Search

Background The fat‐to‐muscle mass ratio (FMR) might be an indicator to assess type 2 diabetes risk independent of general obesity. However, no longitudinal studies have explored the extent to which total and regional FMRs may confer risks. We aimed to measure the sex‐specific associations between FMRs of the arm, leg, trunk and whole body and incident type 2 diabetes. Methods A total of 464 817 participants (207 286 men and 257 531 women, mean age 56.5 ± 8.2 and 56.2 ± 8.0 years old, respectively) free of diabetes at baseline were included in this prospective cohort study with UK Biobank data. Fat mass and muscle mass were estimated using a bioelectrical impedance assessment device (Tanita BC 418MA). FMR was calculated as fat mass divided by muscle mass in corresponding body parts (total body, arm, leg and trunk). Cox proportional hazard models were used to estimate the aforementioned associations among men and women. Interaction analyses were performed between FMRs and body mass index (BMI) categories (BMI

Background: Coronary heart disease (CHD) is an important cause of chronic heart failure, and chronic heart failure is also a serious complication in the end stage of coronary heart disease. At present, there is no specific treatment plan. Shenfu injection has advantages in the treatment of heart failure in patients with coronary heart disease, but there is a lack of standard clinical study to verify this. Therefore, the purpose of this randomized controlled trial is to evaluate the efficacy and safety of Shenfu injection combined with furosemide in the treatment of chronic heart failure in patients with coronary heart disease. Methods: This is a prospective randomized controlled trial to study the efficacy and safety of Shenfu injection combined with furosemide in the treatment of coronary heart disease and chronic heart failure. This study will be approved by the clinical research ethics committee of our hospital. The patients will be randomly divided into two groups according to 1:1:(a) Shenfu injection combined with furosemide group and (b) simple furosemide group. Standard treatment for 7 days followed up for 30 days at the same time, pay attention to its efficacy and safety indicators. The total effective rate of cardiac function improvement, left ventricular ejection fraction (LVEF), N-terminal pro-brain natriuretic peptide (NT-pro BNP), 6-minute walk test (6-MWTD), and adverse reactions will be observed. Data will be analyzed using the statistical software package SPSS version 25.0 (Chicago, IL). Discussion: This study will evaluate the efficacy and safety of Shenfu injection combined with furosemide in the treatment of coronary heart disease with chronic heart failure. The results of this experiment will provide a clinical basis for Shenfu injection combined with furosemide in the treatment of coronary heart disease and chronic heart failure. OSF Registration number: doi: 10.17605/OSF.IO/27FPM

Prostate cancer (PCa) is a common high-incidence malignancy in men, some of whom develop biochemical recurrence (BCR) in the advanced stage. However, there are currently no accurate prognostic indicators of BCR in PCa. The aim of our study was to identify an autophagy-related circular RNA prognostic factor of BCR for patients with PCa. In this study, immunochemistry revealed that the classic autophagy marker MAP1LC3B was positively correlated with Gleason score. Least absolute shrinkage and selector operator regression were conducted to develop a novel prognostic model with tenfold cross-validation and an L1 penalty. Five autophagy-related circRNA signatures were included in the prognostic model. Patients with PCa were ultimately divided into high- and low-risk groups, based on the median risk score. Patients with PCa, who had a high risk score, were more likely to develop BCR in a shorter period of time. Univariate and multivariate Cox regression analyses demonstrated that the risk score was an independent variable for predicting BCR in PCa. In addition, a prognostic nomogram integrated with the risk score and numerous clinicopathological parameters was developed to accurately predict 3- and 5-year BCR of patients with PCa. Finally, the hsa_circ_0001747 signature was selected for further experimental verification in vitro and in vivo, which showed that downregulated hsa_circ_0001747 might facilitate PCa via augmenting autophagy. Our findings indicate that the autophagy-related circRNA signature hsa_circ_0001747 may serve as a promising indicator for BCR prediction in patients with PCa.

Aims Observational studies have suggested strong associations between sleep duration and many cardiovascular diseases (CVDs), but causal inferences have not been confirmed. We aimed to determine the causal associations between genetically predicted sleep duration and 12 CVDs using both linear and nonlinear Mendelian randomization (MR) designs. Methods and results Genetic variants associated with continuous, short (≤6 h) and long (≥9 h) sleep durations were used to examine the causal associations with 12 CVDs among 404 044 UK Biobank participants of White British ancestry. Linear MR analyses showed that genetically predicted sleep duration was negatively associated with arterial hypertension, atrial fibrillation, pulmonary embolism, and chronic ischaemic heart disease after correcting for multiple tests (P Conclusion This study suggests that genetically predicted short sleep duration is a potential causal risk factor of several CVDs, while genetically predicted long sleep duration is unlikely to be a causal risk factor for most CVDs.

A lower day-to-night systolic blood pressure (BP) dip has previously been associated with poor brain health and cognitive functions. Here, we sought to examine whether reduced (nighttime/daytime ratio of systolic BP >0.9 and ≤1) and reverse (nighttime/daytime ratio of systolic BP >1) dipping of systolic BP is associated with the prospective risk of being diagnosed with any dementia in Swedish older men. Twenty-four-hour ambulatory BP monitoring was used to estimate the nocturnal systolic BP dipping status of men at mean age 71 (n=997; 35% on antihypertensive medication) and 77.6 (n=611; 41% on antihypertensive medication). Dementia incidence during the observational period up to 24 years (n=286 cases) was determined by reviewing participants’ medical history and independently confirmed by at least 2 experienced geriatricians. Using time-updated Cox regression (ie, time-updated information on covariates and exposure), we found that reverse systolic BP dipping was associated with a higher risk of being diagnosed with any dementia (adjusted HR, 1.64 [95% CI, 1.14–2.34], P =0.007) and Alzheimer’s disease (1.67 [1.01–2.76], P =0.047) but not vascular dementia (1.29 [0.55–3.06], P =0.559). In contrast, reduced dipping of nocturnal systolic BP was not associated with a higher risk of being diagnosed with dementia. Our findings suggest that reverse systolic BP dipping may represent an independent risk factor for dementia and Alzheimer’s disease in older men. Future studies should decipher whether therapies lowering nocturnal systolic BP below daytime levels, such as bedtime dosing of antihypertensive medication, can meaningfully curb the development of dementia.

