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Acoustic fingerprinting aims to identify a mobile device based on its internal microphone(s) and speaker(s) which are unique due to manufacturing imperfection. This paper seeks a thorough understanding of the (in)security of exploring acoustic fingerprints for achieving distributed mobile authentication. Our contributions are threefold. First, we present a new acoustic fingerprint-emulation attack and demonstrate that it is a common vulnerability of acoustic mobile authentication systems. Second, we propose a dynamic challenge-response defense to secure acoustic mobile authentication systems against the acoustic fingerprint-emulation attack. Finally, we thoroughly investigate existing acoustic fingerprinting schemes and identify the best option for accurate, secure, and deployable acoustic mobile authentication systems.

Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies for this task obviously have great benefits in reducing the drug search space. Most of computational methods to predict DTI are proposed to solve a binary classification problem, which ignore the influence of binding strength. Therefore, drug-target binding affinity prediction is still a challenging issue. Currently, lots of studies only extract sequence information that lacks feature-rich representation, but we consider more spatial features in order to merge various data in drug and target spaces. In this study, we propose a two-stage deep neural network ensemble model for detecting drug-target binding affinity, called DeepFusionDTA, via various information analysis modules. First stage is to utilize sequence and structure information to generate fusion feature map of candidate protein and drug pair through various analysis modules based deep learning. Second stage is to apply bagging-based ensemble learning strategy for regression prediction, and we obtain outstanding results by combining the advantages of various algorithms in efficient feature abstraction and regression calculation. Importantly, we evaluate our novel method, DeepFusionDTA, which delivers 1.5 percent CI increase on KIBA dataset and 1.0 percent increase on Davis dataset, by comparing with existing prediction tools, DeepDTA. Furthermore, the ideas we have offered can be applied to in-silico screening of the interaction space, to provide novel DTIs which can be experimentally pursued. The codes and data are available from https://github.com/guofei-tju/DeepFusionDTA.

Background Human epidermal growth factor receptor2+ subtype breast cancer has a high degree of malignancy and a poor prognosis. The aim of this study is to develop a prediction model for the human epidermal growth factor receptor2+ subtype (non-luminal) of breast cancer based on the clinical and ultrasound features related with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor2. Methods We collected clinical data and reviewed preoperative ultrasound images of enrolled breast cancers from September 2017 to August 2020. We divided the data into in three groups as follows. Group I: estrogen receptor ± , Group II: progesterone receptor ± and Group III: human epidermal growth factor receptor2 ± . Univariate and multivariate logistic regression analyses were used to analyze the clinical and ultrasound features related with biomarkers among these groups. A model to predict human epidermal growth factor receptor2+ subtype was then developed based on the results of multivariate regression analyses, and the efficacy was evaluated using the area under receiver operating characteristic curve, accuracy, sensitivity, specificity. Results The human epidermal growth factor receptor2+ subtype accounted for 138 cases (11.8%) in the training set and 51 cases (10.1%) in the test set. In the multivariate regression analysis, age ≤ 50 years was an independent predictor of progesterone receptor + (p = 0.007), and posterior enhancement was a negative predictor of progesterone receptor + (p = 0.013) in Group II; palpable axillary lymph node, round, irregular shape and calcifications were independent predictors of the positivity for human epidermal growth factor receptor-2 in Group III (p = 0.001, p = 0.007, p = 0.010, p Conclusions Our study established a model to predict the human epidermal growth factor receptor2-positive subtype with moderate performance. And the results demonstrated that clinical and ultrasound features were significantly associated with biomarkers.

We report the results of a novel protocol for running online experiments using a combination of an online experimental platform in parallel with web-conferencing software in two formats—with and without subject webcams—to improve subjects’ attention and engagement. We compare the results between our online sessions with the offline (lab) sessions of the same experiment. We find that both online formats lead to comparable subject characteristics and performance as the offline (lab) experiment. However, the webcam-on protocol has less noisy data, and hence better statistical power, than the protocol without a webcam. The webcam-on protocol can detect reasonable effect sizes with a comparable sample size as in the offline (lab) protocol. Supplementary Information The online version contains supplementary material available at 10.1007/s40881-021-00112-w.

Dysbiosis of the human microbiome has long been reported to be closely associated with various cancers. Accumulating studies have shown that microbial dysbiosis can accelerate tumorigenesis through tumor-promoting inflammation, DNA damage, and inducing immune evasion. Differential composition of microbiome could be novel biomarkers for cancer detection or biomarkers of successful immunotherapy. More importantly, emerging evidence demonstrates that alterations of circulating microbiome DNA (cmDNA) could serve as promising noninvasive biomarkers for cancer detection. It has been reported that distinct circulating bacterial DNA could distinguish prostate cancer, lung cancer, and melanoma patients from healthy populations. Therefore, in this review, we summarized current literature on microbial biomarkers for cancer detection and unraveled the potential of cmDNA as a promising cancer detection tool.

Online learning has become one of the most important learning styles, yet with the need of supervisors to consistently keep the learners motivated and on-task. Some learners could be supervised by outer factors, and distance learners have to be motivated by themselves. However, online learning engagement is hardly to be assessed by supervisors in real time. With the rapid development of information technology, it is able to remedy the above problem by using intelligent video surveillance techniques. In this paper, we propose a novel framework of learning engagement assessment which introduces facial expression recognition to timely acquire the emotional changes of the learners. Moreover, a new facial expression recognition method is proposed based on domain adaptation, which is suitable for the MOOC scenario. The experiments show the effectiveness of our proposed framework on assessing learners’ learning engagement. The comparisons with the state-of-the-art methods also demonstrate the superiority of our proposed facial emotion recognition method.

Wireless networks of different technologies may interfere with each other when they are deployed at proximity. Such cross-technology interference (CTI) has become prevalent with the surge of IoT devices. In this paper, we exploit CTI in coexisting WiFi-Zigbee networks and propose DeepJam, a new stealthy jamming strategy, to jam Zigbee traffic. DeepJam relies on deep learning techniques to capture the temporal pattern of the past wireless traffic and predict the future wireless traffic. By only jamming the victim’s transmissions that are not disrupted by CTI, DeepJam can significantly reduce the victim’s throughput with far fewer jamming signals and is thus much more stealthy than conventional jamming strategies. Detailed evaluations show that DeepJam can converge within 10 sec and achieve the jamming-efficiency gains of up to 742% and 285% over conventional random and reactive jamming strategies, respectively, in practical scenarios. We also propose a simple yet effective countermeasure against DeepJam.

