22 research outputs found
FPGA-accelerated machine learning inference as a service for particle physics computing
New heterogeneous computing paradigms on dedicated hardware with increased
parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting
solutions with large potential gains. The growing applications of machine
learning algorithms in particle physics for simulation, reconstruction, and
analysis are naturally deployed on such platforms. We demonstrate that the
acceleration of machine learning inference as a web service represents a
heterogeneous computing solution for particle physics experiments that
potentially requires minimal modification to the current computing model. As
examples, we retrain the ResNet-50 convolutional neural network to demonstrate
state-of-the-art performance for top quark jet tagging at the LHC and apply a
ResNet-50 model with transfer learning for neutrino event classification. Using
Project Brainwave by Microsoft to accelerate the ResNet-50 image classification
model, we achieve average inference times of 60 (10) milliseconds with our
experimental physics software framework using Brainwave as a cloud (edge or
on-premises) service, representing an improvement by a factor of approximately
30 (175) in model inference latency over traditional CPU inference in current
experimental hardware. A single FPGA service accessed by many CPUs achieves a
throughput of 600--700 inferences per second using an image batch of one,
comparable to large batch-size GPU throughput and significantly better than
small batch-size GPU throughput. Deployed as an edge or cloud service for the
particle physics computing model, coprocessor accelerators can have a higher
duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table
MRMer, an Interactive Open Source and Cross-platform System for Data Extraction and Visualization of Multiple Reaction Monitoring Experiments*S⃞
Multiple reaction monitoring (MRM) mass spectrometry identifies and quantifies specific peptides in a complex mixture with very high sensitivity and speed and thus has promise for the high throughput screening of clinical samples for candidate biomarkers. We have developed an interactive software platform, called MRMer, for managing highly complex MRM-MS experiments, including quantitative analyses using heavy/light isotopic peptide pairs. MRMer parses and extracts information from MS files encoded in the platform-independent mzXML data format. It extracts and infers precursor-product ion transition pairings, computes integrated ion intensities, and permits rapid visual curation for analyses exceeding 1000 precursor-product pairs. Results can be easily output for quantitative comparison of consecutive runs. Additionally MRMer incorporates features that permit the quantitative analysis experiments including heavy and light isotopic peptide pairs. MRMer is open source and provided under the Apache 2.0 license
Gene Expression Profiling of the Cellular Transcriptional Network Regulated by Alpha/Beta Interferon and Its Partial Attenuation by the Hepatitis C Virus Nonstructural 5A Protein
Alpha/beta interferons (IFN-α/β) induce potent antiviral and antiproliferative responses and are used to treat a wide range of human diseases, including chronic hepatitis C virus (HCV) infection. However, for reasons that remain poorly understood, many HCV isolates are resistant to IFN therapy. To better understand the nature of the cellular IFN response, we examined the effects of IFN treatment on global gene expression by using several types of human cells, including HeLa cells, liver cell lines, and primary fetal hepatocytes. In response to IFN, 50 of the approximately 4,600 genes examined were consistently induced in each of these cell types and another 60 were induced in a cell type-specific manner. A search for IFN-stimulated response elements (ISREs) in genomic DNA located upstream of IFN-stimulated genes revealed both previously identified and novel putative ISREs. To determine whether HCV can alter IFN-regulated gene expression, we performed microarray analyses on IFN-treated HeLa cells expressing the HCV nonstructural 5A (NS5A) protein and on IFN-treated Huh7 cells containing an HCV subgenomic replicon. NS5A partially blocked the IFN-mediated induction of 14 IFN-stimulated genes, an effect that may play a role in HCV resistance to IFN. This block may occur through repression of ISRE-mediated transcription, since NS5A also inhibited the IFN-mediated induction of a reporter gene driven from an ISRE-containing promoter. In contrast, the HCV replicon had very little effect on IFN-regulated gene expression. These differences highlight the importance of comparing results from multiple model systems when investigating complex phenomena such as the cellular response to IFN and viral mechanisms of IFN resistance
Identification and functional analysis of ‘hypothetical’ genes expressed in Haemophilus influenzae
The progress in genome sequencing has led to a rapid accumulation in GenBank submissions of uncharacterized ‘hypothetical’ genes. These genes, which have not been experimentally characterized and whose functions cannot be deduced from simple sequence comparisons alone, now comprise a significant fraction of the public databases. Expression analyses of Haemophilus influenzae cells using a combination of transcriptomic and proteomic approaches resulted in confident identification of 54 ‘hypothetical’ genes that were expressed in cells under normal growth conditions. In an attempt to understand the functions of these proteins, we used a variety of publicly available analysis tools. Close homologs in other species were detected for each of the 54 ‘hypothetical’ genes. For 16 of them, exact functional assignments could be found in one or more public databases. Additionally, we were able to suggest general functional characterization for 27 more genes (comprising ∼80% total). Findings from this analysis include the identification of a pyruvate-formate lyase-like operon, likely to be expressed not only in H.influenzae but also in several other bacteria. Further, we also observed three genes that are likely to participate in the transport and/or metabolism of sialic acid, an important component of the H.influenzae lipo-oligosaccharide. Accurate functional annotation of uncharacterized genes calls for an integrative approach, combining expression studies with extensive computational analysis and curation, followed by eventual experimental verification of the computational predictions
Integrative Analysis of N-Linked Human Glycoproteomic Data Sets Reveals PTPRF Ectodomain as a Novel Plasma Biomarker Candidate for Prostate Cancer
In an attempt to identify prostate cancer biomarkers
with greater diagnostic and prognostic capabilities, we have developed
an integrative proteomic discovery workflow focused on N-linked glycoproteins
that refines the target selection process. In this work, hydrazide-based
chemistry was used to identify N-linked glycopeptides from 22Rv1 prostate
cancer cells cultured in vitro, which were compared with glycopeptides
identified from explanted 22Rv1 murine tumor xenografts. One hundred
and four human glycoproteins were identified in the former analysis
and 75 in the latter, with 40 proteins overlapping between data sets.
Of the 40 overlapping proteins, 80% have multiple literature references
to the neoplastic process and ∼40% to <i>prostatic</i> neoplasms. These include a number of well-known prostate cancer-associated
biomarkers, such as prostate-specific membrane antigen (PSMA). By
integrating gene expression data and available literature, we identified
members of the overlap data set that deserve consideration as potential
prostate cancer biomarkers. Specifically, the identification of the
extracellular domain of protein tyrosine phosphatase receptor type
F (PTPRF) was of particular interest due to the direct involvement
of PTPRF in the control of β-catenin signaling, as well as dramatically
elevated gene expression levels in the prostate compared to other
tissues. In this investigation, we demonstrate that the PTPRF E-subunit
is more abundant in human prostate tumor tissue compared to normal
control and also detectable in murine plasma by immunoblot and ELISA.
Specifically, PTPRF distinguishes between animals xenografted with
the 22Rv1 cells and control animals as early as 14 days after implantation.
This result suggests that the ectodomain of PTPRF has the potential
to function as a novel plasma or tissue-based biomarker for prostate
cancer. The workflow described adds to the literature of potential
biomarker candidates for prostate cancer and demonstrates a pathway
to developing new diagnostic assays