Performance of genetic imputation across commercial crop species

AbstractWe show that accurate imputation can be carried out in three commercial plant species (maize, sugar beet and wheat) and that accurate imputation does not require a pedigree, although pedigree information can improve accuracy and speed. Our approach uses a hidden Markov model to build a haplotype library from individuals genotyped at high-density and then uses this library to impute low-density genotyped individuals to high-density. To build the library, we use founders when the pedigree is known, or a sample of progeny when the pedigree is unknown. Without a pedigree, and with 50 individuals genotyped at high-density and 100 low-density markers per chromosome, the median accuracies were 0.97 (maize), 0.96 (sugar beet), and 0.94 (wheat). We obtained similar accuracies with a pedigree. For biparental crosses with 100 markers per chromosome, median accuracies were 0.96 (maize), 0.96 (sugar beet) and 0.94 (wheat). For the imputation scenarios without a pedigree, we compared accuracies with those obtained by running Beagle 5.1. In all but one scenario, our method outperformed Beagle. We believe that plant breeders can effectively apply imputation in many crop species.

Download Full-text

Subvector-quantized high-density discrete hidden Markov model and its re-estimation

2010 7th International Symposium on Chinese Spoken Language Processing ◽

10.1109/iscslp.2010.5684838 ◽

2010 ◽

Author(s):

Guoli Ye ◽

Brian Mak

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

High Density

Download Full-text

Pelabelan Kelas Kata Bahasa Jawa Menggunakan Hidden Markov Model

Mobile and Forensics ◽

10.12928/mf.v2i2.2450 ◽

2020 ◽

Vol 2 (2) ◽

pp. 71-83

Author(s):

Mohammad Mursyit ◽

Aji Prasetya Wibawa ◽

Ilham Ari Elbaith Zaeni ◽

Harits Ar Rosyid

Keyword(s):

Short Stories ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Improve Accuracy ◽

Unknown Words ◽

Speech Tagging

Part of Speech TaggingÂ atauÂ POS TaggingÂ adalah proses memberikan label pada setiap kata dalam sebuah kalimat secara otomatis. Penelitian ini menggunakan algoritmaÂ Hidden Markov ModelÂ (HMM) untuk prosesÂ POS Tagging. Perlakuan untukÂ unknown wordsÂ menggunakanÂ Most Probable POS-Tag.Â DatasetÂ yang digunakan berupa 10 cerita pendek berbahasa Jawa terdiri dari 10.180 kata yang telah diberikanÂ tagsetBahasa Jawa. Pada penelitian ini prosesÂ POS TaggingÂ menggunakan dua skenario. Skenario pertama yaitu menggunakan algoritmaÂ Hidden Markov ModelÂ (HMM) tanpa menggunakan perlakuan untukÂ unknown words. Skenario yang kedua menggunakan HMM danÂ Most Probable POS-TagÂ untukÂ perlakuan unknown words. Hasil menunjukan skenario pertama menghasilkan akurasi sebesar 45.5% dan skenario kedua menghasilkan akurasi sebesar 70.78%.Â Most Probable POS-TagÂ dapat meningkatkan akurasi padaÂ POS TaggingÂ tetapi tidak selalu menunjukan hasil yang benar dalam pemberian label.Â Most Probable POS-TagÂ dapat menghilangkan probabilitas bernilai Nol dariÂ POS Tagging Hidden Markov Model. Hasil penelitian ini menunjukan bahwaÂ POS TaggingÂ dengan menggunakanÂ Hidden Markov ModelÂ dipengaruhi oleh perlakuan terhadapÂ unknown words, perbendaharaan kata dan hubungan label kata padaÂ dataset.Â Â Part of Speech Tagging or POS Tagging is the process of automatically giving labels to each word in a sentence. This study uses the Hidden Markov Model (HMM) algorithm for the POS Tagging process. Treatment for unknown words uses the Most Probable POS-Tag. The dataset used is in the form of 10 short stories in Javanese consisting of 10,180 words which have been given the Javanese tagset. In this study, the POS Tagging process uses two scenarios. The first scenario is using the Hidden Markov Model (HMM) algorithm without using treatment for unknown words. The second scenario uses HMM and Most Probable POS-Tag for treatment of unknown words. The results show that the first scenario produces an accuracy of 45.5% and the second scenario produces an accuracy of 70.78%. Most Probable POS-Tag can improve accuracy in POS Tagging but does not always produce correct labels. Most Probable POS-Tag can remove zero-value probability from POS Tagging Hidden Markov Model. The results of this study indicate that POS Tagging using the Hidden Markov Model is influenced by the treatment of unknown words, vocabulary and word label relationships in the dataset.

