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BackgroundLaparoscopic sleeve gastrectomy (LSG) is a sustainable technique that effectively treats morbid obesity. However, the molecular mechanisms underlying the improvement of metabolic health following this process warrants more investigation. This study investigates LSG-related molecules and uses bulk RNA-sequencing high-throughput analysis to unravel their regulatory mechanisms.MethodsPeripheral blood mononuclear cells (PBMC) were collected from ten obese patients with BMI ≥ 32.5 kg/m2 in the Department of General Surgery of Kunming First People’s Hospital. After LSG, patients were followed up for one month, and blood samples were retaken. Blood samples from ten patients before and after LSG and bulk RNA-Seq data were analyzed in this study. LSG-associated gene expression was detected by weighted gene coexpression network analysis (WGCNA) and differential analysis. Subsequently, essential signature genes were identified using logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were utilized to reveal the potential functions of the target genes. Furthermore, the Pearson correlation of signature genes with leptin and lipocalin was also explored. Finally, we constructed a robust endogenous RNA (ceRNA) network based on miRWalk and starBase databases.ResultsWe identified 18 overlapping genes from 91 hub genes, and 165 differentially expressed mRNAs (DE-mRNA), which were revealed to be significantly associated with immune cells, immune response, inflammatory response, lipid storage, and localization upon functional enrichment analysis. Three signature genes, IRF1, NFKBIA, and YRDC, were identified from the 18 overlapping genes by LASSO and SVM-REF algorithms. The logistic regression model based on the three signature genes highlighted how robustly they discriminated between samples. ssGSEA indicated these genes to be involved in lipid metabolism and degradation pathways. Moreover, leptin levels were significantly reduced in patients undergoing LSG, and NFKBIA significantly negatively correlated with leptin. Finally, we identified how the long non-coding RNA (lncRNA) ATP2B1-AS1 regulated the expression of the signature genes by competitively binding to six microRNAs (miRNAs), which were hsa-miR-6509-5p, hsa-miR-330-5P, hsa-miR-154-5P, hsa-miR-145-5P, hsa-miR4726-5P and hsa-miR-134-5P.ConclusionThis study identified three critical regulatory genes significantly differentiated between patients before and after LSG treatment and highlighted their potentially crucial role after bariatric surgery. This provides novel insights to increase our understanding of the underlying mechanisms of weight loss and associated metabolic improvement after bariatric surgery.

Based on people’s enthusiasm for swimming and their pursuit of swimming skill at a higher level, this paper proposes a non-contact motion guidance system based on optical motion capture technology, which aims to improve their swimming technique conveniently, scientifically and quickly. The swimming actions can be captured using one smartphone underwater, and the app on the phone will process the video and give suggestions based on the reference postures derived from statistics of professional swimmers for improvement of the swimming performance. In this system, the Canny operator was applied to detect the edge of the image captured from the swimming video, and the Hough line was implemented to recognize and locate the key position information which refers to the angle between knee and hip joints, and the angle between the trunk and horizontal plane. Furthermore, by comparing the key position information with the data of standard swimming, improvement measures can be given to improve the swimming skills of swimmers. As the result of the experiments, the system can accurately capture the swimmer’s movement state, locate the incorrect posture of the swimmer, and provide the corrective information in real-time. Although the paper only took breaststroke as an example, the proposed method can be extended to the other swimming styles.

International audience; The Jiangmen Underground Neutrino Observatory (JUNO), the first multi-kton liquid scintillator detector, which is under construction in China, will have a unique potential to perform a real-time measurement of solar neutrinos well below the few MeV threshold typical for Water Cherenkov detectors. JUNO's large target mass and excellent energy resolution are prerequisites for reaching unprecedented levels of precision. In this paper, we provide estimation of the JUNO sensitivity to 7Be, pep, and CNO solar neutrinos that can be obtained via a spectral analysis above the 0.45 MeV threshold. This study is performed assuming different scenarios of the liquid scintillator radiopurity, ranging from the most opti mistic one corresponding to the radiopurity levels obtained by the Borexino experiment, up to the minimum requirements needed to perform the neutrino mass ordering determination with reactor antineutrinos - the main goal of JUNO. Our study shows that in most scenarios, JUNO will be able to improve the current best measurements on 7Be, pep, and CNO solar neutrino fluxes. We also perform a study on the JUNO capability to detect periodical time variations in the solar neutrino flux, such as the day-night modulation induced by neutrino flavor regeneration in Earth, and the modulations induced by temperature changes driven by helioseismic waves.

