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opencc-zeroMar 2023View details →

Data from: Changes in movement characteristics in response to private and social information acquisition of socially foraging fish

To overcome the cost of competition resulting from foraging socially, individuals may balance their use of private (i.e. acquired from personal sampling) and social (i.e. acquired by watching other individuals) information to adjust their foraging strategy accordingly. Reliability of private information about environmental characteristics, such as the spatial distribution of prey, is thus likely to affect individual movement and social interactions. We aimed to investigate how movement characteristics of foraging individuals changed as they acquired reliable information about the spatial occurrence of prey in a foraging context. We allowed guppies (Poecilia reticulata) to develop the reliability of their private knowledge about prey spatial occurrence by repeatedly testing shoals in a foraging task under three experimental distributions of prey: 1) aggregated prey forming three patches located in fixed locations, 2) scattered distribution of prey with random locations, or 3) no prey (used as control). Using individual time series of spatial coordinates, we computed a suite of movement variables reflecting search effort, social proximity and locomotion characteristics during foraging, to examine changes occurring over repeated trials. Over time, individuals foraging on either scattered or aggregated prey travelled greater distances, showed an increasing distance to their closest neighbour and became more stochastic in their acceleration profile, compared to control individuals. We found that behaviour changed as private information increased over time, with a behavioural shift and an increase of collective foraging efficiency occurring on the third testing day. Social proximity was the major predictor of foraging success in the absence of prior foraging information, while search effort became the most important predictors of foraging success as information increased. In conclusion, we show that individual movement patterns changed as they acquired private information. Contrary to our predictions, the spatial distribution of prey did not affect any of the movement variables of interest.

openmit-licenseMar 2023View details →

Analyzing variational quantum landscapes with information content

This repository contains the code, data and notebooks to reproduce the figures of "Analyzing variational quantum landscapes with information content".

opencc-zeroApr 2023View details →

Recorded plant sounds from: Sounds emitted by plants under stress are airborne and informative

Stressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, airborne sounds emitted by stressed plants have not been investigated before. Here we show that stressed plants emit airborne sounds that can be recorded from a distance and classified. We recorded ultrasonic sounds emitted by tomato and tobacco plants inside an acoustic chamber, and in a greenhouse, while monitoring the plant's physiological parameters. We developed machine learning models that succeeded in identifying the condition of the plants, including dehydration level and injury, based solely on the emitted sounds. These informative sounds may also be detectable by other organisms. This work opens new avenues for understanding plants and their interactions with the environment and may have a significant impact on agriculture.

FIG. 4. — A in The use of photographic color information for highthroughput phenotyping of pigment composition in Agarophyton vermiculophyllum (Ohmi) Gurgel, J.N.Norris & Fredericq

FIG. 4. — A, After color correction procedures, color values estimated for four areas on the color card (i.e., black, white, red, and blue swatches) included in each photograph showed no significant differences among light condition groups (shapes); B, After color correction using values standardized using a color card, color traits (RGB values analyzed with PCA) were more similar within thallus (numbered clusters) than among light conditions (shapes), with a few exceptions. C, Centroids estimated for each thallus (numbered clusters) and light condition (shape and color) combination showed that some light conditions could be effectively standardized with color correction procedures (e.g. blue, direct sunlight, and white light), whereas thalli photographed under other conditions showed different color profiles even after correction (e.g. indirect sunlight (triangles), and to a lesser extend yellow light (boxed Xs).

FIG. 2 in The use of photographic color information for highthroughput phenotyping of pigment composition in Agarophyton vermiculophyllum (Ohmi) Gurgel, J.N.Norris & Fredericq

FIG. 2. — Principal components of variation for: A, R, G, and B values; B, pigment variables. Arrow directions indicate the association of each variable to PC 1 and PC 2. Arrow length indicates the relative strength of the contribution of each variable. Point color reflects the average color of each thallus; C, the major axis of variation in pigments (PC1) is best predicted by a combination of color PC1 and PC2. The dashed line represents predicted values when PC2 is low, compared to when PC2 is high (solid line); D, pigment variation along PC2 is best predicted by color variation along PC2.

FIG. 1 in The use of photographic color information for highthroughput phenotyping of pigment composition in Agarophyton vermiculophyllum (Ohmi) Gurgel, J.N.Norris & Fredericq

FIG. 1. — Agarophyton vermiculophyllum color variation. Photographs used for color information arranged (from top left) in rank order of increasing hue value. Lower right panel shows the color of the average RGB values estimated from each image. Labels indicated the region and site of origin for each thallus. Diameter of dish in each image is 100 mm.

