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Owen Nelson
Owen Nelson

The Interview V1.0 !!INSTALL!!



We reviewed the literature and data from a previous qualitative study of FCS to identify key FCS symptoms and impacts, which were mapped to PROMIS domains to create a pool of eligible items. Candidate items were reduced per expert feedback and patients with FCS completed cognitive interviews to confirm content validity and measure content.




The Interview v1.0


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First, two Northwestern University (NU) researchers with extensive experience with PROMIS and PROM development independently reviewed concept elicitation interview transcripts and results from a previous study of FCS quality of life conducted with ten individuals with FCS in the United States (data on file, Ionis) [18]. The interviews consisted of open-ended questions about FCS symptoms, symptom frequency and severity, and impacts of FCS on daily life. The researchers met to discuss their impressions of the most important symptoms and impacts represented in the data and study report, and created a preliminary list of key symptoms and impacts for FCS.


Interviewers first collected sociodemographic and key disease information from the participant. Next, participants completed the draft measure and the interviewer led them through a series of questions about the measure, using a semi-structured cognitive interview guide based on the work of Willis [20] to ascertain comprehension of the measure items and the response processes. Specifically, the interviewer asked participants to: (1) describe how they arrived at their answer; (2) restate each item in their own words; (3) discuss the clarity of the item; (4) describe any questions they had about the item; and (5) indicate whether the question was relevant to their experience. Participants received a $100 USD electronic gift card for participating. Trained interviewers took detailed field notes, and interviews were audiotaped to ensure comprehensive capture of all relevant information. Cognitive interview recordings were transcribed and transcripts were de-identified. Transcripts were used to confirm field notes and to provide supporting quotations.


We identified nine key published articles about patient-reported symptoms and HRQOL in the context of FCS [3,4,5,6,7,8,9,10,11]. In the prior qualitative study by Davidson and colleagues, FCS interview participants reported 16 symptoms of FCS [18]. Of these, the most prevalent/important symptoms were abdominal pain, diarrhea, brain fog, and fatigue. Commonly reported symptoms per the key literature were abdominal pain, bloating, fatigue [5, 7]. Emotional, social and cognitive impacts of FCS in the literature included anxiety, cognitive difficulties, and work and social limitations [5, 7]. Based upon our review of the key literature and the Davidson article and data, we identified ten important FCS symptoms and 12 impacts, for a total of 22 key concepts (Table 1). The following symptoms, which were mentioned by patients in the prior qualitative study, were excluded from our list of the most import symptoms: blurred vision, poor appetite, difficulty concentrating, weight loss, indigestion, muscle weakness. The 12 impacts shown in Table 1 expand upon the findings by Davidson by, for example, detailing specific impacts related to mental and emotional well-being and adding the concept of sleep disruption [6].


Plot: You are Adam. Adam wanted a job, so he searched for one. He found one and today is his interview. He was curious about it, but he was even more curious about that red little box at the end of the hallway.


A brief moment in time. Two young girls get offered a lifetime opportunity to model for a famous clothing line.A lot is at stake for them, and it all depends on the outcome of an interview with one of the top executives of the advertisement agency.Just how far will they go to score these jobs? Will their boyfriends be able to rescue them should the girls need it?


HRS added COVID-19-related questions to the 2020 core interview and to the psychosocial self-administered questionnaire (pages 36-43), and a special midterm data release is now available. In addition, a new Contextual Data Resource on state-level COVID-19 policies has been added as part of the HRS restricted data products. A supplementary 2021 COVID-19 Mailout Survey is currently in the field through August 2021.


ELSI-Brazil obtained an extra grant from the Ministry of Health to conduct telephone interviews about COVID 19. This short interview included questions about the diagnosis of the disease, carrying out confirmatory tests, adopting preventive measures, using health services and a few questions about mental health. The first series of telephone interviews was held between May 26 and June 8 with approximately 6000 participants of ELSI. Two new interviews are scheduled to take place in July and August.


Conclusions: The PROMIS Profile v1.0-familial chylomicronemia syndrome (FCS) 28 provides strong content validity for assessing quality of life among patients with FCS. The benefits of PROMIS, including norm-referenced mean values for each measure, will facilitate comparison of patients with FCS to other clinical populations.


