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Feature related particle data analysis plays an important role in many scientific applications such as fluid simulations, cosmology simulations and molecular dynamics. Compared to conventional methods that use hand-crafted feature descriptors, some recent studies focus on transforming the data into a new latent space, where features are easier to be identified, compared and extracted. However, it is challenging to transform particle data into latent representations, since the convolution neural networks used in prior studies require the data presented in regular grids. In this paper, we adopt Geometric Convolution, a neural network building block designed for 3D point clouds, to create latent representations for scientific particle data. These latent representations capture both the particle positions and their physical attributes in the local neighborhood so that features can be extracted by clustering in the latent space, and tracked by applying tracking algorithms such as mean-shift. We validate the extracted features and tracking results from our approach using datasets from three applications and show that they are comparable to the methods that define hand-crafted features for each specific dataset.

Adjuvant intravesical chemotherapy following tumour resection is recommended for intermediate-risk non-muscle-invasive bladder cancer (NMIBC).To assess the efficacy and safety of adjuvant intravesical chemohyperthermia (CHT) for intermediate-risk NMIBC.HIVEC-II is an open-label, phase 2 randomised controlled trial of CHT versus chemotherapy alone in patients with intermediate-risk NMIBC recruited at 15 centres between May 2014 and December 2017 (ISRCTN 23639415). Randomisation was stratified by treating hospital.Patients were randomly assigned (1:1) to adjuvant CHT with mitomycin C at 43°C or to room-temperature mitomycin C (control). Both treatment arms received six weekly instillations of 40 mg of mitomycin C lasting for 60 min.The primary endpoint was 24-mo disease-free survival as determined via cystoscopy and urinary cytology. Analysis was by intention to treat.A total of 259 patients (131 CHT vs 128 control) were randomised. At 24 mo, 42 patients (32%) in the CHT group and 49 (38%) in the control group had experienced recurrence. Disease-free survival at 24 mo was 61% (95% confidence interval [CI] 51-69%) in the CHT arm and 60% (95% CI 50-68%) in the control arm (hazard ratio [HR] 0.92, 95% CI 0.62-1.37; log-rank p = 0.8). Progression-free survival was higher in the control arm (HR 3.44, 95% CI 1.09-10.82; log-rank p = 0.02) on intention-to-treat analysis but was not significantly higher on per-protocol analysis (HR 2.87, 95% CI 0.83-9.98; log-rank p = 0.06). Overall survival was similar (HR 2.55, 95% CI 0.77-8.40; log-rank p = 0.09). Patients undergoing CHT were less likely to complete their treatment (n =75, 59% vs n = 111, 89%). Adverse events were reported by 164 patients (87 CHT vs 77 control). Major (grade III) adverse events were rare (13 CHT vs 7 control).CHT cannot be recommended over chemotherapy alone for intermediate-risk NMIBC. Adverse events following CHT were of low grade and short-lived, although patients were less likely to complete their treatment.The HIVEC-II trial investigated the role of heated chemotherapy instillations in the bladder for treatment of intermediate-risk non-muscle-invasive bladder cancer. We found no cancer control benefit from heated chemotherapy instillations over room-temperature chemotherapy. Adverse events following heated chemotherapy were low grade and short-lived, although these patients were less likely to complete their treatment.

Public opinion surveys constitute a powerful tool to study peoples' attitudes and behaviors in comparative perspectives. However, even worldwide surveys provide only partial geographic and time coverage, which hinders comprehensive knowledge production. To broaden the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or years. The resulting new datasets can be analyzed as a single source, which can be flexibly accessed through many data portals. However, such portals offer little guidance to explore the data in-depth or query data with user-customized needs. As a result, it is still challenging for social scientists to efficiently identify related data for their studies and evaluate their theoretical models based on the sliced data. To overcome them, in the Survey Data Recycling (SDR) international cooperation research project, we propose SDRQuerier and apply it to the harmonized SDR database, which features over two million respondents interviewed in a total of 1,721 national surveys that are part of 22 well-known international projects. We design the SDRQuerier to solve three practical challenges that social scientists routinely face. First, a BERT-based model provides customized data queries through research questions or keywords. Second, we propose a new visual design to showcase the availability of the harmonized data at different levels, thus helping users decide if empirical data exist to address a given research question. Lastly, SDRQuerier discloses the underlying relational patterns among substantive and methodological variables in the database, to help social scientists rigorously evaluate or even improve their regression models. Through case studies with multiple social scientists in solving their daily challenges, we demonstrated the novelty, effectiveness of SDRQuerier.

