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Background: We performed a meta-analysis to evaluate the effect of home enteral nutrition compared with oral diet after the hospital discharge of subjects with esophagectomy of esophageal cancer. Methods: A systematic literature search up to July 2021 was performed and 12 studies included 970 subjects with esophagectomy of esophageal cancer at the start of the study; 481 of them were using home enteral nutrition after the hospital discharge and 579 were given oral diet. They were reporting relationships between the effects of home enteral nutrition compared with oral diet after the hospital discharge of subjects with esophagectomy of esophageal cancer. We calculated the odds ratio (OR) and the mean difference (MD) with 95% confidence intervals (CIs) to assess the effects of home enteral nutrition compared with oral diet after the hospital discharge of subjects with esophagectomy of esophageal cancer using the dichotomous and contentious method with a random or fixed-effect model. Results: Home enteral nutrition had significantly higher body weight (MD, 31.4; 95% CI, 2.55-3.72, p < 0.001), body mass index (MD, 1.20; 95% CI, 0.66-1.74, p < 0.001), and serum albumin (MD, 3.14; 95% CI, 2.07-4.20, p < 0.001), hemoglobin (MD, 8.57; 95% CI, 6.52–10.63, p < 0.001), total protein (MD, 4.86; 95% CI, 2.93-6.80, p < 0.001), and pre-albumin (MD, 30.39; 95% CI, 1.27-59.50, p = 0.04) compared to oral diet after the hospital discharge of subjects with esophagectomy of esophageal cancer. Additionally, home enteral nutrition compared with oral diet after the hospital discharge of subjects with esophagectomy of esophageal cancer had no significant difference in feeding-related complications (OR, 1.04; 95% CI, 0.44-2.43, p = 0.93). Conclusions: Home enteral nutrition had significantly higher body weight, body mass index, and serum albumin, hemoglobin, total protein, and pre-albumin compared to oral diet after the hospital discharge of subjects with esophagectomy of esophageal cancer with no significant difference in feeding-related complications. Furthers studies are needed to confirm these findings.

Quantitative differential phase-contrast (qDPC) imaging is a label-free phase retrieval method for weak phase objects using asymmetric illumination. However, qDPC imaging with fewer intensity measurements leads to anisotropic phase distribution in reconstructed images. In order to obtain isotropic phase transfer function, multiple measurements are required; thus, it is a time-consuming process. Here, we propose the feasibility of using deep learning (DL) method for isotropic qDPC microscopy from the least number of measurements. We utilize a commonly used convolutional neural network namely U-net architecture, trained to generate 12-axis isotropic reconstructed cell images (i.e. output) from 1-axis anisotropic cell images (i.e. input). To further extend the number of images for training, the U-net model is trained with a patch-wise approach. In this work, seven different types of living cell images were used for training, validation, and testing datasets. The results obtained from testing datasets show that our proposed DL-based method generates 1-axis qDPC images of similar accuracy to 12-axis measurements. The quantitative phase value in the region of interest is recovered from 66% up to 97%, compared to ground-truth values, providing solid evidence for improved phase uniformity, as well as retrieved missing spatial frequencies in 1-axis reconstructed images. In addition, results from our model are compared with paired and unpaired CycleGANs. Higher PSNR and SSIM values show the advantage of using the U-net model for isotropic qDPC microscopy. The proposed DL-based method may help in performing high-resolution quantitative studies for cell biology.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson’s correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = −0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis.

Background Diagnostic criteria for sarcopenia have not been established in Chinese. This study established criteria based on the L3‐skeletal muscle index (L3‐SMI) and assessed its value for outcomes predicting in cirrhotic Chinese patients. Methods Totally 911 subjects who underwent a CT scan at two centres were enrolled in Cohort 1 (394 male and 417 female subjects, aged 20–80 years). The data of those subjects younger than 60 years (365 male and 296 female subjects) were used to determine the reference intervals of the L3‐SMI and its influencing factors. Cohort 2 consisted of 480 patients (286 male and 184 female patients) from three centres, and their data were used to investigate the prevalence of sarcopenia and evaluate the value of L3‐SMI for predicting the prognosis and complications of cirrhosis. Results Age and sex had the greatest effects on the L3‐SMI (P 14 (RR = 4.310, 95% CI 2.091–8.882, P

Cyber-physical systems (CPS) have been widely deployed in critical infrastructures and are vulnerable to various attacks. Data integrity attacks manipulate sensor measurements and cause control systems to fail, which are one of the prominent threats to CPS. Anomaly detection methods are proposed to secure CPS. However, existing anomaly detection studies usually require expert knowledge (e.g., system model-based) or are lack of interpretability (e.g., deep learning-based). In this paper, we present Deepnoise, a deep learning-based anomaly detection method for CPS with interpretability. Specifically, we utilize the sensor and process noise to detect data integrity attacks. Such noise represents the intrinsic characteristics of physical devices and the production process in CPS. One key enabler is that we use a robust deep autoencoder to automatically extract the noise from measurement data. Further, an LSTM-based detector is designed to inspect the obtained noise and detect anomalies. Data integrity attacks change noise patterns and thus are identified as the root cause of anomalies by Deepnoise. Evaluated on the SWaT testbed, Deep-noise achieves higher accuracy and recall compared with state-of-the-art model-based and deep learning-based methods. On average, when detecting direct attacks, the precision is 95.47%, the recall is 96.58%, and F is 95.98%. When detecting stealthy attacks, precision, recall, and F scores are between 96% and 99.5%.

