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openaire: EC/H2020/725513/EU//SuperRepel A myriad of natural surfaces such as plant leaves and insect wings can repel water and remain unwetted inspiring scientists and engineers to develop water-repellent surfaces for various applications. Those natural and artificial water-repellent surfaces are typically opaque, containing micro- and nano-roughness, and their wetting properties are determined by the details at the actual liquid-solid interface. However, a generally applicable way to directly observe moving contact lines on opaque water-repellent surfaces is missing. Here, we show that the advancing and receding contact lines and corresponding contact area on micro- and nano-rough water-repellent surfaces can be readily and reproducibly quantified using a transparent droplet probe. Combined with a conventional optical microscope, we quantify the progression of the apparent contact area and apparent contact line irregularity in different types of superhydrophobic silicon nanograss surfaces. Contact angles near 180° can be determined with an uncertainty as low as 0.2°, that a conventional contact angle goniometer cannot distinguish. We also identify the pinning/depinning sequences of a pillared model surface with excellent repeatability and quantify the progression of the apparent contact interface and contact angle of natural plant leaves with irregular surface topography.

This paper reports the control method for the core power of the China initiative Accelerator Driven System (CiADS) facility. In the CiADS facility, an intense external neutron source provided by a proton accelerator coupled to a spallation target is used to drive a sub-critical reactor. Without any control rod inside the sub-critical reactor, the core power is controlled by adjusting the proton beam intensity. In order to continuously change the beam intensity, an adjustable aperture is considered to be used at the Low Energy Beam Transport (LEBT) line of the accelerator. The aperture size is adjusted based on the Proportional Integral Derivative (PID) controllers, by comparing either the setting beam intensity or the setting core power with the measured value. To evaluate the proposed control method, a CiADS core model is built based on the point reactor kinetics model with six delayed neutron groups. The simulations based on the CiADS core model have indicated that the core power can be controlled stably by adjusting the aperture size. The response time in the adjustment of the core power depends mainly on the adjustment time of the beam intensity.

Drug–target interaction (DTI) prediction can identify novel ligands for specific protein targets, and facilitate the rapid screening of effective new drug candidates to speed up the drug discovery process. However, the current methods are not sensitive enough to complex topological structures, and complicated relations between multiple node types are not fully captured yet. To address the above challenges, we construct a metapath-based heterogeneous bioinformatics network, and then propose a DTI prediction method with metapath-based hierarchical transformer and attention network for drug–target interaction prediction (MHTAN-DTI), applying metapath instance-level transformer, single-semantic attention and multi-semantic attention to generate low-dimensional vector representations of drugs and proteins. Metapath instance-level transformer performs internal aggregation on the metapath instances, and models global context information to capture long-range dependencies. Single-semantic attention learns the semantics of a certain metapath type, introduces the central node weight and assigns different weights to different metapath instances to obtain the semantic-specific node embedding. Multi-semantic attention captures the importance of different metapath types and performs weighted fusion to attain the final node embedding. The hierarchical transformer and attention network weakens the influence of noise data on the DTI prediction results, and enhances the robustness and generalization ability of MHTAN-DTI. Compared with the state-of-the-art DTI prediction methods, MHTAN-DTI achieves significant performance improvements. In addition, we also conduct sufficient ablation studies and visualize the experimental results. All the results demonstrate that MHTAN-DTI can offer a powerful and interpretable tool for integrating heterogeneous information to predict DTIs and provide new insights into drug discovery.

