Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of low-frequency repetitive transcranial magnetic stimulation (rTMS) on the treatment of aphasia caused by cerebrovascular accident (CVA)

Background: Aphasia is a common outcome of Cerebrovascular Accident (CVA) in which clinical interventions have limited effectiveness. Some evidence suggests that noninvasive stimulation of the brain can have beneficial effects in the treatment of CVA induced aphasia. In patients with motor aphasia, repetitive Transcranial Magnetic Stimulation (rTMS) is used to facilitate long-term improvement in speech ability. Since identifying effective methods for treating CVA induced aphasia can be very important in subsequent decision-making and treatment interventions, the objective of this study was to evaluate the effect of low-frequency TMS in Broca’s area in the right hemisphere on the treatment of CVA induced motor aphasia. Methods: This clinical trial enrolled 24 patients with a clinical diagnosis of motor aphasia caused by CVA using convenient sampling. In this study, the effect of stimulation of Broca’s area in the right hemisphere was examined by low-frequency rTMS (one Hz) on aphasia caused by CVA. To conduct verbal fluency test in patients, their correct responses to the selected images before and after rTMS during a certain time were recorded and compared by non-parametric Wilcoxon test using SPSS16 and the significance level was considered <0.05. Registration ID of this research in IRCT is IRCT2014052417814N1. Results: The study findings suggested a significant difference between Wilcoxon test results of patients before and after rTMS (z= -4.401), and it was found that using low-frequency rTMS in the right hemisphere was effective on improving dysarthria in the study population with 95 percent confidence interval (p<0.001). Conclusion: According to the findings, low-frequency rTMS has the potential to be considered as a treatment for patients with nonfluent aphasia caused by CVA.
Use of an electronic malnutrition screening tool in a hospital setting: effects on knowledge, attitudes and perceived practices of healthcare staff

Abstract Malnutrition risk screening is essential for the adequate identification and treatment of malnourished hospitalised patients. The aim of this study was to determine the effect of the use of an electronic malnutrition screening tool on the knowledge, attitudes and perceived practices (KAP) of a pool of nurses, nurses’ aides and physicians. A controlled study using a pre-test–post-test design was carried out in two Austrian hospitals. The hospital that was assigned to the intervention group used the Graz malnutrition screening tool. The hospital that was assigned to the control group received no intervention. To collect data, a questionnaire was filled out by the study participants at baseline (T0) and 1 month after the implementation (T1) to assess KAP. All data were analysed using descriptive statistics, χ 2 tests, Wilcoxon signed-rank tests and Student’s t tests. A total of 269 nurses, nurses’ aides and physicians participated in the study and completed the questionnaires at T0, and 190 people at T1. The sum score for the KAP questionnaire changed significantly after the implementation of the malnutrition screening tool in the intervention group (P<0·001), but not in the control group. The use of a valid and reliable malnutrition screening tool effectively improved the KAP of healthcare staff. The KAP described here are essential for providing successful nutritional care in malnourished patients, and improving these factors may result in improved patient outcomes. To attain these outcomes, stakeholders, as well as members of all professions involved in multidisciplinary nutritional care, must invest significant efforts.
Text-Independent Speaker Verification Based on Deep Neural Networks and Segmental Dynamic Time Warping

In this paper we present a new method for text-independent speaker verification that combines segmental dynamic time warping (SDTW) and the d-vector approach. The d-vectors, generated from a feed forward deep neural network trained to distinguish between speakers, are used as features to perform alignment and hence calculate the overall distance between the enrolment and test utterances. We present results on the NIST 2008 data set for speaker verification where the proposed method outperforms the conventional i-vector baseline with PLDA scores and outperforms d-vector approach with local distances based on cosine and PLDA scores. Also score combination with the i-vector/PLDA baseline leads to significant gains over both methods.
The two faces of pannexins: new roles in inflammation and repair

Pannexins belong to a family of ATP-release channels expressed in almost all cell types. An increasing body of literature on pannexins suggests that these channels play dual and sometimes contradictory roles, contributing to normal cell function, as well as to the pathological progression of disease. In this review, we summarize our understanding of pannexin “protective” and “harmful” functions in inflammation, regeneration and mechanical signaling. We also suggest a possible basis for pannexin’s dual roles, related to extracellular ATP and K+ levels and the activation of various types of P2 receptors that are associated with pannexin. Finally, we speculate upon therapeutic strategies related to pannexin using eyes, lacrimal glands, and peripheral nerves as examples of interesting therapeutic targets.
Effectiveness of a home fortification programme with multiple micronutrients on infant and young child development: a cluster-randomised trial in rural Bihar, India

