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Purpose Flexible mechanical gripper has better safety and adaptability than a rigid mechanical hand. At present, there are few soft grippers for small objects on a millimeter scale. Therefore, the purpose of this paper is to design a soft pneumatic gripper for grasping millimeter-scale small and fragile objects such as jewelry and electronic components. Design/methodology/approach By simulating the clamping action of the bird’s mouth and combining the high flexibility of the soft material, the bird’s beak soft pneumatic gripper is designed. First, the internal cavity of the gripping end of the gripper is determined by bending deformation calculation, and the brief manufacturing process of the gripper is outlined. Then, the single finger of the soft gripper is modeled mechanically, and the relationship between air pressure and bending deformation of the single finger is obtained. Finally, the experimental platform of the soft mechanical gripper is built, and the gripping performance of silicone rubber material is tested by comparison test, bending deformation test, stability test, adaptability test and gripping accuracy test. Findings The designed gripper has the advantages of simple structure, convenient operation, easy grasping of different small objects of millimeter-scale and good adaptability. It can grasp the precise dispensing needle with a minimum diameter of 0.19 mm, and its accuracy meets daily use. Originality/value A new type of soft pneumatic, the mechanical gripper is proposed and manufactured. According to the shape of the bird’s beak and the calculation of bending performance, a hollow finger gripper with better bending performance is designed. Various test results show that the gripper has a significant clamping effect on millimeter small objects, which supplements the research field of millimeter small object gripper.

For the sake of comprehend the influence of locally enhanced heat dissipation of the polar on Li-ion power batteries, coupling the thermal effect of anode and cathode, the heat generation model of Li-ion battery with different discharge magnification is obtained. Based on this model, the discharge process simulation analysis of the single battery is carried out and compared with the experimental results. The experiment results show that battery polarity heat effect has great influence on the temperature field distribution of the battery. Then, a locally enhanced heat dissipation structure is set up near the polar region and a comparative experiment is carried out by changing the discharge rate. Although the heat pipe can only slightly improve the discharge capacity of lithium ion battery by enhancing the heat dissipation capability of polarity, the heat generated under low discharge rate can be reduced by using the heat pipe with low emission rate, the heat generated by the polar region can be effectively and timely exported to reduce the temperature of the polar region and it can greatly reduce the overall temperature of the battery. Then, according to the thermal characteristics and the results of locally enhanced heat dissipation analysis, a new battery module is designed and simulated. And the prototype is completed and tested. The results show that this method can effectively reduce the temperature rise and temperature difference of the battery module by using locally enhanced heat dissipation structure. Finally, combined with the simulation and the experimental results, some useful suggestions are put forward for the design and manufacture of battery modules and battery boxes.

BackgroundHealth interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer.ObjectiveThis study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer.MethodsWe described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features.ResultsWe observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier.ConclusionsThe results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.

Early detection of influenza-like symptoms can prevent widespread flu viruses and enable timely treatments, particularly in the post-pandemic era. Mobile sensing leverages an increasingly diverse set of embedded sensors to capture fine-grained information of human behaviors and ambient contexts, and can serve as a promising solution for influenza-like symptom recognition. Traditionally, handcrafted and high level features of mobile sensing data are extracted by manual feature engineering and convolutional/recurrent neural network respectively. In this work, we apply graph representation to encode the dynamics of state transitions and internal dependencies in human behaviors, leverage graph embeddings to automatically extract the topological and spatial features from graph inputs, and propose an end-to-end graph neural network (GNN) model with multi-channel mobile sensing input for influenzalike symptom recognition based on people's daily mobility, social interactions, and physical activities. Using data generated from 448 participants, we show that GNN with GraphSAGE convolutional layers significantly outperforms baseline models with handcrafted features. Furthermore, we use GNN interpretability method to generate insights (e.g., important nodes and graph structures) about the importance of mobile sensing for recognizing Influenza-like symptoms. To the best of our knowledge, this is the first work that applies graph representation and graph neural network on mobile sensing data for graph-based human behavior modeling and health symptoms prediction.

