Molecular identification and whole genome sequence analyses of methicillin-resistant and mastitis-associated Staphylococcus aureus sequence types 6 and 2454 isolated from dairy cows.

The emergence of antimicrobial-resistant and mastitis-associated Staphylococcus aureus is of great concern due to the huge economic losses worldwide. Here, we report draft genome sequences of two Staphylococcus aureus strains which were isolated from raw milk samples obtained from mastitis-infected cows in Bangladesh. The strains were isolated and identified using conventional microbiological and molecular polymerase chain reaction (PCR) methods. Antibiotic susceptibility testing was performed. Genomic DNA of the two strains was extracted and the strains were sequenced using the Illumina NextSeq 550 platform. The assembled contigs were analyzed for virulence determinants, antimicrobial resistance genes, extra-chromosomal plasmids, and multi-locus sequence type (MLST). The genomes of the two strains were compared with other publicly available genome sequences of Staphylococcus aureus strains. The raw read sequences were downloaded and all sequence files were analyzed identically to generate core genome phylogenetic trees. The genome of BR-MHR281strain did not harbour any antibiotic resistance determinants, however BR-MHR220 strain harbored mecA and blaZ genes. Analysis of BR-MHR220 strain revealed that it was assigned to sequence type (ST-6), clonal complex (CC) 5 and spa type t304, while BR-MHR281 strain belonged to ST-2454, CC8, and harbored the spa type t7867. The findings of the present study and the genome sequences of BR-MHR220 and BR-MHR281 strains will provide data on the detection and genomic analysis and characterization of mastitis-associated Staphylococcus aureus in Bangladesh. In addition, the findings of the present study will serve as reference genomes for future molecular epidemiological studies and will provide significant data which help understand the prevalence, pathogenesis and antimicrobial resistance of mastitis-associated Staphylococcus aureus.

Walking position commanded NAO robot using nonlinear disturbance observer-based fixed-time terminal sliding mode.

The walking stability of a humanoid robot is a fundamental problem due to the complex nonlinear dynamic model of the robot's legs. This work introduces the performance tracking control for the humanoid NAO robot by using a Nonlinear Disturbance Observer (NDO)-based Fixed-time Terminal Sliding Mode (FTSM). The influence of uncertain external disturbance is considered while implementing the control strategy to improve the walking motion of the NAO robot. An NDO is adapted to estimate the uncertainties and external disturbances. A novel FTSM surface is proposed to drive the tracking errors to zero in fixed-time. The designed NDO-based FTSM control law achieves robustness while reducing the chattering phenomenon. The Lyapunov's stability theory is used to establish the fixed-time stability of the sliding surface and system states under the proposed control method. To validate the performance of the proposed NDO-based FTSM control, a real-time experiment was conducted on a humanoid NAO robot to demonstrate the improved tracking performance in the presence of the uncertain perturbation effect. The effectiveness of the proposed controller design is validated on a flat, upward inclined surface, and compared to another controller.

COVID-19 infection segmentation using hybrid deep learning and image processing techniques.

The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people's lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.

Unfavorable clinical outcomes in patients with good collateral scores following endovascular treatment for acute ischemic stroke of the anterior circulation: The UNCLOSE study.

