From high-level languages to dataflow circuits

La manera tradicional de computar alguna cosa és creant software que es pot executar en la unitat de processament central (CPU) d'un processador. El problema és que una CPU no té la capacitat de còmput suficient per executar correctament aplicacions pertanyents a certs àmbits, com per exemple l'aprenentatge profund o la mineria de cripto-monedes. Amb el pas del temps, les unitats de processament gràfic (GPUs) es van començar a utilitzar en altres camps més enllà dels ideats inicialment (p.e. videojocs), permetent l'execució d'aquelles aplicacions que les CPU no podien. No obstant, existeix una altra manera per executar programes o algorismes, la qual és molt més eficient en el consum de temps i energia que executar software en CPUs i GPUs. Aquesta altra manera consisteix a dissenyar i implementar directament un circuit hardware per executar alguna cosa en particular, en lloc d'utilitzar un circuit de propòsit general que permet executar qualsevol cosa. Per aquesta raó, l'objectiu d'aquest projecte és el de desenvolupar una eina de síntesis que generi circuits de data flow a partir de llenguatges de programació d'alt nivell. Aquests circuits es poden implementar en tecnologies com les matrius de portes programables (FPGAs). Aquest projecte crearà el back end d'un compilador, amb l'ajuda d'algun front end d'un compilador que permeti la traducció de codi d'alt nivell en una representació intermèdia, com per exemple LLVM. La idea és tenir un únic codi intermedi per múltiples llenguatges d'alt nivell. Aleshores, aquesta representació intermèdia es passarà al nostre back end, i aquest generarà un conjunt de mòduls amb diferents funcionalitats, i canals per transmetre dades entre mòduls, en la forma d'un graf dirigit. Finalment, aquests grafs s'implementaran en les mencionades FGPAs, creant el circuit hardware final que s'executarà. El funcionament d'aquests circuits seguirà el paradigma del data flow, proposat en el MIT a mitjans dels anys 70.The traditional way to compute something is writing software that can be executed in the processor's central processing unit (CPU). However, a CPU does not have the computing capacity to properly run applications belonging to certain fields like for example, deep learning and cryptocurrency mining. With the passage of time, graphic processing units (GPUs) began to be used in other fields besides the initially intended ones (e.g. video games), permitting the execution of those applications that CPUs could not. Nevertheless, exists a different way to execute programs or algorithms, that is much more efficient in time and power consumption than executing software in CPUs and GPUs. This other way consists in directly designing and implementing a hardware circuit to particularly execute something, instead of using a general-purpose circuitry that can compute anything. For this reason, the goal of this project is the development of a synthesis tool that generates data flow circuits from high-level languages. These circuits can be later be implemented in technologies such as field-programmable gate arrays (FPGAs). This project will create a compiler back end, with the help of some existing compiler front end that can translate the initial high-level code into some intermediate representation, such as LLVM. The idea is to have a unique intermediate code for multiple high-level languages. Then, this intermediate representation will be fed to our back end, and it will generate a set of modules with different functions, and channels to transmit data between modules, in the form of directed graphs. Finally, these graphs will be implemented in the mentioned FPGAs, creating the final hardware circuit that will be run. The functioning of these circuits, will follow the data flow paradigm proposed at the MIT in the mid 70's

Merging microsatellite data: enhanced methodology and software to combine genotype data for linkage and association analysis

<p>Abstract</p> <p>Background</p> <p>Correctly merged data sets that have been independently genotyped can increase statistical power in linkage and association studies. However, alleles from microsatellite data sets genotyped with different experimental protocols or platforms cannot be accurately matched using base-pair size information alone. In a previous publication we introduced a statistical model for merging microsatellite data by matching allele frequencies between data sets. These methods are implemented in our software MicroMerge version 1 (v1). While MicroMerge v1 output can be analyzed by some genetic analysis programs, many programs can not analyze alignments that do not match alleles one-to-one between data sets. A consequence of such alignments is that codominant genotypes must often be analyzed as phenotypes. In this paper we describe several extensions that are implemented in MicroMerge version 2 (v2).</p> <p>Results</p> <p>Notably, MicroMerge v2 includes a new one-to-one alignment option that creates merged pedigree and locus files that can be handled by most genetic analysis software. Other features in MicroMerge v2 enhance the following aspects of control: 1) optimizing the algorithm for different merging scenarios, such as data sets with very different sample sizes or multiple data sets, 2) merging small data sets when a reliable set of allele frequencies are available, and 3) improving the quantity and 4) quality of merged data. We present results from simulated and real microsatellite genotype data sets, and conclude with an association analysis of three familial dyslipidemia (FD) study samples genotyped at different laboratories. Independent analysis of each FD data set did not yield consistent results, but analysis of the merged data sets identified strong association at locus D11S2002.</p> <p>Conclusion</p> <p>The MicroMerge v2 features will enable merging for a variety of genotype data sets, which in turn will facilitate meta-analyses for powering association analysis.</p

