There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth science data are collected from modern satellites, in-situ sensors and different climate models. Similarly, huge amount of flight operational data is downloaded for different commercial airlines. These different types of datasets need to be analyzed for finding outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations with only a subset of features available at any location. Moving these petabytes of data to a single location may waste a lot of bandwidth. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the entire data without moving all the data to a single location. The method we propose only centralizes a very small sample from the different data subsets at different locations. We analytically prove and experimentally verify that the algorithm offers high accuracy compared to complete centralization with only a fraction of the communication cost. We show that our algorithm is highly relevant to both earth sciences and aeronautics by describing applications in these domains. The performance of the algorithm is demonstrated on two large publicly available datasets: (1) the NASA MODIS satellite images and (2) a simulated aviation dataset generated by the ‘Commercial Modular Aero-Propulsion System Simulation’ (CMAPSS).

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..One person in each household is designated as the householder. In most cases, this is the person or one of the people in whose name the home is owned, being bought, or rented and who is listed on line one of the survey questionnaire. If there is no such person in the household, any adult household member 15 years old and over could be designated as the householder.Households are classified by type according to the presence of relatives. Two types of householders are distinguished: a family householder and a nonfamily householder. A family householder is a householder living with one or more individuals related to him or her by birth, marriage, or adoption. The householder and all people in the household related to him or her are family members. A nonfamily householder is a householder living alone or with non-relatives only.To determine poverty status of a householder in family households, one compares the total income in the past 12 months of all family members with the poverty threshold appropriate for that family size and composition. If the total family income is less than the threshold, then the householder together with every member of his or her family are considered as having income below the poverty level.In determining poverty status of a nonfamily householder, only the householder's own personal income is compared with the appropriate threshold for a single person. The poverty status of a nonfamily householder does not affect the poverty status of the other unrelated individuals living in the household and the incomes of people living in the household who are not related to the householder are not considered when determining the poverty status of a householder. The income of each unrelated individual is compared to the appropriate threshold for a single person..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of erro...

Temperate winters can impose severe conditions on songbirds that threaten survival, including shorter days and often lower temperature and food availability. One well-studied mechanism by which songbirds cope with such conditions is seasonal acclimatization of thermal metabolic traits, with strong evidence for both preparative and responsive changes in thermogenic capacity (i.e., the ability to generate heat) to low winter temperature. However, a bird's ability to cope with seasonal extremes or unpredictable events is likely dependent on a combination of behavioral and physiological traits that function to maintain allostatic balance. The ability to cope with reduced food availability may be an important component of organismal response to temperate winters in songbirds. Here we compare responses to experimentally reduced food availability at different times of year in captive red crossbills (Loxia curvirostra) and pine siskins (Spinus pinus) – two species that cope with variable food resources and live in cold places – to investigate seasonal changes in the organismal response to food availability. Further, red crossbills are known to use social information to improve response to reduced food availability, so we also examine whether use of social information in this context varies seasonally in this species. We find that pine siskins and red crossbills lose less body mass during time-restricted feedings in late winter compared to summer, and that red crossbills further benefit from social information gathered from observing other food-restricted red crossbills in both seasons. Observed changes in body mass were only partially explained by seasonal differences in food intake. Our results demonstrate seasonal acclimation to food stress and social information use across seasons in a controlled captive environment and highlight the importance of considering diverse physiological systems (e.g., thermogenic, metabolic, digestive, etc) to understand organismal responses to environmental challenges. Methods These data were collected in an experimental, repeated measures design across multiple different experiments. Experiments were identical in their food restriction methodology but were performed with different individuals and in different years. We use this database to compare seasonal differences in food intake and change in body mass during food restriction in captive finches - the red crossbill and pine siskin. Food was restricted by limiting the access to food cups to two 45-minute feeding sessions per day. We also compare seasonal differences in food intake and change in body mass during food restriction between red crossbills with and without predictive social information from food-restricted neighbors. The data deposited here are raw scores of food intake and body mass for each individual in the experiment across three days of food restriction. Some birds were housed as dyads and some individually. For dyads we calcualted food intake as the change in the mass of the food cup divided by two. Only one food intake score per pair was included in analysis of food intake, although the a food intake value is provided in this database for every individual.