BACKGROUND: Patients with acute ischemic stroke secondary to large vessel occlusions and good collaterals are frequently associated with favorable outcomes after mechanical thrombectomy, although poor outcomes are observed also in this subgroup. We aimed to investigate the factors associated with unfavorable outcomes (modified Rankin Scale3-6) in this specific subgroup of patients. METHODS: In total, 219 patients (117 females) with anterior circulation stroke and good collaterals (American Society for Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology grades 3-4), treated by mechanical thrombectomy between 2016 and 2021 at our institution were included in this study. Clinical files and neuroimaging were retrospectively reviewed. Univariate and multivariate analyses were performed to identify the predictors of unfavorable outcomes in the overall population (primary endpoint). Secondary endpoints focused on the analysis of the predictors of unfavorable outcomes in the subgroup of successfully recanalized patients, mortality, and symptomatic intracerebral hemorrhages in the overall population. RESULTS: Poor outcome was observed in 47% of the patients despite the presence of good collaterals. Older age (p < 0.001), higher baseline National Institute of Health stroke scale (p < 0.001), no intravenous thrombolysis administration (p = 0.004), > 3 passes (p = 0.01), and secondary transfers (p < 0.001) were associated with the primary endpoint. The multivariate analysis showed a predictive effect of modified treatment in cerebral infarction 2b-3 and of first pass effect on symptomatic intracerebral hemorrhage. CONCLUSIONS: Despite good collaterals, defined through the American Society for Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology scale, poor outcomes occurred in almost half of the patients. Patients with good collaterals not receiving intravenous thrombolysis were significantly associated with unfavorable outcomes, whereas first pass effect was not significantly correlated with clinical outcome in this specific cohort of patients. Different methods to assess collaterals should also be investigated.

Assessing the Practical Differences in LDL-C Estimates Calculated by Friedewald, Martin/Hopkins, or NIH Equation 2: An Observation Cross-Sectional Study.

Objective: Low-density lipoprotein-cholesterol (LDL-C) remains a clinically important cholesterol target in primary prevention of atherosclerotic cardiovascular disease. The present study aimed to assess the practical differences among three equations utilized for the estimation of LDL-C: the Friedewald, the Martin/Hopkins, and the NIH equation 2. Methods: Blood lipid measurements from 4,556 noninstitutionalized participants, aged 12 to 80, were obtained from the 2017-2020 National Health and Nutrition Examination Survey study. We 1) assessed the differences between three calculated LDL-C estimates, 2) examined the correlations between LDL-C estimates using correlation coefficients and regression, and 3) investigated the degree of agreement in classifying individuals into the LDL-C category using weighted Kappa and percentage of agreement. Results: The differences in LDL-C estimates between equations varied by sex and triglyceride levels (p<0.001). Overall, the mean of absolute differences between Friedewald and Martin/Hopkins was 3.17 mg/dL (median=2.0, 95% confidence interval [CI] [3.07-3.27]). The mean of absolute differences between Friedewald and NIH Equation 2 was 2.08 mg/dL (median=2.0, 95% CI [2.03-2.14]). Friedewald correlated highly with Martin/Hopkins (r=0.991, rho=0.989) and NIH Equation 2 (r=0.998, rho=0.997). Cohen's weighted Kappa=0.92 between Friedewald and Martin/Hopkins, and 0.95 between Friedewald and NIH equation 2. The percentage of agreement in classifying individuals into the same LDL-C category was 93.0% between Friedewald and Martin/Hopkins, and 95.4% between Friedewald and NIH equation 2. Conclusion: Understanding the practical differences in LDL-C calculations can be helpful in facilitating decision-making during a paradigm shift.

Draft Genome Sequences of Two Clinical Mastitis-Associated Escherichia coli Strains, of Sequence Type 101 and Novel Sequence Type 13054, Isolated from Dairy Cows in Bangladesh.

Here, we report the draft genome sequences of two Escherichia coli strains that were isolated from raw milk samples obtained from lactating cows with mastitis in Bangladesh. One strain was assigned to a novel sequence type 13054, and the other strain belonged to sequence type 101.

Will Your Next Therapist Be a Robot?-A Review of the Advancements in Robotic Upper Extremity Rehabilitation.

Several recent studies have indicated that upper extremity injuries are classified as a top common workplace injury. Therefore, upper extremity rehabilitation has become a leading research area in the last few decades. However, this high number of upper extremity injuries is viewed as a challenging problem due to the insufficient number of physiotherapists. With the recent advancements in technology, robots have been widely involved in upper extremity rehabilitation exercises. Although robotic technology and its involvement in the rehabilitation field are rapidly evolving, the literature lacks a recent review that addresses the updates in the robotic upper extremity rehabilitation field. Thus, this paper presents a comprehensive review of state-of-the-art robotic upper extremity rehabilitation solutions, with a detailed classification of various rehabilitative robots. The paper also reports some experimental robotic trials and their outcomes in clinics.

