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Organism Database (FASTA file) programs, including PRISMSeq Uploader and the FastaFileMaker console application. This repository includes several software utilities for working with Protein Collections stored in PRISM, the Proteomics Research Information and Management System.
Abstract Bulk ice-microphysical models parameterize the dynamic evolution of ice particles from advection, collection, and sedimentation through a cloud layer to the surface. Frozen hydrometeors can grow to acquire a multitude of shapes and sizes, which influence the distribution of mass within cloud systems. Aggregates, defined herein as the collection of ice particles, have a variety of formations based on initial ice particle size, shape, falling orientation, and the number of particles that collect. This work focuses on using the Ice Particle and Aggregate Simulator (IPAS) as a statistical tool to repetitively collect ice crystals of identical properties to derive bulk aggregate characteristics. A database of 9 744 000 aggregates is generated with resulting properties analyzed. After 150 single ice crystals (monomers) collect, the most extreme aggregate aspect ratio calculations asymptote toward and ϕ ca ≈ 0.50 for aggregates composed of quasi-horizontally oriented and randomly oriented monomers, respectively. The results presented are largely consistent with both a previous theoretical study and estimates derived from ground-based observations from two different geographic locations. Particle falling orientation highly influences newly formed aggregate aspect ratios from the collection of particles with extreme aspect ratios; quasi-horizontally oriented particles can produce aggregate aspect ratios an order of magnitude more extreme than randomly oriented particles but can also produce near-spherical aggregates as the number of monomers comprising the aggregate reach approximately 100. Finally, a majority of collections result in aggregates that are closer to prolate than oblate spheroids.
Protein kinases catalyze the transfer of a phosphate moiety from a phosphate donor to the substrate molecule, thus playing critical roles in cell signaling and metabolism. Although plant genomes contain more than 1000 genes that encode kinases, knowledge is limited about the function of each of these kinases. A major obstacle that hinders progress towards kinase characterization is functional redundancy. To address this challenge, we previously developed the rice kinase database (RKD) that integrated omics-scale data within a phylogenetics context.
Background: Current treatment guidelines for male breast cancer are predominantly guided by female-only clinical trials. With scarce research, it is unclear whether breast-conserving therapy (BCT) is equivalent to mastectomy in men. We sought to compare overall survival (OS) among male breast cancer patients who underwent BCT versus mastectomy. Methods: We performed a retrospective analysis of 8445 stage I–II (T1–2 N0–1 M0) male breast cancer patients from the National Cancer Database (2004–2014). Patients were grouped according to surgical and radiation therapy (RT). BCT was defined as partial mastectomy followed by RT. Multivariable and inverse probability of treatment-weighted (IPTW) Cox proportional hazards models were used to compare OS between treatment groups, controlling for demographic and clinicopathologic characteristics. Results: Most patients underwent total mastectomy (61.2%), whereas 18.2% underwent BCT, 12.4% underwent total mastectomy with RT, and 8.2% underwent partial mastectomy alone. In multivariable and IPTW models, partial mastectomy alone, total mastectomy alone, and total mastectomy with RT were associated with worse OS compared with BCT (p < 0.001 all). Ten-year OS was 73.8% for BCT and 56.3, 58.0 and 56.3% for other treatment approaches. Older age, higher T/N stage, histological grade, and triple-negative receptor status were associated with poorer OS (p < 0.05). Subgroup analysis by stage demonstrated similar results. Conclusions: In this national sample of male breast cancer patients, BCT was associated with greater survival. The underlying mechanisms of this association warrant further study, because more routine adoption of BCT in male breast cancer appears to translate into clinically meaningful improvements in survival.