Gut microbiota and their metabolic products might play important roles in regulating the pathogenesis of choledocholithiasis concurrent with cholangitis (CC). The aim of this study was to explore the characteristic gut dysbiosis, metabolite profiles and the possible roles in patients with CC.A case-control study was carried out to analyze the alterations in the intestinal microbiota and their metabolites in patients with CC (n = 25) compared with healthy controls (HCs) (n = 25) by metagenomic sequencing to define the gut microbiota community and liquid chromatography/mass spectrometry (LC/MS) analysis to characterize the metabolite profiles.Significantly reduced Shannon diversity index (p = 0.043) and differential overall fecal microbiota community in CCs were observed. Twelve dominant altered species were identified and analyzed (LDA score 3.0, p 0.05) (Q value 0.05), including unclassified_f_Enterobacteriaceae, Escherichia_coli, Roseburia_faecis and Eubacterium rectale. Moreover, the levels of KEGG pathways related to biofilm formation of Escherichia coli, lipopolysaccharide (LPS) biosynthesis, and the metabolism of propanoate and glutathione in CCs were significantly altered. Finally, 47 markedly changed metabolites (VIP 1.0 and p 0.05), including low level of kynurenic acid (KYNA) and high concentration of N-palmitoylsphingosine involving tryptophan metabolism and sphingolipid signaling pathways, were identified to validate aberrant metabolic patterns in CCs, and multiple correlated metabolic modules involving bile inflammation were altered in CCs.Our study provides novel insights into compositional and functional alterations in the gut microbiome and metabolite profiles in CC and the underlying mechanisms between gut microbiota and bile inflammation.

Fluorescence microscopy with optical sectioning capabilities is extensively utilized in biological research to obtain three-dimensional structural images of volumetric samples. Tunable lenses have been applied in microscopy for axial scanning to acquire multiplane images. However, images acquired by conventional tunable lenses suffer from spherical aberration and distortions. Here, we design, fabricate, and implement a dielectric Moiré metalens for fluorescence imaging. The Moiré metalens consists of two complementary phase metasurfaces, with variable focal length, ranging from ∼10 to ∼125 mm at 532 nm by tuning mutual angles. In addition, a telecentric configuration using the Moiré metalens is designed for high-contrast multiplane fluorescence imaging. The performance of our system is evaluated by optically sectioned images obtained from HiLo illumination of fluorescently labeled beads, as well as ex vivo mice intestine tissue samples. The compact design of the varifocal metalens may find important applications in fluorescence microscopy and endoscopy for clinical purposes.

Background Elastography has not been widely applied to the gastrointestinal tract. The bowel wall's normal elasticity values are still unknown and are necessary for studies of gastrointestinal diseases. This study explores the feasibility of using shear wave elastography (SWE) to measure the terminal ileum wall stiffness in healthy subjects and establish the corresponding normal ranges of elasticity values. Methods This observational study recruited 139 healthy adult volunteers from April to July 2020. All examinations were performed in the anterior terminal ileum wall. Shear wave velocity (SWV) and Young's modulus (E) values were measured in the midline on longitudinal sections and replicated different operators' obtained data. Also, bowel wall thickness (BWT) and depth were recorded. Subgroups were classified according to the volunteers' gender, age, body mass index (BMI), BWT, and depth. The intra-class correlation coefficient was calculated to analyze inter- and intra-operator consistency, and independent t-tests and one-way analysis of variance were used to explore the differences in variables. Results The inter- and intra-operator agreements were good to excellent by different operators and in the replicated measurements (intra-operator consistency: 0.963; inter-operator consistency: 0.842). In all volunteers, the mean SWV was 1.08±0.25 m/s, the mean E value was 3.84±1.84 kPa, and the median BWT was 2 mm. SWV and E did not show significant differences according to gender (P=0.589), age (P=0.738), BMI (P=0.678), depth (P=0.375), or BWT (P=0.410). BWT did not show significant differences according to age (P=0.142), BMI (P=0.863), or depth (P=0.368). Conclusions SWE can be used in terminal ileum wall stiffness measurements with good reliability, and the SWE values do not appear to vary significantly according to different physiological factors. The corresponding elasticity ranges of the terminal ileum in normal adults were acquired.

The current mainstream methods of loop closure detection in visual simultaneous localization and mapping (SLAM) are based on bag-of-words (BoW). However, traditional BoW-based approaches are strongly affected by changes in the appearance of the scene, which leads to poor robustness and low precision. In order to improve the precision and robustness of loop closure detection, a novel approach based on stacked assorted auto-encoder (SAAE) is proposed. The traditional stacked auto-encoder is made up of multiple layers of the same autoencoder. Compared with the visual BoW model, although it can better extract the features of the scene image, the output feature dimension is high. The proposed SAAE is composed of multiple layers of denoising auto-encoder, convolutional auto-encoder and sparse auto-encoder, it uses denoising auto-encoder to improve the robustness of image features, convolutional auto-encoder to preserve the spatial information of the image, and sparse auto-encoder to reduce the dimensionality of image features. It is capable of extracting low to high dimensional features of the scene image and preserving the spatial local characteristics of the image, which makes the output features more robust. The performance of SAAE is evaluated by a comparison study using data from new college dataset and city centre dataset. The methodology proposed in this paper can effectively improve the precision and robustness of loop closure detection in visual SLAM.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with high prevalence rate among the elderly population. A large number of clinical studies have suggested repetitive transcranial magnetic stimulation (rTMS) as a promising non-invasive treatment for patients with mild to moderate AD. However, the underlying cellular and molecular mechanisms remain largely uninvestigated. In the current study, we examined the effect of high frequency rTMS treatment on the cognitive functions and pathological changes in the brains of 4- to 5-month old 5xFAD mice, an early pathological stage with pronounced amyloidopathy and cognitive deficit. Our results showed that rTMS treatment effectively prevented the decline of long-term memories of the 5xFAD mice for novel objects and locations. Importantly, rTMS treatment significantly increased the drainage efficiency of brain clearance pathways, including the glymphatic system in brain parenchyma and the meningeal lymphatics, in the 5xFAD mouse model. Significant reduction of Aβ deposits, suppression of microglia and astrocyte activation, and prevention of decline of neuronal activity as indicated by the elevated c-FOS expression, were observed in the prefrontal cortex and hippocampus of the rTMS-treated 5xFAD mice. Collectively, these findings provide a novel mechanistic insight of rTMS in regulating brain drainage system and β-amyloid clearance in the 5xFAD mouse model, and suggest the potential use of the clearance rate of contrast tracer in cerebrospinal fluid as a prognostic biomarker for the effectiveness of rTMS treatment in AD patients. Supplementary Information The online version contains supplementary material available at 10.1186/s40478-021-01198-3.