The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or erroneous reporting in FDMRS, which significantly increase the cost of related epidemic investigations. To solve this problem, we designed a model to identify suspected outbreaks from the data generated by the FDMRS of CFSA. In this study, machine learning models were used to fit the data. The recall rate and F1-score were used as evaluation metrics to compare the classification performance of each model. Feature importance and pathogenic factors were identified and analyzed using tree-based and gradient boosting models. Three real foodborne disease outbreaks were then used to evaluate the best performing model. Furthermore, the SHapley Additive exPlanation value was used to identify the effect of features. Among all machine learning classification models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with the highest recall rate and F1-score of 0.9699 and 0.9582, respectively. In terms of model validation, the model provides a correct judgment of real outbreaks. In the feature importance analysis with the XGBoost model, the health status of the other people with the same exposure has the highest weight, reaching 0.65. The machine learning model built in this study exhibits high accuracy in recognizing foodborne disease outbreaks, thus reducing the manual burden for medical staff. The model helped us identify the confounding factors of foodborne disease outbreaks. Attention should be paid not only to the health status of those with the same exposure but also to the similarity of the cases in time and space.

Background Foodborne disease is a common threat to human health worldwide, leading to millions of deaths every year. Thus, the accurate prediction foodborne disease risk is very urgent and of great importance for public health management. Objective We aimed to design a spatial–temporal risk prediction model suitable for predicting foodborne disease risks in various regions, to provide guidance for the prevention and control of foodborne diseases. Methods We designed a novel end-to-end framework to predict foodborne disease risk by using a multigraph structural long short-term memory neural network, which can utilize an encoder–decoder to achieve multistep prediction. In particular, to capture multiple spatial correlations, we divided regions by administrative area and constructed adjacent graphs with metrics that included region proximity, historical data similarity, regional function similarity, and exposure food similarity. We also integrated an attention mechanism in both spatial and temporal dimensions, as well as external factors, to refine prediction accuracy. We validated our model with a long-term real-world foodborne disease data set, comprising data from 2015 to 2019 from multiple provinces in China. Results Our model can achieve F1 scores of 0.822, 0.679, 0.709, and 0.720 for single-month forecasts for the provinces of Beijing, Zhejiang, Shanxi and Hebei, respectively, and the highest F1 score was 20% higher than the best results of the other models. The experimental results clearly demonstrated that our approach can outperform other state-of-the-art models, with a margin. Conclusions The spatial–temporal risk prediction model can take into account the spatial–temporal characteristics of foodborne disease data and accurately determine future disease spatial–temporal risks, thereby providing support for the prevention and risk assessment of foodborne disease.

BACKGROUND Foodborne disease is a common threat to human health worldwide, leading to millions of deaths every year. Thus, the accurate prediction foodborne disease risk is very urgent and of great importance for public health management. OBJECTIVE We aimed to design a spatial–temporal risk prediction model suitable for predicting foodborne disease risks in various regions, to provide guidance for the prevention and control of foodborne diseases. METHODS We designed a novel end-to-end framework to predict foodborne disease risk by using a multigraph structural long short-term memory neural network, which can utilize an encoder–decoder to achieve multistep prediction. In particular, to capture multiple spatial correlations, we divided regions by administrative area and constructed adjacent graphs with metrics that included region proximity, historical data similarity, regional function similarity, and exposure food similarity. We also integrated an attention mechanism in both spatial and temporal dimensions, as well as external factors, to refine prediction accuracy. We validated our model with a long-term real-world foodborne disease data set, comprising data from 2015 to 2019 from multiple provinces in China. RESULTS Our model can achieve F1 scores of 0.822, 0.679, 0.709, and 0.720 for single-month forecasts for the provinces of Beijing, Zhejiang, Shanxi and Hebei, respectively, and the highest F1 score was 20% higher than the best results of the other models. The experimental results clearly demonstrated that our approach can outperform other state-of-the-art models, with a margin. CONCLUSIONS The spatial–temporal risk prediction model can take into account the spatial–temporal characteristics of foodborne disease data and accurately determine future disease spatial–temporal risks, thereby providing support for the prevention and risk assessment of foodborne disease.

