the oxidants and potentially produced oxidation by-products can penetrate the membranes into permeate. However, little knowledge is, Sanitary Engineering

Selecting appropriate distributed join methods for logical join operations in a query plan is crucial for the performance of data-intensive scalable computing (DISC). Different network communication patterns in the data exchange phase generate varying network communication workloads and significantly affect the distributed join performance. However, most cost-based query optimizers focus on the local computing cost and do not precisely model the network communication cost. We propose a cost model for various distributed join methods to optimize join queries in DISC platforms. Our method precisely measures the network and local computing workloads in different execution phases, using information on the size and cardinality statistics of datasets and cluster join parallelism. Our cost model reveals the importance of the relative size of the joining datasets. We implement an efficient distributed join selection strategy, known as RelJoin in SparkSQL, which is an industry-prevalent distributed data processing framework. RelJoin uses runtime adaptive statistics for accurate cost estimation and selects optimal distributed join methods for logical joins to optimize the physical query plan. The evaluation results on the TPC-DS benchmark show that RelJoin performs best in 62 of the 97 queries and can reduce the average query time by 21% compared with other strategies.

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate chest X-ray report generation with Contrastive Learning, named DCL. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Then we explore specific knowledge extracted from the retrieved reports to add additional nodes or redefine their relations in a bottom-up manner. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation. Finally, this paper introduces Image-Report Contrastive and Image-Report Matching losses to better represent visual features and textual information. Evaluated on IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art models on these two benchmarks.

Understanding the decay and maintenance of long-term SARS-CoV-2 neutralizing antibodies in infected or vaccinated people and how vaccines protect against other SARS-CoV-2 variants is critical for assessing public vaccination plans. Here, we measured different plasm antibody levels 2 and 12 months after disease onset, including anti-RBD, anti-N, total neutralizing antibodies, and two neutralizing-antibody clusters. We found that total neutralizing antibodies declined more slowly than total anti-RBD and anti-N IgG, and the two neutralizing-antibody clusters decayed even more slowly than total neutralizing antibodies. Interestingly, the level of neutralizing antibodies at 12 months after disease onset was significantly lower than that at 2 months but more broadly neutralized SARS-CoV-2 variants, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Lambda (C.37). Significant immune escape by the Omicron variant (B.1.1.529) was also observed 2 months post-recovery. Furthermore, we revealed that a high percentage of virus-specific CD4+ T cells and cTfh1 were associated with a slower decline in humoral immunity, accompanied by higher levels of CXCR3 ligands such as CXCL9 and CXCL10, higher frequency of cTfh1, and lower levels of cTfh2 and cTfh17. Our data highlight the importance of coordinating T-cell and humoral immunity to achieve long-term protective immunity.

Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10-3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10-4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10-3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10-7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective treatment for severe major depressive episodes (MDEs). Nonetheless, firmly established associations between ECT outcomes and biological variables are currently lacking. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response in psychiatry are seldom reported. Here, we examined whether PRSs for major depressive disorder, schizophrenia (SCZ), cross-disorder, and pharmacological antidepressant response are associated with ECT effectiveness. METHODS: A total of 288 patients with MDE from 3 countries were included. The main outcome was a change in the 17-item Hamilton Depression Rating Scale scores from before to after ECT treatment. Secondary outcomes were response and remission. Regression analyses with PRSs as independent variables and several covariates were performed. Explained variance (R2) at the optimal p-value threshold is reported. RESULTS: In the 266 subjects passing quality control, the PRS-SCZ was positively associated with a larger Hamilton Depression Rating Scale decrease in linear regression (optimal p-value threshold = .05, R2 = 6.94%, p < .0001), which was consistent across countries: Ireland (R2 = 8.18%, p = .0013), Belgium (R2 = 6.83%, p = .016), and the Netherlands (R2 = 7.92%, p = .0077). The PRS-SCZ was also positively associated with remission (R2 = 4.63%, p = .0018). Sensitivity and subgroup analyses, including in MDE without psychotic features (R2 = 4.42%, p = .0024) and unipolar MDE only (R2 = 9.08%, p < .0001), confirmed the results. The other PRSs were not associated with a change in the Hamilton Depression Rating Scale score at the predefined Bonferroni-corrected significance threshold. CONCLUSIONS: A linear association between PRS-SCZ and ECT outcome was uncovered. Although it is too early to adopt PRSs in ECT clinical decision making, these findings strengthen the positioning of PRS-SCZ as relevant to treatment response in psychiatry.

