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opencc-by-4.0Apr 2023View details →

Modelling and simulating age-dependent pedestrian behaviour with an autonomous vehicle

Video "1_SimulationWithoutAgel" shows the simulation before the modifications of the model and the implementation. Video "2_SimulationWithAge" shows the simulation after the modifications of the model and the implementation depending on the age data.   Videos of the article: "Modelling and simulating age-dependent pedestrian behaviour with an autonomous vehicle" Abstract: In shared spaces, autonomous vehicles (AVs) will have to move efficiently and safely, without normal road signage, and with other users such as pedestrians, cyclists and drivers. To achieve this, AVs need to anticipate the behaviours of other road users in order to adapt their navigation accordingly. This paper focuses on age-related pedestrian behaviours with an autonomous vehicle. Looking at age as one of the main factors determining behaviour, a literature review is conducted. The results are used to integrate age-dependent pedestrian behaviours into a model for simulating more realistic pedestrian behaviours in shared spaces with an AV.

opencc-by-4.0Apr 2023View details →

Age of FGK Dwarfs Observed with LAMOST and GALAH: Considering the Oxygen Enhancement

MESA (r12115) inlist files used in the preparation of the paper "Age of FGK Dwarfs Observed with LAMOST and GALAH: Considering the Oxygen Enhancement ". The '<a href="https://zenodo.org/api/files/7cd5636b-f6f5-4baa-be00-b248a0a93018/inlist_project_MIST_sun">inlist_project_MIST_sun</a>' file evolves the model from pre-main sequence to the TAMS '.  This '<a href="https://zenodo.org/api/files/7cd5636b-f6f5-4baa-be00-b248a0a93018/inlist_project_MIST_sun">inlist_project_MIST_sun</a>' file is an example file for an O-enhanced stellar model, with an input [α/Fe] value of 0.1 and a [O/Fe] value of 0.5. We use a Python code to drive the calculations of the stellar model grid, by modifying the initial y and z values, as well as the initial mass of the '<a href="https://zenodo.org/api/files/7cd5636b-f6f5-4baa-be00-b248a0a93018/inlist_project_MIST_sun">inlist_project_MIST</a>' file, to obtain the '<a href="https://zenodo.org/api/files/7cd5636b-f6f5-4baa-be00-b248a0a93018/inlist_project_MIST_sun">inlist_project_MIST_sun</a>' file.

opencc-zeroApr 2023View details →

Age-specific survival of territorial and nonterritorial male chamois

How alternative reproductive tactics (ARTs) are maintained in wildlife populations is one of the major questions in evolutionary biology. As a dominant status, territoriality is typically linked to increased mating opportunities, and one explanation for why this behaviour coexists with other tactics is that dominance implies survival costs. Such a trade-off may occur in the Northern chamois Rupicapra rupicapra, as reproductive advantages of territorial males over nonterritorial males could be counterbalanced by a reduction in survival mediated through energy expenditure, stress and parasitic infections, ultimately favouring ART coexistence. Here, we analysed age-dependent survival probabilities of territorial (n=15) and nonterritorial (n=16) adult chamois using information collected over 12 years between 2010 and 2021 in the Gran Paradiso National Park (Western Italian Alps). Survival rates were estimated with a CMR approach using Burnham's joint modelling of live encounter and dead recovery data. The model selection procedure, based on AICc value minimisation, supported a linear decrease of survival with age but did not match our predictions, as territorial chamois did not have lower survival rates than nonterritorial chamois. In contrast, territorial males appeared to enjoy reproductive success at lower survival costs. This, in turn, supports the role of other factors, such as snow-dependent environmental stochasticity, in the maintenance of ARTs in chamois populations. The limited sample size, however, calls for caution in interpretation, and long-term studies of lifetime reproductive success and survival are necessary to clarify the mechanisms underlying the expression and coexistence of different reproductive behaviours in this species.

opencc-by-4.0Sep 2019View details →

Metabolomics analysis of Sleep and Aging

UPLC-MS/MS based semi-targeted analysis of overlapping effects of sleep and aging using a mouse model of sleep deprivation.