Additional file 1: Fig S1. The number of the nonsense genetic signatures (GS) for each breed. The y-axis represents the number of nonsense variants in GS represented by dots. The x-axis corresponds to each of the 76 breeds in the alphabetical order. Fig S2. Histogram showing the breed sample size distribution. The histogram showed the sample size within each valid breed enrolled in the GS discovery process. The minimal breed sample size was capped at three as shown in the histogram. Fig S3. Phylogenetic tree showing the breed membership of samples in the collection. Each sample was colored based on its breed label. Samples from the same breed were marked with the same color. a) Tree A includes 451 pure-bred dogs from 97 breeds in the collection. b) Tree B includes 412 pure-bred dogs from 76 valid breeds after removing potential wrongfully labeled samples. Fig S4. Pairwise genetic signature sharing between all 76 breeds. The 76 by 76 grid plot indicates the genome-wide GS sharing for all breed-pairs. The upper triangular part shows the standardized GS sharing score (See Methods). All scores were scaled between 0 and 1 with the darkest grid representing the highest GS sharing between Shetland sheepdog and Collie. The lower triangular part shows the absolute number of GS discovered across the genome. Similarly, the darker grid color indicates larger number of GS shared between two breeds. Fig S5. General pipeline of the population-frequency based variant analyzing tool. The flowchart showing the general workflow of the population frequency. Fig S6. Performance benchmark of the variant analyzing tool. a) Line plot showing the real-world run-time of using this tool to discover GS, BSGS and breed-pair unique shared GS in the dataset of 412 samples, when different number of CPUs were provided. b) The relative per-CPU efficiency for each of the three analyses when different number of CPUs were provided.

Efficient exploration of the chemical space to search the candidate drugs that satisfy various constraints is a fundamental task of drug discovery. Advanced deep generative methods attempt to optimize the molecules in the compact latent space instead of the discrete original space, but the mapping between the original and latent spaces is always kept unchanged during the entire optimization process. The unchanged mapping makes those methods challenging to fast adapt to various optimization scenes and leads to the great demand for assessed molecules (samples) to provide optimization direction, which is a considerable expense for drug discovery. To this end, we design a sample-efficient molecular generative method, HelixMO, which explores the scene-sensitive latent space to promote sample efficiency. The scene-sensitive latent space focuses more on modeling the promising molecules by dynamically adjusting the space mapping by leveraging the correlations between the general and scene-specific characteristics during the optimization process. Extensive experiments demonstrate that HelixMO can achieve competitive performance with only a few assessed samples on four molecular optimization scenes. Ablation studies verify the positive impact of the scene-specific latent space, which is capable of identifying the critical characteristics of the promising molecules. We also deployed HelixMO on the website PaddleHelix (https://paddlehelix.baidu.com/app/drug/drugdesign/forecast) to provide drug design service.

Background A major hand-foot-and-mouth disease (HFMD) pathogen, coxsackievirus A16 (CVA16), has predominated in several of the last 10 years and caused the largest number of HFMD outbreaks between 2011 and 2018 in China. We evaluated the efficacy of maternal anti-CVA16 antibody transfer via the placenta and explored the dynamics of maternal and natural infection-induced neutralizing antibodies in children. Methods Two population-based longitudinal cohorts in southern China were studied during 2013–2018. Participants were enrolled in autumn 2013, including 2475 children aged 1–9 years old and 1066 mother-neonate pairs, and followed for 3 years. Blood/cord samples were collected for CVA16-neutralizing antibody detection. The maternal antibody transfer efficacy, age-specific seroprevalence, geometric mean titre (GMT) and immune response kinetics were estimated. Results The average maternal antibody transfer ratio was 0.88 (95% CI 0.80–0.96). Transferred maternal antibody levels declined rapidly (half-life: 2.0 months, 95% CI 1.9–2.2 months). The GMT decayed below the positive threshold (8) by 1.5 months of age. Due to natural infections, it increased above 8 after 1.4 years and reached 32 by 5 years of age, thereafter dropping slightly. Although the average duration of maternal antibody-mediated protection was < 3 months, the duration extended to 6 months on average for mothers with titres ≥ 64. Conclusions Anti-CVA16 maternal antibodies are efficiently transferred to neonates, but their levels decline quickly. Children aged 0–5 years are the main susceptible population and should be protected by CVA16 vaccination, with the optimal vaccination time between 1.5 months and 1 year of age.

The European physical journal / C 82(12), 1168 (2022). doi:10.1140/epjc/s10052-022-11002-8, Published by Springer, Heidelberg

BackgroundArtificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.MethodsStudies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.ResultsThe systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).ConclusionThe models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.

Exposure to phthalate acid esters (PAEs) has been found to have adverse effects on reproduction of human and animal. However, the effects on the semen quality are controversial. In this study, we have collected paired blood and sperm samples from patients (n = 103) at a medical clinic in Guangzhou, China, to determine the concentrations of eight phthalate metabolites (m-PAEs). The concentration ranges of ∑8m-PAEs were 2.7–55 and 0.99–17 ng/mL in blood and semen samples, respectively. MEHP (median, 3.6 ng/mL in blood and 0.55 ng/mL in semen) and MnOP (median, 3.4 ng/mL in blood and 0.69 ng/mL in semen) were the predominant analytes in both blood and semen samples. However, analytes were not correlated in paired blood and semen. Semen motility was significantly associated to the concentrations of ∑8m-PAEs in semen, but it did not associate to the concentrations of PAEs in blood. Multi-linear regression analysis, including concentrations of PAEs in both blood and semen, as well as BMI, occupation, living habits, suggested that exposure to PAEs at current environmental levels had very limited effects on semen quality of population in Guangzhou, China.

