Paragon Genomics (United States)

Data discovery refers to a set of tasks that enable users and downstream applications to explore and gain insights from massive collections of data sources such as data lakes. In this tutorial, we will provide a comprehensive overview of the most recent table discovery techniques developed by the data management community. We will cover table understanding tasks such as domain discovery, table annotation, and table representation learning which help data lake systems capture semantics of tables. We will also cover techniques enabling various query-driven discovery and table exploration tasks, as well as how table discovery can support key data science applications such as machine learning and knowledge base construction. Finally, we will discuss future research directions on developing new table discovery paradigms by combining structured knowledge and dense table representations, as well as improving the efficiency of discovery using state-of-the-art indexing techniques, and more.

Despite an appetite among UK veterinarians (vets) and farmers to improve calf health, vets face challenges in delivering and sustaining proactive calf health services. Forty-six vets and 10 veterinary technicians (techs) participated in a project to determine what makes calf health services successful while improving their own services. In four facilitated workshops and two seminars, carried out between August 2021 and April 2022, participants described their approaches to calf work, discussed measures of success, identified challenges and success factors, and addressed knowledge gaps. Many approaches to calf health services were described, and these could be categorised into three overlapping models. Success involved enthusiastic, knowledgeable vets/techs, supported by their practice team, fostering positive attitudes among farmers by providing the services they need, creating a tangible return on investment for farmers and the practice. Lack of time was identified as the most prominent challenge to achieving success. Participants were self-selected from one nationwide group of practices. Successful calf health services depend on identifying the needs of calves, farmers and veterinary practices, and delivering measurable benefits to each. More calf health services embedded as a core part of farm veterinary practice could bring wide ranging benefits to calves, farmers and vets.

The emergence of novel hardware accelerators has powered the tremendous growth of machine learning in recent years. These accelerators deliver incomparable performance gains in processing high-volume matrix operators, particularly matrix multiplication, a core component of neural network training and inference. In this work, we explored opportunities of accelerating database systems using NVIDIA's Tensor Core Units (TCUs). We present TCUDB, a TCU-accelerated query engine processing a set of query operators including natural joins and group-by aggregates as matrix operators within TCUs. Matrix multiplication was considered inefficient in the past; however, this strategy has remained largely unexplored in conventional GPU-based databases, which primarily rely on vector or scalar processing. We demonstrate the significant performance gain of TCUDB in a range of real-world applications including entity matching, graph query processing, and matrix-based data analytics. TCUDB achieves up to 288x speedup compared to a baseline GPU-based query engine.

Entity Matching (EM) refers to the problem of determining whether two different data representations refer to the same real-world entity. It has been a long-standing interest of the data management community and many efforts have been paid in creating benchmark tasks as well as in developing advanced matching techniques. However, existing benchmark tasks for EM are limited to the case where the two data collections of entities are structured tables with the same schema. Meanwhile, the data collections for matching could be structured, semi-structured, or unstructured in real-world scenarios of data science. In this paper, we come up with a new research problem -- Generalized Entity Matching to satisfy this requirement and create a benchmark Machamp for it. Machamp consists of seven tasks having diverse characteristics and thus provides good coverage of use cases in real applications. We summarize existing EM benchmark tasks for structured tables and conduct a series of processing and cleaning efforts to transform them into matching tasks between tables with different structures. Based on that, we further conduct comprehensive profiling of the proposed benchmark tasks and evaluate popular entity matching approaches on them. With the help of Machamp, it is the first time that researchers can evaluate EM techniques between data collections with different structures.

Creating and collecting labeled data is one of the major bottlenecks in machine learning pipelines and the emergence of automated feature generation techniques such as deep learning, which typically requires a lot of training data, has further exacerbated the problem. While weak-supervision techniques have circumvented this bottleneck, existing frameworks either require users to write a set of diverse, high-quality rules to label data (e.g., Snorkel), or require a labeled subset of the data to automatically mine rules (e.g., Snuba). The process of manually writing rules can be tedious and time consuming. At the same time, creating a labeled subset of the data can be costly and even infeasible in imbalanced settings.

To further characterize the cardiac safety of pitolisant in adults with narcolepsy.

