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Dataset for paper "Freihardt (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment. DOI 10.1007/s11111-025-00478-7"
<p>This is the dataset underlying the paper: </p> <p>Freihardt, J. (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment, 47, 6. DOI: 10.1007/s11111-025-00478-7.</p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Red vision in animals is broadly associated with lighting environment but not types of visual task
<p>Red sensitivity is the exception rather than the norm in most animal groups. Among species with a long wavelength sensitive (LWS) photoreceptor, peak wavelength sensitivity (λ<sub>max</sub>) varies substantially and it is unclear whether this variation can be explained by visual tuning to the light environment or to visual tasks such as signalling or foraging. Here, we examine long wavelength sensitivity across a broad range of taxa showing diversity in LWS photoreceptor λ<sub>max</sub>: insects, crustaceans, arachnids, amphibians, reptiles, fish, sharks and rays. We identified 161 species with a LWS photoreceptor (λ<sub>max</sub> ≥ 550 nm). We found evidence supporting visual tuning to the light environment: terrestrial species had longer λ<sub>max</sub> than aquatic species, and of these, species from turbid shallow waters had longer λmax than those from clear or deep waters. Of the terrestrial species, diurnal species had longer λ<sub>max</sub> than nocturnal species, but we did not detect any differences across terrestrial habitats (closed, intermediate or open). We found no association with proxies for visual tasks such as having red morphological features or utilising flowers or coral reefs. These results support the emerging consensus that, in general, visual systems are adapted to broad range of tasks, rather than tuned to certain tasks.</p>
Rodents show darker and redder coloration in warm and rainy environments
<p><strong>Aim: </strong>Gloger's rule predicts that warmer and wetter areas favor more pigmented animals. Yet, the original formulation lacks differentiation between the two primary pigments: eu- and pheomelanin. We examined geographic variation in eumelanin and pheomelanin to unravel how various ecological factors influence pigment deposition, and to assess support for the complex version of Gloger's rule.</p> <p><strong>Location:</strong> South America.</p> <p><strong>Time period:</strong> Contemporary.</p> <p><strong>Major Taxa Studied:</strong> Sigmodontine rodents.</p> <p><strong>Methods:</strong> We extracted pelage color data from 231 species and quantified the variation in eu- and pheomelanin deposition at the assemblage level. We performed linear multiple regression to investigate the influence of temperature, precipitation, predator diversity, and UVA-B radiance in eumelanin (lightness) and pheomelanin (redness).</p> <p><strong>Results:</strong> Our findings support the original formulations of Gloger's rule. Rodents in warmer and rainier regions, which also entails greater exposure to UV radiation and a diverse range of predators, exhibit darker-colored pelage. In addition, redder rodents prevail in warmer environments. However, contrary to the rule predictions, we observe a reversal for reddish patterns in relation to precipitation, with rainier regions showcasing more intense red rodents.</p> <p><strong>Main conclusions:</strong> Our study breaks new ground by investigating previously unexplored facets of Gloger's rule in a continental mammalian group. We discovered compelling evidence that darker and redder coloration align closely with temperature and rainfall gradients. Although we found support for eumelanin-pelage predictions, expectations for pheomelanin pigmentation were only partially met. Our results might suggest that selective pressures act differently on dark and reddish coloration, revealing that coloration patterns in response to climate are more intricate than previously formulated.</p>
