Implementation of a 10.24 GS/s 12-bit Optoelectronics Analog-to-Digital Converter Based on a Polyphase Demultiplexing Architecture

AbstractIn this paper we present the practical implementation of a high-speed polyphase sampling and demultiplexing architecture for optoelectronics analog-to-digital converters (OADCs). The architecture consists of a one-stage divide-by-eight decimator circuit where optically-triggered samplers are cascaded to sample an analog input signal, and demultiplex different phases of the sampled signal to yield low data rate for electronic quantization. Electrical-in to electrical-out data format is maintained through the sampling, demultiplexing and quantization processes of the architecture thereby avoiding the need for electrical-to-optical and optical-to-electrical signal conversions. We experimentally demonstrate a 10.24 giga samples per second (GS/s), 12-bit resolution OADC system comprising the optically-triggered sampling circuits integrated with commercial electronic quantizers. Measurements performed on the OADC yielded an effective bit resolution (ENOB) of 10.3 bits, spurious free dynamic range (SFDR) of -32 dB and signal-to-noise and distortion ratio (SNDR) of 63.7 dB

Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs

Abstract Background This paper uses simulation to explore how gene drives can increase genetic gain in livestock breeding programs. Gene drives are naturally occurring phenomena that cause a mutation on one chromosome to copy itself onto its homologous chromosome. Methods We simulated nine different breeding and editing scenarios with a common overall structure. Each scenario began with 21 generations of selection, followed by 20 generations of selection based on true breeding values where the breeder used selection alone, selection in combination with genome editing, or selection with genome editing and gene drives. In the scenarios that used gene drives, we varied the probability of successfully incorporating the gene drive. For each scenario, we evaluated genetic gain, genetic variance ( \u3c3 A 2 ) , rate of change in inbreeding ( \u394 F ), number of distinct quantitative trait nucleotides (QTN) edited, rate of increase in favourable allele frequencies of edited QTN and the time to fix favourable alleles. Results Gene drives enhanced the benefits of genome editing in seven ways: (1) they amplified the increase in genetic gain brought about by genome editing; (2) they amplified the rate of increase in the frequency of favourable alleles and reduced the time it took to fix them; (3) they enabled more rapid targeting of QTN with lesser effect for genome editing; (4) they distributed fixed editing resources across a larger number of distinct QTN across generations; (5) they focussed editing on a smaller number of QTN within a given generation; (6) they reduced the level of inbreeding when editing a subset of the sires; and (7) they increased the efficiency of converting genetic variation into genetic gain. Conclusions Genome editing in livestock breeding results in short-, medium- and long-term increases in genetic gain. The increase in genetic gain occurs because editing increases the frequency of favourable alleles in the population. Gene drives accelerate the increase in allele frequency caused by editing, which results in even higher genetic gain over a shorter period of time with no impact on inbreeding

Planck Intermediate Results. IV. The XMM-Newton validation programme for new Planck galaxy clusters

We present the final results from the XMM-Newton validation follow-up of new Planck galaxy cluster candidates. We observed 15 new candidates, detected with signal-to-noise ratios between 4.0 and 6.1 in the 15.5-month nominal Planck survey. The candidates were selected using ancillary data flags derived from the ROSAT All Sky Survey (RASS) and Digitized Sky Survey all-sky maps, with the aim of pushing into the low SZ flux, high-z regime and testing RASS flags as indicators of candidate reliability. 14 new clusters were detected by XMM, including 2 double systems. Redshifts lie in the range 0.2 to 0.9, with 6 clusters at z>0.5. Estimated M500 range from 2.5 10^14 to 8 10^14 Msun. We discuss our results in the context of the full XMM validation programme, in which 51 new clusters have been detected. This includes 4 double and 2 triple systems, some of which are chance projections on the sky of clusters at different z. We find that association with a RASS-BSC source is a robust indicator of the reliability of a candidate, whereas association with a FSC source does not guarantee that the SZ candidate is a bona fide cluster. Nevertheless, most Planck clusters appear in RASS maps, with a significance greater than 2 sigma being a good indication that the candidate is a real cluster. The full sample gives a Planck sensitivity threshold of Y500 ~ 4 10^-4 arcmin^2, with indication for Malmquist bias in the YX-Y500 relation below this level. The corresponding mass threshold depends on z. Systems with M500 > 5 10^14 Msun at z > 0.5 are easily detectable with Planck. The newly-detected clusters follow the YX-Y500 relation derived from X-ray selected samples. Compared to X-ray selected clusters, the new SZ clusters have a lower X-ray luminosity on average for their mass. There is no indication of departure from standard self-similar evolution in the X-ray versus SZ scaling properties. (abridged)Comment: accepted by A&

Genomic evaluations with many more genotypes

<p>Abstract</p> <p>Background</p> <p>Genomic evaluations in Holstein dairy cattle have quickly become more reliable over the last two years in many countries as more animals have been genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as the 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. The overall cost of selection is reduced by genotyping most animals at less than the highest density and imputing their missing genotypes using haplotypes. Algorithms to combine different densities need to be efficient because numbers of genotyped animals and markers may continue to grow quickly.</p> <p>Methods</p> <p>Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage disequilibrium was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations using linear and nonlinear methods were compared.</p> <p>Results</p> <p>Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations with 50,000 markers and 1.6% lower with 500,000 markers.</p> <p>Conclusions</p> <p>Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can be modified to reflect success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.</p

Comparison of linkage disequilibrium and haplotype diversity on macro- and microchromosomes in chicken

