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380 results for “age differences”
Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
<p><strong>Abstract</strong></p> <p>Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.</p> <p><strong>Data Description</strong></p> <p>This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. </p> <p>As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.</p> <p>Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.</p> <p><strong>Paper & Code</strong></p> <p>The original paper for this article can be accessed here:</p> <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> <p>To access the codes relevant for this project, please access the project GitHub Repos:</p> <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> <p>If using this work, please cite it based on the above paper, or using the following BibTex:</p> <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <p> </p> <p><strong>Data Access</strong></p> <p>The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi</p> <p><strong>Funding</strong></p> <p>ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).</p>
Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Turner, A., Heard, G., Hall, A., Wassens, S. (in review). Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk.</p> <p>The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod)</p> <p>1. <strong>Skeleto_dat_ready_Jan2021.csv</strong> Data from frog surveys conducted by Anna Turner</p> <p>2. <strong>Geoffs_data.csv</strong> Data from frog surveys conducted by Geoff Heard</p> <p>3. <strong>Environmental_variables_skeleto.csv</strong> Environmental data collected during surveys </p> <p>4. <strong>sk.dat_July21.csv</strong> Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis</p> <p>5. <strong>Variables_that_are_highly_correlated_with_each_other_season_wide.csv</strong> Testing for correlation</p> <p>6. <strong>Model_structure_skeleto_2.csv </strong>creates model structure for analysis</p> <p>7. <strong>Model_selection_statistics_June_21.csv </strong>Output from model</p> <p>R code is provided seperately for each of the following components:</p> <p>1. <strong>Data_collation_for_analysis_2.R</strong> Collating data from Anna and Geoffs datasets</p> <p>2. <strong>Skeleto_analysis_5.R - </strong>First uses regression modelling to explore factors correlated with variation in age</p> <p> - Following Scheele et al. (2016) regression models with a poisson distribution</p> <p> - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data</p> <p> - Plots male and female growth curves</p> <p> - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie (2012) but with bayesian implementation</p>
Dataset: Insular cortex dopamine 1 and 2 receptors in methamphetamine conditioned place preference and aversion: Age and sex differences
<p>Dataset for Insular cortex dopamine 1 and 2 receptors in methamphetamine conditioned place preference and aversion: Age and sex differences</p>
Brinell-Hardness (HBW 2.5/62.5) of Al-alloy EN AW-2618A after different aging times and temperatures
<p>The dataset contains data from Brinell hardness measurements of Al-alloy EN AW-2618A after aging for different times and temperatures. Aging was either load free or with applied tensile load (creep). The investigated material and the applied methods were described in detail in two publications.</p> <p>Version 2.0 has been extended with additional data for further ageing temperatures.</p> <p>Further information is provided in the file content.pdf.</p>
Neglected patterns of variation in transgenerational plasticity: The importance of different sources of environmental variation differs across ages and sexes in a cyprinid fish
<p>Adaptive transgenerational plasticity (TGP) requires individuals to integrate environmental experience across multiple sources. However, few empirical studies have considered that the relative relevance of certain sources might vary across ontogeny and sexes.</p> <p>Here, we address this knowledge gap by studying inducible antipredator defenses, one of the most convincing examples of TGP. We assessed individual and combined effects of perceived high predation risk in mothers, fathers, caring males and personal environments on the morphology of juvenile, adult male and adult female cyprinids Pimephales promelas.</p> <p>Parental rather than personal environmental experience determined morphological defense expression across ages and sexes, likely because parents had a longer sampling period.</p> <p>In juveniles and adult males, egg-mediated environmental experience outweighed sperm-mediated environmental experience in the induction of body shape differences, likely because eggs can transmit information beyond epigenomes. However, in adult females, where body shape responses can be interpreted as life-history plasticity, information from egg and sperm were equally important, likely resulting from different integration mechanisms between morphological and life-history plasticity.</p> <p>The importance of care-mediated relative to gamete-mediated variation changed between juveniles and adult males, likely because they represent short- and long-term environmental experience, respectively. Instead, in adult females, both sources were again equally important, potentially owing to lag-times of life-history plasticity. Parental care intensity only contributed marginally to defense formation.</p> <p>These results highlight age- and sex-specific prioritization of different environmental experiences so as to generate optimal phenotypes.</p>
