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1,708 results for “young adults”
EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks
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From the Childhood Past: Views of Young Adults on Parental Sharing of Children's Photos
<p>The dataset contains the responses of a questionnaire asking young adults about their perspectives on parental sharing of children's photos on social media.</p> <p>The four files in this dataset contain:<br>1) README: This file provides detailed instructions on how to use the code.<br>2) Data File: A CSV file containing the raw data collected from the study participants.<br>3) Codebook: This file describes every item in the CSV file in detail.<br>4) R Code: This script conducts the analysis of the findings described in the paper.</p>
Raw data of healthy young adults in the Weather Prediction Task
<p>Raw data of 22 healthy young adults (11 females; average age: 26.29 years; range: 21.72–30.82) in the Weather Prediction Task with 100 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Raw data of 15 healthy young adults (9 females; average age: 26.58 years; range: 20.37–28.84) in the Weather Prediction Task with 200 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials.</p> <p>Bochud-Fragnière E, Banta Lavenex P and Lavenex P (2022) What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task. Front. Psychol. 13:886339. doi: 10.3389/fpsyg.2022.886339</p>
Differences in Chemo-signaling Compound-Evoked Brain Activity in Male and Female Young Adults: A Pilot Study in the Role of Sexual Dimorphism in Olfactory Chemo-Signaling
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Dataset and codebook for the article by Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: 10.1001/jamanetworkopen.2022.37563
<p>Dataset and codebook for the article Gaume J, Bertholet N, McCambridge J, et al. <strong>Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial</strong>. JAMA Netw Open. 2022;5(10):e2237563. doi: <a href="http://jamanetwork.com/article.aspx?doi=10.1001/jamanetworkopen.2022.37563">10.1001/jamanetworkopen.2022.37563</a></p> <p>The dataset contains all data needed to reproduce the results in the above cited article.</p> <p>Variable description and labels can be found in the codebook.</p> <p>Please refer to the published article and supplemental online content for further information about the data and the study procedures.</p>
Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"
<p>Written by: Paolo Mistretta<br> Contact information: paolo.mistretta@phd.unipd.it<br> Date: 24/01/2020</p> <p><br> This document contains supplementary material for the article<br> “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition”<br> (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone)<br> presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020.</p> <p><br> Data are contained in the file: “database_ISEA2020.mat”</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</p>
Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults
<p>There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age.</p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the code to process the data. In total, 520 trials from 215 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Group: the participant's group (G1=young adults, walking; G2=old adults, walking; G3=young adults, running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (walking or running)</li> <li>Speed: the speed at which the recordings were conducted in [m/s]</li> <li>Speed_type: the distinction between fixed (decided by the researchers) or preferred (selected by the participant) speed</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Trials are named like “ID0020_M_YOUNG_TW_01,” where the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with one row for each gait cycle and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_ID0020_M_YOUNG_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_ID0003_F_OLD_TW_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “ID0003” indicate the participant number (in this example the 3rd), the character “F” indicates the sex, the characters “OLD” indicate the age group, the characters “TW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”. The latest version of this code can be found at https://github.com/alesantuz/musclesyneRgies.</p>
Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan in Brown bear (Ursus arctos) of Zavyalov Island (Sea of Okhotsk): Abundance and possible migration routes
Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan
Fig. 7 in Reciprocal Predation Between Preserved And Invasive Species: Adult Bombina Bombina Predate Young Whitebaits Of Alien Fish Perccottus Glenii
Fig. 7. Dynamics of relative predation (% from existed number of live whitebaits) for all model populations of B. bombina.
Fig. 1 in Reciprocal Predation Between Preserved And Invasive Species: Adult Bombina Bombina Predate Young Whitebaits Of Alien Fish Perccottus Glenii
Fig. 1. Overlapping areas of B. bombina and P. glenii distribution in Latvia (Pupina et al. In press).