Data from: Mating environments mediate the evolution of behavioral isolation during ecological speciation
<p>The evolution of behavioral isolation is often the first step towards speciation. While past studies show that behavioral isolation will sometimes evolve as a by-product of divergent ecological selection, we lack a more nuanced understanding of factors that may promote or hamper its evolution. The environment in which mating occurs may be important in mediating whether behavioral isolation evolves for two reasons. Ecological speciation could occur as a direct outcome of different sexual interactions being favored in different mating environments. Alternatively, mating environments may vary in the constraint they impose on traits underlying mating interactions, such that populations evolving in a 'constraining' mating environment would be less likely to evolve behavioral isolation than populations evolving in a less constraining mating environment. In the latter, mating environment is not the direct cause of behavioral isolation but rather permits its evolution only if other drivers are present. We test these ideas with a set of 28 experimental fly populations, each of which evolved under one of two mating environments and one of two larval environments. Counter to the prediction of ecological speciation by mating environment, behavioral isolation was not maximal between populations evolved in different mating environments. Nonetheless, mating environment was an important factor as behavioral isolation evolved among populations from one mating environment but not among populations from the other. Though one mating environment was conducive to the evolution of behavioral isolation, it was not sufficient: assortative mating only evolved between populations adapting to different larval environments within that mating environment, indicating a role for ecological speciation. Intriguingly, the mating environment that promoted behavioral isolation is characterized by less sexual conflict compared to the other mating environment. Our results suggest that mating environments plays a key role in mediating ecological speciation via other axes of divergent selection.</p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Best of both worlds: Acclimation to fluctuating environments confers advantages and minimizes costs of constant environments
<ol> <li><span>Thermal acclimation is often considered critical in organismal responses to novel thermal conditions. Our understanding of the physiological implications of acclimation is largely derived from lab studies with simplified thermal regimes that fail to account for any variation that animals would experience naturally (i.e. diel variation). As such, constant temperature acclimation experiments may produce a flawed understanding of acclimation in the wild. </span></li> <li><span>To fill this gap, we acclimated lizards (<em>Amphibolurus</em> <em>muricatus</em>) under three thermal regimes (Hot Constant, Cold Constant and Alternating) and compared their physiological responses (Metabolic Rate, Sprint Speed, Thermal Preferences and Thermal Limits). </span></li> <li> <span>We found that animals maintained constantly at hot temperatures (preferred temperature, 35</span>°<span>C) gained sprint performance increases, not seen in those maintained constantly at cold temperatures (20</span>°<span>C), yet suffered costs to growth (in younger animals) and maintenance (mass loss in older animals). Animals maintained at alternating temperatures (12 hr 20</span>°<span>C; 12 hr 35</span>°<span>C) had performance benefits matching animals in the hot treatment, without experiencing reductions in juvenile growth and adult mass. </span> </li> <li><span>Animals