Complete database of Sage Group's mergers and acquisitions

Supplementary Figure 2 from A Serial Analysis of Gene Expression (SAGE) Database Analysis of Chemosensitivity

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9, 2022. The SAGE Genie website provides highly intuitive, visual displays of human and mouse gene expression, based on a unique analytical process (Boon et al.) that reliably matches SAGE tags, 10 or 17 nucleotides in length, to known genes. Recently, with the construction and incorporation of a reference database of SNP-associated alternative tags into SAGE Genie (Silva et al.), the interpretation of tag to human gene mapping has been enhanced. Human SAGE Genie Tools The SAGE Anatomic Viewer visually displays the relative expression of a given gene in normal and malignant tissues of the human body. In addition, there is access to: o The Digital Northern, which shows the relative expression of the gene in each library. o The Ludwig Transcript (LT) Viewer, which visually represents a particular transcript with up to four possible virtual SAGE tag locations (starting from the 3' end) and locations of internally primed or alternatively polyadenylated transcripts. o Expanded access to brain, hES cells, and breast cell subtypes. * The SAGE Digital Gene Expression Displayer distinguishes significant differences in gene expression profiles between two pools of SAGE libraries. * The SAGE Experimental Viewer provides DGED results for pre-set pairs of libraries, one under control and the other under experimental conditions. * The SAGE Absolute Level Lister lists all SAGE libraries and links to the distribution of transcript expression levels in any given library. * The SAGE Library Finder tool searches for one or more tissue-specific libraries from the SAGE collection. * SAGE Genie Downloads provide files of genes, tags, datasets, mappings, and more. * SAGE Genomics Finder tool searches for genomics info for tags. * DKView tool for viewing Digital Karyotyping data. * DK Microbe tool searches for exact matches of 17 base-pair digital karyotyping tags in bacterial and viral genomes. * SAGE Tag Extraction tool to extract tags from sequence files. Mouse SAGE Genie Tools * The mSAGE Expression Matrix visually displays the relative expression of a given gene through stages of mouse development. In addition, there is access to: o The Digital Northern, which shows the relative expression of the gene in each library. o The Ludwig Transcript (LT) Viewer, which visually represents a particular transcript with up to four possible virtual SAGE tag locations (starting from the 3' end) and locations of internally primed or alternatively polyadenylated transcripts. * The mSAGE Absolute Level Lister lists all mouse SAGE libraries, organized by either tissue (normal or malignant) or developmental stage, and links to the distribution of transcript expression levels in any given library. * The mSAGE Digital Gene Expression Displayer distinguishes significant differences in gene expression profiles between two pools of mouse SAGE libraries. * The mSAGE Experimental Viewer automatically sets up the DGED with mouse libraries, prepared as stand alone experiments, for gene expression comparison. * The mSAGE Library Finder tool searches for one or more tissue-specific mouse libraries from the SAGE collection. * mSAGE Genie Downloads provide files of mouse genes, tags, datasets, mappings, and more. * SAGE Genomics Finder tool searches for genomics info for tags. * SAGE Tag Extraction tool to extract tags from sequence files. Notes: The majority of human libraries are short whereas the majority of mouse libraries are long. Several mouse developmental libraries are prepared by the SAGELite method. SAGELite is an extension of the long Sage protocol that includes a PCR-based amplification stage to allow the use of 10ng to 100ng of total RNA to produce a Sage library. However, it has been observed that a significant bias incurred by this amplification process. SAGE Genie was produced as part of the NCI CGAP SAGE project with collaborators from Johns Hopkins University, the Ludwig Institute for Cancer Research, Sao Paulo Branch, the University of Texas MD Anderson Cancer Center, and the Genome Sciences Center of the BCCRC.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on October 30, 2012. A database that displays the observed frequencies of individual 5' end SAGE tags and previously unknown transcription start sites in the promoter regions, introns and intergenic regions of known genes. 5'SAGE will be useful for analyzing promoter regions and start site variation in different tissues, and is freely available.