Fatigue damage accumulation is a critical factor resulting in the failure of prestressed concrete (PC) bridges. The fatigue damage is usually caused by the coupled effect of cyclic vehicle loading and environmental corrosion. This study investigated probabilistic fatigue damage on aging PC bridges considering both stochastic traffic loading and corrosion. A stochastic traffic model was derived based on long-term monitoring data aiming to simulate fatigue stress spectra of critical rebar. The effect of cracks on the fatigue stress spectra was investigated in order to model the fatigue stress state more realistically. A three-stage traffic growth model was established based on traffic volume histories of three highways in China. A fatigue limit state function considering traffic growth and corrosion effect was deduced for fatigue reliability assessment of PC bridges. Numerical results show that the stress amplitude of rebar considering cracks is 1.53 times greater than the rebar with no-cracks, resulting in a decrease of fatigue life by 68 years. In addition, the three-stage traffic growth models lead to 25 years shorter fatigue life than the one considering a linear traffic growth model. Finally, the corrosion effect results in a fatigue life of 44 years. The numerical results provide a theoretical basis for fatigue life estimation and maintenance of aging PC bridges.

Although hepatocellular carcinoma (HCC) is one of the deadliest health burdens worldwide, few drugs are available for its clinical treatment. However, in recent years, major breakthroughs have been made in the development of new drugs due to intensive fundamental research and numerous clinical trials in HCC. Traditional systemic therapy schemes and emerging immunotherapy strategies have both advanced. Between 2017 and 2020, the United States Food and Drug Administration (FDA) approved a variety of drugs for the treatment of HCC, including multikinase inhibitors (regorafenib, lenvatinib, cabozantinib, and ramucirumab), immune checkpoint inhibitors (nivolumab and pembrolizumab), and bevacizumab combined with atezolizumab. Currently, there are more than 1000 ongoing clinical trials involving HCC, which represents a vibrant atmosphere in the HCC drug research and development field. Additionally, traditional Chinese medicine approaches are being gradually optimized. This review summarizes FDA-approved agents for HCC, elucidates promising agents evaluated in clinical phase I/II/III trials and identifies emerging targets for HCC treatment. In addition, we introduce the development of HCC drugs in China. Finally, we discuss potential problems in HCC drug therapy and possible future solutions and indicate future directions for the development of drugs for HCC treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-021-01968-w.

To evaluate the performance of noninvasive prenatal screening (NIPS) for the fetal common aneuploidy screening in twin pregnancies.The data of 5469 women with twin pregnancies were collected in this retrospective observational study between January 2017 and December 2018. Patients underwent NIPS as first-line screening or after standard serum screening for fetal aneuploidy. The performance of NIPS was examined, and a regression analysis was performed to investigate testing failure in cases of low fetal fraction.In this study, 2231 (40.8%) patients opted for NIPS as the primary prenatal screening test, and 3238 (59.2%) opted for serum screening, including 440 patients who opted for NIPS after serum screening. Among the 2671 pregnancies with available NIPS outcomes, 11 cases of aneuploidy were identified, seven of trisomy 21 and four of sex chromosome aneuploidy (SCA). The sensitivity and specificity for trisomy 21 were 100% (95% CI, 56.1-100.0%) and 100% (95% CI, 99.8-100.0%), respectively. The positive predictive value (PPV) for SCA was 40.0% (95% CI, 13.7-72.6%). No false negatives were found, with a negative predictive value (NPV) of 100% (95% CI, 99.8-100.0%) in total. In 32 pregnancies who failed NIPS test without available NIPS outcomes due to low fetal fraction, the regression analysis demonstrated that increasing BMI and assisted reproductive technology treatment were significant independent predictors.NIPS is a high-performing routine primary prenatal screening test in twin pregnancies, with a high PPV and low false positive rate for detecting trisomy 21. It is also useful to identify common sex chromosome aneuploidies in twin pregnancies, with similar performance to that reported in singleton pregnancy.

Background: The incidence of oral squamous cell carcinoma (OSCC) is increasing, with an estimated 369,000 new patients each year worldwide. Surgery is the primary treatment modality for early-stage OSCC, but there is scant evidence to prove the value of elective neck dissection (END) for relatively small early-stage OSCC. This study aimed to identify factors predicting survival for patients with clinical stage T1N0M0 (cT1N0M0) OSCC and whether up-front END improved survival. Patients and Methods: Patients with cT1N0M0 OSCC who underwent tumor resection with or without END were identified and extracted from the SEER database. Kaplan-Meier survival analysis was used to assess overall survival and disease-specific survival. Prognostic factors were determined using Cox regression analysis. Results: A total of 5,752 patients with cT1N0M0 OSCC were extracted, of whom 2,194 (38.1%) underwent tumor resection surgery with concurrent END and 3,558 (61.9%) underwent only tumor resection. In a multivariate Cox analysis, a relatively advanced age (>62 years) and relatively high pathologic grade were the significant negative predictors, but married status (hazard ratio, 0.709; P=.006) and undergoing END (hazard ratio, 0.708; PConclusions: Patients with cT1N0M0 OSCC gain significant overall and disease-specific survival benefit from END.