opencc-by-4.0Oct 2022View details →

age metallicity of simulated galaxy g2.79e12 from the NIHAO-UHD suite

python pickle file of a high redshift snapshot from a Milky Way mass galaxy from the NIHAO-UHD suite

opencc-zeroMay 2023View details →

Data from: Mercury concentrations decline with age in the fur of females of an insectivorous terrestrial mammal (Myotis lucifugus)

Methylmercury (MeHg) is a toxic form of mercury that bioaccumulates in organisms and biomagnifies through food webs. MeHg concentrations can be high in aquatic environments, and this puts high trophic-level predators who derive energy originating from aquatic environments at risk of toxic effects. Due to the potential for bioaccumulation of MeHg over an individual's life, the risk of MeHg toxicity may increase as animals age, and this risk may be especially high in species with relatively high metabolic rates. Total mercury (THg) concentrations were measured from the fur of adult female little brown bats (Myotis lucifugus) collected between 2012–2017 in Salmonier Nature Park, Newfoundland and Labrador. Using linear mixed-effects models, the effects of age, year, and day of capture on THg concentrations were evaluated and interpreted with AICc and multi-model inference. We expected that THg concentrations would increase with age, and that due to annual summer moulting, individuals captured earlier in the season would have lower THg concentrations than individuals captured later in the season. Contrary to expectations, THg concentrations decreased with age, and date of capture did not explain any variation in concentration. Among individuals, there was a negative relationship between the initial THg concentration of an individual and the rate of change in THg concentrations with age. Using a regression analysis, we found evidence of a population-level decline in THg concentrations in fur over the 6-year study period. Overall, the results indicate that adult female bats eliminate enough MeHg from their tissues to affect a decrease in THg concentrations in their fur over time and that young adults are potentially at the greatest risk of experiencing toxic effects from high MeHg concentrations; this could result in reduced reproductive output, and warrants further research.

opencc-zeroMay 2023View details →

Host age affects the performance of the root hemiparasitic plant Rhinanthus alectorolophus

Interactions between root hemiparasitic plants and their hosts are strongly affected by host identity but may also depend on the condition of the host. An important determinant of host quality could be host age, as it may influence host size, allocation patterns, responses to infection, and the strength of competition for light between parasite and host. We investigated the effects of host species identity, host age and above-ground separation of hemiparasite and host on the interactions between the hemiparasite Rhinanthus alectorolophus and five host species in a factorial experiment. The host species were planted at six different times, from ten weeks before the parasite was planted to four weeks after. Host age strongly influenced the performance of the parasite, but these effects also varied among host species. Parasites grew largest with hosts planted at the same time or two weeks earlier, but their performance strongly declined both with increasing host age and with the time they grew autotrophically. A large part of the variation due to host age but not of that due to host species identity could be related to the negative influence of host size at the likely time of parasite attachment. The low quality of older hosts was not due to light competition, suggesting that effective exploitation of these hosts was prevented by other factors like harder roots, stronger defence against parasite attack or competition for resources taken up by the host roots. Suppression of host growth by the parasites declined with increasing host age. The results indicate that the choice of host age may influence the results of studies on hemiparasites. They also highlight the importance for annual root hemiparasites of attachment in early spring, i.e. at a time when their mostly perennial hosts produce fresh roots but are still poorly developed above ground.

opencc-by-4.0Jun 2023View details →

Evolution of light absorption enhancement of black carbon aerosols from biomass burning in atmospheric photooxidation aging

The Dataset of the evolution of light absorption enhancement of black carbon aerosols from biomass burning in atmospheric photooxidation aging.

opencc-zeroJun 2023View details →

Evidence for maintenance of key components of vocal learning in aging budgerigars despite diminished affiliative social interaction

In some species, the ability to acquire new vocalizations persists into adulthood and may be an important mediator of social interactions. While it is generally assumed that vocal learning persists undiminished throughout the lifespan of these open-ended learners, the stability of this trait remains largely unexplored. We hypothesize that vocal learning exhibits senescence, as is typical of complex cognitive traits, and that this decline may relate to age-dependent changes in sociality. The budgerigar (Melopsittacus undulatus), an open-ended learner which develops new contact call types that are shared with social associates upon joining new flocks, provides a robust assay for measuring the effects of aging on vocal learning ability. We formed captive flocks of 4 previously unfamiliar adult males of the same age class, either "young adults" (6 mo.-1 yr.) or "older adults" (≥ 3 yr.), and concurrently tracked changes in contact call structure and social interactions over time. Older adults exhibited decreased vocal diversity, which may be related to the sparser and weaker affiliative bonds observed in older adults. Older adults, however, displayed equivalent levels of vocal plasticity and vocal convergence compared to young adults, suggesting vocal learning ability is largely maintained into later adulthood in an open-ended learner.