Objective: The aim of this study was to evaluate socioeconomic discrepancies in current treatment approaches and survival trends among patients with intrahepatic cholangiocarcinoma (ICC). Methods: The 2004–2015 National Cancer Database was retrospectively analyzed for histopathologically proven ICC. Treatment predictors were evaluated using multinomial logistic regression and overall survival via multivariable Cox models. Results: Overall, 12,837 ICC patients were included. Multiple factors influenced treatment allocation, including age, education, comorbidities, cancer stage, grade, treatment center, and US state region (multivariable p < 0.05). The highest surgery rates were observed in the Middle Atlantic (28.7%) and lowest rates were observed in the Mountain States (18.4%). Decreased ICC treatment likelihood was observed for male African Americans with Medicaid insurance and those with low income (multivariable p < 0.05). Socioeconomic treatment discrepancies translated into decreased overall survival for patients of male sex (vs. female; hazard ratio [HR] 1.21, 95% confidence interval [CI] 1.16–1.26, p < 0.001), with low income (< $37,999 vs. ≥ $63,000 annually; HR 1.07, 95% CI 1.01–1.14, p = 0.032), and with Medicaid insurance (vs. private insurance; HR 1.13, 95% CI 1.04–1.23, p = 0.006). Both surgical and non-surgical ICC management showed increased survival compared with no treatment, with the longest survival for surgery (5-year overall survival for surgery, 33.5%; interventional oncology, 11.8%; radiation oncology/chemotherapy, 4.4%; no treatment, 3.3%). Among non-surgically treated patients, interventional oncology yielded the longest survival versus radiation oncology/chemotherapy (HR 0.73, 95% CI 0.65–0.82, p < 0.001). Conclusions: ICC treatment allocation and outcome demonstrated a marked variation depending on socioeconomic status, demography, cancer factors, and US geography. Healthcare providers should address these discrepancies by providing surgery and interventional oncology as first-line treatment to all eligible patients, with special attention to the vulnerable populations identified in this study.
Background: The American College of Surgeons Oncology Group (ACOSOG) Z1071 and Sentinel Neoadjuvant (SENTINA) trials of sentinel node biopsy for node-positive breast cancer treated with neoadjuvant chemotherapy (NAC) demonstrated false-negative rates that varied on the basis of surgical technique. This study evaluated trends in axillary operations before and after publication of these trials. Methods: This study analyzed patients from National Cancer Database (NCDB) with clinical T0 through T4, N1 and N2, M0 breast cancer who received NAC from 1 January 2012 to 31 December 2015 and sentinel lymph node biopsy (SNB) or axillary lymph node dissection (ALND). The patients were divided into the following groups: SNB, ALND, and (SNB + ALND). Results: Of the 32,036 evaluable patients identified in this study. 5565 had SNB, 19,930 had ALND, and 6541 had SNB + ALND. Compared with the ALND group, the SNB group was younger, had more invasive ductal cancers, and had lower clinical T- and N-stage disease (p < 0.001). The patients in the SNB group had a higher rate of estrogen receptor-positive and triple-negative breast cancers, but a lower rate of human epidermal growth factor receptor 2 (HER2)-positive cancer (p < 0.001). The nodal pathologic complete response (PCR) rate, defined as no residual invasive cancer, was 66.5% in the SNB group and 33.1% in the ALND group. Since 2013, the rate of ALND has decreased from 88.7 to 77.1% in both community and academic institutions (p < 0.001). Conclusion: Since publication of the ACOSOG Z1071 and SENTINA trials, the national rates of ALND in node positive breast cancer treated with NAC have decreased despite reported false-negative SNB rates and lack of prospective outcome data regarding the oncologic safety of ALND omission.
Background: The feasibility of sentinel lymph node biopsy (SLNB) in patients with clinically node-positive (cN+) disease who convert to clinically node-negative (cN0) disease following neoadjuvant chemotherapy (NAC) has been evaluated in several large clinical trials, but it remains unclear whether the approach has been broadly adopted in the United States. Methods: The National Cancer Database was used to identify women diagnosed with cN+ breast cancer who received NAC followed by surgery between 2012 and 2015. Trends in axillary surgery were evaluated and multivariable logistic regression analyses performed to determine factors associated with receipt of SLNB. Results: Of 12,965 women cN+ at baseline, the use of SLNB increased from 31.8% in 2012 to 49% in 2015 (p < 0.001). Using axillary pCR as a surrogate for patients who convert to cN0 following NAC, among 5127 (39.5%) ypN0 patients, SLNB increased from 38.2 to 58.4% over the study period (p < 0.001), resulting in avoidance of axillary dissection in 42.2% of ypN0 patients by 2015. In adjusted analyses, factors significantly associated with SLNB attempt included cN1 disease, age < 45 years, treatment facility type, triple-negative and HER2-positive subtypes, and year of diagnosis. In women with residual isolated tumor cells (ITCs), micrometastases, and ypN1 disease, SLNB was the only axillary procedure performed in 36.9%, 23.6%, and 13.0% of cases. Conclusions: The use of SLNB in cN+ patients receiving NAC increased significantly between 2012 and 2015. SLNB alone was performed in more than 10% of patients with ypN1 disease, 20% with micrometastases, and 35% with ITCs; the oncologic safety of omitting axillary dissection in these patients requires further evaluation.
This paper updates recent work to populate spectral databases for (near-) infrared sensing of solids in different modalities including passive and active sensing of minerals, ores and other mining chemicals and minerals.
