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BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p> A. Segmentation It seeks to establish a model that describes the shape and typical fluctuations. This requires first the preparation of a learning base to reflect the possible variations in shape of the structure. The preparation of the training set Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 3.Proposed Computer Assisted Diagnosis
<p>The figure below presents our proposed Computer Assisted Diagnosis. Our CAD includes 3 steps: Preprocessing, Segmentation and Classification. For the step of preprocessing, we used the NLMS (Non Local Means) to improve the quality of image. For the step of segmentation: we have a learning phase to extract the different shapes and to determine the average shape. Our proposed automatic method is based on the deformable model. For the step of classification, we present a new supervised method to distinguish between Normal, MCI and AD. The figure below presents our proposed system.</p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training
<p>Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below. </p>
Standing repetitive pointing task in individuals with and without Parkinson's disease
<p>[ These data refer to a manuscript currently under revision in PlosOne. In this MS we aimed to determine the effects of levodopa medication on the performance of a repetitive pointing task while standing, and to investigate the optimal trial duration in individuals with Parkinson’s disease, and older adults. Seventeen individuals with Parkinson’s disease (5 freezers) and 9 older adults stood on force platforms for 30 s and 120 s while performing a bilateral repetitive pointing task, tracked by motion capture. Participants with Parkinson’s disease were assessed on and off medication and older adults were also assessed on separate days. The main findings were that: 1) on medication, participants with Parkinson’s exhibited greater center of pressure root mean square in the medial-lateral direction, greater velocity in the medial-lateral and anterior-posterior directions, and greater range in the medial-lateral direction than off medication; 2) longer trial durations resulted in greater center of pressure range in the medial-lateral and anterior-posterior directions and greater coefficient of variation in finger pointing on the least affected side; 3) Parkinson’s participants exhibited larger range in the medial-lateral direction compared to older adults; 4) off medication, freezers presented with less range and root mean square in the anterior-posterior direction than non-freezers; and 5) a correlation emerged between the freezing of gait questionnaire and pointing asymmetry and the coefficient of variation of pointing on the most affected side. Therefore, Parkinson’s medication may increase instability during a repetitive pointing task. Longer trials may provide a better depiction of sway by discriminating between those with and without neurological impairment. Individuals with Parkinson’s were less stable than older adults, supporting that they are at a greater risk for falls. The greater restrictive postural strategy in freezers compared to non-freezers is likely a factor that augments fall-risk. Lastly, the link between freezing of gait and upper-limb movement indicates that freezing may manifest first in the lower-limbs. Add description ]</p>
Literature Review 13/02/2018: Genetic risk of Parkinson's disease dementia due to APOE4 or MAPT
<p>Literature review assessing genetic risk of dementia due to <em>APOE4</em> or <em>MAPT </em>in Parkinson's disease, performed on the 13<sup>th</sup> February 2018. All studies had to fulfil three<em> a priori </em>inclusion criteria:</p> <p>1) Case control studies using clinically diagnosed or pathologically confirmed PD and PDD.</p> <p>2) Time between motor diagnosis and experimental assessment could be defined or estimated.</p> <p>3) Genotype information supplied, allowing the odds ratio (OR) and confidence intervals (CI) to be calculated that aligned with the genotype categories used in this work.</p> <p>For <em>MAPT</em>, a PubMed search for the term “<em>MAPT Parkinson’s dementia</em>” identified 105 potential matches, of which 9 met the inclusion criteria. For <em>APOE4</em>, a PubMed search for the term “<em>APOE Parkinson’s dementia</em>” identified 188 potential matches, of which 19 met the inclusion criteria. Note, the review includes several publications arising from the CamPaIGN cohort; As we were interested in genetic risk as a function of time from diagnosis, we included each unique study time-point. </p>
