Aspectual verbs (e.g. begin) and intensional verbs (e.g. want) can both take entity-denoting NPs as a complement (begin/want the book) and acquire an implicit meaning (e.g. reading). Linguistic theory posits that such enriched implicit meanings can be acquired either by semantic enrichment with aspectual verbs or by syntactic enrichment with intensional verbs. To investigate whether semantic and syntactic enrichment share enrichment operations, we conducted a structural priming study. Experiment 1 repeated the verb on prime and target trials and found evidence for enrichment priming for both verb types. Experiment 2 crossed the verb type and found no evidence for priming. These results suggest that enrichment operations are distinct for aspectual and intensional verbs. However, Experiment 3 repeated Experiment 1 without lexical boost and found no enrichment priming within the verb type. Thus, producing an enriched structure may not robustly activate enrichment structures, leaving open questions concerning shared mechanisms.

Establishment of a syndication flow from the department of Vendée (85) with the data from the “Restoration” schedule with enriched data: name, address, type of restaurant, means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, payment methods accepted, opening dates, rates, details of visits, videos.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Automated Indicator Enrichment Market Outlook

According to our latest research, the global Automated Indicator Enrichment market size reached USD 1.26 billion in 2024, reflecting robust adoption across sectors. The market is expected to grow at a CAGR of 16.7% during the forecast period, with the market size projected to reach USD 4.01 billion by 2033. This growth is primarily driven by the increasing sophistication of cyber threats and the urgent need for organizations to automate threat intelligence processes, enabling faster and more accurate response to security incidents. The convergence of AI, machine learning, and security automation technologies is further accelerating the adoption of automated indicator enrichment solutions globally.

One of the key growth factors for the Automated Indicator Enrichment market is the escalating volume and complexity of cyber threats targeting organizations of all sizes. With threat actors employing advanced tactics, techniques, and procedures (TTPs), traditional manual threat analysis processes are proving inadequate. Automated indicator enrichment enables security teams to automatically contextualize, validate, and prioritize threat indicators, significantly reducing the mean time to detect (MTTD) and respond (MTTR) to incidents. The proliferation of endpoints, cloud workloads, and interconnected digital assets has necessitated a scalable approach to threat intelligence, further fueling demand for automated solutions that can process vast amounts of threat data in real time.

Another significant driver is the increasing regulatory pressure on organizations to maintain robust cybersecurity postures and ensure compliance with international standards such as GDPR, HIPAA, and PCI DSS. Automated indicator enrichment solutions facilitate compliance management by providing auditable, consistent, and timely threat intelligence workflows. This not only helps organizations avoid costly penalties but also enhances their overall security posture. The market is also benefitting from the growing awareness among enterprises regarding the benefits of automation in reducing human error, improving operational efficiency, and enabling proactive security measures. As a result, both large enterprises and small and medium enterprises (SMEs) are investing in advanced automated indicator enrichment platforms to stay ahead of evolving cyber threats.

The rapid advancements in artificial intelligence (AI) and machine learning (ML) technologies have also played a pivotal role in shaping the Automated Indicator Enrichment market. Modern solutions leverage AI and ML algorithms to enrich threat indicators with contextual data from multiple sources, including threat intelligence feeds, internal logs, and external databases. This automated enrichment process enhances the accuracy of threat detection and enables security analysts to focus on high-priority incidents. Additionally, the integration of automated indicator enrichment tools with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platforms is creating new opportunities for seamless, end-to-end security automation, further driving market growth.

From a regional perspective, North America currently dominates the Automated Indicator Enrichment market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of major cybersecurity vendors, high adoption rates of advanced security solutions, and stringent regulatory frameworks are key factors contributing to North America's leadership. Meanwhile, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by increasing digital transformation initiatives, rising cybercrime rates, and growing investments in cybersecurity infrastructure across emerging economies such as India, China, and Southeast Asia. Europe continues to show strong growth potential, particularly in sectors like BFSI, healthcare, and government, where data protection and compliance are top priorities.

Component Analysis

The Automated Indicator Enrichment market is segmented by component into software and services, each playing a vital role in the ecosystem. The software segment currently holds the largest market share, owing to the increasing deployment of advanced enrichment platforms that leverage AI, ML, and big data analytics to automate the enrichment of threat indicators. These platforms

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.

Methods 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 paper co-authored by said authors. Then, the edges have been enriched with edge-related (i.e., collaboration-related) attributes.

