Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 co...

Table S7. Subtype-specific CNV and their values in TCGA and external dataset. The table show the previously reported subtype-specific CNV and their values (mean and standard deviation) for (A) breast cancer and (B) GBM TCGA and external dataset. The values were CDFs (ranged from 0 to 1) for TCGA data, log2 of estimated copy numbers (centered at 0) for METABRIC, and estimated copy numbers (centered at 2) for REMBRANDT, respectively. (XLSX 11 kb)

The Intelligent Semantic Data Service market is experiencing robust growth, driven by the increasing need for organizations to extract actionable insights from rapidly expanding data volumes. The market's complexity necessitates sophisticated solutions that go beyond traditional data analytics, focusing on understanding the meaning and context of data. This demand is fueled by advancements in artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), which power semantic analysis engines. Key players like Google, IBM, Microsoft, Amazon, and others are heavily investing in this space, developing and deploying powerful solutions that cater to various industries, from finance and healthcare to retail and manufacturing. The market's projected Compound Annual Growth Rate (CAGR) suggests a significant expansion over the forecast period (2025-2033). We estimate the 2025 market size to be approximately $15 billion, based on industry reports and observed growth trajectories in related AI segments. This figure is expected to reach approximately $35 billion by 2033. Several factors contribute to this growth, including the rising adoption of cloud-based solutions, the need for improved data governance, and a growing emphasis on data-driven decision-making. However, the market also faces certain restraints. High implementation costs, the need for specialized expertise, and data security concerns can hinder widespread adoption. Furthermore, the market is characterized by a relatively high barrier to entry, favoring established players with significant R&D capabilities. Nevertheless, the potential benefits of unlocking the true value of unstructured data through intelligent semantic analysis are compelling enough to drive continued investment and innovation in this rapidly evolving market. Segmentation within the market is likely based on deployment type (cloud, on-premise), service type (data enrichment, knowledge graph creation, semantic search), and industry vertical. The geographic distribution shows a strong concentration in North America and Europe, followed by a steady growth in the Asia-Pacific region, driven by increasing digitalization efforts.

DESeq2 differential abundance output for genes with q < 0.05 and |log₂ fold change| > 1 in cancer vs control plasma samples:

• differentialabundance_pancancer.txt: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in a pan-cancer plasma sample cohort (25 locally advanced to metastatic cancer types - 7 or 8 patients per type - vs 8 cancer-free control donors)
• differentialabundance_threecancer.txt: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in the three-cancer plasma cohort (ovarian, prostate and uterine cancer - 11 or 12 patients per type - vs 20 cancer-free controls)
  • Gene_id: Ensembl gene id (GChr38 v91); baseMean: mean of normalized counts for all samples; log2FoldChange: log2 fold change for cancer vs control; lfcSE: standard error for cancer vs control; stat: Wald statistic for cancer vs control; pvalue: Wald test p-value for cancer vs control; padj: Benjamini-Hochberg corrected p-value; cancertype: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls.

Gene set enrichment analyses based on fold change ranked gene lists (cancer versus control) - results obtained with fgea (v1.22.0):

• customgenesets.txt: custom gene set lists based on RNA Atlas (&Human Protein Atlas), Tabula Sapiens, GTEX, TCGA data.
  • Reference: reference to create gene sets (including RNA Atlas, Human Protein Atlas, Tabula Sapiens, GTEX, and TCGA); set: set name; genes: gene list for set
• GSEA_pancancer.txt & GSEA_threecancer.txt: gene set enrichment results based on fold change ranked gene list (specific cancer type versus controls) in pan-cancer cohort and three-cancer cohort, respectively
  • Sets: gene set category (HALLMARK and KEGG: Hallmark and Canonical Pathways gene sets obtained from MSigDB (v2022.1); CUSTOM: custom tissue and cell type specific gene sets as defined in customgenesets.txt); pathway: pathway/set name; pval: enrichment p-value; padj: Benjamini-Hochberg adjusted p-value; log2err: expected error for the standard deviation of the P-value logarithm; ES: enrichment score, same as in Broad GSEA implementation; NES: enrichment score normalized to mean enrichment of random samples of the same size; size: size of the pathway after removing genes without statistic values; leadingEdge: leading edge genes that drive the enrichment; Disease: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls

Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Data normalization is a critical step in RNA sequencing (RNA-seq) analysis, aiming to remove systematic effects from the data to ensure that technical biases have minimal impact on the results. Analyzing numerous RNA-seq datasets, we detected a prevalent sample-specific length effect that leads to a strong association between gene length and fold-change estimates between samples. This stochastic sample-specific effect is not corrected by common normalization methods, including reads per kilobase of transcript length per million reads (RPKM), Trimmed Mean of M values (TMM), relative log expression (RLE), and quantile and upper-quartile normalization. Importantly, we demonstrate that this bias causes recurrent false positive calls by gene-set enrichment analysis (GSEA) methods, thereby leading to frequent functional misinterpretation of the data. Gene sets characterized by markedly short genes (e.g., ribosomal protein genes) or long genes (e.g., extracellular matrix genes) are particularly prone to such false calls. This sample-specific length bias is effectively removed by the conditional quantile normalization (cqn) and EDASeq methods, which allow the integration of gene length as a sample-specific covariate. Consequently, using these normalization methods led to substantial reduction in GSEA false results while retaining true ones. In addition, we found that application of gene-set tests that take into account gene–gene correlations attenuates false positive rates caused by the length bias, but statistical power is reduced as well. Our results advocate the inspection and correction of sample-specific length biases as default steps in RNA-seq analysis pipelines and reiterate the need to account for intergene correlations when performing gene-set enrichment tests to lessen false interpretation of transcriptomic data.

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts. , The proposed network is built starting from two distinct data sources:

the entire dataset dump from Semantic Scholar (with particular emphasis on the authors and papers datasets) the entire list of Italian faculty members as maintained by Cineca (under appointment by the Italian Ministry of University and Research).

By means of a custom name-identity recognition algorithm (details are available in the accompanying paper published in Scientific Data), the names of the authors in the Semantic Scholar dataset have been mapped against the names contained in the Cineca dataset and authors with no match (e.g., because of not being part of an Italian university) have been discarded. The remaining authors will compose the nodes of the network, which have been enriched with node-related (i.e., author-related) attributes. In order to build the network edges, we leveraged the papers dataset from Semantic Scholar: specifically, any two authors are said to be connected if there is at least one pap..., , # Data cleaning and enrichment through data integration: networking the Italian academia

https://doi.org/10.5061/dryad.wpzgmsbwj

Manuscript published in Scientific Data with DOI .

Description of the data and file structure

This repository contains two main data files:

• edge_data_AGG.csv, the full network in comma-separated edge list format (this file contains mainly temporal co-authorship information);
• Coauthorship_Network_AGG.graphml, the full network in GraphML format.Â

along with several supplementary data, listed below, useful only to build the network (i.e., for reproducibility only):

• University-City-match.xlsx, an Excel file that maps the name of a university against the city where its respective headquarter is located;
• Areas-SS-CINECA-match.xlsx, an Excel file that maps the research areas in Cineca against the research areas in Semantic Scholar.

Description of the main data files

The `Coauthorship_Networ...

Raw sequencing data from Denitrifying Anaerobic Methane Oxidation (DAMO) experiments and the relevant statistical data generated by various bioinformatics tools. This dataset is not publicly accessible because: All the experiments for this study were not performed in EPA but in co-authors' institution which has managed the project and prepared a manuscript for peer-reviewed journal submission. It can be accessed through the following means: The raw data will be made available by the authors on request (Dr. Yaohuan Gao, gaoyaohuan@xjtu.edu.cn). Format: Not available because the raw data was not generated in EPA. This dataset is associated with the following publication: Xia, L., Y. Wang, P. Yao, H. Ryu, Z. Dong, C. Tan, S. Deng, H. Liao, and Y. Gao. The effects of model insoluble copper compounds in anoxic sedimentary environment on denitrifying anaerobic methane oxidation (DAMO) activity. Microorganisms. MDPI, Basel, SWITZERLAND, 12(11): 2259, (2024).

