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1,961 results for “Sensing”
Tree ring, leaf mining, climate, and remote sensing data from aspen leaf miner survey sites: II - Tree DBH and age
This dataset contiains tree level measurements of diameter at breast height (DBH) and age of aspen that were sampled in 2015 for tree ring anlyses. The tree ages provided are the age of the tree in 2015.
Bioclimatic predictors in Maricopa County, Arizona derived from remotely sensed, daily weather parameters (NASA DAYMET): 2000-2016
overview There is considerable interest in using climatic variables and bioclimatic predictors not only in ecological species distribution models but in interdisciplinary studies of urban environments. We compiled an downloadable geodatabase of monthly environmental variables on a 1km x 1km spatial resolution including raw climate variables such as precipitation, minimum and maximum air temperature, and water vapor pressure obtained from NASA Earth Science Data and Information System Daily Surface Weather and Climatological Summaries (DAYMET) for Maricopa County. We then used the continuous environmental data from DAYMET to create 19 different annual bioclimatic predictors for Maricopa County (as defined by Nix, 1986 and Hijmans, 2004). Our study is the first to utilize NASA DAYMET model data to generate bioclimatic predictors. Bioclimatic predictors are important variables to use in model development to study nuances of seasonality especially when compiling models of species and vegetation. This geodatabase of environmental variables provides accessible vital data for the entire Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) study area that can be used in an array of interdisciplinary studies. related data set Processed DAYMET data from which the data in this data set were derived are accessible from: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=662 literature cited Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A., 2004. The WorldClim interpolated global terrestrial climate surfaces. Version 1.3. Nix, Henry A., 1986, A biogeographic analysis of Australian elapid snakes, in Longmore, Richard, ed., Atlas of elapid snakes of Australia: Canberra, Australian Flora and Fauna Series 7, Australian Government Publishing Service, p. 4‒15.
PIE LTER, Year 2013-2018, remote sensing derived sediment concentration maps, movies, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges at Plum Island Sound, Massachusetts.
PIE LTER, Year 2013-2018, remote sensing (Landsat8 OLI sensors and Sentinel-2A/2B) derived sediment concentration maps, transect averaged sediment concentation, water level, dh/dt, wind direction and speed, river discharges for Plum Island Sound estuary, Massachusetts.
Fine-Grained Activities of Daily Living Data with Structural Vibration and Electrical Load Sensing
<p>Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. We utilize structural vibration sensing and electrical load sensing to acquire multiple fine-grained kitchen activities under a lab structure setting.</p> <p>Each file contains the following values:<br> -RawData: time series of data for each channel (vibration on the table, vibration on the floor, load)<br> -Label: manually fine-grained labels of events<br> -Table: detected events start/stop index for vibration sensor on the table<br> -Floor: detected events start/stop index for vibration sensor on the floor<br> -Load: detected events start/stop index for load sensor<br> <br> Label notation:<br> 1 -- operating the kettle<br> 2 -- kettle on<br> 3 -- operating the microwave<br> 4 -- microwave on<br> 5 -- put things on the stove<br> 6 -- operating with stove<br> 7 -- stove on<br> 8 -- operating vacuum<br> 9 -- sweep floor<br> 10 -- walking/step<br> 11 -- miscellaneous<br> 12 -- synchronization signal (knock on the floor)<br> 13 -- vacant<br> 14 -- microwave door open</p>
Dataset for Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals
<p>Dataset of paper "Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals" presented in International Conference on Localization and GNSS (ICL-GNSS) 2020.</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted at an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing.</p> <p>The file "indoorMapping_processing.m" shows how to process and plot the shared data.</p> <p> </p> <p> </p>
Isoprene in the Southern Ocean and remote sensed variables
<p>%%%%%%<br> Variables' names contained in "rodriguezrosetal_2020_isorems_data.csv"<br> %%%%%%</p> <p>"id" = source of the data ("peg" = PEGASO cruise, "ace" = ACE Expedition, "pml" = ANDREXII, "ooki" = Ooki et al. 2015, "hack" = Hackemberg et al. 2017)</p> <p>"solar_time" = solar time estimated with solaR package on R. </p> <p>"month" = month of the year. </p> <p>"iso_pm" = Isoprene concentration (pM)</p> <p>"chla_fluo" = Chlorophyll-a (fluorometric)</p> <p>"chla_matchup" = Chlorophyll-a (MODIS Aqua)</p> <p>"sst_matchup" = Sea Surface Temperature (MODIS Aqua)</p> <p>"zeu_matchup" = Depth of the Euphotic Layer (MODIS Aqua)</p> <p>"poc_matchup" = Particulate Organic Carbon (MODIS Aqua)</p> <p>"pic_matchup" = Particulate Inorganic Carbon (MODIS Aqua)</p> <p>"mld_matchup" = Mixing Layer Depth (Holte et al. 2017)</p> <p>"par_matchup" = PAR radiation (MODIS Aqua)</p> <p>"lat" = Latitude (decimal degrees)</p> <p>"lon" = Longitude (decimal degrees)</p>