Wireless sensor technology plays an important role in the military, medical, and commercial fields nowadays. Wireless Body Area Network (WBAN) is a special application of the wireless sensor network in human health monitoring, through which patients can know their physical condition in real time and respond to emergencies on time. Data reliability, guaranteed by the trust of nodes in WBAN, is a prerequisite for the effective treatment of patients. Therefore, authenticating the sensor nodes and the sink nodes in WBAN is necessary. This paper proposes a lightweight Physical Unclonable Function (PUF)-based and cloud-assisted authentication mechanism for multi-hop body area networks, which compared with the star single-hop network, can enhance the adaptability to human motion and the integrity of data transmission. Such authentication mechanism can significantly reduce the storage overhead and resource loss in the data transmission process.

Learning multiple related graphs from many distributed and privacy-required resources is an important and common task in neuroscience applications. Medical researchers can comprehensively investigate the diagnostic evidence and understand the cause of certain brain diseases via analyzing the commonalities and differences of the brain connectomes predicted from the fMRI data across multiple hospitals. Previous sparse Undirected Graphical Model (sUGM) methods either cannot take full usage of the heterogeneous data while preserving privacy or miss the capability of handling the nonparanormal data, which is highly non-independent and identically distributed (non-i.i.d.). This paper proposes a novel and efficient approach, FEDJEM (federated joint estimator of multiple sUGMs), that trains the multi-sUGMs over a massive network encompassing various local devices and the global center. In order to efficiently process the datasets with different nonparanormal distributions, the proposed federated algorithm fully exploits the computing power of the local devices and cloud center while federated updates ensure that personal data remain local, thus defending the privacy. We also implement a general federated learning framework for multi-task learning based on our method. We apply our method on multiple simulation datasets to evaluate its speed and accuracy in comparison with relevant baselines and develop a strategy accordingly to balance its computation and communication abilities. Finally, we predict several informative groups of connectomes based on the real-world dataset.

A moving bed biofilm reactor (MBBR) is a kind of commonly used biological sewage treatment process. A carrier, the core of MBBR, could directly affect the treatment efficiency of MBBR. In this experiment, a hybrid carrier composed of an MBBR carrier and fluidized bed porous carrier was innovatively utilized to treat low-concentration simulated domestic sewage through an MBBR reactor to investigate the effects of different hydraulic retention times (HRT) and different carrier dose ratios on the reactor performance. The results indicated that when the volume ratio of the carrier dosage was 5% : 20% when the reactor HRT was 5 h, the removal rates of ammonia nitrogen, total nitrogen (TN) and chemical oxygen demand (CODCr) were optimal, which were 96.5%, 60.9% and 91.5%, respectively. The ammonia nitrogen, total nitrogen and CODCr concentrations of the effluent were 1.04 mg L−1, 12.20 mg L−1 and 29.02 mg L−1, respectively. Furthermore, the total biomass concentration in the hybrid carrier biofilm reactor (HCBR) was 3790.35 mg L−1, which also reached the highest value. As the experiment progressed, the concentrations of protein, polysaccharide and soluble microbial products (SMP) were reduced to 7.68 mg L−1, 11.10 mg L−1 and 18.08 mg L−1, respectively. This was basically consistent with the results of the three-dimensional fluorescence spectrum. The results showed that the combined-carrier biofilm reactor could reduce the volumetric filling rate, improving the removal capability of organic matter and the denitrification efficiency. This study provided technical support for the composite carrier biofilm wastewater treatment technology, and also had a good prospect of application.

Previous studies have pointed out that indoleamine 2,3-dioxygenase (IDO), the rate-limiting enzyme initiating tryptophan metabolism, plays a role in the regulation of the immune system. This project was designed to investigate the potential role of IDO in monocyte-derived dendritic cells (moDCs) obtained from active Vogt-Koyanagi-Harada (VKH) disease patients. In this study, we found that the IDO mRNA expression and enzyme activity were increased in active VKH patients as compared with healthy controls and patients in remission. To investigate the role of IDO in immune regulation, an effective inhibitor 1-methyl-L-tryptophan (1-MT) was used to suppress its activity in DCs. The results showed that inhibition of IDO with 1-MT significantly decreased the expression of DC marker CD86. IDO inhibition did not affect the cytokine production of IL-6, IL-1β, TNF-α, IL-10, and TGF-β in DCs. Downregulation of IDO in DCs also led to the reduction of regulatory T (Treg) cells and an increased CD4 + T cell proliferation. Treatment with 1-MT did not affect the phosphorylation of the MAPK pathway in DCs. In general, our study suggests that IDO may play an important role in the pathogenesis of VKH disease by regulating DC and CD4 + T cell function. Tryptophan deficiency and kynurenine accumulation may account for the complicated effects of IDO. Further research is needed to study the precise tryptophan metabolites that may limit immune responses in VKH disease.

HCC (hepatocellular carcinoma) is a major health threat for the Chinese population and has poor prognosis because of strong resistance to chemotherapy in patients. For instance, a considerable challenge for the treatment of HCC is sorafenib resistance. The aberrant glucose metabolism in cancer cells aerobic glycolysis is associated with resistance to chemotherapeutic agents. Drug-resistance cells and tumors were exposed to sorafenib to establish sorafenib-resistance cell lines and tumors. Western blotting and real-time PCR or IHC staining were used to analyze the level of CLCF1 in the sorafenib resistance cell lines or tumors. The aerobic glycolysis was analyzed by ECAR assay. The mechanism mediating the high expression of CLCF1 in sorafenib-resistant cells and its relationships with miR-130-5p was determined by bioinformatic analysis, dual luciferase reporter assays, real-time PCR, and western blotting. The in vivo effect was evaluated by xenografted with nude mice. The relation of CLCF1 and miR-30a-5p was determined in patients’ samples. In this study, we report the relationship between sorafenib resistance and increased glycolysis in HCC cells. We also show the vital role of CLCF1 in promoting glycolysis by activating PI3K/AKT signaling and its downstream genes, thus participating in glycolysis in sorafenib-resistant HCC cells. Furthermore, we also show that miR-30a-5p directly targets CLCF1 and that sorafenib-mediated suppression of miR-30a-5p results in the upregulation of CLCF1 in HCC cells resistant to sorafenib. We also found that when a cholesterol modified agomiR-30a-5p was delivered systemically to mice harboring sorafenib-resistant HCC tumors, tumor growth decreased significantly. There is an uncharacterized mechanism of biochemical resistance to hormone therapies orchestrated by the miR-30a-5p/CLCF1 axis to mediate sorafenib resistance and aerobic glycolysis in HCC. Therefore, this study indicates that targeting the miR-30a-5p/CLCF1 axis may hold promise for therapeutic intervention in HCC sorafenib resistance patients.

