Buffer Engineering for M|G|infinity Input Processes

We suggest the $M|G|infty$ input process as a viable model forrepresenting the heavy correlations observed in network traffic.Originally introduced by Cox, this model represents the busy-serverprocess of an $M|G|infty$ queue with Poisson inputs and generalservice times distributed according to $G$, and provides a large andversatile class of traffic models. We examine various properties ofthe $M|G|infty$ process, focusing particularly on its richcorrelation structure. The process is shown to effectively portrayshort or long-range dependence simply by controlling the tail of thedistribution $G$.In an effort to understand the dynamics of a system supporting$M|G|infty$ traffic, we study the large buffer asymptotics of amultiplexer driven by an $M|G|infty$ input process. Using the largedeviations framework developed by Duffield and O'Connell, weinvestigate the tail probabilities for the steady-state buffercontent. The key step in this approach is the identification of theappropriate large deviations scaling. This scaling is shown to beclosely related to the forward recurrence time of the service timedistribution, and a closed form expression is derived for thecorresponding limiting log-moment generating functionassociated with the input process. Three different regimes areidentified.The results are then applied to obtain the large bufferasymptotics under a variety of service time distributions. In eachcase, the derived asymptotics are compared with simulation results. While the general functional form of buffer asymptotics may be derivedvia large deviations techniques, direct arguments often provide a moreprecise description when the input traffic is heavily correlated.Even so, several significant inferences may be drawn from thefunctional dependencies of the tail buffer probabilities. Theasymptotics already indicate a sub-exponential behavior in the caseof heavily-correlated traffic, in sharp contrast to the geometricdecay usually observed for Markovian input streams. This difference,along with a shift in the explicit dependence of the asymptotics onthe input and output rates $r_{in}$ and $c$, from $ho=r_{in}/c$ when$G$ is exponential, to $Delta = c - r_{in}$ when $G$ issub--exponential, clearly delineates the heavy and light tailed cases.Finally, comparison with similar asymptotics for a different class ofinput processes indicates that buffer sizing cannot be adequatelydetermined by appealing solely to the short versus long-rangedependence characterization of the input model used

A. Heteroplasmic sites per sample.

<p>Total number of heteroplasmic sites found per sample for each YRI and CEU sample. <b>B</b>. <b>Level of heteroplasmy per position across the mitochondrial genome</b>. Top and bottom displays the level of heteroplasmy for all sites found in YRI and CEU samples, respectively, across the mitochondrial genome.</p

Coverage across the mitochondrial genome.

<p>The top portion of the figure shows where the three amplicons lie and overlap across the mtgenome. A) Coverage for all samples per population. For each sample the coverage at a particular position was normalized by dividing the total number of reads obtained for that sample by 1,000. B) The forward to reverse read ratio for the modal base was centered to 0 using the following statistic: [(forward/reverse)−1]/[(forward/reverse)+1]. C) GC content across the mtgenome was calculated using a sliding window of 51 bp centered on the position in question.</p

Multi-gene testing in neurological disorders showed an improved diagnostic yield: data from over 1000 Indian patients

Background Neurological disorders are clinically heterogeneous group of disorders and are major causes of disability and death. Several of these disorders are caused due to genetic aberration. A precise and confirmatory diagnosis in the patients in a timely manner is essential for appropriate therapeutic and management strategies. Due to the complexity of the clinical presentations across various neurological disorders, arriving at an accurate diagnosis remains a challenge. Methods We sequenced 1012 unrelated patients from India with suspected neurological disorders, using TruSight One panel. Genetic variations were identified using the Strand NGS software and interpreted using the StrandOmics platform. Results We were able to detect mutations in 197 genes in 405 (40%) cases and 178 mutations were novel. The highest diagnostic rate was observed among patients with muscular dystrophy (64%) followed by leukodystrophy and ataxia (43%, each). In our cohort, 26% of the patients who received definitive diagnosis were primarily referred with complex neurological phenotypes with no suggestive diagnosis. In terms of mutations types, 62.8% were truncating and in addition, 13.4% were structural variants, which are also likely to cause loss of function. Conclusion In our study, we observed an improved performance of multi-gene panel testing, with an overall diagnostic yield of 40%. Furthermore, we show that NGS (next-generation sequencing)-based testing is comprehensive and can detect all types of variants including structural variants. It can be considered as a single-platform genetic test for neurological disorders that can provide a swift and definitive diagnosis in a cost-effective manner