In January 2009, Steven Davis was chopping up meat at a local grocery store in Seattle, WA. A month later, he and his wife were living in Phoenix, AZ, where he began study to be a luthier at Roberto-Venn. In 2011, Steve built a guitar for Meshuggah guitarist Mårten Hagström (which may be seen on the DVD of their latest release, Koloss). And today, Steve is a full-time teacher at Roberto-Venn and builds custom guitars, as well. Check out the interview with Steve below, then check out his custom work at EIR Guitars. Thanks for taking time out of your busy schedule to answer some questions, Steve!


We derive Exit and Post-Exit variables from the Tracker file variable xIWTYPE (Interview Type), where x=Survey Year letter. The EXIT1 variable contains the year the Exit Interview was conducted. For example, if you merge all Respondents in 1992 with the RAND HRS Exit/Post-Exit Finder File by HHIDPN, and they have missing values for the EXIT1 variable, these Respondents do not have an Exit interview.


This paper describes JATS2RDF v1.0 ( ), a mapping of the principle metadata components of the ANSI/NISO JATS Journal Publishing Tag Library Version 1.0 from XML to RDF. Our mapping uses the SPAR ontologies, together with elements from other well-known vocabularies such as the Dublin Core Metadata Initiative (DCMI) Metadata Terms and the Friend of a Friend (FOAF) Vocabulary. By means of an Extensible Stylesheet Language Transformation (XSLT) transform that we have also created ( ), this JATS2RDF mapping now permit the JATS metadata elements and their attributes, from documents marked up in XML using the NISO-JATS Journal Publishing Tag Library v1.0, to be converted automatically to RDF, enabling this information to be published to the Semantic Web as linked open data in a manner that is unambiguous and universally understood.


Thus the JATS element may be used to describe an XML representation of a research article, or an XML representation of many other kinds of journal content, such as an editorial, obituary, list of events, book review, puzzle, game, quiz, interview or photo-essay, depending upon the meaning an individual publisher chooses for this tag element. This goes beyond what the average publisher or average person means by "journal article."


In this section of the paper, we describe the mapping decisions we made, in those cases where these were not obvious. Many of these decisions are also noted as footnotes in the JATS2RDF v1.0 mapping document ( ).


According to the suggestions made by the professor during the interview, we plan to make the following improvements in the future: First, for the optical promoter itself, reduce its requirements on light and increase the practicability of the product. Second, if in the future will be put into large-scale production, the problems encountered in the production should be timely improved in the laboratory.


Considering the particularity of the research objects of our project (patchouli and patchouli alcohol), the team decided to interview some practitioners of traditional Chinese medicine working in the frontline in the community clinic, and further discuss the current situation of the use of patchouli and patchouli alcohol and the social value of our project.


As a small team, insights were shared minutes after interviews were done. Our questions were broad and our approach was designed to be convenient enough to make simple changes, not to uncover deeper, unmet needs.


Participants were asked to share their screens (hacked together using Google Meet's screen sharing feature) and talk/think-out-loud as they moved about the v1.0 Perspectives page. We had participants interact with the desktop and mobile versions of the site.


It was also much easier to get interrupted with notifications when reading on mobile (something that happened in many of our interviews), making it even less likely that someone would read all of the content.


MLPerf is an industry-wide AI consortium tasked with developing a suite of performance benchmarks that cover a range of leading AI workloads widely in use. The latest MLPerf v1.0 training round includes vision, language and recommender systems, and reinforcement learning tasks. It is continually evolving to reflect the state-of-the-art AI applications.


NVIDIA submitted MLPerf v1.0 training results for all eight benchmarks, as is our tradition. In fact, systems built upon the NVIDIA AI platform are the only commercially available systems to make submissions across the board.


Compared to the v0.7 submission where we used a batch size of 2048, the v1.0 batch size was 3072, which required 22% fewer iterations. Because the larger iteration was only 20% slower, the result was an 8% faster time to convergence.


For scoring, we used nv-cocoapi, which is a C++ implementation of cocoapi and 60x times faster. For v1.0, we improved the nv-cocoapi performance by 2x with multithreaded results accumulation, faster indices sorting, and caching the ground truth data structures. 041b061a72


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