opencc-by-4.0Jun 2023View details →

Demystifying Economic Growth Implications of Population Ageing

This is part of a study attempting to estimate the impact of population ageing on economic growth in a panel of nations.

opencc-by-4.0Jun 2023View details →

Using a Web Map Service to map Little Ice Age glacier extents at regional scales

Extending the record of glacier area changes into the past improves our understanding of climate change impacts. Although analogue maps showing historic glacier extents are abundant, digital outlines from before the satellite era are sparse as the digitisation of moraines and trimlines on freely available satellite images is challenging. With the now available very high-resolution images provided by Web Map Services (WMS), new doors are open for the precise digitisation. Here, we used the ESRI WMS to digitise Little Ice Age (LIA) glacier extents and present area changes since the LIA in four selected regions along with a detailed uncertainty analysis. We used modern glacier outlines as a starting point and additionally consulted Sentinel-2 images, the ArcticDEM and historic maps for interpretation. Dating records from the literature allowed calculating area change rates. In total, 493 LIA glaciers (4640 km2, now 891 ice bodies with 3590 km2) were digitised, yielding relative area changes of −20% (−0.14% a−1), −15% (−0.10% a−1), −26% (−0.16% a−1) and −61% (−0.19% a−1) for Alaska, Baffin Island, Novaya Zemlya and the tropics, respectively. The ESRI WMS images are a great asset to precisely map moraines and trimlines, but information about the timing of the related extents requires further sources.