Abstract Research demonstrates the importance of nutrition for early brain development. Few studies have examined the effectiveness of multiple micronutrient powders (MNP) on child development. This study examined the impacts of home fortification with MNP on motor and mental development, executive function and memory of children living in Bihar. This two-arm cluster-randomised effectiveness trial selected seventy health sub-centres to receive either MNP and nutrition counselling (intervention) or nutrition counselling alone (control) for 12 months. Front-line health workers delivered the intervention to all households in study communities with a child aged 6–18 months. Data were collected using cross-sectional surveys at baseline and endline by selecting households from intervention (baseline, n 2184; endline, n 2170) and control (baseline, n 2176; endline, n 2122) communities using a two-stage cluster-randomised sampling strategy. Children in the intervention group had a significantly larger improvement from baseline to endline compared with those in the control group on scores for motor and mental development (Cohen’s d, motor=0·12; 95 % CI 0·03, 0·22; mental=0·15; 95 % CI 0·06, 0·25). Greater impacts of MNP on motor and mental development were observed in children from households with higher stimulation scores at baseline compared with those with lower stimulation (Cohen’s d, motor=0·20 v. 0·09; mental=0·22 v. 0·14; P interaction<0·05). No significant treatment differences were seen for executive function or memory. Home fortification with MNP through the existing health infrastructure in Bihar was effective in improving motor and mental development and should be considered in combination with other child development interventions such as stimulation.
Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning

Basketball is one of the world's most popular sports because of the agility and speed demonstrated by the players. This agility and speed makes designing controllers to realize robust control of basketball skills a challenge for physics-based character animation. The highly dynamic behaviors and precise manipulation of the ball that occur in the game are difficult to reproduce for simulated players. In this paper, we present an approach for learning robust basketball dribbling controllers from motion capture data. Our system decouples a basketball controller into locomotion control and arm control components and learns each component separately. To achieve robust control of the ball, we develop an efficient pipeline based on trajectory optimization and deep reinforcement learning and learn non-linear arm control policies. We also present a technique for learning skills and the transition between skills simultaneously. Our system is capable of learning robust controllers for various basketball dribbling skills, such as dribbling between the legs and crossover moves. The resulting control graphs enable a simulated player to perform transitions between these skills and respond to user interaction.
Separation and determination of corynoxine and corynoxine B using chiral ionic liquid and hydroxypropyl‐β‐cyclodextrin as additives by field‐amplified sample stacking in capillary electrophoresis

A sensitive, fast, and effective method, field‐amplified sample stacking (FASS) in capillary electrophoresis, has been established for the separation and determination of corynoxine and corynoxine B. Hydroxypropyl‐β‐CD (HP‐β‐CD) and tetrabutylammonium‐L‐glutamic acid (TBA‐L‐Glu) were used as additives in the separation system. Electrokinetic injection was chosen to introduce sample from inlet at 10 kV for 50 s after a water plug (0.5 psi, 4 s) was injected to permit FASS. The running buffer (pH 6.1) was composed of 40 mM sodium dihydrogen phosphate solution, 130 mM HP‐β‐CD, and 10 mM TBA‐L‐Glu and the separation voltage was 20 kV. Under the optimum conditions, corynoxine and corynoxine B were successfully enriched and separated within 12 min and the sensitivity was improved approximately by 700–900 folds. Calibration curves were in a good linear relationship within the range of 62.5–5.00 × 103 ng/mL for both corynoxine and corynoxine B. The limits of detection (S/N = 3) and quantitation (S/N = 10) were 14.9, 45.2 ng/mL for corynoxine and 11.2, 34.5 ng/mL for corynoxine B, respectively. Finally, this method was successfully applied for the determination of corynoxine and corynoxine B in the stems with hooks of Uncaria rhynchophylla and its formulations.
Leadership in physical activity: is this the currency of change in the student healthcare curriculum?

