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Fig. 1 MapshowingtherangeofthestudypopulationofEasternImperialEaglesinHungaryandthelocationofsampledandnotsampledterritoriesin 2003 in High Turnover Rate Revealed By Non-Invasive Genetic Analyses In An Expanding Eastern Imperial Eagle Population
Fig. 1 MapshowingtherangeofthestudypopulationofEasternImperialEaglesinHungaryandthelocationofsampledandnotsampledterritoriesin 2003 (35 ofthe 61 nesting
Fig. 2 in High Turnover Rate Revealed By Non-Invasive Genetic Analyses In An Expanding Eastern Imperial Eagle Population
Fig. 2. Firstidentification (1999, territorycodeBS-02) andre-identification (2003, BS-03) of afemale. Theterritorieswereapproximately 10 kmawayfromeachotherandtheoriginal BS-02 territorywasvacantin 2001-2002, butitwasoccupiedbyapairwithanewfemale in 2003; differentmarkingsrepresentdifferentgeneticallytaggedfemales, blackmarkings representthenestsfromtheBS-02 territory, greymarkingsrepresentnestsfromtheBS-03 territory; yearsinitalic (nestsmarkedbycircles) representnestingsiteswithoutsamples.
Differences in dogs' event related potentials in response to human and dog vocal stimuli: A non-invasive study
<p>Recent advances in the field of canine neuro-cognition allow for the non-invasive research of brain mechanisms in family dogs. Considering the striking similarities between dog's and human (infant)'s socio-cognition at the behavioural level, both similarities and differences in neural background can be of particular relevance. The current study investigates brain responses of N=17 family dogs to human and conspecific emotional vocalisations using a fully non-invasive ERP paradigm. We found that similarly to humans, dogs show a differential ERP response depending on the species of the caller demonstrated by a more positive ERP response to human vocalisations compared to dog vocalisations in a time-window between 250-650 ms after stimulus onset. A later time-window between 800-900 ms also revealed a valence sensitive ERP response in interaction with the species of the caller. Our results are the first ERP evidence to show the species sensitivity of vocal neural processing in dogs along with indications of valence sensitive processes in later post-stimulus time-periods.</p>
Figure 2 in Phallus eversion sexing in Phrynops geoffroanus (Testudines: Chelidae): a new non-invasive approach
Figure 2. Frequency polygon between success rate and phallus eversion methods in Phrynops geoffroanus.
Figure 1 in Phallus eversion sexing in Phrynops geoffroanus (Testudines: Chelidae): a new non-invasive approach
Figure 1. Phrynops geoffroanus in (A) ventral view of the male; (B) Ventral view of the female; Greater tail prominence in female (B) and absence of plastron concavity in both sexes.
Figure 3 in Phallus eversion sexing in Phrynops geoffroanus (Testudines: Chelidae): a new non-invasive approach
Figure 3. Timing of stimulus time required for phallus eversion in Phrynops geoffroanus, for methods 4 and 5.
Figure 4 in Phallus eversion sexing in Phrynops geoffroanus (Testudines: Chelidae): a new non-invasive approach
Figure 4. Phrynops geoffroanus' phallus eversion in three stages: (A) primary with only the apical portion; (B) partial engorgement; (C) total engorgement; (D) engorgement with apical exposure.
Fig. 5 in Surface Coat Differences between and Non-Invasive Entamoeba dispar Invasive Entamoeba histolytica
Fig. 5. Confocal, and fluorescence intensity assays were carried-out using FITC-conjugated streptavidin (1: 100) for the localization of biotinylated proteins in E. dispar (A, C) and E. histolytica (B, D). Observation conditions were the same for both species, but had to be modified for E. dispar to improve image quality, since the high intensity of fluorescence produced distortion of the image. E – Graphical representation in arbitrary fluorescence units. Bar: 20 µm.
Fig. 1 in Surface Coat Differences between and Non-Invasive Entamoeba dispar Invasive Entamoeba histolytica
Fig. 1. Transmission electron photomicrographs of amoebas treated with ruthenium red and cationized ferritin. E. histolytica (A) and E. dispar (B) stained with ruthenium red. The stain on the cell surface of E. histolytica is seen as a dense solid layer. In contrast, in E. dispar the stain is observed as a slight deposit. Small groups of ferritin particles were observed in E. histolytica (C) while substantially large particles clumps were found in E. dispar (D). Bar: 0.1 µm.
Fig. 4 in Surface Coat Differences between and Non-Invasive Entamoeba dispar Invasive Entamoeba histolytica
Fig. 4. (A) Silver staining of the protein profile of E. dispar and E. histolytica, and densitometry analysis of the 60 kDa band as loading control. (B) Biotinylated patterns of membrane proteins of E. dispar and E. histolytica.
