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Background Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy. Funding The Wellcome Trust, the UK National Institute of Health Research, and the Oxford NIHR Biomedical Research Centre.

Accessing difficult to reach hydrocarbon reservoirs while simultaneously reducing risk and increasing efficiency demonstrates a need for improved autonomous directional control of rotary steerable systems (RSS). The inherently uncertain drilling environment presents a challenge for control algorithms and human operators alike, where model mismatch can be significant and the parameters are time varying. Parameter estimation can improve the performance of steering controllers through model adaptation as well as provide valuable information to human operators. This paper proposes the use of a Markov Chain Monte Carlo (MCMC) method to estimate time-varying model parameters in real-time using only measurements commonly obtained while drilling. The proposed method is evaluated on historical field data and its accuracy is quantified by prediction accuracy to achieve a mean absolute error of 0.68 deg over 30 m. Next, the proposed method is used to adapt the model of a model predictive controller (MPC) and its performance is compared with a static MPC in closed-loop simulation of a prototypical drilling scenario. The estimator reduces tracking error of the MPC by 93.36% and produces a higher quality borehole. Finally, the utility of estimation for human-in-the-loop operation is explored through the design of an early warning system (EWS). The posterior distribution produced by MCMC is utilized in the EWS to predict the probability of undesirable future trajectories. By providing automatic alerts, the EWS serves as a safety mechanism that enhances operators' proficiency when monitoring several autonomously drilled wells.

Objectives: There are limited data regarding long-term follow-up and therapeutic outcomes in Sjogren's syndrome (SS)-associated peripheral neuropathy. In this study, we aim to study the clinical, electrophysiological spectrum and therapeutic responses among the different subtypes of SS-associated neuropathy. The predictors of suboptimal treatment response will be identified. Methods: The study included a retrospective cohort of patients with SS-associated neuropathy between January 2012 and November 2015. Baseline clinical, laboratory, electrophysiological data and details of treatment were noted. Therapeutic outcomes were assessed at follow-up and compared among the different subtypes. Prognostic predictors were determined using logistic regression analysis. Results: Fifty-four patients were included in the study. Sensory ataxic neuropathy (17, including 9 with sensory ganglionopathy) and radiculoneuropathy (11) were the main subtypes. Notable atypical presentations included acute neuropathies, pure motor neuropathies, and hypertrophic neuropathy. Concomitant autoimmune disorders were present in 24 (44.4%) patients. Most presentations were subacute-chronic (51, 94.4%). Minor salivary gland biopsy had a higher yield compared to serological markers (81.5 vs. 44.4%). Sensory ataxic neuropathy was associated with greater severity and autonomic dysfunction. Improvement was noted in 33 (61%) patients. Cranial neuropathy and radiculoneuropathy subtypes were associated with the best treatment responses. Chronicity, orthostatic hypotension, baseline severity, and marked axonopathy (nerve biopsy) were predictive of a suboptimal therapeutic response. Conclusions: The study highlights the heterogeneous spectrum, atypical presentations, and differential therapeutic responses. SS-associated neuropathy remains underdiagnosed. Early diagnosis and prompt initiation of immunotherapy before worsening axonal degeneration is paramount. SS-associated neuropathy need not necessarily be associated with a poor prognosis.

Introduction: In chronic inflammatory demyelinating polyneuropathy (CIDP), emphasis has been on motor disabilities, and autonomic dysfunction in these patients has not been addressed systematically. Materials and Methods: Autonomic function was prospectively analyzed in 38 patients with CIDP. Quantitative autonomic function testing was done using Finometer® PRO and severity of adrenergic and cardiovagal dysfunction graded according to composite autonomic severity score and sudomotor dysfunction assessed using sympathetic skin response. Results: Thirty-four (89%) patients had features of autonomic dysfunction. Thirty-three (86%) patients had cardiovagal dysfunction, 21 (55%) had adrenergic dysfunction, and 24 (63%) had sudomotor dysfunction. Autonomic dysfunction was mild to moderate in the majority (86%). Conclusions: Autonomic dysfunction in CIDP is underreported and potentially amenable to therapy. Our cohort had a high proportion of adrenergic dysfunction compared to previous studies.

Identity crime is common, and pricey, and credit card fraud is a specific case of identity crime. The existing systems of known fraud matching and business rules have restrictions. To remove these negative aspects in real world, this paper proposes a data mining approach: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is impervious to fake social relationships. This approach on a fixed set of attributes is whitelist-oriented. SD increases the suspicion score by finding discrepancies in duplicates. These data mining approaches can detect more types of attacks and removes the unnecessary attributes.