BACKGROUND Sepsis is a severe medical condition. Approximately 0.75 million people are diagnosed with sepsis in the USA annually. Several of anti-inflammatory drugs are used to manage sepsis, but with a very low success rate. This study examined the possible protective effects of a naturally occurring flavanone, quercetin, in a rat model of sepsis. MATERIAL AND METHODS The study was carried out using Wistar albino rats. Sepsis was induced by cecal ligation and puncture methods. Histological analysis was performed by hematoxylin and eosin (HE) staining. Reactive oxygen species (ROS) levels were determined by flow cytometery. Superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) activities were determined by standard assays. Protein expression was determined by Western blot analysis. RESULTS The results showed that quercetin reduced the tissue edema, congestion, and hemorrhage, increased the alveolar volume, and helped to maintain the lung anatomy of septic rats. Admistration of quercetin at the dosage of 15 and 20 mg/kg to septic rats caused significant reduction in the ROS levels. The activities and the expression of SOD, CAT, and APX were significantly decreased upon administration of quercetin in the septic rats at the dosage of 15 and 20 mg/kg. The effects of quercetin were also examined on the expression of the High mobility group box 1 (HMGB1) protein in septic rats. The results showed that quercetin caused a significant decrease in HMGB1 protein levels. CONCLUSIONS The findings of this study suggest that quercetin has therapeutic potential in the treatment of sepsis.

BACKGROUND Sepsis is a devastating medical condition. In the USA, about 745 000 people are diagnosed with sepsis annually. Although many anti-inflammatory drugs have been used to manage sepsis, the treatment success rate is very low. This study was undertaken to examine the protective effects of naringenin on sepsis-induced kidney injury in rats. MATERIAL AND METHODS Sepsis was induced in Wistar albino rats by cecal ligation and puncture methods. Histological analysis was performed with hematoxylin and eosin (HE) staining. Reactive oxygen species (ROS) levels were determined by flow cytometery. TUNEL assay was used to demonstrate apoptosis. Sandwich ELISA method was used for the determination of urinary angiotensinogen, and protein expression was determined by Western blot analysis. RESULTS We found that naringenin decreased atrophy in the glomerulus and enabled maintenance of the capsule area and normal tubular cavity of the septic rats. Admistration of naringenin at the dosage of 10 and 20 mg/kg to sepsis rats caused significant reduction in the sepsis-induced apoptosis of kidney cells, accompanied by decrease in Bax and increase in Bcl-2 expression. Moreover, naringenin also decreased the ROS levels in septic rats and downregulated the expression of SOD, CAT, and APX. The effects of naringenin were also examined on the levels of urinary angiotensinogen in sepsis rats. We found that naringenin caused a significant decrease in urinary angiotensinogen levels of septic rats. CONCLUSIONS Naringenin appears to have potential in the treatment of sepsis.

In an accelerator driven sub-critical system (ADS), a spallation target is irradiated by high-power proton beam to drive a sub-critical reactor. To continuously produce very intense neutrons, it is very important to prevent the unsafe operation of the target. This paper reports an over-temperature protection system for the spallation target in China initiative ADS system, where tungsten granules in the target container are used as both coolant and target materials. In order to protect the target from abnormal irradiation by the accelerator's beam when the granules are blocked or when the beam deviates from the target center, 12 sets of thermocouples are mounted inside the target container to monitor the target temperature. To evaluate the protection functions of these thermocouples, the heat transfer model of particles has been employed to investigate the spatio-temporal variations of the target temperatures when tungsten granules stay static in the target. The simulation results indicate that the temperatures of the thermocouples increase with the irradiation time and the granular blockage can be detected by analyzing the temperatures of these thermocouples. On the other hand, when the beam center deviates from the target center, the azimuthal distribution of the target temperature becomes non-uniform, and the temperature of the thermocouple close to the beam center increases faster. Finally, in the monitoring system for the over-temperature protection, 12 thermocouples divided into 3 groups which are fixed within the target in the upper, central and lower parts of the target, and 4 thermocouples in each group are located in four directions of east, south, west, and north. If two or more channels of four directions in one group exceed the set point, the protective function of granular blockage will be initiated; if two or more channels in one direction are greater than the set point, the protective function of beam deviation will be triggered.

