NATIONAL DRIVER REGISTER & TRAFFIC RECORDS (NVS-422). National Sobriety Testing Resource Center and DRE Data system. National Sobriety Testing Resource Center & DRE Data System (hereatfer referred to as the DRE Data System), a NHTSA data system used to collect drug impaired driving evaluation and toxicology data from the system's end-users: Drug Recognition Experts (DREs) from across the nation. To ensure that law enforcement officers apply DEC procedures correctly and uniformly, officers must undergo IACP-approved training in how to conduct evaluations of suspects prior to being certified as a Drug Recognition Expert (DRE). The DRE Data System serves as a respository for the evualtion records created by certifies DREs.

Dataset

This dataset was created by Breno Couto

Contents

Drug Recognition Expert (DRE) officers utilize standardized evaluations to assess physiological and behavioral indicators of drug impairment. This study analyzed data from Maryland DRE officers (2017–2021), comparing their drug category/ies assessments with blood tests results. DRE evaluation records were linked to citations issued for alcohol/drug-impaired driving, to examine the agreement between charges, DRE evaluation, arrest outcomes, and repeat offenses. Data from 4,931 DRE evaluations were analyzed, involving 4,727 drivers linked to citation records for alcohol/drug-impaired driving offenses. Agreement between DRE opinions and blood test results was quantified by estimating binomial success probabilities with 95% confidence intervals. Citation outcomes and repeat offense rates for DRE and non-DRE cases were also presented. Of 4,931 unique evaluations, blood specimens were collected in 2,118 (42.9%), yielding 1,599 positive drug test results (75.5%). Most evaluated drivers were white (67.6%), male (73.8%), and aged 21–34 years (43.2%). Comparison of DRE opinion with blood test results revealed an overall success probability of 84.2 ± 0.65%. DRE accuracy improved to 91.8 ± 0.85% when none or one drug was detected and decreased to 80.5 ± 0.87% when two or more drugs were involved. When linked to citation data, 3,237 drivers (68.5%) received 36,878 citations, with 88.1% having two or more drug-related offenses and 72.5% having at least one negligent driving offense. Matched DRE drivers were involved in 9,105 traffic stops, with approximately 48.4% receiving over five citations during their first stop. 97.3% were cited for drug impairment, with only 87 drivers avoiding such citations. This study highlights the effectiveness of the DRE program in identifying impaired drivers, providing insights into driver demographics and impairment patterns, while emphasizing need for improved polysubstance impairment data collection. A high degree of agreement between DRE opinions and blood test results for all tested drug categories were statistically established. Despite the program’s success, significant gaps remain in testing methods and integrating alcohol and drug evaluations. Future research should enhance testing protocols, expanding data collection, and examining the link between substance use disorders and impaired driving to strengthen prevention, enforcement, and intervention efforts.

This is a step-by-step demonstration of how to browse NASA data services for land surface maps and time series data using the Data Rods Explorer (DRE) App [1]; followed by a step by step demonstration of how to compare a single model variable for a single location over multiple years. See the DRE User Guide [2] for complete description of this application.

References [1] Data Rods Explorer App [https://apps.hydroshare.org/apps/data-rods-explorer/] [2] DRE User Guide [https://github.com/gespinoza/datarodsexplorer/blob/master/docs/DREUserGuide.md]

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Urinary expressed prostatic secretion or “EPS-urine” is proximal tissue fluid that is collected after a digital rectal exam (DRE). EPS-urine is a rich source of prostate-derived proteins that can be used for biomarker discovery for prostate cancer (PCa) and other prostatic diseases. We previously conducted a comprehensive proteome analysis of direct expressed prostatic secretions (EPS). In the current study, we defined the proteome of EPS-urine employing Multidimensional Protein Identification Technology (MudPIT) and providing a comprehensive catalogue of this body fluid for future biomarker studies. We identified 1022 unique proteins in a heterogeneous cohort of 11 EPS-urines derived from biopsy negative noncancer diagnoses with some benign prostatic diseases (BPH) and low-grade PCa, representative of secreted prostate and immune system-derived proteins in a urine background. We further applied MudPIT-based proteomics to generate and compare the differential proteome from a subset of pooled urines (pre-DRE) and EPS-urines (post-DRE) from noncancer and PCa patients. The direct proteomic comparison of these highly controlled patient sample pools enabled us to define a list of prostate-enriched proteins detectable in EPS-urine and distinguishable from a complex urine protein background. A combinatorial analysis of both proteomics data sets and systematic integration with publicly available proteomics data of related body fluids, human tissue transcriptomic data, and immunohistochemistry images from the Human Protein Atlas database allowed us to demarcate a robust panel of 49 prostate-derived proteins in EPS-urine. Finally, we validated the expression of seven of these proteins using Western blotting, supporting the likelihood that they originate from the prostate. The definition of these prostatic proteins in EPS-urine samples provides a reference for future investigations for prostatic-disease biomarker studies.

