A 15-Gbps BiCMOS XNOR gate for fast recognition of COVID-19 in binarized neural networks

The COVID-19 pandemic is spreading around the world causing more than 177 million cases and over 3.8 million deaths according to the European Centre for Disease Prevention and Control. The virus has devastating effects on economies, health, and well-being of worldwide population. Due to the high increase in daily cases, the available number of COVID-19 test kits in under-developed countries is scarce. Hence, it is vital to implement an effective screening method of patients using chest radiography since the equipment already exists. With the presence of automatic detection systems, any abnormalities in chest radiography that characterizes COVID-19 can be identified. Several artificial-intelligence algorithms have been proposed to detect the virus. However, neural networks training is considered to be time-consuming. Since computations in training neural networks are spent on floating-point multiplications, high computational power is required. Multipliers consume the most space and power among all arithmetic operators in deep neural networks. This paper proposes a 15 Gbps high-speed bipolar-complementary-metal-oxide-semiconductor (BiCMOS) exclusive-nor (XNOR) gate to replace multipliers in binarized neural networks. The proposed gate can be implemented on BiCMOS-based field-programmable gate arrays (FPGAs). This will significantly improve the response time in identifying chest abnormalities in CT scans and X-rays.

Research on the Unstable Branch Screening Method for Power System With High-Proportion Wind Power

The sooner the system instability is predicted and the unstable branches are screened, the timelier emergency control can be implemented for a wind power system. In this paper, aiming at the problem that the existing unstable branch screening methods are lack prejudgment, an unstable branch screening method for power system with high-proportion wind power is proposed. Firstly, the equivalent external characteristics model of the wind farm was deduced. And based on this, the out-of-step oscillation characteristics of the power system with high proportion wind power was analyzed. Secondly, based on the oscillation characteristics, line weak-connection index (LWcI) was proposed to quantify the stability margin of a branch. Then an instability prediction method and an unstable branch screening method were proposed based on LWcI and voltage phase angle difference. Finally, the rapidity and effectiveness of the proposed method are verified through the simulation analysis of IEEE-118 system.

Accuracy of Heart Auscultation Examination by General Practitioners From Various Levels of Competency in Detection of Heart Abnormality in Basic School Children

Background. Most of heart defects in children do not show typical clinical symptoms. Ten percent of the cases are late detected. Echocardiography is an examination with high sensitivity and specificity in detecting heart defects in children, but it cannot be performed by all health workers, expensive and not always available in hospitals. Auscultation is an important part of a physical examination that inexpensive, easy examination, and becomes a competency of all doctors. The aim of this study to determine the accuracy of the screening method by listening to murmurs on heart auscultation by various levels of physician competence. Methods. This is a diagnostic test of 250 elementary school children held in the pediatric ward of dr. Mohammad Hoesin Palembang from September to November 2019. The auscultation examination was performed by three pediatrics resident from three stages (i.e. junior, middle and senior), followed by echocardiography examinations by a pediatric cardiologist. Results. The highest sensitivity of auscultation was found in senior resident, 42.4%, while the lowest was found in junior resident, 12.1%. The results of the kappa analysis of the cardiac auscultation examination on the three examiners showed a poor level of agreement on junior stage compared to senior resident (k = 0.189; CI = 0.033-0.346) and the level of agreement was sufficient in junior stage compared to middle stage resident (k = 0.297; CI = 0.134 -0.461) and middle stage compared to senior resident (k = 0.301; CI = 0.147-0.456). Conclusion. Experience and length of learning will affect the accuracy of the auscultation examination in detecting heart defects in children.

Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data

Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.

Triage by PAX1 and ZNF582 methylation in women with cervical intraepithelial neoplasia grade 3: a multicenter case-control study

Background The colposcopy-conization inconsistency is common in women with cervical intraepithelial neoplasia grade 3 (CIN3). No adequate method has been reported to identify the final pathology of conization. In this study, we explored the ability of PAX1 and ZNF582 methylation to predict the pathological outcome of conization in advance. Methods This was a multicenter study and included 277 histologically confirmed CIN3 women who underwent cold knife conization (CKC) from January 2019 to December 2020. The methylation levels of PAX1 (PAX1m) and ZNF582 (ZNF582 m) were determined by quantitative methylation specific PCR (qMSP) and expressed in ΔCp. Receiver-operating characteristic (ROC) curves were used to evaluate predictive accuracy. Results The final pathological results in 48 (17.33%) patients were inflammation or low-grade squamous intraepithelial lesion (LSIL), 190 (68.59%) were high grade squamous intraepithelial lesion (HSIL) and 39 (14.08%) were squamous cervical cancer (SCC). PAX1 m and ZNF582 m increased as lesions progressed from inflammation/LSIL, HSIL to SCC. PAX1 and ZNF582 methylation yielded better prediction performance compared to common screening strategies, whether individually or combined. ΔCpZNF582 ≥19.18). A 6.53-fold increase in SCC risk was observed in patients with elevated ZNF582 methylation (ΔCpZNF582 < 7.09). Conclusion DNA Methylation would be an alternative screening method to triage and predict the final outcome of conization of the CIN3 cases.

Configuration design and screening of multi-mode double-planetary-gears hybrid powertrains

Hybrid powertrains with planetary gearset(PG) have been widely used. However, there are few types of powertrains in use, more powertrains have not been found. Based on the principle of organic chemistry, a design and screening method of multi-mode 2-PGs hybrid powertrain is proposed, which is divided into five stages. Firstly, powertrains are expressed in the form of molecules. Secondly, powertrains split into the libraries of PGs and power sources. The power sources can be mutually identified to construct new library. Thirdly, the mode switching rules are defined to screen power source group. Fourthly, two libraries interact with each other to promote the generation of new molecules, namely, new powertrains. And the more modes, the greater the vehicle performance potential. Powertrains are screened with mode richness theory firstly. Finally, taking the comprehensive evaluation of power performance and fuel economy as the optimal standard, powertrains are screened and evaluated twice. Through the method, hybrid powertrains with smooth mode switching, simpler structure, and optimal power and economy can be obtained.

Recovery of anosmia in hamsters infected with SARS-CoV-2 is correlated with repair of the olfactory epithelium

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for a pandemic affecting billions of people worldwide. Apart from the extreme global economic impact, the pandemic will likely have a lasting impact through long-term sequelae not yet fully understood. Fully understanding the mechanisms driving the various symptoms and sequelae of SARS-CoV-2 infection will allow for the eventual development of therapeutics to prevent or treat such life-altering symptoms. In this study, we developed a behavioral test of anosmia in SARS-CoV-2-infected hamsters. We find a moderately strong correlation between the level of anosmia and the score of histological damage within the olfactory epithelium. We also find a moderately strong correlation between the level of anosmia and the thickness of the olfactory epithelium, previously demonstrated to be severely damaged upon infection. Thus, this food-searching behavioral test can act as a simple and effective screening method in a hamster model for various therapeutics for SARS-CoV-2-related anosmia.