User-oriented security supporting inter-disciplinary life science research across the grid

Understanding potential genetic factors in disease or development of personalised e-Health solutions require scientists to access a multitude of data and compute resources across the Internet from functional genomics resources through to epidemiological studies. The Grid paradigm provides a compelling model whereby seamless access to these resources can be achieved. However, the acceptance of Grid technologies in this domain by researchers and resource owners must satisfy particular constraints from this community - two of the most critical of these constraints being advanced security and usability. In this paper we show how the Internet2 Shibboleth technology combined with advanced authorisation infrastructures can help address these constraints. We demonstrate the viability of this approach through a selection of case studies across the complete life science spectrum

High-Performance Bioinstrumentation for Real-Time Neuroelectrochemical Traumatic Brain Injury Monitoring

Traumatic brain injury (TBI) has been identified as an important cause of death and severe disability in all age groups and particularly in children and young adults. Central to TBIs devastation is a delayed secondary injury that occurs in 30–40% of TBI patients each year, while they are in the hospital Intensive Care Unit (ICU). Secondary injuries reduce survival rate after TBI and usually occur within 7 days post-injury. State-of-art monitoring of secondary brain injuries benefits from the acquisition of high-quality and time-aligned electrical data i.e., ElectroCorticoGraphy (ECoG) recorded by means of strip electrodes placed on the brains surface, and neurochemical data obtained via rapid sampling microdialysis and microfluidics-based biosensors measuring brain tissue levels of glucose, lactate and potassium. This article progresses the field of multi-modal monitoring of the injured human brain by presenting the design and realization of a new, compact, medical-grade amperometry, potentiometry and ECoG recording bioinstrumentation. Our combined TBI instrument enables the high-precision, real-time neuroelectrochemical monitoring of TBI patients, who have undergone craniotomy neurosurgery and are treated sedated in the ICU. Electrical and neurochemical test measurements are presented, confirming the high-performance of the reported TBI bioinstrumentation

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide Assessment, Intervention and Performance Optimization

Heart rate variability (HRV) is increasingly recognized as a central variable of interest in health maintenance, disease prevention and performance optimization. It is also a sensitive biomarker of health status, disease presence and functional abilities, acquiring and processing high fidelity inter beat interval data, along with other psychophysiological parameters that can assist in clinical assessment and intervention, population health studies/digital epidemiology and positive performance optimization. We describe a system using high-throughput artificial intelligence based on the KUBIOS platform to combine time, frequency and nonlinear data domains acquired by wearable or implanted biosensors to guide in clinical assessment, decision support and intervention, population health monitoring and individual self-regulation and performance enhancement, including the use of HRV biofeedback. This approach follows the iP4 health model which emphasizes an integral, personalized, predictive, preventive and participatory approach to human health and well-being. It therefore includes psychological, biological, genomic, sociocultural, evolutionary and spiritual variables as mutually interactive elements in embodying complex systems adaptation

Neuro-critical multimodal Edge-AI monitoring algorithm and IoT system design and development