FIG. 3 in The use of photographic color information for highthroughput phenotyping of pigment composition in Agarophyton vermiculophyllum (Ohmi) Gurgel, J.N.Norris & Fredericq

FIG. 3. — Examples of images taken of the same thallus and color card under five light conditions (left column), and those same images after white balancing (right column). Width of the color card in each image is 178 mm.

opencc-by-4.0Jan 2022View details →

Detect Related Bugs from Lucene Source Code Using Bug Information

This dataset uses web metrics. More information at http://openscience.us/repo/defect/ck/lucene.html

Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses - Supporting Information for Trajectories

We provide videos of trajectories for a test charge, bound by Lennard-Jones potential. First video, titled "PW Trajectory - Point 2 " refers to the particle under the effect of a plane wave pulse. The other video , titled "Emitter Trajectory - Point 2" refers to the particle affected by an orbital angular momentum carrying pulse. These videos are supplementary materials for the article titled "Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses".

Appendix: Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition

Appendix tables for the paper "Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition". Feature informativeness information was gathered using SHAP values. TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral TABLE X: Table of Statistics and Individual t-Test Results Between Genders TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values

Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.

Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. Ecological Informatics. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256   Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location. To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database. The dataset contains songs of 22 bird species from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was manually annotated by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from 967 file recordings. A precise description of the distribution by species and country can be found in the associated article.   The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github The files were divided into a training folder and a validation folder to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation. Process to select the species : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were  selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.

opencc-zeroApr 2023View details →

Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information

Monitoring fish communities is central to the evaluation of ecological health of rivers. Not only presence/absence of species is important to assess, but also the species composition of local fish assemblages is a crucial parameter. Lotic fish communities are traditionally monitored via electrofishing, characterized by a known limited efficiency and high survey costs. The use of environmental DNA-based analyses could serve as a non-destructive alternative, but this approach requires further insights in practical sampling schemes incorporating transport and dilution of the eDNA fragments; as well as optimization of molecular detection in terms of predictive power and quality assurance. By introducing fifteen species known to occur in Belgian waters via a controlled cage experiment, we aim to extend the knowledge on streamreach of eDNA in small rivers and large brooks, as laid out in the European Water Framework Directive's water typology. Introducing fish communities in two transects of a species poor river characterized by contrasting river discharge rates, we found strong and significant correlations between the eDNA relative abundances and the relative biomass per species in the cage community. Despite a decreasing correlation over distance, the underlying community composition remained stable over a distance of 300 m up to 1 km downstream of the cages, depending on the river discharge rate. Such decrease in similarity between relative source biomass and the corresponding eDNA-based community profile with increasing distance downstream from the source, can partly be attributed to variation in species-specific eDNA persistence. Our findings offer novel insights on eDNA behaviour and characterization of riverine fish communities. We conclude that water sampled from a relatively small river offers an adequate snapshot of the total fish community composition occurring within an upstream perimeter ranging between 300 and 1000 meters. The potential application for other river systems is discussed in this study.

SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation

The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA. The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively. The structure of the data is as follows: 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks. 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels. Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type. 1-Panels-Ortho     |----Train     |----Test     -----Validation 1-Panels-Mask     |----Train     |----Test     -----Validation 2-NoPanels-Ortho     |----Train     |----Test     -----Validation

Needs and preferences of different groups of informal caregivers towards designing digital solutions