The World Health Organisation has deemed several multi-drug resistant (MDR) nosocomial bacterial pathogens to be of significant threat to human health. A stark increase in morbidity, mortality and the burden to healthcare systems around the world can be attributed to the development of resistance in these bacteria. Accordingly, alternative antimicrobial agents have been sought as an attractive means to combat MDR pathogens, with one such example being antimicrobial peptides (AMPs). Given the reported activity of AMPs, including Pardaxin, MSI-78, dermaseptin-PC (DMPC) and Cecropin B, it is important to understand their activities and modes of action against bacteria for further AMP design. In this study, we compared these AMPs against a panel of nosocomial bacterial pathogens, followed by detailed mechanistic studies. It was found that Pardaxin (1-22) and MSI-78 (4-20) displayed the most pronounced antimicrobial activity against the tested bacteria. The mechanistic studies by membrane permeability and molecular dynamics simulation further confirmed the strong membrane interaction and structure of Pardaxin (1-22) and MSI-78 (4-20), which contributed to their potent activity. This study demonstrated a structure and activity guidance for further design of Pardaxin (1-22) and MSI-78 (4-20) as therapeutics against MDR pathogens. The different effects of DMPC (1-19) and Cecropin B (1-21) on membrane integrity and phospholipid membrane interactions provided critical information for the rational design of next-generation analogues with specificity against either Gram-negative or Gram-positive bacteria.

Background: The number of randomized trials of agents targeting oncogene-addicted tumors has surged in the past 10 years. Using a meta-analysis, we explored whether improvements in objective response rate (ORR) in comparative trials using targeted agents could serve as a potential surrogate endpoint for improvements in progression-free survival (PFS) or overall survival (OS) in populations with oncogene-addicted cancer. Patients and methods: Using commercial text mining software I2E, we searched ClinicalTrials.gov and MEDLINE databases for randomized, phase III trials based on prospectively defined criteria, including (i) use of agents targeting EGFR activating mutations, ALK rearrangements, BRAF V600E or V600K mutations, and BCR-ABL fusion protein; (ii) molecularly enriched trial population or subpopulation; (iii) control arm only randomized to chemo/ cytotoxic therapy. Correlative analyses were performed using ORR, OS, and PFS data from trials that met these criteria. Results: A total of 62 trials were identified; 15 met all of the prespecified criteria. The ORR effect size (both the difference in ORR between arms and the log odds ratio) and log PFS hazard ratio were strongly correlated: -0.78 (P = 0.0007) for the ORR difference model; -0.74 (P = 0.0017) for the log odds ratio model. ORR effect size was positively correlated with the log OS hazard ratio, but more weakly: -0.67 (P = 0.013) for the ORR difference model and -0.58 (P = 0.036) for the log odds ratio model. Analysis of the treatment effects between OS and PFS found no correlation. Conclusions: These analyses identified a strong correlation between treatment effects on ORR and PFS in randomized clinical trials investigating agents targeting oncogene-driven cancers. A weaker correlation was observed between ORR and OS. These meta-analysis results support the use of a high ORR forming the basis of an initial regulatory approval in biomarker-driven studies.

Designing neural network architecture for embedded devices is practical but challenging because the models are expected to be not only accurate but also enough lightweight and robust. However, it is challenging to balance those trade-offs manually because of the large search space. To solve this problem, we propose an Effective, Efficient, and Robust Neural Architecture Search (E2RNAS) method to automatically search a neural network architecture that balances the performance, robustness, and resource consumption. Unlike previous studies, the objective function of the proposed E2RNAS method is formulated as a multi-objective bi-level optimization problem with the upper-level subproblem as a multi-objective optimization problem that considers the performance, robustness, and resource consumption. To solve the proposed objective function, we integrate the multiple-gradient descent algorithm, a widely studied gradient-based multi-objective optimization algorithm, with the bi-level optimization. Experiments on benchmark datasets show that the proposed E2RNAS method can find robust architecture with low resource consumption and comparable classification accuracy.

Gait recognition is one of the most important biometric technologies and has been applied in many fields. Recent gait recognition frameworks represent each gait frame by descriptors extracted from either global appearances or local regions of humans. However, the representations based on global information often neglect the details of the gait frame, while local region based descriptors cannot capture the relations among neighboring regions, thus reducing their discriminativeness. In this paper, we propose a novel feature extraction and fusion framework to achieve discriminative feature representations for gait recognition. Towards this goal, we take advantage of both global visual information and local region details and develop a Global and Local Feature Extractor (GLFE). Specifically, our GLFE module is composed of our newly designed multiple global and local convolutional layers (GLConv) to ensemble global and local features in a principle manner. Furthermore, we present a novel operation, namely Local Temporal Aggregation (LTA), to further preserve the spatial information by reducing the temporal resolution to obtain higher spatial resolution. With the help of our GLFE and LTA, our method significantly improves the discriminativeness of our visual features, thus improving the gait recognition performance. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art gait recognition methods on two popular datasets.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial–macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.