The ATF6-Met [67] Val substitution is associated with increased plasma cholesterol levels

Objective— Activating transcription factor 6 (ATF6) is a sensor of the endoplasmic reticulum stress response and regulates expression of several key lipogenic genes. We used a 2-stage design to investigate whether ATF6 polymorphisms are associated with lipids in subjects at increased risk for cardiovascular disease (CVD). Methods and Results— In stage 1, 13 tag-SNPs were tested for association in Dutch samples ascertained for familial combined hyperlipidemia (FCHL) or increased risk for CVD (CVR). In stage 2, we further investigated the SNP with the strongest association from stage 1, a Methionine/Valine substitution at amino-acid 67, in Finnish FCHL families and in subjects with CVR from METSIM, a Finnish population-based cohort. The combined analysis of both stages reached region-wide significance (P=9x10–4), but this association was not seen in the entire METSIM cohort. Our functional analysis demonstrated that Valine at position 67 augments ATF6 protein and its targets Grp78 and Grp94 as well as increases luciferase expression through Grp78 promoter. Conclusions— A common nonsynonymous variant in ATF6 increases ATF6 protein levels and is associated with cholesterol levels in subjects at increased risk for CVD, but this association was not seen in a population-based cohort. Further replication is needed to confirm the role of this variant in lipids. We report the association of the ATF6-methionine [67]valine amino-acid substitution with plasma cholesterol levels. Association analyses in 2674 subjects and functional data suggest that the ATF6 gene may influence cholesterol levels in subjects at increased risk to develop cardiovascular disease

A point-of-care test of active matrix metalloproteinase-8 predicts triggering receptor expressed on myeloid cells-1 levels in saliva

Background This cross-sectional study aims to investigate if a point-of-care (PoC) test of active matrix metalloproteinase-8 (aMMP-8) predicts levels of inflammation amplifier triggering receptor expressed on myeloid cells-1 (TREM-1) and its putative ligand the neutrophil peptidoglycan recognition protein 1 (PGLYRP1) in saliva. Methods Forty-seven adolescents, aged 15 to 17 years, were tested with aMMP-8 PoC test, which was followed by a full-mouth clinical examination of the assessment of periodontal, mucosal, and oral health. TREM-1 and PGLYRP1 levels were analyzed by ELISA. The immunofluorometric assay (IFMA) specific for aMMP-8 was used as the reference method. Results Fourteen saliva samples out of a total of 47 showed positivity for aMMP-8 PoC test. Both the TREM-1 and the aMMP-8 (IFMA) levels were significantly elevated among the aMMP-8 PoC test positives compared with the PoC test negatives (P = 4 mm was significantly lower among the adolescents that had a negative aMMP-8 PoC test result, and TREM-1 levels = 4 mm (P <0.001). Conclusion The present study validated usability of aMMP-8 PoC test for predicting "proinflammatory" salivary profile and periodontal health status in adolescents.Peer reviewe

Long-range chromosomal interactions increase and mark repressed gene expression during adipogenesis

Obesity perturbs central functions of human adipose tissue, centred on differentiation of preadipocytes to adipocytes, i.e., adipogenesis. The large environmental component of obesity makes it important to elucidate epigenetic regulatory factors impacting adipogenesis. Promoter Capture Hi-C (pCHi-C) has been used to identify chromosomal interactions between promoters and associated regulatory elements. However, long range interactions (LRIs) greater than 1 Mb are often filtered out of pCHi-C datasets, due to technical challenges and their low prevalence. To elucidate the unknown role of LRIs in adipogenesis, we investigated preadipocyte differentiation to adipocytes using pCHi-C and bulk and single nucleus RNA-seq data. We first show that LRIs are reproducible between biological replicates, and they increase >2-fold in frequency across adipogenesis. We further demonstrate that genomic loci containing LRIs are more epigenetically repressed than regions without LRIs, corresponding to lower gene expression in the LRI regions. Accordingly, as preadipocytes differentiate into adipocytes, LRI regions are more likely to contain repressed preadipocyte marker genes; whereas these same LRI regions are depleted of actively expressed adipocyte marker genes. Finally, we show that LRIs can be used to restrict multiple testing of the long-range cis-eQTL analysis to identify variants that regulate genes via LRIs. We exemplify this by identifying a putative long range cis regulatory mechanism at the LYPLAL1/TGFB2 obesity locus. In summary, we identify LRIs that mark repressed regions of the genome, and these interactions increase across adipogenesis, pinpointing developmental regions that need to be repressed in a cell-type specific way for adipogenesis to proceed.Peer reviewe

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005–2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a “systems genetics” approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.Peer reviewe