Digitalizing highway infrastructure is gaining interest in Germany and other countries due to the need for greater efficiency and sustainability. The maintenance of the built infrastructure accounts for nearly 30% of greenhouse gas emissions in Germany. To address this, Digital Twins are emerging as tools to optimize road systems. A Digital Twin of a built asset relies on a geometric-semantic as-is model of the area of interest, where an essential step for automated model generation is the semantic segmentation of reality capture data. While most approaches handle data without considering real-world context, our approach leverages existing geospatial data to enrich the data foundation through an adaptive feature extraction workflow. This workflow is adaptable to various model architectures, from deep learning methods like PointNet++ and PointNeXt to traditional machine learning models such as Random Forest. Our four-step workflow significantly boosts performance, improving overall accuracy by 20% and unweighted mean Intersection over Union (mIoU) by up to 43.47%. The target application is the semantic segmentation of point clouds in road environments. Additionally, the proposed modular workflow can be easily customized to fit diverse data sources and enhance semantic segmentation performance in a model-agnostic way.

This dataset contains the Paris subset of the Tourpedia dataset, specifically focusing on points of interest (POIs) categorized as attractions (dataset available at http://tour-pedia.org/download/paris-attraction.csv). The original dataset comprises 4,351 entries that encompass a variety of attractions across Paris, providing details on several attributes for each POI. These attributes include a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. The review fields contain textual feedback from users, aggregated from platforms such as Google Places, Foursquare, and Facebook, offering a qualitative insight into each location.

However, due to the initial dataset's high proportion of incomplete or inconsistently structured entries, a rigorous cleaning process was implemented. This process entailed the removal of erroneous and incomplete data points, ultimately refining the dataset to 477 entries that meet criteria for quality and structural coherence. These selected entries were subjected to further validation to ensure data integrity, enabling a more accurate representation of Paris' attractions.

• Paris.csv It contains columns including a unique identifier, POI name, category, location information (address), latitude, longitude, specific details, and user-generated reviews. Those reviews have been previously retrieved and pre-processed from Google Places, Foursquare, and Facebook, and have different formats: all words, only nouns, nouns + verbs, noun + adjectives and nouns + verbs + adjectives.

• Paris_annotated.csv It contains the ground truth relating to the previous dataset, with manual annotations made by humans on the categorisation of each of the POIs into 12 different pre-defined categories. It has the following columns:

  • POI name
  • POI's address
  • One column for each of the above categories. 1 means that the POI belongs to the category while blank indicates that it does not.

Success.ai’s Startup Data with Contact Data for Startup Founders Worldwide provides businesses with unparalleled access to key entrepreneurs and decision-makers shaping the global startup landscape. With data sourced from over 170 million verified professional profiles, this dataset offers essential contact details, including work emails and direct phone numbers, for founders in various industries and regions.

Whether you’re targeting tech innovators in Silicon Valley, fintech entrepreneurs in Europe, or e-commerce trailblazers in Asia, Success.ai ensures that your outreach efforts reach the right individuals at the right time.

Why Choose Success.ai’s Startup Founders Data?

1. Comprehensive Contact Information
2. Access verified work emails, phone numbers, and LinkedIn profiles for founders and key startup executives worldwide.

3. AI-driven validation ensures 99% accuracy, providing reliable data for effective outreach.

4. Global Reach Across Startup Ecosystems

5. Includes profiles of startup founders from tech, healthcare, fintech, sustainability, and other emerging sectors.

6. Covers North America, Europe, Asia-Pacific, South America, and the Middle East, helping you connect with founders on a global scale.

7. Continuously Updated Datasets

8. Real-time updates mean you always have the latest contact information, ensuring your outreach is timely and relevant.

9. Ethical and Compliant

10. Adheres to GDPR, CCPA, and global data privacy regulations, ensuring ethical and compliant use of data.

Data Highlights

• 170M+ Verified Professional Profiles: Includes startup founders and their teams across a range of industries.
• 50M Work Emails: AI-validated for precision in communication.
• 30M Company Profiles: Gain insights into the startups’ size, location, and industry focus.
• 700M Global Professional Profiles: Enriched data for comprehensive outreach and analysis.

Key Features of the Dataset:

1. Founder Decision-Maker Profiles
2. Identify and connect with founders and key executives who drive strategy, funding, and product decisions.

