Stability of the crosslinked tropomyosin dimer: crosslink effect on the cooperativity of the ordering process and on the maximum in the helix probability profile

1982 ◽  
Vol 15 (4) ◽  
pp. 1088-1093 ◽  
Author(s):  
Wayne L. Mattice ◽  
Jeffrey Skolnick
Keyword(s):  
Probability Profile

Instruction Cache Locking for Embedded Systems using Probability Profile

2011 ◽  
Vol 69 (2) ◽  
pp. 173-188 ◽  
Author(s):  
Tiantian Liu ◽  
Minming Li ◽  
Chun Jason Xue
Keyword(s):  
Embedded Systems ◽  
Instruction Cache ◽  
Probability Profile ◽  
Cache Locking

scholarly journals Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance

2018 ◽  
Author(s):  
Omichessan Hanane ◽  
Severi Gianluca ◽  
Perduca Vittorio
Keyword(s):  
Somatic Mutations ◽  
Empirical Evaluation ◽  
Simulated Data ◽  
Evaluation Study ◽  
Systematic Evaluation ◽  
Mutational Signatures ◽  
Computational Tools ◽  
Probability Profile ◽  
Mathematical Algorithms ◽  
Mutational Processes

AbstractMutational signatures refer to patterns in the occurrence of somatic mutations that reflect underlying mutational processes. To date, after the analysis of tens of thousands of genomes and exomes from about 40 different cancers types, 30 mutational signatures characterized by a unique probability profile across the 96 mutation types have been identified, validated and listed on the COSMIC (Catalogue of Somatic Mutations in Cancer) website. Simultaneously with this development, the last few years saw the publication of several concurrent methods (mathematical algorithms implemented in publicly available software packages) for either the quantification of the contribution of prespecified signatures (e.g. COSMIC signatures) in a given cancer genome or the identification of new signatures from a sample of cancer genomes. A review about existing computational tools has been recently published to guide researchers and practitioners in conducting their mutational signatures analysis, however, other tools have been introduced since its publication and, to date, there has not been a systematic evaluation and comparison of the performance of such tools. In order to fill this gap, we carry on an empirical evaluation study of all available packages to date, using both real and simulated data.

QLREDActive Queue Management Algorithm

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
S.O. Hassan ◽  
A.O. Oluwatope ◽  
C. Ajaegbu ◽  
K-K.A. Abdullah ◽  
A.O. Olasupo
Keyword(s):  
Queue Size ◽  
Packet Dropping ◽  
Simulation Performance ◽  
Internet Routers ◽  
Dropping Probability ◽  
Management Algorithm ◽  
Traffic Loads ◽  
Probability Profile ◽  
Dropping Function ◽  
Minimal Effort

The Random Early Detection (RED) algorithm has not been successful in keeping the average queue size low. In this paper, we an improved RED-based algorithm called QLRED which divides the dropping probability function of the RED algorithm into two equal segments. The first segment utilises a quadratic packet dropping function while the second segment deploys a linear packet dropping function respectively so as to distinguish between light and high traffic loads. The ns-3 simulation performance evaluations clearly showed that QLRED algorithm effectively controls the average queue size under various network conditions resulting in a low delay. Replacing/upgrading the RED algorithm in Internet routers requires minimal effort since only the packet dropping probability profile needs to be adjusted.

scholarly journals Engineering Breakdown Probability Profile for PDP and DCR Optimization in a SPAD Fabricated in a Standard 55nm BCD Process

2021 ◽  
pp. 1-1
Author(s):  
Francesco Gramuglia ◽  
Pouyan Keshavarzian ◽  
Ekin Kizilkan ◽  
Claudio Bruschini ◽  
Shyue Seng Tan ◽  
...  
Keyword(s):  
Probability Profile ◽  
Breakdown Probability

An age-structured state-space stock–recruit model for Pacific salmon (Oncorhynchusspp.)

2013 ◽  
Vol 70 (3) ◽  
pp. 401-414 ◽  
Author(s):  
Steven J. Fleischman ◽  
Matthew J. Catalano ◽  
Robert A. Clark ◽  
David R. Bernard
Keyword(s):  
State Space ◽  
Oncorhynchus Tshawytscha ◽  
Pacific Salmon ◽  
State Space Model ◽  
Auxiliary Information ◽  
Observation Error ◽  
Age Composition ◽  
Probability Profile ◽  
Explicit Consideration ◽  
Age Structured

We describe an age-structured state-space model for stock–recruit analysis of Pacific salmon data. The model allows for incorporation of process variation in stock productivity, recruitment, and maturation schedules, as well as observation error in run abundance, harvest, and age composition. Explicit consideration of age structure allows for realistic depiction of system dynamics and sample design, more complete use of recent data, and forecasts that consider sibling relationships. A Bayesian framework is adopted, implemented with Markov chain Monte Carlo methods, which provides an enhanced ability to incorporate auxiliary information, convenient and rigorous consideration of measurement error and missing data, and a more complete assessment of uncertainty. We fit the model to annual upstream weir counts, commercial and recreational harvest estimates, and age composition data from Chinook salmon (Oncorhynchus tshawytscha) in Karluk River, Alaska. For the case study, the model is configured with a Ricker stock–recruit relationship, autoregressive lag-1 productivity, and Dirichlet age-at-maturity. Details of alternate configurations are also described. We introduce the optimal yield probability profile as an objective tool for informing the selection of escapement goals based on yield considerations and describe alternative versions useful for addressing other management questions.

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