Comparing with lecturer marked assessments, peer assessment is a more comprehensive learning process and many of the associated problems have occurred. In this research work, we study the peer-assessment impact on group learning activities in order to provide a complete and systematic review, increase the practice and quality of the peer assessment process. Pilot studies were conducted and took the form of surveys, focus group interviews, and questionnaires. Prelimi-nary surveys were conducted with 582 students and 276 responses were received, giving a response rate of 47.4%. The results show 37% student will choose individual work over group work if given the choice. In the case study, 82.1% of the total of 28 students have en-joyed working in a group using Facebook as communication tools. 89.3% of the students can demonstrate their skills through group-working and most importantly, 82.1% of them agree that peer assess-ment is an impartial method of assessment with the help of Facebook as proof of self-contribution. Our suggestions to make group work a pleasant experience are by identifying and taking action against the freeloader, giving credit to the deserving students, educating students on how to give constructive feedback and making the assessment pro-cess transparent to all., Comment: Regular Research Paper Accepted by FECS'21 (The 17th Int'l Conf on Frontiers in Education: Computer Science and Computer Engineering)

Additional file 1: Fig. S1. Time series and predicting epidemic magnitude of CVA16 activity in Anhua county during 2013-2016 (A) and that in Hunan province during 2009-2018 (B). Fig. S2. Participants’ included time in baseline and follow-up times (A) and the epidemic seasons of CVA16 activity during 2009-2018 in Anhua, Hunan province (B). Fig. S3. Distribution of maternal antibody titre transfer ratio against CVA16 for all pairs of neonate-mother (A) and positive mothers with antibody titre ≥8 (B). Fig. S4. The aggregated maternal titre specified transfer ratio. Fig. S5. Maternal antibody titre transfers efficacy against CVA16. Fig. S6. Transfer ratio trend with all pairs of mother-neonate. Fig. S7. Dynamic of seroprevalence, neutralization antibody titre by age for neonates using 16 as positive threshold. Fig. S8. Dynamic of neutralization antibody titre by age for neonates using full follow-up subset. Fig. S9. Dynamics of neutralization antibody titer by age for neonates stratified by maternal antibody levels. Fig. S10. Waning rate of maternal transmitted CVA16 antibody and natural infection induced antibody. Fig. S11. Age distribution and cumulative distribution of HFMD associated to CVA16 by clinical severity in Hunan province during 2009-2018 and Anhua county during 2013-2016. Table S1. Distribution of participants aged 1-9 years and sampling ratio. Table S2. Seasonality comparison between Anhua county and Hunan province. Table S3. Epidemic duration for CVA16 circulation in Anhua. Table S4. (a) Baseline characteristics of mothers and neonates in neonate cohort between participants and non-participants, selected participants and non-selected participants for CVA16 assay. (b) Baseline characteristics of neonates in neonate cohort between full follow-up participants and incomplete follow-up participants. Table S5. Comparison of baseline characteristics of 1-9 years old children between participants and non-selected objects through sampling, full follow-up and incomplete follow-up. Table S6. Seroprevalence and GMT of mothers and neonates at birth. Table S7. Sample size (n, %) for CVA16 antibody transfer ratio analysis. Table S8. Multivariate analysis with factors associated with neonates’ seropositivity, antibody titre at birth and transfer ratio. Table S9. Univariate analysis with factors associated with neonates’ seropositivity at birth. Table S10. Univariate analysis with factors associated with the transfer ratio of maternal antibody. Table S11. Univariate analysis with factors associated with neonates’ antibody titre at birth. Table S12. Sensitive analysis for multivariate analysis with factors associated with maternal antibody transfer ratio. Table S13. Sensitive analysis of median age of neonate with positive antibody. Appendix.

Additional file 1: Figure S1. Schematic Figure of SARS-CoV-2 transmission and vaccination model. Figure S2. Vaccine administration capacity in China. Figure S3. Sensitivity analysis on the basic reproduction number. Figure S4. Sensitivity analysis on the initial number of infectious individuals. Figure S5. Sensitivity analysis on the susceptibility to infection. Figure S6. Sensitivity analysis on the number of age groups. Figure S7. Sensitivity analysis on the natural immunity. Figure S8. Sensitivity analysis on the maximum vaccine efficacy. Figure S9. Sensitivity analysis on the relative vaccine efficacy for individuals aged 3-17 and 60+ years relative to that of individuals aged 18-59 years. Figure S10. Sensitivity analysis on the vaccine efficacy within 14 days after second dose. Figure S11. Sensitivity analysis on the time intervals between the two doses. Figure S12. Impact of delaying the start of the epidemic on vaccine coverage and daily incidence. Figure S13. Impact of adopting NPIs in case of a new outbreak. Figure S14. Impact of adopting NPIs in case of a new outbreak on daily incidence. Figure S15. Impact of delaying the start of the epidemic and adopting NPIs on infections. Figure S16. Comparison of contact matrix in Shanghai and China. Figure S17. Impact of delaying the start of the epidemic start and adopting NPIs on estimated net reproduction number using China contact matrix. Figure S18. Effective reproduction number and infection attack rate under different vaccine coverage. Figure S19. Impact of vaccine efficacy and vaccine coverage on estimated net reproduction umber under different intensity of NPIs. Figure S20. Results of model with no age structure. Figure S21. Comparison between China and a scenario with natural immunity and an mRNA vaccine. Tab S1. Summary of parameters used to model Delta Strain. Tab S2. The proportion of pregnant women and vaccine contraindications by age groups.