acclimated under hot temperatures showed a significant lower preferred and voluntary maximum temperatures compared to animals acclimated under a cold temperature regime. </span></li> <li><span>We found no impact of acclimation treatment on behavioural thermal limits or Standard Metabolic Rate. </span></li> </ol> <p><span>Overall, we show that alternating between access to preferred temperatures and having periods of energetic rest confer the greatest benefits for our animals. These results highlight the importance of natural body temperature variation for enhancing overall ectotherm performance and physiology, and the costs of novel thermal environments that fail to provide this variation.</span><span><br></span></p>
Application of LiDAR to assess the habitat selection of an endangered small mammal in an estuarine wetland environment
<p>Light detection and ranging (lidar) has emerged as a valuable tool for examining the fine-scale characteristics of vegetation. However, lidar is rarely used to examine coastal wetland vegetation or the habitat selection of small mammals. Extensive anthropogenic modification has threatened the endemic species in the estuarine wetlands of the California coast, such as the endangered salt marsh harvest mouse (<em>Reithrodontomys raviventris</em>; SMHM). A better understanding of SMHM habitat selection could help managers better protect this species. We assessed the ability of airborne topographic lidar imagery in measuring the vegetation structure of SMHM habitats in a coastal wetland with a narrow range of vegetation heights. We also aimed to better understand the role of vegetation structure in habitat selection at different spatial scales. Habitat selection was modeled from data compiled from 15 small mammal trapping grids collected in the highly urbanized San Francisco Estuary in California, USA. Analyses were conducted at three spatial scales: microhabitat (25 m<sup>2</sup>), mesohabitat (2,025 m<sup>2</sup>), and macrohabitat (10,000 m<sup>2</sup>). A suite of structural covariates was derived from raw lidar data to examine vegetation complexity. We found that adding structural covariates to conventional habitat selection variables significantly improved our models. At the microhabitat scale in managed wetlands, SMHM preferred areas with denser and shorter vegetation, and selected for proximity to levees and taller vegetation in tidal wetlands. At the mesohabitat scale, SMHM were associated with a lower percentage of bare ground and with pickleweed (<em>Salicornia pacifica</em>) presence. All covariates were insignificant at the macrohabitat scale.<em> </em>Our results suggest that SMHM preferentially selected microhabitats with access to tidal refugia and mesohabitats with consistent food sources. Our findings showed that lidar can contribute to improving our understanding of habitat selection of wildlife in coastal wetlands and help to guide future conservation of an endangered species.</p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Code and data to support 'Street view imagery for built environment auditing: a systematic review'
<p>Code and data to support the manuscript entitled 'Street view imagery for built environment auditing: a systematic review'</p>
Simulation dataset and plotting scripts used for journal article "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024)
<p>Simulation data underlying all figures presented in "Surface modulated dissociation of organic aerosol acids and bases in different atmospheric environments" by Sengupta and Prisle (2024) <a href="http://dx.doi.org/10.1080/02786826.2024.2323641" target="_blank" rel="noopener noreferrer">http://dx.doi.org/10.1080/02786826.2024.2323641</a>. </p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
Supplementary data for the paper: "Enhancing concrete durability in chloride-rich environments through manual application of healing agents"