Biomonitoring surveys from environmental DNA make use of metabarcoding tools to describe the community composition. These studies match their sequencing results against public genomic databases to identify the species. However, mitochondrial genomic reference data are yet incomplete, only a few genes may be available, or the suitability of existing sequence data is suboptimal for species-level resolution. Here we present a dedicated and cost-effective workflow with no DNA amplification for generating complete fish mitogenomes for the purpose of strengthening fish mitochondrial databases. Two different long-fragment sequencing approaches using Oxford Nanopore sequencing coupled with mitochondrial DNA enrichment were used. One where the enrichment is achieved by preferential isolation of mitochondria followed by DNA extraction and nuclear DNA depletion (‘mitoenrichment’). A second enrichment approach takes advantage of the CRISPR-Cas9 targeted scission on previously dephosphorylated DNA (‘targeted mitosequencing’). The sequencing results varied between tissue, species, and integrity of the DNA. The mitoenrichment method yielded 0.17-12.33 % of sequences on target and a mean coverage ranging from 74.9 to 805-fold. The targeted mitosequencing experiment from native genomic DNA yielded 1.83-55 % of sequences on target and a 38 to 2123-fold mean coverage. This produced complete the mitogenome of species with homopolymeric regions, tandem repeats, and gene rearrangements. We demonstrate that deep sequencing of long fragments of native fish DNA is possible and can be achieved with low computational resources in a cost-effective manner, opening the discovery of mitogenomes of non-model or understudied fish taxa to a broad range of laboratories worldwide.

Determining the optimal targets of genomic sub-sampling for phylogenomics, phylogeography, and population genomics remains a challenge for evolutionary biologists. Of the available methods for sub-sampling the genome, hybrid enrichment (sequence capture) has become one of the primary means of data collection for systematics, due to the flexibility and cost efficiency of this approach. Despite the utility of this method, information is lacking as to what genomic targets are most appropriate for addressing questions at different evolutionary scales. In this study, first we compare the benefits of target loci developed for deep- and shallow-scales by comparing these loci at each of three taxonomic levels: within a genus (phylogenetics), within a species (phylogeography) and within a hybrid zone (population genomics). Specifically, we target evolutionary conserved loci that are appropriate for deep phylogenetic scales and more rapidly evolving loci that are informative for phylogeographic a...

The search terms 'phosphorus', 'nitrogen', 'nutrient-enrichment' and 'limitation' were used in BIOSIS. From this search a collection of peer-reviewed literature spanning 1992-2008 was compiled. Specifically, we were looking for nutrient enrichment experiment data for coastal and wetland sites. From the literature we extracted nutrient dosages, significant functional trait changes, the associated 'new' means and associatd standard errors/variances and p-values.

Xverum empowers tech-driven companies to elevate their solutions by providing comprehensive global company data. With over 50 million comprehensive company profiles, we help you enrich and expand your data, conduct extensive company analysis, and tailor your digital strategies accordingly.

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This dataset contains the supplementary data for the research paper "Haploinsufficiency of the intellectual disability gene SETD5 disturbs developmental gene expression and cognition".