3. Engage with individuals who can approve partnerships, investments, and collaborations.

4. Advanced Filters for Precision Targeting

5. Filter by industry, funding stage, region, or startup size to narrow down your outreach efforts.

6. Ensure your campaigns target the most relevant contacts for your products, services, or investment opportunities.

7. AI-Driven Enrichment

8. Profiles are enriched with actionable data, offering insights that help tailor your messaging and improve response rates.

Strategic Use Cases:

1. Investor Relations and Funding Opportunities

2. Connect with founders seeking investment, pitch your venture capital or angel investment services, and establish long-term partnerships.

3. Business Development and Partnerships

4. Offer collaboration opportunities, strategic alliances, and joint ventures to startups in need of new market entries or product expansions.

5. Marketing and Sales Campaigns

6. Launch targeted email and phone outreach to founders who match your ideal customer profile, driving product adoption and long-term client relationships.

7. Recruitment and Talent Acquisition

8. Reach founders who may be open to recruitment partnerships or HR solutions, helping them build strong teams and scale effectively.

Why Choose Success.ai?

1. Best Price Guarantee

2. Enjoy top-quality, verified startup founder data at competitive prices, ensuring maximum return on investment.

3. Seamless Integration

4. Easily integrate verified contact data into your CRM or marketing platforms via APIs or customizable downloads.

5. Data Accuracy with AI Validation

6. With 99% data accuracy, you can trust the information to guide meaningful and productive outreach campaigns.

7. Customizable and Scalable Solutions

8. Tailor the dataset to your needs, focusing on specific industries, regions, or funding stages, and easily scale as your business grows.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enrich your existing CRM records with verified founder contact data, adding valuable insights for targeted engagements.

3. Lead Generation API

4. Automate lead generation and streamline your campaigns, ensuring efficient and scalable outreach to startup founders worldwide.

Leverage Success.ai’s B2B Contact Data for Startup Founders Worldwide to connect with the entrepreneurs driving innovation across global markets. With verified work emails, phone numbers, and continuously updated profiles, your outreach efforts become more impactful, timely, and effective.

Experience AI-validated accuracy and our Best Price Guarantee. Contact Success.ai today to learn how our B2B contact data solutions can help you engage with the startup founders who matter most.

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Establishment of a syndication flow from the department of Vendée (85) with data from the “Festivals and Events” schedule with enriched data: name, address, type of FMA (Fests and events), means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, methods of payment accepted, opening dates, rates, details of visits, videos.

ABSTRACT Enriched semantic markup provides meaning to content and allows interoperability between machines, encouraging the visualization of information for users, such as the use of rich snippets, which expand the information provided by search engines. The objective was to analyze and enrich semantically the contents of the graduate programs delivered to the users, so that they are interoperable and contribute to the community through the reuse and proposal of extension of a new entity. The methodology used was descriptive based on the compilation and systematization of qualitative and quantitative information, analysis and characterization of the contents that currently contain the studied web pages and the Schema.org vocabulary. As result, a content semantically enriched markup proposal is presented for the postgraduate programs offered by some Latin American universities contained in a new entity named ProgramaPosgrado, based on the vocabulary of Schema.org. It was concluded that enriched semantic markup using rich snippets is a true Semantic Web application that adds visibility and interoperability to Web content, verifying that Schema.org is a vocabulary that can be extended for use in different fields.

Abstract Background: Arterial hypertension is a precursor to the development of heart and renal failure, furthermore is associated with elevated oxidative markers. Environmental enrichment of rodents increases performance in memory tasks, also appears to exert an antioxidant effect in the hippocampus of normotensive rats. Objectives: Evaluate the effect of environmental enrichment on oxidative stress in the ventrolateral medulla, heart, and kidneys of renovascular hypertensive rats. Methods: Forty male Fischer rats (6 weeks old) were divided into four groups: normotensive standard condition (Sham-St), normotensive enriched environment (Sham-EE), hypertensive standard condition (2K1C-St), and hypertensive enriched environment (2K1C-EE). Animals were kept in enriched or standard cages for four weeks after all animals were euthanized. The level of significance was at p < 0.05. Results: 2K1C-St group presented higher mean arterial pressure (mmHg) 147.0 (122.0; 187.0) compared to Sham-St 101.0 (94.0; 109.0) and Sham-EE 106.0 (90.8; 117.8). Ventrolateral medulla from 2K1C-EE had higher superoxide dismutase (SOD) (49.1 ± 7.9 U/mg ptn) and catalase activity (0.8 ± 0.4 U/mg ptn) compared to SOD (24.1 ± 9.8 U/mg ptn) and catalase activity (0.3 ± 0.1 U/mg ptn) in 2K1C-St. 2K1C-EE presented lower lipid oxidation (0.39 ± 0.06 nmol/mg ptn) than 2K1C-St (0.53 ± 0.22 nmol/mg ptn) in ventrolateral medulla. Furthermore, the kidneys of 2K1C-EE (11.9 ± 2.3 U/mg ptn) animals presented higher superoxide-dismutase activity than those of 2K1C-St animals (9.1 ± 2.3 U/mg ptn). Conclusion: Environmental enrichment induced an antioxidant effect in the ventrolateral medulla and kidneys that contributes to reducing oxidative damage among hypertensive rats.