Protein-Protein, Genetic, and Chemical Interactions for DRE-1 (Caenorhabditis elegans) curated by BioGRID (https://thebiogrid.org); DEFINITION: Protein DRE-1

Dre Medical Equipment Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The Data Rods Explorer (DRE) is a web client app that enables users to browse several NASA-hosted data sets. The interface enables visualization and download of NASA observation retrievals and land surface model (LSM) outputs by variable, space and time. The key variables are precipitation, wind, temperature, surface downward radiation flux, heat flux, humidity, soil moisture, groundwater, runoff, and evapotranspiration. These variables describe the main components of the water cycle over land masses.

These data are diversity responses to three treatments over four years of vegetation sampling. Note SLA and height were only measured in 2014.

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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.

Should You Buy, Sell, or Hold? (DRE 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

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.

DRE DREADNOUGHT RESOURCES LTD

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

The aryl hydrocarbon receptor (AhR) mediates responses elicited by 2,3,7,8-tetrachlorodibenzo-p-dioxin by binding to dioxin response elements (DRE) containing the core consensus sequence 5′-GCGTG-3′. The human, mouse, and rat genomes were computationally searched for all DRE cores. Each core was then extended by 7 bp upstream and downstream, and matrix similarity (MS) scores for the resulting 19 bp DRE sequences were calculated using a revised position weight matrix constructed from bona fide functional DREs. In total, 72318 human, 70720 mouse, and 88651 rat high-scoring (MS ≥ 0.8437) putative DREs were identified. Gene-encoding intragenic DNA regions had ∼1.6 times more putative DREs than the noncoding intergenic DNA regions. Furthermore, the promoter region spanning ±1.5 kb of a TSS had the highest density of putative DREs within the genome. Chromosomal analysis found that the putative DRE densities of chromosomes X and Y were significantly lower than the mean chromosomal density. Interestingly, the 10 kb upstream promoter region on chromosome X of the genomes were significantly less dense than the chromosomal mean, while the same region in chromosome Y was the most dense. In addition to providing a detailed genomic map of all DRE cores in the human, mouse, and rat genomes, these data will further aid the elucidation of AhR-mediated signal transduction.

Historical ownership data of DRE by DEUTSCHE BANK AG

Protein-Protein, Genetic, and Chemical Interactions for Fielenbach N (2007):DRE-1: an evolutionarily conserved F box protein that regulates C. elegans developmental age. curated by BioGRID (https://thebiogrid.org); ABSTRACT: During metazoan development, cells acquire both positional and temporal identities. The Caenorhabditis elegans heterochronic loci are global regulators of larval temporal fates. Most encode conserved transcriptional and translational factors, which affect stage-appropriate programs in various tissues. Here, we describe dre-1, a heterochronic gene, whose mutant phenotypes include precocious terminal differentiation of epidermal stem cells and altered temporal patterning of gonadal outgrowth. Genetic interactions with other heterochronic loci place dre-1 in the larval-to-adult switch. dre-1 encodes a highly conserved F box protein, suggesting a role in an SCF ubiquitin ligase complex. Accordingly, RNAi knockdown of the C. elegans SKP1-like homolog SKR-1, the cullin CUL-1, and ring finger RBX homologs yielded similar heterochronic phenotypes. DRE-1 and SKR-1 form a complex, as do the human orthologs, hFBXO11 and SKP1, revealing a phyletically ancient interaction. The identification of core components involved in SCF-mediated modification and/or proteolysis suggests an important level of regulation in the heterochronic hierarchy.

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Protein-Protein, Genetic, and Chemical Interactions for Horn M (2014):DRE-1/FBXO11-Dependent Degradation of BLMP-1/BLIMP-1 Governs C. elegans Developmental Timing and Maturation. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Developmental timing genes catalyze stem cell progression and animal maturation programs across taxa. Caenorhabditis elegans DRE-1/FBXO11 functions in an SCF E3-ubiquitin ligase complex to regulate the transition to adult programs, but its cognate proteolytic substrates are unknown. Here, we identify the conserved transcription factor BLMP-1 as a substrate of the SCF(DRE-1/FBXO11) complex. blmp-1 deletion suppressed dre-1 mutant phenotypes and exhibited developmental timing defects opposite to dre-1. blmp-1 also opposed dre-1 for other life history traits, including entry into the dauer diapause and longevity. BLMP-1 protein was strikingly elevated upon dre-1 depletion and dysregulated in a stage- and tissue-specific manner. The role of DRE-1 in regulating BLMP-1 stability is evolutionary conserved, as we observed direct protein interaction and degradation function for worm and human counterparts. Taken together, posttranslational regulation of BLMP-1/BLIMP-1 by DRE-1/FBXO11 coordinates C. elegans developmental timing and other life history traits, suggesting that this two-protein module mediates metazoan maturation processes.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.