The high voltage direct hanging energy storage system can effectively solve the problems of fluctuation and intermittence caused by environmental factors, and improve the ability of power system to absorb new energy. By controlling the energy storage, the new energy station has certain inertia and damping characteristics, so that the new energy power station can be connected to the grid friendlier. Starting from the time scale division method of inertial response control, this paper studies the energy demand of inertial response of large-scale new energy power station in different time scales, and gives the inertial response control strategy under different time scales. The model of high voltage direct hanging energy storage system is established, and the inertia response characteristics control technology is verified.

Objective: To investigate the implementation effects of new paths and methods of occupational ethics education for medical students in higher vocational colleges. Methods: A total of 668 students in higher vocational colleges were selected as the study subjects, and divided into traditional group (n=349) and innovative group (n=319) in accordance with their grades. The data pertaining to the students’ views on the educational model of medical and occupational ethics, occupational passion, spirit of occupational risk, occupational ethics and codes, and doctor-patient rights and obligations and communication abilities and skills in the two groups were compared, so as to explore the influences of new paths and methods of occupational ethics education on the occupational ethics of medical students in higher vocational colleges. Results: The study results showed that the innovative group was significantly superior to the traditional group in the satisfaction with the courses of occupational ethics, pedagogical effects, occupational dedication and passion, doctor-patient communication ability, the fulfillment of doctor-patient rights and obligations, and the cognition of occupational ethics and codes (P < 0.05). Conclusion: The implementation of innovative methods of occupational ethics education for medical students in higher vocational colleges can produce significant feedback effects. The improved teachers’ abilities and pedagogical methods, innovative teaching approaches and optimized curriculum can effectively improve the occupational ethics of medical students in higher vocational colleges.

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.

Delivering treatment recommendations via pervasive electronic devices such as mobile phones has the potential to be a viable and scalable treatment medium for long-term health behavior management. But active experimentation of treatment options can be time-consuming, expensive and altogether unethical in some cases. There is a growing interest in methodological approaches that allow an experimenter to learn and evaluate the usefulness of a new treatment strategy before deployment. We present the first development of a treatment recommender system for emotion regulation using real-world historical mobile digital data from n = 114 high socially anxious participants to test the usefulness of new emotion regulation strategies. We explore a number of offline contextual bandits estimators for learning and propose a general framework for learning algorithms. Our experimentation shows that the proposed doubly robust offline learning algorithms performed significantly better than baseline approaches, suggesting that this type of recommender algorithm could improve emotion regulation. Given that emotion regulation is impaired across many mental illnesses and such a recommender algorithm could be scaled up easily, this approach holds potential to increase access to treatment for many people. We also share some insights that allow us to translate contextual bandit models to this complex real-world data, including which contextual features appear to be most important for predicting emotion regulation strategy effectiveness., Accepted at RecSys 2020

BackgroundIn December 2019, human infection with a novel coronavirus, known as SARS-CoV-2, was identified in Wuhan, China. The mortality of critical illness was high in Wuhan. Information about critically ill patients with SARS-CoV-2 infection outside of Wuhan is scarce. We aimed to provide the clinical features, treatment, and prognosis of the critically ill patients with SARS-CoV-2 infection in Guangdong Province.MethodsIn this multi-centered, retrospective, observational study, we enrolled critically ill patients with SARS-CoV-2 pneumonia who were admitted to the intensive care unit (ICU) in Guangdong Province. Demographic data, symptoms, laboratory findings, comorbidities, treatments, and prognosis were collected. Data were compared between patients with and without intubation.ResultsForty-five critically ill patients with SARS-CoV-2 pneumonia were identified in 7 ICUs in Guangdong Province. The mean age was 56.7 years, and 29 patients (64.4%) were men. The most common symptoms at the onset of illness were high fever and cough. Majority of patients presented with lymphopenia and elevated lactate dehydrogenase. Treatment with antiviral drugs was initiated in all the patients. Thirty-seven patients (82.2%) had developed acute respiratory distress syndrome, and 13 (28.9%) septic shock. A total of 20 (44.4%) patients required intubation and 9 (20%) required extracorporeal membrane oxygenation. As of February 28th 2020, only one patient (2.2%) had died and half of them had discharged of ICU.ConclusionsInfection with SARS-CoV-2 in critical illness is characterized by fever, lymphopenia, acute respiratory failure and multiple organ dysfunction. Compared with critically ill patients infected with SARS-CoV-2 in Wuhan, the mortality of critically ill patients in Guangdong Province was relatively low. These data provide some general understandings and experience for the critical patients with SARS-CoV-2 outside of Wuhan.