Greater sage-grouse (Centrocercus urophasianus; hereinafter sage-grouse) is a sagebrush obligate species and widely considered an indicator species for sagebrush ecosystems and other sagebrush-dependent species (Hanser and Knick, 2011; Prochazka and others, 2023). Sagebrush ecosystems are threatened by a wide range of disturbances and anthropogenic factors, including climate change, severe drought, altered wildfire regimes, expansion of invasive species, and anthropogenic development. Collectively, these threats have led to reduced ecological integrity and sage-grouse habitat quality within the sagebrush biome (Doherty and others, 2022). Steady and long-term declines in sage-grouse populations have led to large-scale efforts to improve population performance and prevent additional loss of habitat for sage-grouse and other sagebrush-dependent species (Coates and others, 2021). Due to their complex space use and habitat selection patterns during different life stages, requirements for large intact tracts of sagebrush, declining population trends, and status as a proposed protected species, sage-grouse have become integral to land management and conservation policy throughout the western United States (Western Association of Fish and Wildlife Agencies, 2015; Doherty and others, 2022). References cited: Coates, P.S., Prochazka, B.G., Aldridge, C.L., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)-Updated 1960-2022: U.S. Geological Survey Data Report 1175, 17 p., accessed December 7, 2023, at [Available at https://doi.org/10.3133/dr1175.] Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2022, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022-1081, 38 p., accessed December 6, 2023, at https://doi.org/10.3133/ofr20221081. Hanser, S.E., and Knick, S.T., 2011, Greater sage-grouse as an umbrella species for shrubland passerine birds-A multiscale assessment, chap. 19 in Knick, S.T., eds., Greater sage grouse-Ecology and conservation of a landscape species and its habitats: University of California Press, p. 474-487. [Available at https://doi.org/10.1525/california/9780520267114.003.0020.] Prochazka, B.G., Coates, P.S., O’Donnell, M.S., Edmunds, D.R., Monroe, A.P., Ricca, M.A., Wann, G.T., Hanser, S.E., Wiechman, L.A., Doherty, K.E., Chenaille, M.P., and Aldridge, C.L., 2023, A targeted annual warning system developed for the conservation of a sagebrush indicator species: Ecological Indicators, v. 148. [Available at https://doi.org/10.1016/j.ecolind.2023.110097.] Western Association of Fish and Wildlife Agencies, 2015, Greater sage-grouse population trends: an analysis of lek count databases 1965-2015: Cheyenne, Wyo., Western Association of Fish and Wildlife Agencies, 55 p., accessed 07 12, 2023, at https://ir.library.oregonstate.edu/concern/technical_reports/ng451p621

SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) atte ...

Sage-Grouse habitat areas divided into proposed management categories within Nevada and California project study boundaries.MANAGEMENT CATEGORY DETERMINATION The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was determined from a statewide resource selection function model and first categorized into 4 classes: high, moderate, low, and non-habitat. The standard deviations (SD) from a normal distribution of RSF values created from a set of validation points (10% of the entire telemetry dataset) were used to categorize habitat ‘quality’ classes. High quality habitat comprised pixels with RSF values < 0.5 SD, Moderate > 0.5 and < 1.0 SD, Low < 1.0 and > 1.5, Non-Habitat > 1.5 SD. Proposed Habitat Management Categories were then defined and calculated as follows.1) Core habitat: Defined as the intersection between all suitable habitat (high, moderate, and low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality falling outside the 85% SUI and all non-habitat falling within the 85% SUI. 3) General habitat: Defined as moderate and low quality habitat falling outside the 85% SUI. 4) Non habitat. Defined as non-habitat falling outside the 85% SUI. SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

This CED spatial web service (ESRI ArcGIS Online Hosted Feature Layer) is a optimized quick visualization and unique value source for optimized CED web startup. The CED provides Sagebrush biome spatial representations and attribute information of conservation efforts entered into the Conservation Efforts Database (https://conservationefforts.org) by various data providers. This spatial web service is made of point and polygon layers and non-spatial tables. Feature records are group with their respective spatial feature type layers (point, polygon). The two spatial layers have identical attribute fields.Read only access to this data is ONLY available via an interactive web map on the Conservation Efforts Database website or authorized websites. Users who are interested in more access can directly contact the data providers by using the contact information available through the CED interactive map's pop-up/identify feature.The spatially explicit, web-based Conservation Efforts Database is capable of (1) allowing multiple-users to enter data from different locations, (2) uploading and storing documents, (3) linking conservation actions to one or more threats (one-to-many relationships), (4) reporting functions that would allow summaries of the conservation actions at multiple scales (e.g., management zones, populations, or priority areas for conservation), and (5) accounting for actions at multiple scales from small easements to statewide planning efforts.The sagebrush ecosystem is the largest ecosystem type in the continental U.S., providing habitat for more than 350 associated fish and wildlife species. In recognition of the need to conserve a healthy sagebrush ecosystem to provide for the long-term conservation of its inhabitants, the US Fish and Wildlife Service (Service) and United States Geological Survey (USGS) developed the Conservation Efforts Database version 2.0.0 (CED). The purpose of the CED is to efficiently capture the unprecedented level of conservation plans and actions being implemented throughout the sagebrush ecosystem and designed to capture actions not only for its most famous resident, the greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) but for the other species that rely on sagebrush habitats. Understanding the distribution and type of conservation actions happening across the landscape will allow visualization and quantification of the extent to which threats are being addressed.The purpose of this CED spatial web service (ESRI ArcGIS Online Hosted Feature Layer) is to provide CED data for authorized web sites or authorized users.