Fig. 2. A and B in Surface Coat Differences between and Non-Invasive Entamoeba dispar Invasive Entamoeba histolytica
Fig. 2. A and B. Cell coat of E. histolytica and E. dispar as observed in thin sections after treatment with Con A. The cell coat of E. histolytica (A) was observed as a thick layer of relatively homogenous electron- dense precipitate all along the cell surface. In contrast, in E. dispar, (B) the cell coat was strongly positive. Bar: 0.1 µm. C to D. Confocal and phase contrast microscopy images of amoebae after incubation with fluoresce- in-tagged Concanavalin A. As observed by confocal microscopy (C) and phase contrast (D) in E. histolytica trophozoites the displacement of surface lectin receptors formed a defined cap at the posterior pole of the cell, but such structure is not formed by E. dispar and only irregular patches were seen (E) confocal and (F) phase contrast. Bar: 20 µm.
Fig. 2 in The non-invasive measurement of faecal immunoglobulin in African equids
Fig. 2. IgA was higher in Grevy's zebras than donkeys and, when other factors were taken into account in the zebra model, Grevy's zebra IgA was higher than plains zebra IgA. Conversely, FEC was higher in donkeys than the other two species (and at the population level plains zebras have higher FEC than Grevy's zebras; DIR and KJT unpublished data, Rubenstein et al., 2010). Horizontal bars indicate means, horizontal box edges standard errors, and asterisks and symbols above the brackets denote level of significance for differences that emerged in simple tests (ANOVA with Tukey post hoc for sqrtIgA, Kruskal-Wallis with Dunn's post hoc for FEC; p <0.05*, p <0.01**, p <0.001***).
Fig. 3 in The non-invasive measurement of faecal immunoglobulin in African equids
Fig. 3. In donkeys, FEC and IgA are correlated among individuals in high body condition (with a body condition score> 2.5), in data pooled across years. Shading represents standard error, significant correlations are shown in solid lines, and non-significant trends in dotted lines.
Fig. 1 in The non-invasive measurement of faecal immunoglobulin in African equids
Fig. 1. In donkeys, IgA increased with logFEC but only in 2018, when rainfall was higher. Shading represents standard error, significant correlations are shown in solid lines, and non-significant trends in dotted lines.
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning
<p>This dataset is a subset of the MIMIC-III dataset used for non-invasive blood pressure prediction. PPG and ABP data were divided into windows of 7s length (875 data points). Systolic and diastolic blood pressure values were derived from the ABP windows. Each sample of the dataset consists of a PPG signal and blood pressure values as well as a unique subject identifier. The file consists of three datasets:</p> <ul> <li>PPG: PPG data of size 905,400 x 875</li> <li>label: BP data of size 905,400 x 2</li> <li>subject_idx: subject affiliation of each sample (size 905,400 x 1)</li> </ul> <p>Furthermore, this submission contains the following models:</p> <ul> <li>AlexNet</li> <li>ResNet50</li> <li>LSTM</li> <li>Architecture published by Slapnicar et al. 2019</li> </ul> <p>The architectures were trained using a non-mixed dataset derived from the MIMIC-III waveform database. Samples were divided between training, validation and test set based on their subject affiliation preventing contamination of validation and test sets with samples from subjects used for training.</p>
Testing the effectiveness of genetic monitoring using genetic non-invasive sampling
<p>1. Effective conservation requires accurate data on population genetic diversity, inbreeding, and genetic structure. Increasingly, scientists are adopting genetic non-invasive sampling as a cost-effective population-wide genetic monitoring approach. Genetic non-invasive sampling has, however, known limitations which may impact the accuracy of downstream genetic analyses.</p> <p>2. Here, using high quality SNP data from blood/tissue sampling of a free-ranging koala population (n = 430), we investigated how the reduced SNP panel size and call rate typical of genetic non-invasive samples (derived from experimental and field trials) impacts the accuracy of genetic measures, and also the effect of sampling intensity on these measures.</p> <p>3. We found that genetic non-invasive sampling at small sample sizes (14% of population) can provide accurate population diversity measures, but slightly underestimated population inbreeding coefficients. Accurate measures of internal relatedness required at least 33% of the population to be sampled. Accurate geographic and genetic spatial autocorrelation analysis requires between 28% and 51% of the population to be sampled.</p> <p>4. We show that genetic non-invasive sampling at low sample sizes can provide a powerful tool to aid conservation decision-making and provide recommendations for researchers looking to apply these techniques to free-ranging systems.</p>
Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells
<p>The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following: </p> <ul> <li>Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes.</li> <li>Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts.</li> <li>Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.</li> </ul>
Glucose and Non-Invasive Brain Stimulation
ClinicalTrials.gov study NCT04031404. IPD Sharing: YES. Countries: 1. Publications: 1.
Non-invasive Cervical Electrical Stimulation for SCI
ClinicalTrials.gov study NCT03414424. IPD Sharing: YES. Countries: 1. Publications: 2.
Testing the effectiveness of genetic monitoring using genetic non-invasive sampling
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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)
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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.