Metabolomics of pediatric fatty liver disease
<p>Fecal and plasma metabolite profiles of non-alcoholic fatty liver disease (NAFLD) in a pediatric cohort</p>
Mobile Device Voice Recordings at King's College London (MDVR-KCL) from both early and advanced Parkinson's disease patients and healthy controls
<p><strong>Dataset description</strong></p> <p>The dataset description will start with describing the local conditions and other metadata, then will continue with describing the recording procedure and annotation methodology. Finally, a brief description of the dataset deployment and publication will be given.</p> <p><strong>Meta Information</strong></p> <p>The dataset was recorded at King's College London (KCL) Hospital, Denmark Hill, Brixton, London SE5 9RS in the period from 26 to 29 September 2017. We used a typical examination room with about ten square meters area and a typical reverberation tome of approx. 500ms to perform the voice recordings. Due to the fact, that the voice recordings are performed in the realistic situation of doing a phone call (i.e. participant holds the phone to the preferred ear and microphone is in direct proximity to the mouth), one can assume that all recordings were performed within the reverberation radius and thus can be considered as “clean”.</p> <p><strong>Recording Procedure</strong></p> <p>We used a Motorola Moto G4 Smartphone as recording device. To perform the voice recordings on the device, we developed a “Toggle Recording App”, which uses the same functionalities as the voice recording module used within the i-PROGNOSIS Smartphone application, but deployed as a standalone android application. This means, that the voice capturing service runs as a standalone background service on the recording device and triggers voice recordings via on- and off-hook signals of the Smartphone. Due to the fact, that we directly record the microphone signal, and not the GSM (“Global System for Mobile Communications”) compressed stream, we end up with high quality recordings with a sample rate of 44.1 kHz and a bit depth of 16 Bit (audio CD quality). The raw, uncompressed data is directly written to the external storage of the Smartphone (SD-card) using the well-known WAVE file format (.wav). We used the following workflow to perform a voice recording:</p> <ul> <li>Ask the participant to relax a bit and then to make a phone call to the test executor (off-hook signal triggered).}</li> <li>Ask the participant to read out “The North Wind and the Sun”</li> <li>Depending on the constitution of the participant either ask to read out “Tech. Engin. Computer applications in geography snippet”</li> <li>Start a spontaneous dialog with the participant, the test executor starts asking random questions about places of interest, local traffic, or personal interests if acceptable.</li> <li>Test executor ends call by farewell (on-hook signal triggered).</li> </ul> <p><strong>Annotation Scheme</strong></p> <p>For each HC and PD participant, we labeled the data regarding scores on the Hoehn & Yahr (H&Y), as well as the UPDRS II part 5 and UPDRS III part 18 scale. The voice recordings are labeled in the following scheme:</p> <p>SI_ HS_ HYR_ UPDRS II-5_UPDRS III-18</p> <p>with</p> <ul> <li>SI as subject identification in the form ID<em>NN</em>, <em>N</em> in [0, 9]</li> <li>HS as the health status label (hc or pd accordingly)</li> <li>HYR as the expert assessed H&Y scale rating</li> <li>UPDRS II-5 as the according expert peer-reviewed score</li> <li>UPDRS III-18 as the according expert assessed score</li> </ul> <p>For example, an audio recording with the file name “ID02_pd_1_2_1.wav” represents a recording of the third participant (First participant was anonymized as ID00), which has PD and a H&Y rating of 1, a UPDRS II-5 score of 2 and a UPDRS III-18 score of 1. At this point, it should be noted, that also all healthy controls were evaluated with regard to the introduced scales, because Parkinson's disease and voice degradation correlate, but don't match exactly. This means, that the data set includes one HC participant (ID31) with UPDRS II-5 and III-18 rating of 1, and also includes PD patients with UPDRS II-5 and III-18 ratings of 0. It should be emphasized, that this does not mean the data set includes ambiguous information, but that an expert was not able to hear voice degradation that would end up in a UPDRS rating greater than zero. Machine learning approaches may be able to nevertheless classify correctly, or at least learn to correlate, but not match PD and voice degradation at any time.