Download Full-text

Quantitative defect studies in semiconductor TEM samples prepared by low angle ion milling

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100138932 ◽

1995 ◽

Vol 53 ◽

pp. 512-513

Author(s):

L. Mulestagno ◽

J.C. Holzer ◽

P. Fraundorf

Keyword(s):

Sample Preparation ◽

Milling Time ◽

High Density ◽

Low Density ◽

Ion Milling ◽

High Quality ◽

Variable Angle ◽

Major Disadvantage ◽

Induced Defects

Due to the wealth of information, both analytical and structural that can be obtained from it TEM always has been a favorite tool for the analysis of process-induced defects in semiconductor wafers. The only major disadvantage has always been, that the volume under study in the TEM is relatively small, making it difficult to locate low density defects, and sample preparation is a somewhat lengthy procedure. This problem has been somewhat alleviated by the availability of efficient low angle milling.Using a PIPS® variable angle ion -mill, manufactured by Gatan, we have been consistently obtaining planar specimens with a high quality thin area in excess of 5 × 104 μm2 in about half an hour (milling time), which has made it possible to locate defects at lower densities, or, for defects of relatively high density, obtain information which is statistically more significant (table 1).

Download Full-text

Scanning Electron Microscopic and Electrophoretic Observations on Barium Sulphate Used to Adsorb Clotting Factors

Thrombosis and Haemostasis ◽

10.1055/s-0038-1647879 ◽

1975 ◽

Vol 33 (02) ◽

pp. 256-270

Author(s):

R. M Howell ◽

S. L. M Deacon

Keyword(s):

Electron Microscopy ◽

Factor Xa ◽

Barium Sulphate ◽

High Density ◽

Low Density ◽

Factor X ◽

Clotting Factors ◽

Electron Microscopic ◽

Complementary Techniques ◽

Scanning Electron

SummaryElectron microscopy and particle electrophoresis were found to be complementary techniques with which to complete the physical data from an earlier study on barium sulphates used to adsorb clotting factors from serum. The differences revealed by scanning electron microscopy (S. E. M.) in the physical shape of low and high density grades of barium sulphate particles appear to be of greater significance than charge as expressed by electrophoretic mobility, in determining whether or not precursor or preformed factor Xa is eluted.This conclusion was based on the finding that at pH values close to 7, where the adsorption from serum occurs, all samples with the exception of natural barytes were uncharged. However as the high-density, or soil-grade, was found by S. E. M. to consist of large solid crystals it was suggested that this shape might induce activation of factor X as a result of partial denaturation and consequent unfolding of the adsorbed protein. In contrast, uptake of protein into the centre of the porous aggregates revealed by S. E. M. pictures of low-density or X-ray grade barium sulphate may afford protection against denaturation and exposure of the enzyme site.The porous nature of particles of low-density barium sulphate compared with the solid crystalline forms of other grades accounts not only for its lower bulk density but also for its greater surface/gram ratio which is reflected by an ability to adsorb more protein from serum.Neither technique produced evidence from any of the samples to indicate the presence of stabilising agents sometimes used to coat particles in barium meals.

Download Full-text