We wish to answer this question: If you observe a ‘significant’ p-value after doing a single unbiased experiment, what is the probability that your result is a false positive? The weak evidence provided by p-values between 0.01 and 0.05 is explored by exact calculations of false positive risks. When you observe p = 0.05, the odds in favour of there being a real effect (given by the likelihood ratio) are about 3 : 1. This is far weaker evidence than the odds of 19 to 1 that might, wrongly, be inferred from the p-value. And if you want to limit the false positive risk to 5%, you would have to assume that you were 87% sure that there was a real effect before the experiment was done. If you observe p = 0.001 in a well-powered experiment, it gives a likelihood ratio of almost 100 : 1 odds on there being a real effect. That would usually be regarded as conclusive. But the false positive risk would still be 8% if the prior probability of a real effect were only 0.1. And, in this case, if you wanted to achieve a false positive risk of 5% you would need to observe p = 0.00045. It is recommended that the terms ‘significant’ and ‘non-significant’ should never be used. Rather, p-values should be supplemented by specifying the prior probability that would be needed to produce a specified (e.g. 5%) false positive risk. It may also be helpful to specify the minimum false positive risk associated with the observed p-value. Despite decades of warnings, many areas of science still insist on labelling a result of p < 0.05 as ‘statistically significant’. This practice must contribute to the lack of reproducibility in some areas of science. This is before you get to the many other well-known problems, like multiple comparisons, lack of randomization and p-hacking. Precise inductive inference is impossible and replication is the only way to be sure. Science is endangered by statistical misunderstanding, and by senior people who impose perverse incentives on scientists.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..A "limited English speaking household" is one in which no member 14 years old and over (1) speaks only English or (2) speaks a non-English language and speaks English "very well." In other words, all members 14 years old and over have at least some difficulty with English. By definition, English-only households cannot belong to this group. Previous Census Bureau data products have referred to these households as "linguistically isolated" and "Households in which no one 14 and over speaks English only or speaks a language other than English at home and speaks English 'very well'." This table is directly comparable to tables from earlier years that used these labels..The household language assigned to the housing unit is the non-English language spoken by the first person with a non-English language in the following order: reference person, spouse, parent, sibling, child, grandchild, in-law, other relative, unmarried partner, housemate/roommate, roomer/boarder, foster child, or other nonrelative. If no member of the household age 5 and over speaks a language other than English at home then the household language is English only..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin o...

This dataset includes the number of people enrolled in DSS services by town and by medical benefit plan from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here. About this data For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

Abstract Objective: The purpose of this study was to compare the operative mortality rate and outcomes of endovascular aneurysm repair (EVAR) between young and geriatric people in a single center. Methods: Eighty-five patients with abdominal aortic aneurysms who underwent EVAR between January 2012 and September 2016 were included. Outcomes were compared between two groups: the young (aged < 65 years) and the geriatric (aged ≥ 65 years). The primary study outcome was technical success; the secondary endpoints were mortality and secondary interventions. The mean follow-up time was 36 months (3-60 months). Results: The study included 72 males and 13 females with a mean age of 71.08±8.6 years (range 49-85 years). Of the 85 patients analyzed, 18 (21.2%) were under 65 years old and 67 patients (78.8%) were over 65 years old. There was no statistically significant correlation between chronic disease and age. We found no statistically significant difference between aneurysm diameter, neck angle, neck length, or right and left iliac angles. The secondary intervention rate was 7% (six patients). The conversion to open surgery was necessary for only one patient and only three deaths were reported (3.5%). There was no statistically significant difference in the mortality and reintervention rates between the age groups. The three deaths occurred only in the geriatric group and two died secondary to rupture. Kidney failure was observed in three patients in the geriatric group (4.5%). Conclusion: Our single-center experience shows that EVAR can be used safely in both young and geriatric patients.

