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Figure 5 in Reconstructing the life cycle of the isopodan group Aegidae with morphological descriptions and the importance of immature stages
Figure 5. Aegiochus antarctica (Hodgson, 1910) immatura staga 1 (NIWA 23664). A, Dorsal viaw. B, Vantral viaw. C, Lataral viaw. D, Antarovantral viaw. E, Antannula. F, Antanna. G, Mandibla. H, Maxillula. I, Maxilla. J, Maxillipad. K, Plaon appandaga 2 without appandix masculina. Scalas: A–C, 1 mm; E–F, 500 µm; G–J, 200µm.
Figure 4 in Reconstructing the life cycle of the isopodan group Aegidae with morphological descriptions and the importance of immature stages
Figure 4. Aegiochus antarctica (Hodgson, 1910) immatura staga 2 (NIWA 23671). A, Dorsal viaw. B, Vantral viaw. C, Antarovantral viaw. D, Antannula. E, Antanna. F, Mandibla. G, Maxillula. H, Maxilla. I, Maxillipad. J, Closa-up of starnita 7 (without panas). K, Plaon appandaga 2 without appandix masculina. L, Exampla of panial lobas of adult mala. Scalas: A–B, 2 mm; D–E, 0.5 mm (top, right); F–I, 250 µm.
Figure 1. Aega monophthalma Johnston, 1834 immatura mala staga 3 in Reconstructing the life cycle of the isopodan group Aegidae with morphological descriptions and the importance of immature stages
Figure 1. Aega monophthalma Johnston, 1834 immatura mala staga 3 (NIWA 23759). A, Dorsal viaw. B, Vantral viaw. C, Lataral viaw. D, Antarovantral viaw. E, Antannula. F, Antanna. G, Mandibla. H, Maxillula. I, Maxilla. J, Maxillipad. K, Closa-up of undardavalopad thoracopod 7. L, Closa-up of panial opanings. M, Plaon appandaga 2 with appandix masculina. Scalas: A–C, 5 mm; E–F, 1 mm (top, right); G–J, 1 mm (bottom, right).
Neolithic life tables de 2024.02
<p>Data from the literature containing information on the age at death of Neolithic burials from Germany. The data were taken from mortality tables (Dx), individual data or bar charts. The data set does not claim to be complete, but focuses on larger burial groups.</p>
Title: Health-Related Quality of Life and DNA Methylation-Based Aging Biomarkers among Survivors of Childhood Cancer
<p><strong><span>Abstract</span></strong></p> <p><strong><span>Background: </span></strong><span>Childhood cancer survivors are at high risk for morbidity and mortality and poor patient-reported outcomes, typically health-related-quality-of-life (HRQOL). However, </span><span>associations between DNA methylation (DNAm)-based aging biomarkers and HRQOL have not been evaluated.</span></p> <p><strong><span>Methods: </span></strong><span>DNAm was generated with Infinium EPIC BeadChip on blood-derived DNA (median[range] for age at blood draw=34.5[18.5-66.6] years) and HRQOL was assessed with age at survey (32.3[18.4-64.5] years) from 2,206 survivors in the St. Jude Lifetime Cohort. DNAm-based aging biomarkers, including epigenetic age using multiple clocks (e.g., <span>GrimAge</span>) and others (e.g., DNAmB2M: beta-2-microglobulin; DNAmADM: adrenomedullin), were derived from the DNAm Age Calculator (https://dnamage.genetics.ucla.edu). HRQOL was assessed using the Medical Outcomes Study 36-Item Short-Form Health Survey to capture eight domains, and physical and mental component summaries (PCS and MCS). General linear models evaluated associations between HRQOL and epigenetic age acceleration (EAA, e.g., EAA_GrimAge) or other age-adjusted DNAm-based biomarkers (e.g., ageadj_DNAmB2M) after adjusting for age at blood draw, sex, cancer treatments, and DNAm-based surrogate for smoking pack-years. All P values were 2-sided.</span></p> <p><strong><span>Results: </span></strong><span>Worse HRQOL was associated with greater EAA_GrimAge (</span><span>PCS: β[95%CI]=-0.18[-0.251,-0.11] years, P=1.85×10<sup>-5</sup>; and four individual HRQOL domains), followed by ageadj_DNAmB2M (PCS: -0.08[-0.124,-0.037], P=0.003; and three individual HRQOL domains), and ageadj_DNAmADM (PCS: -0.082[-0.125,-0.039], P=0.002; and two HRQOL domains). EAA_Hannum (Hannum clock) was not associated with any HRQOL.</span></p> <p><strong>Conclustion: </strong>Overall and domain-specific measures of HRQOL are associated with DNAm measures of biological aging. Future longitudinal studies should test biological aging as a potential mechanism.</p>
Data and code for: Nonlinear life table response analysis: Decomposing nonlinear and nonadditive population growth responses to changes in environmental drivers
<p>Life table response experiments (LTREs) decompose differences in population growth rate between environments into separate contributions from each underlying demographic rate. However, most LTRE analyses make the unrealistic assumption that the relationships between demographic rates and environmental drivers are linear and independent, which may result in diminished accuracy when these assumptions are violated. In this study, we compare the relative efficacy of linear and second-order LTRE analyses in capturing changes in population growth rate caused by environmental driver changes. To explore this question, we analyze demographic data collected for three long-lived plant species: <em>Ardisia escallonioides</em> (Pascarella & Horvitz, 1998), <em>Silene acaulis</em>, and <em>Bistorta vivipara</em> (Doak & Morris, 2010). This repository includes data files containing vital rate (survival, growth, reproduction) observations or models for our three case studies, as well as an R script in which we use these demographic data to calculate linear and second-order LTRE approximations of changes in population growth rate for each system and generate the figures we present in our paper.</p>
Figure 2 in Development and life table parameters of the Phytoseius corniger Wainstein (Acari: Phytoseiidae) feeding on the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae) under laboratory conditions
Figure 2. The age-specific survival rate (lx), and fecundity (mx) of Phytoseius corniger fed on Tetranychus urticae under laboratory conditions (25 ± 2 °C, 55 ± 5% of RH, and 16L: 8D h photoperiod).