Dataset - Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2020) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.txt</strong></em> file.</p> <p>The dataset contains:</p> <p>- libRadtran input files</p> <p>- libRadtran output</p> <p>- name lists</p> <p>- GRAL simulation outputs</p>
Tracking sickness effects on social encounters via continuous proximity-sensing in wild vampire bats
Sickness behaviors can slow the spread of pathogens across a social network. We conducted a field experiment to investigate how sickness behavior affects individual connectedness over time using a dynamic social network created from high-resolution proximity data. After capturing adult female vampire bats (Desmodus rotundus) from a roost, we created 'sick' bats by injecting a random half of bats with the immune-challenging substance, lipopolysaccharide, while the control group received saline injections. Over the next three days, we used proximity sensors to continuously track dyadic associations between 16 'sick' bats and 15 control bats under natural conditions. Compared to control bats, 'sick' bats associated with fewer bats, spent less time near others, and were less socially connected to more well-connected individuals (sick bats had on average a lower degree, strength, and eigenvector centrality). High-resolution proximity data allow researchers to flexibly define network connections (association rates) based on how a particular pathogen is transmitted (e.g., contact duration of >1 vs >60 minutes, contact proximity of <1 vs <10 meters). Therefore, we inspected how different ways of measuring association rates changed the observed effect of LPS. How researchers define association rates influences the magnitude and detectability of sickness effects on network centrality.
Dataset associated to Bioinspired electro-permeable glycans on carbon: Fouling control for sensing in complex matrices
<p>This dataset is associated to the publication "Bioinspired electro-permeable glycans on carbon: Fouling control for sensing in complex matrices" performed in Trinity College, Dublin, Ireland. The dataset contains raw data associated to the measures contained in the article: X ray photoelectron spectroscopy, Atomic force microscopy, cyclic voltammetries. This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) grant No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319311455#gs1">13/CDA/2213</a>. AM and JAB gratefully acknowledge support from the School of Chemistry and the Irish Research Council Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319311455#gs2">GOIPG/2014/399</a>, respectively. EW is grateful for support by the Undergraduate Research Bursary Program of the Royal Society of Chemistry and Nuffield Foundation. Use of the XPS of Prof. I. V. Shvets and C. McGuinness provided under SFI Equipment Infrastructure funds. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319311455#gs6">799175</a> (HiBriCarbon). The results of this publication reflect only the authors’ view and the Commission is not responsible for any use that may be made of the information it contains.</p>
Monitoring recent changes in the Beaufort Sea coast using very high resolution remote sensing
<p>Arctic permafrost coasts are major carbon (Schuur et al., 2015) and mercury pools (Schuster et al., 2018). They represent about 34% of the Earth’s coastline, with long sections affected by high erosion rates (Fritz et al, 2017), increasingly threatening coastal communities. Year-round reduction in Arctic sea ice is forecasted and by the end of the 21st century, models indicate a decrease in sea ice area from 43 to 94% in September and from 8 to 34% in February (IPCC, 2014). An increase of the sea-ice free season leads to a longer exposure of coasts to wave action. Further, climate warming is also expected to modify the contribution of terrestrial erosion (Fritz et al., 2015, Ramage et al., 2018, Irrgang et al., 2018). Within the project EU Horizon2020 project NUNATARYUK, we are updating the mapping of the Arctic coast, with the Canadian Beaufort coast as a case-study. The surveying methodology includes: i. a high resolution update of the coastline mapping and change rates using Pleiades (CNES) satellite acquisitions from 2018, ii. a survey using RTK-UAV aerial imagery of long-term monitoring sites from the Canada-US border to King Point, and iii. the experimental use of TerraSAR-X staring spotlight scenes and PAZ at key sites to monitor intraseasonal dynamics of cliff edge retreat. This research is funded by the EC H2020 Project NUNATARYUK. Support on remote sensing imagery access by the WMO Polar Space Task Group.</p>
Math anxiety mediates the link between number sense and math achievements in high math anxiety young adults
<p>The data are organized into three subfolders. One subfolder for each task:</p> <p><strong>MOT:</strong></p> <p>Each file contains a 3x4 matrix called “MatriceRisultati”.</p> <p>Each row of the matrix “MatriceRisultati” is a condition.</p> <p>The columns contain the following information:</p> <p>1<sup>st</sup>: Condition</p> <p>2<sup>nd</sup>: Number of response correct</p> <p>3<sup>rd</sup>: Total number of trials </p> <p>4<sup>th</sup>: proportion between column 2 and 3</p> <p> </p> <p><strong>Numerosity discrimination: </strong></p> <p>Each file contains a matrix called “a”. </p> <p>Each row of the matrix “a” is a trial.</p> <p>The columns contain the following information:</p> <p>1<sup>st</sup>: Test Numerosity</p> <p>2<sup>nd</sup>: Subject response </p> <p>3<sup>rd</sup>: Reference position: 1= left; 2=right</p> <p> </p> <p> <strong>Size discrimination:</strong> </p> <p>Each file contains a matrix called “MATR”. </p> <p>Each row of the matrix “MATR” is a trial.</p> <p>The columns contain the following information:</p> <p>1<sup>st</sup>: Delta (%) </p> <p>2<sup>nd</sup>: Subject response </p> <p>3<sup>rd</sup>: Test stimulus size (deg)</p> <p>4<sup>th</sup>: Probe stimulus size (deg)</p> <p>5<sup>th</sup>: Delta (%)</p> <p>6<sup>th</sup>: delta (deg)</p>
Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'
<p><strong>Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>.<br> The licences valid for the elements of this repository are discussed under point "3. Licenses" below.</p> <p><strong>1. Structure</strong></p> <p>The repository contains the following items:</p> <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>We now focus on the structure of the file CODE_AND_RESULTS.zip.<br> It contains the following items:</p> <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>Inside the folder "new_methods," the following sub-folders are provided:</p> <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> <p>In our experiments, we used two datasets:</p> <p>"WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.<br> The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>].</p> <p><strong>2. References</strong></p> <p>[<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, <em>IEEE Access</em> 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>. <br> [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>.<br> [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em> 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>. <br> [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</em> (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>. <br> [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: <em>Proceedings of the IEEE/CVF International Conference on Computer Vision</em> (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>.</p> <p><strong>3. Licenses</strong></p> <p>The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip":</p> <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><strong>4. Contact</strong></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn<br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn</p> <p>Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China<br> </p>
Pressure and Inertial sensing drifter data for glacial hydrology flow path.
<p>Raw data for paper titled 'Topology and pressure distribution reconstruction of an englacial channel'</p> <p>The dataset consists of field measurements conducted on Austre Brøggerbreen, Ny-Ålesund, Svalbard. The dataset consists of:</p> <p>(1)Raw englacial data (6 deployments), 2019; (2) Raw supraglacial data (11 deployments), 2019; (3) Average GNSS drifter path along the supraglacial channel, 2019; (4) GNSS drifter path along the englacial channel, 2020; (5) Englacial river mapped from a satellite image.</p> <p>The experimental work was conducted between 30.06.2019 and 05.07.2019, during the period of the main spring snow melt. All drifters were recovered by hand from the river.</p>
Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
<p><strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below.</p> <p><em><strong>1. Structure</strong></em></p> <p>The repository contains the following items:</p> <ol> <li>"data" - the results from our experiments</li> <li>"lib" - some external functions used in the experiments</li> <li>"make_data" - the training and test data</li> <li>"fmt-vgg.py" - the FMT-RAN model</li> <li>"stn.py" - the STN module of ST-RAN</li> <li>"st_ran.py" - the ST-RAN model</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p><strong><em>2. License</em></strong></p> <p>The following licenses apply for the files and folders:</p> <ul> <li>The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository <a href="https://github.com/GHamrouni/stn-tuto">https://github.com/GHamrouni/stn-tuto</a> and therefore are under the copyright of its repository owner Ghassen Hamrouni.</li> <li>All other files are under the <a href="https://mit-license.org/">MIT License</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><em><strong>3. Contact</strong></em></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, <a href="mailto:wuzz@hfuu.edu.cn">wuzz@hfuu.edu.cn</a><br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a>, <a href="http://mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a></p> <p><a href="http://iao.hfuu.edu.cn">Institute of Applied Optimization</a>, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China</p>
Data from: Canine sense of quantity: evidence for numerical ratio-dependent activation in parietotemporal cortex
<p></p><p>The approximate number system (ANS), which supports the rapid estimation of quantity, emerges early in human development and is widespread across species. Neural evidence from both human and non-human primates suggests the parietal cortex as a primary locus of numerical estimation, but it is unclear whether the numerical competencies observed across non-primate species are subserved by similar neural mechanisms. Moreover, because studies with non-human animals typically involve extensive training, little is known about the spontaneous numerical capacities of non-human animals. To address these questions, we examined the neural underpinnings of number perception using awake canine functional magnetic resonance imaging. Dogs passively viewed dot arrays that varied in ratio and, critically, received no task-relevant training or exposure prior to testing. We found evidence of ratio-dependent activation, which is a key feature of the ANS, in canine parietotemporal cortex in the majority of dogs tested. This finding is suggestive of a neural mechanism for quantity perception that has been conserved across mammalian evolution.</p><p></p>
