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Record W2810364471 · doi:10.1111/tan.13326

Urdu speaking population from South India: Six extended haplotypes in linkage disequilibrium in Urdu Speaking Population

2018· article· en· W2810364471 on OpenAlexfundno aff
Vani Seshasubramanian, N. K. Manisekar, Aruna Devi SathishKannan, Chandramouleeswaran Naganathan, Yogesh Nandakumar, Sneha Narayan

Bibliographic record

VenueHLA · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsnot available
FundersInstitute of GeneticsUniversité de GenèveLife Technologies Corporation
KeywordsLinkage disequilibriumHaplotypePopulationTeluguUrduGeneticsAllele frequencyAlleleSanger sequencingBiologyDemographyDNA sequencingGeneSociologyNatural language processing

Abstract

fetched live from OpenAlex

HLA A: B:C:DRB1:DQB1 allele and haplotype frequencies were determined among Urdu speaking population from South India by Sanger and Next Generation Sequencing. Seventy bone marrow registry donors and 327 cord blood units from the Jeevan Stem Cell Foundation (part of Be The Cure Registry), Chennai, Tamilnadu were included in the study. No overall deviations from expected Hardy-Weinberg equilibrium proportions were observed at all the five loci studied. The most frequent HLA class I alleles observed in this population were A*11:01:01, B*40:06:01, and C*06:02:01, showing a frequency of 15.87%, 12.72%, and 13.22%, respectively. The most frequent class II alleles observed in this population were DRB1*07:01:01 (15.62%) and DQB1*06:01:01 (21.41%). The top ranked haplotype A*01:01:01~B*57:01:01~C*06:02:01~DRB1*07:01:01~DQB1*03:03:02 (2.77%) reported in the present study, was the highly frequent haplotype found in Telugu speaking population who lived in the same region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.284
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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