</p> <p><strong>Appendix</strong></p> <p>North Wind and the Sun (Orthographic Version):</p> <p>“The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak. They agreed that the one who first succeeded in making the traveler take his cloak off should be considered stronger than the other. Then the North Wind blew as hard as he could, but the more he blew the more closely did the traveler fold his cloak around him; and at last the North Wind gave up the attempt. Then the Sun shone out warmly, and immediately the traveler took off his cloak. And so the North Wind was obliged to confess that the Sun was the stronger of the two.”</p> <p>BNC – Tech. Engin. Computer applications in geography snippet:</p> <p>“[...] This is because there is less scattering of blue light as the atmospheric path length and consequently the degree of scattering of the incoming radiation is reduced. For the same reason, the sun appears to be whiter and less orange-coloured as the observer's altitude increases; this is because a greater proportion of the sunlight comes directly to the observer's eye. Figure 5.7 is a schematic representation of the path of electromagnetic energy in the visible spectrum as it travels from the sun to the Earth and back again towards a sensor mounted on an orbiting satellite. The paths of waves representing energy prone to scattering (that is, the shorter wavelengths) as it travels from sun to Earth are shown. To the sensor it appears that all the energy has been reflected from point P on the ground whereas, in fact, it has not, because some has been scattered within the atmosphere and has never reached the ground at all. [...]”</p>
Distribution of invalid children due to forms of diseases in Russian regions
<p>It is a dataset, which shows distribution of invalid children due to forms of diseases in Russian regions. Data is focused on children under 18 years old.</p>
Ontology based mining of pathogen-disease associations from literature
<p>This dataset contains a number of manually evaluated text mined pathogen-disease associations for the study described at</p> <p>doi: https://doi.org/10.1101/437558</p>
Data from: Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance
<p>Data and code associated with the submitted manuscript "Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance". Detailed descriptions of each file can be found in the README.txt . The raw sequence data associated with this work can be downloaded from the NCBI Sequence Read Archive, listed under BioProject #PRJNA565009<strong>.</strong></p>
Figure 4 in Geometric morphometrics of Aedes aegypti populations and study of transmission of arboviral diseases in Barreiras, Brazil
Figure 4 Graphical representation of wing shape comparison of different areas.(A) Diagrams of first canonical variable from the comparison (discriminant analysis) between A and B areas (considering the highway as a barrier). (B) Diagrams of first canonical variable from the comparison between A and C areas (considering the river as a barrier). (C) Diagrams of first canonical variable of the comparison between B and C areas (considering both, the highway and the river as barriers).
Figure 1 in Geometric morphometrics of Aedes aegypti populations and study of transmission of arboviral diseases in Barreiras, Brazil
Figure 1 Sampling strategy in the urban area of Barreiras, Bahia, Brazil. The city is crossed by the Federal BR-242 Highway and by the Rio Grande River. Collection areas A, B and C were defined according to anthropic (highway) and natural (river) barriers.
Figure 5 in Geometric morphometrics of Aedes aegypti populations and study of transmission of arboviral diseases in Barreiras, Brazil
Figure 5 Morphological space of 1st and 2nd canonical variates derived from wing shape comparison (discriminant analysis) among A, B and C areas. Contribution of each Canonical Variation is indicated between brackets. Red, blue and green dots represent populations of A, B and C areas, respectively.
Fig. 4 in Standard method for detecting Bombyx mori nucleopolyhedrovirus disease-resistant silkworm varieties
Fig. 4. IIM for placing silkworms on mulberry leaves. (A) One larva was placed on each leaf. (B) The larva eating the leaf. (C) The leaf after being eaten by the silkworm.
Fig. 3 in Standard method for detecting Bombyx mori nucleopolyhedrovirus disease-resistant silkworm varieties
Fig. 3. GIM for placing silkworms on mulberry leaves. (A) The leaves were arranged in the box after smearing them with BmNPV, and then they were air dried. (B) Five larvae were placed on each leaf. (C) The leaves after being eaten by the silkworms.