Background In early 2015, a ZIKA Virus (ZIKV) infection outbreak was recognized in northeast Brazil, where concerns over its possible links with infant microcephaly have been discussed. Providing a causal link between ZIKV infection and birth defects is still a challenge. MicroRNAs (miRNAs) are small noncoding RNAs (sncRNAs) that regulate post-transcriptional gene expression by translational repression, and play important roles in viral pathogenesis and brain development. The potential for flavivirus-mediated miRNA signalling dysfunction in brain-tissue development provides a compelling hypothesis to test the perceived link between ZIKV and microcephaly. Methodology/Principal Findings Here, we applied in silico analyses to provide novel insights to understand how Congenital ZIKA Syndrome symptoms may be related to an imbalance in miRNAs function. Moreover, following World Health Organization (WHO) recommendations, we have assembled a database to help target investigations of the possible relationship between ZIKV symptoms and miRNA-mediated human gene expression. Conclusions/Significance We have computationally predicted both miRNAs encoded by ZIKV able to target genes in the human genome and cellular (human) miRNAs capable of interacting with ZIKV genomes. Our results represent a step forward in the ZIKV studies, providing new insights to support research in this field and identify potential targets for therapy.
Air emissions from the U.S. pulp and paper sector have been federally regulated since 1978; however, regulations are periodically reviewed and revised to improve efficiency and effectiveness of existing emission standards. The Industrial Sectors Integrated Solutions (ISIS) model for the pulp and paper sector is currently under development at the U.S. Environmental Protection Agency (EPA), and can be utilized to facilitate multi-pollutant, sectorbased analyses that are performed in conjunction with regulatory development. The model utilizes a multi-sector, multi-product dynamic linear modeling framework that evaluates the economic impact of emission reduction strategies for multiple air pollutants. The ISIS model considers facility-level economic, environmental, and technical parameters, as well as sector-level market data, to estimate the impacts of environmental regulations on the pulp and paper industry. Specifically, the model can be used to estimate U.S. and global market impacts of new or more stringent air regulations, such as impacts on product price, exports and imports, market demands, capital investment, and mill closures. One major challenge to developing a representative model is the need for an extensive amount of data. This article discusses the collection and processing of data for use in the model, as well as the methods used for building the ISIS pulp and paper database that facilitates the required analyses to support the air quality management of the pulp and paper sector.
Nuclear factor erythroid-2 related factor 2 (NRF2) encoded by the NFE2L2 gene is a transcription factor critical for protecting cells from chemically-induced oxidative stress. We developed computational procedures to identify chemical modulators of NRF2 in a large database of human microarray data. A gene expression biomarker was built from statistically-filtered gene lists derived from microarray experiments in primary human hepatocytes and cancer cell lines exposed to NRF2-activating chemicals (oltipraz, sulforaphane, CDDOIm) or in which the NRF2 suppressor Keap1 was knocked down by siRNA. Directionally consistent biomarker genes were further filtered for those dependent on NRF2 using a microarray dataset from cells after NFE2L2 siRNA knockdown. The resulting 143-gene biomarker was evaluated as a predictive tool using the correlation-based Running Fisher algorithm. Using 59 gene expression comparisons from chemically-treated cells with known NRF2 activating potential, the biomarker gave a balanced accuracy of 93%. The biomarker was comprised of many well-known NRF2 target genes (AKR1B10, AKR1C1, NQO1, TXNRD1, SRXN1, GCLC, GCLM), 69% of which were found to be bound directly by NRF2 using ChIP-Seq. NRF2 activity was assessed across ~9840 microarray comparisons from ~1460 studies examining the effects of ~2260 chemicals in human cell lines. A total of 260 and 43 chemicals were found to activate or suppress NRF2, respectively, most of which have not been previously reported to modulate NRF2 activity. Using a NRF2-responsive reporter gene in HepG2 cells, we confirmed the activity of a set of chemicals predicted using the biomarker. The biomarker will be useful for future gene expression screening studies of environmentally-relevant chemicals.