A Novel Multi-Modal Teleoperation of a Humanoid Assistive Robot with Real-Time Motion Mimic.

This research shows the development of a teleoperation system with an assistive robot (NAO) through a Kinect V2 sensor, a set of Meta Quest virtual reality glasses, and Nintendo Switch controllers (Joycons), with the use of the Robot Operating System (ROS) framework to implement the communication between devices. In this paper, two interchangeable operating models are proposed. An exclusive controller is used to control the robot's movement to perform assignments that require long-distance travel. Another teleoperation protocol uses the skeleton joints information readings by the Kinect sensor, the orientation of the Meta Quest, and the button press and thumbstick movements of the Joycons to control the arm joints and head of the assistive robot, and its movement in a limited area. They give image feedback to the operator in the VR glasses in a first-person perspective and retrieve the user's voice to be spoken by the assistive robot. Results are promising and can be used for educational and therapeutic purposes.

Evaluation of Objective Functions for the Optimal Design of an Assistive Robot.

The number of individuals with upper or lower extremities dysfunction (ULED) has considerably increased in the past few decades, resulting in a high economic burden for their families and society. Individuals with ULEDs require assistive robots to fulfill all their activities of daily living (ADLs). However, a theory for the optimal design of assistive robots that reduces energy consumption while increasing the workspace is unavailable. Thus, this research presents an algorithm for the optimal link length selection of an assistive robot mounted on a wheelchair to minimize the torque demands of each joint while increasing the workspace coverage. For this purpose, this research developed a workspace to satisfy a list of 18 ADLs. Then, three torque indices from the literature were considered as performance measures to minimize; the three torque measures are the quadratic average torque (QAT), the weighted root square mean (WRMS), and the absolute sum of torques (AST). The proposed algorithm evaluates any of the three torque measures within the workspace, given the robot dimensions. This proposed algorithm acts as an objective function, which is optimized using a genetic algorithm for each torque measure. The results show that all tree torque measures are suitable criteria for assistance robot optimization. However, each torque measures yield different optimal results; in the case of the QAT optimization, it produces the least workspace with the minimum overall torques of all the joints. Contrarily, the WRMS and AST optimization yield similar results generating the maximum workspace coverage but with a greater overall torque of all joints. Thus, the selection between the three methods depends on the designer's criteria. Based on the results, the presented methodology is a reliable tool for the optimal dimensioning of assistive robots.

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks.

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human-machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1-3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success.

Predicting the handgrip strength across the age span: Cross-validating reference equations from the 2011 NIH toolbox norming study.

STUDY DESIGN: This is a cross-sectional observational study. BACKGROUND: Reference equations for describing hand-grip strength across the age span were derived from the 2011 NIH Toolbox norming study. PURPOSE: The purpose of this study was to cross-validate reference equations by evaluating its predicting power on a separate, independent data set from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) study METHODS: Observed hand-grip strength data from 13,676 noninstitutionalized participants were obtained from the NHANES study. Best values (best from 3 trials) and the mean values (averaged from 3 trials) were determined for each hand. Using the age (yr), height (m), and weight (kg), we computed predicted grip strength values for dominant and nondominant hands using the reference equations. For validation, three predictability measures: the correlation coefficient, residuals, and accuracy, were used along with the Bland-Altman plot. RESULTS: The predicted values highly correlated with observed values (r = 0.90, ICC = 0.89). In predicting best values, means (SD) of residuals were 1.41 (5.57) and 1.03 (5.44) kg for dominant and nondominant hands, respectively. In predicting mean values, means (SD) of residuals were -0.23 (5.42) and -0.54 (5.31) kg for dominant and nondominant hands, respectively. Root mean square error ranged from 4.10 (female's nondominant mean values) to 6.74 (male's dominant best values). About 5.56% fell outside of the 95% confidence interval of the prediction. CONCLUSIONS: We acknowledged that the two studies' hand-grip protocols (NIH Toolbox, NHANES) were different. Results provided the preliminary predicting performance of the reference equations derived from the NIH Toolbox study.