Figure 1 in Development and life table parameters of the Phytoseius corniger Wainstein (Acari: Phytoseiidae) feeding on the two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae) under laboratory conditions
Figure 1. Age-stage specific survival rate (sjx) of the parent cohort of bisexual Phytoseius corniger fed on Tetranychus urticae under laboratory conditions (25 ± 2 °C, 55 ± 5% of RH, and 16L: 8D h photoperiod). Note: L stands for larva, N1 for protonymph, and N2 for deutonymph, respectively.
Data from: Early-life variation in migration is subject to strong fluctuating survival selection in a partially migratory bird
<p>Population dynamic and eco-evolutionary responses to environmental variation and change fundamentally depend on combinations of within- and among-cohort variation in phenotypic expression of key life-history traits, and on corresponding variation in selection on those traits. Specifically, in partially migratory populations, spatio-seasonal dynamics depend on the degree of adaptive phenotypic expression of seasonal migration versus residence, where more individuals migrate when selection favours migration.</p> <p>Opportunity for adaptive (or, conversely, maladaptive) expression could be particularly substantial in early life, through initial development of migration versus residence. However, within- and among-cohort dynamics of early-life migration, and of associated survival selection, have not been quantified in any system, preventing any inference on adaptive early-life expression. Such analyses have been precluded because data on seasonal movements and survival of sufficient young individuals, across multiple cohorts, have not been collected.</p> <p>We undertook extensive year-round field resightings of 9,359 colour-ringed juvenile European Shags (<em>Gulosus aristotelis</em>) from 11 successive cohorts in a partially-migratory population. We fitted advanced Bayesian multi-state capture-mark-recapture models to quantify early-life variation in migration versus residence and associated survival across short temporal occasions through each cohort's first year from fledging, thereby quantifying the degree of adaptive phenotypic expression of migration within and across years.</p> <p>All cohorts were highly partially migratory, but the degree and timing of migration varied considerably within and among cohorts. Episodes of strong survival selection on migration versus residence occurred both on short timeframes within years, and cumulatively across whole years, generating instances of instantaneous and cumulative net selection that would be obscured at coarser temporal resolutions. Further, the magnitude and direction of selection varied among years, generating strong fluctuating survival selection on early-life migration across cohorts, as rarely evidenced in nature. Yet, the degree of migration did not strongly covary with the direction of selection, indicating limited early-life adaptive phenotypic expression.</p> <p>These results reveal how dynamic early-life expression and selection on a key life-history trait, seasonal migration, can emerge across seasonal, annual, and multi-year timeframes, yet be substantially decoupled. This restricts the potential for adaptive phenotypic, micro-evolutionary, and population dynamic responses to changing seasonal environments.</p>
Figure 8 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 8. Hypothesized life cycle of Plesionika edwardsii in the Azorean region. After the incubation period of shrimp eggs, (1) larvae are released into the water column and (2) juveniles develop in shallow waters. Mature females and males are distributed up to 600 m with a sexual segregation by depth: (3) non-ovigerous females are mainly found up to 200 m, (4) ovigerous females between 200 and 300 m, and (5) males from 400 to 500 m deep. Females are bigger than males, and ovigerous females are bigger than nonovigerous females. A bigger-deeper trend is observed up to 400 m. (6) Long larval stages of P. edwardsii increases its potential for dispersal (Landeira et al., 2009), favoring connectivity and stock homogeneity between adjacent areas.
Figure 5 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 5. Sex ratio of Plesionika edwardsii by depth stratum in the Azorean region during the period 1999–2000.
