Biochemical Analysis of SNARE Protein Interactions – Role of Transmembrane Domain

SNARE (soluble NSF attachment protein receptor) Proteine umfassen verschiedene Familien von Proteinen, welche in Eukaryonten von der Hefe bis hin zum Menschen vorkommen. Sie sind für die Membranfusion bei zellulären Prozessen, wie Vesikeltransport, Neurotransmitterausschüttung oder Hefevakuolenfusion, unerlässlich. Vor der Membranfusion assemblieren SNARE zu einem stabilen Komplex. Dies beginnt nach dem „zipper“ Modell am zytoplasmatischen Bereich und setzt sich dann zu der Transmembrandomäne (TMD) fort. Bisher deuten Ergebnisse darauf hin, dass die TMD von SNARE Proteinen nicht nur deren Verankerung in der Membran übernimmt, sondern auch für die Funktion wichtig ist. In dieser Arbeit habe ich die Rolle der TMD bei der Assemblierung des neuronalen SNARE Proteins Synaptobrevin II und der SNAREs aus Hefevakuolen erforscht. Andere Arbeitsgruppen zeigten, dass die Homodimerisierung des bei der Neurotransmitterausschüttung beteiligten Synaptobrevin II von dessen TMD abhängt. Hier wurde gezeigt, dass diese Assemblierung in Anwesenheit von Harnstoff besser erfolgt, als ohne ihn. Dabei wurde durch CD-Messungen nachgewiesen, dass Harnstoff die -helikale Struktur der TMD nicht beeinflusst. Die Charakterisierung der TMD bei der Assemblierung von SNARE Proteinen während der Hefevakuolenfusion erfolgte unter nativen Bedingungen. Mit Saccharosegradienten wurde gezeigt, dass das vakuoläre SNARE Vam3p Homodimere ausbildet und dies deutlich von der TMD abhängt, während dies bei Selbstinteraktionen von Nyv1p und Vti1p nicht der Fall ist. Die SNARE Proteine Vam7p und Ykt6p, ohne TMD, lagen überwiegend als Monomere vor. Die in vitro Assemblierung der vakuolären SNARE Proteine Vam3p, Nyv1p, Vam7p und Vti1p, wurde durch Saccharosegradient mit anschließender co-Immunopräzipitation untersucht. Der Komplex wurde spezifisch durch Sec18p und Sec17p in Anwesenheit von ATP getrennt, wie dies in Hefe der Fall ist, und somit dessen biologische Relevanz verdeutlicht. Die Ausbildung des Komplexes wird auch durch die TMD von Vam3p beeinflusst, da teilweise bei deren Nichtvorhandensein unspezifische Multimere entstehen. Bei der Untersuchung der Bildung des zytoplasmatischen SNARE-Komplexes an Proteinen ohne TMD wurden im Vergleich zu den Proteinen von voller Länge Multimere von höherer molekularer Masse nachgewiesen. Ein pentamerer SNARE Komplex mit Ykt6p konnte unter diesen experimentellen Bedingungen nicht nachgewiesen werden

SQUAD: Combining Sketching and Sampling Is Better than Either for Per-item Quantile Estimation

Latency quantiles measurements are essential as they often capture the user's utility. For example, if a video connection has high tail latency, the perceived quality will suffer, even if the average and median latencies are low. In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, latency) tuples, and we wish to track the latency quantiles for each ID. Existing quantile sketches are designed for a single number stream (e.g., containing just the latency). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand. We first present a simple sampling algorithm that serves as a benchmark. Then, we design an algorithm that augments a quantile sketch within each entry of a heavy hitter algorithm, resulting in similar space complexity but with a deterministic error guarantee. Finally, we present SQUAD, a method that combines sampling and sketching while improving the asymptotic space complexity. Intuitively, SQUAD uses a background sampling process to capture the behaviour of the latencies of an item before it is allocated with a sketch, thereby allowing us to use fewer samples and sketches. Our solutions are rigorously analyzed, and we demonstrate the superiority of our approach using extensive simulations

Crimean Congo Hemorrhagic Fever (CCHF): An Investigation Report, India, 2015

Background: In India, first confirmed outbreak of Crimean Congo Hemorrhagic Fever (CCHF) was reported in 2011. Since then till now clusters of outbreaks were reported from various parts of Rajasthan. A team from National Centre for Disease Control, New Delhi, investigated the CCHF outbreak in Jodhpur, Rajasthan, and reporting here their result.Methodology: A team conducted a CCHF outbreak investigation January 2015, with review of hospital records, discussion with hospital staffs and community members along with contact tracing. Environmental examination, collection of human and animal serum sample, collection of tick sample and entomological survey was also carried out.Results: Four laboratories confirmed CCHF cases reported among male nurses working in the ICU of a private Hospital. Two expired and other two cases took treatment from private hospitals and have now recovered and are healthy. As per records, one case admitted in ICU was suspected as with possible symptoms of CCHF. Seventy-five percent of confirmed cases did not follow proper biosafety precautions. The blood and ticks’ samples of domestic animals were found to be negative for CCHF. Overall CFR in this outbreak was 50%.Conclusion: CCHF outbreak was propagated nosocomially due to poor infection control practice and low index of suspicion for CCHF amongst treating physicians

Comparative Analysis of Data Mining Techniques for Heart Disease Prediction: A Focus on Neural Networks and Decision Trees

Heart disease is a general term used to describe numerous medical conditions that directly affect the heart and its various components. It is a prevalent health concern in modern times. The focus of this paper is to evaluate different data mining techniques for the prediction of heart disease, which have been introduced in recent years. The findings indicate that neural networks using 15 attributes demonstrate the best performance among all other data mining techniques. Additionally, the analysis concludes that decision trees, with the assistance of genetic algorithms and feature subset selection, also exhibit high accuracy. The study concludes that data mining techniques can effectively predict heart disease and that the choice of technique depends on the specific context of the analysis. The study suggests that decision trees and artificial neural network models are suitable for heart disease prediction. The study also recommends further research to explore the use of other data mining techniques for heart disease prediction

Cosmological QCD Phase Transition and Dark Matter

We calculate the size distribution of quark nuggets, which could be formed due to first order QCD phase transition in the early universe. We find that there are a large number of stable Quark Nuggets which could be a viable candidate for cosmological dark matter.Comment: To appear in Quark Matter 99 proceeding