Since the recent success of Vision Transformers (ViTs), explorations toward ViT-style architectures have triggered the resurgence of ConvNets. In this work, we explore the representation ability of modern ConvNets from a novel view of multi-order game-theoretic interaction, which reflects inter-variable interaction effects w.r.t.~contexts of different scales based on game theory. Within the modern ConvNet framework, we tailor the two feature mixers with conceptually simple yet effective depthwise convolutions to facilitate middle-order information across spatial and channel spaces respectively. In this light, a new family of pure ConvNet architecture, dubbed MogaNet, is proposed, which shows excellent scalability and attains competitive results among state-of-the-art models with more efficient use of parameters on ImageNet and multifarious typical vision benchmarks, including COCO object detection, ADE20K semantic segmentation, 2D\&3D human pose estimation, and video prediction. Typically, MogaNet hits 80.0\% and 87.8\% top-1 accuracy with 5.2M and 181M parameters on ImageNet, outperforming ParC-Net-S and ConvNeXt-L while saving 59\% FLOPs and 17M parameters. The source code is available at \url{https://github.com/Westlake-AI/MogaNet}., Preprint V2 (15 pages + 10 pages), updating the models and more experiment results

Additional file 1: Figure S1. The distribution of whether local residents need to pay for vaccine. Figure S2. Geographic distribution of overall technical platforms for vaccines. Figure S3. Proportion administered by vaccine technical platforms. Figure S4. Proportion administered by vaccine types. Figure S5. Date at which achieved one dose per 100 people in total population by country. Figure S6. Vaccine coverage over time stratified by income groups and role of vaccine seller/donor or recipient. Figure S7. Vaccine coverage stratified by SDI quintile and WHO region. Figure S8. The association between vaccine coverage with physician density and government health spending per capita. Figure S9. Corrections between vaccine coverage and country-level vaccine acceptance. Table S1. Authorization information of COVID-19 vaccines by technical platforms and country. Table S2. Target populations and contraindications recommended by regulatory agencies. Table S3. Policies on additional or booster dose of COVID-19 vaccine. Table S4. Country lists of selling/donating or receiving COVID-19 vaccines. Table S5. Categories and definitions of special population groups belonging to indication and contraindication lists. Table S6. Global, regional, and national target population (TP). Table S7. The list of variables for investigating associations with vaccine coverage. Table S8. Analysis of multicollinearity. Table S9. Global, regional, and national demand of vaccine dose.

Customer service is the lifeblood of any business. Excellent customer service not only generates return business but also creates new customers. Looking at the demanding market to provide a 24/7 service to customers, many organisations are increasingly engaged in popular social media and text messaging platforms such as WhatsApp and Facebook Messenger in providing a 24/7 service to customers in the current demanding market. In this paper, we present an intent matching based customer services chatbot (IMCSC), which is capable of replacing the customer service work of sales personnel, whilst interacting in a more natural and human-like manner through the employment of Natural Language Understanding (NLU). The bot is able to answer the most common frequently asked questions and we have also integrated features for the processing and exporting of customer orders to a Google Sheet., Comment: Accepted by "the 5th International Conference on Communication and Information Systems (ICCIS 2021)"

Extrinsic manipulation, a technique that enables robots to leverage extrinsic resources for object manipulation, presents practical yet challenging scenarios. Particularly in the context of extrinsic manipulation on a supporting plane, regrasping becomes essential for achieving the desired final object poses. This process involves sequential operation steps and stable placements of objects, which provide grasp space for the robot. To address this challenge, we focus on predicting diverse placements of objects on the plane using deep neural networks. A framework that comprises orientation generation, placement refinement, and placement discrimination stages is proposed, leveraging point clouds to obtain precise and diverse stable placements. To facilitate training, a large-scale dataset is constructed, encompassing stable object placements and contact information between objects. Through extensive experiments, our approach is demonstrated to outperform the start-of-the-art, achieving an accuracy rate of 90.4\% and a diversity rate of 81.3\% in predicted placements. Furthermore, we validate the effectiveness of our approach through real-robot experiments, demonstrating its capability to compute sequential pick-and-place steps based on the predicted placements for regrasping objects to goal poses that are not readily attainable within a single step. Videos and dataset are available at https://sites.google.com/view/pmvlr2022/.

Discriminating the source of water inrush accurately and efficiently is necessary for water control in the coal mining industry. We combined the Fisher feature extraction and support vector machine (SVM) methods and applied this new model to the Wuhai mining area. The method extracts features from the raw data and integrated SVM, and synthetically considers the influence of geographical factors. Cross-analysis was tested 100 times, which arbitrarily selected 12 samples for the prediction and discrimination process. The results indicate that this new combined model of linear dimension reduction and non-linear dimension elevation was more accurate and efficient in discriminating water inrush sources than the traditional SVM model. Moreover, by reducing the penalty term of SVM model, we analyzed the correlation among the aquifers. We concluded that aquifers II and IV correlated strongly with each other, and that aquifer III was poorly connected with the other aquifers.