Metabolic and bariatric surgery is the most effective weight loss treatment for severe obesity. The number of adolescents undergoing sleeve gastrectomy is increasing. We investigated changes in body composition in adolescents undergoing sleeve gastrectomy 12-26 weeks post-operatively using whole-body magnetic resonance imaging (WB-MRI).This prospective cohort study assessed changes in adipose tissue compartments (ie, visceral, subcutaneous, and intermuscular) and muscle in 18 obese adolescents, ages 14-19, 89% female, with body mass index z -score of 2.6 ± 0.25 (range 2.16-3.2). All underwent WB-MRI 1.5-17 weeks pre-operatively and 12-26 weeks post-operatively.Pre- and post-operative WB-MRI showed decreases in all adipose tissue compartments, as well as decreased skeletal muscle and liver fat fraction ( P0.0001). The post-operative percentage loss of adipose tissue in subcutaneous, visceral, and intermuscular compartments (89.0%, 5.8%, 5.2%, respectively) was similar to the pre-operative percentages of corresponding adipose tissue compartments (90.5%, 5.0%, 4.5%, respectively). Of note, participants with obstructive sleep apnea had significantly higher pre-operative volume of subcutaneous and intermuscular adipose tissue than participants without obstructive sleep apnea ( P = 0.003).We found, contrary to what is reported to occur in adults, that pre-operative percentage loss of adipose tissue in subcutaneous, visceral, and intermuscular compartments was similar to the post-operative percentage loss of corresponding adipose tissue compartments in adolescents 12-26 weeks after sleeve gastrectomy.

To investigate the predictive value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) features on the pathologic grade, microvascular invasion (MVI), and cytokeratin-19 (CK19) expression in hepatocellular carcinomas (HCC), and to evaluate their association with postoperative recurrence of HCC.This retrospective study included 147 patients with surgically confirmed HCCs who underwent gadoxetic-enhanced MRI. The lesions were evaluated quantitatively in terms of the relative enhancement ratio (RER), and qualitatively based on imaging features and clinical parameters. Logistic regression analyses were performed to investigate the value of these parameters in predicting the pathologic grade, MVI, and CK19 in HCC. Predictive factors for postoperative recurrence were determined using a Cox proportional hazards model.Peritumoral enhancement (odds ratio [OR], 3.396; p = 0.025) was an independent predictor of high pathologic grades. Serum protein induced by vitamin K absence or antagonist (PIVKA) level40 mAU/mL (OR, 3.763; p = 0.018) and peritumoral hypointensity (OR, 4.343; p = 0.003) were independent predictors of MVI. Predictors of CK19 included serum alpha-fetoprotein (AFP) level400 ng/mL (OR, 4.576; p = 0.005), rim enhancement (OR, 5.493; p = 0.024), and lower RER (OR, 0.013; p = 0.011). Peritumoral hypointensity (hazard ratio [HR], 1.957; p = 0.027) and poor pathologic grades (HR, 2.339; p = 0.043) were independent predictors of recurrence.We demonstrated the value of preoperative gadoxetic-enhanced MRI in predicting aggressive pathological features of HCC. Poor pathologic grades and peritumoral hypointensity may independently predict the recurrence of HCC.

Yuan Liu,* Chen Song,* Zhiqiang Tian, Wei Shen Department of General Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Shen, Department of General Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, People’s Republic of China, Tel +86 13385110723, Email shenweijs@outlook.comPurpose: This study aims to construct a machine learning model that can recognize preoperative, intraoperative, and postoperative high-risk indicators and predict the onset of venous thromboembolism (VTE) in patients.Patients and Methods: A total of 1239 patients diagnosed with gastric cancer were enrolled in this retrospective study, among whom 107 patients developed VTE after surgery. We collected 42 characteristic variables of gastric cancer patients from the database of Wuxi People’s Hospital and Wuxi Second People’s Hospital between 2010 and 2020, including patients’ demographic characteristics, chronic medical history, laboratory test characteristics, surgical information, and patients’ postoperative conditions. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN), were employed to develop predictive models. We also utilized Shapley additive explanation (SHAP) for model interpretation and evaluated the models using k-fold cross-validation, receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and external validation metrics.Results: The XGBoost algorithm demonstrated superior performance compared to the other three prediction models. The area under the curve (AUC) value for XGBoost was 0.989 in the training set and 0.912 in the validation set, indicating high prediction accuracy. Furthermore, the AUC value of the external validation set was 0.85, signifying good extrapolation of the XGBoost prediction model. The results of SHAP analysis revealed that several factors, including higher body mass index (BMI), history of adjuvant radiotherapy and chemotherapy, T-stage of the tumor, lymph node metastasis, central venous catheter use, high intraoperative bleeding, and long operative time, were significantly associated with postoperative VTE.Conclusion: The machine learning algorithm XGBoost derived from this study enables the development of a predictive model for postoperative VTE in patients after radical gastrectomy, thereby assisting clinicians in making informed clinical decisions.Keywords: gastric neoplasms, gastrectomy, venous thromboembolism, risk factors, machine learning, prediction model