Fig. 2. Flow diagrams showing a in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 2. Flow diagrams showing a weight of evidence framework using the (A) 7 questions proposed by Burkhardt-Holm and Scheurer (2007) and the (B) modified questions for addressing disease(s) in wildlife.
Fig. 1 in Review on parasites of wild and captive giant pandas (Ailuropoda melanoleuca): Diversity, disease and conservation impact
Fig. 1. Distribution of wild giant pandas in six mountain regions (Qinling, Minshan, Qionglai, Liangshan, Daxiangling and Xiaoxiangling) in three Provinces (Gansu, Shaanxi, and Sichuan) of China. Adapted from Wang et al. (2018).
Fig. 1 in "Weight of evidence" as a tool for evaluating disease in wildlife: An example assessing parasitic infection in Northern bobwhite (Colinus virginianus)
Fig. 1. Timeline depicting the history of wildlife diseases in the United States: blue boxes are for disease reports and outbreaks, green for improvements to disease research, and red for events that hindered disease research. Abbreviations: foot-and-mouth disease (FMD), Smoot-Hawley Tariff Act (SHTA), State-Federal Cooperative Brucellosis Eradication Program (SFCBER), Bear River Research Station (BRRS), Wildlife Disease Investigations Laboratory (WDIL), Southeastern Cooperative Wildlife Disease Study (SCWDS), epizootic hemorrhagic disease (EHD), World Organisation for Animal Health's (OIE), National Wildlife Research Center (NWRC), U. S. Fish and Wildlife Service (USFWS). References: 1. Antolin et al. (2002), 2. Creel (1941), 3. Anderson (1978), 4. Locke and Friend (1987), 5. McCoy and Chapin (1912), 6. Wherry and Lamb (1914), 7. Meagher and Meyer (1994), 8. Clements (2007), 9, Bachrach (1968), 10. Busch and Parker (1972), 11. USFWS (1991), 12. Tunnicliff and Marsh (1935), 13. Brooks and Buchanan (1970), 14. Elton (1931), 15. Brown (2007), 16. CDFW 2019, 17. Friend (2014), 18. SCWDS 2019, 19. Shope et al. (1960), 20. Cohen (2000), 21. Cross et al. (2013), 22. Samuel et al. (2007), 23. Carvalho et al. (2017), 24. Dobson and Hudson (1986), 25. Jones et al. (2008), 26. Berger et al. (1998), 27. Laurance et al. (1996), 28. Collins and Crump (2009), 29. OIE 2008, 30. Voyles et al. (2015), 31. Fagerstone (2014), 32. USFWS (2016), 33. Scheele et al. (2019). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Data from: Mouse α-synuclein fibrils are structurally and functionally distinct from human fibrils associated with Lewy body diseases
<p>Tabular raw data corresponding to figure sets used in the study. </p> <p><span>Fig. 1 Mouse α-syn fibrils are structurally similar to human E46K-mutated and MSA-amplified α-syn fibrils</span></p> <p><span>Chemical structure and binding curves of ThT (J), Nile Red (K), and FSB (L) to mouse and human sonicated α-syn fibrils (average radii: 16.68±1.44 nm and 15.14±4.02 nm, respectively). Data points indicate means from three independent experiments and error bars are S.E.M. ****p<0.0001 and ***p<0.01 from 2-tailed t-tests. </span></p> <p><span>Fig. 2 Distinct β-fold stacking arrangements in mouse α-syn fibrils contribute to low tensile strength and resilience</span></p> <p><span>(C) Representation of proposed model of fibril fragmentation for tensile strength estimation used to simulate the MMGBSA energy of α-syn fibrils rupture and group analysis of MMGBSA energy required to disrupt a stack of six rungs. Error bars represent S.D. from 100 independent simulations. (D) Group comparison of fibril breakage under sonication conditions shown as the percent of size population of 10-100 nm (e.g., short fibrils) at 0, 2, and 30 minutes, measured by DLS. Error bars indicate S.E.M of three independent experiments with 30 acquisition measurements corresponding to each experiment. (E) Filter-trap slot-blot analysis of sonicated fibrils (i.e., PFFs) exposed to different concentrations of guanidinium chloride (GuHCl), and then remaining fibrils detected with the fibril-selective antibody MJFR14-6-4-2. Error bars indicate S.E.M from three independent experiments. (F) DLS analysis of PFFs after incubation with GuHCL. Error bars indicate S.E.M of three independent experiments with 10 acquisition measurements for each biological sample. Curves in (E) and (F) show asymmetric sigmoidal models with a goodness of fit >0.96, and **p<0.01, **p<0.001, ****p < 0.0001 from unpaired 2-tailed t-tests.