Digital image correlation (DIC) and the strain gage method are both widely used in characterizing the fracture mechanisms of materials. However, the quantitative comparisons of rock fracture parameters between the two methods have been studied insufficiently. In this paper, dynamic three-point bending impact tests were performed on white marble specimens using DIC and the strain gage method simultaneously. The complete cracking process was recorded by two high-speed cameras situated on either side of the specimen. The crack propagation velocities were obtained and the dynamic stress intensity factors (DSIFs) were calculated by both methods for analysis and comparison. The experimental results show that the crack propagation velocities almost remain constant during the cracking process, and the evolutions of strain values recorded by gages are consistent with the modified strain variations obtained theoretically. DSIFs calculated by DIC show good agreement with the results obtained from strain gages #1 to #4, while the size effect and the multiple stress waves interfere with the strain signals sensed in strain gage #5, causing a relatively larger deviation in the DSIF. In addition, the complete fracture process (including the variations in successive displacement and strain fields) can be obtained by the DIC method, while a rough changing trend in the DSIF can be given by the strain gage method with a limited number of strain gages.

Service manual documents are crucial to the engineering company as they provide guidelines and knowledge to service engineers. However, it has become inconvenient and inefficient for service engineers to retrieve specific knowledge from documents due to the complexity of resources. In this research, we propose an automated knowledge mining and document classification system with novel multi-model transfer learning approaches. Particularly, the classification performance of the system has been improved with three effective techniques: fine-tuning, pruning, and multi-model method. The fine-tuning technique optimizes a pre-trained BERT model by adding a feed-forward neural network layer and the pruning technique is used to retrain the BERT model with new data. The multi-model method initializes and trains multiple BERT models to overcome the randomness of data ordering during the fine-tuning process. In the first iteration of the training process, multiple BERT models are being trained simultaneously. The best model is then selected for the next phase of the training process with another two iterations and the training processes for other BERT models will be terminated. The performance of the proposed system has been evaluated by comparing with two robust baseline methods, BERT and BERT-CNN. Experimental results on a widely used Corpus of Linguistic Acceptability (CoLA) dataset have shown that the proposed techniques perform better than these baseline methods in terms of accuracy and MCC score., Comment: This paper has been submitted to journal of System and Management Sciences

Data mixing augmentation have proved to be effective in improving the generalization ability of deep neural networks. While early methods mix samples by hand-crafted policies (e.g., linear interpolation), recent methods utilize saliency information to match the mixed samples and labels via complex offline optimization. However, there arises a trade-off between precise mixing policies and optimization complexity. To address this challenge, we propose a novel automatic mixup (AutoMix) framework, where the mixup policy is parameterized and serves the ultimate classification goal directly. Specifically, AutoMix reformulates the mixup classification into two sub-tasks (i.e., mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework. For the generation, a learnable lightweight mixup generator, Mix Block, is designed to generate mixed samples by modeling patch-wise relationships under the direct supervision of the corresponding mixed labels. To prevent the degradation and instability of bi-level optimization, we further introduce a momentum pipeline to train AutoMix in an end-to-end manner. Extensive experiments on nine image benchmarks prove the superiority of AutoMix compared with state-of-the-art in various classification scenarios and downstream tasks., Comment: ECCV 2022 (Oral presentation paper). Updated version. The source code is available at https://github.com/Westlake-AI/openmixup

BackgroundCardiopulmonary bypass (CPB) is an important cause of significant systemic inflammatory response syndrome (SIRS) in the surgical treatment of acute type A aortic dissection (ATAAD). In patients with arch vessel involvement, extensive surgical repairs often necessitate prolonged use of CPB and results in extensive inflammatory responses. Cytokines and chemokines released during CPB contribute to the progression of SIRS, increase perioperative complications, and negatively impact surgical outcomes. A cytokine adsorber (HA380) is expected to reduce the level of cytokines during CPB, which may decrease both intraoperative and postoperative inflammation. The purpose of this study is to investigate if HA380 is able to reduce the levels of inflammatory cytokines and decrease perioperative complications in ATAAD patients undergoing CPB and deep hypothermic circulatory arrest (DHCA).MethodsThis study is a single-center, randomized, controlled, double-blind clinical trial. The study aims to recruit 88 patients with ATAAD and aortic arch involvement who will undergo CPB and DHCA to repair the dissected aorta. Patients will be randomized equally into the CPB/DHCA only group (control group) and the CPB/DHCA + HA380 hemoperfusion group (intervention group), with 44 patients each. Patients in the control group will undergo CPB and DHCA only, while patients in the intervention group will undergo continuous hemoperfusion with HA380, in addition to CPB and DHCA. The primary outcome is a composite of major perioperative complications. The secondary outcomes include related inflammatory markers, coagulation parameters, and minor perioperative complications. To comprehensively evaluate the effect of hemoperfusion on the perioperative outcomes, we will also determine if there are differences in perioperative all-cause mortality, length of ICU stay, and total hospitalization costs.DiscussionIn the current trial, hemoperfusion will be applied in patients undergoing CPB and DHCA for repair of the aorta involving the aortic arch. This trial aims to test the safety and efficacy of our hemoperfusion device (HA380) in such settings. Upon completion of the trial, we will determine if HA380 is effective in reducing perioperative proinflammatory cytokine levels. Further, we will also verify if reduction in the proinflammatory cytokine levels, if present, translates to improvement in patient outcomes.Trial registrationClinicalTrials.govNCT04007484. Registered on 1 July 2019 (retrospectively registered).