Background Rectal subepithelial lesions (SELs) are commonly seen in endoscopic examination, generally manifested as bumps with a smooth surface. Precise preoperative diagnoses for rectal SELs are difficult because abnormal tissues are not easily to be obtained by regular endoscopic forceps biopsy. Traditional guidance modalities of preoperative biopsy, including endoscopic ultrasound, computed tomography, and transabdominal ultrasound, are often unsatisfactory. An updated, safe, and effective biopsy guidance method is required. We herein report a new biopsy guidance modality-endorectal ultrasound (ERUS) combined with contrast-enhanced ultrasound (CEUS). Case summary A 32-year-old woman complained of a mass inside the rectovaginal space for 9 years, which became enlarged within 1 year. A rectal SEL detected by endoscopy was suspected to be a gastrointestinal stromal tumor or exophytic uterine fibroid. Pathological diagnosis was difficult because of unsuccessful transabdominal core needle biopsy with insufficient tissues, as well as vaginal hemorrhage. A second biopsy was suggested after multiple disciplinary treatment discussion, which referred to a transperineal core needle biopsy (CNB) guided by ERUS combined with CEUS. Adequate samples were procured and rectal gastrointestinal stromal tumor was proved to be the pathological diagnosis. Imatinib was recommended for first-line therapy by multiple disciplinary treatment discussion. After the tumor shrunk, resection of the rectal gastrointestinal stromal tumor was performed through the posterior vaginal wall. Adjuvant therapy was applied and no recurrence or metastasis has been found by the last follow-up on December 13, 2019. Conclusion Transperineal CNB guided by ERUS and CEUS is a safe and effective preoperative biopsy of rectal SELs yet with some limitations.

Exercise has been recommended as an important strategy to improve glucose metabolism in obesity. Adipose tissue fibrosis is associated with inflammation and is implicated in glucose metabolism disturbance and insulin resistance in obesity. However, the effect of exercise on the progression of adipose tissue fibrosis is still unknown. The aim of the present study was to investigate whether exercise retarded the progression of adipose tissue fibrosis and ameliorated glucose homeostasis in diet-induced obese mice. To do so, obesity and adipose tissue fibrosis in mice were induced by high-fat diet feeding for 12 weeks and the mice subsequently received high-fat diet and exercise intervention for another 12 weeks. Exercise alleviated high-fat diet-induced glucose intolerance and insulin resistance. Continued high-fat diet feeding exacerbated collagen deposition and further increased fibrosis-related gene expression in adipose tissue. Exercise attenuated or reversed these changes. Additionally, PPARγ, which has been shown to inhibit adipose tissue fibrosis, was observed to be increased following exercise. Moreover, exercise decreased the expression of HIF-1α in adipose fibrosis, and adipose tissue inflammation was inhibited. In conclusion, our data indicate that exercise attenuates and even reverses the progression of adipose tissue fibrosis, providing a plausible mechanism for its beneficial effects on glucose metabolism in obesity.

Emerging Internet of Things (IoT) systems leverage connected devices to enable intelligent and automated functionalities. Despite the benefits, there exist privacy risks of network traffic, which have been studied by the previous research. However, with the current privacy inference remaining at the event-level, potential privacy risks are underestimated, which, as our study shows, can be much higher than previously reported through app-level traffic analysis. A key observation of our research is that IoT event-triggered traffic is generated by apps, which often adopt an if-trigger-then-action (trigger–action) programming paradigm. We utilize this feature to develop fingerprints to differentiate running apps and learn context-rich privacy-sensitive information from apps. In this article, we present a privacy leakage analysis called ALTA to infer running apps in smart home IoT environments. First, ALTA identifies app fingerprints through static analysis and extracts sensitive information from app descriptions and input prompts. Then, through dynamic traffic profiling, it learns traffic fingerprints of apps. Finally, ALTA matches the fingerprints of app and traffic, and thus is able to pinpoint which app is running from IoT traffic at runtime. To demonstrate the feasibility of our approach, we analyze 254 SmartThings applications via program and natural language processing (NLP) analysis. We also perform the app inference evaluation on 31 apps executed in a simulated smart home. The results suggest that ALTA can effectively infer running apps from IoT traffic and learn context-rich information (e.g., health conditions, daily routines, and user activities) from apps with high accuracy.

Context Qingre Huoxue (QRHX) decoction, a traditional Chinese medicine, has been widely used to prevent and treat myocardial infarction (MI). Objective This study elucidates the possible mechanisms of QRHX in preventing or treating MI in a rat model. Materials and methods The chemical constituents of QRHX were identified by UPLC-MS. Sprague-Dawley rats were randomly divided into the Sham (normal saline), Model (normal saline), QRHX-L, QRHX-M and QRHX-H group (n = 10 per group). QRHX decoction was administered by gavage to the rats for 14 days (5, 10 and 20 g/kg/day). The left anterior descending ligation method was performed to develop MI in Model and QRHX groups, and the same surgical procedures excluding ligation sutures were performed for the sham group. Finally, we evaluated cardiac function, myocardial fibrosis degree, serum inflammatory factors, autophagy levels and verified the signalling pathways in vivo. Results A total of 68 active components of QRHX corresponding to 223 active targets were obtained and 2558 MI-related disease targets were collected. After integration, 123 QRHX anti-MI targets were obtained, and 70 signalling pathways, such as PI3K/Akt, were identified by enrichment analysis. In vivo experiments suggest that QRHX could reduce the degree of myocardial fibrosis, downregulate serum inflammatory factors, and promote autophagy in MI rats. Discussion and Conclusions QRHX plays a protective role in the myocardium by mediating PI3K/Akt signalling pathway to activate autophagy and inhibiting inflammatory factor expression. These findings provide a scientific basis for further research and validation of QRHX as a potential therapeutic for MI.