Outscraper's Global Location Data service is an advanced solution for harnessing location-based data from Google Maps. Equipped with features such as worldwide coverage, precise filtering, and a plethora of data fields, Outscraper is your reliable source of fresh and accurate data.

Outscraper's Global Location Data Service leverages the extensive data accessible via Google Maps to deliver critical location data on a global scale. This service offers a robust solution for your global intelligence needs, utilizing cutting-edge technology to collect and analyze data from Google Maps and create accurate and relevant location datasets. The service is supported by a constant stream of reliable and current data, powered by Outscraper's advanced web scraping technology, guaranteeing that the data pulled from Google Maps is both fresh and accurate.

One of the key features of Outscraper's Global Location Data Service is its advanced filtering capabilities, allowing you to extract only the location data you need. This means you can specify particular categories, locations, and other criteria to obtain the most pertinent and valuable data for your business requirements, eliminating the need to sort through irrelevant records.

With Outscraper, you gain worldwide coverage for your location data needs. The service's advanced data scraping technology lets you collect data from any country and city without restrictions, making it an indispensable tool for businesses operating on a global scale or those looking to expand internationally. Outscraper provides a wealth of data, offering an unmatched number of fields to compile and enrich your location data. With over 40 data fields, you can generate comprehensive and detailed datasets that offer deep insights into your areas of interest.

The global reach of this service spans across Africa, Asia, and Europe, covering over 150 countries, including but not limited to Zimbabwe in Africa, Yemen in Asia, and Slovenia in Europe. This broad coverage ensures that no matter where your business operations or interests lie, you will have access to the location data you need.

Experience the Outscraper difference today and elevate your location data analysis to the next level.

This describe names of girls and boys, include the meaning of there names.

The cells are: Gender, Name, Meaning, Origin

This data can help to enrich other's data sets.

ABSTRACT This study compared the growth curve models for the live weight and body length of Japanese quail raised under lights of various colors. The Brody, Gompertz, and von Bertalanffy growth models were used to investigate the effect of different colored lights on Japanese quail growth over a period of six weeks (1-42 days). Four lights of different colors, comprising yellow, red, blue, and white, were used in the study. According to the different light colors, the mean and standard error for the live weight and body length on day 42 were calculated as 196.09 and 3.87 g and 29.48 and 0.192 cm, respectively. Furthermore, while the differences in live weight according to the color of the light being used were statistically significant on days 14, 21, and 28, there were significant differences in body length on days 7, 28, 35, and 42, depending on the color of the light used. The highest values of R2 for body length and live weight were 0.9935 and 0.9988; the lowest sum of square error values for body length and live weight were 9.6588 and 10.6623 according to the Gompertz model. Test results did not reveal autocorrelation among serial data except for those grown under red colored lights.

One manifestation of individualization is a progressively differential response of individuals to the non-shared components of the same environment. Individualization has practical implications in the clinical setting, where subtle differences between patients are often decisive for the success of an intervention, yet there has been no suitable animal model to study its underlying biological mechanisms. Here we show that enriched environment (ENR) can serve as a model of brain individualization. We kept 40 isogenic female C57BL/6JRj mice for 3 months in ENR and compared these mice to an equally sized group of standard-housed control animals, looking at the effects on a wide range of phenotypes in terms of both means and variances. Although ENR influenced multiple parameters and restructured correlation patterns between them, it only increased differences among individuals in traits related to brain and behavior (adult hippocampal neurogenesis, motor cortex thickness, open field and obje...

Outscraper's Location Intelligence Service is a powerful and innovative tool that harnesses the rich data available from Google Maps to provide valuable Point of Interest (POI) data for businesses. This service is an excellent solution for local intelligence needs, using advanced technology to efficiently gather and analyze data from Google Maps, creating precise and relevant POI datasets​.