</span></p> <p><br><br></p> <p><span>Fig. 3 Mouse α-syn fibrils fail to elicit robust cytokine and lysosome damage in macrophages </span></p> <p><span>(B) Internalized Alexa-647-PFFs (%area) inside cells and (C) % of LAMP1-positive vesicles positive for PFFs. Each data point represents the means of cells analyzed from at least eight images from three independent experiments. (D) ELISA analysis of the extracellular IL-6 and (E) CCL5 from MDM cultures treated with PFFs (1 μg/mL) for 3 and 24 hours. Each data point represents the mean from two technical replicates from four independent experiments. (F) % of Gal3-positive vesicles calculated per mm² of cell surface area in PFF-treated MDM cultures after 24 hours of incubation and (G) % of Gal3-positive vesicles also positive for DQ-PFFs after 48 hours of PFF incubation. Data points show the mean values from cells imaged across three independent experiments with at least eight images analyzed per group. (H) Representative images of Gal3 immunostaining after 24-hours treatment with Alexa-647- or DQ- labeled PFFs. Orthogonal views of sequential z-stacks are shown. Side left image = x,y plane, side right image = y,z plane; top image = x,z plane. Scale bar is 5 um. Error bars represent S.E.M and ***p<0.001, *p<0.05 and ns for not significant from unpaired 2-tailed t-tests (panels B, C, F, G) or from Tukey’s post-hoc test after ANOVA (panels D,E). </span></p> <p><span>Fig. 4. Mouse and human α-syn fibril uptake in neurons is similar and clathrin-dependent</span></p> <p><span>(A) Time-dependent dynamics of pHrodo-labeled mouse or human PFF (1 µg/mL) internalization over 24 hours into human-PAC-wt-SNCA<sup>+/+</sup>/Snca<sup>-/- </sup>hippocampal primary neuronal cultures at DIV7, with normalized pHrodo-channel intensity to DAPI counts at the indicated time point. Each dot represents mean values from four images each from three independent neuronal cultures. </span><span>(D) Alexa-568 intensity in dopaminergic neurons and (E) intensity exclusively in the perinuclear area 8 hours post PFF incubation. Each dot represents the mean value of one image with at least 20 images collected from three independent experiments. (F) Calculated uptake of pHrodo-labeled α-syn PFFs at 24 hours in the presence of endocytosis inhibitors with signals normalized to vehicle only controls. Each dot in panel F represents the mean value of four images evaluated per condition from three independent experiments. Error bars for each group analysis represent S.E.M., and ns is not-significant from 2-tailed t tests.</span></p> <p><span>Fig. 5. Mouse α-syn fibrils pathology propagation is more efficient than human α-syn in primary neurons. </span></p> <p><span>(A) Levels of pS129-α-syn signal assessed relative to the number of neurons in the corresponding cultures cultured from human-PAC-wt-SNCA<sup>+/+</sup>/Snca<sup>-/- </sup>hippocampal primary neuron culture treated with 1 µg/mL of α-syn PFFs for 14 days, or equivalent amounts of monomer as indicated, and stained against pS129-α-syn, Tau and NeuN. (B) To ensure the specificity of pS129-α-syn signals, control groups show the lack of signal in neurons cultured from Snca<sup>-/- </sup>mice following 14 days of incubation with α-syn PFFs or monomer as shown. (D) Abundance of distinct pS129-α-syn signals in cell bodies or neuritic morphology in neuronal cells treated with 1 µg/mL of mouse or human α-syn PFFs. (E) Proportion of pS129-α-syn occupancy in cell body and neurites in primary hippocampal