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination.

Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

Kinematics and Workspace Analysis of xArm6 Robot for Activities of Daily Living.

Recent statistics reveal that the number of individuals with upper or lower extremity dysfunctions has increased alarmingly. It is estimated that approximately 3.3 million Americans use a wheelchair, with an expected 2 million new wheelchair users every year. To assist powered wheelchair users with limited upper limb function, we have been exploring assistive robots that can be mounted on a wheelchair to perform essential activities of daily living (ADL), such as picking/placing an object from out of reach, feeding, etc. In this research, a 6DoF robot, xArm-6 was used as an assistive robot to provide ADL assistance. Experiments were conducted with xArm6 Robot to investigate the motion trajectories and workspace covering essential ADLs. In kinematic analysis, modified Denavit-Hartenberg parameters are used to identify the Robot's motion path and workspace. On the other hand, the iterative Newton-Euler method was used for dynamic analysis to estimate the joint torques corresponding to each ADL. Experimental results show that xArm-6 can be used for some selected ADLs tasks but not for all essential ADLs.

Psychometric evaluation of the disabilities of the arm, shoulder and hand (DASH) in patients with orthopedic shoulder impairments seeking outpatient rehabilitation.

STUDY DESIGN: This is a cross-sectional, observational study. INTRODUCTION: The disability of the arm, shoulder and hand (DASH) questionnaire is an upper-extremity specific outcome measure commonly used in routine clinical care and clinical trials. PURPOSE OF THE STUDY: Our purposes were to: (1) examine the psychometric properties of the DASH questionnaire using factor analysis, one- and two-parameter item response theory models, and (2) develop a functional staging map illustrating the relationships between the item difficulty hierarchy of the DASH items and the patient's DASH total score. METHODS: Data from 2724 patients with orthopedic shoulder impairments seeking outpatient physical therapy in 79 clinics in the US were analyzed. RESULTS: Factor analysis supported a general factor, explaining 62.2% of the total variance. The coverage of DASH items was suitable for patients with shoulder impairments with no ceiling or floor effect. Endorsed items representing the most difficult items were related to feeling less capable, executing recreational activities with force or impact, and performing recreational activities in which you move your arm freely. Items with higher discriminating abilities were those related to do heavy household chores, garden/yard work, and change a light bulb overhead. With a separation index equaled to 4.94, the DASH can differentiate persons into at least 6 statistically distinct person strata. None of the DASH items exhibited differential item functioning by gender or symptom acuity, except two items by age group. DISCUSSION: Besides a total (summed) final score obtained from a specific measure, the keyform and functional staging plots/maps can be used to assist clinicians in clinical interpretation of the scores. CONCLUSIONS: Results supported the clinical usage of the DASH questionnaire in patients with orthopedic shoulder impairments seeking outpatient rehabilitation.

Improvement of sliding mode controller by using a new adaptive reaching law: Theory and experiment.

In this paper, a new sliding mode control (SMC) is applied to a physical nonlinear system. The novelty of this approach is related to the proposed reaching law by overcoming the main limitations of SMC. Unlike existing reaching laws, the suggested one can achieve high performance with significant reducing of a chattering problem and has a very fast convergence time of the system trajectories into the origin. This law benefits from the advantages and overcomes the limitations of both the exponential reaching law (ERL) and the conventional sliding mode control (SMC). Simulation results and comparison study with ERL and SMC are presented and applied on two degrees of freedom robot in order to show the advantage of the proposed adaptive reaching law. Experiments results are performed with electric cylinder (DC Motor) to confirm this proposition in real-time implementation.

NAO robot for cooperative rehabilitation training.