Background: Major improvements are required in the coverage and quality of essential childhood interventions to achieve Millennium Development Goal Four (MDG 4). Long distance to health facilities is one of the known barriers to access. We investigated the effect of networked and Euclidean distances from home to formal health facilities on childhood mortality in rural Tanzania between 2005 and 2007. Methods: A secondary analysis of data from a cohort of 28,823 children younger than age 5 between 2005 and 2007 from Ifakara Health and Demographic Surveillance System was carried out. Both Euclidean and networked distances from the household to the nearest health facility were calculated using geographical information system methods. Cox proportional hazard regression models were used to investigate the effect of distance from home to the nearest health facility on child mortality. Results: Children who lived in homes with networked distance >5 km experienced approximately 17% increased mortality risk (HR=1.17; 95% CI 1.02–1.38) compared to those who lived

Background: Major improvements are required in the coverage and quality of essential childhood interventions to achieve Millennium Development Goal Four (MDG 4). Long distance to health facilities is one of the known barriers to access. We investigated the effect of networked and Euclidean distances from home to formal health facilities on childhood mortality in rural Tanzania between 2005 and 2007. Methods: A secondary analysis of data from a cohort of 28,823 children younger than age 5 between 2005 and 2007 from Ifakara Health and Demographic Surveillance System was carried out. Both Euclidean and networked distances from the household to the nearest health facility were calculated using geographical information system methods. Cox proportional hazard regression models were used to investigate the effect of distance from home to the nearest health facility on child mortality. Results: Children who lived in homes with networked distance >5 km experienced approximately 17% increased mortality risk (HR=1.17; 95% CI 1.02-1.38) compared to those who lived

An adult male presented with dyskinetic movements of the face and choreiform movements associated with dysphagia and vocaltics.Obsessive compulsive symptoms and depressive symptoms occurred 3 months after the onset of the illness.Peripheral blood smear revealed an excess of acanthocytes. A diagnosis of neuroacanthocytosis was made and he was started on treatment.

Using social identity perspective and a moderated mediation analysis, we tested a model integrating Transactive Memory System (TMS) with team identity and communication openness in a sample of 138 ...

Measles is still a big killer, especially in poor malnourished children in India. Yet professional bodies have yet to recommend to the government the inclusion of the measles, mumps, and rubella …

Autonomous ground vehicles (AGVs) have been deployed for industrial automation. Despite the advancement that has been achieved in the field of AGV technology, there are still many challenges associated with this domain. For instance, it is highly challenging to optimally schedule and plan the routing of an AGV in real time, where the AGV is stochastically assigned tasks with various priorities. The AGV routing based on task priority might be different from that based on the shortest path. There will be a tradeoff between completing tasks in their priority order and completing the tasks within the shortest time. Additionally, real time planning of the routing is desirable so that the AGV can update its path to accommodate the assigned tasks and achieve optimal efficiency. In order to address these challenges, this paper introduces an AGV development platform that can optimally generate an optimal path to route the AGV in real time considering both to achieve the shortest route and to accommodate the task priority. The platform also provides a tool for the actual design of the AGV.

Ten studies are presented which show how and why individual differences in gratitude are related to well-being, with six key conclusions.\ud \ud Grateful people view the help they receive in everyday life as more costly, valuable, and altruistically intended. Cross-sectional (n=253), multi-level process (n=113), and experimental (n=200) studies showed these attributional biases explain why trait and state levels of gratitude are linked.\ud \ud Trait gratitude involves the habitual focusing on the positive in the world, suggesting why gratitude is linked to well-being. Two studies (n=206 and n=389) presented exploratory and confirmatory factor analysis showing that each of the existing measure of gratitude and appreciation (the GQ-6, GRAT, and Appreciation Scale) assess the same latent construct.\ud \ud Two studies (n=389) and (n=201) show gratitude is uniquely linked to subjective well-being (satisfaction with life) and psychological well-being (personal growth, positive relationships with others, purpose in life, and self-acceptance), after controlling for the 30 facets of the Five Factor Model.\ud \ud Two longitudinal studies (n=156 and n=87) showed that during a life transition, gratitude led to lower stress and depression, and higher perceived social support. Structural equation modelling disproved other models of causality.\ud \ud Grateful people were shown to use more adaptive coping strategies, characterised by seeking help from others and actively coping rather than avoiding the problem. Across two samples (n=236) these adaptive coping strategies were shown to partially explain why grateful people feel lower level of stress in life.\ud \ud In a large community sample (n=401, 40% with clinically impaired sleep) grateful people had a better quality of sleep.\ud \ud Together, the ten studies show that individual differences in gratitude (1) are related to specific information processing biases, (2) involved a habitual orientation towards noticing and appreciating the positive in life, (3) uniquely predict well-being, (4) lead to well-being over time, (5) are related to positive coping, and (6) predict better sleeping quality.