cultures incubated with mouse or human α-syn PFFs for 14 days. (F) ELISA quantification of α-syn aggregate levels in cell lysates from human-PAC-wt-SNCA<sup>+/+</sup>/Snca<sup>-/- </sup> or Snca<sup>-/- </sup>neuronal cultures treated with fibril PFFs or monomeric protein for 14 days. (G) Group analysis of NeuN-positive nuclei abundance normalized to DAPI count. Each data point in a group in the graphs represents the mean of signal from an individual litter with two technical replicates per litter and at least 25 images analyzed for each replicate, with error bars indicating S.E.M. Significance was determined by 2-tailed t-tests; **p<0.001, ****p < 0.0001, ns for not significant.</span></p> <p><span>Fig. 6. Mouse PFF induced α-syn pathology spreads through the mouse brain to seed human α-syn pathology more efficiently than human α-syn PFFs</span></p> <p><span>(B) Group analysis of α-syn pathology propagation ratio to contralateral side in piriform cortex, (C) thalamus, and (D) dorsal striatum, quantified as proportion of pSyn neuronal inclusion spread between Ipsi and Contr. areas. (E) Analysis of pS129-α-syn pathology near the injection site within the ipsilateral dorsal striatum. Each data point (n=5 per group) in group analysis plots represents the mean of the signal from 20-25 sections from an individual animal, and error bars indicate S.E.M. Significance was determined by 2-tailed t-tests; *p<0.05, ns for not significant</span>.</p> <p><span>Fig. 7. Recruitment of human α-syn monomer into mouse PFFs leads to the generation of mouse-like fibrils.</span></p> <p><span>(A) Representative aggregation assays showing mouse and human PFF-templated aggregation with human α-syn monomer, with (B) the calculated lag phase. Data points represent normalized ThT fluorescence from three independent experiments with error bars indicating S.E.M. (C) Representative filter-trap slot-blot membranes stained with MJFR14-6-4-2 α-syn aggregate-specific antibodies for the detection of aggregated α-syn in corroboration of aggregation kinetics without amyloid dyes. </span><span> (E) Group analysis of ThT binding (fluorescence units, F.U.) from chimeric or homogenous-sequence extracted, monomer-free, sonicated α-syn fibril (PFF) products, as well as (F) Nile Red binding. Each data point in panels E and F represents a mean from an individual experiment measured in duplicate with three independent experiments.E<a name="_Hlk173655145"></a>rror bars indicate S.E.M., **p<0.01, from a 2-tailed t-test, and ***p<0.001 from Tukey’s post-hoc test after ANOVA. </span></p> <p>fig. S2. High resolution estimation of the cryo-EM map of mouse and human recombinant α-syn fibrils</p> <p>Fourier shell correlation (FSC) resolution estimation and validation for the 3D reconstruction of the cryo-EM collected images of procured mouse fibrils generated by Duke (a) and EPFL (b) research groups. (c) FSC estimation plot of human α-syn fibrils collected and generated at Duke University.</p> <p> </p> <p>fig. S7. Generation and validation of mouse and human α-syn PFFs</p> <p>(c) Group analysis of the size population proportions of sonicated mouse and human α-syn preparations, (e) representative average radii of human α-syn,(f) UV absorbance spectra and (g) coefficient extinction adjusted concentration. Each data point or S.E.M in panel c, e and g are extracted from a single acquisition from three independent experiments with ten measurements analyzed per group. Each dot in panel f is the mean of two technical replicates from three independent batches. Significance was assessed via 2-tailed t-tests with ns for not significant.</p> <p> </p> <p>fig. S9. Mouse α-syn PFFs are highly susceptible to sarkosyl denaturation.