In mines, the exhaust gas is mainly discharged from main ventilators in free dissipation state. The effective recovery of its kinetic energy is lucrative from economical and technical standpoints. This study is focused on recovering the kinetic energy of the exhaust gas without deteriorating the main ventilator's safe operation or increasing power dissipation. The related experiments and numerical simulations were performed for the conical diffusers with diffusing angles of 16° and inlet diameters of 220 and 320 mm, respectively. By examining the start-up and operation states of the wind turbines using inlet collectors with conical, circular arc, and cone-arc shapes, it was found that the wind speed at the inlet of the drainage tube exceeded 4 and 20 m/s at the minimal and maximal distances from the wind turbine, respectively. The wind speed at the overflow tube outlet was no less than 1.8 m/s, proving a certain recovery of kinetic energy by the wind turbine. Thus, the critical distance between the diffuser and the inlet collector should be at least four times the diffuser's inlet diameter. Finally, the velocity, pressure, and flow fields in the vortex core regions of the kinetic recovery system with different types of inlet collectors were simulated with the FLUENT commercial software package. The simulation results revealed that the circular-arc collector outperformed other three tested configurations in kinetic energy recovery.Implications: This study is focused on recovering the kinetic energy of the exhaust gas without deteriorating the main ventilator's safe operation or increasing power dissipation. China is a country with coal as its main energy source, which releases over ten billion cubic meters of the exhaust gas annually. In addition to low-concentration methane, there is also the exhaust gas released at a velocity of about 20 m/s, which kinetic energy recovery would be very lucrative.The significance of this study is that the mine's main ventilator outlet was simplified as two conical diffusers with parameters α=16°/n=2 and α=16°/n=2.5, and the optimal critical distance between the diffuser and the collector was determined as 4Di, where Di was the diffuser's inlet diameter. Under that optimal condition, the system can achieve the maximum power without affecting the ventilator's normal operation. It is instructive to make full use of and manage the exhaust gas in mine that fits the journal on the purpose of air waste management.Finally, we provide a statement serious that the article is original and unpublished and is not being considered for publication elsewhere.

Electrocardiography (ECG) arrhythmia heartbeat classification is essential for automatic cardiovascular diagnosis system. However, the enormous differences of ECG signals among individuals and high price of labeled data have brought huge challenges for current classification algorithms based on deep neural networks and prevented these models from achieving satisfactory performance on new data. In order to build a classification system with better adaptability, we propose a novel Domain-Adaptative ECG Arrhythmia Classification (DAEAC) model based on convolutional network and unsupervised domain adaptation (UDA). Based on observation of clustering characteristics of data, we present two original objective functions to enhance the inter-patient performance. A Cluster-Aligning loss is presented to align the distributions of training data and test data. Simultaneously, a Cluster-Maintaining loss is proposed to reinforce the discriminability and structural information of features. The proposed method requires no expert annotations but a short period of unsupervised data in new records to make deep models more adaptive. Extensive experimental results on three public databases demonstrate that our method achieves competitive performance with other state-of-the-arts on the detection of ventricular ectopic beats (V), supraventricular ectopic beats (S) and fusion beats (F). The cross-dataset experimental results also verify the great generalization capability.

BACKGROUND: Growing use of social media has led to the emergence of virtual controlled substance and illicit drug marketplaces, prompting calls for action by government and law enforcement. Previous studies have analyzed Instagram drug selling via posts. However, comments made by users involving potential drug selling have not been analyzed. In this study, we use unsupervised machine learning to detect and classify prescription and illicit drug-related buying and selling interactions on Instagram. METHODS: We used over 1,000 drug-related hashtags on Instagram to collect a total of 43,607 Instagram comments between February 1(st), 2019 and May 31st, 2019 using data mining approaches in the Python programming language. We then used an unsupervised machine learning approach, the Biterm Topic Model (BTM), to thematically summarize Instagram comments into distinct topic groupings, which were then extracted and manually annotated to detect buying and selling comments. RESULTS: We detected 5,589 comments from sellers, prospective buyers, and online pharmacies from 531 unique posts. The vast majority (99.7%) of comments originated from drug sellers and online pharmacies. Key themes from comments included providing contact information through encrypted third-party messaging platforms, drug availability, and price inquiry. Commonly offered drugs for sale included scheduled controlled substances such as Adderall and Xanax, as well as illicit hallucinogens and stimulants. Comments from prospective buyers of drugs most commonly included inquiries about price and availability. CONCLUSIONS: We detected prescription controlled substances and other illicit drug selling interactions via Instagram comments to posts. We observed that comments were primarily used by sellers offering drugs, and typically not by prospective buyers interacting with sellers. Further research is needed to characterize these “social” drug marketplace interactions on this and other popular social media platforms.

Salvia miltiorrhiza Bunge, known as red sage or Danshen, is commonly applied in Chinese herbal therapeutics for various biological effects. In 25–220 C.E., the earliest Chinese Materia Medica referred to as Shengong’s Classic of Materia Medica (Eastern Han Dynasty of China, 200 C.E.) initially documented Salvia miltiorrhiza Bunge. Currently, Salvia miltiorrhiza Bunge is one of the most frequently prescribed traditional Chinese medicines in clinics for antidiabetic, antianxiety, antihypertensive, antidepressant, antilipidemic, antiarrhythmic, and anticancer effects. Undoubtedly, it is best known for improving and promoting blood circulation; thus, its recommendation for use in cardiovascular disorders. Regarding this article, important information was sourced both manually and electronically using patent compendium and databases, including Google Scholar, herbal plant monographs, Hindawi, PubMed, Embase, CNKI, pharmacopoeias, Springer, etc. Well-concise details and discussions on the botany, traditional medicinal uses, chemical constituents-phytochemistry, pharmacokinetics, pharmacology, and toxicology information are presented. In perspective, understanding the comprehensive information of the above-stated fields with regards to Salvia miltiorrhiza Bunge would yield an additional positive knowledge on areas that need urgent attention and identify gaps in research and safety for future consideration.