Background Trimethylamine-N-Oxide (TMAO) is a proatherogenic and prothrombotic metabolite. Our study examined the association of plasma TMAO level with cardiovascular and all-cause mortality in hemodialysis (HD) patients. Methods Patients who were at least 18 years-old and received HD for at least 6 months were enrolled within 6 months. Patients with coronary heart disease, congestive heart failure, arrhythmia, or stroke within 3 months before study onset were excluded. The primary endpoints were cardiovascular and all-cause death, and the secondary endpoint was cerebrovascular death. Results We recruited 252 patients and divided them into a high-TMAO group (>4.73 μg/mL) and a low-TMAO group (≤4.73 μg/mL). The median follow-up time was 73.4 months (interquartile range: 42.9, 108). A total of 123 patients died, 39 from cardiovascular disease, 19 from cerebrovascular disease, and 65 from other causes. Kaplan-Meier analysis indicated that the high-TMAO group had a greater incidence of cardiovascular death (Log-Rank: p = 0.006) and all-cause death (Log-Rank: p

The consensus reaching process (CRP) is essential to obtain a final solution in group decision making (GDM). This paper focuses on constructing a consensus mechanism where decision makers (DMs) use linguistic term sets (LTSs) to express their preferences. LTSs can describe real decision making well. Adjusting opinions can improve the consensus level, we assume that the adjusted opinion combines an initial preference and a referenced preference. The subjective behavior characteristics expressed by DMs would affect the CRP. To simulate human behavior, quantum probability theory is applied to aggregate referenced opinions, reflecting the interference effect caused by subjective beliefs flowing toward decision classification paths. The paths are composed of the clusters with the highest consensus and consistency degrees. Moreover, the combination is generated based on the subjective willingness defined by the consensus position of the recognized DM in the group. An optimization model is set to control individual consistency after implementing modifications. Finally, a numerical example illustrates the principle of the constructed model.

Ovarian cancer is the most lethal malignant tumor of female reproductive system. It is well-known that induction of STING-mediated type I interferons can enhance the resultant antitumor activity. However, STING pathway is usually inactivated in cancer cells at multiple levels. Here, we identified deubiquitinase USP35 is upregulated in ovarian cancer tissues. High level of USP35 was correlated with diminished CD8(+) T cell infiltration and poor prognosis in ovarian cancer patients. Mechanistically, we found that silencing USP35 reinforces the activation of STING-TBK1-IRF3 pathway and promotes the expression of type I interferons. Our data further showed that USP35 can directly deubiquitinate and inactivate STING. Interestingly, activation of STING promotes its binding to USP35 in a STING phosphorylation-dependent manner. Functionally, we found that knockdown of USP35 sensitizes ovarian cancer cells to the DNA-damage chemotherapeutic drug cisplatin. Overall, our study indicates that upregulation of USP35 may be a mechanism of the restricted STING activity in cancer cells, and highlights the significance of USP35 as a potential therapeutic target for ovarian cancer.

OBJECTIVE The common MTNR1B single nucleotide polymorphism rs10830963 associates with risk of type 2 diabetes (T2D). Here, we examine the association between this gene variant and the risk of myocardial infarction (MCI) among patients with T2D. MCI is a main cause of death and disability among such individuals. RESEARCH DESIGN AND METHODS Data from the UK Biobank cohort were used in order to examine the association between rs10830963 and incidence of MCI (fatal and nonfatal) among 13,655 participants with probable T2D during a follow-up period of 6.8 years. RESULTS Assuming an additive genetic model, a positive association was found between the rs10830963 variant in the MTNR1B gene and the risk for incident MCI during the 6.8-year follow-up (adjusted hazard ratio per G allele 1.19 [95% CI 1.02, 1.40], P = 0.03). CONCLUSIONS The rs10830963 polymorphism may be a useful genetic marker for MCI in patients with T2D.

Accumulating evidence show that exercise and diet interventions are associated with improved sleep quality. Studies investigating the effects of exercise and dieting on circulating metabolomics in people with sleep disorders, particularly insomnia, are scarce. This 6-month randomized study aimed to assess the effects of exercise and dietary interventions on serum metabolites in men with insomnia symptoms. Seventy-two Finnish men (age: 51.6 ± 10.1 years) with chronic insomnia symptoms who were assigned to different intervention groups completed this study (exercise, n = 24; diet, n = 27; and control, n = 21). The Shapiro-Wilk W-test, Levene test, Spearman correlation analysis, and analysis of variance were used for data analysis. We found that exercise and diet intervention were associated with improved sleep quality and with a number of metabolites across different biochemical pathways. Although we could not show causality, our findings provide new insight into the biological mechanisms underlying the health effects of physical activity, diet, and sleep quality. Further investigation is needed to better understand the link among lifestyle, sleep quality, and metabolic health.

With significant development of Internet of medical things (IoMT) and cloud-fog-edge computing, medical industry is now involving medical big data to improve quality of service in patient care. Karyotyping refers classifying human chromosomes. However, performing karyotyping task generally requires domain expertise in cytogenetics, long-period experience for high accuracy, and considerable manual efforts. An end-to-end chromosome karyotype analysis system is proposed over medical big data to automatically and accurately perform chromosome related tasks of detection, segmentation, and classification. Facing image data generated and collected by means of edge computing, we firstly utilize visual feature to generate chromosome candidates with Extremal Regions (ER) technology. Due to severe occlusion and cross overlapping, we utilize ring radius transform to cluster pixels with same property to approximate chromosome shapes. To solve the problem of unbalanced and small dataset by covering diverse data patterns, we proposed multidistributed generated advertising network (MD-GAN) to perform data enhancement by generating additional training samples. Afterwards, we fine-tune CNN for chromosome classification task by involving generated and sufficient training images. Through experiments in self-collected datasets, the proposed method achieves high accuracy in tasks of chromosome detection, segmentation, and classification. Moreover, experimental results prove that MD-GAN-based data enhancement contributes to classification results of CNN to a certain extent.