Figure 2 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 2. Seasonal predicted mean catch per unit effort (CPUE, g trap-1) by depth stratum for males, non-ovigerous and ovigerous females of Plesionika edwardsii in the Azorean region for the period 1999–2000. Light-colored symbols represent raw data. Detailed parameter estimates are in Tab. S4.
Figure 7 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 7. Size at which 50 % of the shrimps are mature (L 50) estimated for Plesionika edwardsii in the Azorean region fitting a logistic curve to the proportion of ovigerous females. Logistic curve was estimated combining all data obtained during the period 1999–2000.
Figure 4 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 4. Seasonal predicted mean cephalothorax length (CL) by depth stratum for males, non-ovigerous and ovigerous females of Plesionika edwardsii in the Azorean region for the period 1999–2000. Light-colored symbols represent raw data. Detailed parameter estimates are in Tab. S4.
Figure 1 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 1. Sampling areas of Plesionika edwardsii in the mid-North Atlantic Ocean, Azorean region (ICES Subdivision 10a2) between 1999 and 2000. Orange dots represent each site sampled by a trap.
Figure 6 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 6. Sex ratio of Plesionika edwardsii by size class in the Azorean region during the period 1999–2000.
Figure 3 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 3. Size frequency distribution of males, non-ovigerous and ovigerous females Plesionika edwardsii in the Azorean region during the period 1999-2000.
Daily Life Activities Dataset
<p><strong>Experiment design</strong></p> <p>The Daily Life Activities (DLA) dataset consists of trials of daily life object manipulation tasks performed by a human. The dataset consists of ten tasks: <em>cutting, painting, pouring with cup, putting cup away, quarter turn, scooping and pouring, scooping food, shaking, sinusoidal motion</em>, and <em>table wiping</em>. In this dataset, a high variation in the context was purposefully introduced. That is, the tasks were performed with respect to three different viewpoints (V1, V2, V3) and with four different execution styles (normal, with larger spatial scale, with different velocity profile, and with longer time duration). This resulted in a total of (3x4=12) twelve different contexts in which the tasks were performed. Each task was performed ten times in every context, resulting in a total of (10x3x4x10=1200) trials.</p> <p><strong>Experimental setup</strong></p> <p>The trials were recorded using a Krypton K600 camera from NIKON Metrology by tracking up to nine LED markers attached to the manipulated object. The 3D position of each LED marker was recorded with a sampling rate of 50 Hz and expected accuracy of 0.4mm with respect to the measurement frame of the camera system.</p> <p><strong>Data format</strong></p> <p>Every trial_xxx.mat file is a Matlab structure array. The trailing number xxx refers to the order in which the trials were performed. For every task:</p> <ul> <li> <em>trial_001.mat</em> up to <em>trial_040.mat </em>were recorded in sensor viewpoint 1. <ul> <li><em>trial_001.mat</em> up to <em>trial_010.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_011.mat</em> up to <em>trial_020.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_021.mat</em> up to <em>trial_030.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_031.mat</em> up to <em>trial_040.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> <li> <em>trial_041.mat</em> up to <em>trial_080.mat </em>were recorded in sensor viewpoint 2. <ul> <li><em>trial_041.mat</em> up to <em>trial_050.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_051.mat</em> up to <em>trial_060.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_061.mat</em> up to <em>trial_070.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_071.mat</em> up to <em>trial_080.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> <li><em> trial_081.mat</em> up to <em>trial_120.mat </em>were recorded in sensor viewpoint 3. <ul> <li><em>trial_081.mat</em> up to <em>trial_090.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_091.mat</em> up to <em>trial_100.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_101.mat</em> up to <em>trial_110.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_111.mat</em> up to <em>trial_120.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> </ul> <p>The structure array trial_xxx.mat has the following fields:</p> <ul> <li>'number_of_timesamples': the total number of timesamples (N) for the recorded task,</li> <li>'K6C_12250_3_x': a 4xN matrix containing the 3D position coordinates of the LED marker expressed in millimeters. The trailing number x in 'K6C_12250_3_x' refers to the LED number, which can range from 1 to 9. <ul> <li>In case the LED marker was visible, the first, second and third row contain the x-, y-, and z-coordinates of the marker, respectively. The fourth row contains the zero value in this case.</li> <li>In case the LED marker was not visible, the first, second and third row contain zero values. The fourth row contains a non-zero value in this case.</li> </ul> </li> </ul>
Figure 1 in Influence of temperature and prey type on life-table parameters and consumption rate of Stethorus gilvifrons (Mulsant) (Coleoptera: Coccinellidae) on three tetranychid mites
Figure 1. Age-stage-specific survival rate (lx) and age-specific fecundity (mx) curves of Stethorus gilvifrons on different prey types and different temperatures.
F I G U R E 7 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 7 Model prediction of the proportion of juvenile Atlantic salmon choosing to smolt as 1-year-olds (full saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), 2-year-olds (medium saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), and 3-year-olds (low saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5). The red line is the point of reaction norm calibration to Piggins and Mills (1985).
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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.