Tumor recurrence is one of the major life-threatening complications after liver transplantation for liver cancer. In addition to the common mechanisms underlying tumor recurrence, another unavoidable problem is that the immunosuppressive therapeutic regimen after transplantation could promote tumor recurrence and metastasis. Transplant oncology is an emerging field that addresses oncological challenges in transplantation. In this context, a comprehensive therapeutic management approach is required to balance the anti-tumor treatment and immunosuppressive status of recipients. Double-negative T cells (DNTs) are a cluster of heterogeneous cells mainly consisting of two subsets stratified by T cell receptor (TCR) type. Among them, TCRαβ(+) DNTs are considered to induce immune suppression in immune-mediated diseases, while TCRγδ(+) DNTs are widely recognized as tumor killers. As a composite cell therapy, healthy donor-derived DNTs can be propagated to therapeutic numbers in vitro and applied for the treatment of several malignancies without impairing normal tissues or being rejected by the host. In this work, we summarized the biological characteristics and functions of DNTs in oncology, immunology, and transplantation. Based on the multiple roles of DNTs, we propose that a new balance could be achieved in liver transplant oncology using them as an off-the-shelf adoptive cell therapy (ACT).

Background: ADRC (active disturbance rejection controller) technology is a new practical technology that does not rely on the mathematical model of the controlled object and has strong robustness. It integrates the essence of modern control theory and classical proportion integral derivative theory and has good engineering application prospects. But with the research and development of the adaptive disturbance rejection controller, the problem of many parameters and the difficulty to adjust also arises. Objective: To act as a guide for future studies on the enhancement of ADRC parameter setting, to support the growth of ADRC technology, and to promote the effective use of the technology in other control fields. Method: The basic concepts and principles of ADRC control, the research methodologies for ADRC parameter tuning, and the research progress for ADRC parameter tuning in each direction are all introduced in this paper. The benefits and drawbacks of each method are then compiled, and a potential course of future development is suggested. This information is expected to serve as a guide for future studies on the enhancement of ADRC parameter tuning. Conclusion: The parameter tuning of the ADRC is a complex adjustment process. At present, the mainstream parameter tuning methods include the empirical method, the bandwidth method, the intelligent algorithm tuning method, and the time scale tuning method. Among them, the empirical method demands the debugging personnel to adjust conform to the accumulated experience, and the adjustment process is cumbersome; The bandwidth method needs to go through a lot of tedious calculations to determine the control parameters according to the model of the system, and the generality is poor; and the use of intelligent algorithms to tune ADRC parameters has become the most widely used method for tuning parameters.

In May 2022, the monkeypox epidemic broke out worldwide. According to the cases collected by the Centers for Disease Control and Prevention (CDC), as of August 17, 2022, 13,517 cases were confirmed in the United States. Based on the 2022 U.S. monkeypox epidemic data collected by the CDC, the Prophet prediction model released by Facebook in 2017 was used to predict the future trend of the monkeypox epidemic in the United States. The range of daily confirmed cases for the 2022 U.S. Monkeypox epidemic applied from May 17, 2022, to August 10, 2022, with reporting date. The results forecast monkeypox trends in American monkeypox over periods as short as seven days and as long as 17 days, with all values of MAPE in this prediction having good forecasting accuracy between 0.018 and 0.117. The prediction model’s accuracy can be affected by the k-value biasing due to strange events and the implementation of epidemic prevention policies.