Summary Background Chemoprevention of colorectal adenoma and colorectal cancer remains an important public health goal. The present study aimed to investigate the clinical potential and safety of berberine for prevention of colorectal adenoma recurrence. Methods This double-blind, randomised, placebo-controlled trial was done in seven hospital centres across six provinces in China. Individuals aged 18–75 years who had at least one but no more than six histologically confirmed colorectal adenomas that had undergone complete polypectomy within the 6 months before recruitment were recruited and randomly assigned (1:1) to receive berberine (0·3 g twice daily) or placebo tablets via block randomisation (block size of six). Participants were to undergo a first follow-up colonoscopy 1 year after enrolment, and if no colorectal adenomas were detected, a second follow-up colonoscopy at 2 years was planned. The study continued until the last enrolled participant reached the 2-year follow-up point. All participants, investigators, endoscopists, and pathologists were blinded to treatment assignment. The primary efficacy endpoint was the recurrence of adenomas at any follow-up colonoscopy. Analysis was based on modified intention-to-treat, with the full analysis set including all randomised participants who received at least one dose of study medication and who had available efficacy data. The study is registered with ClinicalTrials.gov , number NCT02226185 ; the trial has ended and this report represents the final analysis. Findings Between Nov 14, 2014, and Dec 30, 2016, 553 participants were randomly assigned to the berberine group and 555 to the placebo group. The full analysis set consisted of 429 participants in the berberine group and 462 in the placebo group. 155 (36%) participants in the berberine group and 216 (47%) in the placebo group were found to have recurrent adenoma during follow-up (unadjusted relative risk ratio for recurrence 0·77, 95% CI 0·66–0·91; p=0·001). No colorectal cancers were detected during follow-up. The most common adverse event was constipation (six [1%] of 446 patients in the berberine group vs one [ Interpretation Berberine 0·3 g twice daily was safe and effective in reducing the risk of recurrence of colorectal adenoma and could be an option for chemoprevention after polypectomy. Funding National Natural Science Foundation of China.

Objectives: Using a comprehensive indicator to measure the association between psychological wellbeing score and type 2 diabetes and whether this association was modified by genetic predisposition. Methods: In UK Biobank, a total of 127,496 participants were included in this prospective cohort study without type 2 diabetes at baseline. The healthy psychological wellbeing score was calculated according four factors: happiness, life satisfaction, broad depression and neuroticism. Cox proportional hazard models were used to test the association between quartile of weighted psychological wellbeing score and type 2 diabetes risk. Interaction analysis between psychological wellbeing and weighted genetic risk score to diabetes was further performed. Results: During the median follow-up of 10.0 years (about 1.3 million person-years), 2,547 incident type 2 diabetes were documented. Moderate to extreme unhappiness, satisfaction score ≤3, presence of broad depression and neuroticism score ≥3 were all significantly associated with increased risk of incident diabetes when mutually adjusted. When considered as a comprehensive indicator, there was a significant association between weighted psychological wellbeing score quartiles and type 2 diabetes. Compared with individuals in the highest quartile, which means healthiest, the fully adjusted HRs (95% CI) of type 2 diabetes in lower quintile groups were 1.39(1.19-1.63), 1.44(1.23-1.67) and 1.86(1.60-2.17), respectively. In the stratified analysis, we observed the significant interactions among age, physical activity, HbA1c and type 2 diabetes (P interaction

Exploring and utilizing the agronomic potential of deep-underground is one of the ways to cope with the challenges of sudden environmental change on agriculture. Understanding the effects of environmental stresses on the morphological and physiological indicators of crop seeds after their storage deep-underground is crucial to developing and implementing strategies for agriculture in the deep-underground space. In this study, we stored canola seeds in envelopes or sealed packages at 0, 240, 690, and 1,410 m in a gold mine. Seeds in envelopes were retrieved at 42, 66, 90, and 227 days of storage, whereas seeds in sealed packages were retrieved at 66 and 227 days of storage. The germination tests were conducted to investigate the effects of storage depth, duration, and packing method on stored and non-stored seeds. Results showed that increased depth and duration led to decreased seed germination rate, with the germination and vigor indexes also descending to varying degrees. Increased hypocotyl length and biomass accumulation suggested that deep-underground environment had more significant compensatory effect on seed germination. For all indicators, the performance of seeds sealed in packages was superior to those stored in envelopes. Regression analysis showed that it was difficult to obtain the optimal value of each indicator simultaneously. The successful germination experiment foreshadowed the possibilities of deep-underground agriculture in the future.

Previous small-scale studies have found that oral antidiabetic therapy is associated with sleep difficulties among patients with type 2 diabetes (T2D). Here, we used data from 11 806 T2D patients from the UK Biobank baseline investigation to examine the association of oral antidiabetic therapy with self-reported difficulty falling and staying asleep and daily sleep duration. As shown by logistic regression adjusted for, e.g., age, T2D duration, and HbA1c, patients on non-metformin therapy (N=815; 86% were treated with sulphonylureas) had a 1.24-fold higher odds ratio of reporting regular difficulty falling and staying asleep at night compared to those without antidiabetic medication use (N=5 366, PDe två sista författarna delar sistaförfattarskapet

Objectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI).Methods: This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients. Three feature selection methods and four classifiers, such as k-nearest neighbors (KNN), random forest classifier (RFC), logistic regression (LR), and support vector machine (SVM), for distinguishing brain abscess from cystic glioma were compared. The best feature combination and classifier were chosen according to the quantitative metrics including area under the curve (AUC), Youden Index, and accuracy.Results: In most cases, deep learning-based radiomics (DLR) features, i.e., DTL features combined with HCR features, contributed to a higher accuracy than HCR and DTL features alone for distinguishing brain abscesses from cystic gliomas. The AUC values of the model established, based on the DLR features in T2WI, were 0.86 (95% CI: 0.81, 0.91) in the training cohort and 0.85 (95% CI: 0.75, 0.95) in the test cohort, respectively.Conclusions: The model established with the DLR features can distinguish brain abscess from cystic glioma efficiently, providing a useful, inexpensive, convenient, and non-invasive method for differential diagnosis. This is the first time that conventional MRI radiomics is applied to identify these diseases. Also, the combination of HCR and DTL features can lead to get impressive performance.

Without appealing to exhaustive labeled data, self-supervised monocular depth estimation (MDE) plays a fundamental role in computer vision. Previous methods usually adopt a one-stage MDE network, which is insufficient to achieve high performance. In this paper, we dig deep into this task to propose an aggressive framework termed AggNet. The framework is based on a training-only progressive two-stage module to perform pseudo counter-surveillance as well as a simple yet effective dual-warp loss function between image pairs. In particular, we first propose a residual module, which follows the MDE network to learn a refined depth. The residual module takes both the initial depth generated from MDE and the initial color image as input to generate refined depth with residual depth learning. Then, the refined depth is leveraged to supervise the initial depth simultaneously during the training period. For inference, only the MDE network is retained to regress depth from a single image, which gains better performance without introducing extra computation. In addition to self-distillation loss, a simple yet effective dual-warp consistency loss is introduced to encourage the MDE network to keep depth consistency between stereo image pairs. Extensive experiments show that our AggNet achieves state-of-the-art performance on the KITTI and Make3D datasets.

