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12,871 results for “aging;”
Fig. 2 in Age affects antibody levels and anthelmintic treatment efficacy in a wild rodent
Fig. 2. Relationship between mouse age (scaled ELW), treatment and post-treatment H. polygyrus burden as predicted by GLM model. The red line represents model estimates for treated animals, black lines represent model estimates for control animals. Shaded areas represent estimated standard errors. Points represent raw data.
Fig. 4 in Age determination of the Mongolian wild ass (Equus hemionus Pallas, 1775) by the dentition patterns and annual lines in the tooth cementum
Fig. 4. The times of eruption, development of cementum and formation of the annual lines in the first upper incisor (I1) at the labial (left) and lingual side (right).
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 100 Schizophrenia Patients to 48 Patients with Major Depression -Figure 4. Histogram age distributions of the 100 Schizophrenia patients illustrating clusters of patients at 20, 30, 40, and 50 years old.
<p>The figure below is a histogram distribution of the ages of one hundred Schizophrenia<br> patients in this study. There appears to be a cyclical peak every ten-years cycles at 20, 30, 40, and<br> 50-year-old patients. This may suggest a recent finding that CD8 T cells, which play a pivotal role<br> in mediating long-term immunity to Toxoplasma, are down-regulated in schizophrenia patients<br> (Bhadra et al., 2013). Based on the aforementioned statements, a vaccine for Toxoplasmosis may be<br> difficult to achieve, but the treatment and screening possibilities in patients with psychosis are<br> available today.</p>
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 4. Facebook adoption rate by age group in Romania
<p>Also, according to Facebrands statistics of 15 October 2015, the Facebook penetration rate in the population is 39.76% and in the total number of Romanian Internet users is 82.97%. The statistics infirm the preconceived ideas of skeptics that the websites of socialization have no relevance for tourism organizations because the contained information in these websites is unstructured, inconsistent in terms of content and irrelevant for tourism, or newer, the ideas of those who believe that the majority of Facebook users are young and very young (under 18). From Figure 4 we can see that the segment of major users (over 18 years) totals 88.3% of the total number of Facebook users and these users may have at any time the quality of tourists.</p>
Age-resolved chemistry of red giants in the solar neighbourhood
<p>In our associated paper we find age-abundance trends for individual elements using a sample of solar-neighborhood stars observed with the SDSS-APOGEE instrument. Here we publish the likelihood functions from a Bayesian isochrone matching to determine the most probable age.</p>
Data set for the paper "What are the Effects of History Length and Age on Mining Software Change Impact?"
<p>Data set for the paper What are the Effects of History Length and Age on Mining Software Change Impact?<br> by Leon Moonen, Thomas Rolfsnes, David Binkley and Stefano di Alesio.<br> In Journal of Empirical Software Engineering (EMSE), 2018, Springer. https://doi.org/10.1007/s10664-017-9588-z<br> Available from https://evolveit.bitbucket.io/publications/emse2018/</p> <p>Please cite this work by referring to the corresponding journal publication (a preprint is included in this package).</p> <p>The goal of Software Change Impact Analysis is to identify artifacts (typically source-code files or individual methods therein) potentially affected by a change. Recently, there has been increased interest in <em>mining</em> software change impact based on evolutionary coupling. A particularly promising approach uses association rule mining to uncover potentially affected artifacts from patterns in the system’s change history. Two main considerations when using this approach are the <em>history length</em>, the number of transactions from the change history used to identify the impact of a change, and <em>history age</em>, the number of transactions that have occurred since patterns were last mined from the history. Although history length and age can significantly affect the quality of mining results, few guidelines exist on how to best select appropriate values for these two parameters.</p> <p>In this paper, we empirically investigate the effects of history length and age on the quality of change impact analysis using mined evolutionary coupling. Specifically, we report on a series of systematic experiments using three state-of-the-art mining algorithms that involve the change histories of two large industrial systems and 17 large open source systems. In these experiments, we vary the length and age of the history used to mine software change impact, and assess how this affects precision and applicability. Results from the study are used to derive practical guidelines for choosing history length and age when applying association rule mining to conduct software change impact analysis. </p>
Influence of young age microbiome on adult sleep behavior in D. Melanogaster
<p>There is growing evidence for the interaction between the gut microbiome and the brain. Several studies report strong correlations between the composition of the gut microbiome and various neurological diseases. Moreover, gut bacteria are shown to influence levels of neurotransmitters, e.g GABA, which are unbalanced in stress related disorders, such as anxiety and depression but also in in sleep disorders.</p> <p><em>Drosophila Melanogaster</em> is a powerful model organism for investigating the interaction between the microbiome and the brain. In addition to available genetic techniques, yielding germ free (axenic) flies and establishing gnotobiotic cultures is faster and easier with fruit flies compared to other model organisms. Moreover, <em>Drosophila</em> microbiome is much simpler in complexity, in contrast to the vertebrate microbiome.</p> <p>We investigated the significance of the young age microbiome on adult sleep behaviour in <em>Drosophila</em>. Our hypothesis was that differences in microbiome composition might elucidate the reason for the behavioral variability in resilience/vulnerability to sleep deprivation, amongst individuals with same genetic background. However, our results suggest that there is no/ minor effect of the <em>Drosophila </em>microbiome on sleep behaviour. </p> <p> </p>
Marriage Proportion, Age Specific Fertility, Births within Marriage ratios for US, Japan, and selected European countries