<p>Supplementary data for the paper: “Enhancing concrete durability in chloride-rich environments through manual application of healing agents”.<br><br>Open data concerning experimental work. The paperinvestigates the use of three potential healing agents: water-repellent agent (WRA), sodium silicate (SS), and polyurethane (PU). The agents are tested separately under two conditions: exposure to a 3.3% NaCl concentration at 20°C and exposure to cyclic freeze-thaw conditions. The agents are manually injected into the cracks and evaluated for their healing efficiency using the capillary absorption test and microscopy analysis. Additionally, their resistance to freeze-thaw cycles by monitoring mass loss and chloride bulk diffusion is measured using sprayed silver nitrate, titration, and EDX mapping. </p>
Figure 3 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 3. Error bars of mean values of SOS and AOS variables are shown with 99% confidence intervals.
Figure 4 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 4. Individual scores of the Helix lucorum along the first 2 PCA axes defined by size variables and 2 ratios.
Figure 2 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 2. Measurements of the shells of Helix lucorum used in the analysis: SH, shell height; SW, shell width; AH, aperture height; AW, aperture width.
LoLiPoP-IoT TU/e Dataset: Preferred Illuminance at Different Correlated Color Temperatures for Paper- and Tablet-based Reading Tasks in an Office Environment
<p>The dataset has the following <strong>576</strong> (=i.e., <strong>32</strong> Participants × <strong>3</strong> CCTs × <strong>2</strong> task media × <strong>3</strong> repetitions) preferred illuminance values (unit: lx).</p> <table> <tbody> <tr> <td> </td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> </tr> <tr> <td><strong>Participant ID</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> </tr> <tr> <td><strong>1</strong></td> <td>1098.00</td> <td>1668.00</td> <td>452.70</td> <td>396.20</td> <td>812.70</td> <td>589.60</td> <td>1139.00</td> <td>603.40</td> <td>502.60</td> </tr> <tr> <td><strong>2</strong></td> <td>275.90</td> <td>322.20</td> <td>316.00</td> <td>455.60</td> <td>412.30</td> <td>387.40</td> <td>382.30</td> <td>346.10</td> <td>411.10</td> </tr> <tr> <td><strong>3</strong></td> <td>2872.00</td> <td>399.70</td> <td>304.60</td> <td>1814.00</td> <td>339.80</td> <td>574.70</td> <td>1750.00</td> <td>725.60</td> <td>442.20</td> </tr> <tr> <td><strong>4</strong></td> <td>843.20</td> <td>686.40</td> <td>1088.00</td> <td>755.40</td> <td>1001.00</td> <td>818.20</td> <td>817.10</td> <td>790.70</td> <td>810.40</td> </tr> <tr> <td><strong>5</strong></td> <td>541.00</td> <td>404.10</td> <td>561.10</td> <td>513.50</td> <td>380.20</td> <td>419.70</td> <td>335.20</td> <td>343.30</td> <td>459.10</td> </tr> <tr> <td><strong>6</strong></td> <td>308.30</td> <td>289.90</td> <td>237.20</td> <td>485.20</td> <td>437.00</td> <td>403.90</td> <td>385.90</td> <td>462.80</td> <td>478.00</td> </tr> <tr> <td><strong>7</strong></td> <td>1111.00</td> <td>1531.00</td> <td>1280.00</td> <td>1422.00</td> <td>1273.00</td> <td>1812.00</td> <td>1408.00</td> <td>1618.00</td> <td>1734.00</td> </tr> <tr> <td><strong>8</strong></td> <td>744.20</td> <td>545.00</td> <td>462.90</td> <td>605.50</td> <td>616.00</td> <td>575.10</td> <td>1020.00</td> <td>721.20</td> <td>661.10</td> </tr> <tr> <td><strong>9</strong></td> <td>422.50</td> <td>707.40</td> <td>363.40</td> <td>460.30</td> <td>514.00</td> <td>353.40</td> <td>433.00</td> <td>463.80</td> <td>481.60</td> </tr> <tr> <td><strong>10</strong></td> <td>833.00</td> <td>626.80</td> <td>595.60</td> <td>773.60</td> <td>727.40</td> <td>363.40</td> <td>746.90</td> <td>723.70</td> <td>214.30</td> </tr> <tr> <td><strong>11</strong></td> <td>379