Joint learning of scene parsing and depth estimation remains a challenging task due to the rivalry between the two tasks. In this paper, we revisit the mutual enhancement for joint semantic segmentation and depth estimation. Inspired by the observation that the competition and cooperation could be reflected in the feature frequency components of different tasks, we propose a Frequency Aware Feature Enhancement (FAFE) network that can effectively enhance the reciprocal relationship whereas avoiding the competition. In FAFE, a frequency disentanglement module is proposed to fetch the favorable frequency component sets for each task and resolve the discordance between the two tasks. For task cooperation, we introduce a re-calibration unit to aggregate features of the two tasks, so as to complement task information with each other. Accordingly, the learning of each task can be boosted by the complementary task appropriately. Besides, a novel local-aware consistency loss function is proposed to impose on the predicted segmentation and depth so as to strengthen the cooperation. With the FAFE network and new local-aware consistency loss encapsulated into the multi-task learning network, the proposed approach achieves superior performance over previous state-of-the-art methods. Extensive experiments and ablation studies on multi-task datasets demonstrate the effectiveness of our proposed approach.

In this paper, we propose a dimension apart network (DANet) for radar object detection task. A Dimension Apart Module (DAM) is first designed to be lightweight and capable of extracting temporal-spatial information from the RAMap sequences. To fully utilize the hierarchical features from the RAMaps, we propose a multi-scale U-Net style network architecture termed DANet. Extensive experiments demonstrate that our proposed DANet achieves superior performance on the radar detection task at much less computational cost, compared to previous pioneer works. In addition to the proposed novel network, we also utilize a vast amount of data augmentation techniques. To further improve the robustness of our model, we ensemble the predicted results from a bunch of lightweight DANet variants. Finally, we achieve 82.2% on average precision and 90% on average recall of object detection performance and rank at 1st place in the ROD2021 radar detection challenge. Our code is available at: \urlhttps://github.com/jb892/ROD2021_Radar_Detection_Challenge_Baidu.

In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. Specifically, given an untrimmed video, WSSTAD aims to localize a spatio-temporal tube (i.e., a sequence of bounding boxes at consecutive times) that encloses the abnormal event, with only coarse video-level annotations as supervision during training. To address this challenging task, we propose a dual-branch network which takes as input the proposals with multi-granularities in both spatial-temporal domains. Each branch employs a relationship reasoning module to capture the correlation between tubes/videolets, which can provide rich contextual information and complex entity relationships for the concept learning of abnormal behaviors. Mutually-guided Progressive Refinement framework is set up to employ dual-path mutual guidance in a recurrent manner, iteratively sharing auxiliary supervision information across branches. It impels the learned concepts of each branch to serve as a guide for its counterpart, which progressively refines the corresponding branch and the whole framework. Furthermore, we contribute two datasets, i.e., ST-UCF-Crime and STRA, consisting of videos containing spatio-temporal abnormal annotations to serve as the benchmarks for WSSTAD. We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task., accepted by IJCAI2021

In this paper, a plaintext related image hybrid encryption scheme is proposed based on Lagrange interpolation, generalized Henon map and nonlinear operations of matrices. The proposed scheme consists of three parts. In the first part, a generalized chaotic map is constructed on the basis of Henon map. Using the novel map, a chaotic sequence is built. And then, both the chaotic sequence and the plaintext pixels are used to implement the first nonlinear operation for generating the first cipher matrix associated with the plaintext. By performing an exclusive XOR operation between the original pixels matrix and the first cipher matrix, the diffusion encryption is carried out. In the second part, Lagrange interpolation is used to create the second cipher matrix related to the diffused image; the second nonlinear transformation is developed between the diffused image and the second cipher matrix; and sequence rearrangement is adopted to scramble the diffused image. In the third part, the third nonlinear transformation of matrices based on point operation and rounding operation is implemented on the scrambled image to complete the image encryption. Accordingly, the decryption process is executed by the inverse operations in the opposite order. The proposed algorithm has some distinctive features: a variety of nonlinear tools such as nonlinear polynomial interpolation, nonlinear chaotic map, and nonlinear operations were involved in the scheme. The cryptosystem is designed with the plaintext to enhance the algorithm security. Due to the combination of multiple nonlinear methods and random factors, the scheme is one time pad, which can withstand multiple types of attacks. The algorithm has a clear structure and a simple calculation, so it is easy to program. In addition, encryption simulation and performance analysis are carried out. The feasibility and effectiveness of the algorithm are verified by the simulated results. The security of the algorithm are proved by the objective indicators such as the running time, key space, statistical properties, key sensitivity, and differential analysis, etc.

The Ordos Basin is one of the largest continental sedimentary basins and it represents one major and famous production area of coal, oil and gas resources in China. The Jurassic non-marine deposits are well developed and cropped out in the basin. The Middle Jurassic Yan’an Formation is rich in coal and contains diverse plant remains. We recognize 40 species in 25 genera belonging to mosses, horsetails, ferns, cycadophytes, ginkgoaleans, czekanowskialeans and conifers. This flora is attributed to the early Middle Jurassic Epoch, possibly the Aalenian-Bajocian. The climate of the Ordos Basin during the Middle Jurassic was warm and humid with seasonal temperature and precipitation fluctuations. The result tends to support the palaeophytogeographic/palaeoclimatic setting of the Ordos Basin in the North Floristic Province, or the North Chinese Province in the warm temperate zone during the Middle Jurassic.

Object tracking is to accurately track target information in continuous video sequences, and the bounding box of target is also indispensable as an important metric for evaluating tracker algorithms. In recent years, correlation filter trackers have been successfully achieved powerful robustness. To estimate object scale, most correlation filter trackers use a simple multi-scale search that has limited the development of correlation filters in precision tracking. We propose a scale estimation network to solve the problem, which uses the experience of bounding box estimation in object detection. Through extensive offline learning, high-level knowledge is incorporated into target estimation. Our scale estimation network is trained to optimize object scale. We further introduce the fusion method between correlation filter and scale estimation network coordinated operation. Our improved tracker evaluated on three benchmarks: OTB2015, VOT2018, and VOT2019. The evaluation experiments on these benchmarks demonstrate that our tracker has better performance.