Radii of S-phase Al2CuMg in Al-alloy EN AW-2618A after different aging times at 190°C
<p>The dataset contains data from quantitative microstructural analysis of transmission electron microscopy (TEM) studies of the S-phase (Al<sub>2</sub>CuMg) radii in Al-alloy EN AW 2618A. The investigated material and the applied methods were described in detail in two publications. Further information is provided in the file content.pdf.</p> <p>The conversion factor between pixel and length/area has been corrected/clarified in version 1.2 of the content file.</p>
Can disease resistance evolve independently at different ages? Genetic variation in age-dependent resistance to disease in three wild plant species
<p>1. Juveniles are typically less resistant (more susceptible) to infectious disease than adults, and this difference in susceptibility can help fuel the spread of pathogens in age-structured populations. However evolutionary explanations for this variation in resistance across age remain to be tested.</p> <p>2. One hypothesis is that natural selection has optimized resistance to peak at ages where disease exposure is greatest. A central assumption of this hypothesis is that hosts have the capacity to evolve resistance independently at different ages. This would mean that hosts populations have a) standing genetic variation in resistance at both juvenile and adult stages, and b) that this variation is not strongly correlated between age-classes so that selection acting at one age does not produce a correlated response at the other age</p> <p>3. Here we evaluated the capacity of three wild plant species (Silene latifolia, S. vulgaris, and Dianthus pavonius) to evolve resistance to their anther-smut pathogens (Microbotryum fungi), independently at different ages. The pathogen is pollinator-transmitted, and thus exposure risk is considered to be highest at the adult flowering stage.</p> <p>4. Within each species we grew families to different ages, inoculated individuals with anther smut, and evaluated the effects of age, family and their interaction on infection.</p> <p>5. In two of the plant species, S. latifolia and D. pavonius, resistance to smut at the juvenile stage was not correlated with resistance to smut at the adult stage. In all three species, we show there are significant age*family interaction effects, indicating that age-specificity of resistance varies among the plant families.</p> <p>6. Synthesis: These results indicate that different mechanisms likely underlie resistance at juvenile and adult stages and support the hypothesis that resistance can evolve independently in response to differing selection pressures as hosts age. Taken together our results provide new insight into the structure of genetic variation in age-dependent resistance in three well-studied wild host-pathogen systems.</p>
Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird
<p>The diet of an individual animal is subject to change over time, both in response to short-term food fluctuations and over longer time scales as an individual ages and meets different challenges over its life cycle. A metabarcoding approach was used to elucidate the diet of different life stages of a migratory songbird, the Eurasian reed warbler (<em>Acrocephalus scirpaceus</em>) over the 2017 summer breeding season in Somerset, UK. The faeces of adult, juvenile and nestling warblers were screened for invertebrate DNA, enabling the identification of prey species. Dietary analysis was coupled with monitoring of Diptera in the field using yellow sticky traps. Seasonal changes in warbler diet were subtle whereas age class had a greater influence on overall diet composition. Age classes showed high dietary overlap, but significant dietary differences were mediated through the selection of prey; i) from different taxonomic groups, ii) with different habitat origins (aquatic versus terrestrial) and iii) of different average approximate sizes. Our results highlight the value of metabarcoding data for enhancing ecological studies of insectivores in dynamic environments. </p>
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 5. Коррелограммы покаЗателей обилиЯ наЗемных моллюсков раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – H. lucorum, участок № 1, 2010 г.; B – Ch. tridens, участок № 2, 2011 г.; C – Ch. tridens, участок № 4, 2012 г.); D – Ch. tridens, участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 5. Spatial correlogram of the land snail different age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – H. lucorum, site 1, 2010; B – Ch. tridens, site 2, 2011; C – Ch. tridens, site 4, 2012; D – Ch. tridens, site 5, 2012 (Moran index confidence value presented by filled signs).