Recent discoveries of two-dimensional transitional metal based materials have emerged as an excellent candidate for fabricating nanostructured flame-retardants. Herein, we report an eco-friendly flame-retardant for flexible polyurethane foam (PUF), which is synthesised by hybridising MXene (Ti3C 2) with biomass materials including phytic acid (PA), casein, pectin, and chitosan (CH). Results show that coating PUFs with 3 layers of CH/PA/Ti3C 2 via layer-by-layer approach reduces the peak heat release and total smoke release by 51.1% and 84.8%, respectively. These exceptional improvements exceed those achieved by a CH/Ti3C 2 coating. To further understand the fundamental flame and smoke reduction phenomena, a pyrolysis model with surface regression was developed to simulate the flame propagation and char layer. A genetic algorithm was utilised to determine optimum parameters describing the thermal degradation rate. The superior flame-retardancy of CH/PA/Ti3C 2 was originated from the shielding and charring effects of the hybrid MXene with biomass materials containing aromatic rings, phenolic and phosphorous compounds.

AIMS: To understand how survivors of stroke perceive secondary prevention and explore their perceived barriers and facilitators using the Theoretical Domains Framework. DESIGN: A qualitative descriptive study. METHOD(S): Nineteen survivors of stroke from three hospitals were recruited and interviewed from April 2019 to April 2020. The data were analysed deductively and inductively by content analysis strategies. RESULT(S): Three main themes of perception of secondary prevention were identified, these being active treatment-seeking, attention to taking medications and negative attitude towards lifestyle changes. Using deductive analysis, eight domains of the Theoretical Domains Framework were reported to be relevant in the secondary prevention behaviour of survivors of stroke that mapped to five 'barrier' domains (i.e. knowledge, physical skills, beliefs about capability, beliefs about consequences and optimism) as well as six 'facilitator' domains (i.e. knowledge, interpersonal skills, beliefs about capability, intention, emotion and social influences). Using inductive analysis we identified two additional important factors not falling in the domains of the Theoretical Domains Framework. These comprised female spouses' support and patients' economic autonomy, both of which could be classified as a facilitator or barrier. CONCLUSION(S): Survivors of stroke perceive seeking treatment and using preventive medication as more important than modifying lifestyle behaviours. Knowledge and insight into the barriers and facilitators of secondary prevention in this specific context provides a theoretical and practical basis for the design of future secondary prevention interventions. IMPACT: Stroke survivors' perceptions of secondary prevention, barriers and facilitators were explored in the context of a developing country. These findings highlight the need to better communicate the importance of improving lifestyle modification and medication adherence, and provide evidence f

Emerging testing technologies for detection of SARS-CoV-2 include those that are rapid and can be used at point-of-care (POC), and those facilitating high throughput laboratory-based testing. Tests designed to be performed at POC (such as antigen tests and molecular assays) have the potential to expedite isolation of infectious patients and their contacts, but most are less sensitive than standard-of-care reverse transcription polymerase chain reaction (RT-PCR). Data on clinical performance of the majority of emerging assays are limited with most evaluations performed on contrived or stored laboratory samples. Further evaluations of these assays are required, particularly when performed at POC on symptomatic and asymptomatic patients and at various time-points after symptom onset. A few studies have so far shown several of these assays have high specificity. However, large prospective evaluations are needed to confirm specificity, particularly before the assays are implemented in low prevalence settings or asymptomatic populations. High throughput laboratory-based testing includes the use of new sample types (e.g., saliva to increase acceptability) or innovative uses of existing technology (e.g., sample pooling). Information detailing population-wide testing strategies for SARS-COV-2 is largely missing from peer-reviewed literature. Logistics and supply chains are key considerations in any plan to 'scale up' testing in the Australian context. The strategic use of novel assays will help strike the balance between achieving adequate test numbers without overwhelming laboratory capacity. To protect testing of high-risk populations, the aims of testing with respect to the phase of the pandemic must be considered.Crown Copyright © 2021. Published by Elsevier B.V. All rights reserved.