This study aimed to investigate the alterations in brain networks in patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) based on a population-specific brain template. Previous studies on AD brain networks using graph theory rarely adopted brain templates specific for certain ethnicities. In this study, patients were divided into 3 groups: AD (n = 24), MCI (n = 27), and healthy controls (HCs, n = 33), and all of the subjects are Chinese. Functional brain networks were constructed for each group based on a Chinese brain template using resting-state functional magnetic resonance imaging (rs-fMRI) data; several graph metrics were calculated. Graph metrics with significant differences after false discovery rate (FDR) correction were analyzed with respect to correlations with four neuropsychological test scores: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), and Clinical Dementia Rating (CDR), which assessed the subjects’ cognitive functions and ability to engage in ADL. Graph metrics including assortativity coefficient, nodal degree centrality, nodal clustering coefficient, nodal efficiency, and nodal local efficiency of the frontal gyrus and cerebellum were significantly altered in AD and MCI compared with HC. Several graph metrics were significantly correlated with cognitive function and the ability to engage in daily activities. The findings suggest that altered graph metrics in the frontal gyrus may reflect brain plasticity, and that patients with MCI may have unique graph metric alterations in the cerebellum. Future graph analysis studies on functional brain networks in AD and MCI based on population-specific brain atlases for particular ethnicities may prove valuable.

Phototherapy holds promise in cancer treatment for its prominent antitumor efficacy and low systematic toxicity compared with traditional chemotherapy. However, the higher risk of tumor metastasis caused by the severe hypoxic state during phototherapy is a threat in practical use. Here, in order to tackle this challenge, we developed a delivery system via loading the photosensitizer indocyanine green (ICG) into the low molecular weight heparin (LMWH) modified liposomes (LMWH-ICG-Lip) to realize the synergistic effects between photosensitizer and drug vehicle, achieving better phototherapeutic efficacy and meanwhile alleviating the potential risk of tumor metastasis caused by phototherapy. In this system, besides elongating the photosensitizers' circulation time and enhancing their accumulating efficacy to tumor tissues, LMWH itself also exhibited anti-metastasis efficacy via inhibiting adhesion of platelets to tumor cells and decreasing migration and invasion capability of tumor cells. In vivo efficacy evaluation was conducted on orthotopic 4T1 breast cancer model, and the system of LMWH-ICG-Lip could alleviate metastasis potential of residual tumor cells after irradiation, and elicit optimistic antitumor and anti-metastasis efficacy for phototherapy.

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19 In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect Finally, it discusses the problem of information processing in art teaching evaluation © 2020 - IOS Press and the authors All rights reserved

Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.

BackgroundIncreased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study the general ICU population of the Medical Information Mart for Intensive Care III (MIMIC-III) database to corroborate these associations, and propose a supervised learning model for the prediction of hyperchloremia in ICU patients.MethodsWe assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortality, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regression analysis. Four predictive supervised learning classifiers were trained to predict hyperchloremia using features representative of clinical records from the first 24h of adult ICU stays.ResultsHyperchloremia was shown to have an independent association with increased odds of ICU mortality, new AKI by day 7, and MODS on day 7. High chloride load was also associated with increased odds of ICU mortality. Our best performing supervised learning model predicted second-day hyperchloremia with an AUC of 0.76 and a number needed to alert (NNA) of 7—a clinically-actionable rate.ConclusionsOur results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes.

Lung adenocarcinoma (LUAD) is the most common and lethal cancer worldwide. Radiotherapy (RT) is widely used at all stages of LUAD, and the development of immunotherapy substantially enhances the survival of LUAD patients. Although the emerging treatments for LUAD have improved prognosis, only a small fraction of patients can benefit from clinical therapies. Thereby, approaches assessing responses to RT and immunotherapy in LUAD patients are essential. After integrating the analysis of RT, immunization, mRNA, and clinical information, we constructed a signature based on 308 tumor‐infiltrating B lymphocyte‐specific genes (TILBSig) using a machine learning method. TILBSig was composed of 6 B cell‐specific genes (PARP15, BIRC3, RUBCNL, SP110, TLE1, and FADS3), which were highly associated with the overall survival as independent factors. TILBSig was able to differentiate better survival compared with worse survival among different patients, and served as an independent factor for clinical characteristics. The low‐risk TILBSig group was correlated with more immune cell infiltration (especially B lineages) and lower cancer stem cell characteristics than the high‐risk group. The patients with lower risk scores were more likely to respond to RT and immunotherapy. TILBSig served as an excellent predicator for prognosis and response to immunotherapy and RT in LUAD patients., Our studies identified a 6‐genes signature TILBSig based on tumor‐infiltrating B lymphocyte specific genes, which might serve as predictors of survival and response to immunotherapy and radiotherapy for LUAD patients.

Viral myocarditis (VMC) is a cardiac disease associated with myocardial inflammation and injury induced by virus infection. Cardiomyocytes have recently been regarded as key players in eliciting and modulating inflammation within the myocardium. Kruppel-like factor 10 (KLF10) is a crucial regulator of various pathological processes and plays different roles in a variety of diseases. However, its role in VMC induced by coxsackievirus B3 (CVB3) infection remains unknown. In this study, we report that cardiac KLF10 confers enhanced protection against viral myocarditis. We found that KLF10 expression was downregulated upon CVB3 infection. KLF10 deficiency enhanced cardiac viral replication and aggravated VMC progress. Bone marrow chimera experiments indicated that KLF10 expression in nonhematopoietic cells was involved in the pathogenesis of VMC. We further identified MCP-1 as a novel target of KLF10 in cardiomyocytes, and KLF10 cooperated with histone deacetylase 1 (HDAC1) to negatively regulate MCP-1 expression by binding its promoter, leading to activation of MCP-1 transcription and recruitment of Ly6C(high) monocytes/macrophages into the myocardium. This novel mechanism of MCP-1 regulation by KLF10 might provide new insights into the pathogenesis of VMC and a potential therapeutic target for VMC.

