Exploring Mechanisms of Bacterial Adaptation to Seasonal Temperature Change

This research examines three potential mechanisms by which bacteria can adapt to different temperatures: changes in strain-level population structure, gene regulation and particle colonization. For the first two mechanisms, I utilize bacterial strains from the Vibrionaceae family due to their ease of culturability, ubiquity in coastal environments and status as a model system for marine bacteria. I first examine vibrio seasonal dynamics in temperate, coastal water and compare the thermal performance of strains that occupy different thermal environments. Our results suggest that there are tradeoffs in adaptation to specific temperatures and that thermal specialization can occur at a very fine phylogenetic scale. The observed thermal specialization over relatively short evolutionary time-scales indicates that few genes or cellular processes may limit expansion to a different thermal niche. I then compare the genomic and transcriptional changes associated with thermal adaptation in closely-related vibrio strains under heat and cold stress. The two vibrio strains have very similar genomes and overall exhibit similar transcriptional profiles in response to temperature stress but their temperature preferences are determined by differential transcriptional responses in shared genes as well as temperature-dependent regulation of unique genes. Finally, I investigate the temporal dynamics of particle-attached and free-living bacterial community in coastal seawater and find that microhabitats exert a stronger forcing on microbial communities than environmental variability, suggesting that particle-attachment could buffer the impacts of environmental changes and particle-associated communities likely respond to the presence of distinct eukaryotes rather than commonly-measured environmental parameters. Integrating these results will offer new perspectives on the mechanisms by which bacteria respond to seasonal temperature changes as well as potential adaptations to climate change-driven warming of the surface oceans.

A prospective comparison of a noninvasive cardiac output monitor versus esophageal doppler monitor for goal-directed fluid therapy in colorectal surgery patients

Copyright © 2014 International Anesthesia Research Society.BACKGROUND: Goal-directed fluid therapy (GDFT) is associated with improved outcomes after surgery. The esophageal Doppler monitor (EDM) is widely used, but has several limitations. The NICOM, a completely noninvasive cardiac output monitor (Cheetah Medical), may be appropriate for guiding GDFT. No prospective studies have compared the NICOM and the EDM. We hypothesized that the NICOM is not significantly different from the EDM for monitoring during GDFT. METHODS: One hundred adult patients undergoing elective colorectal surgery participated in this study. Patients in phase I (n = 50) had intraoperative GDFT guided by the EDM while the NICOM was connected, and patients in phase II (n = 50) had intraoperative GDFT guided by the NICOM while the EDM was connected. Each patient's stroke volume was optimized using 250- mL colloid boluses. Agreement between the monitors was assessed, and patient outcomes (postoperative pain, nausea, and return of bowel function), complications (renal, pulmonary, infectious, and wound complications), and length of hospital stay (LOS) were compared. RESULTS: Using a 10% increase in stroke volume after fluid challenge, agreement between monitors was 60% at 5 minutes, 61% at 10 minutes, and 66% at 15 minutes, with no significant systematic disagreement (McNemar P > 0.05) at any time point. The EDM had significantly more missing data than the NICOM. No clinically significant differences were found in total LOS or other outcomes. The mean LOS was 6.56 ± 4.32 days in phase I and 6.07 ± 2.85 days in phase II, and 95% confidence limits for the difference were -0.96 to +1.95 days (P = 0.5016). CONCLUSIONS: The NICOM performs similarly to the EDM in guiding GDFT, with no clinically significant differences in outcomes, and offers increased ease of use as well as fewer missing data points. The NICOM may be a viable alternative monitor to guide GDFT.

The Synthesis of Novel N-Heterocyclic Scaffolds and Diazirine-Based Molecular Tags

N-Heterocycles are ubiquitous in biologically active natural products and pharmaceuticals. Yet, new syntheses and modifications of N-heterocycles are continually of interest for the purposes of expanding chemical space, finding quicker synthetic routes, better pharmaceuticals, and even new handles for molecular labeling. There are several iterations of molecular labeling; the decision of where to place the label is as important as of which visualization technique to emphasize.

Piperidine and indole are two of the most widely distributed N-heterocycles and thus were targeted for synthesis, functionalization, and labeling. The major functionalization of these scaffolds should include a nitrogen atom, while the inclusion of other groups will expand the utility of the method. Towards this goal, ease of synthesis and elimination of step-wise transformations are of the utmost concern. Here, the concept of electrophilic amination can be utilized as a way of introducing complex secondary and tertiary amines with minimal operations.

Molecular tags should be on or adjacent to an N-heterocycle as they are normally the motifs implicated at the binding site of enzymes and receptors. The labeling techniques should be useful to a chemical biologist, but should also in theory be useful to the medical community. The two types of labeling that are of interest to a chemist and a physician would be positron emission tomography (PET) and magnetic resonance imaging (MRI).

Coincidentally, the 3-positions of both piperidine and indole are historically difficult to access and modify. However, using electrophilic amination techniques, 3-functionalized piperidines can be synthesized in good yields from unsaturated amines. In the same manner, 3-labeled piperidines can be obtained; the piperidines can either be labeled with an azide for biochemical research or an 18F for PET imaging research. The novel electrophiles, N-benzenesulfonyloxyamides, can be reacted with indole in one of two ways: 3-amidation or 1-amidomethylation, depending on the exact reaction conditions. Lastly, a novel, hyperpolarizable 15N2-labeled diazirine has been developed as an exogenous and versatile tag for use in magnetic resonance imaging.

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.