In an accelerator driven critical or sub-critical system (ADS), a spallation target is irradiated by high-power proton beam to drive a reactor. To continuously produce very intense neutrons, it is very important to ensure that the target operates in a setting temperature environment. This paper reports several techniques of temperature control for the spallation target in China initiative ADS system, where tungsten granules in the target container are used as both coolant and target materials. In order to avoid high temperatures at the target center due to the Gaussian distribution of beam intensity, the proton beam should be scanned on the surface of the target to provide a high degree of uniformity in energy deposition. The target temperature can be controlled by adjusting automatically the speed of the coolant. If the speed attains the maximum setting value, the beam intensity will be decreased for a given value to reduce the heat generation. To evaluate the proposed control techniques, the heat transfer model of particles has been employed to investigate the spatio-temporal variations of the target temperatures. The simulation results indicate that the beam scanning technique reduces dramatically the temperatures. The target temperatures can be controlled by adjusting automatically both the granular speed and the beam intensity. The target can operate normally in the setting working temperature range even if the target is irradiated by the proton beam with the maximum power.

The fundamental understanding of the icephobic performance of two model superhydrophobic surfaces (lotus and petal surfaces) is important for many industrial applications. The effect of wettability and surface energy on ice adhesion has recently received attention. However, the verification of icephobicity is typically carried out with a single observation method. For this study, rough modified rod-coated surfaces, structured polymer surfaces and nine smooth surfaces with tunable wettability were fabricated. Combining several observation techniques of icephobicity indicate that petal and lotus surfaces lose their superhydrophobicity, being less useful for ice repellency due to poor humidity tolerance in condensation conditions. The ice adhesion of rough surfaces enhanced with increasing hydrophobicity. The increased interface area leads to a reduction of the icephobic performance. The stress concentrator on the lotus surface was better than on the petal surface. In addition, the role of dynamic wettability on ice adhesion was checked. Smooth hydrophobic surfaces were found to be better icephobic materials compared to rough surfaces. The intrinsic surface energy of smooth surfaces is significantly linear with the ice adhesion. High freezing delay times were found for smooth low-density polyethylene resin and fluorinated surfaces.

The gravity-driven dense granular target (DGT) was proposed as a new concept for a high-power spallation target in China initiative Accelerator Driven System (CiADS). A DGT has several attractive features, coexisting with some new challenges such as the inner temperature measurement for the target. It has been analyzed and proved that a traditional method cannot be used for the inner temperature measurement. A natural viewpoint is that measuring the temperature by means of the other physical quantities. In this paper, the minimum velocity distribution along the radial direction was firstly gained by investigating the relationship between the velocity and energy deposited in grains. Then, the relationship between the temperature increment and flow rate was studied based on the velocity and flow characteristics of grains in the target. At the same time, the temperature field at a specific flow rate was also studied. The method was finally proved feasible and practical theoretically. The trial of the research thought is new and also suitable for other cases similar with the one in this paper.

BACKGROUND Foodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life. OBJECTIVE We aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested. METHODS We extracted features such as space, time, and exposed food from foodborne disease case data and analyzed the relationship between these features and the foodborne disease pathogens using a variety of machine learning methods to classify foodborne disease pathogens. We compared the results of 4 models to obtain the pathogen prediction model with the highest accuracy. RESULTS The gradient boost decision tree model obtained the highest accuracy, with accuracy approaching 69% in identifying 4 pathogens including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. By evaluating the importance of features such as time of illness, geographical longitude and latitude, and diarrhea frequency, we found that they play important roles in classifying the foodborne disease pathogens. CONCLUSIONS Data analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.