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Project Summary Refugees are often considered to be among the world’s most powerless groups; they face significant structural barriers to political mobilization, including often extreme poverty and exposure to repression. Yet despite these odds refugee groups do occasionally mobilize to demand better services and greater rights. This paper examines varying levels of mobilization among Syrian refugees living in camps and informal settlements in Turkey, Lebanon, and Jordan in order to explain how marginalized and dispossessed groups manage to develop autonomous political strength. I explain the surprisingly high levels of mobilization in Jordan’s Za’atari Camp, compared to the relative quiescence of refugees in Turkish camps and Lebanese informal settlements, as the product of a set of strong informal leadership networks. These networks emerged due to two unique facets of the refugee management regime in Jordan: 1) the concentration of refugees in the camp, and 2) a fragmented governance system. In Turkey and Lebanon, where these two conditions were absent, refugees did not develop the strong leadership networks necessary to support mobilization. I develop this argument through structured comparison of three cases and within-case process tracing, using primary source documents from humanitarian agencies, contentious event data, and 87 original interviews conducted in the summer of 2015. Data Abstract The data in this deposit comprise part of the empirical material behind the 2018 Perspectives on Politics article "When Do the Dispossessed Protest? Informal Leadership and Mobilization in Syrian Refugee Camps." The article examines why Syrian refugees living in some refugee camps in the Middle East, but not others, engage in contentious mobilization against humanitarian authorities. I am sharing two types of data, both of which were compiled from web-based sources. First, I collected humanitarian documents from the data-sharing portal of the United Nations High Commission for Refugees (UNHCR). Specifically, I collected meeting minutes, governance plans, security reports, maps, and statistical reports used for the management of the Zaatari refugee camp in northern Jordan. I used these documents to study governance dynamics and contention patterns in the camp. The documents are mostly comprehensive within a given category. For example, I include all the Camp Management Committee meeting minutes and all the community mobilization meeting minutes that I was able to access through the portal. Similarly, both of the two safety and security reports available have been included, and the governance plan is the only one that was produced. I only include maps that I used to inform the analysis in the paper, as UNHCR produces hundreds of maps of different types to facilitate its governance of the camp. In the camp overview and population camps folder, I included the earliest statistical factsheet that I could find (April 2015), as that was closest to the period I was writing about (2012-2014), and the only REACH population survey that I was able to identify. Second, I worked with a data analytics company in Lebanon to web-scrape Internet content on Syrian refugee protests in Lebanon in order to build a catalog of protest events in that country. The web-scraping technique delivered a catchment of web content (mostly links to news articles and social media posts) referring to specific protest events. I then individually examined each of these events, using the web material, to confirm its validity and relevance to the project. Through this method I was able to identify eighteen events from February 2014 to January 2017. In an appendix to the article each event is enumerated in a table and the corroborating sources and original links are listed next to each event. In the depository the content of each corroborating source is also included (where possible), as is an archived url to the original source. Files DescriptionThe data are of two types: humanitarian documents and web-scraped material on protest events. The humanitarian documents are organized by topic. First are two documents with statistical information on the camp. Following these are files with the meeting minutes for the weekly Camp Coordinating Meeting in Zaatari (50 total). Then come meeting minutes for the community mobilization initiative. Next come two security reports and the camp governance plan. Finally, there are four maps. A full list of these files, including their original URL, generated by QDR curators, is also included as documentation. Most of these documents are in PDF format. Second, the catalog of protest events in Lebanon are included as a spreadsheet. Each event listed includes the details of the event as well as links to web content (usually news articles or social media posts) corroborating the occurrence of the event. These links were also archived using perma.cc by QDR, and the perma.cc links are included as well. This list is included...

N_PLAN_EAU_IFN_S_019 The DIREN Limousin has entrusted to the National Forestry Institute (IFN) map inventory of Limousin ponds from