.20</td> <td>689.10</td> <td>703.60</td> <td>526.80</td> <td>711.30</td> <td>981.40</td> <td>431.40</td> <td>511.20</td> <td>873.10</td> </tr> <tr> <td><strong>12</strong></td> <td>683.30</td> <td>764.60</td> <td>1124.00</td> <td>995.10</td> <td>990.10</td> <td>1116.00</td> <td>901.10</td> <td>962.50</td> <td>1296.00</td> </tr> <tr> <td><strong>13</strong></td> <td>318.30</td> <td>406.10</td> <td>492.20</td> <td>424.80</td> <td>531.40</td> <td>549.80</td> <td>510.70</td> <td>471.70</td> <td>529.20</td> </tr> <tr> <td><strong>14</strong></td> <td>228.80</td> <td>200.40</td> <td>233.30</td> <td>326.00</td> <td>322.30</td> <td>342.00</td> <td>425.40</td> <td>337.70</td> <td>411.10</td> </tr> <tr> <td><strong>15</strong></td> <td>252.10</td> <td>296.60</td> <td>293.60</td> <td>323.90</td> <td>318.40</td> <td>351.70</td> <td>328.10</td> <td>321.50</td> <td>572.70</td> </tr> <tr> <td><strong>16</strong></td> <td>473.70</td> <td>682.90</td> <td>709.00</td> <td>468.60</td> <td>855.50</td> <td>896.50</td> <td>611.00</td> <td>1165.00</td> <td>846.30</td> </tr> <tr> <td><strong>17</strong></td> <td>388.20</td> <td>266.90</td> <td>300.00</td> <td>386.30</td> <td>404.30</td> <td>372.30</td> <td>383.60</td> <td>364.20</td> <td>370.60</td> </tr> <tr> <td><strong>18</strong></td> <td>626.90</td> <td>740.70</td> <td>891.80</td> <td>644.60</td> <td>815.60</td> <td>938.50</td> <td>541.50</td> <td>558.20</td> <td>780.10</td> </tr> <tr> <td><strong>19</strong></td> <td>1025.00</td> <td>558.70</td> <td>535.70</td> <td>1056.00</td> <td>747.30</td> <td>1608.00</td> <td>819.70</td> <td>1092.00</td> <td>1259.00</td> </tr> <tr> <td><strong>20</strong></td> <td>661.80</td> <td>533.20</td> <td>464.60</td> <td>1093.00</td> <td>861.80</td> <td>1070.00</td> <td>894.60</td> <td>738.40</td> <td>1459.00</td> </tr> <tr> <td><strong>21</strong></td> <td>466.60</td> <td>401.70</td> <td>455.00</td> <td>542.90</td> <td>550.00</td> <td>491.30</td> <td>482.00</td> <td>540.60</td> <td>431.10</td> </tr> <tr> <td><strong>22</strong></td> <td>1093.00</td> <td>1037.00</td> <td>1106.00</td> <td>612.30</td> <td>777.20</td> <td>1041.00</td> <td>353.00</td> <td>848.10</td> <td>478.20</td> </tr> <tr> <td><strong>23</strong></td> <td>688.80</td> <td>1034.00</td> <td>746.90</td> <td>752.60</td> <td>1301.00</td> <td>817.80</td> <td>717.70</td> <td>730.20</td> <td>893.70</td> </tr> <tr> <td><strong>24</strong></td> <td>468.00</td> <td>231.50</td> <td>150.20</td> <td>666.90</td> <td>304.20</td> <td>282.40</td> <td>486.20</td> <td>319.30</td> <td>276.60</td> </tr> <tr> <td><strong>25</strong></td> <td>646.80</td> <td>1064.00</td> <td>743.10</td> <td>1072.00</td> <td>996.10</td> <td>961.20</td> <td>623.60</td> <td>1203.00</td> <td>1026.00</td> </tr> <tr> <td><strong>26</strong></td> <td>307.90</td> <td>224.70</td> <td>255.40</td> <td>410.10</td> <td>331.50</td> <td>331.80</td> <td>259.00</td> <td>354.60</td> <td>311.10</td> </tr> <tr> <td><strong>27</strong></td> <td>609.00</td> <td>661.80</td> <td>526.00</td> <td>451.50</td> <td>496.20</td> <td>557.70</td> <td>421.30</td> <td>475.60</td> <td>594.70</td> </tr> <tr> <td><strong>28</strong></td> <td>456.40</td> <td>479.60</td> <td>412.40</td> <td>458.60</td> <td>314.70</td> <td>349.90</td> <td>445.40</td> <td>331.80</td> <td>445.80</td> </tr> <tr> <td><strong>29</strong></td> <td>546.40</td> <td>921.10</td> <td>872.40</td> <td>906.50</td> <td>1001.00</td> <td>873.70</td> <td>576.90</td> <td>897.10</td> <td>840.70</td> </tr> <tr> <td><strong>30</strong></td> <td>438.90</td> <td>398.30</td> <td>372.90</td> <td>559.30</td> <td>344.60</td> <td>475.30</td> <td>359.90</td> <td>314.90</td> <td>403.50</td> </tr> <tr> <td><strong>31</strong></td> <td>667.10</td> <td>554.10</td> <td>449.70</td> <td>737.10</td> <td>730.10</td> <td>589.60</td> <td>789.00</td> <td>474.60</td> <td>603.80</td> </tr> <tr> <td><strong>32</strong></td> <td>1234.00</td> <td>1198.00</td> <td>1007.00</td> <td>849.00</td> <td>1158.00</td> <td>770.00</td> <td>909.70</td> <td>989.80</td> <td>726.70</td> </tr> </tbody> </table> <p> </p> <p> </p> <table> <tbody> <tr> <td><strong>Participant