Video retrieval is becoming increasingly important owing to the rapid emergence of videos on the Internet. The dominant paradigm for video retrieval learns video-text representations by pushing the distance between the similarity of positive pairs and that of negative pairs apart from a fixed margin. However, negative pairs used for training are sampled randomly, which indicates that the semantics between negative pairs may be related or even equivalent, while most methods still enforce dissimilar representations to decrease their similarity. This phenomenon leads to inaccurate supervision and poor performance in learning video-text representations. While most video retrieval methods overlook that phenomenon, we propose an adaptive margin changed with the distance between positive and negative pairs to solve the aforementioned issue. First, we design the calculation framework of the adaptive margin, including the method of distance measurement and the function between the distance and the margin. Then, we explore a novel implementation called "Cross-Modal Generalized Self-Distillation" (CMGSD), which can be built on the top of most video retrieval models with few modifications. Notably, CMGSD adds few computational overheads at train time and adds no computational overhead at test time. Experimental results on three widely used datasets demonstrate that the proposed method can yield significantly better performance than the corresponding backbone model, and it outperforms state-of-the-art methods by a large margin., Comment: Accepted by SIGIR 2021

Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt 2D or 3D convolutions to extract instance embeddings for instance association. However, due to the large receptive field of deep convolutional neural networks, the foreground areas of the current instance and the surrounding areas containing the nearby instances or environments are usually mixed up in the learned instance embeddings, resulting in ambiguities in tracking. In this paper, we propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation. In this way, the non-overlapping nature of instance segments can be fully exploited by strictly separating the foreground point cloud and the background point cloud. Moreover, multiple informative data modalities are formulated as point-wise representations to enrich point-wise features. For each instance, the embedding is learned on the foreground 2D point cloud, the environment 2D point cloud, and the smallest circumscribed bounding box. Then, similarities between instance embeddings are measured for the inter-frame association. In addition, to enable the practical utility of MOTS, we modify the one-stage instance segmentation method SpatialEmbedding for instance segmentation. The resulting efficient and effective framework, named PointTrackV2, outperforms all the state-of-the-art methods including 3D tracking methods by large margins (4.8 percent higher sMOTSA for pedestrians over MOTSFusion) with the near real-time speed (20 FPS evaluated on a single 2080Ti). Extensive evaluations on three datasets demonstrate both the effectiveness and efficiency of our method. Furthermore, as crowded scenes for cars are insufficient in current MOTS datasets, we provide a more challenging dataset named APOLLO MOTS with a much higher instance density.

Vehicle re-identification (re-id) is one of the most important components in the current intelligence transport system, benefiting both the smart traffic management and the optimal path planning. In this paper, we focus on developing a robust part-aware structure-based vehicle re-id system against the massive appearance changes due to the pose and illumination variants. Specifically, we apply the strong convolutional neural networks to extract the visual representation, which is based on the detected vehicle images. Taking one step further, we deploy a part detector to recognize different vehicle parts, such as front, back, left, and right, which explicitly introduce the prior knowledge on the structure of the rigid objective, i.e., vehicle. With the geometry information, we further harness different part feature extractors to filter wrong matches. By using this simple but effective strategy, we remove the hard negative candidates while maintaining high recall accuracy, combing general global-level coarse-grained re-id feature models with part-level fine-grained features. We achieved 71.51% mAP in the vehicle re-id track of the AI City Challenge 2021, which verified the effectiveness and scalability of the proposed structure-based method. The code will be available at https://github.com/XuanmengZhang/AICITY2021-Track2.

Multi-Target Multi-Camera tracking (MTMC) is an essential task in the intelligent city and traffic analysis. It is a great challenging task due to several problems such as heavy occlusions and appearance variance caused by various camera perspectives and congested vehicles. In this paper, we propose a practical framework for dealing with the MTMC problem. The proposed framework contains three stage. Firstly, in the vehicles detection and Re-ID stage, the proposed system leverages Cascade R-CNN to detect all vehicles and extract appearance features with a Re-ID module for all cameras. Secondly, in the Multi-Target Single-Camera tracking (MTSC) stage, on the basis of the detected boxes and appearance features, it tracks multiple vehicles to generate candidate trajectories within each single camera with Tracklet-Plane Matching (TPM) tracking algorithm. Finally, in the Inter-Camera Association (ICA) stage, it associates all candidate trajectories between two successive cameras using the established distance matrix, and combines all successively matching results for final submission. The established distance matrix is simply computed by the Re-ID features and refined by the constraints of traveling time, road structures, and traffic rules to accelerate matching time as well as reduce search space. Extensive experiments on the public track3 test set of NVIDIA AI CITY 2021 CHALLENGE demonstrate the effectiveness of our method, which achieves IDF1 of 77.87%.

In this paper, we propose an online movement-specific vehicle counting system to realize robust traffic flow analysis at crowded intersections. Our proposed framework adopts PP-YOLO as the vehicle detector and adapts the Deep-Sort algorithm to perform multi-object tracking. In order to realize online and robust vehicle counting, we further adopt a shape-based movement assignment strategy to differentiate movements and carefully designed spatial constraints to effectively reduce false-positive counts. Our proposed framework achieves the overall S1-score of 0.9467, ranking the first in the AICITY2021-track1 challenge.

The detection of traffic anomalies is a critical component of the intelligent city transportation management sys-tem. Previous works have proposed a variety of notable in-sights and taken a step forward in this field, however, dealing with the complex traffic environment remains a challenge. Moreover, the lack of high-quality data and the complexity of the traffic scene, motivate us to study this problem from a hand-crafted perspective. In this paper, we propose a straightforward and efficient framework that includes pre-processing, a dynamic track module, and post-processing. With video stabilization, background modeling, and vehicle detection, the pro-processing phase aims to generate candidate anomalies. The dynamic tracking module seeks and locates the start time of anomalies by utilizing vehicle motion patterns and spatiotemporal status. Finally, we use the post-processing to fine-tune the temporal boundary of anomalies. Not surprisingly, our proposed framework was ranked 1st in the NVIDIA AI CITY 2021 leaderboard for traffic anomaly detection. Codes will be available.

The sharing economy plays an important role in economic development, and the sharing platform is the core point in the sharing economy. However, the large-scale sharing platform results in low-efficiencies and weak matching. To address the problem, we design a two-stage consensus reaching process for resources sharing in platform. Firstly, considering the time-consuming process of generating satisfaction, we design a new method to generate satisfaction based on large-scale mixed historical data, the cloud model is used to unify the mixed uncertain information. Then, we design a two-stage consensus reaching process to realize the stable matching. For the first stage, we maximize the total consensus of the two-sided individuals. For the second stage, to realize stable sharing, we focus on the individuals’ requirement of consensus, the platform’s strategies, such as discount and scheduling, are used to adjust their consensus. Finally, considering the hierarchy of the two-stage consensus reaching process, we establish bi-level programming to embody the features. We design an improved algorithm to deal with bi-level programming. Also, an industrial internet platform is used as an example to verify the method and algorithm.

