Selected Gamma Aminobutyric Acid (GABA) Esters may Provide Analgesia for Some Central Pain Conditions.

Central pain is an enigmatic, intractable condition, related to destruction of thalamic areas, resulting in likely loss of inhibitory synaptic transmission mediated by GABA. It is proposed that treatment of central pain, a localized process, may be treated by GABA supplementation, like Parkinson's disease and depression. At physiologic pH, GABA exists as a zwitterion that is poorly permeable to the blood brain barrier (BBB). Because the pH of the cerebral spinal fluid (CSF) is acidic relative to the plasma, ion trapping may allow a GABA ester prodrug to accumulate and be hydrolyzed within the CSF. Previous investigations with ester local anesthetics may be applicable to some GABA esters since they are weak bases, hydrolyzed by esterases and cross the BBB. Potential non-toxic GABA esters are discussed. Many GABA esters were investigated in the 1980s and it is hoped that this paper may spark renewed interest in their development.

Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.