Integrated energy system(IES) refers to a variety of energy in the coupling of electricity, gas, heat energy in the form of management, distribution and application of the system, is the expression of energy in the Internet area, this paper constructs the IES structure of the park, using the energy hub model to analyze the cell transformation relationship, and constructed the mathematical model of the structure of the unit, and presents a total cost as the objective function, considering the photovoltaic output and the user can use kinds of uncertainty planning method. The example is solved by using BONMIN, and in a 3 energy unit planning examples verily the effectiveness of the proposed method.

This paper aims at improving the classification accuracy of a Support Vector Machine (SVM) classifier with Sequential Minimal Optimization (SMO) training algorithm in order to properly classify failure and normal instances from oil and gas equipment data. Recent applications of failure analysis have made use of the SVM technique without implementing SMO training algorithm, while in our study we show that the proposed solution can perform much better when using the SMO training algorithm. Furthermore, we implement the ensemble approach, which is a hybrid rule based and neural network classifier to improve the performance of the SVM classifier (with SMO training algorithm). The optimization study is as a result of the underperformance of the classifier when dealing with imbalanced dataset. The selected best performing classifiers are combined together with SVM classifier (with SMO training algorithm) by using the stacking ensemble method which is to create an efficient ensemble predictive model that can handle the issue of imbalanced data. The classification performance of this predictive model is considerably better than the SVM with and without SMO training algorithm and many other conventional classifiers., Comment: This paper is published at IEEE RTCSA 2013

Perfluoroalkyl acids (PFAAs) have been suspected to act as endocrine disruptors and adversely affect human reproductive health. We aimed to investigate the association between PFAAs in blood and semen, explore a potential link between PFAAs exposure and semen quality in the population of the Pearl River Delta (PRD) region in China, one of the “world factories”. The monitoring results demonstrated that the population (103 male participants) from the PRD region in this study had higher PFAAs levels in blood and semen than some other areas in China. PFOS was found at the highest mean concentrations of 118.16 ng/mL in blood and 5.31 ng/mL in semen among the nine PFAAs. Significant associations were found between concentrations of several analytes in blood and semen, including Σ9 PFAAs (r = 0.475, P

The working state and operating parameters of the automobile transmission system play a key role in the vehicle noise and vibration performance. Based on the basic calculation method of relative rotational angle, this paper proposes two methods for calculating the working rotational angle and torsional stiffness of the transmission system, which can effectively obtain the key information of the transmission system under the vehicle operating state. The working rotational angle, whose initial angle should be corrected by the average angle in the neutral gear coasting condition, can reflect the actual working state of the torsional vibration damper effectively. And the accuracy of the linear torsional stiffness obtained will be above 90%. Both simulation and experimental analysis results show that these two proposed application methods have high calculation accuracies and engineering feasibility.

How to correctly stress hard samples in metric learning is critical for visual recognition tasks, especially in challenging person re-ID applications. Pedestrians across cameras with significant appearance variations are easily confused, which could bias the learned metric and slow down the convergence rate. In this paper, we propose a novel weighted complete bipartite graph based maximum-value perfect (MVP) matching for mining the hard samples from a batch of samples. It can emphasize the hard positive and negative sample pairs respectively, and thus relieve adverse optimization and sample imbalance problems. We then develop a new batch-wise MVP matching based loss objective and combine it in an end-to-end deep metric learning manner. It leads to significant improvements in both convergence rate and recognition performance. Extensive empirical results on five person re-ID benchmark datasets, i.e., Market-1501, CUHK03-Detected, CUHK03-Labeled, Duke-MTMC, and MSMT17, demonstrate the superiority of the proposed method. It can accelerate the convergence rate significantly while achieving state-of-the-art performance. The source code of our method is available at \url{https://github.com/IAAI-CVResearchGroup/MVP-metric}.

With the continuous increase of battery capacity in the power battery charging and discharging system(PBCDS), the charging and discharging equipment of power battery also needs to output large power or energy, so the multi-DC-DC parallel-connected structure on the battery side becomes the first choice. In addition, considering the traditional PI control method have a complex parameter adjustment process and the complexity of parameter adjustment increases sharply with the parallel-connected of multi-DC-DC, but also it cannot meet the special requirements of high dynamic response speed and non-overshoot in the power battery charging and discharging process(PBCDP). Therefore, this paper innovatively proposed a model predictive control method for the parallel-connected system with n DC-DC converters, which does not require complicated parameter tuning, but also achieves the special requirements of high dynamic response speed and non-overshoot in the PBCDP. The method has strong universality and high flexibility, and can be applied to power battery charge and discharge conditions with any level of power. Finally, the simulation is conducted to validate the feasibility and correctness of the proposed MPC control method.