ID</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> </tr> <tr> <td> </td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> </tr> <tr> <td><strong>1</strong></td> <td>154.80</td> <td>149.50</td> <td>98.75</td> <td>630.00</td> <td>288.90</td> <td>304.10</td> <td>181.80</td> <td>192.80</td> <td>199.60</td> </tr> <tr> <td><strong>2</strong></td> <td>179.40</td> <td>225.40</td> <td>241.00</td> <td>242.60</td> <td>316.80</td> <td>372.50</td> <td>206.40</td> <td>333.80</td> <td>275.20</td> </tr> <tr> <td><strong>3</strong></td> <td>681.70</td> <td>662.10</td> <td>703.70</td> <td>484.90</td> <td>662.70</td> <td>468.90</td> <td>2085.00</td> <td>537.40</td> <td>385.70</td> </tr> <tr> <td><strong>4</strong></td> <td>632.10</td> <td>1171.00</td> <td>998.30</td> <td>613.10</td> <td>1012.00</td> <td>818.40</td> <td>678.30</td> <td>693.70</td> <td>675.30</td> </tr> <tr> <td><strong>5</strong></td> <td>421.70</td> <td>349.30</td> <td>234.40</td> <td>392.50</td> <td>303.40</td> <td>280.20</td> <td>307.20</td> <td>428.70</td> <td>268.00</td> </tr> <tr> <td><strong>6</strong></td> <td>217.40</td> <td>206.00</td> <td>198.10</td> <td>324.40</td> <td>304.40</td> <td>343.70</td> <td>283.70</td> <td>285.70</td> <td>293.10</td> </tr> <tr> <td><strong>7</strong></td> <td>992.50</td> <td>1338.00</td> <td>1807.00</td> <td>1849.00</td> <td>1301.00</td> <td>1423.00</td> <td>1637.00</td> <td>1442.00</td> <td>1700.00</td> </tr> <tr> <td><strong>8</strong></td> <td>840.20</td> <td>1131.00</td> <td>778.60</td> <td>891.20</td> <td>1019.00</td> <td>651.80</td> <td>810.80</td> <td>1016.00</td> <td>866.20</td> </tr> <tr> <td><strong>9</strong></td> <td>391.40</td> <td>337.40</td> <td>355.70</td> <td>314.90</td> <td>510.80</td> <td>463.60</td> <td>209.80</td> <td>450.60</td> <td>435.30</td> </tr> <tr> <td><strong>10</strong></td> <td>493.40</td> <td>535.00</td> <td>691.80</td> <td>657.60</td> <td>740.50</td> <td>342.60</td> <td>1710.00</td> <td>781.00</td> <td>339.60</td> </tr> <tr> <td><strong>11</strong></td> <td>361.70</td> <td>277.90</td> <td>431.00</td> <td>342.80</td> <td>327.80</td> <td>337.60</td> <td>350.40</td> <td>237.20</td> <td>308.90</td> </tr> <tr> <td><strong>12</strong></td> <td>857.10</td> <td>1114.00</td> <td>1348.00</td> <td>1302.00</td> <td>1408.00</td> <td>1294.00</td> <td>1031.00</td> <td>1142.00</td> <td>1078.00</td> </tr> <tr> <td><strong>13</strong></td> <td>371.80</td> <td>358.20</td> <td>731.40</td> <td>320.10</td> <td>459.50</td> <td>360.00</td> <td>440.40</td> <td>507.10</td> <td>436.90</td> </tr> <tr> <td><strong>14</strong></td> <td>361.20</td> <td>267.00</td> <td>285.80</td> <td>414.80</td> <td>398.70</td> <td>365.90</td> <td>411.50</td> <td>381.50</td> <td>392.30</td> </tr> <tr> <td><strong>15</strong></td> <td>376.00</td> <td>297.80</td> <td>294.20</td> <td>326.20</td> <td>330.50</td> <td>351.70</td> <td>327.90</td> <td>397.00</td> <td>357.00</td> </tr> <tr> <td><strong>16</strong></td> <td>221.20</td> <td>557.40</td> <td>536.30</td> <td>885.40</td> <td>518.90</td> <td>688.50</td> <td>192.40</td> <td>703.50</td> <td>568.90</td> </tr> <tr> <td><strong>17</strong></td> <td>435.70</td> <td>479.40</td> <td>444.50</td> <td>391.00</td> <td>452.90</td> <td>426.60</td> <td>421.40</td> <td>390.60</td> <td>397.80</td> </tr> <tr> <td><strong>18</strong></td> <td>431.50</td> <td>728.60</td> <td>985.70</td> <td>319.40</td> <td>542.10</td> <td>890.40</td> <td>326.90</td> <td>696.70</td> <td>575.80</td> </tr> <tr> <td><strong>19</strong></td> <td>470.80</td> <td>586.10</td> <td>476.90</td> <td>667.10</td> <td>570.10</td> <td>614.20</td> <td>803.80</td> <td>638.80</td> <td