Simultaneous Sparse Approximation Using an Iterative Method with Adaptive Thresholding

This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem arises in many applications which work with multiple signals maintaining some degree of dependency such as radar and sensor networks. In this paper, we introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion on the convergence of our method called Simultaneous Iterative Method with Adaptive Thresholding (SIMAT). Additionally, we compare our method with other group-sparse reconstruction techniques, i.e., Simultaneous Orthogonal Matching Pursuit (SOMP), and Block Iterative Method with Adaptive Thresholding (BIMAT) through numerical experiments. The simulation results demonstrate that SIMAT outperforms these algorithms in terms of the metrics Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIMAT is considerably less complicated than BIMAT, which makes it feasible for practical applications such as implementation in MIMO radar systems

Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks

Outlier detection has received special attention in various fields, mainly for those dealing with machine learning and artificial intelligence. As strong outliers, anomalies are divided into the point, contextual and collective outliers. The most important challenges in outlier detection include the thin boundary between the remote points and natural area, the tendency of new data and noise to mimic the real data, unlabelled datasets and different definitions for outliers in different applications. Considering the stated challenges, we defined new types of anomalies called Collective Normal Anomaly and Collective Point Anomaly in order to improve a much better detection of the thin boundary between different types of anomalies. Basic domain-independent methods are introduced to detect these defined anomalies in both unsupervised and supervised datasets. The Multi-Layer Perceptron Neural Network is enhanced using the Genetic Algorithm to detect newly defined anomalies with higher precision so as to ensure a test error less than that calculated for the conventional Multi-Layer Perceptron Neural Network. Experimental results on benchmark datasets indicated reduced error of anomaly detection process in comparison to baselines

Range of motion and angular velocity analysis during landing from different heights, of the lower limb joints in patients with reconstructed anterior cruciate ligaments

 Aims and background: Anterior cruciate ligament injury is the most common ligament injury of the lower limb that necessitates reconstruction as a proper treatment approach. It is now up to the researchers to ask, are the altered kinematic patterns in the lower limbs corrected by this reconstruction? The present study aims to analyze the range of motion and angular velocity of the lower limb joints in patients with reconstructed Anterior cruciate ligaments during landing. Materials and Methods: 20 male subjects who participated voluntarily were divided into 2 groups. One group consisted of healthy subjects (control) the other of patients with reconstructed Anterior cruciate ligament (experimental). They were evaluated in 3 assignments: landing from a box, a vertical jump-landing, and a jump from an obstacle-landing. 3-Dimensional kinematics of the range of motion and angular velocity of the lower limb joints were recorded using 4 cameras and processed by visual 3D software. Findings: In the sagittal plane, the experimental group’s knee and hip joints range of motion was less than the control group (p=0.00) The ankle joint  was more (p≤0.05) than the control group. In per 3 planes, the experimental group’s ankle and knee joints angular velocity was lower than the control group. The hip joint was higher than the control group (p≤0.05). In most cases, both variables showed the smallest value in the landing from the box task and the highest value in the jump from obstacle-landing task (p≤0.05). conclusion: During complex tasks such as landing, patients exhibit altered kinematic patterns that are followed by compensatory mechanisms in adjacent joints. These altered patterns will persist for at least 2 years after the Anterior cruciate ligament is reconstructed. These patients, in the absence of care, are at risk for Anterior cruciate ligament re-injury and prone to develop knee osteoarthritis in future

Conductance modulation of charged lipid bilayer using electrolyte-gated graphene-field effect transistor

Graphene is an attention-grabbing material in electronics, physics, chemistry, and even biology because of its unique properties such as high surface-area-to-volume ratio. Also, the ability of graphene-based materials to continuously tune charge carriers from holes to electrons makes them promising for biological applications, especially in lipid bilayer-based sensors. Furthermore, changes in charged lipid membrane properties can be electrically detected by a graphene-based electrolyte-gated graphene field effect transistor (GFET). In this paper, a monolayer graphene-based GFET with a focus on the conductance variation caused by membrane electric charges and thickness is studied. Monolayer graphene conductance as an electrical detection platform is suggested for neutral, negative, and positive electric-charged membrane. The electric charge and thickness of the lipid bilayer (Q LP and L LP) as a function of carrier density are proposed, and the control parameters are defined. Finally, the proposed analytical model is compared with experimental data which indicates good overall agreemen

Diagnostic values of bronchodilator response versus 9-question questionnaire for asthma