The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg ivers. This tool (“Caregiver Balance”; <a href="https://balans.mantelzorg.nl">https://balans.mantelzorg.nl</a>) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs.   In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3). Table: Study details and associated files <table> <tbody> <tr> <td>Number</td> <td>Study Name</td> <td>Study Aim</td> <td>Study Type</td> <td>Type of data</td> <td>Associated Files</td> </tr> <tr> <td>1</td> <td>Perceived life balance among young adult students: a comparison between caregivers and non-caregivers</td> <td>Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities</td> <td>Survey study</td> <td>Quantitative</td> <td> <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData</li> <li>ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData</li> <li>ENTWINE_ PerceivedLifeBalanceSurvey _Syntax</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook</li> </ol> </td> </tr> <tr> <td>2</td> <td>Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers</td> <td>Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.</td> <td>Survey study</td> <td>Quantitative</td> <td> <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData</li> <li>ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData</li> <li>ENTWINE_PositiveAspectsCaregiving_Survey_Syntax</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook</li> </ol> </td> </tr> <tr> <td>3</td> <td>Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool</td> <td>Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.</td> <td>Usability study</td> <td> Qualitative and Quantitative </td> <td> <ol> <li>ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]</li> <li>ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData</li> </ol> </td> </tr> </tbody> </table> Description of the files to be uploaded Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.</li> <li>ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata - <ol> <li>Population: young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;</li> <li>Number of participants: 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)</li> <li>Time point of measurement: Data was collected from December 2020 till March 2022</li> <li>Type of data: quantitative</li> <li>Measurements included, topics covered: perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])</li> <li>Short procedure conducted to receive data: online survey on Qualtrics platform</li> </ol> </li> <li>SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset</li> </ol> Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers <ol> <li>ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.</li> <li>ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata - <ol> <li>Population: young adult caregivers aged 18-25 years studying in the Netherlands;</li> <li>Number of participants: 74 young adult caregivers</li> <li>Time point of measurement: Data was collected from December 2020 till March 2022</li> <li>Type of data: quantitative</li> <li>Measurements included, topics covered: positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )</li> <li>Short procedure conducted to receive data: online survey on Qualtrics platform.</li> </ol> </li> </ol> <ol> <li>SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.</li> <li>ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.</li> </ol> Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool <ol> <li>ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata – <ol> <li>Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total</li> <li>Time point of measurement: data was collected from October 2021 till February 2022</li> <li>Type of data: qualitative and quantitative</li> <li>Measurements included, topics covered: Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool</li> <li>Short procedure conducted to receive data: Online interviews</li> </ol> </li> <li>ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata <ol> <li>Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total</li> <li>Time point of measurement: data was collected from October 2021 till February 2022</li> <li>Type of data: qualitative and quantitative</li> <li>Measurements included, topics covered: User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])</li> <li>Short procedure conducted to receive data: Online questionnaire</li> </ol> </li> </ol> Data collection details <ol> <li>All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.</li> <li>The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.</li> <li>The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).</li> <li>Participants digitally signed informed consent for participating in the study.</li> </ol> Terms of use <ol> <li>Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands <a href="mailto:mariet.hageboorn@umcg.nl">mariet.hageboorn@umcg.nl</a>).</li> <li>Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.</li> <li>The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).</li> <li>(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.</li> <li>Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.</li> </ol> References 1.  Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971 2.  Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940 3.  Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales. J Pers Soc Psychol 1988;54(6):1063–1070. PMID:3397865 4.  Pavot W, Diener E, Colvin CR, Sandvik E. Further Validation of the Satisfaction With Life Scale; Evidence for the Cross-Method Convergence of Well-Being Measures. J Pers Assess 1991;57(1):149–161. PMID:1920028 5.  Tarlow BJ, Wisniewski SR, Belle SH, Rubert M, Ory MG, Gallagher-Thompson D. Positive aspects of caregiving: Contributions of the REACH project to the development of new measures for Alzheimer’s caregiving. Res Aging 2004;26(4):429–453. [doi: 10.1177/0164027504264493] 6.  Hendrick SS, Dicke A, Hendrick C. The relationship assessment scale. J Soc Pers Relat 1998;15(1):137–142. [doi: 10.1177/0265407598151009] 7.  Berg CA, Schindler I, Maharajh S. Adolescents’ and Mothers’ Perceptions of the Cognitive and Relational Functions of Collaboration and Adjustment in Dealing With Type 1 Diabetes. J Fam Psychol 2008;22(6):865–874. PMID:19102607 8.  Laugwitz B, Held T, Schrepp M. Construction and evaluation of a user experience questionnaire. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2008;5298 LNCS:63–76. [doi: 10.1007/978-3-540-89350-9_6] 9.  Lewis JR. Psychometric evaluation of an after-scenario questionnaire for computer usability studies. ACM SIGCHI Bull 1991;23(1):78–81. [doi: 10.1145/122672.122692] 10. Meschtscherjakov A, Gärtner M, Mirnig A, Rödel C, Tscheligi M. The Persuasive Potential Questionnaire (PPQ): Challenges, drawbacks, and lessons learned. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2016;9638:162–175. [doi: 10.1007/978-3-319-31510-2_14]

Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.

Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.   List of files used for each dataset.   Dataset 0 : MS2-CY5_Cyto543_560_woStim     Images in the dataset :         ROI001_XY1657814108_Z00_T0_merged.tif   - Image Id Number:  0         ROI002_XY1657815441_Z00_T0_merged.tif   - Image Id Number:  1         ROI003_XY1657814110_Z00_T0_merged.tif   - Image Id Number:  2         ROI004_XY1657814111_Z00_T0_merged.tif   - Image Id Number:  3         ROI005_XY1657814112_Z00_T0_merged.tif   - Image Id Number:  4         ROI006_XY1657814113_Z00_T0_merged.tif   - Image Id Number:  5         ROI007_XY1657814114_Z00_T0_merged.tif   - Image Id Number:  6         ROI008_XY1657814115_Z00_T0_merged.tif   - Image Id Number:  7         ROI009_XY1657814116_Z00_T0_merged.tif   - Image Id Number:  8         ROI010_XY1657814117_Z00_T0_merged.tif   - Image Id Number:  9         ROI011_XY1657814118_Z00_T0_merged.tif   - Image Id Number:  10         ROI012_XY1657814119_Z00_T0_merged.tif   - Image Id Number:  11   Datset 1 : MS2-CY5_Cyto543_560_18minTPL_5uM     Images in the dataset :         ROI001 - Position 1_XY1657818948_Z00_T0_merged.tif   - Image Id Number:  0         ROI001 - Position 2_XY1657818949_Z00_T0_merged.tif   - Image Id Number:  1         ROI001 - Position 4_XY1657818951_Z00_T0_merged.tif   - Image Id Number:  2         ROI001 - Position 5_XY1657818952_Z00_T0_merged.tif   - Image Id Number:  3         ROI001 - Position 6_XY1657818953_Z00_T0_merged.tif   - Image Id Number:  4         ROI001 - Position 7_XY1657818954_Z00_T0_merged.tif   - Image Id Number:  5         ROI001 - Position 8_XY1657818955_Z00_T0_merged.tif   - Image Id Number:  6         ROI001 - Position 9_XY1657818956_Z00_T0_merged.tif   - Image Id Number:  7         ROI001 - Position 10_XY1657818957_Z00_T0_merged.tif   - Image Id Number:  8         ROI001 - Position 11_XY1657818958_Z00_T0_merged.tif   - Image Id Number:  9         ROI001 - Position 12_XY1657818959_Z00_T0_merged.tif   - Image Id Number:  10   Dataset 2: MS2-CY5_Cyto543_560_5hTPL_5uM     Images in the datset :         ROI001_XY1657822809_Z00_T0_merged.tif   - Image Id Number:  0         ROI002_XY1657822933_Z00_T0_merged.tif   - Image Id Number:  1         ROI003_XY1657822934_Z00_T0_merged.tif   - Image Id Number:  2         ROI005_XY1657822936_Z00_T0_merged.tif   - Image Id Number:  3         ROI006_XY1657822937_Z00_T0_merged.tif   - Image Id Number:  4         ROI007_XY1657822938_Z00_T0_merged.tif   - Image Id Number:  5         ROI008_XY1657822939_Z00_T0_merged.tif   - Image Id Number:  6         ROI010_XY1657822941_Z00_T0_merged.tif   - Image Id Number:  7         ROI013_XY1657822944_Z00_T0_merged.tif   - Image Id Number:  8         ROI014_XY1657822945_Z00_T0_merged.tif   - Image Id Number:  9         ROI015_XY1657822946_Z00_T0_merged.tif   - Image Id Number:  10         ROI016_XY1657822947_Z00_T0_merged.tif   - Image Id Number:  11         ROI017_XY1657822948_Z00_T0_merged.tif   - Image Id Number:  12         ROI018_XY1657822949_Z00_T0_merged.tif   - Image Id Number:  13

opencc-zeroApr 2023View details →

Supplementary information for: Multisensory integration by polymodal sensory neurons dictates settlement in actinulae larvae