Aims The aim of this study was to test whether current and past night shift work was associated with incident atrial fibrillation (AF) and whether this association was modified by genetic vulnerability. Its associations with coronary heart disease (CHD), stroke, and heart failure (HF) were measured as a secondary aim. Methods and results This cohort study included 283 657 participants in paid employment or self-employed without AF and 276 009 participants free of CHD, stroke, and HF at baseline in the UK Biobank. Current and lifetime night shift work information was obtained. Cox proportional hazard models were used. Weighted genetic risk score for AF was calculated. During a median follow-up of 10.4 years, 5777 incident AF cases were documented. From ‘day workers’, ‘shift but never/rarely night shifts’, and ‘some night shifts’ to ‘usual/permanent night shifts’, there was a significant increasing trend in the risk of incident AF (P for trend 0.013). Usual or permanent night shifts were associated with the highest risk [hazard ratio (HR) 1.16, 95% confidence interval (CI) 1.02–1.32]. Considering a person’s lifetime work schedule and compared with shift workers never working nights, participants with a duration over 10 years and an average 3–8 nights/month frequency of night shift work exposure possessed higher AF risk (HR 1.18, 95% CI 0.99–1.40 and HR 1.22, 95% CI 1.02–1.45, respectively). These associations between current and lifetime night shifts and AF were not modified by genetic predisposition to AF. Usual/permanent current night shifts, ≥10 years and 3–8 nights/month of lifetime night shifts were significantly associated with a higher risk of incident CHD (HR 1.22, 95% CI 1.11–1.35, HR 1.37, 95% CI 1.20–1.58 and HR 1.35, 95% CI 1.18–1.55, respectively). These associations in stroke and HF were not significant. Conclusion Both current and lifetime night shift exposures were associated with increased AF risk, regardless of genetic AF risk. Night shift exposure also increased the risk of CHD but not stroke or HF. Whether decreasing night shift work frequency and duration might represent another avenue to improve heart health during working life and beyond warrants further study.

Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at:https://github.com/BiomedicalMachineLearning/HEMnet

The human circadian system consists of the master clock in the suprachiasmatic nuclei of the hypothalamus as well as peripheral molecular clocks located in organs throughout the body. Together, these systems establish normal sleep-wake and eating cycles synced to the day-night cycle. Several biologic and physiologic processes such as body temperature, blood pressure, hormone secretion, gene expression, and immune functions manifest diurnal patterns. Many facets of modern life, such as work schedules, travel, and social engagements, can lead to sleep-wake and eating schedules that are not completely in sync with the biological clock. In its extreme form, this can disrupt and impair physiological and psychological parameters that may ultimately put people at higher risk for chronic diseases like cancer, cardiovascular disease, and metabolic disorders. Understanding the mechanisms that regulate sleep and behavioral effects on sleep and circadian rhythms may ultimately lead to insights on behavioral changes that can lower the risk of these diseases. On February 25, 2021, experts in sleep, circadian rhythms, and chronobiology met virtually for the Keystone eSymposium “Sleep & Circadian Rhythms: Pillars of Health” to discuss the latest research on understanding the bi-directional relationships between sleep, circadian rhythms, and health and disease.

With the increasing exploitation and utilization of underground spaces, the excavation of deep foundation pits adjacent to existing metro tunnels is becoming increasingly common. These excavations have the potential to cause safety problems for the operation of the nearby metro. Therefore, to prevent metro tunnel accidents from occurring during the construction process and to ensure the safety of lives and property, it is necessary to establish a risk-based early warning system. During the excavation process, the main methods for preventing accidents in excavations adjacent to existing metro tunnels are manual analyses based on on-site monitoring data. However, these methods make it difficult to enact effective control measures in a timely manner owing to the lag of information processing. However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. This study uses a backpropagation neural network to predict the real-time deformation of the tunnel based on monitoring data from the adjacent construction site. A safety risk assessment model is then established based on the relevant specifications. Through the establishment of an intelligent warning system, the safety risk to the metro tunnel during the construction process can be displayed in a three-dimensional (3D) form using the BIM. The operation results of the ANN–BIM system show that it can effectively present the safety risk to existing metro tunnels in a 3D manner, which can provide managers with rapid and convenient visual information to inform their decision-making.

Previous methods of highlight removal in image processing have exclusively addressed images taken in specific illumination environments. However, most of these methods have limitations in natural scenes and thus, introduce artifacts to nature images. In this work, we propose a specular highlight removal method that is applicable to natural scene image. Firstly, we decompose the input image into a reflectance image and an illumination image based on Retinex theory, and show that the illumination image of natural scene is obviously different from that of commonly used experimental scene. Then, the smooth features of the input image are extracted to help estimate the specular reflection coefficient in chromaticity space. Finally, the space transformation is used to calculate the specular components and the highlight removal is achieved by subtracting the specular reflection component from the original image. The experimental results show that our method outperforms most of existing methods on natural scene images, especially in some challenging scenarios with saturated pixels and complex textures.

Remote monitoring of vital signs in infectious patients minimizes the risks of viral transmissions to healthcare professionals. Evidence indicates that donning face masks reduces the risk of viral transmissions and is now the norm in medical facilities. We propose attaching an acoustic-sensing device onto face masks to assist medical facilities in monitoring patients' respiration remotely. Usability and functionality studies of the modified face mask were evaluated on 16 healthy participants, who were blindfolded throughout the data collection. Around half of the participants noticed the difference between the modified and unmodified masks but they also reported there was no discomfort in using the modified mask. Respiratory rates of the participants were evaluated for one minute and the mean error of respiratory rate was found to be 2.0 +/- 1.3 breath per minute. As all participants were healthy, the wheeze detection algorithm was assessed by playing 176 wheezes and 176 normal breaths through a foam mannequin. The recordings were played at three different times to account for varying environmental noise. The overall accuracy of the wheeze detection algorithm was 91.9%. The current findings support and suggest the use of the mask attachment in medical facilities., Comment: Published on Inventions, 10 pages

Artificial neural network (ANN) inherently cannot explain in a comprehensible form how a given decision or output is generated, which limits its extensive use. Fuzzy rules are an intuitive and reasonable representation to be used for explanation, model checking, and system integration. However, different methods may extract different rules from the same ANN. Which one can deliver good quality such that the ANN can be accurately described by the extracted fuzzy rules? In this paper, we perform an empirical study on three different rule extraction methods. The first method extracts fuzzy rules from a fuzzy neural network, while the second and third ones are originally designed to extract crisp rules, which can be transformed into fuzzy rules directly, from a well-trained ANN. In detail, in the second method, the behavior of a neuron is approximated by (continuous) Boolean functions with respect to its direct input neurons, whereas in the third method, the relationship between a neuron and its direct input neurons is described by a decision tree. We evaluate the three methods on discrete, continuous, and hybrid data sets by comparing the rules generated from sample data directly. The results show that the first method cannot generate proper fuzzy rules on the three kinds of data sets, the second one can generate accurate rules on discrete data, while the third one can generate fuzzy rules for all data sets but cannot always guarantee the accuracy, especially for data sets with poor separability. Hence, our work illustrates that, given an ANN, one should carefully select a method, sometimes even needs to design new methods for explanations. Published version This research was supported by National Nature Science Foundation of China (Grant Nos.61751210).

