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We Are What We Eat: Nutrigenomics of the “Diet” Exposome
Michael Muller 한국식품영양과학회 2021 한국식품영양과학회 학술대회발표집 Vol.2021 No.10
We are currently facing significant human health and societal challenges that are linked to our unhealthy food patterns and lifestyles with consequence for the ageing process across the life course. To improve such situation an in-depth understanding of the exact mechanisms underpinning the role of nutrition in controlling human health is required. Nutrigenomics is the combination of molecular nutrition research and applications of high-throughput genomics tools in nutrition research to understand how nutrition influences immune-metabolic pathways and homeostatic control, how this regulation is disturbed in the early phase of a diet-related disease and to what extent individual sensitizing (epi)genotypes contribute to such diseases. Our own research is focused on the better understanding of the role of the gut for human health. The gut is essential for efficient absorption and metabolic processing of nutrients and food bioactives and represents a critical barrier and gatekeeper for our body. The gut microbiota contributes to nutrient processing and signalling, and produces metabolites with essential functions, such as vitamins, short-chain fatty acids and certain bile acids. Compromised functionality of the gut has been linked to several metabolic complications and complex diseases emphasizing its relevance for optimal human health and effective prevention of diseases. Recently the gut microbiota-brain axis got a lot of attention because of the potential influence of gut microbiota in the progression of diseases affecting the central nervous system. Patients affected by such diseases shared the prevalence of the same families of microorganisms in their microbiota that differs from that of healthy controls. Targeting the gut microbiome is worth further investigation as a potential target to mitigate cognitive decline during aging. These strategies may include individualized healthier food pattern, probiotics, food bioactives, or drugs that target specifically the small intestine to improve metabolic health of the gut-microbiota-liver/brain axis.
Muller-Putz, Gernot,Leeb, Robert,Tangermann, Michael,Hohne, Johannes,Kubler, Andrea,Cincotti, Febo,Mattia, Donatella,Rupp, Rudiger,Muller, Klaus-Robert,Del R Millan, Jose IEEE 2015 Proceedings of the Institute of Electrical and Ele Vol.103 No.6
<P>In their early days, brain-computer interfaces (BCIs) were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cannot only translate brain signals directly into control signals, but also can combine such kind of artificial output with a natural muscle-based output. Thus, the integration of multiple biological signals for real-time interaction holds the promise to enhance a much larger population than originally thought end users with preserved residual functions who could benefit from new generations of assistive technologies. A BCI system that combines a BCI with other physiological or technical signals is known as hybrid BCI (hBCI). In this work, we review the work of a large scale integrated project funded by the European commission which was dedicated to develop practical hybrid BCIs and introduce them in various fields of applications. This article presents an hBCI framework, which was used in studies with nonimpaired as well as end users with motor impairments.</P>
Add-on Therapy for Symptomatic Asthma despite Long-Acting Beta-Agonists/Inhaled Corticosteroid
( Michael Dreher ),( Tobias Muller ) 대한결핵 및 호흡기학회 2018 Tuberculosis and Respiratory Diseases Vol.81 No.1
Asthma, remains symptomatic despite ongoing treatment with high doses of inhaled corticosteroids (ICS) in conjunction with long-acting beta-agonists (LABA), is classified as “severe” asthma. In the course of caring for those patients diagnosed with severe asthma, stepping up from ICS/LABA to more aggressive therapeutic measures would be justified, though several aspects have to be checked in advance (including inhaler technique, adherence to therapy, and possible associated comorbidities). That accomplished, it would be advisable to step up care in accordance with the Global Initiative for Asthma (GINA) recommendations. Possible strategies include the addition of a leukotriene receptor antagonist or tiotropium (to the treatment regimen). The latter has been shown to be effective in the management of several subgroups of asthma. Oral corticosteroids have commonly been used for the treatment of patients with severe asthma in the past; however, the use of oral corticosteroids is commonly associated with corticosteroid-related adverse events and comorbidities. Therefore, according to GINA 2017 these patients should be referred to experts who specialize in the treatment of severe asthma to check further therapeutic options including biologics before starting treatment with oral corticosteroids.
Add-on Therapy for Symptomatic Asthma despite Long-Acting Beta-Agonists/Inhaled Corticosteroid
Dreher, Michael,Muller, Tobias The Korean Academy of Tuberculosis and Respiratory 2018 Tuberculosis and Respiratory Diseases Vol.81 No.1
Asthma, remains symptomatic despite ongoing treatment with high doses of inhaled corticosteroids (ICS) in conjunction with long-acting beta-agonists (LABA), is classified as "severe" asthma. In the course of caring for those patients diagnosed with severe asthma, stepping up from ICS/LABA to more aggressive therapeutic measures would be justified, though several aspects have to be checked in advance (including inhaler technique, adherence to therapy, and possible associated comorbidities). That accomplished, it would be advisable to step up care in accordance with the Global Initiative for Asthma (GINA) recommendations. Possible strategies include the addition of a leukotriene receptor antagonist or tiotropium (to the treatment regimen). The latter has been shown to be effective in the management of several subgroups of asthma. Oral corticosteroids have commonly been used for the treatment of patients with severe asthma in the past; however, the use of oral corticosteroids is commonly associated with corticosteroid-related adverse events and comorbidities. Therefore, according to GINA 2017 these patients should be referred to experts who specialize in the treatment of severe asthma to check further therapeutic options including biologics before starting treatment with oral corticosteroids.
The Brownfield of the Eiffel Tower Steel Mill: A Highly Contaminated but Well-Functioning Ecosystem
Pierre Lucisine,Michael Danger,Vincent Felten,Delphine Aran,Sonia Henry,Hermine Huot,Antoine Lecerf,Gabriel Moinet,Jean-Louis Morel,Serge Muller,Johanne Nahmani,Florence Maunoury-Danger 한국토양비료학회 2014 한국토양비료학회 학술발표회 초록집 Vol.2014 No.6
Hubner, David,Verhoeven, Thibault,Muller, Klaus-Robert,Kindermans, Pieter-Jan,Tangermann, Michael IEEE 2018 IEEE computational intelligence magazine Vol.13 No.2
<P>One of the fundamental challenges in brain-computer interfaces (BCIs) is to tune a brain signal decoder to reliably detect a user's intention. While information about the decoder can partially be transferred between subjects or sessions, optimal decoding performance can only be reached with novel data from the current session. Thus, it is preferable to learn from unlabeled data gained from the actual usage of the BCI application instead of conducting a calibration recording prior to BCI usage. We review such unsupervised machine learning methods for BCIs based on event-related potentials of the electroencephalogram. We present results of an online study with twelve healthy participants controlling a visual speller. Online performance is reported for three completely unsupervised learning methods: (1) learning from label proportions, (2) an expectation-maximization approach and (3) MIX, which combines the strengths of the two other methods. After a short ramp-up, we observed that the MIX method not only defeats its two unsupervised competitors but even performs on par with a state-of-the-art regularized linear discriminant analysis trained on the same number of data points and with full label access. With this online study, we deliver the best possible proof in BCI that an unsupervised decoding method can in practice render a supervised method unnecessary. This is possible despite skipping the calibration, without losing much performance and with the prospect of continuous improvement over a session. Thus, our findings pave the way for a transition from supervised to unsupervised learning methods in BCIs based on eventrelated potentials.</P>