708 research outputs found

    Integrated parylene-cabled silicon probes for neural prosthetics

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    Recent advances in the field of neural prosthetics have demonstrated the thought control of a computer cursor. This capability relies primarily on electrode array surgically implanted into the brain as an acquisition source of neural activity. Various technologies have been developed for signal extraction; however most suffer from either fragile electrode shanks and bulky cables or inefficient use of surgical site areas. Here we present a design and initial testing results from high electrode density, silicon based arrays system with an integrated parylene cable. The greatly reduced flexible rigidity of the parylene cable is believed to relief possible mechanical damages due to relative motion between a brain and its skull

    Diabetic Foot Due to Anaphylactic Shock: A Case Report

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    Introduction: Diabetic foot is a clinical disorder, which is commonly seen in patients with diabetes mellitus. It is also the major cause of below knee amputation in the world. There are many underlying causes such as neuropathic, ischemic, and infectious causes for diabetic foot. Local or systemic complications may develop after snake bite. Case Presentation: We reported a very rare case, involving a 78-year-old male admitted to the Emergency Department, who developed anaphylactic shock and diabetic foot after the snake bite. Conclusions: Reviewing the literature, this is the second reported case of snake bite associated with diabetic foot

    Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

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    Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity

    Explanations of Black-Box Model Predictions by Contextual Importance and Utility

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    The significant advances in autonomous systems together with an immensely wider application domain have increased the need for trustable intelligent systems. Explainable artificial intelligence is gaining considerable attention among researchers and developers to address this requirement. Although there is an increasing number of works on interpretable and transparent machine learning algorithms, they are mostly intended for the technical users. Explanations for the end-user have been neglected in many usable and practical applications. In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We present an example of providing explanations for linear and non-linear models to demonstrate the generalizability of the method. CI and CU are numerical values that can be represented to the user in visuals and natural language form to justify actions and explain reasoning for individual instances, situations, and contexts. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation (i.e. contrasting instance against the instance of interest). The experimental results show the feasibility and validity of the provided explanation methods

    ANFIS-based droop control of an AC microgrid system: considering intake of water treatment plant

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    Provision of an efficient water supply system (WSS) is one of the top priorities of all municipals to ascertain adequate water supply to the city. Intake is the lifeline of the water supply system and largely effects the overall plant efficiency. The required power supply is generally fed from the main grid, and a diesel generator is commonly used as a power backup source. This results in high pumping cost as well as high operational cost. Moreover, due to operation of motor pumps and other auxiliary loads, frequent maintenance is required. Therefore, to avoid various challenges and to efficiently operate the intake system, microgrid concept has been introduced in this paper. Various distributed generations (DGs) such as solar photovoltaic (PV), interior permanent magnet machine (IPM) wind turbine generator and Battery energy storage system (BESS) are incorporated in the microgrid system. Additionally, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is proposed, where P-f and Q-V droop is considered while training the ANFIS data; after successful training, the microgrid voltage and frequency are controlled as per system requirement. Simulation of the microgrid system shows good results and comparison with the generalized droop control (GDC) method is done using MATLAB/Simulink software

    Development of lifetime comorbidity in the world health organization world mental health surveys

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    CONTEXT: Although numerous studies have examined the role of latent variables in the structure of comorbidity among mental disorders, none has examined their role in the development of comorbidity. OBJECTIVE: To study the role of latent variables in the development of comorbidity among 18 lifetime DSM-IV disorders in the World Health Organization World Mental Health Surveys. DESIGN: Nationally or regionally representative community surveys. SETTING: Fourteen countries. PARTICIPANTS: A total of 21 229 survey respondents. MAIN OUTCOME MEASURES: First onset of 18 lifetime DSM-IV anxiety, mood, behavior, and substance disorders assessed retrospectively in the World Health Organization Composite International Diagnostic Interview. RESULTS: Separate internalizing (anxiety and mood disorders) and externalizing (behavior and substance disorders) factors were found in exploratory factor analysis of lifetime disorders. Consistently significant positive time-lagged associations were found in survival analyses for virtually all temporally primary lifetime disorders predicting subsequent onset of other disorders. Within-domain (ie, internalizing or externalizing) associations were generally stronger than between-domain associations. Most time-lagged associations were explained by a model that assumed the existence of mediating latent internalizing and externalizing variables. Specific phobia and obsessive-compulsive disorder (internalizing) and hyperactivity and oppositional defiant disorders (externalizing) were the most important predictors. A small number of residual associations remained significant after controlling the latent variables. CONCLUSIONS: The good fit of the latent variable model suggests that common causal pathways account for most of the comorbidity among the disorders considered herein. These common pathways should be the focus of future research on the development of comorbidity, although several important pairwise associations that cannot be accounted for by latent variables also exist that warrant further focused study

    Estimated Risk of HIV Acquisition and Practice for Preventing Occupational Exposure: A Study of Healthcare Workers at Tumbi and Dodoma Hospitals, Tanzania.

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    Health care workers (HCWs) are at risk of acquiring human immuno-deficiency virus (HIV) and other infections via exposure to infectious patients' blood and body fluids. The main objective of this study was to estimate the risk of HIV transmission and examine the practices for preventing occupational exposures among HCWs at Tumbi and Dodoma Hospitals in Tanzania. This study was carried out in two hospitals, namely, Tumbi in Coast Region and Dodoma in Dodoma Region. In each facility, hospital records of occupational exposure to HIV infection and its management were reviewed. In addition, practices to prevent occupational exposure to HIV infection among HCWs were observed. The estimated risk of HIV transmission due to needle stick injuries was calculated to be 7 cases per 1,000,000 HCWs-years. Over half of the observed hospital departments did not have guidelines for prevention and management of occupational exposure to HIV infections and lacked well displayed health and safety instructions. Approximately, one-fifth of the hospital departments visited failed to adhere to the instructions pertaining to correlation between waste materials and the corresponding colour coded bag/container/safety box. Seventy four percent of the hospital departments observed did not display instructions for handling infectious materials. Inappropriate use of gloves, lack of health and safety instructions, and lack of use of eye protective glasses were more frequently observed at Dodoma Hospital than at Tumbi Hospital. The poor quality of the hospital records at the two hospitals hampered our effort to characterise the risk of HIV infection acquisition by HCWs. Greater data completeness in hospital records is needed to allow the determination of the actual risk of HIV transmission for HCWs. To further reduce the risk of HIV infection due to occupational exposure, hospitals should be equipped with sufficient personal protective equipment (PPE) and HCWs should be reminded of the importance of adhering to universal precautions
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