Properties of finite groups determined by the product of their element orders

http://dx.doi.org/10.1017/S000497271200033

Blastocystis sp. in Small Ruminants: A Universal Systematic Review and Meta-analysis

Purpose The present review was done to evaluate the prevalence and subtype distribution of Blastocystis infection among small ruminants, at a global perspective. Methods Systematic search was performed in PubMed, Scopus, Google Scholar, and Web of Science until 30th January 2022 and total estimates along with 95 confidence intervals (CIs) were computed using a random-effects model. Results Ultimately, the required data were extracted from 25 papers including 19 datasets for each animal. Among 3125 sheep, the Blastocystis prevalence was 25.3 (95 CI 16.1-37.4) (10 countries), being lower in comparison to that in 2869 examined goats 20.5% (95% CI 11-35.1%) (12 countries). Regarding STs distribution, fourteen genetically diverse STs of Blastocystis (ST1-ST5, ST7, ST10, ST12, ST14, ST15, ST21, ST23, ST24, ST26) have been reported in sheep, and the highest pooled prevalence was related to ST10 11 datasets, 57.8% (95% CI 43.7-70.8%), followed by ST14 8 datasets, 28.4% (95% CI 20.2-38.4%), and ST7 2 datasets, 21.1% (95% CI 4.5-60.3%). Compared to sheep, more STs (ST1, ST3-ST7, ST10, ST12, ST14, ST21, ST23-ST26, and ST32) were reported from goats, and the highest weighted frequency was related to ST10 6 datasets, 45.1% (95% CI: 25.6-66.2%), followed by ST7 2 datasets, 40.4% (95% CI 30-51.7%), and ST14 4 datasets, 29% (95% CI 15.5-47.7%). Out of ten known zoonotic STs reported for Blastocystis (ST1-ST9, and ST12), 7 were isolated from sheep (ST1-ST5, ST7, and ST12) and 7 were reported from goats (ST1, ST3-ST7, ST12). Conclusions Overall, Blastocystis epidemiology in sheep and goats is yet to be elucidated and demands more comprehensive studies

Design, construction, and evaluation of �sensor lock�: an electromechanical stance control knee joint

Background and aim: Most currently-available stance control knee ankle foot orthoses (SCKAFOs) still need full knee extension to lock the knee joint, and they are still noisy, bulky, and heavy. Therefore, the aim of this study was to design, construct, and evaluate an original electromechanical SCKAFO knee joint that could feasibly solve these problems, and thus address the problems of current stance control knee joints with regards to their structure, function, cosmesis, and cost. Method: Ten able-bodied (AB) participants and two (knee ankle foot orthosis) KAFO users were recruited to participate in the study. A custom SCKAFO with the same set of components was constructed for each participant. Lower limb kinematics were captured using a 6-camera, video-based motion analysis system. Results: For AB participants, significant differences were found between normal walking and walking with the SCKAFO for temporal-spatial parameters and between orthoses with two modes of knee joints in the healthy subjects. Walking with stance control mode produced greater walking speed and step length, greater knee flexion during swing, and less pelvic obliquity than walking with a locked knee, for both AB and KAFO users. Conclusions: The feasibility of this new knee joint with AB people was demonstrated.Implications for rehabilitation Stance control knee ankle foot orthoses (SCKAFOs) are designed to stop knee flexion in stance phase and provide free knee movement during swing phase of walking. Due to their high cost, size, excessive weight, and poor performance, few SCKAFO were optimal clinically and commercially. The feasibility of the new knee joint with able-bodied people and poliomyelitis subjects was demonstrated. © 2017 Informa UK Limited, trading as Taylor & Francis Group

Design, construction, and evaluation of �sensor lock�: an electromechanical stance control knee joint

Background and aim: Most currently-available stance control knee ankle foot orthoses (SCKAFOs) still need full knee extension to lock the knee joint, and they are still noisy, bulky, and heavy. Therefore, the aim of this study was to design, construct, and evaluate an original electromechanical SCKAFO knee joint that could feasibly solve these problems, and thus address the problems of current stance control knee joints with regards to their structure, function, cosmesis, and cost. Method: Ten able-bodied (AB) participants and two (knee ankle foot orthosis) KAFO users were recruited to participate in the study. A custom SCKAFO with the same set of components was constructed for each participant. Lower limb kinematics were captured using a 6-camera, video-based motion analysis system. Results: For AB participants, significant differences were found between normal walking and walking with the SCKAFO for temporal-spatial parameters and between orthoses with two modes of knee joints in the healthy subjects. Walking with stance control mode produced greater walking speed and step length, greater knee flexion during swing, and less pelvic obliquity than walking with a locked knee, for both AB and KAFO users. Conclusions: The feasibility of this new knee joint with AB people was demonstrated.Implications for rehabilitation Stance control knee ankle foot orthoses (SCKAFOs) are designed to stop knee flexion in stance phase and provide free knee movement during swing phase of walking. Due to their high cost, size, excessive weight, and poor performance, few SCKAFO were optimal clinically and commercially. The feasibility of the new knee joint with able-bodied people and poliomyelitis subjects was demonstrated. © 2017 Informa UK Limited, trading as Taylor & Francis Group

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach

Purpose: Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE). Methods: Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE. Results: Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5 for dynamic features compared with 82 for static features). Selecting the best dynamic features also elevates the accuracy to 91.5. Conclusion: Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE. © 2020, Fondazione Società Italiana di Neurologia