Caffeine

Caffeine is a central nervous system (CNS) stimulant of the methylxanthine class. It is mainly used recreationally, as a eugeroic (wakefulness promoter) or as a mild cognitive enhancer to increase alertness and attentional performance. Caffeine acts by blocking binding of adenosine to the adenosine A1 receptor, which enhances release of the neurotransmitter acetylcholine. Caffeine has a three-dimensional structure similar to that of adenosine, which allows it to bind and block its receptors. Caffeine also increases cyclic AMP levels through nonselective inhibition of phosphodiesterase. Caffeine is a bitter, white crystalline purine, a methylxanthine alkaloid, and is chemically related to the adenine and guanine bases of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). It is found in the seeds, fruits, nuts, or leaves of a number of plants native to Africa, East Asia and South America, and helps to protect them against herbivores and from competition by preventing the germinati

The effect of frontoparietal paired associative stimulation on decision-making and working memory

Traian Popa

Previous single-site neurostimulation experiments have unsuccessfully attempted to shift decision-making away from habitual control, a fast, inflexible cognitive strategy, towards goal-directed control, a flexible, though computationally expensive strategy. We employed a dual-target neurostimulation approach in 30 healthy participants, using cortico-cortical paired associative stimulation (ccPAS) to target two key nodes: lateral prefrontal cortex (LPFC) and intraparietal sulcus (IPS), to test whether decision-making can be artificially shifted from habitual toward goal-directed control. Participants received three active stimulations, delivered at least six days apart (each involving 100 paired pulses over the IPS and LPFC, varying the interstimulus interval): two interventional, time-relevant ccPAS (10 msec interval) and one control, non-time-relevant ccPAS (100 msec interval). Following stimulation, participants completed a sequential learning task, measuring goal-directed/habitual control, and a working memory task. IPS -> LPFC ccPAS (stimulating IPS, then LPFC with a 10 msec interval) shifted decision-making from habitual toward goal-directed control, compared to control ccPAS. There was no effect of LPFC -> IPS ccPAS, nor an effect of any PAS condition on working memory. Previous studies have shown ccPAS effects outside the motor domain targeting prefrontal regions on response inhibition, attentional bias, and alpha asymmetry. The present study measures the behavioural effects of parietal-prefrontal PAS, focusing on a highly complex decision-making task and working memory. If confirmed in larger studies, this would be the first instance of neurostimulation successfully shifting decision-making from habitual to goal-directed control, putatively via inducing long-term potentiation between the IPS and LPFC. However, we found no effect in the other direction (LPFC -> ccPAS), and no effect on working memory overall. PAS is a relatively new neuromodulatory technique in the cognitive arsenal, and this study could help guide future approaches in healthy and disordered decision-making. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

ELSEVIER MASSON, CORPORATION OFFICE2019

Computational studies of industrial hosts for improved production of the 2nd generation of biofuels

Milenko Tokic

In an effort of overcoming the limited availability of fossil energy resources and moving toward a sustainable economy, the focus of the research and development in the area of biofuels has shifted towards developing the 2nd generation of fuels that should be produced via microbial fermentation. The 2nd generation biofuels should satisfy several criteria such as lower emission, higher energy density and should be less corrosive to engines. Although for many of these molecules, natural producers are known, they are not produced in the appreciated quantities. Heterologous expression of biosynthetic pathways taken from natural producers or expression of de novo synthetic pathways into microbial workhorses such as E. coli allows for production of a wide spectra of biofuels. Recently, P. putida has emerged as an amenable production hosts with a number of advantages over natural producers. P. putida is a non-pathogenic soil bacterium known for its versatile metabolism. This highly adaptive bacterium has been found to survive and grow on a wide range of substrates from pure caffeine to toxic industrial waste. Moreover, P. putida is tolerant to high toxicity compounds such as 2nd generation biofuel butanol. Counterintuitively, P. putida was seldom used as a host for the production of biofuels. In this thesis, we performed a computational analysis of this organism to evaluate its metabolic capacities to serve as a potential 2nd generation biofuels production host. Its capacity was compared against heavily used host E. coli on the test example of production of one of the most prominent fuel candidate Methyl Ethyl Ketone (MEK). To this end, we first performed a thermodynamic curation of the genome-scale iJN1411 model of P. putida, and we then used redGEM and lumpGEM algorithms to derive a consistently reduced large-scale stoichiometric model of P. putida. We integrated different omics data into resulting models and we proposed a novel way of constraining concentrations of the same species across different compartments while maintaining the consistency with the experimental measurements. To assess its capability to serve as a host, we evaluated and analyzed more than 3.6 millions biosynthetic pathways for production of 5 MEK precursors, in both heavily used industrial workhorse E. coli and rising P. putida. We compared their capability and performance with respect to thermodynamic feasibility and yield and we identified the most promising pathways for MEK production. Beside the discovered and evaluated pathways, we present a new way of clustering of feasible pathways and pathway precursors that allows us to classify and evaluate alternative ways for production and to better understand chemistry that leads towards the target molecule. Identification of metabolic engineering targets for the improved biofuel production requires kinetic models. We used the ORACLE framework to generate a population of large-scale kinetic models of P. putida, and we employed these models in two studies. In the first study, for a wild-type strain of P. putida grown under aerobic conditions using glucose as a carbon source, we evaluated and validated the predictions of the generated kinetic models against a collection of experimental single-gene knockouts. In the second study, we analyzed the capacity of P. putida to adapt to increased energy demand, and we identified potential metabolic engineering targets for improved resistance of this organism to stress conditions.

EPFL2019

The effect of frontoparietal paired associative stimulation on decision-making and working memory

Traian Popa

ELSEVIER MASSON, CORPORATION OFFICE2019

Computational studies of industrial hosts for improved production of the 2nd generation of biofuels

Milenko Tokic

EPFL2019

Removal of persistent pollutants of wastewater treatment caffeine

The effect of frontoparietal paired associative stimulation on decision-making and working memory

Computational studies of industrial hosts for improved production of the 2nd generation of biofuels

The effect of frontoparietal paired associative stimulation on decision-making and working memory

Removal of persistent pollutants of wastewater treatment caffeine

Computational studies of industrial hosts for improved production of the 2nd generation of biofuels