China initiative Accelerator Driven System (CiADS) has been approved as a strategic plan to build an ADS demonstration facility in the next few years. It proposed a new concept for a high-power spallation target: the gravity-driven dense granular target (DGT). As the same with a monolithic target (MT), both solid and liquid target, energy deposition and neutron flux are two critical issues. In this paper, we focus on these two issues and long for some valuable results for the project. Unlike a solid target, the internal geometry structure of a DGT is very complicated. To be as much as closer with the reality, we designed an algorithm and firstly packed the grains randomly in a cylindrical container in GEANT4 software. The packing result was in great agreement with the experimentally measured results. It shows that the algorithm is practicable. In the next step, all the simulations about energy deposition and neutron flux of a DGT were performed with the GEANT4 codes, and the results were compared with the data of a MT. Compared to a MT, a DGT has inarguable advantages in both terms of energy deposition and neutron flux. In addition, the simulations with different radius of grains were also performed. Finally, we found that both the energy deposition and neutron flux are nearly irrelevant to the radius of the grains in the range of 0.5 mm–5 mm when the packing density is same by analyzing the results meticulously.

TOPIC: Education, Research, and Quality Improvement TYPE: Original Investigations PURPOSE: To describe the development, implementation, and learner engagement in a longitudinal international remote critical care continuing medical education program in China. METHODS: Based on the Mayo Clinic Checklist for Early Recognition and Treatment of Acute and Illness and iNjury (CERTAIN) program, we designed and delivered a longitudinal 40-week remote coaching program for a community-based teaching hospital in Shandong Province, China. Based on a mixed-methods needs assessment that included learner feedback using an exploratory sequential design and ICU process and outcomes data, we developed a curriculum that included asynchronous, online learning modules with multiple language captions and weekly remote education sessions using a blend of didactic presentations on common critical care syndromes, virtual simulation, journal club, and case-based discussions. Faculty included a diverse group of critical care experts and bilingual facilitators. The program also included clinical research and quality improvement workshops to facilitate implementation of key concepts identified during these activities. Participants completed a survey using a 5 point anchored Likert scale after each educational activity to provide feedback and guide course improvement. RESULTS: Twenty-two Chinese clinicians (18 physicians, 4 nurses) enrolled in this CERTAIN longitudinal program. Learners completed a total of 163 hours (mean 7.4 hr/learner) asynchronous online learning, and to date have completed 23 weekly education sessions (total 28 hours, including an extended virtual simulation experience). Survey response rate was 56%. Learners reported a high rate of overall satisfaction with the course (112, 61% Excellent;55 (30%) Very Good) and specific topic discussions (110,60% Excellent;57, 31% Very Good), with steady improvement over time. Perceived practice relevance was also high (110, 59% Excellent;55,30% Very Good), and this effort has informed ongoing local performance improvement initiatives. CONCLUSIONS: Remote delivery of longitudinal critical care continuing education program in China using asynchronous learning, case-based discussion, and virtual simulation is feasible, associated with a high rate of learner satisfaction, and increases engagement in quality improvement initiatives. This innovative global education initiative offers an important potential solution to strengthen critical care services in remote, resource-limited settings at low cost, especially during the ongoing COVID-19 pandemic. CLINICAL IMPLICATIONS: The World Health Organization has identified remote education programs as a priority to strengthen international critical care services and better meet growing global clinical demand. The best method to effectively deliver continuing medical education to international healthcare providers with unique cultural, organizational, and practice backgrounds is not well defined. DISCLOSURES: no disclosure on file for Wenjuan Cui;No relevant relationships by Yue Dong, source=Web Response Patent/IP rights for a licensed product relationship with Ambient Clinical Analytics Please note: From 2016 Added 05/23/2021 by Ognjen Gajic, source=Web Response, value=Royalty No relevant relationships by Heyi Li, source=Web Response No relevant relationships by Alexander Niven, source=Web Response no disclosure on file for Lujun Qiao;No relevant relationships by Yuqiang Sun, source=Web Response no disclosure on file for Qingzhong Yuan