Abstract Many detection methods have been used or reported for the diagnosis and/or surveillance of COVID-19. Among them, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used because of its high sensitivity, typically claiming detection of about 5 copies of viruses. However, it has been reported that only 47-59% of the positive cases were identified by some RT-PCR methods, probably due to low viral load, timing of sampling, degradation of virus RNA in the sampling process, or possible mutations spanning the primer binding sites. Therefore, alternative and highly sensitive methods are imperative. With the goal of improving sensitivity and accommodating various application settings, we developed a multiplex-PCR-based method comprising 343 pairs of specific primers and demonstrated its efficiency at detecting SARS-CoV-2 at low copy numbers. The assay produced clean characteristic target peaks of defined sizes, which allowed for direct identification of positives by electrophoresis. We further amplified the entire SARS-CoV-2 genome from 8 to half a million viral copies purified from 13 COVID-19 positive specimens and detected mutations through next-generation sequencing. Finally, we developed a multiplex-PCR-based metagenomic method in parallel that required modest sequencing depth for uncovering SARS-CoV-2 mutational diversity and potentially novel or emerging isolates. Citation Format: Chenyu Li, David N. Debruyne, Julia Spencer, Vidushi Kapoor, Lily Y. Liu, Bo Zhou, Utsav Pandey, Moiz Bootwalla, Dejerianne Ostrow, Dennis T. Maglinte, David Ruble, Alex Ryutov, Lishuang Shen, Lucie Lee, Rounak Feigelman, Grayson Burdon, Jeffrey Liu, Alejandra Oliva, Adam Borcherding, Hongdong Tan, Alexander E. Urban, Xiaowu Gai, Jennifer Dien Bard, Guoying Liu, Zhitong Liu. Highly sensitive and full-genome interrogation of SARS-CoV-2 using multiplexed PCR enrichment followed by next-generation sequencing [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S08-01.

Abstract Purpose: Blood-based candidate biomarkers of disease can be monitored by analyzing CTCs and/or circulating cfDNA isolated from the peripheral blood. Our primary objective is to understand the relative contributions of these circulating factors (i.e., CTCs and cfDNA) to the overall disease profile in MBC. Methods: Clinically archived FFPE tumor tissue and prospective blood samples are collected through a minimal risk protocol approved by the Mayo Clinic IRB (#16-001540) from patients with MBC and objective evidence of disease progression. Blood samples include 20 mL whole blood in Streck blood collection tubes (BCTs) for platelet poor plasma (PPP); 20 mL whole blood in AccuCyte BCTs for CTCs, WBCs, and PPP; and 10 mL whole blood in EDTA BCTs for PPP. Nucleated, EpCAM+/cytokeratin+/CD45- CTCs are identified, assessed for ER/HER2 status, and isolated using a centrifugation and direct imaging platform that allows for single cell retrieval (RareCyte). DNA is extracted from PPP, CTCs, WBCs, and FFPE tumor tissue using established methods. Targeted sequencing for SNVs/indels is performed on paired WBCs and CTC-DNA, AccuCyte-cfDNA, Streck-cfDNA, EDTA-cfDNA, and tumor tissue derived DNA using the same NGS panel and informatics pipeline (65 genes; CleanPlex OncoZoom; Paragon Genomics). Results: Tissue and blood samples were collected from 40 patients with metastatic breast cancer. 10 cases were selected for initial analyses on the basis of CTC yield (range 3-113 per 3.75 mL blood); up to 5 CTCs per subject were isolated and pooled for DNA extraction. Plasma cfDNA yields and variant allele frequencies were highly comparable between AccuCyte and Streck collected blood samples. Mutations (range 1-3) were identified in CTC-DNA and/or cfDNA in 9 of 10 cases for a total of 18 detected mutations: 10 in CTC-DNA and cfDNA (BRCA2 N372H, PIK3CA E542K, PIK3CA E545K (x3), PIK3CA H1047R, PTEN R130P, RET G691S, TP53 C135W, TP53 Q192*); 5 in CTC-DNA only (EGFR R521K, EGFR T790M, PIK3CA H1047R, SMAD4 C363Y, TP53 N263D); and 3 in cfDNA only (DNMT3A W893S, DNMT3A S714C, TP53 Q136E). Parallel analyses of samples from 10 more subjects are in progress. Analysis of tumor tissue for all 20 subjects and of EDTA-cfDNA and single CTCs for a subset of cases is ongoing. Updated results will be presented at the meeting. Conclusions: It is feasible to isolate high quality CTCs and cfDNA from the same blood collection tube to perform targeted sequencing; this streamlines specimen processing, decreases overall costs, and minimizes required blood volumes. Importantly, there is overlap in the majority of mutations identified in CTC-DNA and cfDNA, but actionable mutations (e.g., PIK3CA, EGFR) were detected in CTC-DNA only. The clinical and theranostic relevance of these findings is unclear and warrants further investigation. Citation Format: Minetta C. Liu, Karthik V. Giridhar, Roberto A. Leon Ferre, Jamie L. Carroll, Matthew P. Goetz, Tufia C. Haddad, Deanne R. Smith, Siddhartha Yadav, Vidushi Kapoor, Guoying Liu, Tad George, Nolan Ericson, Arturo B. Ramirez, Eric Kaldjian, Keegan E. Haselkorn. Comparison of circulating tumor cell (CTC) derived DNA and circulating cell-free DNA (cfDNA) from simultaneous blood sampling of patients with metastatic breast cancer (MBC) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3119.