This Location Intelligence Service is backed by reliable and up-to-date data, thanks to Outscraper's advanced web scraping technology. This ensures that the data extracted from Google Maps is both accurate and fresh, providing a dependable source of data for your business operations and strategic planning​.

A key feature of Outscraper's Location Intelligence Service is its advanced filtering capabilities, enabling you to retrieve only the POI data you require. This means you can target specific categories, locations, and other criteria to get the most relevant and valuable data for your business needs, eliminating the need to sift through irrelevant records​.

With Outscraper, you also get worldwide coverage for your POI data needs. The service's advanced data scraping technology allows you to collect data from any country and city without limitations, making it an invaluable tool for businesses with global operations or those seeking to expand internationally​.

Outscraper provides a vast amount of data, offering the largest number of fields available to compile and enrich your POI data. With more than 40 data fields, you can create comprehensive and detailed datasets that provide deep insights into your areas of interest​.

Outscraper's Location Intelligence Service is designed to be user-friendly, even for those without coding skills. Creating a Google Maps scraping task is quick and simple with the Outscraper App Dashboard, where you select a few parameters like category, location, limits, language, and file extension to scrape data from Google Maps​.

Outscraper also offers API support, providing a fast and easy way to fetch Google Maps results in real-time. This feature is ideal for businesses that need to access location data quickly and efficiently​.

BackgroundHigh throughput molecular-interaction studies using immunoprecipitations (IP) or affinity purifications are powerful and widely used in biology research. One of many important applications of this method is to identify the set of RNAs that interact with a particular RNA-binding protein (RBP). Here, the unique statistical challenge presented is to delineate a specific set of RNAs that are enriched in one sample relative to another, typically a specific IP compared to a non-specific control to model background. The choice of normalization procedure critically impacts the number of RNAs that will be identified as interacting with an RBP at a given significance threshold – yet existing normalization methods make assumptions that are often fundamentally inaccurate when applied to IP enrichment data.MethodsIn this paper, we present a new normalization methodology that is specifically designed for identifying enriched RNA or DNA sequences in an IP. The normalization (called adaptive or AD normalization) uses a basic model of the IP experiment and is not a variant of mean, quantile, or other methodology previously proposed. The approach is evaluated statistically and tested with simulated and empirical data.Results and ConclusionsThe adaptive (AD) normalization method results in a greatly increased range in the number of enriched RNAs identified, fewer false positives, and overall better concordance with independent biological evidence, for the RBPs we analyzed, compared to median normalization. The approach is also applicable to the study of pairwise RNA, DNA and protein interactions such as the analysis of transcription factors via chromatin immunoprecipitation (ChIP) or any other experiments where samples from two conditions, one of which contains an enriched subset of the other, are studied.

1. Targeted enrichment of conserved genomic regions (e.g., ultraconserved elements or UCEs) has emerged as a promising tool for inferring evolutionary history in many organismal groups. Because the UCE approach is still relatively new, much remains to be learned about how best to identify UCE loci and design baits to enrich them.

2. We test an updated UCE identification and bait design workflow for the insect order Hymenoptera, with a particular focus on ants. The new strategy augments a previous bait design for Hymenoptera by (a) changing the parameters by which conserved genomic regions are identified and retained, and (b) increasing the number of genomes used for locus identification and bait design. We perform in vitro validation of the approach in ants by synthesizing an ant-specific bait set that targets UCE loci and a set of “legacy” phylogenetic markers. Using this bait set, we generate new data for 84 taxa (16/17 ant subfamilies) and extract loci from an additional 17 genome-enabled taxa. We then use these data to examine UCE capture success and phylogenetic performance across ants. We also test the workability of extracting legacy markers from enriched samples and combining the data with published data sets.

3. The updated bait design (hym-v2) contained a total of 2,590-targeted UCE loci for Hymenoptera, significantly increasing the number of loci relative to the original bait set (hym-v1; 1,510 loci). Across 38 genome-enabled Hymenoptera and 84 enriched samples, experiments demonstrated a high and unbiased capture success rate, with the mean locus enrichment rate being 2,214 loci per sample. Phylogenomic analyses of ants produced a robust tree that included strong support for previously uncertain relationships. Complementing the UCE results, we successfully enriched legacy markers, combined the data with published Sanger data sets, and generated a comprehensive ant phylogeny containing 1,060 terminals.

4. Overall, the new UCE bait design strategy resulted in an enhanced bait set for genome-scale phylogenetics in ants and likely all of Hymenoptera. Our in vitro tests demonstrate the utility of the updated design workflow, providing evidence that this approach could be applied to any organismal group with available genomic information.