<div> <div> <div> Multisensory integration (MSI) combines information from more than one sensory modality to elicit behaviors distinct from unisensory behaviors. MSI is best understood in animals with complex brains and specialized centers for parsing sensory information, but the dispersive larvae of sessile marine invertebrates utilize multimodal environmental sensory stimuli to base irreversible settlement decisions on, and most lack complex brains. Here, we examined the sensory determinants of settlement in actinula larvae of the hydrozoan Ectopleura crocea (Cnidaria), which possess a diffuse nerve net. A factorial settlement study revealed that photo-, chemo-, and mechano-sensory cues each influence the settlement response, which was complex and dependent on specific combinations of cues, therefore indicating MSI. Mechanosensory cues either inhibited or enhanced settlement rates depending on the presence or absence of chemical and light cues in the environment. Sensory gene expression over development peaked with developmental competence to settle, which in actinulae, requires cnidocyte discharge. Transcriptome analyses also highlighted several deep homological links between cnidarian and bilaterian mechano- chemo- and photo-sensory pathways. Fluorescent in situ hybridization studies of candidate transcripts suggested cellular partitioning of sensory function among the few cell types that comprise the actinula nervous system, where ubiquitous polymodal sensory neurons with putative chemo- and photo-sensitivity interface with mechanoreceptive cnidocytes. We propose that a simple multisensory processing circuit, involving polymodal chemo/photosensory neurons and mechanoreceptive cnidocytes, is sufficient to explain MSI in actinulae settlement. Our study demonstrates that MSI is not exclusive to complex brains, but likely predated and contextualized their evolution. </div> </div> </div>

opencc-by-4.0Mar 2023View details →

Supporting information for "Underscreening in concentrated electrolytes: Polyelectrolyte brushes exhibit re-entrant swelling behaviour"

This deposition contains the data and analysis (Jupyter notebooks) detailed in "Underscreening in concentrated electrolytes: Polyelectrolyte brushes exhibit re-entrant swelling behaviour". All Jupyter notebooks have also been converted into PDF files for ease of viewing.  All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data.zip” archive. This archive contains three sub-directories:  Spectroscopic_ellipsometry  <ul> <li> Data directory (data_files) containing all raw ellipsometry data.  </li> <li> “PMETAC_wet_analysis.ipynb” notebooks to reproduce the analysis of a hydrated (solid-liquid) polymer brush. Further relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips</a> repo.  </li> <li> “PMETAC_dry.ipynb” notebook to reproduce the analysis of the dry polymer film and create the areal maps.  </li> </ul> Neutron_reflectometry  <ul> <li> Data directory (NamedData) containing all relevant reduced reflectivity profiles from the Platypus reflectometry at ANSTO.  </li> <li> “NR_dry.ipynb”, “NR_Solid-Liquid.ipynb” notebooks required to reproduce the analysis pertaining to a dry polymer brush and a solvated brush, respectively.  </li> <li> “NR_Solid-Liquid_corefine.ipynb” notebook for reproducing the simultaneous optimisation of the PMETAC brush in D2O and H2O: co-refinement.   </li> <li> Additional code required to model the hydrated polymer brush and various plotting tools can be found in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox</a> repo and the <a href="https://github.com/refnx/refnx">refnx</a> repo.  </li> </ul> nSCFT  <ul> <li> Files containing the polymer, ion, and solvent VF profiles from nSCFT calculations.  </li> </ul>

opencc-zeroMay 2023View details →

Towards a more informative representation of the fetal-neonatal brain connectome using Variational Autoencoder

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the lifespan. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including non-linear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called Variational Autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting state data in healthy adults. Here, we demonstrated that non-linear brain features, i.e., latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, non-linear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.

opencc-zeroMay 2023View details →

Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback

A main goal of evolutionary biology is to understand the genetic basis of adaptive evolution. Although the genes that underlie some adaptive phenotypes are now known, the molecular pathways and regulatory mechanisms mediating the phenotypic effects of those genes often remain a black box. Unveiling this black box is necessary to fully understand the genetic basis of adaptive phenotypes, and to understand why particular genes might be used during phenotypic evolution. Here, we investigated which genes and regulatory mechanisms are mediating the phenotypic effects of the Eda haplotype, a locus responsible for the loss of lateral plates and changes in the sensory lateral line of freshwater threespine stickleback (Gasterosteus aculeatus) populations. Using a combination of RNAseq and a cross design that isolated the Eda haplotype on a fixed genomic background, we found that the Eda haplotype affects both gene expression and alternative splicing of genes related to bone development, neuronal development and immunity. These include genes in conserved pathways, like the BMP, netrin and bradykinin signalling pathways, known to play a role in these biological processes. Furthermore, we found that differentially expressed and differentially spliced genes had different levels of connectivity and expression, suggesting that these factors might influence which regulatory mechanisms are used during phenotypic evolution. Taken together, these results provide a better understanding of the mechanisms mediating the effects of an important adaptive locus in stickleback and suggest that alternative splicing could be an important regulatory mechanism mediating adaptive phenotypes.