Delivering eHealthcare: A Perspective on Opportunities, Challenges, and Information Gaps

Helman, Deborah; Addeo, E. J.; Santoso, Nurudin; Walters, David; Helman, Guy

doi:10.1109/healthcom.2015.7454490

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Burgard et al claimed the development of nonnaturally occurring microbial organism having a methanol metabolic pathway for producing 1,4-butanediol related thereto. All of them mentioned among others the expression of genes encoding for MMO, MDH, HPS, or PHI; however, none of them provided experimental or quantitative evidence for these claims (Helman et al 2015;Burgard et al 2014;Lynch 2014;Coleman et al 2014). Brautaset et al described the expression of RuMP pathway enzymes (i.e., HPS and PHI) together with MDH in E. coli, C. glutamicum, and B. subtilis.…”

Section: Synthetic Methylotrophy In the Patent Literaturementioning

confidence: 99%

Synthetic Methylotrophy: Past, Present, and Future

Heux¹,

Brautaset²,

Vorholt³

et al. 2018

Methane Biocatalysis: Paving the Way to Sustainability

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Methylotrophy is the capacity of certain microorganisms (mostly bacteria and yeasts) to use reduced molecules without C-C bond (also referred to as one-carbon (C1) compounds) as their sole source of carbon and energy. Phylogenetically, natural methylotrophs belong to a rather small number of genera. The majority of methylotrophic bacteria are members of the Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria (e.g., Methylobacterium, Methylobacillus, and Methylococcus); alternatively, they can be found within the Grampositive and Verrucomicrobia (e.g., Bacillus and Methylacidiphilum, respectively) groups (Kolb 2009;Lidstrom 2006). The eukaryotic methylotrophs include the yeasts Candida, Pichia, and some genera that were recently separated from Pichia (i.e., Ogataea, Kuraishia, and Komagataella) (Yurimoto et al. 2011).

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Section: Synthetic Methylotrophy In the Patent Literaturementioning

confidence: 99%

Synthetic Methylotrophy: Past, Present, and Future

Heux¹,

Brautaset²,

Vorholt³

et al. 2018

Methane Biocatalysis: Paving the Way to Sustainability

View full text Add to dashboard Cite

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“…With the goal of developing novel tools for future health care solutions and to broaden our understanding of the biological bases of diseases, many efforts in research have been pushed over the last century [1]. Neuroscience in particular studies the functions and dysfunctions of the nervous system and contributes to improve the health of human brain.…”

Section: Introductionmentioning

confidence: 99%

Rat Cortical Layers Classification extracting Evoked Local Field Potential Images with Implanted Multi-Electrode Sensor

Wang

Magno

Cavigelli

et al. 2018

2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom)

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One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data processing is needed to extract useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, machine learning approaches are increasingly used in many application scenarios. This paper focuses on processing data of evoked local field potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16×16 MEA. We evaluated machine learning algorithms and trained an optimized classifier to detect at which cortical depth the neural activity is measured. We demonstrate with experimental results that machine learning can be applied successfully to noisy single-trial LFPs offering up to 99.11% of test accuracy in classifying signals acquired from different cortical layers. As such, the method is a very promising starting point toward realtime decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications.

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