OBJECTIVE Within the Spine Instability Neoplastic Score (SINS) classification, tumor-related potential spinal instability (SINS 7–12) may not have a clear treatment approach. The authors aimed to examine the proportion of patients in this indeterminate zone who later required surgical stabilization after initial nonoperative management. By studying this patient population, they sought to determine if a clear SINS cutoff existed whereby the spine is potentially unstable due to a lesion and would be more likely to require stabilization. METHODS Records from patients treated at the University of California, San Francisco, for metastatic spine disease from 2005 to 2019 were retrospectively reviewed. Seventy-five patients with tumor-related potential spinal instability (SINS 7–12) who were initially treated nonoperatively were included. All patients had at least a 1-year follow-up with complete medical records. A univariate chi-square test and Student t-test were used to compare categorical and continuous outcomes, respectively, between patients who ultimately underwent surgery and those who did not. A backward likelihood multivariate binary logistic regression model was used to investigate the relationship between clinical characteristics and surgical intervention. Recursive partitioning analysis (RPA) and single-variable logistic regression were performed as a function of SINS. RESULTS Seventy-five patients with a total of 292 spinal metastatic sites were included in this study; 26 (34.7%) patients underwent surgical intervention, and 49 (65.3%) did not. There was no difference in age, sex, comorbidities, or lesion location between the groups. However, there were more patients with a SINS of 12 in the surgery group (55.2%) than in the no surgery group (44.8%) (p = 0.003). On multivariate analysis, SINS > 11 (OR 8.09, CI 1.96–33.4, p = 0.004) and Karnofsky Performance Scale (KPS) score < 60 (OR 0.94, CI 0.89–0.98, p = 0.008) were associated with an increased risk of surgery. KPS score was not correlated with SINS (p = 0.4). RPA by each spinal lesion identified an optimal cutoff value of SINS > 10, which were associated with an increased risk of surgical intervention. Patients with a surgical intervention had a higher incidence of complications on multivariable analysis (OR 2.96, CI 1.01–8.71, p = 0.048). CONCLUSIONS Patients with a mean SINS of 11 or greater may be at increased risk of mechanical instability requiring surgery after initial nonoperative management. RPA showed that patients with a KPS score of 60 or lower and a SINS of greater than 10 had increased surgery rates.

Although the survival rate of patients with cancer have increased due to the use of current chemotherapeutic agents, adverse effects of cancer therapy remain a concern. The reversal of drug resistance, reduction in harmful side effects and accelerated increase in efficiency have often been addressed in the development of combination therapeutics. Tazemetostat (EPZ-6438), a histone methyltransferase EZH2 selective inhibitor, was approved by the FDA for the treatment of advanced epithelioid sarcoma. However, the effect of tazemetostat on colorectal cancer (CRC) and 5-FU sensitivity remains unclear. In this study, the enhancement of tazemetostat on 5-FU sensitivity was examined in CRC cells. Our findings demonstrated that tazemetostat combined with 5-FU exhibits synergistic antitumor function in vitro and in vivo in CRC cells. In addition, tazemetostat promotes PUMA induction through the ROS/ER stress/CHOP axis. PUMA depletion attenuates the antitumor effect of the combination therapy. Therefore, tazemetostat may be a novel treatment to improve the sensitivity of tumors to 5-FU in CRC therapy. In conclusion, the combination of 5-FU and tazemetostat shows high therapeutic possibility with reduced unfavorable effects.

Anomaly detection in the city scenario is a fundamental computer vision task and plays a critical role in city management and public safety. Although it has attracted intense attention in recent years, it remains a very challenging problem due to the complexity of the city environment, the serious imbalance between normal and abnormal samples, and the ambiguity of the concept of abnormal behavior. In this paper, we propose a modularized framework to perform general and specific anomaly detection. A video segment extraction module is first employed to obtain the candidate video segments. Then an anomaly classification network is introduced to predict the abnormal score for each category. A category-sensitive abnormal filter is concatenated after the classification model to filter the abnormal event from the candidate video clips. It is helpful to alleviate the impact of the imbalance of abnormal categories in the test phase and obtain more accurate localization results. The experimental results reveal that our framework obtains a 66.41 MF1 in the test set of the CitySCENE Challenge 2020, which ranks first in the specific anomaly detection task.

Introduction Influenza has been linked to the crowding in emergency departments (ED) across the world. The impact of the Coronavirus Disease 2019 (COVID-19) pandemic on China EDs has been quite different from those during past influenza outbreaks. Our objective was to determine if COVID-19 changed ED visit disease severity during the pandemic. Methods This was a retrospective cross sectional study conducted in Nanjing, China. We captured ED visit data from 28 hospitals. We then compared visit numbers from October 2019 to February 2020 for a month-to-month analysis and every February from 2017 to 2020 for a year-to-year analysis. Inter-group chi-square test and time series trend tests were performed to compare visit numbers. The primary outcome was the proportion of severe disease visits in the EDs. Results Through February 29 th 2020, there were 93 laboratory-confirmed COVID-19 patients in Nanjing, of which 40 cases (43.01%) were first seen in the ED. The total number of ED visits in Nanjing in February 2020, were dramatically decreased (n = 99,949) in compared to January 2020 (n = 313,125) and February 2019 (n = 262,503). Except for poisoning, the severe diseases in EDs all decreased in absolute number, but increased in proportion both in year-to-year and month-to-month analyses. This increase in proportional ED disease severity was greater in higher-level referral hospitals when compared year by year. Conclusion The COVID-19 outbreak has been associated with decreases in ED visits in Nanjing, China, but increases in the proportion of severe ED visits.