(a)ELSD indicated expression level in SAGE database.(b)‘Yes’ indicating at least one putative MoAP1 binding site was identified in the promoter region of the gene while ‘No’ stand for non putative MoAP1 binding site was identified in the promoter region.

To accelerate the process of tumor antigen discovery, we generated a publicly available Human Potential Tumor Associated Antigen database (HPtaa) with pTAAs identified by insilico computing. 3518 potential targets have been included in the database, which is freely available to academic users. It successfully screened out 41 of 82 known Cancer-Testis antigens, 6 of 18 differentiation antigen, 2 of 2 oncofetal antigen, and 7 of 12 FDA approved cancer markers that have Gene ID, therefore will provide a good platform for identification of cancer target genes. This database utilizes expression data from various expression platforms, including carefully chosen publicly available microarray expression data, GEO SAGE data, Unigene expression data. In addition, other relevant databases required for TAA discovery such as CGAP, CCDS, gene ontology database etc, were also incorporated. In order to integrate different expression platforms together, various strategies and algorithms have been developed. Known tumor antigens are gathered from literature and serve as training sets. A total tumor specificity penalty was computed from positive clue penalty for differential expression in human cancers, the corresponding differential ratio, and normal tissue restriction penalty for each gene. We hope this database will help with the process of cancer immunome identification, thus help with improving the diagnosis and treatment of human carcinomas.

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. The Unified Library Database, or UniLib, takes a library-level view of the EST and SAGE libraries present in NCBI's dbEST, UniGene and SAGEmap resources. This database was initially developed by NCBI in order to track and annotate libraries being generated by NCI's CGAP project. The query bar of the UniLib Library browser provides the most friendly way to navigate through these libraries. When matches to the Library browser query are returned as summaries, full library records can be retrieved through the linked Record retriever.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. This database is intended to serve as a learning tool to obtain curated information for the design of microarray targets to scan collecting duct tissues (human, rat, mouse). The database focuses on regulatory and transporter proteins expressed in the collecting duct, but when collecting duct proteins are a member of a larger family of proteins, common additional members of the family are included even if they have not been demonstrated to be expressed in the collecting duct. An Internet-accessible database has been devised for major collecting duct proteins involved in transport and regulation of cellular processes. The individual proteins included in this database are those culled from literature searches and from previously published studies involving cDNA arrays and serial analysis of gene expression (SAGE). Design of microarray targets for the study of kidney collecting duct tissues is facilitated by the database, which includes links to curated base pair and amino acid sequence data, relevant literature, and related databases. Use of the database is illustrated by a search for water channel proteins, aquaporins, and by a subsequent search for vasopressin receptors. Links are shown to the literature and to sequence data for human, rat, and mouse, as well as to relevant web-based resources. Extension of the database is dynamic and is done through a maintenance interface. This permits creation of new categories, updating of existing entries, and addition of new ones. CDDB is a database that organizes lists of genes found in collecting duct tissues from three mammalian species: human, rat, and mouse. Proteins are divided into categories by family relationships and functional classification, and each category is assigned a section in the database. Each section includes links to the literature and to sequence information for genes, proteins, expressed sequence tags, and related information. The user can peruse a section or use a search engine at the bottom of the web page to search the database for a name or abbreviation or for a link to a sequence. Each entry in the database includes links to relevant papers in the kidney and collecting duct literature. It uses links to PubMed to generate MEDLINE searches for retrieval of references. In addition, each entry includes links to curated sequence data available in LocusLink. Individual links are made to sequence and protein data for human, rat, and mouse. Links are then added as curated sequences become available for proteins identified in the renal collecting duct and for proteins identified in kidney and similar in function or homologous to proteins identified in the collecting duct.

This statistic presents the result of a keyword search on five electronic databases to identify the amount of scientific research into the implementation of mHealth apps for dementia healthcare in 2018. In total, 6,195 research articles were found using select search terms in the following databases: MEDLINE, PubMed, SAGE, IEEE Xplore, and Science Direct.