A 57-year-old male presenting with spontaneously relieved abdominal cramp and distension was admitted to the West China Hospital. The diagnosis remained unclear after colonoscopy and computed tomography. Double contrast-enhanced ultrasonography was then performed and a neoplasm in the small intestine was suspected, supported by a thin-section computed tomography and positron emission tomography/computed tomography. This was confirmed pathologically after surgery to be a small intestinal G1 neuroendocrine tumor. Surgery was performed to remove approximately 25 cm of small bowel and a 3-cm solid mass located in the mesentery. The patient had a complete recovery and was tumor-free at the final follow-up. Small intestinal tumors including neuroendocrine tumors have always posed a diagnostic challenge. This case indicated that double contrast-enhanced ultrasonography is feasible in detection of small intestinal neuroendocrine tumors, and it may be an advisable approach assisting diagnosis of small intestinal tumors.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiomyopathy characterized by myocardial necrosis and fibrofatty substitution of the myocardium, predominantly of the right ventricle. The evaluation of risk associated with gestation and delivery in patients with ARVC is difficult due to the small number of already reported cases. We present our experience of patients with ARVC who completed a pregnancy and delivery.A case series of nine women in Calgary, Canada, from 2013 to 2018, who were diagnosed with ARVC before or during pregnancy. Patients were identified using our Cardiac-Obstetrics database, and information was collected through electronic charts and patient recollection.All pregnancies reported were singleton with an average maternal age of 31 years. Six patients had a related genetic mutation. Beta blockers were being used by eight, and five had an implantable cardioverter-defibrillator (ICD) prior to the pregnancy. None of the patients developed heart failure during pregnancy, but one had a complicated antepartum and postpartum course. All pregnancies delivered at term with eight receiving neuroaxial analgesia. Five patients delivered vaginally. Those without an ICD had continuous cardiac monitoring intrapartum. The incidence of small for gestational age (33%) was higher than the general population. All of the patients breastfed the newborns.Pregnancies in these patients with ARVC were generally well tolerated. Given the rarity of the disease and absence of any clinical guidelines, multidisciplinary care is essential in the management of these patients.

Growing evidence highlighted the tumor mutational burden (TMB) as an important feature of carcinogenesis and therapeutic efficacy in esophageal cancer (EC). Our study aimed to explore the genomic landscape and the correlation between TMB and immune cell infiltration in EC patients with or without radiotherapy. The EC patients were categorized into high TMB (TMB-H) and low TMB (TMB-L) groups by the ESTIMATE algorithm, and subgroup analysis was performed based on receiving radiotherapy or not. Univariate regression analysis indicated TMB and TNM stages as high-risk prognostic factors (Hazard ratio > 1 and P < 0.05). Multivariate regression analysis suggested TMB as an independent prognostic factor (Hazard ratio = 1.051, P = 0.003). Kaplan-Meier analysis showed no significant difference of the overall survival (OS) between TMB-H and TMB-L groups (P = 0.082). However, EC patients without radiotherapy in the TMB-H group had significantly decreased OS (P = 0.038) and increased Tregs cell infiltration (P = 0.033). These results suggested TMB as a prognostic marker for EC patients. Especially for patients who did not receive radiotherapy, the prognosis of TMB-H patients was significantly poorer than that of TMB-L patients, which might result from the different regulatory T cell infiltration.

Introduction Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. Methods This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. Results We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. Discussion and conclusion Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses.

Background Perineal tear is a common consequence of vaginal births affecting females globally. Even mild perineal tears could cause short- and long-term complications for females. Though many studies of interventions to prevent or treat perineal tears to minimize the consequences have been conducted, there is a significant heterogeneity in the outcomes measured and reported in existing studies, which makes meaningful comparison difficult and makes the generalizability to clinical practice challenging. Developing a core outcome set (COS) could solve these methodological concerns. In this paper, we report a protocol to develop a COS for clinical trials of mild perineal tears, which shall assist in establishing the evidence base and implementation of effective measures to reduce the incidence and minimize the consequences of mild perineal tears. Methods/design The development of this COS will be guided by a study advisory group composed of obstetricians, midwives, nursing managers, service users, and methodologists. This study will include four stages: (1) a systematic research of the literature to identify outcomes reported in prior studies, (2) a semi-structured interview with key stakeholders to collect their opinions on important outcomes, (3) a panel of experts will be invited to conduct a three-round Delphi survey to prioritize these outcomes, and (4) a consensus meeting with key stakeholders to determine the list of outcomes included in the final COS. Discussion The development of this COS will provide international standards for the outcomes to be collected and reported in all clinical trials and audits of practice, which involve prevention and treatment of first- and second-degree perineal tears for women with vaginal delivery. This will facilitate comparing and contrasting of studies and allow for combining of appropriate studies with the ultimate goal of improved perineal care for women choosing vaginal delivery. Trial registration This study was registered in the database of Core Outcome Measures in Effectiveness Trials (COMET) on June11th, 2021 (https://comet-initiative.org/Studies/Details/1884).

Objective Caries risk assessment tools are essential for identifying and providing treatment for individuals at high risk of developing caries. We aimed to develop a nomogram for the assessment and evaluation of caries risk among Chinese children. Methods We enrolled schoolchildren age 7 years from a primary school in Shanghai. Baseline information of participants was collected using a questionnaire completed by children’s caregivers. A nomogram of a novel prediction scoring model was established based on predictors detected in univariate and multivariate analyses. Predictive accuracy and discriminative ability of the nomogram were calculated using the concordance index (C index). The bootstrap method (1000 samples) was used to decrease overfitting. The net benefit of the model was validated using decision curve analysis. Results Overall, 406 children with complete information and two completed dental examinations were included in the final analysis. The nomogram based on logistic regression model coefficients demonstrated a C index of 0.766 (95% confidence interval: 0.761–0.771) for caries risk. The net benefit of the decision curve analysis was 38.6% at 55% threshold probability. Conclusion This nomogram model, derived using dietary habits, oral hygiene status, and caries experience, showed promising predictive ability to assess the caries risk among Chinese children.