orthophotoplans. The photographs date from 1999 for the Corrèze and 2000 for the Up and down and the Creuse. In view of the resolution of the photographs, only ponds of which the area is more than 1 000 m² have been surveyed. Some summary data on this work as well as the comparison with the SCAN25 and the BD CARTHAGE® can be found in the document (http://diren.dev.eservices.fr/donnees/eau/etangs/rendu.asp?page=etangs_presentation) This inventory is only partial since it concerns only the ponds of more than 1 000 m² and which are visible on the photographs (water to the time of shooting). Finally, it should be mentioned that bodies of water bordering two departments have been recorded in each of the departments and for the area concerned on the department (in particular Vassivière) which breaks into 1 body of water in Haute-Vienne of 448 ha and two bodies of water in Creuse 153 Ha and 331 Ha) or Lavaud or Bort les Organs that are counted only for the area included in the region. This consequent work of scanning on infrared boards colors leads to a fairly complete inventory of water bodies of more than 1 000 m² (which corresponds to the reporting threshold) administrative). The reliability of this inventory is very good both in terms of the completeness of the base and of the layout of the contours and therefore of the surfaces estimated. This leads to a very interesting tool for bets that can now compare their administrative basis of declared ponds with this inventory (undeclared ponds, comparison of areas) declared,...). But it is also an interesting basis for characterisation of pelvis heads since we can quickly know the number of ponds and the amount of water they represent in a given territoryN_PLAN_EAU_IFN_S_019

The DIREN Limousin has entrusted to the National Forestry Institute (IFN) map inventory of Limousin ponds from orthophotoplans. The photographs date from 1999 for the Corrèze and 2000 for the Up and down and the Creuse. In view of the resolution of the photographs, only ponds of which the area is more than 1 000 m² have been surveyed. Some summary data on this work as well as the comparison with the SCAN25 and the BD CARTHAGE® can be found in the document (http://diren.dev.eservices.fr/donnees/eau/etangs/rendu.asp?page=etangs_presentation) This inventory is only partial since it concerns only the ponds of more than 1 000 m² and which are visible on the photographs (water to the time of shooting). Finally, it should be mentioned that bodies of water bordering two departments have been recorded in each of the departments and for the area concerned on the department (in particular Vassivière) which breaks into 1 body of water in Haute-Vienne of 448 ha and two bodies of water in Creuse 153 Ha and 331 Ha) or Lavaud or Bort les Organs that are counted only for the area included in the region. This consequent work of scanning on infrared boards colors leads to a fairly complete inventory of water bodies of more than 1 000 m² (which corresponds to the reporting threshold) administrative). The reliability of this inventory is very good both in terms of the completeness of the base and of the layout of the contours and therefore of the surfaces estimated. This leads to a very interesting tool for bets that can now compare their administrative basis of declared ponds with this inventory (undeclared ponds, comparison of areas) declared,...). But it is also an interesting basis for characterisation of pelvis heads since we can quickly know the number of ponds and

the amount of water they represent in a given territory

This dataset contains the Version 1.2 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.2 CDR represents is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L2X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L2C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.

These Electron Current Spectra in the Jovian Magnetosphere are from the Plasma Spectrometer (PLS) Instrument on Voyager 1 during March 1979 Flyby of Jupiter. The Instrument has four Faraday Cups A, B, C, and D, the Electron Data come only from Faraday Cup D. The Data are specified in Terms of Current per Faraday Cup in femto-amperes (10^-15 A equals 1 fA) versus Channel Number and Energy (eV). This Data Set is for the PLS E1 Mode covering Electron Energies from 10 eV to 140 eV in 16 logarithmic Energy Channels. The PLS Instrument samples only one Mode of Electron (E1, E2) or Ion (L, M) Spectra in each Time Interval, so the E1 Data are not continuous but consecutive with the other Modes in Time. Reference: Bridge, H.S., Belcher, J.W., Butler, R.J., Lazarus, A.J., Mavretic, A.M., Sullivan, J.D., Siscoe, G.L., and V.M. Vasyliunas, The Plasma Experiment on the 1977 Voyager Mission, Space Sci. Rev., 21, 259-287, 1977.