Data set includes the following files:

1. Figures described in the article:

   Figure 1. Changes in the transcriptomic profile of WJ-MSCs (N=7) following the cytokine licensing:
   A – Principal component analysis (PCA) of transcriptome data from unprimed (light blue) and primed (dark blue) samples.
   B – Volcano plot depicting pathway-level enrichment analysis across various biological databases, including Hallmark, KEGG, REACTOME, Gene Ontology, and WikiPathways. Each point represents a distinct pathway, with the x-axis showing the normalized enrichment scores and the y-axis displaying the -log10 adjusted p-value.
   C – Enrichment plots for two hallmark gene sets significantly upregulated in primed samples: Interferon Gamma Response and TNFA signalling via NFKB pathways. Green curves represent the enrichment score (ES), while black bars indicate the position of genes within the ranked list.
   
   Figure 2. Distribution of up- and downregulated genes (fold changes, FC) referred to cell differentiation following cytokine priming based on analysis of the Gene ontology (GO) terms. The following GO terms are presented: GO:0002062 Chondrocyte differentiation, GO:0045445 Myoblast differentiation, GO:0035914 Skeletal muscle cell differentiation, GO:0051145 Smooth muscle cell differentiation, GO:0045444 Fat cell differentiation, GO:0001649 Osteoblast differentiation, GO:0030182 Neuron differentiation, GO:0030154 Cell differentiation.
   
   Figure 3. The expression of osteogenic and adipogenic genes of unprimed and cytokine licensed WJ-MSCs (N=4, in duplicates) following the 7 days of the differentiation induction. Data is presented as Mean ± SD relative expression of induced cells to control cells (not subjected to differentiation induction). Note: Ns – non-significant; * - p<0.05; ** - p<0.01.
   
   Figure 4. The content of secreted growth factors in the conditioned medium of WJ-MSCs (N=4, in duplicates). Data is presented as Mean ± SD. Note: * - p<0.05.
   
   Figure 5. The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of the unprimed and cytokine licensed WJ-MSCs (N=6, in triplicates) assessed using Seahorse assay. Note: Ns – non-significant; * - p<0.05; ** - p<0.01; *** - p<0.001.

   Figure 6. Metabolomic and lipidomic profile of WJ-MSCs following the cytokine licensing (N=4, in duplicates):
   A - Principal component analysis (PCA) of metabolomic data from unprimed (light blue) and primed (dark blue) WJ-MSCs samples.
   B – Volcano plot depicting clear separation of the main metabolite classes in WJ-MSCs induced by the cytokine priming. Each point represents a distinct most enriched metabolite within the pathway. Abbreviations: Tricarboxylic acid cycle metabolites (TCA), triglycerides (TG), free fatty acids (FAs), lysophosphatidylcholines (LPC), lysophosphatidylethanolamines (LPE), cholesterol esters (CE), and amino acids (AAs).
   C – The changes in selected metabolite families in WJ-MSCs following the cytokine priming. Note: Ns – non-significant; * - p<0.05; ** - p<0.01.

   D – Dot plot depicting log2 fold changes in metabolite levels between cytokine-primed WJ-MSCs and unprimed controls. Each dot represents a metabolite, with its position indicating the magnitude of fold change (log2) and its color/size denoting statistical significance.

2. Supplementary materials: Table 1. The list of the most up- or down-regulated genes (log2fold >1, p<0.001), detected in RNA-seq results. Figure 1S. Changes in expression of genes of interest after short-term cytokine licensing. Note: ** -p<0.01; *** - p<0.001. Figure 2S. The expression of osteogenic and adipogenic genes of unprimed and cytokine licensed WJ-MSCs (N=4 in duplicates) with and without IDO inhibition by 1-methyl-DL-tryptophan (1-MT, 1 mM) following the 7 days of the differentiation induction. Data is presented as Mean ± SD relative expression of induced cells to control cells (not subjected to differentiation induction) Ns – non-significant; * - p<0.05; ** - p<0.01.
3. RNA sequencing (initial data)
4. Metabolomic analysis (initial data)
5. List of normalized enrichment scores depicting pathway-level analysis across various biological databases, including Hallmark, KEGG, REACTOME, Gene Ontology, and WikiPathways (*file GO_terms)

This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseSample DataSee Sample Layer Item for sample data during Weather inactivity!Update FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.Additional information on Watches and Warnings.