For acquiring optically sectioned images of volumetric samples from multiple depths without axial scanning, a confocal system which is able to obtain multi-plane images with a Digital Micro-Mirror Device (DMD) has been proposed. The focal points of the laser beam can be arbitrarily positioned to any multiple planes in the three dimensional object space by switching multiplexed binary hologram encoded in the DMD by Lee hologram. The radius of the spherical wavefronts generated directly determines the location of the focal points and DMD can function either as a convex or concave mirror. The optical sectioning capabilities of system are verified and which can be applicable in various biomedical imaging applications.

Background Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction. Results We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p Conclusion Our results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes.

Lung ischemia-reperfusion (IR) injury is induced by pulmonary artery occlusion and reperfusion. Lung IR injury commonly happens after weaning from extracorporeal circulation, lung transplantation, and pulmonary thromboendarterectomy; it is a lethal perioperative complication. A definite therapeutic intervention remains to be determined. It is known that the enzyme activity of angiotensin-converting enzyme 2 (ACE2) is critical in maintaining pulmonary vascular tone and epithelial integrity. In a noxious environment to the lungs, inactivation of ACE2 is mainly due to a disintegrin and metalloprotease 17 (ADAM17) protein-mediated ACE2 shedding. Thus, we assumed that protection of local ACE2 in the lung against ADAM17-mediated shedding would be a therapeutic target for lung IR injury. In this study, we established both in vivo and in vitro models to demonstrate that the damage degree of lung IR injury depends on the loss of ACE2 and ACE2 enzyme dysfunction in lung tissue. Treatment with ACE2 protectant diminazen aceturate (DIZE) maintained higher ACE2 enzyme activity and reduced angiotensin II, angiotensin type 1 receptor, and ADAM17 levels in the lung tissue. Concurrently, DIZE-inhibited oxidative stress and nitrosative stress via p38MAPK and NF-κB pathways consequently reduced release of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-1β. The underlying molecular mechanism of DIZE contributed to its protective effect against lung IR injury and resulted in the improvement of oxygenation index and ameliorating pulmonary pathological damage. We concluded that DIZE protects the lungs from IR injury via inhibition of ADAM17-mediated ACE2 shedding.

The variable head pose and low-quality eye images in natural scenes can lead to low accuracy of gaze estimation. In this paper, we propose a multi-feature fusion gaze estimation model based on the attention mechanism. First, face and eye feature extractors based on the group convolution channel and spatial attention mechanism (GCCSAM) are designed to use channel and spatial information to adaptively select and enhance important features in face images and two eye images, and suppress information irrelevant to gaze estimation. Then we design two feature fusion networks to fuse the features of face, two eyes and pupil center position, thus avoiding the effects of two-eye asymmetry and inaccurate head pose estimation on gaze estimation. The average angular error of the proposed method is 4.1° on MPIIGaze and 5.2° on EyeDiap. Compared with the current mainstream methods, our method effectively improves the accuracy and robustness of gaze estimation in natural scenes.

Supplemental Digital Content is available in the text., OBJECTIVES: Patients with aneurysmal subarachnoid hemorrhage (ruptured brain aneurysm) often have reduced health-related quality of life at follow-up in multiple domains (e.g., cognitive function and social function). We tested the hypothesis that there are distinct patterns of patient outcomes across domains of health-related quality of life, “complex patient outcomes,” in survivors of subarachnoid hemorrhage. DESIGN: Patients with subarachnoid hemorrhage were prospectively identified. Clinical data were prospectively recorded. Health-related quality of life was prospectively assessed at 3-month follow-up using the National Institutes of Health Patient Reported Outcomes Measurement Information System and neuro-quality of life in the domains of mobility, cognitive function, satisfaction with social roles, and depression. We used k-means clustering to analyze prospectively recorded health-related quality of life data, identifying clusters of complex patient outcomes. Decision tree analysis identified index hospital stay factors predictive of a patient having a particular complex patient outcome at follow-up. SETTING: Academic medical center. PATIENTS: One hundred three survivors of subarachnoid hemorrhage. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 103 patients, of whom 75 (72.8%) were female, and mean age was 53.6 ± 13.4 years. There were three complex patient outcomes: health-related quality of life greater than 1 sd better than the U.S. mean across all domains (n = 23, 22.3%), health-related quality of life greater than 1 sd worse than U.S. mean across all domains (n = 26, 25.2%), and satisfaction with social roles greater than 0.5 sd worse than U.S. mean with cognitive function, depression, and mobility scores near the U.S. mean (n = 54, 52.4%). In decision tree analysis, hospital disposition and Hunt and Hess Grade were associated with complex patient outcome. CONCLUSIONS: Complex patient outcomes across multiple domains of health-related quality of life at follow-up after subarachnoid hemorrhage are distinct and may be predictable.

Objective— To investigate the dietary characteristics and nutritional health status of cadets in air force youth aviation school. Methods— The 24-h dietary survey was used to investigate the dietary structure. The body composition index and blood biochemical indexes were measured to evaluate their health status. Results— According to the dietary guidelines for Chinese residents, the intake of fruits was seriously insufficient, reaching only 53.48%–80.23%. The intakes of sodium, potassium and phosphorus were high, calcium, iron, zinc and iodine were low. Folic acid intake reached 50.58% of the recommended amount of Chinese residents. The energy supply for breakfast was 23.97%, which was lower than the recommended amount, while the dinner was higher. Carbohydrate provided 1163.6 kcal (39.80%), which was lower than standard recommended. The proportion of protein intake from animal origin was slightly higher than the upper limit (50%) recommended by military standard. Among the blood biochemical indexes, only VD (25-OH) was low. Conclusions— The dietary structure of the cadets is not reasonable, and the intake of minerals and the energy supply ratio of the three nutrients are unbalanced. The overall nutritional health status was good, individual students had malnutrition.

