Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients

Parkinson's Disease (PD) patients with Freezing of Gait (FOG) often experience sudden and unpredictable failure in their ability to start or continue walking, making it potentially a dangerous symptom. Emerging knowledge about brain connectivity is leading to new insights into the pathophysiology of FOG and has suggested that electroencephalogram (EEG) may offer a novel technique for understanding and predicting FOG. In this study we have integrated spatial, spectral, and temporal features of the EEG signals utilizing wavelet coefficients as our input for the Multilayer Perceptron Neural Network and k-Nearest Neighbor classifier. This approach allowed us to predict transition from walking to freezing with 87 % sensitivity and 73 % accuracy. This preliminary data affirms the functional breakdown between areas in the brain during FOG and suggests that EEG offers potential as a therapeutic strategy in advanced PD. © 2013 IEEE

Prediction of freezing of gait using analysis of brain effective connectivity

© 2014 IEEE. Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patients experience sudden difficulties in starting or continuing locomotion. It is described by patients as the sensation that their feet are suddenly glued to the ground. This, disturbs their balance, and hence often leads to falls. In this study, directed transfer function (DTF) and partial directed coherence (PDC) were used to calculate the effective connectivity of neural networks, as the input features for systems that can detect FOG based on a Multilayer Perceptron Neural Network, as well as means for assessing the causal relationships in neurophysiological neural networks during FOG episodes. The sensitivity, specificity and accuracy obtained in subject dependent analysis were 82%, 77%, and 78%, respectively. This is a significant improvement compared to previously used methods for detecting FOG, bringing this detection system one step closer to a final version that can be used by the patients to improve their symptoms

Identification of EEG Dynamics during Freezing of Gait and Voluntary Stopping in Patients with Parkinson’s Disease

Mobility is severely impacted in patients with Parkinson's disease (PD), who often experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between “voluntary stopping” and “involuntary stopping” caused by FOG is vital for the detection of and potential intervention for FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal “stop” and “walk” instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was performed to study the dynamics of the EEG spectra induced by different walking phases, including normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that during the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared with that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based tool that can accurately predict FOG episodes, excluding the potential confounding of voluntary stopping

Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine

© 2017 IEEE. Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get 'stuck' to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG

Genomorama: genome visualization and analysis

<p>Abstract</p> <p>Background</p> <p>The ability to visualize genomic features and design experimental assays that can target specific regions of a genome is essential for modern biology. To assist in these tasks, we present Genomorama, a software program for interactively displaying multiple genomes and identifying potential DNA hybridization sites for assay design.</p> <p>Results</p> <p>Useful features of Genomorama include genome search by DNA hybridization (probe binding and PCR amplification), efficient multi-scale display and manipulation of multiple genomes, support for many genome file types and the ability to search for and retrieve data from the National Center for Biotechnology Information (NCBI) Entrez server.</p> <p>Conclusion</p> <p>Genomorama provides an efficient computational platform for visualizing and analyzing multiple genomes.</p

Non-indigenous partner perspectives on indigenous peoples' involvement in renewable energy: exploring reconciliation as relationships of accountability or status quo innocence?

This is the author accepted manuscript. The final version is available from Emerald via the DOI in this record This research considers the potential for renewable energy partnerships to contribute to Canada's efforts to overcome its colonial past and present by developing an understanding of how non-Indigenous peoples working in the sector relate to their Indigenous partners. Design/methodology/approach This study is part of a larger research program focused on decolonization and reconciliation in the renewable energy sector. This exploratory research is framed by energy justice and decolonial reconciliation literatures relevant to the topic of Indigenous-led renewable energy. The authors used content and discourse analysis to identify themes arising from 10 semi-structured interviews with non-Indigenous corporate and governmental partners. Findings Interviewees’ lack of prior exposure to Indigenous histories, cultures and acknowledgement of settler colonialism had a profound impact on their engagement with reconciliation frameworks. Partners' perspectives on what it means to partner with Indigenous peoples varied; most dismissed the need to further develop understandings of reconciliation and instead focused on increasing community capacity to allow Indigenous groups to participate in the renewable energy transition. Research limitations/implications In this study, the authors intentionally spoke with non-Indigenous peoples working in the renewable energy sector. Recruitment was a challenge and the sample is small. The authors encourage researchers to extend their questions to other organizations in the renewable energy sector, across industries and with Indigenous peoples given this is an under-researched field. Originality/value This paper is an early look at the way non-Indigenous “partners” working in renewable energy understand and relate to topics of reconciliation, Indigenous rights and self-determination. It highlights potential barriers to reconciliation that are naïvely occurring at organizational and institutional levels, while anchored in colonial power structures.Canadian Institutes for Health Researc