Jiawei Li,1 Guobin Zhong,1 Keqiong Wang,1 Wei Kang,2 Wei Wei1 1Department of Breast Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 2Dominant Cultivation Discipline of Guangxi Medical University Cancer Hospital (Medical Imaging Department), Guangxi Key Clinical Specialty (Medical Imaging Department), Nanning, Guangxi, People’s Republic of ChinaCorrespondence: Wei Wei; Wei Kang Email 605347565@qq.com; xuanbingche0313@126.comBackground: Breast-conserving surgery plus postoperative radiotherapy is the standard surgical treatment mode for early breast cancer. Currently, there are no clear predictive indicators to determine whether a patient can choose breast-conserving surgery, which mainly depends on the surgeon’s clinical experience and subjective judgment. Cone-beam breast computed tomography (CBBCT) reconstructs the breast 3D image from three mutually perpendicular angles, helping surgeons to locate and accurately measure the volume of the tumor, mammary gland, and breast. We used CBBCT to retrospectively measure the tumor-to-gland volume ratio and tumor-to-breast volume ratio in breast cancer cases. Then, we analyzed the correlation between the surgical methods and ratios in breast cancer patients.Methods: We collected 100 patients undergoing breast-conserving surgery as the study group, and 100 patients undergoing mastectomy as the control group. All patients chose the surgical approach after comprehensive consideration of examination results and assessment of patient condition. Patients underwent CBBCT examination before surgery. We retrospectively measured the volume of tumor, mammary glands and breast, then calculated tumor-to-gland and tumor-to-breast volume ratios.Results: Tumor volume and the ratios of the two groups statistically differed (P < 0.001), while the mammary gland and breast volume did not (P > 0.05). The average tumor-to-gland volume ratio was 4.32% in the study group and 10.74% in the control group, and the average tumor-to-breast volume ratio was 0.74% in the study group and 1.36% in the control group. In breast-conserving surgery, the 95% reference range of tumor-to-gland ratio is (0, 12.90%), and the 95% reference range of tumor-to-breast ratio is (0, 2.17%).Conclusion: The tumor-to-gland volume ratio and tumor-to-breast volume ratio measured using CBBCT are correlated with the choice of surgical methods (breast-conserving surgery or mastectomy) for breast cancer patients. This can be used as possible predictor of breast-conserving surgery to help surgeons.Keywords: breast cancer, cone-beam breast computed tomography, CBBCT, tumor-to-gland volume ratio, tumor-to-breast volume ratio, breast-conserving surgery

Background Synovial inflammation plays a major role in the pathogenesis of osteoarthritis (OA). This study investigated the effect of andrographolide (Andro) on synovial inflammation mediated by tumor necrosis factor-alpha receptor 2 (TNFR2) trafficking and its utility in attenuating OA progression. Methods Knee joints were harvested from rats subjected to radial transection of the medial collateral ligament (MCLT) and medial meniscus (MMT) to examine the effect of Andro on synovial inflammation and OA progression. Quantitative real-time polymerase chain reaction was used to evaluate the expression of inflammatory factors in primary fibroblast-like synoviocytes (FLSs) after Andro treatment in vitro. The mechanism underlying Andro-mediated regulation of TNFR2 distribution and nuclear factor-κB (NF-κB) expression was verified using endosome maturation inhibitor hydroxychloroquine (HCQ) through flow cytometry, immunofluorescence, and western blot analysis. Results Andro treatment was found to reduce synovial inflammation and OA progression in vivo. Furthermore, a decrease in pain hypersensitivity and dorsal horn neuron activation was observed after treatment. Andro also downregulated the expression of inflammatory mediators and TNFR2 in FLSs. TNFR2 is crucial for the activation of the NF-κB signaling pathway, and Andro-induced degradation of TNFR2 was associated with lysosomal function, which in turn, reduced the downstream phosphorylation of p65 in the NF-κB signaling pathway. Conclusions Andro could suppress synovial inflammation via regulation of TNFR2 trafficking and degradation. This also suggests it could be a potential treatment for the prevention of synovial inflammation and OA progression. The translational potential of this article This study provides strong evidence that Andro reduces NF-κB activation and inflammatory responses in OA FLSs via regulation of TNFR2 trafficking. The inhibition of TNFR2 and Andro could be a novel therapeutic approach for OA and pain management.

Introduction Early reports of COVID-19 cases and deaths may not accurately convey community-level concern about the pandemic during early stages, particularly in the United States where testing capacity was initially limited. Social media interaction may elucidate public reaction and communication dynamics about COVID-19 in this critical period, during which communities may have formulated initial conceptions about the perceived severity of the pandemic. Methods Tweets were collected from the Twitter public API stream filtered for keywords related to COVID-19. Using a pre-existing training set, a support vector machine (SVM) classifier was used to obtain a larger set of geocoded tweets with characteristics of user self-reporting COVID-19 symptoms, concerns, and experiences. We then assessed the longitudinal relationship between identified tweets and the number of officially reported COVID-19 cases using linear and exponential regression at the U.S. county level. Changes in tweets that included geospatial clustering were also assessed for the top five most populous U.S. cities. Results From an initial dataset of 60 million tweets, we analyzed 459,937 tweets that contained COVID-19-related keywords that were also geolocated to U.S. counties. We observed an increasing number of tweets throughout the study period, although there was variation between city centers and residential areas. Tweets identified as COVID-19 symptoms or concerns appeared to be more predictive of active COVID-19 cases as temporal distance increased. Conclusion Results from this study suggest that social media communication dynamics during the early stages of a global pandemic may exhibit a number of geospatial-specific variations among different communities and that targeted pandemic communication is warranted. User engagement on COVID-19 topics may also be predictive of future confirmed case counts, though further studies to validate these findings are needed.

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.

The wireless power terminals are deployed in harsh public places and lack strict control, facing security problems. Thus, they are faced with security problems such as illegal and counterfeit terminal access, unlawful control of connected terminals, etc. The intrusion detection system based on machine learning and artificial intelligence significantly improve the terminal side’s abnormal detection capacity. In this article, we aim at identifying the abnormal behavior of wireless power terminals based on a double Hidden Markov Model (HMM), which solves the computational complexity problem caused by high dimensions in intrusion detection systems using a single HMM. The lower-layer HMM is used to identify the discrete single network abnormal behavior. Simultaneously, the upper-layer can obtain more extended period attack behavior in multiple independent abnormal events identified by the low-level. The experiment results indicate that the intrusion detection system using proposed double HMM can effectively detect the terminal’s abnormal behavior and identify the network attack behavior for an extended period.

