Apprentice Labor and Firm Output: Evidence from a random match experiment

2014 ◽  
Author(s):  
Jamie McCasland
Keyword(s):  
Random Match

scholarly journals Pathogenic Variant Filtering for Mitochondrial Genome Haplotype Reporting

Genes ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1140
Author(s):  
Charla Marshall ◽  
Kimberly Sturk-Andreaggi ◽  
Joseph D. Ring ◽  
Arne Dür ◽  
Walther Parson
Keyword(s):  
Genetic Information ◽  
Variant Calling ◽  
Pathogenic Variant ◽  
Discriminatory Power ◽  
Coding Region ◽  
Legal Requirements ◽  
Random Match ◽  
Pathogenic Variants ◽  
Disease Information ◽  
Variant Filtering

Given the enhanced discriminatory power of the mitochondrial DNA (mtDNA) genome (mitogenome) over the commonly sequenced control region (CR) portion, the scientific merit of mitogenome sequencing is generally accepted. However, many laboratories remain beholden to CR sequencing due to privacy policies and legal requirements restricting the use of disease information or coding region (codR) information. In this report, we present an approach to obviate the reporting of sensitive codR data in forensic haplotypes. We consulted the MitoMap database to identify 92 mtDNA codR variants with confirmed pathogenicity. We determined the frequencies of these pathogenic variants in literature-quality and forensic-quality databases to be very low, at 1.2% and 0.36%, respectively. The observed effect of pathogenic variant filtering on random match statistics in 2488 forensic-quality mitogenome haplotypes from four populations was nil. We propose that pathogenic variant filtering should be incorporated into variant calling algorithms for mitogenome haplotype reporting to maximize the discriminatory power of the locus while minimizing the reveal of sensitive genetic information.

An algorithm for random match probability calculation from peptide sequences

2020 ◽  
Vol 47 ◽  
pp. 102295
Author(s):  
August E. Woerner ◽  
F. Curtis Hewitt ◽  
Myles W. Gardner ◽  
Michael A. Freitas ◽  
Kathleen Q. Schulte ◽  
...  
Keyword(s):  
Match Probability ◽  
Random Match ◽  
Random Match Probability ◽  
Probability Calculation

scholarly journals Stability of the Bipartite Matching Model

2013 ◽  
Vol 45 (2) ◽  
pp. 351-378 ◽  
Author(s):  
Ana Bušić ◽  
Varun Gupta ◽  
Jean Mairesse
Keyword(s):  
Bipartite Graph ◽  
Stability Region ◽  
Joint Probability ◽  
Matching Model ◽  
Time Step ◽  
Random Match ◽  
Bipartite Matching ◽  
Discrete Time Markov Chain ◽  
The Stability ◽  
Maximal Stability

We consider the bipartite matching model of customers and servers introduced by Caldentey, Kaplan and Weiss (2009). Customers and servers play symmetrical roles. There are finite sets C and S of customer and server classes, respectively. Time is discrete and at each time step one customer and one server arrive in the system according to a joint probability measure μ on C× S, independently of the past. Also, at each time step, pairs of matched customers and servers, if they exist, depart from the system. Authorized matchings are given by a fixed bipartite graph (C, S, E⊂ C × S). A matching policy is chosen, which decides how to match when there are several possibilities. Customers/servers that cannot be matched are stored in a buffer. The evolution of the model can be described by a discrete-time Markov chain. We study its stability under various admissible matching policies, including ML (match the longest), MS (match the shortest), FIFO (match the oldest), RANDOM (match uniformly), and PRIORITY. There exist natural necessary conditions for stability (independent of the matching policy) defining the maximal possible stability region. For some bipartite graphs, we prove that the stability region is indeed maximal for any admissible matching policy. For the ML policy, we prove that the stability region is maximal for any bipartite graph. For the MS and PRIORITY policies, we exhibit a bipartite graph with a non-maximal stability region.

scholarly journals Mu1-related transposable elements of maize preferentially insert into low copy number DNA.

Genetics ◽  
1995 ◽  
Vol 140 (1) ◽  
pp. 315-324 ◽  
Author(s):  
A D Cresse ◽  
S H Hulbert ◽  
W E Brown ◽  
J R Lucas ◽  
J L Bennetzen
Keyword(s):  
Transposable Elements ◽  
High Frequency ◽  
Copy Number ◽  
Consensus Sequence ◽  
Induced Mutations ◽  
Random Match ◽  
Low Copy Number Dna ◽  
Low Copy Number ◽  
Element System ◽  
Selection Of

Abstract The Mutator transposable element system of maize was originally identified through its induction of mutations at an exceptionally high frequency and at a wide variety of loci. The Mu1 subfamily of transposable elements within this system are responsible for the majority of Mutator-induced mutations. Mu 1-related elements were isolated from active Mutator plants and their flanking DNA was characterized. Sequence analyses revealed perfect nine base target duplications directly flanking the insert for 13 of the 14 elements studied. Hybridizational studies indicated that Mu1-like elements insert primarily into regions of the maize genome that are of low copy number. This preferential selection of low copy number DNA as targets for Mu element insertion was not directed by any specific secondary structure(s) that could be detected in this study, but the 9-bp target duplications exhibited a discernibly higher than random match with the consensus sequence 5'-G-T-T-G-G/C-A-G-G/A-G-3'.

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