How to deal with uncertainty in prenatal genomics: A systematic review of guidelines and policies

Exome Sequencing (ES) enhanced the diagnostic yield of genetic testing, but has also increased the possibility of uncertain findings. Prenatal ES is increasingly being offered after a fetal abnormality is detected through ultrasound. It is important to know how to handle uncertainty in this particularly stressful period. This systematic review aimed to provide a comprehensive overview of guidelines available for addressing uncertainty related to prenatal chromosomal microarray (CMA) and ES. Ten uncertainty types associated with prenatal ES and CMA were identified and defined by an international multidisciplinary team. Medline (all) and Embase were systematically searched. Laboratory scientists, clinical geneticists, psychologists, and a fetal medicine specialist screened the papers and performed the data extraction. Nineteen papers were included. Recommendations generally emphasized the importance of trio analysis, clinical information, data sharing, validation and re-analysis, protocols, multidisciplinary teams, genetic counselling, whether to limit the possible scope of results, and when to report particular findings. This systematic review helps provide a vocabulary for uncertainties, and a compass to navigate uncertainties. Prenatal CMA and ES guidelines provide a strong starting point for determining how to handle uncertainty. Gaps in guidelines and recommendations were identified and discussed to provide direction for future research and policy making

Hardness characterisation of grey cast iron and its tribological performance in a contact lubricated with soybean oil

The effect of hardness of grey cast iron flat specimen on its wear and friction on the contact were characterised with the presence of vegetable oil as biolubricant. Prior to the tribological test, the as - received grey cast iron flat specimen hardness was characterised. Friction and wear tests were then conducted using a ball - on - flat reciprocating sliding contact. The one - way analysis of variance (ANOVA) was used to determine the significance of friction and wear data with a 95% significance level. The wear scars after the test were then characterised by surface roughness and wear mechanism. The microstructure and elemental analysis we re also reported. The average value of hardness was 210 HV with a large difference between minimum (185 HV) and maximum (250 HV) values. The friction and wear performance of grey cast iron specimens with soybean oil varied with its hardness. The specimens with higher hardness gave lower friction coefficient and greater wear resistance than the lower hardness specimens. The difference in coefficient of friction produced between high hardness specimens (COF = 0.122) and low hardness specimens (COF = 0.140) wa s 17%. In terms of mass loss, the low hardness 2 specimens (mass loss = 50.38 mg) and the high hardness specimens (mass loss = 12.90 mg) produced a difference of 74%. It is shown that, with soybean oil lubricant, the grey cast iron specimen can produce wide range of tribological data especially on mass loss due to its hardness distribution. The influence of soybean oil lubrication in this work is less in improving the wear resistance (about 7%), but greater for friction reduction (about 24%) compared to an un lubricated grey cast iron surface. The hardness of grey cast iron specimen is an important parameter that needs to be specifically measured and controlled on the contact due to wide hardness distribution of grey cast iron may produce variation in tribologi cal data

Family history of colorectal cancer in Iran

BACKGROUND: Previous reports show a high proportion of young CRC patients in Iran. In this study we aim to look for the clustering of colorectal cancer in families of a series of CRC patients from Iran. METHODS: The family history of cancer is traced in 449 CRC patients of which 112 were 45 yrs or younger and 337 were older than 45 yrs at time of diagnosis. The patients were admitted in two hospitals in Tehran, during a 4-year period. RESULTS: Clinical diagnosis of HNPCC was established in 21 (4.7%) probands. Family history of CRC was more frequently reported by early-onset than by late-onset patients (29.5% vs. 12.8%, p < 0.001). Distribution of tumor site differed significantly between those with and without family history of CRC. Right colon cancer was the most frequent site (23/45, 35.4%) observed in patients with positive family history of colorectal cancer. CONCLUSION: The relatively high frequency of CRC clustering along with HNPCC in our patients should be further confirmed with larger sample size population-based and genetic studies to establish a cost effective molecular screening for the future