A Survey of Attention Deficit Hyperactivity Disorder Identification Using Psychophysiological Data

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders among children, that affects different areas in the brain that allows executing certain functionalities. This may lead to a variety of impairments such as difficulties in paying attention or focusing, controlling impulsive behaviours and overreacting. The continuous symptoms may have a severe impact in the long-term. This paper explores the ADHD identification studies using eye movement data and functional Magnetic Resonance Imaging (fMRI). This study discusses different machine learning techniques, existing models and analyses the existing literature. We have identified the current challenges and possible future directions to provide computational support for early identification of ADHD patients that enable early treatments

Identification of unique release kinetics of serotonin from guinea-pig and human enterochromaffin cells

This is the accepted version of the following article: [Raghupathi, R., Duffield, M. D., Zelkas, L., Meedeniya, A., Brookes, S. J. H., Sia, T. C., Wattchow, D. A., Spencer, N. J. and Keating, D. J. (2013), Identification of unique release kinetics of serotonin from guinea-pig and human enterochromaffin cells. The Journal of Physiology, 591: 5959–5975. doi: 10.1113/jphysiol.2013.259796], which has been published in final form at [http://dx.doi.org/10.1113/jphysiol.2013.259796]. In addition, authors may also transmit, print and share copies with colleagues, provided that there is no systematic distribution of the submitted version, e.g. posting on a listserve, network or automated delivery

Transplantation of Neuronal-Primed Human Bone Marrow Mesenchymal Stem Cells in Hemiparkinsonian Rodents

Bone marrow-derived human mesenchymal stem cells (hMSCs) have shown promise in in vitro neuronal differentiation and in cellular therapy for neurodegenerative disorders, including Parkinson' disease. However, the effects of intracerebral transplantation are not well defined, and studies do not agreed on the optimal neuronal differentiation method. Here, we investigated three growth factor-based neuronal differentiation procedures (using FGF-2/EGF/PDGF/SHH/FGF-8/GDNF), and found all to be capable of eliciting an immature neural phenotype, in terms of cell morphology and gene/protein expression. The neuronal-priming (FGF-2/EGF) method induced neurosphere-like formation and the highest NES and NR4A2 expression by hMSCs. Transplantation of undifferentiated and neuronal-primed hMSCs into the striatum and substantia nigra of 6-OHDA-lesioned hemiparkinsonian rats revealed transient graft survival of 7 days, despite the reported immunosuppressive properties of MSCs and cyclosporine-immunosuppression of rats. Neither differentiation of hMSCs nor induction of host neurogenesis was observed at injection sites, and hMSCs continued producing mesodermal fibronectin. Strategies for improving engraftment and differentiation post-transplantation, such as prior in vitro neuronal-priming, nigral and striatal grafting, and co-transplantation of olfactory ensheathing cells that promote neural regeneration, were unable to provide advantages. Innate inflammatory responses (Iba-1-positive microglia/macrophage and GFAP-positive astrocyte activation and accumulation) were detected around grafts within 7 days. Our findings indicate that growth factor-based methods allow hMSC differentiation toward immature neuronal-like cells, and contrary to previous reports, only transient survival and engraftment of hMSCs occurs following transplantation in immunosuppressed hemiparkinsonian rats. In addition, suppression of host innate inflammatory responses may be a key factor for improving hMSC survival and engraftment

User support for managed immersive education : an evaluation of in-world training for OpenSim

Part of this research was support by the Commonwealth Scholarship (UK) and SICSA Prize studentship.Supporting users for a competent interaction with 3 dimensional virtual worlds can increase their user experience within the immersive education environment. User manuals and other guide documents are popular supporting instruments for training new users of a software system. Quite often these documents have many screenshots of the application user interface which are used to steer a new user through sequential orders of actions. However, for complex scenarios of user interactions, such as those found in virtual worlds, these types of documents can become unhelpfully lengthy and unintuitive. The first part of this research was a comparative analysis of traditional document-based user support with an in-world approach; a prototype training island was developed in OpenSim and evaluated for its training support against the OpenSim user guide documents. The results suggested in-world training can be a better option of training for OpenSim than training documents. Second part of this research was to evaluate a completed training environment, which consist of two OpenSim islands, one for basic user training and one for training advanced OpenSim management. The results suggested that training for advanced OpenSim management, which is not covered in user guide documents, make users competent for managing their immersive environment. The final part of the research, a case study, examined the effective use of this complete training environment for module teaching and learner support. The results suggest that for learning the skills essential for productive use of OpenSim-based educational environments, an in-world approach covering advanced management functions of OpenSim is likely to be a better option than traditional user manuals for the future needs for immersive education as a mainstream practice.Publisher PDFPeer reviewe

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

Next-generation sequencing has revolutionized the field of genomics by producing accurate, rapid and cost-effective genome analysis with the use of high throughput sequencing technologies. This has intensified the need for accurate and performance efficient genome assemblers to assemble a large set of short reads produced by next-generation sequencing technology. Genome assembly is an NP-hard problem that is computationally challenging. Therefore, the current methods that rely on heuristic and approximation algorithms to assemble genomes prevent them from arriving at the most accurate solution. This paper presents a novel approach by gamifying whole-genome shotgun assembly from next-generation sequencing data; we present "Geno", a human-computing game designed with the aim of improving the accuracy of whole-genome shotgun assembly. We evaluate the feasibility of crowdsourcing the problem of whole-genome shotgun assembly by breaking the problem into small subtasks. The evaluation results, for single-cell Escherichia coli K-12 substr. MG1655 with a read length of 25 bp that produced 144,867 game instances of mean 25 sequences per instance at 40x coverage indicate the feasibility of sub-tasking the problem of genome assembly to be solved using crowdsourcing

Educating users for disaster management : an exploratory study on using immersive training for disaster management

Educating users for effective disaster management skills can be a challenge that requires different levels of training support. While the training requirements can be different with respect to the contexts of managing different disaster types there can be generic training requirements that should be incorporated into all types of disaster management training. Another key aspect of disaster management training is to associate new tools and technologies that facilitate disaster management and relief work. Wireless sensor based disaster management is an emerging research area that promotes technology incorporation into different levels of disaster management tasks. In this work we explore the training for disaster management activities with wireless sensor networks. As the training platform we use a novel, yet increasingly popular and learner engaging, immersive environment OpenSim. In an OpenSim installation, a specialised training environment was developed to simulate several disaster scenarios and required wireless sensors. A set of users have successfully used the training environment and provided feedback. The next phase of the research is planned to produce a Massive Open Online Course (MOOC) to facilitate academics and students for disaster management training

WordNet and cosine similarity based classifier of exam questions using bloom's taxonomy

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom's taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used prior to generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom's taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of this classification process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy

An Adapter Architecture for heterogeneous data processing in bioinformatics pipelines

Bioinformatics is a growing field focused on both the domains of computer science and biology. A range of bioinformatics data processing tools exists at present, which takes inputs and produces outputs in varying formats depending on the algorithms and processes being used. The undesirable situation where such processes would produce outputs that may not allow the pipelining of other processes, calls for a generic bioinformatics data format converter. Though such converters currently exist, most of them are limited to text conversions and provide limited functionality. In addition, such functions have the potential capability of supporting parallelism to increase the overall throughput. A solution that can provide the said conversion functions as well as utility functions, while processing with a high throughput via parallelism is proposed through this paper. A utility function of this system requires storing bioinformatics data locally. In addition to facilitating this, an average compression rate of 26% achieved in data storage. Evaluation of the system using a set of 7,000,000 gene data showed the maximum time consumption for retrieval as 400ms