Due to the significant variability in waveforms and characteristics of ECG signals, developing fully automatic (i.e., requires no expert assistance) heartbeat classification algorithms with satisfactory performance on domain-shifted data remains challenging. In this letter, we propose a novel Mixup Asymmetric Tri-training (MIAT) method to improve the generalization ability of heartbeat classifiers in domain shift scenarios. First, we develop an ECG-based tri-branch CNN model, including one shared feature encoder followed by three branch networks. Next, to obtain target-discriminative features progressively, the tri-branch CNN is trained asymmetrically in each domain adaptation cycle, where two branches are used to assign pseudo-labels to the target domain samples and the third branch is trained on these pseudo-labeled target samples. Moreover, three kinds of mixup regularizations are incorporated into the training process. Experimental results on MITDB and SVDB show that the proposed MIAT outperforms the state-of-the-art methods in terms of F1-macro score and demonstrate the effectiveness of each mixup regularization.

The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.

Due to the deep integration of information technology and operational technology, networked control systems are experiencing an increasing risk of international cyber attacks. In practice, industrial cyber security is a significant topic because current networked control systems are supporting various critical infrastructures to offer vital utility services. By comparing with traditional IT systems, this paper first analyzes the uncontrollable cyber threats and classified attack characteristics, and elaborates the intrinsic vulnerabilities in current networked control systems and novel security challenges in future Industrial Internet. After that, in order to overcome partial vulnerabilities, this paper presents a few representative security mechanisms which have been successfully applied in today's industrial control systems, and these mechanisms originally improve traditional IT defense technologies from the perspective of industrial availability. Finally, several popular security viewpoints, adequately covering the needs of industrial network structures and service characteristics, are proposed to combine with burgeoning industrial information technologies. We target to provide some helpful security guidelines for both academia and industry, and hope that our insights can further promote in-depth development of industrial cyber security.

It is of vital importance to identify the pleural line when performing lung ultrasound, as the pleural line not only indicates the interface between the chest wall and lung, but offers additional diagnostic information. In the current clinical practice, the pleural line is visually detected and evaluated by clinicians, which requires experiences and skills with challenges for the novice. In this study, we developed a computer-aided technique for automated pleural line detection using ultrasound. The method first utilized the Radon transform to detect line objects in the ultrasound images. The relation of the body mass index and chest wall thickness was then applied to estimate the range of the pleural thickness, based on which the pleural line was detected together with the consideration of the ultrasonic properties of the pleural line. The proposed method was validated by testing 83 ultrasound data sets collected from 21 pneumothorax patients. The pleural lines were successfully identified in 76 data sets by the automated method (successful detection rate 91.6%). In those successful cases, the depths of the pleural lines measured by the automated method agreed with those manually measured as confirmed with the Bland-Altman test. The measurement errors were below 5% in terms of the pleural line depth. As a conclusion, the proposed method could detect the pleural line in an automated manner in the defined data set. In addition, the method may potentially act as an alternative to visual inspection after further tests on more diverse data sets are performed in future studies.

Breast ultrasound (BUS) is a powerful screening tool for examination of breast lesions. Recently, research attention has been paid to combining doctor’s opinions and machine learning technology to build up a better computer-aided diagnosis (CAD) system. In this paper, we propose an improved approach that uses human-rating BI-RADS scores to classify the BUS samples. A BI-RADS feature scoring scheme is firstly adopted to standardize the descriptions on breast lesions, and then the diagnostic patterns are mined by a biclustering algorithm in the collected BI-RADS feature score dataset. With an input sample, the diagnostic patterns could be activated to different degrees which represent the high-level features by calculating the distance between input sample and patterns. The high-level features of the sample are input into a multi-layer perception neural network (MLPNN) and we use a cost matrix to convert the output from probabilities to classification cost. The structure of the MLPNN and the values of elements in cost matrix are optimized by Particle Swarm Optimization, and it finally classifies the input of BUS sample. According to the comparative experiments with other CAD approaches and the experienced sonographers, the proposed approach achieved the best sensitivity, indicating that it can serve as an assistant diagnostic system in clinical practices.

The bearing fault feature for complex equipment in early failure period is so weak and susceptible to complicated transmission path and random noise that it’s very difficult to be extracted, so Fast Iterative Filtering Decomposition (FIFD) with Probabilistic Neural Network (PNN) are combined for diagnosing the bearing fault. A bearing simulator was used to collect vibration signals of bearing under different fault locations, and then FIFD was applied to decompose them into several Intrinsic Mode Functions, where their energy entropy as an feature vector was calculated respectively. Finally PNN was used to classify different bearing faults. The bearing fault simulator shows that this method can quickly and accurately identify the different fault locations of bearings.

Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods., A non-destructive and efficient method for predicting the risk of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients is highly needed. Here, the authors develop a transfer learning radiomics model for preoperative prediction of LNM in patients with PTC in a multicenter scenario.

In this study, we collected a total of 610 hospitalized patients from Wuhan between February 2, 2020, and February 17, 2020. We reported a potentially high false negative rate of real‐time reverse‐transcriptase polymerase chain reaction (RT‐PCR) testing for SARS‐CoV‐2 in the 610 hospitalized patients clinically diagnosed with COVID‐19 during the 2019 outbreak. We also found that the RT‐PCR results from several tests at different points were variable from the same patients during the course of diagnosis and treatment of these patients. Our results indicate that in addition to the emphasis on RT‐PCR testing, clinical indicators such as computed tomography images should also be used not only for diagnosis and treatment but also for isolation, recovery/discharge, and transferring for hospitalized patients clinically diagnosed with COVID‐19 during the current epidemic. These results suggested the urgent needs for the standard of procedures of sampling from different anatomic sites, sample transportation, optimization of RT‐PCR, serology diagnosis/screening for SARS‐CoV‐2 infection, and distinct diagnosis from other respiratory diseases such as fluenza infections as well.