Introduction: Several studies have investigated different tools for asthma diagnosis in order to reduce the cost and improve its early recognition. The goal of this study is to establish a short questionnaire to be used in practice for asthma screening and compare diagnostic values between this method and spirometric response to bronchodilators.Material and method: 208 patients presenting with chronic stable dyspnea (> 6 months) and definite clinical diagnosis of chronic obstructive pulmonary disease, bronchiectasis, pulmonary fibrosis or asthma, were enrolled. 9 questions out of 43 based on the literature search were selected by regression analysis. Patients were asked to complete the questionnaire and then their spirometric responses to bronchodilators were evaluated. Results: Of all, 53.8% of cases were diagnosed clinically to have asthma. For establishing diagnosis of asthma, the bronchodilator test had 48.2% sensitivity, 78.1% specificity, 72% positive, 56.4% negative predictive values, 2.2 positive, 0.66 negative likeli-hood ratios, and false positive, false negative and accuracy of 21.9%, 51.8% and 62.01%, respectively. The revised 9 questions from the questionnaire had 97.3% sensitivity, 77.1% specificity, 83.2% positive, 96.1% negative predictive values, 4.24 positive,  0.03 negative likelihood ratios, 22.9% false positive, 2.7% false negative and 87.98% accuracy.Conclusions: The 9-question questionnaire had better diagnostic values in defining asthma in patients with chronic dyspnea than reversibility of airway obstruction to salbutamol and can be used as a useful screening test for diagnosis of asthma in clinical practice and for investigational purposes

Disturbing-free determination of yeast concentration in DI water and in glucose using impedance biochips

Deionized water and glucose without yeast and with yeast (Saccharomyces cerevisiae) of optical density OD600 that ranges from 4 to 16 has been put in the ring electrode region of six different types of impedance biochips and impedance has been measured in dependence on the added volume (20, 21, 22, 23, 24, 25 µL). The measured impedance of two out of the six types of biochips is strongly sensitive to the addition of both liquid without yeast and liquid with yeast and modelled impedance reveals a linear relationship between the impedance model parameters and yeast concentration. The presented biochips allow for continuous impedance measurements without interrupting the cultivation of the yeast. A multiparameter fit of the impedance model parameters allows for determining the concentration of yeast (cy) in the range from cy = 3.3 × 107 to cy = 17 × 107 cells/mL. This work shows that independent on the liquid, i.e., DI water or glucose, the impedance model parameters of the two most sensitive types of biochips with liquid without yeast and with liquid with yeast are clearly distinguishable for the two most sensitive types of biochips

The Effect of Remote Ischemic Preconditioning on the Incidence of Acute Kidney Injury in Patients Undergoing Coronary Artery Bypass Graft Surgery: A Randomized Controlled Trial

Background: Remote ischemic preconditioning (RIPC) protects other organs from subsequent lethal ischemic injury, but uncertainty remains. We investigated if RIPC could prevent acute kidney injury (AKI) in patients undergoing coronary artery bypass graft (CABG) surgery. Methods: This parallel-group, double-blind, randomized, controlled trial was done on adults undergoing elective or urgent on-pump CABG surgery from 2013 to 2017 in Shiraz, Iran. Patients were allocated to RIPC or control groups through permuted blocking. The patients in the RIPC group received three cycles of 5 min ischemia and 5 min reperfusion in the upper arm after induction of anesthesia. We placed an uninflated cuff on the arm for 30 min in the control group. The study primary endpoint was an incidence of AKI. Secondary endpoints included short-term clinical outcomes. We compared categorical and continuous variables using Pearson χ2 and unpaired t tests, respectively. P<0.05 was considered significant. Results: Of the 180 patients randomized to RIPC (n=90) and control (n=90) groups, 87 patients in the RIPC and 90 patients in the control group were included in the analysis. There was no significant difference in the incidence of AKI between the groups (38 patients [43.7%] in the RIPC group and 41 patients [45.6%] in the control group; relative risk, 0.96; 95% confidence interval, 0.69 to 1.33; P=0.80). No significant differences were seen regarding secondary endpoints such as postoperative liver function, atrial fibrillation, and inpatient mortality. Conclusion: RIPC did not reduce the incidence of AKI, neither did it improve short-term clinical outcomes in patients undergoing on-pump CABG surgery. Trial Registration Number: IRCT2017110537254N1

A machine learning based exploration of COVID-19 mortality risk

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, noninvasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO2, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage

Towards Bacteria Counting in DI Water of Several Microliters or Growing Suspension Using Impedance Biochips

We counted bacterial cells of E. coli strain K12 in several-microliter DI water or in several-microliter PBS in the low optical density (OD) range (OD = 0.05–1.08) in contact with the surface of Si-based impedance biochips with ring electrodes by impedance measurements. The multiparameter fit of the impedance data allowed calibration of the impedance data with the concentration cb of the E. coli cells in the range of cb = 0.06 to 1.26 × 109 cells/mL. The results showed that for E. coli in DI water and in PBS, the modelled impedance parameters depend linearly on the concentration of cells in the range of cb = 0.06 to 1.26 × 109 cells/mL, whereas the OD, which was independently measured with a spectrophotometer, was only linearly dependent on the concentration of the E. coli cells in the range of cb = 0.06 to 0.50 × 109 cells/mL