Tensile tests results on artificially aged aluminum-magnesium-silicon alloy blanks subjected to different localized heat treatment strategies
<p>Tensile tests results on artificially aged aluminum-magnesium-silicon alloy blanks subjected to different localized heat treatment strategies </p>
Рис. 2. Линейный (А) и весовой (Б) рост бурого морского петушка Alectrias alectrolophus из разных районов Тауйской губы Охотского моря Fig. 2. Linear (A) and weight (Б) growth of stone cockscomb Alectrias alectrolophus from different regions of Taui Bay, the Sea of Okhotsk in Size-age structure, growth, and feeding of stone cockscomb Alectrias alectrolophus (Stichaeidae) from different areas of Taui Bay, the Sea of Okhotsk
Рис. 2. Линейный (А) и весовой (Б) рост бурого морского петушка Alectrias alectrolophus из разных районов Тауйской губы Охотского моря Fig. 2. Linear (A) and weight (Б) growth of stone cockscomb Alectrias alectrolophus from different regions of Taui Bay, the Sea of Okhotsk
Рис. 3. Возрастная Δинамика относитеΛьных приростов ΔΛины (А) и массы теΛа (А) бурого морского петушка Alectrias alectrolophus из разных районов Тауйской губы Охотского моря Fig. 3. Age-related dynamics of relative gains in length (A) and body weight (A) of stone cockscomb Alectrias alectrolophus from different regions of Taui Bay, the Sea of Okhotsk in Size-age structure, growth, and feeding of stone cockscomb Alectrias alectrolophus (Stichaeidae) from different areas of Taui Bay, the Sea of Okhotsk
Рис. 3. Возрастная Δинамика относитеΛьных приростов ΔΛины (А) и массы теΛа (А) бурого морского петушка Alectrias alectrolophus из разных районов Тауйской губы Охотского моря Fig. 3. Age-related dynamics of relative gains in length (A) and body weight (A) of stone cockscomb Alectrias alectrolophus from different regions of Taui Bay, the Sea of Okhotsk
Рис. 1. Возрастной (А), размерный (Б) и весовой (В) состав бурого морского петушка Alectrias alectrolophus из разных районов Тауйской губы Охотского моря in Size-age structure, growth, and feeding of stone cockscomb Alectrias alectrolophus (Stichaeidae) from different areas of Taui Bay, the Sea of Okhotsk
Рис. 1. Возрастной (А), размерный (Б) и весовой (В) состав бурого морского петушка Alectrias alectrolophus из разных районов Тауйской губы Охотского моря
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 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>
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>
Fig. 1 in The Study Of Age-Related Variability Of Pigmentation Patterns Of The Shells Of Dreissena Polymorpha (Bivalvia, Dreissenidae) From Different Parts Of It'S Range
Fig. 1. Change of pattern types on zebra mussel shell. The present shell has four age zones (0+, 1+, 2+, 3+). The pattern sequence is С–АС–А–А.
Figure. Age-specific survival rate (lx) and natality (m x) of Axinoscymnus apioides at different temperatures (20 °C, 23 °C, 26 °C, 29 °C, and 32 °C). in Temperature influences the development, survival, and life history of Axinoscymnus apioides Kuznetsov & Ren (Coleoptera: Coccinellidae), a predator of whitefly
Figure. Age-specific survival rate (lx) and natality (m x) of Axinoscymnus apioides at different temperatures (20 °C, 23 °C, 26 °C, 29 °C, and 32 °C).
Fig. 3 in The trade-off between the transmission of chemical cues and parasites: behavioral interactions between leaf-cutting ant workers of different age classes
Fig. 3. Mean ± s.e. frequencies that young and old ants were observed giving or receiving allogrooming during a 15 s observation period.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.