Examination of Endogenous Rotund Expression and Function in Developing Drosophila Olfactory System Using CRISPR-Cas9-Mediated Protein Tagging.

The zinc-finger protein Rotund (Rn) plays a critical role in controlling the development of the fly olfactory system. However, little is known about its molecular function in vivo. Here, we added protein tags to the rn locus using CRISPR-Cas9 technology in Drosophila to investigate its subcellular localization and the genes that it regulates . We previously used a reporter construct to show that rn is expressed in a subset of olfactory receptor neuron (ORN) precursors and it is required for the diversification of ORN fates. Here, we show that tagged endogenous Rn protein is functional based on the analysis of ORN phenotypes. Using this method, we also mapped the expression pattern of the endogenous isoform-specific tags in vivo with increased precision. Comparison of the Rn expression pattern from this study with previously published results using GAL4 reporters showed that Rn is mainly present in early steps in antennal disc patterning, but not in pupal stages when ORNs are born. Finally, using chromatin immunoprecipitation, we showed a direct binding of Rotund to a previously identified regulatory element upstream of the bric-a-brac gene locus in the developing antennal disc.

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.