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Hawaiian hoary bats ('ōpe'ape'a; Lasiurus semotus) were captured and tracked back to roosting locations on Hawaiʻi Island. Roost tree metrics were observed and collected from 2018 to 2021. We observed a total of 56 roost trees used by 46 bats (18 female; 25 male; 3 unknown). We examined roost preferences at the tree-level with discrete choice analysis. Discrete model choice sets were developed based on distinct selection events and served as the observational units at each level, that is roost tree and roost stand. The number of choice sets was determined both by the number of unique roost sites to which a bat was tracked and the duration of the sampling period over which it was confirmed at one or more roosts. A “basic” choice set was comprised of one used site and two random sites for each selection event. For bats observed for a short period (<3 days) at only one roost, we produced a choice set limited to only a single selection event. For bats tracked to only one roost but confirmed at that roost on at least three days, we included an additional independent selection event for that roost. An additional selection event was also assigned to bats that returned to the same roost locations during more than one season (Reproductive season = May to September; non-reproductive season = October to April) and/or year. Bats that used multiple roosts were assigned an equivalent number of selection events, and additional events if confirmed at a particular roost on at least three days. The method estimates the probability of specific habitat attributes being used by comparing selected to available but unselected random sites. We modeled day-roost selection at the tree-level with 91 choice sets for 45 (18F, 24M, 3 unknown) unique ‘ōpe‘ape‘a that included the habitat attributes of 55 unique trees. This data file includes data pertaining to random tree metrics including, height, canopy cover, and habitat classification.

BackgroundThe intestinal microbiota contributes to the colonization resistance of the gut towards bacterial pathogens. Antibiotic treatment often negatively affects the microbiome composition, rendering the host more susceptible for infections. However, a correct interpretation of such a perturbation requires quantitative microbiome profiling to reflect accurately the direction and magnitude of compositional changes within a microbiota. Standard 16S rRNA gene amplicon sequencing of microbiota samples offers compositional data in relative, but not absolute abundancies, and the presence of multiple copies of 16S rRNA genes in bacterial genomes introduces bias into compositional data. We explored whether improved sequencing data analysis influences the significance of the effect exerted by antibiotics on the faecal microbiota of young pigs using two veterinary antibiotics. Calculation of absolute abundances, either by flow cytometry-based bacterial cell counts or by spike-in of synthetic 16S rRNA genes, was employed and 16S rRNA gene copy numbers (GCN) were corrected.ResultsCell number determination exhibited large interindividual variability in two pig studies, using either tylosin or tulathromycin. Following tylosin application, flow cytometry-based cell counting revealed decreased absolute abundances of five families and ten genera. These results were not detectable by standard 16S analysis based on relative abundances. Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. In another experimental setting with tulathromycin treatment, bacterial abundance quantification by flow cytometry and by a spike-in method yielded similar results only on the phylum level. Even though the spike-in method identified the decrease of four genera, analysis by fluorescence-activated cell sorting (FACS) uncovered eight significantly reduced genera, such as Prevotella and Paraprevotella upon antibiotic treatment. In contrast, analysis of relative abundances only showed a decrease of Faecalibacterium and Rikenellaceae RC9 gut group and, thus, a much less detailed antibiotic effect.ConclusionFlow cytometry is a laborious method, but identified a higher number of significant microbiome changes in comparison to common compositional data analysis and even revealed to be superior to a spike-in method. Calculation of absolute abundances and GCN correction are valuable methods that should be standards in microbiome analyses in veterinary as well as human medicine.

This dataset includes the number of people enrolled in DSS services by town and by race from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here. About this data For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: A place in which, at the time of the census, one or more persons regularly sleep. It may be a room in a factory, store or office building, a loft over a stable, a canal boat, tent, or a wigwam. A building like a tenement or apartment house, if it has only one front door, counts as only one dwelling house, no matter how many persons or families live in it. But one building with a partition wall through it and a front door for each of the two parts, counts as two dwelling houses. So in a block of houses there are as many dwelling-houses as front doors. - Households: A group of individuals who occupy jointly a dwelling place or part of a dwelling place. A person who boards in one place and lodges in another should be returned as a member of the family where he lodges. A domestic servant, unless she sleeps elsewhere, is to be returned as a member of the family in which she works. All the occupants and employees of a hotel, if they regularly sleep there, make up, for census purposes, a single family, because they occupy one dwelling place. The same is true of all officials and inmates of an institution who live in the institution building. But where officers or employees of an institution sleep in detached houses or separate dwelling places, they are separate families. - Group quarters: Yes

Universe

All persons living in the United States including temporarily absent residents and sailors at sea. Native Americans living on reservations or under tribal rule were enumerated using a separate schedule.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Department of the Interior

SAMPLE SIZE (person records): 3852852.

SAMPLE DESIGN: 1-in-20 national random sample of the population. Alaska and Hawaii are not included in this dataset.