The following files are designed to be run using the Path Landscape Model software, version 3.0.4. Later versions of the software cannot run these files. To get a copy of this software, please contact Apex RMS at path@apexrms.com. 1) "Path Landscape Mode" folder contains files to be run in the PLM softwarel, version 3.0.4 or later. Path models MUST be run with the provided MCM mulitplier files to apply the required transition probability adjustments for procesess such as insect outbreaks, wildfire, and climate change trends. Each Path database is set up with three folders: - The 'Common' folder contains a single Path scenario (also named 'Common'). The Transitions tab within the Common scenario contains the climate-smart STM. - The 'Multipliers' folder contains multipliers specific to each ownership-allocation to activate or deactivate transitions (both climate change and management). Actual treatments are input in the Treatments tab for each stratum in the 'Runs' folder. - The 'Runs' folder contains one Path scenario per modeling stratum, with initial conditions specific to each stratum (combination of watershed and ownership-allocation). The models are stored as a dependency from the 'Common' folder and the multipliers as a dependency from the 'Multipliers' scenario. For the scenarios that have management activities (current and restoration management), a specified number of acres for each treatment is shown in the Treatments tab for treated stata. There are 12 databases, one for each combintation of management and climate scenario run in southeast Oregon. Climate scenarios include: - No climate change - continuing current climate (NoCC) - HadGEM global circulation model, representative concentration pathway 8.5 (HadGEM) - NorESM global circulation model, representative concentration pathway 8.5 (NorESM) - MRI global circulation model, representative concentration pathway 8.5 (MRI) Management scenarios include: - No management - no restoration treatments (NoMgt) - Current management - current treatment rates compiled from managers in the region (CurMgt) - Restoration management - a restoration scenario to restore sage-grouse habitat (RestMgt) 2) The "lookupTables" folder contains files necessary for providing definitions and context for the information located in other folders. 3) The 'Spatial' folder contains the SEO region stratum map, called SEO_Modeling_Strata.tif. This map can be joined to output from the climate-informed state-and-transition models to map projected future condition or sage-grouse habitat on the "Strata" field. Modeling strata consist of the intersection of watershed (Hydrologic unit code [HUC]) and ownership-allocation map. Watersheds are three digit codes starting at 101. Ownership-allocation categories are a two-character label, and are described in the attribute table through the fields "Ownership", and "Allocation". The field ScenarioID indicates the internal ID number used by the Path software to link results for modeling strata to its Scenario names. 4) This folder contains summarized modeling results that can be viewed as nonspatial trends across the whole landscape or displayed across mapped modeling strata. The .tif file in the 3Spatial folder shows the spatial distribution of modeling strata. There are 3 subfolders: ClassesSummary, TransitionSummary, and HabitatSummary. The csv files within ClassesSummary contain summaries of state class area (in Acres) for each timestep, over monte carlo repetitions, within each modeling stratum. Those within TransitionSummary are summarized in the same manner, but contain summaries of area affected by each Transition Type. The csv files within HabitatSummary are designed to be joined ot the grid mentioned above, to allow for a spatial depiction of habitat projections. There is one column per modeled year, containing a summary that indicates the proportion of each modeling stratum that is comprised of potential greater sage grouse habitat (the general summary, not the high-quality summary, averaged over monte carlo repetitions). Sample queries outlining how to build new summaries of output data for mapping from the ClassesSummary files and lookup tables are included in the database: SEO_Summaries.accdb. Due to a minor error in starting conditions, summaries of sage-grouse habitat may be underestimated in the early years, especially within the northwestern quadrant of the map. Because of this, we have removed the summaries of the first four years within the Habitat summary files, and constrain the sample queries to only show projections for after 2011. If the early years of this summary data are needed for viewing other parts of the map, they can be constructed by removing a constraint within query1 of the sample queries shown above.

This dataset contains two predictive lek (breeding site) persistence raster layers covering the U.S. greater sage-grouse distribution. In the United States, locations where males display and breed with females (i.e., leks) are often monitored annually by state wildlife agencies, providing valuable information on the persistence of birds in the surrounding areas. A U.S. range-wide lek database was recently compiled for greater sage-Grouse (O’Donnell et al. 2021), providing a standardized source of information to build statistical models to evaluate environmental characteristics associated with lek persistence. The compiled lek database classified a subset of leks as being either active (leks currently used for breeding activities) or inactive (leks no longer used for breeding activities) based on count data collected over a 20-year monitoring period. We fit the outcome of a lek being active or inactive as a function of environmental predictors characterizing surrounding conditions in a logistic regression model. Covariates included sagebrush cover, pinyon-juniper cover, topography, precipitation, point and line disturbance densities, and landscape configuration metrics. We included the Bureau of Land Management habitat assessment areas (termed mid-scales) as regional random effects in the form of random intercepts and random slopes (for a subset of covariates). The final model included 13 covariates. We predicted conditional probabilities of lek persistence across the U.S. occupied range using the covariate layers and regional mid-scales, which we make available here as a 30-meter resolution continuous raster dataset. The predictions were conditional because they were specific to each mid-scale factor level (i.e., pixel predictions were influenced by the regional mid-scale polygon they fell within via the associated mid-scale intercept and random slope deviations). We applied sensitivity thresholds (capturing percentage of leks correctly classified as active) to the continuous probability layer to bin persistence probabilities into high, medium, low, and marginal areas of persistence, which we make available here as a 30-m categorical raster dataset.