opencc-by-4.0Dec 2022View details →

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

This is the online data repository accompanying the following manuscript: Changes in patterns of age-related network connectivity are associated with risk for schizophrenia Roberta Passiatorea,b,c, Linda A. Antonuccia, Thomas P. DeRamusb, Leonardo Faziod, Giuseppe Stolfaa, Leonardo Sportellia,e, Gianluca C. Kikidisa,e, Giuseppe Blasia,f, Qiang Chene, Juergen Dukartc,g, Aaron L. Goldmane, Venkata S. Mattaye,h, Teresa Popolizioi, Antonio Rampinoa,f, Fabio Sambataroj, Pierluigi Selvaggia,f, William Ulriche, Apulian Network on Risk for Psychosisa,k,l,m,n,o, Daniel R. Weinbergere,h,p,q,r, Alessandro Bertolinoa,f1, Vince D. Calhounb1, Giulio Pergolaa,e,p1 a Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124 Bari, Italy b Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303 Atlanta, GA c Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428 Jülich, Germany d Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010 Casamassima, Italy e Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, MD f Psychiatric Unit, University Hospital, 70124 Bari, Italy g Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany h Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287 Baltimore, MD i Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Foggia, Italy j Section of Psychiatry, Department of Neuroscience, University of Padova, 35121 Padova, Italy k Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121 Foggia, Italy l Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy m Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123 Andria, Italy n Department of Mental Health, Azienda Sanitaria Locale Bari, 70132 Bari, Italy o Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100 Brindisi, Italy p Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205 Baltimore, MD q Department of Neuroscience, Johns Hopkins University School of Medicine, 21287 Baltimore, MD r Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287 Baltimore, MD   Members of the Apulian Network on Risk for Psychosis include Ileana Andriola, Lucia Mare, Nicolò Parente, Alessandra Raio, Veronica D. Toro, (Department of Translational Biomedicine and Neuroscience – University of Bari Aldo Moro, Bari, IT), Mario Altamura, Marimar Castrigno, Melania Difino (Department of Mental Health, ASL Foggia, Foggia, IT; Department of Clinical and Experimental Medicine, University of Foggia, Foggia, IT), Flora Brudaglio, Domenico S. Savino, Rossana Vista (Department of Mental Health, ASL Barletta-Andria-Trani, Andria, IT), Angela Carofiglio, Barbara Gelao, Marina Mancini (Department of Mental Health, ASL Bari, Bari, IT), Alessandro Saponaro, Anna Manzari, Domenico Suma (Department of Mental Health, ASL Brindisi, Brindisi, IT). Abstract Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single- session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age-stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlate with genetic risk for SCZ and are detectable with MRI in young participants. DOI: 10.1073/pnas.2221533120   Files: - NeuroMark_1.0_template  <ol> <li>NeuroMark_1.0.nii:  4D Nifti containing the 100 Independent Components (ICs) spatial maps that was used for individual IC extraction based on Du Y, Fu Z, Sui J, Gao S, Xing Y, Lin D, Salman M, Abrol A, Rahaman MA, Chen J, Hong LE, Kochunov P, Osuch EA, Calhoun VD; Alzheimer's Disease Neuroimaging Initiative. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. Neuroimage Clin. 2020;28:102375. doi: 10.1016/j.nicl.2020.102375. Epub 2020 Aug 11. PMID: 32961402; PMCID: PMC7509081.</li> <li>NeuroMark_1.0_Label.doc: document containing labels for each IC extracted including brain regions, peak coordinates of ICs, corresponding functional network in which ICs are arranged, and the corresponding image number in the NeuroMark_1.0.nii file. NOTE</li> </ol> - AveragedMatrix <ol> <li>vectorized_FNC_LIBD.RData, vectorized_FNC_UNIBA1.RData, vectorized_FNC_UNIBA1.RData:</li> <li>FNC_all_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects averaged across groups, sessions and cohorts</li> <li>FNC_all_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during resting state fMRI averaged across groups and cohorts</li> <li>FNC_all_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during encoding (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li>FNC_all_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during retrieval (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li>FNC_all_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during working memory (2-Back) fMRI averaged across groups and cohorts</li> <li>FNC_all_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix across subjects during emotion recognition (Faces/FMT) fMRI averaged across groups and cohorts</li> <li>FNC_yNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC averaged across sessions</li> <li>FNC_yNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during resting state fMRI</li> <li>FNC_yNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during encoding (RISE/PEAR) fMRI</li> <li>FNC_yNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during retrieval (RISE/PEAR) fMRI</li> <li>FNC_yNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during working memory (2-Back) fMRI</li> <li>FNC_yNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during emotion recognition (Faces/FMT) fMRI</li> <li>FN_oNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC averaged across sessions</li> <li>FNC_oNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during resting state fMRI</li> <li>FNC_oNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during encoding (RISE/PEAR) fMRI</li> <li>FNC_oNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during retrieval (RISE/PEAR) fMRI</li> <li>FNC_oNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during working memory (2-Back) fMRI</li> <li>FNC_oNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during emotion recognition (Faces/FMT) fMRI</li> <li>FNC_ySIB_averaged (UNIBA1, LIBD): mean FNC matrix on ySIB averaged across sessions</li> <li>FNC_ySIB_rest (LIBD): mean FNC matrix on ySIB during resting state fMRI</li> <li>FNC_ySIB_enc (UNIBA1, LIBD): mean FNC matrix on ySIB during encoding (PEAR) fMRI</li> <li>FNC_ySIB_ret (LIBD): mean FNC matrix on ySIB during retrieval (PEAR) fMRI</li> <li>FNC_ySIB_nback (UNIBA1, LIBD): mean FNC matrix on ySIB during working memory (2-Back) fMRI</li> <li>FNC_ySIB_emo (UNIBA1, LIBD): mean FNC matrix on ySIB during emotion recognition (Faces/FMT) fMRI</li> <li>FNC_oSIB_averaged (UNIBA1, LIBD): mean FNC matrix on oSIB averaged across sessions</li> <li>FNC_oSIB_rest (LIBD): mean FNC matrix on oSIB during resting state fMRI</li> <li>FNC_oSIB_enc (UNIBA1, LIBD): mean FNC matrix on oSIB during encoding (PEAR) fMRI</li> <li>FNC_oSIB_ret (LIBD): mean FNC matrix on oSIB during retrieval (PEAR) fMRI</li> <li>FNC_oSIB_nback (UNIBA1, LIBD): mean FNC matrix on oSIB during working memory (2-Back) fMRI</li> <li>FNC_oSIB_emo (LIBD): mean FNC matrix on oSIB during emotion recognition (FMT) fMRI</li> <li>FNC_yPSY_averaged (UNIBA2): mean FNC matrix on yPSY averaged across sessions</li> <li>FNC_yPSY_rest (UNIBA2): mean FNC matrix on yPSY during resting state fMRI</li> <li>FNC_yPSY_enc (UNIBA2): mean FNC matrix on yPSY during encoding (RISE) fMRI</li> <li>FNC_yPSY_ret (UNIBA2): mean FNC matrix on yPSY during retrieval (RISE) fMRI</li> <li>FNC_yPSY_nback (UNIBA2): mean FNC matrix on yPSY during working memory (2-Back) fMRI</li> <li>FNC_yPSY_emo (UNIBA2): mean FNC matrix on yPSY during emotion recognition (Faces) fMRI</li> <li>FNC_oSCZ_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ averaged across sessions</li> <li>FNC_oSCZ_rest (UNIBA2, LIBD): mean FNC matrix on oSCZ during resting state fMRI</li> <li>FNC_oSCZ_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during encoding (PEAR/RISE) fMRI</li> <li>FNC_oSCZ_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during retrieval (PEAR/RISE) fMRI</li> <li>FNC_oSCZ_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during working memory (2-Back) fMRI</li> <li>FNC_oSCZ_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during emotion recognition (Faces/FMT) fMRI</li> <li>PlotMatrixFNC.m (Script): code used to generate FNC plots. Example of FNC plots are included in Figure 1.</li> </ol> - Connectogram <ol> <li>MeanMatrix.mat: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Matlab object.</li> <li>MeanMatrix.txt: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Text file.</li> <li>ConnectogramNeuroMark_1.0.m (Script): code used to generate Figure 2. Graphical and statistical options are provided. Please refer to <a href="https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf">https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf</a> for further details.</li> </ol> - ResFNCAgeDifferences <ol> <li>data_example.RData (Data): example data frame for AgeDiff code.</li> <li>AgeDiff_real.R (Script): code to test age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li>AgeDiff_permutation.R (Script): code to permute age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li>ResFNCAgeDifference.RData: summary statistics obtained on UNIBA1, UNIBA2, LIBD cohorts included in the manuscript.</li> <li>boxplot_permutation_p_LIBD.pdf, boxplot_permutation_p_UNIBA1.pdf, boxplot_permutation_p_UNIBA2.pdf: error bars showing results obtained after permutation (10,000 iteration) across IC pairs.</li> </ol> - dataFNC <ol> <li>data_final.RData: de-identified individuals' aggregate FNC data for each cohort (UNIBA1, UNIBA2, LIBD, PNC, ABCD, UKB)</li> <li>plotData.R (Script): code used to plot FNC differences across groups (Figure 3). Code used to assess group differences - Wilcoxon Rank sum test - is included.</li> </ol> - ResFNCRiskDifferences <ol> <li>res_cerebellar_occipitoparietal_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on the cerebellar-occipitoparietal FNC</li> <li>res_dorsolateralPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on medial PFC-sensorimotor FNC</li> <li>res_medialPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on dorsolateral PFC-sensorimotor FNC</li> </ol> - ResFNCxPRS <ol> <li>ResMetanalysisPRS_FNC_R.csv: summary statistics of metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups</li> <li>ResMetanalysisPRS_FNC_children_R.csv: summary statistics of  metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li>t2r.R (Script): code for t to R2 transform</li> <li>MetanalysisAveragedFNC_PRS.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> <li>MetanalysisAveragedFNC_PRS_children.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li>MetanalysisPRS_FNC_sessions_R.R (Script): code used perform metanalysis on the session-specific FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> </ol>   The present work includes data from UK Biobank (ID #41655), Adolescence Brain Cognitive Development (ABCD; ID #12036) Study, and Philadelphia Neurodevelopmental Cohort (PNC; ID #20998). Data are publicly accessible under approved request at the following links: - UK Biobank: <a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a> - ABCD: <a href="https://abcdstudy.org/">https://abcdstudy.org/</a> - PNC: <a href="https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html">https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html</a> The ABCD data used in this report came from NIMH Data Archive DOI: https://doi.org/10.15154/1528793. Additional code for MRI data processing and Independent Component Analysis are publicly available at the following link: <a href="https://trendscenter.org/software/">https://trendscenter.org/software/</a>   Please, check https://doi.org/10.5281/zenodo.7853030 for future updates. For any data inquiries please contact: <ul> <li>Roberta Passiatore: Roberta.Passiatore@uniba.it</li> <li>Giulio Pergola: Giulio.Pergola@uniba.it - Giulio.Pergola@libd.org</li> <li>Vince D Calhoun: VCalhoun@gsu.edu</li> <li>Alessandro Bertolino: Alessandro.Bertolino@uniba.it</li> </ul>   Funding This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 798181 awarded to G.P. (PI) and A.B., D.R.W.; from the European Union funding within the MUR PNRR Extended Partnership initiative on Neuroscience and Neuropharmacology (Project no. PE00000006 CUP H93C22000660006 “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) to G.B., A.R., A.B., and G.P.; from the funding initiative Horizon Europe Seeds 2021 (Next Generation 8 EU–MUR D.M. 737/2021) for the project S68 CUP: H99J21017550006 to G.P. (PI) and L.A.A., G.B., A.R., A.B.; from the Apulian regional government for the project: “Early Identification of Psychosis Risk” to A.B.; from the Research Projects of National Relevance (PRIN) 2017 Prot. 2017K2NEF4 awarded to G.P.; from a Collaboration Grant from Exprivia Spa to G.P. under the ministerial decree D.M. n. 352/22 and A.B. under a collaboration agreement; from a Collaboration Grant from ITEL Telecomunicazioni Srl awarded to A.B.; from the 2021 Helmholtz Information and Data Science Academy Grant No. 12429 awarded to R.P. and J.D.; and from the NIH grant #R01MH118695 awarded to V.D.C. The collection of the MRI and genetic data for the LIBD cohort was supported by direct funding from the Intramural Research Program of the NIMH to the Clinical Brain Disorders Branch (PI: D.R.W., protocol 95-M-0150).