The contained files have the following content: 'Supplementary Figures.pdf' Additional figures (as referenced in the paper). 'Supplementary Table 1. Statistics.xlsx' Details on statistical tests performed in the paper. 'Supplementary Table 2. Differentially expressed gene analysis.xlsx' Results for the differential gene expression analysis for embryonic (E9.5; analysis with edgeR) and in vitro (ESCs, EBs, NPCs; analysis with DESeq2) samples. 'Supplementary Table 3. Gene Ontology (GO) term enrichment analysis.xlsx' Results for the GO term enrichment analysis for differentially expressed genes in embryonic (GO E9.5) and in vitro (GO ESC, GO EBs, GO NPCs) samples. Differentially expressed genes for in vitro samples were split into upregulated and downregulated genes (up/down) and the analysis was performed on each subset (e.g. GO ESC up / GO ESC down). 'Supplementary Table 4. Differentially expressed gene analysis for CFC samples.xlsx' Results for the differential gene expression analysis for samples from adult mice before (HC - Homecage) and 1h and 3h after contextual fear conditioning (1h and 3h, respectively). Each sheet shows the results for a different comparison. Sheets 1-3 show results for comparisons between timepoints for wild type (WT) samples only and sheets 4-6 for the same comparisons in mutant (Het) samples. Sheets 7-9 show results for comparisons between genotypes at each time point and sheet 10 contains the results for the analysis of differential expression trajectories between wild type and mutant. 'Supplementary Table 5. Cluster identification.xlsx' Results for k-means clustering of genes by expression. Sheet 1 shows clustering of just the genes with significantly different expression trajectories between genotypes. Sheet 2 shows clustering of all genes that are significantly differentially expressed in any of the comparisons (includes also genes with same trajectories). 'Supplementary Table 6. GO term cluster analysis.xlsx' Results for the GO term enrichment analysis and EWCE analysis for enrichment of cell type specific genes for each cluster identified by clustering genes with different expression trajectories (see Table S5, sheet 1). 'Supplementary Table 7. Setd5 mass spectrometry results.xlsx' Results showing proteins interacting with Setd5 as identified by mass spectrometry. Sheet 1 shows protein protein interaction data generated from these results (combined with data from the STRING database. Sheet 2 shows the results of the statistical analysis with limma. 'Supplementary Table 8. PolII ChIP-seq analysis.xlsx' Results for the Chip-Seq analysis for binding of RNA polymerase II (PolII). Sheet 1 shows results for differential binding of PolII at the transcription start site (TSS) between genotypes and sheets 2+3 show the corresponding GO enrichment analysis for these differentially bound genes. Sheet 4 shows RNAseq counts for genes with increased binding of PolII at the TSS.

Unfortunately, no README file was found for the datano extension, limiting the ability to provide a detailed and comprehensive description. Therefore, the following description is based on the extension name and general assumptions about data annotation tools within the CKAN ecosystem. The datano extension for CKAN, presumably short for "data annotation," likely aims to enhance datasets with annotations, metadata enrichment, and quality control features directly within the CKAN environment. It potentially introduces functionalities for adding textual descriptions, classifications, or other forms of annotation to datasets to improve their discoverability, usability, and overall value. This extension could provide an interface for users to collaboratively annotate data, thereby enriching dataset descriptions and making the data more useful for various purposes. Key Features (Assumed): * Dataset Annotation Interface: Provides a user-friendly interface within CKAN for adding structured or unstructured annotations to datasets and associated resources. This allows for a richer understanding of the data's content, purpose, and usage. * Collaborative Annotation: Supports multiple users collaboratively annotating datasets, fostering knowledge sharing and collective understanding of the data. * Annotation Versioning: Maintains a history of annotations, enabling users to track changes and revert to previous versions if necessary. * Annotation Search: Allows users to search for datasets based on annotations, enabling quick discovery of relevant data based on specific criteria. * Metadata Enrichment: Integrates annotations with existing metadata, enhancing metadata schemas to support more detailed descriptions and contextual information. * Quality Control Features: Includes options to rate, validate, or flag annotations to ensure they are accurate and relevant, improving overall data quality. Use Cases (Assumed): 1. Data Discovery Improvement: Enables users to find specific datasets more easily by searching for datasets based on their annotations and enriched metadata. 2. Data Quality Enhancement: Allows data curators to improve the quality of datasets by adding annotations that clarify the data's meaning, provenance, and limitations. 3. Collaborative Data Projects: Facilitates collaborative data annotation efforts, wherein multiple users contribute to the enrichment of datasets with their knowledge and insights. Technical Integration (Assumed): The datano extension would likely integrate with CKAN's existing plugin framework, adding new UI elements for annotation management and search. It could leverage CKAN's API for programmatic access to annotations and utilize CKAN's security model for managing access permissions. Benefits & Impact (Assumed): By implementing the datano extension, CKAN users can leverage improvements to data discoverability, quality, and collaborative potential. The enhancement can help data curators to refine the understanding and management of data, making it easier to search, understand and promote data driven decision-making.