We compiled and verified a dataset which represents a comprehensive inventory of communication tower infrastructure across the range of the greater sage-grouse (Centrocercus urophasianus) from 1990 to 2023. Our dataset is an annual spatial time series product that allows users to visualize, assess, and analyze tower locations and duration (i.e., including date of construction through date of dismantlement) on western landscapes within the sagebrush ecosystem. Tower data were acquired from four publicly available infrastructure databases, data records were filtered to only include communication tower structures within the spatial extent of interest. Data records were then validated and checked for accuracy using Google Earth. The filtered dataset comprises 3,272 tower site locations verified via satellite imagery or field visits, and a further 799 tower site unverified records.

This data set consists of two reconstructed historical land use databases. One database was developed at the National Institute of Public Health and the Environment (RIVM) in the Netherlands and the other was developed at the Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison. These two databases used historical statistical inventories on agricultural lands (census data, tax records, land surveys, etc.) and applied different spatial analysis techniques to reconstruct land cover change due to land use for the last 300 years. The data sets focused on reconstructing the historical expansion of cropland and pasture areas. The data show that cropland areas expanded from 3-4 million km^2 1700 to 15-18 km^2 in 1990, while pasture areas expanded from 4-5 million km^2 in 1700 to 31-33 million km^2 in 1990. The database also includes a global potential natural vegetation data set (developed by SAGE). The databases are available on CD-ROM at a spatial resolution of 0.5 degree latitude and longitude at an annual resolution (SAGE) or decadal/multi-decadal resolution (RIVM) from 1700-1992.

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on March 24, 2014. A resource for gene expression studies, storing highly curated MIAME-compliant studies (i.e. experiments) employing a variety of technologies such as filter arrays, 2-channel microarrays, Affymetrix chips, SAGE, MPSS and RT-PCR. Data were available for querying and downloading based on the MGED ontology, publications or genes. Both public and private studies (the latter viewable only by users having appropriate logins and permissions) were available from this website. Specific details on protocols, biomaterials, study designs, etc., are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format. RAD is part of a more general Genomics Unified Schema (http://gusdb.org), which includes a richly annotated gene index (http://allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. NOTE: Due to changes in technology and funding, the RAD website is no longer available. RAD as a schema is still very much active and incorporated in the GUS (Genomics Unified Schema) database system used by CBIL (EuPathDB, Beta Cell Genomics) and others. The schema for RAD can be viewed along with the other GUS namespaces through our Schema Browser.

BackgroundSAGE (serial analysis of gene expression) is a powerful method of analyzing gene expression for the entire transcriptome. There are currently many well-developed SAGE tools. However, the cross-comparison of different tissues is seldom addressed, thus limiting the identification of common- and tissue-specific tumor markers.Methodology/Principal FindingsTo improve the SAGE mining methods, we propose a novel function for cross-tissue comparison of SAGE data by combining the mathematical set theory and logic with a unique “multi-pool method” that analyzes multiple pools of pair-wise case controls individually. When all the settings are in “inclusion”, the common SAGE tag sequences are mined. When one tissue type is in “inclusion” and the other types of tissues are not in “inclusion”, the selected tissue-specific SAGE tag sequences are generated. They are displayed in tags-per-million (TPM) and fold values, as well as visually displayed in four kinds of scales in a color gradient pattern. In the fold visualization display, the top scores of the SAGE tag sequences are provided, along with cluster plots. A user-defined matrix file is designed for cross-tissue comparison by selecting libraries from publically available databases or user-defined libraries.Conclusions/SignificanceThe hSAGEing tool provides a combination of friendly cross-tissue analysis and an interface for comparing SAGE libraries for the first time. Some up- or down-regulated genes with tissue-specific or common tumor markers and suppressors are identified computationally. The tool is useful and convenient for in silico cancer transcriptomic studies and is freely available at http://bio.kuas.edu.tw/hSAGEing