Medical research has found strong connections between cognitive stress and various physical and mental health conditions. This paper addresses the need for more timely, less obtrusive measurements of cognitive stress. Given recent advances in and uptake of smartphone technology, we hypothesize significant correlations between a subject׳s smart-phone Bluetooth encounter networks and their cognitive stress levels. A review of related research indicates a lack of systematic feature engineering for these networks as well as evidence of their predictive power in mental health outcomes. We base our feature engineering on structural, edge, and neighbor attributes that incorporate network analysis measures as well as social and temporal commonalities. These features offer significant improvement in goodness-of-fit and prediction performance (AUC) for momentary stress outcomes in multiple experimental and statistical evaluation settings. Our results indicate potential value of incorporating Bluetooth encounter data into mental health monitoring practice via mobile sensing technology.

Background Alstonia scholaris is a folk medicine used to treat cough, asthma and chronic obstructive pulmonary disease in China. Total alkaloids (TA) from A. scholaris exhibit anti-inflammatory properties in acute respiratory disease, which suggests their possible anti-inflammatory effect on influenza virus infection. Purpose To assess the clinical use of TA by demonstrating their anti-influenza and anti-inflammatory effects and the possible mechanism underlying the effect of TA on influenza A virus (IAV) infection in vitro and to reveal the inhibitory effect of TA on lung immunopathology caused by IAV infection. Methods Antiviral and anti-inflammatory activities were assessed in Madin-Darby canine kidney (MDCK) and A549 cells and U937-derived macrophages infected with influenza A/PR/8/34 (H1N1) virus. Proinflammatory cytokine levels were measured by real-time quantitative PCR and Bio-Plex assays. The activation of innate immune signaling induced by H1N1 virus in the absence or presence of TA was detected in A549 cells by Western blot. Furthermore, mice were infected intranasally with H1N1 virus and treated with TA (50, 25 and 12.5 mg/kg/d) or oseltamivir (60 mg/kg/d) for 5 days in vivo. The survival rates and body weight were recorded, and the viral titer, proinflammatory cytokine levels, innate immune cell populations and histopathological changes in the lungs were analyzed. Results TA significantly inhibited viral replication in A549 cells and U937-derived macrophages and markedly reduced cytokine and chemokine production at the mRNA and protein levels. Furthermore, TA blocked the activation of pattern recognition receptor (PRR)- and IFN-activated signal transduction in A549 cells. Critically, TA also increased the survival rate, reduced the viral titer, suppressed proinflammatory cytokine production and innate immune cell infiltration and improved lung histopathology in a lethal PR8 mouse model. Conclusion TA exhibits anti-viral and anti-inflammatory effects against IAV infection by interfering with PRR- and IFN-activated signal transduction.

Despite the proliferation of mobile interventions that are delivered through smartphones, and their potential impact on promoting mental health in chronic health populations, little research has examined how to leverage the advanced technological capabilities of smartphones to also monitor response to app-based interventions. Current methods used to evaluate response to digital interventions rely on static and retrospective self-report measures to infer crucial behavioral and affective patterns, which provide little contextual information and limits generalizability. Importantly, this approach can also increase burden among chronic health populations that are already experiencing a heavy treatment load. Using data collected from 7 recently diagnosed breast cancer patients undergoing active cancer treatment (average time from initial diagnosis to study enrollment is 13 days), we propose a framework for integrating self-report surveys and fine-grained mobile sensing data. Preliminary results demonstrate the feasibility and utility of this framework to reduce burden and improve detection of mood in response to an app-based intervention among chronic health populations.

Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives., BHI2018

Blood flow during ECMO, changes in IPPV parameters and physiological indicators pre-ECMO and during ECMO. (DOCX 107Â kb)

ECMO Cases Per Year for Each Hospital. (DOCX 60Â kb)

The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples. In this study, a total of 300 biomarkers were selected from the microbiome of 806 Chinese individuals (383 controls, 170 with type 2 diabetes, 130 with rheumatoid arthritis, and 123 with liver cirrhosis), and then logistic regression prediction algorithm was applied to those markers as the model intrinsic features. The estimated model produced an F1 score of 0.9142, which was better than other popular classification methods, and an average receiver operating characteristic (ROC) of 0.9475 showed a significant correlation between these selected biomarkers from microbiome and corresponding phenotypes. The results from this study indicate that machine learning is a vital tool in data mining from microbiome in order to identify disease-related biomarkers, which may contribute to the application of microbiome-based precision medicine in the future.

Sensor-based activity recognition seeks to provide higher-level knowledge about human activities from multiple sensors such as accelerometer and gyroscope. Thanks to growing ubiquity of sensor-rich smartphones and wearable devices, activity recognition research has made tremendous progress in recent years. Many sensors, such as motion sensors and cameras, have been previously used for this task. In this paper, we present the first work to recognize human activities solely from photoplethysmography (PPG) data. PPG sensors use a light-based technology to sense the rate of blood flow as controlled by the heart's pumping action. Although they are not originally intended to sense body motions and gestures, in this paper, we propose a novel method to extract meaningful features from the PPG to predict human activities. We exploit PPG signals' susceptibility to corruption by noise introduced by motion artifact and extract, as opposed to discard, the motion artifact signals combined with cardiac and respiratory signals to predict the types of activities performed by users. We combine convolutional and recurrent neural networks to predict different types of daily activities (e.g., walking, running, jumping) from the raw PPG signal. Results using data generated by wrist-worn smartwatches of 12 participants demonstrate the feasibility of our approach at predicting five types of activities (standing, walking, jogging, jumping, and sitting), and highlight new insights about how to use PPG sensors for human activity recognition. Results also highlight the importance of extracting motion artifact signals to understand human behaviors.

A stiffened cylindrical shell is normally used in refuge chambers of a coal mine. Based on the method of application and shape characteristics of a refuge chamber, we simplified its shell as an orthotropic cylinder. The basic buckling equation of the stiffened cylindrical shell under uniform axial compression was deduced by using a Donnell function. The factors affecting its buckling capacity were studied by theoretical analysis and numerical calculations. The results reveal that the torsional rigidity of the longitudinal stiffener had little effect on the buckling capacity of the shell and that the critical load of an externally stiffened cylindrical shell is higher than that of an internally stiffened cylindrical shell. Keywords: Refuge chamber, Buckling, Axial compression, Stiffened cylindrical shell

Type 2 diabetes, which is a complex metabolic disease influenced by genetic and environment, has become a worldwide problem. Previous published results focused on genetic components through genome-wide association studies that just interpret this disease to some extent. Recently, two research groups published metagenome-wide association studies (MGWAS) result that found meta-biomarkers related with type 2 diabetes. However, One key problem of analyzing genomic data is that how to deal with the ultra-high dimensionality of features. From a statistical viewpoint it is challenging to filter true factors in high dimensional data. Various methods and techniques have been proposed on this issue, which can only achieve limited prediction performance and poor interpretability. New statistical procedure with higher performance and clear interpretability is appealing in analyzing high dimensional data. To address this problem, we apply an excellent statistical variable selection procedure called iterative sure independence screening to gene profiles that obtained from metagenome sequencing, and 48/24 meta-markers were selected in Chinese/European cohorts as predictors with 0.97/0.99 accuracy in AUC (area under the curve), which showed a better performance than other model selection methods, respectively. These results demonstrate the power and utility of data mining technologies within the large-scale and ultra-high dimensional genomic-related dataset for diagnostic and predictive markers identifying.