The size of the Data as a Service market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 20.00% during the forecast period.Data as a Service, in its most simplistic form, provides an on-demand cloud-based service model for data and analytics. The model will help business use the power of data by not requiring large upfront investments in data storage, processing, and analysis infrastructure. Therefore, data and insights as a service will make DaaS simple to manage, reduce operational costs, and accelerate time-to-value.DaaS suppliers deliver a collection of data services which may include data integration, data cleansing, data enrichment, and data analytics. These services ensure businesses are able to access, and thereby use, hundreds and thousands of data sources located internally or externally for valuable insight and informed decisions. Primarily, DaaS can help out those organizations lacking internal resources and expertise or in their means to gather, handle, and process significant data. Business results are therefore better outsourced with DaaS because they can, at a given time, tend to more core competencies related to the business. Recent developments include: September 2022: Asigra Inc., an ultra-secure backup and recovery pioneer, declared the general availability of Tigris Data Protection software with Content Disarm & Reconstruction (CDR). The addition of CDR makes Asigra the most security-forward backup and recovery software platform available, adding to its extensive suite of security features., June 2022: IMAT Solutions, a real-time healthcare data management and population health reporting solutions provider, announced the launch of a new Data-as-a-Service (DaaS) offering for health payers. The new DaaS solution meets the new Centers for Medicare & Medicaid Services (CMS) effort to transition all quality measures used in its reporting programs to digital quality measures (dQMs).. Key drivers for this market are: Growing Penetration of Data-based Decisions Among Enterprises, Transformation of Enterprises Leading to Real-time Analytics Demand. Potential restraints include: Concerns Regarding Privacy and Security. Notable trends are: BFSI Sector to Witness High Growth.

Many ecosystems are now co-invaded by alien plant and herbivore species. The evolutionary naivety of native plants to alien herbivores can make the plants more susceptible to detrimental effects of herbivory than co-occurring invasive plants, in accordance with the apparent competition hypothesis. Moreover, the invasional meltdown hypothesis predicts that in multiply invaded ecosystems, invasive species can facilitate each other's impacts on native communities. Although there is growing empirical support for these hypotheses, facilitative interactions between invasive plants and herbivores remain underexplored in aquatic ecosystems. Many freshwater ecosystems are co-invaded by aquatic macrophytes and mollusks and simultaneously experience nutrient enrichment. However, the interactive effects of these ecological processes on native macrophyte communities remain an underexplored area. To test these effects, we performed a freshwater mesocosm experiment in which we grew a synthetic native community of three macrophyte species under two levels of invasion by an alien macrophyte Myriophyllum aquaticum (invasion vs. no-invasion) and fully crossed with two levels of nutrient enrichment (enrichment vs. no-enrichment) and herbivory by an invasive snail Pomacea canaliculata (herbivory vs. no-herbivory). In line with the invasional meltdown and apparent competition hypotheses, we found that the proportional above-ground biomass yield of the invasive macrophyte, relative to that of the native macrophyte community, was significantly greater in the presence of the invasive herbivore. Evidence of a reciprocal facilitative effect of the invasive macrophyte on the invasive herbivore is provided by the results showing that the herbivore produced greater egg biomass in the presence than in the absence of M. aquaticum. However, nutrient enrichment reduced the mean proportional above-ground biomass yield of the invasive macrophyte. Our results suggested that herbivory by invader P. canaliculata may enhance invasiveness of M. aquaticum. However, nutrient enrichment of habitats that already harbor M. aquaticum may slow down invasive spread of the macrophyte. Broadly, our study underscores the significance of considering several factors and their interactions when assessing the impact of invasive species, especially considering that many habitats experience co-invasion by plants and herbivores and simultaneously undergo various other disturbances, including nutrient enrichment.