Emerging testing technologies for detection of SARS-CoV-2 include those that are rapid and can be used at point-of-care (POC), and those facilitating high throughput laboratory-based testing. Tests designed to be performed at POC (such as antigen tests and molecular assays) have the potential to expedite isolation of infectious patients and their contacts, but most are less sensitive than standard-of-care reverse transcription polymerase chain reaction (RT-PCR). Data on clinical performance of the majority of emerging assays are limited with most evaluations performed on contrived or stored laboratory samples. Further evaluations of these assays are required, particularly when performed at POC on symptomatic and asymptomatic patients and at various time-points after symptom onset. A few studies have so far shown several of these assays have high specificity. However, large prospective evaluations are needed to confirm specificity, particularly before the assays are implemented in low prevalence settings or asymptomatic populations. High throughput laboratory-based testing includes the use of new sample types (e.g., saliva to increase acceptability) or innovative uses of existing technology (e.g., sample pooling). Information detailing population-wide testing strategies for SARS-COV-2 is largely missing from peer-reviewed literature. Logistics and supply chains are key considerations in any plan to 'scale up' testing in the Australian context. The strategic use of novel assays will help strike the balance between achieving adequate test numbers without overwhelming laboratory capacity. To protect testing of high-risk populations, the aims of testing with respect to the phase of the pandemic must be considered.Crown Copyright © 2021. Published by Elsevier B.V. All rights reserved.

AIMS: To understand how survivors of stroke perceive secondary prevention and explore their perceived barriers and facilitators using the Theoretical Domains Framework. DESIGN: A qualitative descriptive study. METHOD(S): Nineteen survivors of stroke from three hospitals were recruited and interviewed from April 2019 to April 2020. The data were analysed deductively and inductively by content analysis strategies. RESULT(S): Three main themes of perception of secondary prevention were identified, these being active treatment-seeking, attention to taking medications and negative attitude towards lifestyle changes. Using deductive analysis, eight domains of the Theoretical Domains Framework were reported to be relevant in the secondary prevention behaviour of survivors of stroke that mapped to five 'barrier' domains (i.e. knowledge, physical skills, beliefs about capability, beliefs about consequences and optimism) as well as six 'facilitator' domains (i.e. knowledge, interpersonal skills, beliefs about capability, intention, emotion and social influences). Using inductive analysis we identified two additional important factors not falling in the domains of the Theoretical Domains Framework. These comprised female spouses' support and patients' economic autonomy, both of which could be classified as a facilitator or barrier. CONCLUSION(S): Survivors of stroke perceive seeking treatment and using preventive medication as more important than modifying lifestyle behaviours. Knowledge and insight into the barriers and facilitators of secondary prevention in this specific context provides a theoretical and practical basis for the design of future secondary prevention interventions. IMPACT: Stroke survivors' perceptions of secondary prevention, barriers and facilitators were explored in the context of a developing country. These findings highlight the need to better communicate the importance of improving lifestyle modification and medication adherence, and provide evidence f

Ceramic nanofiltration (NF) is a newly-developed technology for water recycling, but is still limited to pilot-scale applications. Lacking efficient and eco-friendly strategies for cleaning ceramic NF membrane impedes its scaling-up in industries. Forward flush, backwash and acidic/caustic cleaning are not efficient enough. In this work, a novel oxalic acid-aided Fenton process was proposed for synergistic relaxation/oxidation of persistent Ca2+-mediated gel-like fouling of ceramic NF membrane. A reactive catalyst layer was online pre-coated on top of the membrane via a pressure-driven cross-flow pre-filtration of Fe3O4 hydrosols. The gel-like fouling was simulated by alginate in the presence of Ca2+ ions. Results show that the Fe3O4 loading could be readily tuned from 0.16 to 1.34 g m−2 by altering the permeate flux during the pre-coating. The membrane permeability loss due to the pre-coating was minimal (<10%). The combination of oxalic acid chelation and Fenton-based oxidation resulted in high flux recovery (85.07%) for the iron-oxide pre-coated membrane, whereas the single treatment by hydrogen peroxide (H2O2) or oxalic acid was inefficient. This synergistic effect was attributed to relaxation of the Ca2+-mediated gel layer via oxalic acid/Ca2+ chelation, which presumably facilitated H2O2 diffusion at the Fe3O4/foulant interface. The iron-oxide pre-coated membrane maintained stable initial normalized fluxes (83.33–90.15%) through the oxalic acid/H2O2 cleaning over five cycles, with no need of refreshing the iron-oxide pre-coat. Additionally, the leaching of iron from the iron-oxide pre-coat by oxalic acid was suppressed by the oxalic acid/H2O2 combination, owing to a reactive shielding by competitive sorption of H2O2 onto the Fe, Sanitary Engineering, Water Management