In recent years, with the continuous development of neurocritical medicine, the success rate of treatment of patients with traumatic brain injury (TBI) has continued to increase, and the prognosis has also improved. TBI patients' condition is usually very complicated, and after treatment, patients often need a more extended time to recover. The degree of recovery is also related to prognosis. However, as a young discipline, neurocritical medicine still has many shortcomings. Especially in most hospitals, the condition of Neuro-intensive Care Unit (NICU) is uneven, the equipment has limited functionality, and there is no unified data specification. Most of the instruments are cumbersome and expensive, and patients often need to pay high medical expenses. Recent years have seen a rapid development of big data and artificial intelligence (AI) technology, which are advancing the medical IoT field. However, further development and a wider range of applications of these technologies are needed to achieve widespread adoption. Based on the above premises, the main contributions of this thesis are the following. First, the design and development of a multi-modal brain monitoring system including 8-channel electroencephalography (EEG) signals, dual-channel NIRS signals, and intracranial pressure (ICP) signals acquisition. Furthermore, an integrated display platform for multi-modal physiological data to display and analysis signals in real-time was designed. This thesis also introduces the use of the Qt signal and slot event processing mechanism and multi-threaded to improve the real-time performance of data processing to a higher level. In addition, multi-modal electrophysiological data storage and processing was realized on cloud server. The system also includes a custom built Django cloud server which realizes real-time transmission between server and WeChat applet. Based on WebSocket protocol, the data transmission delay is less than 10ms. The analysis platform can be equipped with deep learning models to realize the monitoring of patients with epileptic seizures and assess the level of consciousness of Disorders of Consciousness (DOC) patients. This thesis combines the standard open-source data set CHB-MIT, a clinical data set provided by Huashan Hospital, and additional data collected by the system described in this thesis. These data sets are merged to build a deep learning network model and develop related applications for automatic disease diagnosis for smart medical IoT systems. It mainly includes the use of the clinical data to analyze the characteristics of the EEG signal of DOC patients and building a CNN model to evaluate the patient's level of consciousness automatically. Also, epilepsy is a common disease in neuro-intensive care. In this regard, this thesis also analyzes the differences of various deep learning model between the CHB-MIT data set and clinical data set for epilepsy monitoring, in order to select the most appropriate model for the system being designed and developed. Finally, this thesis also verifies the AI-assisted analysis model.. The results show that the accuracy of the CNN network model based on the evaluation of consciousness disorder on the clinical data set reaches 82%. The CNN+STFT network model based on epilepsy monitoring reaches 90% of the accuracy rate in clinical data. Also, the multi-modal brain monitoring system built is fully verified. The EEG signal collected by this system has a high signal-to-noise ratio, strong anti-interference ability, and is very stable. The built brain monitoring system performs well in real-time and stability. Keywords: TBI, Neurocritical care, Multi-modal, Consciousness Assessment, seizures detection, deep learning, CNN, IoT

The Integration of Neuroscience and Counseling Using Neuroeducation in Trauma Treatment: A Quantitative Study

Researchers in numerous professional fields, including psychology, have applied neuroscience integration in their studies. Yet research has also demonstrated a hesitancy among counselors to utilize neuro-informed principles in case conceptualization and treatment. No researchers in the studies found among the mental health counseling fields considered this issue. If left unaddressed, counselors and clinicians may avoid the use of an effective and complimentary integrative approach or unintentionally misapply neuro-informed principles and violate ethical standards in practice. This quantitative research used a survey and case study design to consider mental health professional characteristic variables of self-competency, theoretical attitude, and strength of religious beliefs as measured by the Counselor Self-Efficacy Scale, the Theoretical Orientation Profile Scale-Revised, and the Dimensions of Religiosity Scale respectively. Correlation between these variables and neuroeducation use in case conceptualization and treatment was measured via correlation analysis. Results showed a significant positive relationship between the characteristic variables and use of neuroeducation. Moderated regression analysis further indicated strength of religious beliefs had a moderating effect on the relationship between self-competency and neuroeducation use but not in relation to theoretical attitude. Results of a multivariate analysis of variance showed consistency of neuroeducation use among segments of the mental health field. A review of current literature related to neuroscience integration, neuroeducation, and neuro-informed trauma treatment clarifies pertinent issues, defines the problem of limited integration, identifies factors that influence use, and suggests areas of future research. Data was collected through an online survey via Amazon’s Mechanical Turk and Survey Monkey from a diverse group of allied mental health professionals

Stochastic signatures of involuntary head micro-movements can be used to classify females of ABIDE into different subtypes of neurodevelopmental disorders.