</p> <p>(a) Filter-trap slot-blot analysis of sonicated fibrils (PFFs) exposed to different concentrations of sarkosyl (left) and the remaining fibrils detected with the fibril-selective antibody MJFR14-6-4-2 in a dose-response curve (middle), with group analysis between two PFF variants at 1% Sarkosyl compared (right). Error bars indicate S.E.M from three independent experiments. (b) DLS analysis of sonicated fibrils (PFFs) exposed to sarkosyl concentrations in a dose-response curve. Error bars indicate S.E.M of three independent experiments with 10 acquisition measurements for each biological sample. *p<0.05 from unpaired 2-tailed t-tests.</p> <p> </p> <p>fig. S11. Evaluation of α-syn aggregation ELISA using control recombinant mouse and human α-syn fibril PFFs.</p> <p>Standard curves generated from mouse and human PFFs in a pan-α-syn aggregate-specific ELISA. The ELISA approach was utilized to quantify the level of aggregates present in lysates derived from the neuronal cultures. The standard curve, along with the indicated goodness of fit and corresponding r² values, provides reliable measures at physiological ranges. Each data point represents the mean from three technical replicates from two independent experiments with errors bars indicating S.E.M.</p> <p> </p> <p>fig. S9. Evaluation of α-syn ELISA using control recombinant mouse and human α-syn fibril PFFs</p> <p>Standard curve generated from mouse and human PFFs in a pan-α-syn aggregate ELISA. The ELISA was utilized to quantify the level of aggregates present in the cell lysates. The standard curve, along with the indicated goodness of fit and corresponding r² values, provides a reliable measure for interpreting the results. Each data point represents the mean from three technical replicates from two independent experiments with errors bars indicating S.E.M.</p> <p> </p> <p>fig. S12. Elevated p-S129-α-syn levels in iPSC-derived dopaminergic neurons following treatment with mouse α-syn PFFs</p> <p>(b) Levels of pS129-α-syn in the β-III-tubulin area in iPSC-derived DA neurons after 7 days of treatment with 10 μg/mL of PFFs or control. (c) The quantity of pS129-α-syn puncta relative to the β-III-tubulin area measured in each image and compared across conditions. (d) Proportion of abundance of pS129-α-syn puncta localized in cell bodies and neurites in group analysis between mouse and human α-syn PFF treatments. Each data point in c and d represent the mean value of the images from one well (n=4) and errors bars indicate S.E.M with **p<0.01 from 2-tailed t-tests. </p> <p> </p> <p>fig. S13. Evaluation of kinetic properties in aggregation assays with cross-seeded chimeric fibrils show that the chimeric fibrils largely replicate the functional properties of their parental seeds, despite different amino acid sequences between the fibril preparations.</p> <p>(a) Representative RT-QuIC (real-time quaking induced) assays of normalized relative fluorescence values (RFUs) with mouse monomer templating on different PFF seeds in the creation of new fibrils. The lack of formation of spontaneous fibrils (gray color, no PFFs added “no template”) indicate that spontaneous aggregation is not occurring under the given aggregation conditions within the specified timeframes. Line graphs show corresponding lag in amplification (in hours, or time to initial fluorescence threshold) of the different reactions combined with different PFF concentrations added and analyzed to a linear regression model. Data points represent normalized ThT fluorescence to their maximal fluorescence in the individual reactions, accounting for differential ThT binding to different fibril structures. (b) Comparable reactions as above but with human instead of mouse monomer templating with the indicated PFF seed. </p> <p> </p>
Fig. 3 in Disease reservoirs threaten the recently rediscovered Podocarpus Stubfoot Toad (Atelopus podocarpus)
Fig. 3. Comparison of infection burdens in Gastrotheca tadpoles between three of the six sites. The asterisks denote significant differences. The boxplot was produced in ggplot 2 (Ginestet 2011).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.