To evaluate the surgical safety of en bloc resection of bladder tumor (ERBT) and the effectiveness of ERBT combined with near-infrared (NIR) imaging technique in the diagnosis and treatment of non-muscle invasive bladder cancer (NMIBC). From October 2017 to June 2018, 26 patients newly diagnosed with single NMIBC were included in this retrospectively trial. All patients received ERBT with monopolar current. After surgery, the fresh specimen was incubated with anti-CD47-Alexa Fluor 790, and then imaged under NIR imaging technique. Operative details, intraoperative and postoperative complications of ERBT regarded as safety outcomes, the mean fluorescence intensity (MFI) of tumor tissue and adjacent normal background tissue, and 12 months follow-up data were analyzed. Of 26 collected patients, obturator nerve reflex was occurred in six patients during tumor resection, and only one patient was observed with bladder perforation. In NIR gray image, the gray scale of MFI of tumor tissue were 132.31 ± 6.67 and the adjacent normal background tissue were 52.27 ± 12.09. The result showed a significantly higher MFI signals in tumor tissue compared to adjacent normal background tissue (P < 0.001). The recurrence-free survival rate at 12 month was 96.15%. ERBT with monopolar current is a safe and feasible technique to treat patients with NMIBC. A integrated bladder tumor tissue-bound anti-CD47-Alexa Fluor 790 was detected under NIR light, and the NIR image indicates that higher MFI signals in surgical margin is a predictive factor for residual tumor in patients with NMIBC after ERBT.

OBJECTIVES To investigate the correlation between ultrasound (US) appearances of invasive breast cancers and tumor proliferation and invasiveness measured according to the histologic grade, Ki-67 expression, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI). METHODS This study evaluated 676 patients who underwent primary surgical treatment of invasive breast cancers. The preoperative US reports and postoperative pathologic and immunohistochemical results of the patients were retrospectively reviewed. Ultrasound characteristics were evaluated according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon. Logistic regression analyses were used to identify independent predictive US features that were correlated with tumor proliferation and invasiveness of breast cancers. Odds ratios (ORs) were calculated. RESULTS Posterior acoustic enhancement and calcifications on US images were independent predictive factors of a higher histologic grade and a higher Ki-67 level (OR, 1.69-6.54; P

Purpose There is an urgent need for new biomarkers for the diagnosis of breast cancer. Exosomes can communicate with cells through transport molecules, including long-chain noncoding RNA (lncRNA), which is considered as a promising noninvasive biomarker. Here, we aimed to determine the potential of long noncoding RNA (lncRNA) H19 in the circulating exosomes for the diagnosis of breast cancer (BC). Materials and Methods We measured the levels of lncRNA H19 in serum-derived exosomes from patients with breast cancer (BC) or benign breast disease (BBD) and healthy subjects, using quantitative real-time PCR. H19 levels were also measured for pre-operative and post-operative patients. Receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated to determine the applicability of exosomal H19 levels as biomarkers in BC. The relationship between H19 relative expression and clinical features of BC patients was also analyzed. Results Exosomal H19 expression levels were upregulated in patients with BC compared to that in patients with BBD and healthy controls (BC vs BBD, P < 0.001; BC vs healthy subjects, P < 0.001). The median serum exosomal H19 levels were significantly lower in post-operative than that in the pre-operative patients (P < 0.001). The AUC for exosomal H19 analysis was 0.870 (95% CI: 0.774-0.966) with a sensitivity of 87.0% and specificity of 70.6%, which was higher than the AUCs for CA15-3 and CEA, ie, 0.822 and 0.811, respectively. Moreover, exosomal H19 expression levels were associated with lymph node metastasis (P = 0.039), distant metastasis (P = 0.008), TNM stages (P = 0.022), ER (P=0.009), PR (P = 0.018), and Her-2 (P = 0.021). Conclusion Our results indicated that serum exosomal H19 acts as a novel biomarker for the diagnosis of BC.

A high-isolation eight-way power combiner is proposed in this article that is constructed by cascading three stages of power combiners in series. Its high isolation and low return loss are realized by means of lumped isolators, an isolated side network, and a resistive septum. The eight-way power combiner is basically a 17-port mode network, consisting of eight input ports, eight matching ports, and one output port. The mode network matrix cascade computation clearly reveals the working mechanism. Based on the designed 17-port network, an eight-way power combiner is realized with eight matching ports connected to loads. The measurements agree well with the simulations. From 7.8 to 10.2 GHz, the return loss at the input ports is greater than 19 dB, the return loss at the output port is greater than 18 dB, the isolation is at least 19 dB, and the insertion loss is less than 0.4 dB. The proposed eight-way power combiner has the merits of high isolation, low return loss, low insertion loss, high power capacity, and ease of integration, with great potential for integration into high-power amplifiers.

This work reviews the processes of methane (CH4) and carbon dioxide (CO2) adsorption and desorption, as well as the displacement of CH4 by CO2, at the molecular level. For the adsorbate, laboratory...

The Internet of Things (IoT) networks are finding massive applications in mission-critical contexts. A group key is needed to encrypt and authenticate broadcast/multicast messages commonly seen in large-scale wireless networks. In this paper, we propose DroneKey, a novel drone-aided PHY-based Group-Key Generation (GKG) scheme for large-scale IoT networks. In DroneKey, a drone is dispatched to fly along random 3D trajectories and keep broadcasting standard wireless signals to refresh the group keys in the whole network. Every IoT device receives the broadcast signals from which to extract the Channel State Information (CSI) stream which captures the dynamic variations of the individual wireless channel between the IoT device and the drone. DroneKey explores a deep-learning approach to extract the hidden correlation among the CSI streams to establish a common group key. We thoroughly evaluate DroneKey with a prototype in both indoor and outdoor environments. We show that DroneKey can achieve a high key-generation rate of 89.5 bit/sec for 10 devices in contrast to 40 bit/sec in the state-of-art prior work. In addition, DroneKey is much more scalable and can support 100 devices in contrast to 10 nodes in the state-of-art prior work with comparable key-generate rates.