Purpose To explore the risk factors for breast cancer-related lymphedema (BCRL) and upper extremity dysfunction (UED) in patients with early breast cancer after modern comprehensive treatment and to compare the safety of various treatment strategies. Methods From 2017 to 2020, a total of 1369 female patients with pT1-3N0-1M0 breast cancer who underwent adjuvant radiotherapy in our centre were retrospectively reviewed. BCRL and UED were identified by the Norman and QuickDASH questionnaires. The incidence, severity and risk factors for BCRL and UED were evaluated. Results After a median follow-up of 25 months, a total of 249 patients developed BCRL; ALND, increased number of dissected nodes, right-sided and hypofractionated radiotherapy containing RNI were found to be significant risk factors (all p values

Yuan Liu,* Chen Song,* Zhiqiang Tian, Wei Shen Department of General Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei Shen, Department of General Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu Province, 214000, People’s Republic of China, Tel +86 13385110723, Email shenweijs@outlook.comPurpose: The occurrence of myocardial injury, a grave complication post complete mesocolic excision (CME), profoundly impacts the immediate and long-term prognosis of patients. The aim of this inquiry was to conceive a machine learning model that can recognize preoperative, intraoperative and postoperative high-risk factors and predict the onset of myocardial injury following CME.Patients and Methods: This study included 1198 colon cancer patients, 133 of whom experienced myocardial injury after surgery. Thirty-six distinct variables were gathered, encompassing patient demographics, medical history, preoperative examination characteristics, surgery type, and intraoperative details. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor algorithm (KNN), were employed to fabricate the model, and k-fold cross-validation, ROC curve, calibration curve, decision curve analysis (DCA), and external validation were employed to evaluate it.Results: Out of the four predictive models employed, the XGBoost algorithm demonstrated the best performance. The ROC curve findings indicated that the XGBoost model exhibited remarkable predictive accuracy, with an area under the curve (AUC) value of 0.997 in the training set and 0.956 in the validation set. For internal validation, the k-fold cross-validation method was utilized, and the XGBoost model was shown to be steady. Furthermore, the calibration curves demonstrated the XGBoost model’s high predictive capability. The DCA curve revealed higher benefit rates for patients who underwent interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.74, which indicated that the XGBoost prediction model possessed good extrapolative capacity.Conclusion: The myocardial injury prediction model for patients undergoing CME that was developed using the XGBoost machine learning algorithm in this study demonstrates both high predictive accuracy and clinical utility.Keywords: colonic neoplasms, myocardial injury, surgery, prognosis, risk factor, machine learning

Yuan Liu, Wei Shen, Zhiqiang Tian Department of General Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, People’s Republic of ChinaCorrespondence: Wei Shen, Department of General Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, People’s Republic of China, Tel +86 13385110723, Email shenweijs@outlook.comPurpose: Postoperative delirium (POD) is a common postoperative complication in elderly patients, and it greatly affects the short-term and long-term prognosis of patients. The purpose of this study was to develop a machine learning model to identify preoperative, intraoperative and postoperative high-risk factors and predict the occurrence of delirium after nonbrain surgery in elderly patients.Patients and Methods: A total of 950 elderly patients were included in the study, including 132 patients with POD. We collected 30 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination characteristics, type of surgery, and intraoperative information. Three machine learning algorithms, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and k-nearest neighbor algorithm (KNN), were applied to construct the model, and the k-fold cross-validation method, ROC curve, calibration curve, decision curve analysis (DCA) and external validation were used for model evaluation.Results: XGBoost showed the best performance among the three prediction models. The ROC curve results showed that XGBoost had a high area under the curve (AUC) value of 0.982 in the training set; the AUC value in the validation set was 0.924, and the prediction model was highly accurate. The k-fold cross-validation method was used for internal validation, and the XGBoost model was stable The calibration curve showed high predictive power of the XGBoost model. The DCA curve showed a higher benefit rate for patients who received interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.88, indicating that the predictive model was extrapolative.Conclusion: The prediction model of POD derived from the machine learning algorithm in this study has high prediction accuracy and clinical utility, which is beneficial for clinicians to diagnose and treat patients in a timely manner.Keywords: postoperative delirium, prevention, risk factors, machine learning, prediction model

China’s increasing engagement in international governance has had significant ramifications in international rule-making and institutional build-up. The post-COVID-19 era has seen a rise of populism in China, as well as an elevation of China’s significance in international governance. This article offers a new perspective on China’s growing influence in the midst of resurgent populism, by focusing on three case scenarios: defending state liability in public health crisis lawsuits, championing financial multilateralism, and an emerging digital currency landscape. The argument made here is that China’s status as a rising power has led it to advocate for international rules, standards, and institutions in a de-Americanised and anti-populist manner.