Mode of data collection

Face-to-face [f2f]

Research instrument

The census operation involved four schedules. Schedule 1 was used to enumerate households and collected information on individual characteristics. Other schedules were used to enumerate the Native American population, and record information on agriculture and livestock.

This dataset contains the Version 1.0 CYGNSS Level 2 Ocean Surface Heat Flux Science Data Record, which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). Only one netCDF-4 data file is produced each day (each file containing data from a combination of up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. Version 1.0 represents the first release. The Cyclone Global Navigation Satellite System (CYGNSS), launched on 15 December 2016, is a NASA Earth System Science Pathfinder Mission that was launched with the purpose to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the CYGNSS observatories provide nearly gap-free Earth coverage with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. The 35 degree orbital inclination allows CYGNSS to measure ocean surface winds between approximately 38 degrees North and 38 degrees South latitude using an innovative combination of all-weather performance Global Positioning System (GPS) L-band ocean surface reflectometry to penetrate the clouds and heavy precipitation. The Coupled Ocean-Atmosphere Response Experiment (COARE) algorithm is what is used in this dataset to estimate the latent and sensible heat fluxes and their respective transfer coefficients. While COARE's initial intentions were for low to moderate wind speeds, the version used for this product, COARE 3.5, has been verified with direct in situ flux measurements for wind speeds up to 25 m/s. As CYGNSS does not provide air/sea temperature, humidity, surface pressure or density, the producer of this dataset obtains these values from the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), which uses data assimilation to combine all available in situ and satellite observation data with an initial estimate of the atmospheric state, provided by a global atmospheric model. Since the MERRA-2 data is only updated on monthly intervals, this corresponding heat flux dataset is likewise updated on a monthly interval to reflect the latest data available from MERRA-2, thus accounting for measurement latency, with respect to CYGNSS observables, ranging from 1 to 2 months. The data from this release compares well with in situ buoy data, including: Kuroshio Extension Observatory (KEO), National Data Buoy Center (NDBC), Ocean Sustained Interdisciplinary Time-series Environment observation System (OceanSITES), Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and the Tropical Atmosphere Ocean (TAO) array. As this marks only the first data release, future work is expected to provide comparisons and validation with various field campaigns (e.g., PISTON, CAMP2Ex) as well as more buoy data, especially at higher flux estimates.

Identifying what drove the late Pleistocene megafaunal extinctions on the continents remains one of the most contested topics in historical science. This is especially so in Australia, which lost 90% of its large species by 40,000 years ago, more than half of them kangaroos. Determining causation has been obstructed by a poor understanding of their ecology. Using Dental Microwear Texture Analysis, we show that most members of Australia’s richest Pleistocene kangaroo assemblage had diets that were much more generalized than their craniodental anatomy implies. Mixed feeding across most kangaroos pinpoints dietary flexibility as a key behavioral adaptation to climate-driven fluctuations in vegetation structure, dispelling the likelihood that late Pleistocene climatic variation was the sole or primary driver of their disappearance. Methods Modern samples

Modern specimens used in this study are housed in the Australian Museum, Sydney (prefix AM M); Museum and Art Gallery of the Northern Territory, Alice Springs and Darwin (CAM, U); Museum Victoria, Melbourne (MV C, DTC); Flinders University Research Collection, Adelaide (FUR); Queensland Museum, Brisbane (QM A, J, JM), South Australian Museum, Adelaide (SAMA M); Western Australian Museum, Perth (WAM M); American Museum of Natural History, New York (AMNH); and Papua New Guinea National Museum and Art Gallery, Port Moresby (PNG MR). Sixteen extant species were sampled to capture a broad dietary spectrum, with an additional five species of *Dendrolagus *from New Guinea analyzed as a single unit, because no single species had adequate available samples (Table S1, Data S1).

Fossil samples

Paleontological specimens are housed in the South Australian Museum, Adelaide (SAMA P, FU). The sample originated from excavations of the Main Fossil Chamber deposit of Victoria Fossil Cave, Naracoorte, South Australia, which were led by Rod Wells (Flinders University) through the 1970s–1990s. The sequence consists of at least eight superposed infill sedimentary units (19, 42). A flowstone capping the sequence provides a minimum age of ~213 ka (22). Using existing stratigraphic designations (42), unit 8 and upper unit 7 (depth bin 7E, see below) are dated to ~220 ka and ~226 ka, respectively (21).