Background: Just-In-Time (JIT) defect prediction models predict if a commit will introduce defects in the future. DeepJIT and CC2Vec are two state-of-the-art JIT Deep Learning (DL) techniques. Usually, defect prediction techniques are evaluated, treating all training data equally. However, data is usually imbalanced not only in terms of the overall class label (e.g., defect and non-defect) but also in terms of characteristics such as File Count, Edit Count, Multiline Comments, Inward Dependency Sum etc. Prior research has investigated the impact of class imbalance on prediction technique's performance but not the impact of imbalance of other characteristics. Aims: We aim to explore the impact of different commit related characteristic's imbalance on DL defect prediction. Method: We investigated different characteristic's impact on the overall performance of DeepJIT and CC2Vec. We also propose a Siamese network based few-shot learning framework for JIT defect prediction (SifterJIT) combining Siamese network and DeepJIT. Results: Our results show that DeepJIT and CC2Vec lose out on the performance by around 20% when trained and tested on imbalanced data. However, SifterJIT can outperform state-of-the-art DL techniques with an average of 8.65% AUC score, 11% precision, and 6% F1-score improvement. Conclusions: Our results highlight that dataset imbalanced in terms of commit characteristics can significantly impact prediction performance, and few-shot learning based techniques can help alleviate the situation.

Undergraduate research has been shown to enrich education, increase retention and promote critical thinking. Research projects involving extending an already mature and relatively large code base have special value for students, as they mimic industry experience. Students have to understand the existing code structure and figure out where to add their code to extend the functionality of the system. There are obvious challenges for undergraduate students to take on such projects, including lack of knowledge at the onset of the project, limited time span (normally a semester), and limited time availability during a semester. Undergraduate institutions have additional challenges, such as high teaching load for faculty advisors and lack of graduate students who could act as mentors. In this paper, we report our experience with a relatively large project that lasted for several semesters and involved several teams of students. We managed the challenges by being flexible and carving out many mini-projects from this umbrella project, which required that the faculty advisor work closely with students. The project has successfully produced a teaching/learning tool for auto-gradable full tracing exercises. The tool has been prototyped in classrooms and helped students learn various programming constructs. We hope that our experience could encourage larger scale projects in undergraduate institutions.

Acute kidney injury (AKI) and chronic kidney disease (CKD) represent different stages of renal failure; thus, CKD can be regarded as a result of AKI deterioration. Previous studies have demonstrated that immune cell infiltration, oxidative stress, and metabolic mentalism can support renal fibrosis progression in AKI cases. However, the most important triggers and cell types involved in this pathological progression remain unclear. This study was conducted to shed light into the underlying cellular and molecular features of renal fibrosis progression through the analysis of three mouse whole kidney and one human single-cell RNA-sequencing datasets publicly available. According to the different causes of AKI (ischemia reperfusion injury [IRI] or cisplatin), the mouse samples were divided into the CIU [control-IRI-unilateral ureteral obstruction (UUO)] and CCU (control-cisplatin-UUO) groups. Comparisons between groups revealed eight different modules of differentially expressed genes (DEGs). A total of 1,214 genes showed the same expression pattern in both CIU and CCU groups; however, 1,816 and 1,308 genes were expressed specifically in the CCU and CIU groups, respectively. Further assessment of the DEGs according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway and Gene Ontology (GO) showed that T-cell activation, fatty acid metabolic process, and arachidonic acid metabolism were involved in the fibrosis progression in CIU and CCU. Single-cell RNA-sequencing data along with the collected DEGs information also revealed that the T-cell activation mainly happened in immune cells, whereas the fatty acid metabolic process and arachidonic acid metabolism occurred in tubule cells. Taken together, these findings suggest that the fibrosis process differed between the CIU and CCU stages, in which immune and tubule cells have different functions. These identified cellular and molecular features of the different stages of fibrosis progression may pave the way for exploring novel potential therapeutic strategies in the clinic.

The human-computer interaction (HCI) system is the hub of conversation between man and machine. The previous HCI system is challenging to meet the needs of modern society with the continuous progress of computer technology. A new HCI technology different from the past needs to be put forward to solve the problem that the development of computer technology and outdated HCI technology lag behind the times. Machine learning aims to build a behavior method that can obtain information from data and predict the data. Machine learning features are used and integrated into gesture algorithms through the basic principle of a finger-guessing game. Gesture estimation is adopted to detect joint gesture features efficiently, and a convolutional neural network is employed to plan and process the joint features to solve the bottleneck of poor segmentation of gesture images in different environments. HCI system based on gesture recognition algorithm is designed from the perspective of machine learning. The final experimental results show that this method has good recognition accuracy for the performance of different scales of various gestures, and the accuracy of recognition results can reach 70%. It suggests that the system is theoretically conditional on making the recognition result reach the most authentic level. It is demonstrated that gesture recognition is of excellent use value in this HCI system, and the HCI system established here also has an efficient reference value.

BackgroundRenal fibrosis is inevitable in all progressive chronic kidney diseases (CKDs) and represents a serious public health problem. Immune factors contribute to the progression of renal fibrosis. Thus, it is very possible that immunosuppression cells, such as myeloid-derived suppressor cells (MDSCs), could bring benefits to renal fibrosis. Herein, this study investigated the antifibrotic and reno-protective effect of MDSCs and the possible mechanisms.MethodsMurine and cell models of unilateral ureter obstruction (UUO) renal fibrosis were used. Bone marrow-induced MDSCs and granulocyte–macrophage colony-stimulating factor (GM-CSF) were pretreated before surgery. Kidney weight, pathological injury, extracellular matrix deposition, and epithelial–mesenchymal transition progression were examined. Transforming growth factor (TGF)-β1)/Smad/Snail signaling pathway involvement was investigated through Western blotting and quantitative PCR (qPCR). Accumulation of MDSC, CD4+ T cell, regulatory T (Treg), and T helper 1 (TH1) cell accumulation, and CCL5 and CCR5 expression level in MDSCs and non-MDSCs were evaluated using flow cytometry.ResultsIn vitro- and in vivo-induced MDSCs significantly ameliorated UUO-induced tubulointerstitial fibrosis, inhibited the TGF-β1/Smad/Snail signaling pathway, and enhanced MDSC and Treg infiltration in the kidney while downregulating the TH1 cells. Both in vitro and in vivo experiments confirmed CCL5 elevation in the two MDSC-treated groups.ConclusionIn vitro- and in vivo-induced MDSCs alleviated renal fibrosis similarly through promoting the CCL5–CCR5 axis interaction and TGF-β1/Smad/Snail signaling pathway inhibition. Our results indicate an alternative treatment for renal fibrosis.