Non-communicable diseases (NCD) are the major healthcare challenge for many societies, including those that are more developed. Physical inactivity and deconditioning are inextricably linked to the present and future health outcomes for patients, communities and nations. This is a changed environment. Traditionally, Western healthcare was focused on curing disease such as infection and cancers. Now, the NCDs are the bigger problem. For this reason, it is imperative that the next generation of healthcare professionals are able to deliver both prevention and treatment services which emphasise the benefits of physical activity (PA). To do this they must be equipped with the knowledge and implementation skills to provide care and leadership.1 Indeed, given that this is the first generation of society who are expected to outlive their children, this has to be an immediate and transformational community of practice approach rather than the slower, traditional transactional leadership model. Physical inactivity and sedentary behaviour are well-known risk factors for the current epidemic of NCDs and the consequent problems of immobility, disability and premature death.2 Therefore, it is essential that healthcare students possess the basic facts and the necessary skills to enable them to deliver the best care to patients. This means that they must be confident, competent and capable in discussing and recommending PA as an intervention. As a bare minimum, students should be aware of the following four critical points:
Toward Performance Optimization in IoT-based Next-Gen Wireless Sensor Networks

In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is that of data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results.
AirLab: Autograd Image Registration Laboratory

Medical image registration is an active research topic and forms a basis for many medical image analysis tasks. Although image registration is a rather general concept specialized methods are usually required to target a specific registration problem. The development and implementation of such methods has been tough so far as the gradient of the objective has to be computed. Also, its evaluation has to be performed preferably on a GPU for larger images and for more complex transformation models and regularization terms. This hinders researchers from rapid prototyping and poses hurdles to reproduce research results. There is a clear need for an environment which hides this complexity to put the modeling and the experimental exploration of registration methods into the foreground. With the "Autograd Image Registration Laboratory" (AIRLab), we introduce an open laboratory for image registration tasks, where the analytic gradients of the objective function are computed automatically and the device where the computations are performed, on a CPU or a GPU, is transparent. It is meant as a laboratory for researchers and developers enabling them to rapidly try out new ideas for registering images and to reproduce registration results which have already been published. AIRLab is implemented in Python using PyTorch as tensor and optimization library and SimpleITK for basic image IO. Therefore, it profits from recent advances made by the machine learning community concerning optimization and deep neural network models. The presented draft of this paper outlines AIRLab with first code snippets and performance analyses. A more exhaustive introduction will follow as a final version soon.
Fast and Secure Multi-Keyword Searching over Encrypted Cloud Data using Skip Graph

The cloud computing motivates the data owners to outsource their data to the cloud servers. Because of security of outsourced data, encrypt the data before sending to the cloud server. Earlier research works were focused on single keyword search, and some consider the multi-keyword search on encrypted data, but none of them consider the dynamic updating of the index and keywords in the documents. In this paper proposed the fast and secure multi-keyword search over the encrypted cloud data. This scheme uses a hybrid data structure, "skip graph bloom filter" for the fast index management. It also added an algorithm that handles the dynamic updates by the end user by keeping the status, version and modification dates. Performance evaluation and analysis are done to demonstrate the effectiveness of proposed search scheme.
Web-Based Versus Usual Care and Other Formats of Decision Aids to Support Prostate Cancer Screening Decisions: Systematic Review and Meta-Analysis