Coronavirus Disease 2019 (COVID-19) is especially severe in aged patients, defined as 65 years or older, for reasons that are currently unknown. To investigate the underlying basis for this vulnerability, we performed multimodal data analyses on immunity, inflammation, and COVID-19 incidence and severity as a function of age. Our analysis leveraged age-specific COVID-19 mortality and laboratory testing from a large COVID-19 registry, along with epidemiological data of ∼3.4 million individuals, large-scale deep immune cell profiling data, and single-cell RNA-sequencing data from aged COVID-19 patients across diverse populations. To begin, we confirmed a significantly increased rate of severe outcomes in aged COVID-19 patients. Furthermore, we identified increased inflammatory markers (C-reactive protein, D-dimer, and neutrophil-lymphocyte ratio), viral entry factors in secretory cells, and TGFβ-mediated immune-epithelial cell interactions, as well as reduction in both naïve CD8 T cells and expression of interferon antiviral defense genes (i.e.,IFITM3andTRIM22), along with strong TGF-beta mediated immune-epithelial cell interactions (i.e., secretory - T regulatory cells), in aged severe COVID-19 patients. Taken together, our findings point to immuno-inflammatory factors that could be targeted therapeutically to reduce morbidity and mortality in aged COVID-19 patients.

Objective The study sought to test the feasibility of conducting a phenome-wide association study to characterize phenotypic abnormalities associated with individuals at high risk for lung cancer using electronic health records. Materials and Methods We used the beta release of the All of Us Researcher Workbench with clinical and survey data from a population of 225 000 subjects. We identified 3 cohorts of individuals at high risk to develop lung cancer based on (1) the 2013 U.S. Preventive Services Task Force criteria, (2) the long-term quitters of cigarette smoking criteria, and (3) the younger age of onset criteria. We applied the logistic regression analysis to identify the significant associations between individuals’ phenotypes and their risk categories. We validated our findings against a lung cancer cohort from the same population and conducted an expert review to understand whether these associations are known or potentially novel. Results We found a total of 214 statistically significant associations (P 1.5) enriched in the high-risk individuals from 3 cohorts, and 15 enriched in the low-risk individuals. Forty significant associations enriched in the high-risk individuals and 13 enriched in the low-risk individuals were validated in the cancer cohort. Expert review identified 15 potentially new associations enriched in the high-risk individuals. Conclusions It is feasible to conduct a phenome-wide association study to characterize phenotypic abnormalities associated in high-risk individuals developing lung cancer using electronic health records. The All of Us Research Workbench is a promising resource for the research studies to evaluate and optimize lung cancer screening criteria.

To avoid the inconvenience of retyping a user’s ID and password, most mobile apps now provide the automatic login feature for a better user experience. To this end, auto-login credential is stored locally on the smartphone. However, such sensitive credential can be stolen by attackers and placed into their smartphones via the well-known credential-clone attack. Then, attackers can imperceptibly log into the victim’s account, which causes more devastating and covert losses than merely intercepting the user’s password. In this article, we propose a generalized Android credential-clone attack, called data-clone attack. By exploiting the new-found vulnerabilities of original equipment manufacturer (OEM)-made phone clone apps, we design an identity theft method that overcomes the problem of incomplete credential extraction and eliminates the requirement of root authority. To evade the consistency check of device-specific attributes in apps, we design two environment customization methods for app-level and operating system (OS)-level, respectively. Especially, we develop a transparent Android OS customization solution, named CloneDroid, which simulates 101 special attributes of Android OS. We implement a prototype of CloneDroid and the experimental results show that 172 out of 175 most-downloaded apps’ accounts can be jeopardized, such as Facebook and WeChat. Moreover, our study has identified 18 confirmed zero-day vulnerabilities. Our findings paint a cautionary tale for the security community that billions of accounts are potentially exposed to Android OS customization-assisted data-clone attacks.

Resources and the environment are two eternal themes of social development. Nuclear energy, a green source with high energy density, can greatly alleviate the pressure of the energy crisis in today's society. To guarantee the long-term sufficient supply of nuclear fuel, mining seawater uranium is imperative. Meanwhile, the great threats of uranium to ecological security and human health make the removal of uranium from the environment urgent. To achieve these ends, a large number of materials with specific functions have been born as a result. Among them, amidoxime-based materials serve as one of the most promising candidates and are the main tool used for uranium extraction from aqueous systems owing to their special affinity for uranium. However, there is still huge room for improvement in amidoxime-based materials in terms of their economic efficiency and performance. In this paper, we provide a comprehensive review of amidoxime-based materials for uranium recovery and removal, including synthesis strategies, characterization and types of amidoxime-based materials, the factors that influence uranium extraction, and the binding mechanisms between amidoxime ligands and uranyl ions, as well as the cost drivers in applications. Meanwhile, the shortcomings of current research as well as future development directions and research hotspots are also pointed out. Based on the in-depth analysis of the currently available literature, a demand-oriented strategy for fabricating a new generation of amidoxime-based adsorbents was proposed, and means to enhance the adsorbent performance were discussed with regards to four aspects, including adsorption capacity, selectivity, kinetics and regenerability. This paper aims to provide guidance for the purposeful design of novel amidoxime-based materials, and to provide advice on circumventing unfavorable factors and solving the technical problems relating to uranium recovery and removal.

In this paper, a visual focus of attention (VFOA) detection method based on the improved hybrid incremental dynamic Bayesian network (IHIDBN) constructed with the fusion of head, gaze and prediction sub-models is proposed aiming at solving the problem of the complexity and uncertainty in dynamic scenes. Firstly, gaze detection sub-model is improved based on the traditional human eye model to enhance the recognition rate and robustness for different subjects which are detected. Secondly, the related sub-models are described, and conditional probability is used to establish regression models respectively. Also an incremental learning method is used to dynamically update the parameters to improve adaptability of this model. The method has been evaluated on two public datasets and daily experiments. The results show that the method proposed in this paper can effectively estimate VFOA from user, and it is robust to the free deflection of the head and distance change.