opennotspecifiedOct 2022View details →

Fig. 3 in Chemical constituents of Antidesma bunius aerial parts and the anti-AGEs activity of selected compounds

Fig. 3. Experimental CD spectra of 1 and 2; (B–F) Calculated and experimental ECD spectra of 2, 3, 4, 5, and 8.

opencc-by-4.0Aug 2023View details →

Korea Longitudinal Study of Ageing (2008-2020) by KEIS

Korea Longitudinal Study of Ageing (2008-2020) provided by Korea Employment Information Service

Raw data used in the manuscript "Influence of structure on the 40Ar/39Ar ages of alkali feldspar in the volcanic rocks"

This data shows the raw results of some experiments performed in our manuscript: 1) the complete results of 40Ar/39Ar step-heating analysis, 2) the zircon U-Pb analytical results by SIMS, 3) shows the detailed raw data by infrared absorption spectroscopy experiment and 4) the detailed raw data by X-ray diffraction analysis.

Clinical efficacy of conbercept injection on neovascular age-related macular degeneration under different levels of inflammation

Supplementary Tables

Mesh data for multi-impeller mixing performance prediction in stirred tanks using mean age theory approach

The upload files include mesh data for all configurations studied in the research: multi-impeller mixing performance prediction in stirred tanks using mean age theory approach.

2020 forest age map for China with 30 m resolution

We constructed a framework for estimating China’s forest age by combining remote sensing time series analysis and machine learning algorithms based on over 50 000 field measurements and a remote sensing dataset. We use the proposed framework to generate a spatially explicit forest age map for China for the year 2020 and with 30 m resolution. The map is validated by comparison with independent field plots, which produces an R2 for the estimated forest-age map ranging from 0.51 to 0.63. Nationally, the average forest age is 56.1 years (standard deviation =32.7 years).

Stand age diversity weakens forests' sensitivity to climate change

This repository contains the model inputs, model outputs, graphs, and scripts related to the manuscript entitled "Stand age diversity weakens forests' sensitivity to climate change".