Here we assess the diets of the 14 best-represented VFC macropodid species (Table S1), excluding potoroines, because DMTA for modern, largely fungivorous potoroines has yet to be investigated. Insufficient samples could be attained for *Lagorchestes leporides *and Procoptodon goliah. Recently, *Protemnodon brehus *and *Prot. roechus *have been relegated to nomina dubia, and effectively replaced by *Prot. mamkurra and Prot. viator *(12). Unfortunately, the cheek dentition of these two species cannot be distinguished, so samples are here referred to as *Prot. mamkurra *due to its greater abundance in the VFC deposit based on specimens that can be identified to species level (I. Kerr, pers. comm. 21/3/2024). However, there remains a possibility that *Prot. viator *is present in the sample.

Data acquisition

Specimens were cleaned and cast using standard procedures (43), which have been shown to have high fidelity in replicating microwear surfaces (44). Casts were scanned using a Sensofar Plμ NEOX confocal microscope “Bruce” at Flinders University, (100X ELWD objective, neural aperture 0.80, blue light 460nm, spatial sampling 0.17µm, step height <4nm). Four scans were taken of each surface, (2x2, overlap 15%) stitched to a total scan size of 242 × 181 µm2.

To minimize sampling errors, the ‘Soft Filter-edited’ data processing template (45) was used on all scans in SensoMAP 7.1.2.7288 (Digital Surf). Some samples utilized here have been previously published upon in methodological papers (45, 46), though scanning parameters were identical and all data were recollected here to ensure comparability. Photosimulations of representative scans can be found in figure S1, and PDFs of all scanned surfaces can be found at the Dryad page for this article PDFs_of_Specimens_Scanned.zip.

Intraspecific factors were scored for each specimen (46, see table S2). In addition, we also considered the factor islands, i.e., whether or not a specimen originated from an island (47). Due to ease of scanning, we priorirized larger tooth facets (1 and 6 for upper molars, 4 and 9 for lower molars), and macrowear stages 2–3, but not exclusively, which allowed effective modeling of interspecific differences where low numbers of specimens were available (46). It should also be noted that (46) indicates that facet numbering followed (48), when in fact it used that of (49).

Data were collected using six Scale-Sensitive Fractal Analysis variables (Asfc, HAsfc9, HAsfc81, Smc, Lsfc, epLsar and, NepLsar; 43), 25 International Organization for Standardization (ISO) variables (*S10z, Sa, Sal, Sda, Sdax, Sddx, Sdq, Sdv, Sdvx, Shh, Shhq, Shhx, Shv, Sk, Sku, Smc, Smrk1, Spc, Spd, Spk, Ssk, Svd, Svk, Vmc, *and *Vvv; *(50), and four variables relating to Motif, Isotropy and Furrow (Madf, Metf, Medf, and Pc;51).

Analyses

Our sample is analyzed in two ways: 1) As a single time-averaged sample, which is ideal for characterizing diets, and modern and VFC species analyzed in the same way, with species present in both samples kept discreet in analysis. 2) by depth (in seven consecutive units; Table S5) to track intraspecific variation through time for the four most abundant VFC macropodids, as well as across subfamilies, and independent of taxonomy to assess broad temporal trends.

All data were transformed for normality prior to analysis using the *BestNormalize *package in R. version 4.3.2 (52; Data S1). Many of the 35 variables used measure similar aspects of surface metrology, which was investigated through a correlation matrix (Figure S2). Variables with the highest correlation were systematically removed until a smaller set of 18 variables were retained to best describe the data with minimum collinearity (Fig. S3). The variables *Sku, Smrk1, and Ssk *were later removed from analysis having shown no significant differences between species, leaving 15 variables for analysis (Table S3).