Inertial measurement unit (IMU)-based 3-DoF angle estimation methods for lower limb joints have been studied for decades, however the calibration motions and/or careful sensor placement are still necessary due to challenges of real-time application. This study proposes a novel sensormovement-robust 3-DoF method for lower-limb joint angle estimation without calibration. A realtime optimization process, which is based on a feedback iteration progress to identify three joint axes of a 3-DoF joint, has been presented with a reference frame calibration algorithm, and a safe-guarded strategy is proposed to detect and compensate for the errors caused by sensor movements. The experimental results obtained from a 3-DoF gimbal and ten healthy subjects demonstrate a promising performance on 3-DoF angle estimation. Specially, the experiments on ten subjects are performed with three gait modes and a 2-min level walking. The root mean square error is below 2 deg for level walking and 5 deg for other two gait modes. The result of 2-min level walking shows our algorithms stability under long run. The robustness against sensor movement are demonstrated through data from multiple sets of IMUs. In addition, results from the 3-DoF gimbal indicate that the accuracy of 3-DoF angle estimation could be improved by 84.9% with our reference frame calibration algorithm. In conclusion, our study proposes and validates a sensor-movement-robust 3-DoF angle estimation for lowerlimb joints based on IMU. To the best of our knowledge, our approach is the first experimental implementation of IMUbased 3-DoF angle estimation for lower-limb joints without calibration., Comment: 12 pages

Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This paper presents an effective anonymization approach, Multi-level Clustering based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.

This paper proposes an autoencoder (AE) that is used for improving the performance of once-class classifiers for the purpose of detecting anomalies. Traditional one-class classifiers (OCCs) perform poorly under certain conditions such as high-dimensionality and sparsity. Also, the size of the training set plays an important role on the performance of one-class classifiers. Autoencoders have been widely used for obtaining useful latent variables from high-dimensional datasets. In the proposed approach, the AE is capable of deriving meaningful features from high-dimensional datasets while doing data augmentation at the same time. The augmented data is used for training the OCC algorithms. The experimental results show that the proposed approach enhance the performance of OCC algorithms and also outperforms other well-known approaches.

Evolving cybersecurity threats are a persistent challenge for systemadministrators and security experts as new malwares are continu-ally released. Attackers may look for vulnerabilities in commercialproducts or execute sophisticated reconnaissance campaigns tounderstand a targets network and gather information on securityproducts like firewalls and intrusion detection / prevention systems(network or host-based). Many new attacks tend to be modificationsof existing ones. In such a scenario, rule-based systems fail to detectthe attack, even though there are minor differences in conditions /attributes between rules to identify the new and existing attack. Todetect these differences the IDS must be able to isolate the subset ofconditions that are true and predict the likely conditions (differentfrom the original) that must be observed. In this paper, we proposeaprobabilistic abductive reasoningapproach that augments an exist-ing rule-based IDS (snort [29]) to detect these evolved attacks by (a)Predicting rule conditions that are likely to occur (based on existingrules) and (b) able to generate new snort rules when provided withseed rule (i.e. a starting rule) to reduce the burden on experts toconstantly update them. We demonstrate the effectiveness of theapproach by generating new rules from the snort 2012 rules set andtesting it on the MACCDC 2012 dataset [6].

Outlier detection (also known as anomaly detection or deviation detection) is a process of detecting data points in which their patterns deviate significantly from others. It is common to have outliers in industry applications, which could be generated by different causes such as human error, fraudulent activities, or system failure. Recently, density-based methods have shown promising results, particularly among which Local Outlier Factor (LOF) is arguably dominating. However, one of the major drawbacks of LOF is that it is computationally expensive. Motivated by the mentioned problem, this research presents a novel pruning-based procedure in which the execution time of LOF is reduced while the performance is maintained. A novel Prune-based Local Outlier Factor (PLOF) approach is proposed, in which prior to employing LOF, outlierness of each data instance is measured. Next, based on a threshold, data instances that require further investigation are separated and LOF score is only computed for these points. Extensive experiments have been conducted and results are promising. Comparison experiments with the original LOF and two state-of-the-art variants of LOF have shown that PLOF produces higher accuracy and precision while reducing execution time., Comment: Accepted by "The 4th International Conference on Computing, Mathematics and Statistics 2019 (iCMS2019)"

After large-scale access to distributed generation of new energy generation in urban AC/DC distribution network, its own randomness and uncertainty affect the supply-demand balance mechanism of distribution network. Therefore, this paper studies the power exchange flexibility optimization method of urban AC/DC distribution network to provide reference for the operation and scheduling of distribution network and main network. First, the active power exchange model of distribution network is established, the extreme value of power exchange of distribution network is obtained, and the flexible range of exchange power is calculated. Next, the flexibility evaluation index is established, and the operation characteristics of energy storage are transformed to complete the research on power exchange flexibility optimization of urban AC/DC distribution network. Through the actual example analysis, the results show that after the optimization of the design method, the net load curve is smoother, the effect of reducing the peak valley difference is more significant, and the overall curve is relatively smooth, which shows that the design method has certain effectiveness.

The current global energy transition is in a period of acceleration, and the transition process of various countries shows certain characteristics of differentiation. Based on the analysis of the development direction of the energy transition, this paper proposes covering energy structure and energy efficiency. On this basis, a comparative analysis of the energy transition situations of major countries such as China, the European Union, Germany, and Japan was carried out, and then conclusions and enlightenments for my country’s energy transition were put forward.