<p>Dataset to accompany the paper "Marital fertility patterns and nonmarital birth ratios: an integrated approach"</p> <p>Includes:</p> <p>US African American women and White American women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1980, 1985, 1990, 1995, 2000.</p> <p>Selected European country women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1991, 2001, and 2011 (data not available for all countries in all years).</p> <p>Japanese women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1950, 1960, 1970, 1980, 1990, 1995, 2000, 2005, and 2010.</p> <p> </p>
Y-chromosome and mitochondrial data for the article entitled: "The First Horse Herders and the Impact of Early Bronze Age Steppe Expansions into Asia"
<p>Y-chromosome and mitochondrial data for the article entitled: "The First Horse Herders and the Impact of Early Bronze Age Steppe Expansions into Asia"</p>
Artificial Intelligence and the Future of Smart Cities-Figure 4. Influence of AI in the development of smart cities by respondents age
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 5. Smart features as the main beneficiaries of AI in terms of the respondent's age (statistically significant differences only for 7.1 and 7.3)
<p>The majority of the respondents who found the smart features to be the main beneficiaries of AI facilities were ranging between 31-40 years old and +41 age old, followed by the 18-25 age group (M=3.80, SD =0.75), 26-30 (MD=4.0, SD =.75) (Figure 5).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 8. Influence of AI on individual safety by respondents age
<p>On question 11 respondents were asked to consider the influence of AI on individual safety using a 5 points Likert Scale ranging from 1 being “totally unimportant” and 5 being “very important”. The analysis of variance was used to determine the differences by age group and by gender. The analysis shows that no statistical interaction was found between age and gender F=3.081, p=0.08. We found statistically significant differences by gender F=7.639, p<0.05 and age F=6.318, p=.001). Female participants scored significantly higher (M=4.40, SD=.81) than male participants (M=4.00, SD=.60) on question 11 about the influence of AI on individual safety. At the same question: the 41-50 age group scored the highest (M=4.50, SD=.51), followed by the 18-25 age group (M=4.40, SD=.81); the 26-30 age group scored lower than the 18-25 age group and significantly higher than the 31-40 age group (M=3.87, SD=.60) (Figure 8).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 7. Q.9.Which of the following job functions will AI impact the most over the next 10 years? (Statistically significant differences by age for 9.3, 9.4, 9.5 and 9.6)
<p>The analysis reveals that people perceive that AI will have a greater impact over the next 10 years on marketing (for example, intelligent customer targeting, planning and executing marketing campaigns) scored significantly higher (M=4.37, SD=.69) than on finance (for example, robotic financial advisors, automated corporate financial analysis) (M=3.87, SD=.60) (Figure 7). For the same question customer services scored significantly higher (M=3.75, SD=.83) than health (e.g. consultation and diagnosis, surgery) (M=3.25, SD=.83). For the same question, the analyses by gender reveals that the majority of female participants scored significantly higher (M=3.40, SD=.81) than male participants (M=3.18, SD=.57) and those aged in the second group.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 10. Respondents' opinions about the use of robots in different activities (grouped by age)
<p>Question 14 was used to evaluate the respondents’ opinions about the use of robots in the following activities: performing medical surgeries, child care, supply of consumer goods, driving a car, assistance in performing tasks at work and cleaning (Figure 10). The respondents feel most confident and safe to use robots for cleaning (M=4.18, SD=1.07) and for assistance in performing tasks at work (M=4.12, SD=1.05) and less confident and safe to use robots for driving a car (M=3.87, SD=1.11), for supply of consumer goods (M=3.81, SD=.81) and performing medical surgeries (M=3.68, SD=.92) The child care obtained the lower score (M=2.00, SD=86).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age
<p>On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).</p>
Sanderlings (Calidris alba) of two different age classes at the moment of individual colour-marking at four non-breeding sites that were or were not observed during migration following the capture.
<p>The data file contains data of Sanderlings (<em>Calidris alba</em>) of two different age classes at the moment of capture and individual colour-marking at one of four wintering areas that were or were not observed during migration following the capture. Each individual is indicated with a unique number in column “BirdID”. The column “country” indicates which of the four wintering areas (as depicted in Fig. 1 in the manuscript), with GB indicating England, PT indicating Portugal, MR indicating Mauritania and GH indicating Ghana. The “age” of each bird was either juvenile (<1 year old) or adult (>1 year old). Whether an individual was observed during migration, i.e. at least 2 latitudinal degrees north of its average winter location between 15 March – 15 October, in the migration period following capture is indicated with a 0 (not observed) or 1 (observed) in the column “observed”. Further details can be found in the methods section in the manuscript.</p>
Data archive for Gott et al. 'Chronological age, biological age, and individual variation in the stress response in the European starling: A follow-up study'
<p>Data archive for Gott et al. 'Chronological age, biological age, and individual variation in the stress response in the European starling: A follow-up study'.</p> <p>Revised version of September 4 2018.</p> <p>Contains one data file and one R script to reproduce the analyses in the paper.</p>
Age distribution Austria vs. Vienna
<p>Daset produced by the Data Stewardship Project, comparing the Austrian population distribution by Age to the population distribution of Vienna.</p>
Advance and retreat of glaciers during the end of the Little Ice Age in Europe
<p>Input data and model results of Huss&Förster (2019), L'avancée et le recul des glaciers pendant le Petit Age Glaciaire. Actes du colloque du Giétro, 14./15.juin, 2018. In: Annales Valaisannes, publication of the SHVR.</p> <p>Refer to file header for the description of all variables.</p> <p> </p>
Data for the publication "Impact of Isolated Atmospheric Aging processes on the Cloud Condensation Nucleiactivation of Soot Particles"
<p>The repository contains the data for the paper:</p> <p>Friebel, F., Lobo, P., Neubauer, D., Lohmann, U., Drossaart van Dusseldorp, S., Mühlhofer, E., and Mensah, A. A.: Impact of isolated atmospheric aging processes on the cloud condensation nuclei activation of soot particles, Atmos. Chem. Phys., 19, 15545–15567, https://doi.org/10.5194/acp-19-15545-2019, 2019.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3452036)</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.