Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose

Responsibility and non-repudiation in resource-constrained Internet of Things scenarios

The proliferation and popularity of smart autonomous systems necessitates the development of methods and models for ensuring the effective identification of their owners and controllers. The aim of this paper is to critically discuss the responsibility of Things and their impact on human affairs. This starts with an in-depth analysis of IoT Characteristics such as Autonomy, Ubiquity and Pervasiveness. We argue that Things governed by a controller should have an identifiable relationship between the two parties and that authentication and non-repudiation are essential characteristics in all IoT scenarios which require trustworthy communications. However, resources can be a problem, for instance, many Things are designed to perform in low-powered hardware. Hence, we also propose a protocol to demonstrate how we can achieve the authenticity of participating Things in a connectionless and resource-constrained environment

It\u27s You

https://digitalcommons.library.umaine.edu/mmb-vp/5032/thumbnail.jp

Don\u27t Cry Swanee

https://digitalcommons.library.umaine.edu/mmb-vp/3076/thumbnail.jp

Provisional practice recommendation for the management of myopathy in VCP-associated multisystem proteinopathy

Valosin-containing protein (VCP)-associated multisystem proteinopathy (MSP) is a rare genetic disorder with abnormalities in the autophagy pathway leading to various combinations of myopathy, bone diseases, and neurodegeneration. Ninety percent of patients with VCP-associated MSP have myopathy, but there is no consensus-based guideline. The goal of this working group was to develop a best practice set of provisional recommendations for VCP myopathy which can be easily implemented across the globe. As an initiative by Cure VCP Disease Inc., a patient advocacy organization, an online survey was initially conducted to identify the practice gaps in VCP myopathy. All prior published literature on VCP myopathy was reviewed to better understand the different aspects of management of VCP myopathy, and several working group sessions were conducted involving international experts to develop this provisional recommendation. VCP myopathy has a heterogeneous clinical phenotype and should be considered in patients with limb-girdle muscular dystrophy phenotype, or any myopathy with an autosomal dominant pattern of inheritance. Genetic testing is the only definitive way to diagnose VCP myopathy, and single-variant testing in the case of a known familial VCP variant, or multi-gene panel sequencing in undifferentiated cases can be considered. Muscle biopsy is important in cases of diagnostic uncertainty or lack of a definitive pathogenic genetic variant since rimmed vacuoles (present in ~40% cases) are considered a hallmark of VCP myopathy. Electrodiagnostic studies and magnetic resonance imaging can also help rule out disease mimics. Standardized management of VCP myopathy will optimize patient care and help future research initiatives

Clinical classification of variants in the valosin-containing protein gene associated with multisystem proteinopathy

BACKGROUND AND OBJECTIVES: Pathogenic variants in the valosin-containing protein ( METHODS: A 6-item clinical score was developed to evaluate the phenotypic level of evidence to support the pathogenicity of the novel variants. Each item is allocated a value, a score ranging from 0.5 to 5.5 points. A receiver-operating characteristic curve was used to identify a cutoff value of 3 to consider a variant as high likelihood disease associated. The scoring system results were confronted with results of in vitro ATPase activity assays and with in silico analysis. RESULTS: All variants were missense, except for one small deletion-insertion, 18 led to amino acid changes within the N and D1 domains, and 13 increased the enzymatic activity. The clinical score coincided with the functional studies in 17 of 19 variants and with the in silico analysis in 12 of 19. For 12 variants, the 3 predictive tools agreed, and for 7 variants, the predictive tools disagreed. The pooled data supported the pathogenicity of 13 of 19 novel VCP variants identified in the study. DISCUSSION: This study provides data to support pathogenicity of 14 of 19 nove