Wastewater discharge to rivers is a controversial practice that compromises water quality, aquatic habitats and human health worldwide. Here we show how untreated wastewater laced with microplastics and raw sewage is routinely discharged into UK river flows that are too low to disperse the microplastics downstream. These ‘dry weather’ spills lead to acute microplastic contamination of river bed habitats. Many aquatic fauna feed in the benthic zone, the quality of which affects the entire riverine ecosystem. All microplastic types accumulate to high concentrations on the channel bed until flushed downstream by floods. These findings pose fundamental questions about the sustainable management of urban wastewater. Treating the wastewater would shut down the major source of microplastic fragments and microbeads in such rivers and prevent their transport to the ocean. Riverine microplastic transport is primarily partitioned between: (1) continuous transport at low concentrations of synthetic fibres from treated wastewater effluent; and (2) episodic flood-driven transport of the full microplastic assemblage entrained from contaminated channel beds. Focusing only on the buoyant non-flood microplastic load can produce highly unrepresentative assessments of riverine microplastic contamination. Climate warming and urban population growth will intensify the microplastic burden on many river ecosystems as summer baseflows decline and wastewater fluxes increase. The authors show how untreated wastewater laced with microplastics and raw sewage is routinely discharged into UK river flows that are too low to disperse the microplastics downstream. This discharge creates acute microplastic contamination of river beds that threatens biodiversity and the quality of riverine habitats.

Background Ovarian sex cord-stromal tumors (SCSTs) are relatively rare tumor. The standard treatment is surgery, while adjuvant chemotherapy remains controversial. This study evaluated the effect of adjuvant chemotherapy and disease-specific survival (DSS) in patients with SCSTs.Methods 323 Patients with SCSTs were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. Demographic and clinicopathological characteristics were compared using Mann–Whitney U-test, Fisher’s exact test, and chi-square test. DSS were estimated by Kaplan–Meier and compared with the log-rank test. Cox proportional hazards model was used to control for confounders.Results A total of 323 patients were included in this study, and the mean age was 44 years (range, 10-89 years). The most common histologic subtype was granulosa cell tumor (GCT) (n=197, 61%). 60.7% were diagnosed with stage IA or IB disease and 18% with stage IC disease. Among them, 62.2% of patients (n=201) received chemotherapy. The Median follow-up time was 71 months (range,1-224 months). 12.7% (41/323) of patients experienced death. Patients who did not received chemotherapy (n=122) had better DSS compared to those who received chemotherapy, 5-year DSS rates were 93.5% VS 80.3% (P = 0.0005), 10-year DSS rates were 93.0% and 78.69% (P = 0.0002). Multiple Cox regression analysis showed that higher stage (p＜0.001), poor differentiation（P=0.24）, larger tumor size(size＞10cm) （P=0.018） were independently associated with an increased hazard of death，while histological subtype（P=0.082）,adjuvant chemotherapy(p=0.762), complete staging surgery（p=0.554）was not associated with prognosis.ConclusionChemotherapy could not improve the prognosis and may be associated with poorer DSS. Age, histopathologic subtype, complete staging surgery, lymphadenectomy are not associated with prognosis.

As applications are often allocated more memory than they actually used to cope with peak memory demand due to the workload dynamics in virtualized systems running multiple virtual machines, memory adjustment by reclaiming inactive memory in virtual machines is an effective way to enable memory overcommitment so as to reduce the cost. However, existing schemes are designed based on one-shot adjustment, which may reclaim a large size in a single operation, and they are unaware of both memory access dynamics and memory sensitivity of different applications, so they usually result in excessive reclamation and lead to severe performance slowdown. To address this issue, we propose PMA, a progressive memory adjustment scheme that takes into consideration both memory access dynamics and memory sensitivity and leverages virtual machine performance feedback to progressively reclaim inactive memory to avoid performance slowdown. Besides, PMA is designed based on ballooning (i.e., the balloon driver) so it preserves the good isolation between host and virtual machines for full virtualization systems. We also implement a prototype in host user space, and experiments show that PMA effectively limits the performance drop of memory overcommitment (e.g., the performance drop of every virtual machine is limited within 10% for up to 33% memory overcommitment), which is already very close to the optimal case of having enough memory, so PMA is efficient to enable memory overcommitment with performance guarantee in full virtualization systems.

As a novel networking paradigm, Fog radio access networks (F-RANs) have gained great attention due to the advantages such as multiple transmission modes, interference prevention, and edge caching. But security guarantee in F-RANs is a vital problem where illegal users may access to F-RANs without proper authorization. To this end, we design an authorization mechanism using attribute based encryption (ABE) to solve the data security issue of edge caching in F-RANs. Through theoretical analysis, we prove that our proposed mechanism is secure. Extensive simulations are carried out to evaluate the performance of our proposed mechanism by comparing state-of-the-art mechanisms. The results show that our proposed mechanism can solve the authorization problem in F-RANs and performs better in terms of computing consumption.

Knee osteoarthritis (KOA) is the most common form of joint degeneration with increasing prevalence and incidence in recent decades. KOA is a molecular disorder characterized by the interplay of numerous molecules, a considerable number of which can be detected in body fluids, including synovial fluid, urine, and blood. However, the current diagnosis and treatment of KOA mainly rely on clinical and imaging manifestations, neglecting its molecular pathophysiology. The mismatch between participants’ molecular characteristics and drug therapeutic mechanisms might explain the failure of some disease-modifying drugs in clinical trials. Hence, according to the temporal alteration of representative molecules, we propose a novel molecular classification of KOA divided into pre-KOA, early KOA, progressive KOA, and end-stage KOA. Then, progressive KOA is furtherly divided into four subtypes as cartilage degradation-driven, bone remodeling-driven, inflammation-driven, and pain-driven subtype, based on the major pathophysiology in patient clusters. Multiple clinical findings of representatively investigated molecules in recent years will be reviewed and categorized. This molecular classification allows for the prediction of high-risk KOA individuals, the diagnosis of early KOA patients, the assessment of therapeutic efficacy, and in particular, the selection of homogenous patients who may benefit most from the appropriate therapeutic agents.

