Elyes Ben Salah¹, Karim Dorgham¹, Mylène Lesénéchal², Camille Pease², Laure Allard², Céline Dragonetti², Guy Gorochov¹, Amélie Guihot¹, Delphine Sterlin¹

European Cytokine Network, Vol.29, No.4, pp. 136-145, 2018, DOI:10.1684/ecn.2018.0417

Abstract Worldwide there are about 1.7 billion individuals with latent tuberculosis infection (LTBI) and only 5% to 15% will develop active tuberculosis (TB). It is recommended to treat only those most at risk of develop ing active TB to avoid problems of drug resistance. LTBI diagnosis involves reviewing the individual’s medical history, physical examination, and biological tests. Interferon gamma release assays (IGRA) can yield “undetermi nate” or “uncertain” results, which makes clinical management decisions difficult. We assessed an ultra-sensitive immunoassay prototype based on single molecule array (SiMoA) technology to evaluate its overall performance, and in particular,… More >

Vladimir Mouraviev¹, Michael McDonald²

Canadian Journal of Urology, Vol.25, No.3, pp. 9349-9356, 2018

Abstract Introduction: The changing face of current infection phenotypes — from planktonic to biofilm type — has increasingly implicated bacterial biofilms in recurrent infections. To date, no specific medical treatment exists that can effectively target biofilms within the human host. Similarly, the identification of biofilms has traditionally relied on tissue sample analysis using electron microscopy or DNA identification via polymerase chain reaction (PCR) and sequencing. Standard culture and sensitivity tests are not capable of detecting the presence of biofilms.
Materials and methods: Two types or "levels" of molecular microbial diagnostic testing were performed as described below. In both… More >

Mahiye Uluyagmur Ozturk^a, Ayse Rodopman Arman^b, Gresa Carkaxhiu Bulut^c, Onur Tugce Poyraz Findik^b, Sultan Seval Yilmaz^d, Herdem Aslan Genc^b, M. Yanki Yazgan^e,f, Umut Teker^a, Zehra Cataltepe^a

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 891-905, 2018, DOI:10.31209/2018.100000058

Abstract Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data More >

Jun Ye

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 1-8, 2018, DOI:10.1080/10798587.2016.1261955

Abstract This paper proposes a dimension root distance and its similarity measure of single-valued neutrosophic sets (SVNSs), and then develops the fault diagnosis method of hydraulic turbine by using the dimension root similarity measure of SVNSs. By the similarity measures between the fault diagnosis patterns and a testing sample with single-valued neutrosophic information and the relation indices, we can determine the main fault type and the ranking order of various vibration faults for predicting some possible fault trend. Then, the comparison of the fault diagnoses of hydraulic turbine based of the proposed dimension root similarity measure More >

Heather Y. Sun¹, James A. Proudfoot², Rachel T. McCandless¹

Congenital Heart Disease, Vol.13, No.5, pp. 748-756, 2018, DOI:10.1111/chd.12648

Abstract Background: Fetal echocardiography can accurately diagnose critical congenital heart disease prenatally, but relies on referrals from abnormalities identified on routine obstetrical ultrasounds. Critical congenital heart disease that is frequently missed due to inadequate outflow tract imaging includes anomalies such as truncus arteriosus, double outlet right ventricle, transposition of the great arteries, tetralogy of Fallot, pulmonary stenosis, and aortic stenosis.
Objective: This study evaluated the prenatal detection rate of critical outflow tract anomalies in a single urban pediatric hospital before and after “AIUM Practice Guideline for the Performance of Obstetric Ultrasound Examinations,” which incorporated outflow tract imaging.
Design: Infants… More >

Xinyan Li¹, Xiaoxia Qiu¹, Huan Huang¹, Yili Zhao², Xueqin Li¹, Meng Li¹, Xiaoxian Tian¹

Congenital Heart Disease, Vol.13, No.2, pp. 282-287, 2018, DOI:10.1111/chd.12568

Abstract Objective: To evaluate the efficacy of using fetal heart size measurements derived from axial echocardiography to predict homozygous α-thalassemia-1.
Design: Prospective diagnostic study.
Setting: The carrier rate of α-thalassemia-1 (–/αα) in China’s Guangxi Zhuang Autonomous Region is approximately 15%. If both parents are carriers, the risk of homozygous a-thalassemia-1 in one pregnancy is 25%.
Patients: Singleton mid-pregnancies at risk of homozygous α-thalassemia-1 were enrolled.
Outcome Measures: Fetal heart measurements, including heart diameter (HD), heart length (HL), heart circumference (HC), and heart area (HA), were measured. The z-scores for these heart parameters were then calculated separately based on previously constructed z-score… More >

Jinshan Lin^a*, Chunhong Dou^b, Qianqian Wang^a

Sound & Vibration, Vol.52, No.2, pp. 11-15, 2018, DOI:10.32604/sv.2018.03653

Abstract A method for gearbox fault diagnosis consists of feature extraction and fault identification. Many methods for feature extraction have been devised for exposing nature of vibration data of a defective gearbox. In addition, features extracted from gearbox vibration data are identified by various classifiers. However, existing literatures leave much to be desired in assessing performance of different combinatorial methods for gearbox fault diagnosis. To this end, this paper evaluated performance of several typical combinatorial methods for gearbox fault diagnosis by associating each of multifractal detrended fluctuation analysis (MFDFA), empirical mode decomposition (EMD) and wavelet transform More >

C. K. Madhusudana¹, N. Gangadhar¹, Hemantha Kumar, Kumar,^*,1, S. Narendranath¹

Structural Durability & Health Monitoring, Vol.12, No.2, pp. 111-127, 2018, DOI:10.3970/sdhm.2018.01262

Abstract This paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool More >

Xiaoping Zhao¹, Jiaxin Wu^1,*, Yonghong Zhang², Yunqing Shi³, Lihua Wang²

CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 223-242, 2018, DOI:10.32604/cmc.2018.02490

Abstract With the rapid development of mechanical equipment, mechanical health monitoring field has entered the era of big data. Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities, this also brings influence to the mechanical fault diagnosis field. Therefore, according to the characteristics of motor vibration signals (nonstationary and difficult to deal with) and mechanical ‘big data’, combined with deep learning, a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed. The frequency domain signals obtained by the Fourier transform More >

Shengqun Fang¹, Zhiping Cai^1,*, Wencheng Sun¹, Anfeng Liu², Fang Liu³, Zhiyao Liang⁴, Guoyan Wang⁵

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 419-433, 2018, DOI:10.3970/cmc.2018.02289

Abstract By using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease… More >