These data are from a human study collected under IRB protocol: ClinicalTrials.gov # NCT01874834. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: These data are from a human study collected under IRB protocol: ClinicalTrials.gov # NCT01874834. As such, it is a violation of Federal Law to publish them. Format: These data are from a human study collected under IRB protocol: ClinicalTrials.gov # NCT01874834. This dataset is associated with the following publication: Stiegel, M., J. Pleil, J. Sobus, T. Stevens, and M. Madden. Linking physiological parameters to perturbations in the human exposome: Environmental exposures modify blood pressure and lung function via inflammatory cytokine pathway. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH - PART A: CURRENT ISSUES. Taylor & Francis, Inc., Philadelphia, PA, USA, 80(9): 485-501, (2017).

BackgroundBeyond causing significant morbidity and cost, musculoskeletal injuries (MSKI) are among the most common reasons for primary care visits. A validated injury risk assessment tool for MSKI is conspicuously absent from current care. While motion capture (MC) systems are the current gold standard for assessing human motion, their disadvantages include large size, non-portability, high cost, and limited spatial resolution. As an alternative we introduce the Micro Doppler Radar (MDR); in contrast with MC, it is small, portable, inexpensive, and has superior spatial resolution capabilities. While Phase 1 testing has confirmed that MDR can identify individuals at high risk for MSKI, Phase 2 testing is still needed. Our aims are to 1) Use MDR technology and MC to identify individuals at high-risk for MSKI 2) Evaluate whether MDR has diagnostic accuracy superior to MC 3) Develop MDR algorithms that enhance accuracy and enable automation.Methods and findingsA case control study will compare the movement patterns of 125 ACL reconstruction patients to 125 healthy controls. This study was reviewed and approved by the Pennsylvania State University Human Research Protection Program (HRPP) on May 18, 2022, and the IRB approval number is STUDY00020118. The ACL group is used as a model for a “high risk” population as up to 24% will have a repeat surgery within 2 years. An 8-camera Motion Analysis MC system with Cortex 8 software to collect MC data. Components for the radar technology will be purchased, assembled, and packaged. A micro-doppler signature projection algorithm will determine correct classification of ACL versus healthy control. Our previously tested algorithm for processing the MDR data will be used to identify the two groups. Discrimination, sensitivity and specificity will be calculated to compare the accuracy of MDR to MC in identifying the two groups.ConclusionsWe describe the rationale and methodology of a case-control study using novel MDR technology to detect individuals at high-risk for MSKI. We expect this novel approach to exhibit superior accuracy than the current gold standard. Future translational studies will determine utility in the context of clinical primary care.

The Virtual Clinical Trials (VCT) market is experiencing rapid growth, driven by the increasing need for efficient, cost-effective, and accessible clinical research. Market segmentation reveals key trends across study design, therapeutic indication, and technological advancements. By study design, the market is categorized into interventional, observational, and expanded access trials. While interventional trials currently dominate due to their efficacy and safety assessment capabilities, the demand for more agile and flexible trial designs is boosting the growth of observational studies and expanded access programs. This is particularly true in the realm of real-world data collection and analysis.In terms of therapeutic indications, oncology and cardiovascular disease maintain the largest market share, reflecting the high prevalence of these conditions and the ongoing pursuit of novel treatment modalities. Within oncology, solid tumors and hematological malignancies represent key therapeutic areas, while the cardiovascular segment encompasses coronary artery disease, heart failure, and arrhythmias. Beyond these major segments, significant growth potential exists within neurology, infectious diseases, rare diseases, and respiratory disorders, fueled by unmet medical needs and technological advancements enabling efficient patient recruitment and data acquisition in these complex therapeutic areas.Industry collaborations are significantly impacting market dynamics. Strategic partnerships between pharmaceutical companies, technology providers, and Contract Research Organizations (CROs) are fostering innovation, integrating advanced technologies, and expanding the capabilities of VCTs. The adoption of artificial intelligence (AI) for data analysis, wearable sensors for continuous patient monitoring, and remote patient engagement platforms are streamlining trial processes, reducing costs, and improving patient adherence and overall trial efficiency. This increased efficiency leads to faster time-to-market for new therapies and ultimately, improved patient outcomes. Recent developments include: In July 2023, Signant Health completed the acquisition of DSG, strategically augmenting its eClinical solution suite for both traditional and decentralized clinical trials. By integrating DSG's unified platform, the acquisition facilitated the development of a comprehensive trial ecosystem equipped with advanced software, analytics, and logistics solutions, enabling seamless study conduct and data generation across all modalities, thereby accomplishing the goal of fully digitalizing clinical trials. , In June 2023, Medable Inc. unveiled a comprehensive toolkit tailored for Institutional Review Boards (IRBs)/Ethics Committees (ECs), designed to establish standardized ethics review procedures for decentralized clinical trials (DCTs). The implementation of this toolkit successfully simplified, streamlined, and accelerated the IRB/EC process, playing a pivotal role in fostering enhanced efficiency and patient-centeredness in the execution of DCTs. , In October 2022, Oracle and ObvioHealth entered into a strategic collaboration to integrate diverse data sets into virtual/decentralized clinical trials in the Asia Pacific region. This initiative is expected to allow the quick collection, integration and analysis of multi-source data collected from labs, devices, patients, and sites. .

The interview data was gathered for a project that investigated the practices of instructors who use quantitative data to teach undergraduate courses within the Social Sciences. The study was undertaken by employees of the University of California, Santa Barbara (UCSB) Library, who participated in this research project with 19 other colleges and universities across the U.S. under the direction of Ithaka S+R. Ithaka S+R is a New York-based research organization, which, among other goals, seeks to develop strategies, services, and products to meet evolving academic trends to support faculty and students.

The field of Social Sciences has been notoriously known for valuing the contextual component of data and increasingly entertaining more quantitative and computational approaches to research in response to the prevalence of data literacy skills needed to navigate both personal and professional contexts. Thus, this study becomes particularly timely to identify current instructors’ practi..., The project followed a qualitative and exploratory approach to understand current practices of faculty teaching with data. The study was IRB approved and was exempt by the UCSB’s Office of Research in July 2020 (Protocol 1-20-0491).Â

The identification and recruitment of potential participants took into account the selection criteria pre-established by Ithaka S+R: a) instructors of courses within the Social Sciences, considering the field as broadly defined, and making the best judgment in cases the discipline intersects with other fields; b) instructors who teach undergraduate courses or courses where most of the students are at the undergraduate level; c) instructors of any rank, including adjuncts and graduate students; as long as they were listed as instructors of record of the selected courses; d) instructors who teach courses were students engage with quantitative/computational data.Â

The sampling process followed a combination of strategies to more easily identify instructo..., The data folder contains 10Â pdf files with de-identified transcriptions of the interviews and the pdf files with the recruitment email and the interview guide.Â

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How Do You Pick a Stock? (NSE IRB Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data