INTRODUCTION: The aim of this research is to develop a robot-assistive training approach for the disabled individuals with impaired upper limb functions. People with impaired upper limb function can regain their motor functionality undergoing intense rehabilitation exercises. With increasing number of disabled individuals, we face deficiency in the number of expert therapists. One promising remedy could be the use of robotic assistive devices. METHOD: To instruct and demonstrate rehabilitation exercise, this research used NAO robot. A library of recommended rehabilitation exercises involving shoulder (i.e., abduction/adduction, vertical flexion/extension, and internal/external rotation), and elbow (i.e., flexion/extension) joint movements was formed in Choregraphe (graphical programming interface). For this purpose, a kinematic model of human upper-extremity was developed based on modified Denavit-Hartenberg notations. RESULT: In experiments, NAO robot gave voice instruction and was maneuvered to cooperate and demonstrate the exercises from the library. NAO also plays some complex game with the subject that represents a multi-joint movement's exercise, which was also included in the library. CONCLUSIONS: Experimental results with healthy participants reveal that the NAO robot can successfully instruct and demonstrate upper-extremity rehabilitation exercises for single and multi-joint movements. It implies a technical development of cooperative rehabilitation system for which target group will be individuals with upper limb impairment.

A performance based feature selection technique for subject independent MI based BCI.

PURPOSE: Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user as the most successful methodology can vary between individuals and sessions. The objective of this study was to develop a simple yet effective method of feature selection to improve the accuracy of a subject independent BCI algorithm and streamline the process of BCI algorithm development. Over the past several years, several high precision features have been suggested by researchers to classify different motor imagery tasks. This research applies fourteen of these features as a feature pool that can be used as a reference for future researchers. Additionally, we look for the most efficient feature or feature set with four different classifiers that best differentiates several motor imagery tasks. In this work we have successfully employed a feature fusion method to obtain the best sub-set of features. We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been performed in past studies. The features selected by this method were then fed into a Linear Discriminant Analysis, K-nearest neighbor, decision tree, or support vector machine classifier for classification to determine the overall performance. METHODS: The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. The data used for this study was collected from BCI competition III dataset IVa. RESULT: The result of this algorithm was a classification accuracy of 99% for a subject independent algorithm with less computation cost compared to traditional methods, in addition to multiple feature/classifier combinations that outperform current subject independent methods. CONCLUSION: The conclusion of this study and its significance is that it developed a viable methodology for simple, efficient feature selection and BCI algorithm development, which leads to an overall increase in algorithm classification accuracy.

Between-side differences in hand-grip strength across the age span: Findings from 2011-2014 NHANES and 2011 NIH Toolbox studies.

This project was undertaken to describe percentage differences in the grip strength between the dominant and nondominant-sides of left- and right-handed males and females across the age span. Data used in the project were from population-based samples of participants: 13,653 from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) and 3,571 from the 2011 normative phase of the National Institutes of Health (NIH) Toolbox project. Depending on how percentage differences were calculated, the overall grip strength was a mean 5.0-5.6% greater on the dominant than nondominant side. The percentage differences were significantly greater for individuals who were right-hand dominant rather than left-hand dominant. The differences also varied according to gender. We present summary data for percentage differences stratified by study, handedness, and gender. The values can be used to determine whether grip strength on one side is limited relative to the other.

Adaptive control of an exoskeleton robot with uncertainties on kinematics and dynamics.

In this paper, we propose a new adaptive control technique based on nonlinear sliding mode control (JSTDE) taking into account kinematics and dynamics uncertainties. This approach is applied to an exoskeleton robot with uncertain kinematics and dynamics. The adaptation design is based on Time Delay Estimation (TDE). The proposed strategy does not necessitate the well-defined dynamic and kinematic models of the system robot. The updated laws are designed using Lyapunov-function to solve the adaptation problem systematically, proving the close loop stability and ensuring the convergence asymptotically of the outputs tracking errors. Experiments results show the effectiveness and feasibility of JSTDE technique to deal with the variation of the unknown nonlinear dynamics and kinematics of the exoskeleton model.