>679.50</td> </tr> <tr> <td><strong>20</strong></td> <td>186.10</td> <td>386.50</td> <td>335.60</td> <td>457.40</td> <td>471.20</td> <td>370.90</td> <td>324.70</td> <td>292.60</td> <td>273.40</td> </tr> <tr> <td><strong>21</strong></td> <td>420.80</td> <td>494.30</td> <td>447.60</td> <td>500.80</td> <td>470.30</td> <td>613.00</td> <td>581.80</td> <td>411.40</td> <td>361.40</td> </tr> <tr> <td><strong>22</strong></td> <td>510.20</td> <td>221.60</td> <td>228.60</td> <td>300.80</td> <td>285.50</td> <td>282.30</td> <td>477.30</td> <td>326.50</td> <td>185.20</td> </tr> <tr> <td><strong>23</strong></td> <td>202.20</td> <td>294.20</td> <td>252.00</td> <td>261.60</td> <td>308.80</td> <td>259.90</td> <td>476.10</td> <td>157.80</td> <td>313.10</td> </tr> <tr> <td><strong>24</strong></td> <td>464.10</td> <td>477.30</td> <td>516.50</td> <td>629.00</td> <td>516.70</td> <td>568.30</td> <td>842.50</td> <td>507.90</td> <td>613.20</td> </tr> <tr> <td><strong>25</strong></td> <td>451.80</td> <td>288.20</td> <td>444.10</td> <td>317.20</td> <td>326.40</td> <td>324.20</td> <td>456.40</td> <td>299.80</td> <td>294.80</td> </tr> <tr> <td><strong>26</strong></td> <td>157.40</td> <td>104.90</td> <td>198.70</td> <td>343.70</td> <td>328.60</td> <td>316.30</td> <td>157.40</td> <td>210.90</td> <td>225.10</td> </tr> <tr> <td><strong>27</strong></td> <td>510.00</td> <td>488.90</td> <td>552.30</td> <td>467.70</td> <td>572.20</td> <td>511.20</td> <td>440.10</td> <td>587.80</td> <td>376.60</td> </tr> <tr> <td><strong>28</strong></td> <td>257.20</td> <td>195.70</td> <td>240.90</td> <td>275.90</td> <td>286.00</td> <td>269.70</td> <td>410.00</td> <td>219.40</td> <td>235.40</td> </tr> <tr> <td><strong>29</strong></td> <td>732.00</td> <td>614.40</td> <td>567.20</td> <td>1046.00</td> <td>664.60</td> <td>693.30</td> <td>711.40</td> <td>733.80</td> <td>645.80</td> </tr> <tr> <td><strong>30</strong></td> <td>280.90</td> <td>279.90</td> <td>381.70</td> <td>376.30</td> <td>305.10</td> <td>333.30</td> <td>199.20</td> <td>311.20</td> <td>456.50</td> </tr> <tr> <td><strong>31</strong></td> <td>501.40</td> <td>341.50</td> <td>365.80</td> <td>549.80</td> <td>442.70</td> <td>442.00</td> <td>697.00</td> <td>499.20</td> <td>449.20</td> </tr> <tr> <td><strong>32</strong></td> <td>1039.00</td> <td>1198.00</td> <td>1256.00</td> <td>993.80</td> <td>1129.00</td> <td>1166.00</td> <td>916.50</td> <td>858.70</td> <td>881.30</td> </tr> </tbody> </table>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
Vitamin D status is heritable and under environment-dependent selection in the wild
<p>Vitamin D has a well-established role in skeletal health and is increasingly linked to chronic disease and mortality in humans and companion animals. Despite the clear significance of vitamin D for health and obvious implications for fitness under natural conditions, no longitudinal study has tested whether the circulating concentration of vitamin D is under natural selection in the wild. Here, we show that concentrations of dietary-derived vitamin D and endogenously-produced vitamin D metabolites are heritable and largely polygenic in a wild population of Soay sheep (<em>Ovis aries</em>). Vitamin D status was positively associated with female adult survival, and vitamin D status predicted female fecundity in particular, good environment years when sheep density and competition for resources was low. Our study provides evidence that vitamin D status has the potential to respond to selection, as well as new insights into how vitamin D metabolism is associated with fitness in the wild. </p>
A regime shift towards a more anoxic environment in a eutrophic sea in northern Europe - Datasets
<p>Dataset used to produce figures in the paper "A regime shift towards a more anoxic environment in a eutrophic sea in northern Europe"</p>
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