Data for final variables were modelled using linear mixed-effect modeling (LME), where factors were included only where they improve the ability of the model to fit the data (46). LME also allows sub-sampling (53) allowing a larger dataset of n=2650 scans to be used to delineate dietary differences. Multiple models were constructed following (46), with delineating differences between species, being principle and all other factors only included where they improved the ability of the model to fit the data. Models were compared using different measures of model fit, and cross-validation on independent hold-out datasets, in this case using five folds (see table S4; 53).

LME also allows the inclusion of random factors. In DMTA, this is particularly useful as it accounts for inter-individual sample repetition by using specimen as a random factor (46). All remaining variables were also permutated as random effects in the modeling process to see if this better matches the distribution any of the variables. As some factors (e.g., tooth position and tooth facet) may be better considered as nested factors these were also added into the comparison set (Data S2). LME also requires a reference level for each factor, which acts as a base against which all other levels of each factor are compared (see Table S2). For interspecific comparisons, the mixed feeder,* Thylogale stigmatica*, was chosen as reference level as a generalist mixed-feeding species (13).

Comparison used multiple measures of model ‘fit’, (Conditional R2, Marginal R2, Sigma, Performance, AIC, and cross-validated R2 were used; see Table S4 for a glossary of these). Any models with >2 singularities within cross-validated subsets were flagged as suspect and not included for comparison (Table S4; Data S2). To balance remaining models, those which fell in the top ten of the most measures were selected, or where multiple models performed equally at this, models were visually compared to see which best differentiated species. Where these were identical, the model with fewest parameters was chosen (Data S2; Figures S4–6).

ANOVA models were then run to provide statistical support for differences evident in the final models constructed by LME modeling, as well as additional tests considering differences between species only. For the LME-based models, non-significant factors were dropped from comparison reiteratively until only significant factors remained, with significance defined as the F statistic *p<*0.05. As ANOVA cannot handle random effects, the effect of specimen was dropped from ANOVA comparisons. Because of this, a single scan was used for each specimen, with the most commonly sampled teeth and facets used to determine which scan was used. While removing any subsampling effects, this reduced the sample size to n=937 (see Table S1 ‘N. specimens’ for the sample size for each species, and Data S1 for the full dataset). Post-hoc comparisons were undertaken using Tukey’s HSD test of pairwise comparisons (Data S1).

To visualize the dataset across the multiple variables being used, and help attain a simplified consensus, a Principal Components Analysis (PCA) was undertaken using the mean for each of the 15 final variables for each taxon. Subsampled data for modeling was removed prior to PCA visualization as per ANOVA dataset above (Data S1).

A distinct set of analyses considered dietary change through time, restricted to specimens from Pit C in the Main Fossil Chamber of VFC, where the most reliable depth information is available (42). Analysis considered stratigraphic units 4–8, with the deepest units 4 and 5 combined due to low sample sizes (42). In contrast, the

Infectious diseases remain a serious public health concern globally, while the need for reliable and representative surveillance systems remains as acute as ever. The public health surveillance of infectious diseases uses reported positive results from sentinel clinical laboratories or laboratory networks, to survey the presence of specific microbial agents known to constitute a threat to public health in a given population. This monitoring activity is commonly based on a representative fraction of the microbiology laboratories nationally reporting to a single central reference point. However, in recent years a number of clinical microbiology laboratories (CML) have undergone a process of consolidation involving a shift toward laboratory amalgamation and closer real-time informational linkage. This report aims to investigate whether such merging activities might have a potential impact on infectious diseases surveillance. Influenza data was used from Belgian public health surveillance 2014–2017, to evaluate whether national infection trends could be estimated equally as effectively from only just one centralized CML serving the wider Brussels area (LHUB-ULB). The overall comparison reveals that there is a close correlation and representativeness of the LHUB-ULB data to the national and international data for the same time periods, both on epidemiological and molecular grounds. Notably, the effectiveness of the LHUB-ULB surveillance remains partially subject to local regional variations. A subset of the Influenza samples had their whole genome sequenced so that the observed epidemiological trends could be correlated to molecular observations from the same period, as an added-value proposition. These results illustrate that the real-time integration of high-throughput whole genome sequencing platforms available in consolidated CMLs into the public health surveillance system is not only credible but also advantageous to use for future surveillance and prediction purposes. This can be most effective when implemented for automatic detection systems that might include multiple layers of information and timely implementation of control strategies.