Background Prostate cancer is a leading cause of cancer among men. Because screening for prostate cancer is a controversial issue, many experts in the field have defended the use of shared decision making using validated decision aids, which can be presented in different formats (eg, written, multimedia, Web). Recent studies have concluded that decision aids improve knowledge and reduce decisional conflict. Objective This meta-analysis aimed to investigate the impact of using Web-based decision aids to support men’s prostate cancer screening decisions in comparison with usual care and other formats of decision aids. Methods We searched PubMed, CINAHL, PsycINFO, and Cochrane CENTRAL databases up to November 2016. This search identified randomized controlled trials, which assessed Web-based decision aids for men making a prostate cancer screening decision and reported quality of decision-making outcomes. Two reviewers independently screened citations for inclusion criteria, extracted data, and assessed risk of bias. Using a random-effects model, meta-analyses were conducted pooling results using mean differences (MD), standardized mean differences (SMD), and relative risks (RR). Results Of 2406 unique citations, 7 randomized controlled trials met the inclusion criteria. For risk of bias, selective outcome reporting and participant/personnel blinding were mostly rated as unclear due to inadequate reporting. Based on seven items, two studies had high risk of bias for one item. Compared to usual care, Web-based decision aids increased knowledge (SMD 0.46; 95% CI 0.18-0.75), reduced decisional conflict (MD –7.07%; 95% CI –9.44 to –4.71), and reduced the practitioner control role in the decision-making process (RR 0.50; 95% CI 0.31-0.81). Web-based decision aids compared to printed decision aids yielded no differences in knowledge, decisional conflict, and participation in decision or screening behaviors. Compared to video decision aids, Web-based decision aids showed lower average knowledge scores (SMD –0.50; 95% CI –0.88 to –0.12) and a slight decrease in prostate-specific antigen screening (RR 1.12; 95% CI 1.01-1.25). Conclusions According to this analysis, Web-based decision aids performed similarly to alternative formats (ie, printed, video) for the assessed decision-quality outcomes. The low cost, readiness, availability, and anonymity of the Web can be an advantage for increasing access to decision aids that support prostate cancer screening decisions among men.
Acuity and colour vision changes post intravitreal dexamethasone implant injection in patients with diabetic macular oedema

Purpose To evaluate changes in colour vision following intravitreal injection of Dexamethasone implant (Ozurdex) in patients with diabetic macular oedema (DMO). Both red-green (RG) and yellow-blue (YB) chromatic sensitivity were assessed using the Colour Assessment & Diagnosis (CAD) test which isolates the use of colour signals and provides age-corrected, statistical limits for normal trichromats. To determine whether colour changes and visual acuity (VA) post-treatment relate to central sub-field retinal thickness (CST). Methods Fourteen patients with DMO who were undergoing treatment with Ozurdex were recruited for this study. RG and YB colour thresholds were measured using the CAD test, best corrected visual acuity was assessed using the ETDRS chart and CST was measured using spectral domain optical coherence tomography (SD-OCT). All tests were performed monocularly at baseline and 24 weeks post injection. Results All patients (n = 14 eyes), had significant loss of RG and YB chromatic sensitivity at baseline (p<0.05). The mean age was 56 ± 9.5 years. The age specific, monocular, upper normal limits for a 56 year old subject are 2.66 for RG and 2.85 for YB. In this study, the measured, pre injection thresholds (mean±SD) were 22.6 ± 11.3 for RG and 16.2 ± 3.76 for YB. There was significant improvement in RG threshold post injection (i.e., 19.2 ± 10.8 (p<0.05)). No significant changes were found in the YB thresholds with corresponding mean and range values of: 15.8 ± 4.6 (p = 0.23). CST pre-treatment was 542 ±135 μm. After treatment and by week 24 the CST values decreased to 435 ±127 μm. Conclusions RG colour thresholds provide a sensitive measure of functional change in diabetic subjects with macular oedema. The YB system is damaged severely in the DMO patients studied and shows little or no recovery post treatment. The improvement in VA and particularly in RG colour vision correlate well with the measured decrease in CST. The results suggest that the improvement in the RG chromatic sensitivity can provide a useful biomarker for monitoring the efficacy of treatment in DMO.
Challenging information scarcity: The effect of internet use on protest under authoritarian regimes

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3 . 2 Denoising filter method for impulse noise

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The British Rhinological Society multidisciplinary consensus recommendations on the hospital management of epistaxis

Abstract Objective: Epistaxis is a common ENT emergency in the UK; however, despite the high incidence, there are currently no nationally accepted guidelines for its management. This paper seeks to recommend evidence-based best practice for the hospital management of epistaxis in adults. Methods: Recommendations were developed using an Appraisal of Guidelines for Research and Evaluation (‘AGREE II’) framework. A multifaceted systematic review of the relevant literature was performed and a multidisciplinary consensus event held. Management recommendations were generated that linked the level of supporting evidence and a Grading of Recommendations Assessment, Development and Evaluation (‘GRADE’) score explaining the strength of recommendation. Recommendations: Despite a paucity of high-level evidence, management recommendations were formed across five management domains (initial assessment, cautery, intranasal agents, haematological factors, and surgery and radiological intervention). Conclusion: These consensus recommendations combine a wide-ranging review of the relevant literature with established and rigorous methods of guideline generation. Given the lack of high-level evidence supporting the recommendations, an element of caution should be used when implementing these findings.