Basic self-disturbances are associated with sense of coherence in patients with psychotic disorders
<p>Background: The <i>Sense of Coherence </i>(SOC)<i> </i>theory<i> </i>gives a possible explanation of how people can experience subjective good health despite severe illness. Basic self-disturbances (BSDs) are subtle non-psychotic disturbances that may destabilize the person's sense of self, identity, corporeality, and the overall 'grip' of the world. </p> <p>Aim: Our objective was to investigate associations between BSDs and SOC in patients with psychotic disorders.</p> <p>Design: This is a cross-sectional study of 56 patients diagnosed with <em><span>psychotic disorders inside and outside the schizophrenia spectrum (35 schizophrenia, 13 bipolar, and eight other psychoses). SOC was measured using Antonovsky's 13-item SOC questionnaire, and BSDs were assessed using the Examination of Anomalous Self-Experience (EASE) manual. Diagnosis, symptoms, and social and occupational performance were assessed using standardized clinical instruments. </span></em></p> <p>Results: We found a statistically significant correlation (r=) between high levels of BSDs and low levels of SOC (r=-0.64/<i>p</i><0.001). This association was not influenced by diagnostics, clinical symptoms or level of functioning in follow-up multivariate analyses.</p> <p>Conclusion: A statistically significant association between BSDs and SOC indicates that the presence and level of self-disturbances may influence the person's ability to experience life as comprehensive, manageable and meaningful. However, the cross-sectional nature of the study precludes conclusions regarding the direction of this association. </p>
How biomechanics, path-planning and sensing enable gliding flight in a natural environment
<p>Gliding animals traverse cluttered aerial environments when performing ecologically relevant behaviours. However, it is unknown how gliders execute collision-free flight over varying distances to reach their intended target. We quantified complete glide trajectories amid obstacles in a naturally behaving population of gliding lizards inhabiting a rainforest reserve. In this cluttered habitat, the lizards used glide paths with fewer obstacles than alternatives of similar distance. Their takeoff direction oriented them away from obstacles in their path and they subsequently made mid-air turns with accelerations of up to 0.5 g to reorient towards the target tree. These manoeuvres agreed well with a vision-based steering model which maximized their bearing angle with the obstacle while minimizing it with the target tree. Nonetheless, negotiating obstacles reduced mid-glide shallowing rates, implying greater loss of altitude. Finally, the lizards initiated a pitch-up landing manoeuvre consistent with a visual trigger model, suggesting that the landing decision was based on the optical size and speed of the target. They subsequently followed a controlled-collision approach towards the target, ending with variable impact speeds. Overall, the visually guided path-planning strategy that enabled collision-free gliding required continuous changes in the gliding kinematics such that the lizards never attained theoretically ideal steady state glide dynamics.</p>
The cost and benefit of quorum sensing controlled bacteriocin production in Lactobacillus plantarum
Bacteria eliminate competitors via 'chemical warfare' with bacteriocins. Some species appear to adjust bacteriocin production conditionally in response to the social environment. We tested whether variation in the cost and benefit of producing bacteriocins could explain such conditional behaviour, in the bacteria Lactobacillus plantarum. We found that: (1) bacterial bacteriocin production could be upregulated by either the addition of a synthetic autoinducer peptide (PLNC8IF; signalling molecule), or by a plasmid which constitutively encodes for the production of this peptide; (2) bacteriocin production is costly, leading to reduced growth when grown in poor and, to a lesser extent, in rich media; (3) bacteriocin production provides a fitness advantage, when grown in competition with sensitive strains; (4) the fitness benefits provided by bacteriocin production is greater at higher cell densities. These results show how the costs and benefits of upregulating bacteriocin production can depend upon abiotic and biotic conditions.
Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up
Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state, or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate; this approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one, or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.
High-resolution air temperature observations near the surface using fiber-optic distributed temperature sensing
<p>Time-lapse animation of air temperature observations near the surface, highlighting wave-like motion in opposite direction of the mean wind. </p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.