Description of the INSPIRE Download Service (predefined Atom): In the Saarland landscape program, large-scale, low-structured landscapes are dealt with separately. These are to be improved in their structuring by means of measures coordinated with agriculture and forestry. However, it should be noted that some areas of structurally poor agricultural landscapes (Saar-Nied-Gau, Moselgau, Wahler Platte) have a high importance as a resting place and partly also breeding ground of endangered bird species of the Offenlan-des (Kiebitz, gold rain plover, Mornell rain plover). In these areas, the structural enrichment measures must be carefully aligned with the concerns of bird protection, so as not to impair them. Structural enhancements in agricultural landscapes are mainly to be created along major economic routes, preferably in the form of high green as connecting axes between settlement areas. The necessary measures must be concretised and presented in the municipal landscape planning. s. landscape program Saarland, chapter 6.5.3 and chapter 10.3.2 (as of 2009) — The link(s) for downloading the records is/are generated dynamically from getFeature Requests to a WFS 1.1.0

Animal welfare requires the adequate housing of animals to ensure health and well-being. The application of environmental enrichment is a way to improve the well-being of laboratory animals. However, it is important to know whether these enrichment items can be incorporated in experimental mouse husbandry without creating a divide between past and future experimental results. Previous small-scale studies have been inconsistent throughout the literature, and it is not yet completely understood whether and how enrichment might endanger comparability of results of scientific experiments. Here, we measured the effect on means and variability of 164 physiological parameters in 3 conditions: with nesting material with or without a shelter, comparing these 2 conditions to a “barren” regime without any enrichments. We studied a total of 360 mice from each of 2 mouse strains (C57BL/6NTac and DBA/2NCrl) and both sexes for each of the 3 conditions. Our study indicates that enrichment affects the mean values of some of the 164 parameters with no consistent effects on variability. However, the influence of enrichment appears negligible compared to the effects of other influencing factors. Therefore, nesting material and shelters may be used to improve animal welfare without impairment of experimental outcome or loss of comparability to previous data collected under barren housing conditions.

Our Firmographic data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Data Export Methodology: As we collect data dynamically, we are able to provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly), keeping you informed and up-to-date.

Use Cases: 360-Degree Company View: Get a comprehensive image of the company by means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment. Advertising & Marketing: Understand the Company's employee count, revenue, stock ticker, and a lot more to hyper-personalize and offer targeted ads

Biomarker Image Cytometry. The cell-level frequency of NK2 Homeobox 1 (NKX2-1), Keratin 7 (KRT7), and Thyroglobulin (TG) protein staining were quantitatively evaluated by high-content imaging across huThyrEC cell line variants (1-4) in two medium formulations (huThyrEC and h7H) for verification of thyroid follicular epithelial cell enrichment. Data are the % positive expression frequency (mean ± SD) of two replicates. This dataset is associated with the following publication: Hopperstad, K., T. Truschel, T. Wahlicht, W. Stewart, A. Eicher, T. May, and C. Deisenroth. Characterization of Novel Human Immortalized Thyroid Follicular Epithelial Cell Lines. Applied In Vitro Toxicology. Mary Ann Liebert, Inc., Larchmont, NY, USA, 7(2): 39-49, (2021).