Fig. 9 in Reciprocal Predation Between Preserved And Invasive Species: Adult Bombina Bombina Predate Young Whitebaits Of Alien Fish Perccottus Glenii
Fig. 9. Scheme of the reciprocal predation between B. bombina and its invasive threat P. glenii registered in the study.
Fig. 5 in Reciprocal Predation Between Preserved And Invasive Species: Adult Bombina Bombina Predate Young Whitebaits Of Alien Fish Perccottus Glenii
Fig. 5. Number of left live, predated, and died/ killed P. glenii in all experimental groups in total after the experiment.
Fig. 3 in Reciprocal Predation Between Preserved And Invasive Species: Adult Bombina Bombina Predate Young Whitebaits Of Alien Fish Perccottus Glenii
Fig. 3. Dynamics of number of live, predated, and died/killed P. glenii in different B. bombina model populations (Bb-1, Bb-2, Bb-3, and Bb-4).
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>
Our Mythical History: Children's and Young Adults' Culture in Response to the Heritage of Ancient Greece and Rome
<p>A short movie from the international conference <strong><em>Our Mythical History: Children’s and Young Adults’ Culture in Response to the Heritage of Ancient Greece and Rome </em></strong>held at the Faculty of "Artes Liberales", University of Warsaw, May 22-26, 2019</p> <p>available at <a href="https://www.youtube.com/watch?v=jVeEjWSCXD8">https://www.youtube.com/watch?v=jVeEjWSCXD8</a> </p> <p>Music: <em>Brave </em>by WildKitty Tunes, Video: Mirosław Kaźmierczak, Coordination: Katarzyna Marciniak</p> <p>Art works used in the movie: Matylda Tracewska, Zbigniew Karaszewski</p>
Fig. 1.—A young adult male Mustela africana from a in Mustela africana (Carnivora: Mustelidae)
Fig. 1.—A young adult male Mustela africana from a zoological garden in Pará, Brazil (American Museum of Natural History [AMNH] 37475). Total length (from the skin tag) is 548 mm. Photograph by P. M. Velazco, used with permission.
Dissecting Tiktok and social media for children and young adults
<p>This data is related to protocol for systematic literature review and the details of literatures selected for systematic review.</p>
Text-fig. 3. Right maxilla of a young adult individual of Arsinoitherium zitteli from the Fayum, Egypt (M 8802, NHM collection, London). The dotted lines indicate the homologous part preserved in the specimen from Grigema, Tunisia. a) stereo occlusal view, b) stereo buccal view, c) stereo lingual view. in Arsinoitherium (Embrithopoda) And Other Large Mammals And Plants From The Oligocene Of Tunisia
Text-fig. 3. Right maxilla of a young adult individual of Arsinoitherium zitteli from the Fayum, Egypt (M 8802, NHM collection, London). The dotted lines indicate the homologous part preserved in the specimen from Grigema, Tunisia. a) stereo occlusal view, b) stereo buccal view, c) stereo lingual view.
A Spatial Analysis of Food Insecurity and Body Mass Index with Income and Grocery Store Density in a Diverse Sample of Adolescents and Young Adults
<p><strong>Abstract: </strong>Food insecurity occurs when a household lacks consistent access to food and is more prevalent in ethnic and racial minoritized populations. While there has been a proliferation of research linking food insecurity to obesity, these findings are mixed. It may be helpful to consider some additional geographic factors that may be associated with both factors including socioeconomic status and grocery store density. The purpose of the current study aimed to examine spatial relationships between food insecurity and SES/store density and BMI and SES/store density in a diverse sample of adolescents and young adults across two studies in a large, urban city. GIS analysis revealed that participants with the highest food insecurity (larger symbols) tend to live in the zip codes with the lowest median income. There did not appear to be clear a relationship between food insecurity and store density. Participants with the highest BMI tend to live in zip codes with lower median income and participants with higher BMI tended to live further away from downtown, which has the highest concentration of grocery stores in the city. Our findings may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence.</p>
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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.