<p>Abstract</p> <p>Background</p> <p>The chicken (<it>Gallus gallus</it>), like most avian species, has a very distinct karyotype consisting of many micro- and a few macrochromosomes. While it is known that recombination frequencies are much higher for micro- as compared to macrochromosomes, there is limited information on differences in linkage disequilibrium (LD) and haplotype diversity between these two classes of chromosomes. In this study, LD and haplotype diversity were systematically characterized in 371 birds from eight chicken populations (commercial lines, fancy breeds, and red jungle fowl) across macro- and microchromosomes. To this end we sampled four regions of ~1 cM each on macrochromosomes (GGA1 and GGA2), and four 1.5 -2 cM regions on microchromosomes (GGA26 and GGA27) at a high density of 1 SNP every 2 kb (total of 889 SNPs).</p> <p>Results</p> <p>At a similar physical distance, LD, haplotype homozygosity, haploblock structure, and haplotype sharing were all lower for the micro- as compared to the macrochromosomes. These differences were consistent across populations. Heterozygosity, genetic differentiation, and derived allele frequencies were also higher for the microchromosomes. Differences in LD, haplotype variation, and haplotype sharing between populations were largely in line with known demographic history of the commercial chicken. Despite very low levels of LD, as measured by r²for most populations, some haploblock structure was observed, particularly in the macrochromosomes, but the haploblock sizes were typically less than 10 kb.</p> <p>Conclusion</p> <p>Differences in LD between micro- and macrochromosomes were almost completely explained by differences in recombination rate. Differences in haplotype diversity and haplotype sharing between micro- and macrochromosomes were explained by differences in recombination rate and genotype variation. Haploblock structure was consistent with demography of the chicken populations, and differences in recombination rates between micro- and macrochromosomes. The limited haploblock structure and LD suggests that future whole-genome marker assays will need 100+K SNPs to exploit haplotype information. Interpretation and transferability of genetic parameters will need to take into account the size of chromosomes in chicken, and, since most birds have microchromosomes, in other avian species as well.</p

Tracing Cattle Breeds with Principal Components Analysis Ancestry Informative SNPs

The recent release of the Bovine HapMap dataset represents the most detailed survey of bovine genetic diversity to date, providing an important resource for the design and development of livestock production. We studied this dataset, comprising more than 30,000 Single Nucleotide Polymorphisms (SNPs) for 19 breeds (13 taurine, three zebu, and three hybrid breeds), seeking to identify small panels of genetic markers that can be used to trace the breed of unknown cattle samples. Taking advantage of the power of Principal Components Analysis and algorithms that we have recently described for the selection of Ancestry Informative Markers from genomewide datasets, we present a decision-tree which can be used to accurately infer the origin of individual cattle. In doing so, we present a thorough examination of population genetic structure in modern bovine breeds. Performing extensive cross-validation experiments, we demonstrate that 250-500 carefully selected SNPs suffice in order to achieve close to 100% prediction accuracy of individual ancestry, when this particular set of 19 breeds is considered. Our methods, coupled with the dense genotypic data that is becoming increasingly available, have the potential to become a valuable tool and have considerable impact in worldwide livestock production. They can be used to inform the design of studies of the genetic basis of economically important traits in cattle, as well as breeding programs and efforts to conserve biodiversity. Furthermore, the SNPs that we have identified can provide a reliable solution for the traceability of breed-specific branded products

Planck Intermediate Results. XI: The gas content of dark matter halos: the Sunyaev-Zeldovich-stellar mass relation for locally brightest galaxies

Contains fulltext : 119332.pdf (preprint version ) (Open Access

A simple procedure for directly obtaining haplotype sequences of diploid genomes

Background Almost all genome sequencing projects neglect the fact that diploid organisms contain two genome copies and consequently what is published is a composite of the two. This means that the relationship between alternate alleles at two or more linked loci is lost. We have developed a simplified method of directly obtaining the haploid sequences of each genome copy from an individual organism. Results The diploid sequences of three groups of cattle samples were obtained using a simple sample preparation procedure requiring only a microscope and a haemocytometer. Samples were: 1) lymphocytes from a single Angus steer; 2) sperm cells from an Angus bull; 3) lymphocytes from East African Zebu (EAZ) cattle collected and processed in a field laboratory in Eastern Kenya. Haploid sequence from a fosmid library prepared from lymphocytes of an EAZ cow was used for comparison. Cells were serially diluted to a concentration of one cell per microlitre by counting with a haemocytometer at each dilution. One microlitre samples, each potentially containing a single cell, were lysed and divided into six aliquots (except for the sperm samples which were not divided into aliquots). Each aliquot was amplified with phi29 polymerase and sequenced. Contigs were obtained by mapping to the bovine UMD3.1 reference genome assembly and scaffolds were assembled by joining adjacent contigs that were within a threshold distance of each other. Scaffolds that appeared to contain artefacts of CNV or repeats were filtered out leaving scaffolds with an N50 length of 27–133 kb and a 88–98 % genome coverage. SNP haplotypes were assembled with the Single Individual Haplotyper program to generate an N50 size of 97–201 kb but only ~27–68 % genome coverage. This method can be used in any laboratory with no special equipment at only slightly higher costs than conventional diploid genome sequencing. A substantial body of software for analysis and workflow management was written and is available as supplementary data. Conclusions We have developed a set of laboratory protocols and software tools that will enable any laboratory to obtain haplotype sequences at only modestly greater cost than traditional mixed diploid sequences