On the road of CMOS device continuously scaling, there are lots of challenges regarding the device structure and material engineering. GaN channel has recently been used in MOSFETs and achieved excellent performance. In this paper, we study a novel embedded gate GaN nanotube field effect transistor of 5 nm gate length with ION/IOFF as high as 106, and subthreshold swing (SS) as small as 64 mV/dec using Sentaurus TCAD simulation. The device can effectively improve subthreshold characteristics due to the GaN channel and embedded gate design. Compared with Si nanotube FET and GaN nanowire FET, GaN embedded nanotube FET exhibits low SS and high ION/IOFF at the same channel thickness. GaN embedded nanotube FET has also been determined to superior temperature adaptability and performs better in terms of threshold voltage and subthreshold characteristics compared to Si nanotube FET at the same temperature. In addition, we investigated the impact of different lengths and thicknesses of the embedded gate on the subthreshold characteristics. As the length and thickness of the embedded gate are increased, SS and ION/IOFF are improved. This excellent electrical performance demonstrates the possibility of GaN as a channel material in MOSFETs and embedded gate as an effective design to improve subthreshold characteristics, opening a new way for continued device scaling.

The penetration of renewable energy generations, e.g., wind power, not only introduces the randomness and fluctuations into power system operations but also increases the possibilities of cybersecurity issues. Among them, false data injection attack (FDIA) can access and falsify the readings of smart meters, which would impede the functionalities of power systems. In this paper, we first set up an evaluation model to identify the set of high-risk lines by investigating the relationship between FDIA and wind power uncertainty. Then, for a power system with a high wind penetration, a tri-level preventive dispatch strategy is proposed to ensure the system security even under the worst-case of FDIA. It is demonstrated that the impacts of FDIA can cause more serious security issues as the wind penetration level increases. The effectiveness of the proposed tri-level preventive dispatch strategy in mitigating the FDIA caused overloading risk is validated using the IEEE 118-bus system.

In this article, a vertical four-way rectangular waveguide to microstrip power divider is proposed. The power divider achieves perfect power division from 10.1 to 11 GHz with a 20-dB return loss and a short longitudinal length. Based on the four-way power divider, two microstrip ports are added, and a compact improved four-way rectangular waveguide to microstrip power divider is constructed. The 20-dB return loss bandwidth of the power divider can be extended from 9.9 to 12.4 GHz. If all the output microstrip ports are connected to resistive loads, the power dividers become rectangular matching terminations. Thus, the four-way power divider can achieve a rectangular matching load with an 8% bandwidth and a 2-mm matching length. The improved four-way power divider can achieve a rectangular matching load with a 22.4% bandwidth and a 2.5-mm matching length. The measurements agree well with the simulations. The proposed designs of the rectangular loads are superior in their compact volume, high power capacity, and ease of fabrication.

A compact $E$ -plane four-port junction circulator is proposed in this letter. It consists of an X-shape waveguide junction, two X-shape ferrite disks with fillets, and several matching metal posts. Based on the combinations of the TM210 and degenerate TM±110 modes, a four-port gyromagnetic resonator is realized. By introducing the tuning elements, the bandwidth is improved. Measurements show that from 8 to 9 GHz, the return loss is better than 18 dB, the isolation is better than 17 dB, and the insertion loss is 0.3–0.6 dB. Measurements agree well with the simulations. Its cross-sectional size is less than $0.86\lambda \times 0.72\lambda $ at 8.5 GHz. High-power test shows that its pulsed power capacity is no less than 28 kW.

It is difficult to trace grains to their original harvest because doing so requires unique and accurate grain labels, which are mixed when handled in massive quantities and derived from different origins. Food-grade grain tracers have been developed as an identification technology for tracing from original harvest to final destination. To implement online production operation for food-grade grain tracers by QR code scanning and information recording, automatic separation and identification equipment was designed in three parts: vibration screen separation, transmission and recognition assembly and host computer software for traceability. Improvements in the recognition rate, wear rate and screening rate of the equipment in the grain tracing system was achieved based on a Box–Behnken design (BBD) using response surface methodology (RSM). Four factors that affect the working parameters (i.e., vibration frequency, screen angle, tracer density and conveyor speed) were selected, and their value ranges were confirmed. The following optimal working parameters were obtained: a vibration frequency of 40.13 Hz, a screen angle of 5.00°, a tracer density of 5 tracers per 1.5 kg wheat and a conveyor speed of 0.15 m/s. Test results show that the automatic separation and identification equipment is practical for grain tracing systems. The experimental values of the recognition rate and screening rate under optimized conditions were similar to the predicted values, which confirmed the validity of the models. The advantage of this equipment is that it represents an online approach for separating and identifying tracers that would not require production interruptions or manual recognition. Therefore, this automatic separation and identification equipment holds a certain significance for the design and development of grain tracing systems, possibly providing a basis for the implementation and further research of other food traceability systems.

Aims To investigate the protective effects of downregulating ETaR expression on renal ischemia reperfusion injury (IRI). Main methods The renal IRI model was generated by clamping the left renal artery for 60 min followed by nephrectomy of the right kidney. ETaR siRNA were perfused through the renal artery during ischemia. HE staining was performed to assess histological injury. PCR was performed to determine the expression of NF-κb, TNF-α, IFN-γ, IL-6 and TGF-β. ELISA was used to determine the levels of ET-1, TGF-β and eNOS. The level of nitric oxide (NO) was tested by the NO detection kit. The expression of PI3K, Akt, sGC and PKG were evaluated by western blot. Key findings ETaR siRNA treatment reduced the levels of serum creatinine and urea nitrogen, decreased the number of apoptotic cells, and ameliorated histological damage after IRI. PCR results demonstrated that IRI increased mRNA levels of inflammatory factors, which were inhibited by ETaR siRNA treatment. ELISA result showed that ETaR siRNA decreased the levels of ET-1, TGF-β and eNOS in the renal tissues after IRI. Western blot results demonstrated that ETaR siRNA activated the PI3K/Akt and sGC/PKG signaling pathway. Conversely, the NOS inhibitor, L-NAME, reversed the effects of ETaR siRNA treatment. Significance ETaR siRNA treatment inhibited inflammatory response and improved renal function after renal IRI. The underlying mechanisms of ETaR siRNA treatment may be through increasing eNOS activity through PI3K/Akt signaling, which subsequently increased NO production. The increased NO reduces the expression of ET-1 by inhibiting transcription of ET-1-associated genes via the sGC/PKG signaling pathway.