With the combination of Internet, information technology and energy, micro grid plays an important role in the adjustment of energy structure with its abundant resources and friendly environmental benefits. This paper introduces the status of micro grid and renewable Energy of the status quo, In the second part, the development status of the micro-grid is introduced in the second part. In the third part, the future development trend of micro-grid is given. The conclusion of this paper and the suggestion of micro-grid development are given.

With regard to development strategy of “two networks integration”, based on improvement of power supply capacity, power supply security and reliability, fault response, information collection capability as well as improvement, intelligence, integration and automation upgrade on real-time monitoring capability, “two networks integration” planning strategy and implementation plan, such as planning for distribution network, distribution itself, ubiquitous power Internet of Things, are made to greatly improve overall capacity of distribution network, which further provides reference for similar projects, such as high-quality, stable, efficient and green power supply.

Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection. However, it is well-known that adversaries are always trying to adapt their attacks to evade detection. For example, an email spammer may guess what features spam detection models use and modify or remove those features to avoid detection. There has been some work on making machine learning models more robust to such attacks. However, one simple but promising approach called {\em randomization} is underexplored. This paper proposes a novel randomization-based approach to improve robustness of machine learning models against evasion attacks. The proposed approach incorporates randomization into both model training time and model application time (meaning when the model is used to detect attacks). We also apply this approach to random forest, an existing ML method which already has some degree of randomness. Experiments on intrusion detection and spam filtering data show that our approach further improves robustness of random-forest method. We also discuss how this approach can be applied to other ML models.

Model predictive control (MPC) relies heavily on the accurate mathematical model which is usually difficult to be obtained. Parameters of permanent magnet synchronous motor (PMSM) would be changed in real time and influenced by uncertain factors. For this purpose, this paper proposes a multiparameter identification strategy for PMSM based on MPC. The simulation is carried out and its results show that the accurate model of PMSM for MPC can be obtained and the impact of parameter errors can be eliminated by the proposed control method.

The contribution of time series forecasting (TSF) on various aspects from economic to engineering has yielded its importance. Lot of recent studies concentrated on applying and modifying artificial neural network (ANN) to improve forecasting accuracy and achieved promising results. However, the selection of proper set from historical data for forecasting still has limited consideration. In addition, the selection of network structure as well as initial weights in ANN has been proved to have significant impact on the performance. This paper aims to propose a hybrid model that takes advantages of optimization algorithm: differential evolution (DE) in combine with ANN. The DE operates as features selection process that evaluates useful historical data known as lag to involve in learning process. Besides, DE will perform pre-calculation to determine the set of weight use for ANN. This proposed model is examined on agricultural commodity’s price to evaluate its accuracy. The experimental results is compared and surpassed the popular TSF technique autoregressive integrated moving average (ARIMA) and traditional multilayer perceptron (MLP).

This study is the first one investigating the correlation between the concentration of polycyclic aromatic hydrocarbon (PAHs) in blood and semen qualities for residents in the Pearl River Delta (PRD) region in China. Blood samples from 53 infertile volunteers were studied for measures of semen quality and 16 PAHs. Information on the study subjects' living habits (such as smoking, drinking and preference of consumption for food) and general information (age, body-mass-index (BMI) and educational background) were also collected. Statistical results showed that age and BMI were significantly and negatively related to semen motilities. The total concentrations of PAHs (∑16 PAHs) in the blood were 12,010, 7493, 9105 and 8647ng/g for factory workers, office workers, technicians and salespersons, respectively. In addition, ∑16 PAHs in the blood of smokers, drinkers and heavy-taste food consumers were 11,950, 11,266 and 12,141 ng/g, which were higher than those observed in nonsmokers (10,457 ng/g), nondrinkers (10,920 ng/g) and light-taste food consumers (9202 ng/g), individually. Furthermore, the Pearson correlation analysis results showed significant positive correlations between BMI and ∑16 PAHs in the blood. Statistically significant correlations were observed between semen motilities and ∑16 PAHs in the blood as well. Logistic regression results showed that for each 1 ng/g increase in ∑16 PAHs in blood samples, the log odds of experiencing a pregnancy decrease by 0.039 on average. However, more evidences are needed to clarify the impact of PAHs in the blood to male infertility. Keywords: Polycyclic aromatic hydrocarbons, Whole blood, Semen quality, Male infertility, Pearl River Delta

It has been shown that human prostate cancer (PCa) cells induced apoptotic death of the most potent antigen-presenting cells, dendritic cells (DCs), which are responsible for the induction of specific antitumor immune responses. Here, we investigated the function of 4-1BB on protecting DCs from prostate cancer-induced apoptosis with an agonistic mAb to 4-1BB. RM-1 cells and DCs were co-incubated for 48 h and DC apoptosis was assessed by Annexin Vassay. TNF-α and IL-12 production were assessed by enzyme-linked immunosorbent assay (ELISA) and Bcl-2 and Bcl-xL on DCs were analyzed by Western blot. We have shown that co-incubation of RM-1 cells with DCs is accompanied by an increased level of DCs apoptosis. Triggering 4-1BB on DCs resulted in increased resistance of DCs to RM-1 cells-induced apoptosis, which was owing to the up-regulated expression of Bcl-2 and Bcl-xL, and increased secretion of TNF-αand IL-12. These results demonstrate that triggering 4-1BB on DCs could increased resistance of DCs to PCa-induced apoptosis.