Emerging testing technologies for detection of SARS-CoV-2 include those that are rapid and can be used at point-of-care (POC), and those facilitating high throughput laboratory-based testing. Tests designed to be performed at POC (such as antigen tests and molecular assays) have the potential to expedite isolation of infectious patients and their contacts, but most are less sensitive than standard-of-care reverse transcription polymerase chain reaction (RT-PCR). Data on clinical performance of the majority of emerging assays are limited with most evaluations performed on contrived or stored laboratory samples. Further evaluations of these assays are required, particularly when performed at POC on symptomatic and asymptomatic patients and at various time-points after symptom onset. A few studies have so far shown several of these assays have high specificity. However, large prospective evaluations are needed to confirm specificity, particularly before the assays are implemented in low prevalence settings or asymptomatic populations. High throughput laboratory-based testing includes the use of new sample types (e.g., saliva to increase acceptability) or innovative uses of existing technology (e.g., sample pooling). Information detailing population-wide testing strategies for SARS-COV-2 is largely missing from peer-reviewed literature. Logistics and supply chains are key considerations in any plan to 'scale up' testing in the Australian context. The strategic use of novel assays will help strike the balance between achieving adequate test numbers without overwhelming laboratory capacity. To protect testing of high-risk populations, the aims of testing with respect to the phase of the pandemic must be considered.

T-cell immunity is important for recovery from COVID-19 and provides heightened immunity for re-infection. However, little is known about the SARS-CoV-2-specific T-cell immunity in virus-exposed individuals. Here we report virus-specific CD4+ and CD8+ T-cell memory in recovered COVID-19 patients and close contacts. We also demonstrate the size and quality of the memory T-cell pool of COVID-19 patients are larger and better than those of close contacts. However, the proliferation capacity, size and quality of T-cell responses in close contacts are readily distinguishable from healthy donors, suggesting close contacts are able to gain T-cell immunity against SARS-CoV-2 despite lacking a detectable infection. Additionally, asymptomatic and symptomatic COVID-19 patients contain similar levels of SARS-CoV-2-specific T-cell memory. Overall, this study demonstrates the versatility and potential of memory T cells from COVID-19 patients and close contacts, which may be important for host protection.

Dental caries is characterized by a dysbiotic shift at the biofilm–tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study’s analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)–machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10−3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10−6) and N-acetylneuraminate (p = 6.8 × 10−6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10−6) and epicatechin (P = 2.9 ×

The observed trend towards a warmer and wetter climate in northwest China is a controversial issue lacking sufficient scientific research. Based on monthly meteorological data from 201 weather stations in northwest China and surrounding regions from 1959 to 2019, we calculated potential evapotranspiration using Penman-Monteith (PM) equation. By analyzing the spatial–temporal variations in temperature and precipitation and by studying changing drought trends, we systematically explored the climate trends in northwest China over the past 60 years. Our findings include: (1) From 1959 to 2019, during the growing season, there was a significant upward trend in temperature across northwest China. The most obvious trend, ranged between 0.4 °C–0.6 °C per decade, was observed in northern Qinghai and northeastern Xinjiang. On a per-month basis, the average temperature increased in all months, with April showing a maximum rate of 0.41 °C per decade. (2) The amount of precipitation in the growing season increased in most regions, especially in western Xinjiang and Qinghai. The areas of reduced precipitation were mainly concentrated in the south of Gansu and Ningxia, the west of Shaanxi and the northeast of Xinjiang. The average precipitation also increased in all months, with June showing a maximum change rate of 1.8 mm per decade. (3) There were obvious spatial differences in the climate trends in northwest China with the warming shifting from areas of bare land to grassland and the trend towards increased rainfall shifting from grassland to bare land. These effects were most obvious in Xinjiang's Tarim Basin. (4) From 1959 to 2019, the degree of drought in northwest China significantly decreased in most areas. The areas where drought decreased and precipitation increased were fairly consistent, which produced a strong spatial correlation between them.

Dental caries is characterized by a dysbiotic shift at the biofilm–tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study’s analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)–machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10−3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10−6) and N-acetylneuraminate (p = 6.8 × 10−6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10−6) and epicatechin (P = 2.9 ×