Background Genomic islands (GIs) are mobile genetic elements that integrate site-specifically into bacterial chromosomes, bearing genes that affect phenotypes such as pathogenicity and metabolism. GIs typically occur sporadically among related bacterial strains, enabling comparative genomic approaches to GI identification. For a candidate GI in a query genome, the number of reference genomes with a precise deletion of the GI serves as a support value for the GI. Our comparative software for GI identification was slowed by our original use of large reference genome databases (DBs). Here we explore smaller species-focused DBs. Results With increasing DB size, recovery of our reliable prophage GI calls reached a plateau, while recovery of less reliable GI calls (FPs) increased rapidly as DB sizes exceeded ~500 genomes; i.e., overlarge DBs can increase FP rates. Paradoxically, relative to prophages, FPs were both more frequently supported only by genomes outside the species and more frequently supported only by genomes inside the species; this may be due to their generally lower support values. Setting a DB size limit for our SMA ll R anked T ailored (SMART) DB design speeded runtime ~65-fold. Strictly intra-species DBs would tend to lower yields of prophages for small species (with few genomes available); simulations with large species showed that this could be partially overcome by reaching outside the species to closely related taxa, without an FP burden. Employing such taxonomic outreach in DB design generated redundancy in the DB set; as few as 2984 DBs were needed to cover all 47894 prokaryotic species. Conclusions Runtime decreased dramatically with SMART DB design, with only minor losses of prophages. We also describe potential utility in other comparative genomics projects.
Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present Coco, the first tool that identifies data relationships by analyzing database-backed applications. Once identified, Coco leverages the constraints to optimize the application's physical design and query execution. Instead of developing a fixed set of predefined rewriting rules, Coco employs an enumerate-test-verify technique to automatically exploit the discovered data constraints to improve query execution. Each resulting rewrite is provably equivalent to the original query. Using 14 real-world web applications, our experiments show that Coco can discover numerous data constraints from code analysis and improve real-world application performance significantly.
Master well database for all logs and other well data pertaining to the Fallon FORGE site. Tables are formatted in NGDS Teir 3 schema where appropriate; redundancies between tables are omitted, and should be queried off of the main WellHeader table. All relationships are set on the "HeaderURI" primary key. Tables: WellHeader - well locations and information ChemFluid - aqueous chemistry analyses ChemRock - rock chemistry analyses DensityCore - rock densities from core FracturesInduced - induced borehole fractures FracturesNatural - natural borehole factures LithIntercepts - lithology picks from cuttings and core MagSuscCore - magnetic susceptibility for core samples MagSuscPmag - magnetic susceptibility for pmag samples PTSLogs - pressure-temperature-spin logs WirelineLogs - digital wireline logs (21-31 only)
This dataset include standardized phenological transition dates, and derived season lengths of active, dormant and transition seasons of ecosystem carbon and energy fluxes, as reported in FLUXNET. Such metrics are unique in that they characterize the seasonal dynamics of these fluxes in their own terms rather than in reference to traditional phenology indicators that mostly relate to vegetation structural characteristics. The Flux Seasonality Metrics Database (FSMD) generated 17 standardized seasonality metrics for 4 different fluxes including gross primary productivity (GPP), ecosystem respiration (RE), latent heat (LE) and sensible heat (H) reported in FLUXNET2015 Dataset for about 200 sites and 1500 site-years of data globally. Each metric is reported as an estimate and a confidence interval, which is a function of data coverage and flux variability. We anticipate that the FSMD will facilitate new model validation exercises, and stimulate new research in global change and Earth science disciplines where land-atmosphere exchange dynamics play a central role.
Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.The Defense Coastal/Estuarine Research Program (DCERP) Monitoring and Research Data Information System (MARDIS) database is the long-term repository for all DCERP monitoring and research data. Metadata provide supporting information about monitoring and research data that include, but are not limited to, a description of the location where the data were collected, the field and laboratory methods used to collect and analyze the data, and the spatial extent and temporal frequency of the data collected. In MARDIS, metadata fields have been combined with the actual sampling data fields; therefore, DCERP metadata are not a separate element, table, or document, but instead they are an integral component of each data record.
This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.
The paradigm of molecular discovery in the chemical and pharmaceutical industry has followed a repetitive succession of screening and synthesis, involving the analysis of individual molecules that were both natural and produced. This ability to generate and screen libraries of compounds has found an echo in solid-state physics with the demand to explore and produce new materials for testing. In response to this demand, a golden age of materials discovery is being developed, with progress on important areas of both basic science and device applications. The confluence of theoretical and simulation methods, together with the availability of computation resources, has established the “materials genome” approach that is used by a growing number of research groups around the world with the goal of innovating on materials through systematic discovery. Here, an overview of this group of methodologies in tackling the ever-increasing complexity of computational materials science simulations is provided. Computational simulation is highlighted as a major component of rational design and synthesis of new materials with targeted properties, describing progress on databases and large data treatment. Tools for new materials discovery, including progress on the deployment of new data repositories, the implementation of high-throughput simulation approaches, and the development of artificial intelligence algorithms, are discussed.