© 2017 Torres, Mistry, Caballero and Whyatt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Background: The approximate 5:1 male to female ratio in clinical detection of Autism Spectrum Disorder (ASD) prevents research from characterizing the female phenotype. Current open access repositories [such as those in the Autism Brain Imaging Data Exchange (ABIDE I-II)] contain large numbers of females to help begin providing a new characterization of females on the autistic spectrum. Here we introduce new methods to integrate data in a scale-free manner from continuous biophysical rhythms of the nervous systems and discrete (ordinal) observational scores. Methods: New data-types derived from image-based involuntary head motions and personalized statistical platform were combined with a data-driven approach to unveil sub-groups within the female cohort. Further, to help refine the clinical DSM-based ASD vs. Asperger's Syndrome (AS) criteria, distributional analyses of ordinal score data from Autism Diagnostic Observation Schedule (ADOS)-based criteria were used on both the female and male phenotypes. Results: Separate clusters were automatically uncovered in the female cohort corresponding to differential levels of severity. Specifically, the AS-subgroup emerged as the most severely affected with an excess level of noise and randomness in the involuntary head micro-movements. Extending the methods to characterize males of ABIDE revealed ASD-males to be more affected than AS-males. A thorough study of ADOS-2 and ADOS-G scores provided confounding results regarding the ASD vs. AS male comparison, whereby the ADOS-2 rendered the AS-phenotype worse off than the ASD-phenotype, while ADOS-G flipped the results. Females with AS scored higher on severity than ASD-females in all ADOS test versions and their scores provided evidence for significantly higher severity than males. However, the statistical landscapes underlying female and male scores appeared disparate. As such, further interpretation of the ADOS data seems problematic, rather suggesting the critical need to develop an entirely new metric to measure social behavior in females. Conclusions: According to the outcome of objective, data-driven analyses and subjective clinical observation, these results support the proposition that the female phenotype is different. Consequently the “social behavioral male ruler” will continue to mask the female autistic phenotype. It is our proposition that new observational behavioral tests ought to contain normative scales, be statistically sound and combined with objective data-driven approaches to better characterize the females across the human lifespan.Peer reviewe

Inhibition of Calpains by Calpastatin: Implications for Cellular and Functional Damage Following Traumatic Brain Injury

Traumatic brain injury (TBI) is a devastating health problem based on its high incidence, economic burden, and lack of effective pharmacological treatment. Individuals who suffer an injury often experience lifelong disability. TBI results in abrupt, initial cell damage leading to delayed neuronal death. The calcium-activated proteases, calpains, are known to contribute to this secondary neurodegenerative cascade. Prolonged activation of calpains results in proteolysis of numerous cellular substrates including cytoskeletal components, membrane receptors, and cytosolic proteins, contributing to cell demise despite coincident expression of calpastatin, the specific inhibitor of calpains. A comprehensive analysis using two separate calpastatin transgenic mouse lines was performed to test the hypothesis that calpastatin overexpression will reduce posttraumatic calpain activity affording neuroprotection and behavioral efficacy. Increased calpastatin expression was achieved using transgenic mice that overexpress the human calpastatin (hCAST) construct under control of a neuron-specific calcium-calmodulin dependent kinase II alpha or a ubiquitous prion protein promoter. Both transgenic lines exhibited enhanced calpastatin expression within the brain, extending into peripheral tissues under the prion protein promoter. hCAST overexpression significantly reduced protease activity confirmed by reductions in acute calpain-mediated substrate proteolysis in the cortex and hippocampus following controlled cortical impact brain injury. Aspects of posttraumatic motor and cognitive behavioral deficits were also lessened in hCAST transgenic mice compared to their wildtype littermates. However, volumetric analyses of neocortical contusion revealed no histological neuroprotection at either acute or long-term time points in either transgenic line. Partial hippocampal neuroprotection observed at a moderate injury severity in neuron-specific calpastatin overexpressing transgenic mice was lost after severe TBI. Greater levels of calpastatin under the prion protein promoter line failed to protect against hippocampal cell loss after severe brain injury. This study underscores the effectiveness of calpastatin overexpression in reducing calpain-mediated proteolysis and behavioral impairment after TBI, supporting the therapeutic potential for calpain inhibition. However, the reduction in proteolysis without accompanied neocortical neuroprotection suggests the involvement of other factors that are critical for neuronal survival after contusion brain injury. Augmenting calpastatin levels may be an effective method for calpain inhibition and may have efficacy in reducing behavioral morbidity after TBI and neurodegenerative disorders