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Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise"
<p>Data relating to the manuscript "Dataset for manuscript "During haptic communication, the central nervous system compensates distinctly for delay and noise". This includes the experiment dataset (in file experiment_dataset.csv) as well as the MATLAB functions used for the development of the simulation model (with main function main_delay.m)</p>
The genomic and transcriptional landscape of primary central nervous system lymphoma
<p>Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Despite extensive research, the molecular alterations leading to PCNSL have not been fully elucidated. In order to provide a comprehensive description of the genomic and transcriptional landscape of PCNSL, we here performed whole-genome and transcriptome sequencing and integrative analysis of 51 lymphomas presenting in the CNS, including 42 EBV-negative PCNSL, 6 secondary CNS lymphomas (SCNSL) and 3 EBV+ CNSL and matched controls. The results were compared to an independent validation cohort of 31 FFPE CNSL specimens (PCNSL, n = 19; SCNSL, n = 9; EBV+ CNSL, n = 3) and 36 systemic DLBCL cases outside the CNS.</p> <p>This repository contains tab separated value text files:</p> <p>- Radke_et_al_supplementary_somatic_CNVs.tsv (somatic copy number variations predicted by ACEseq)<br> - Radke_et_al_supplementary_somatic_indels.tsv (somatic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_indels_exonic.tsv (somatic exonic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_mutations_integrated.tsv (table of gene by patients, stating which mutations were observed) <br> - Radke_et_al_supplementary_somatic_mutations_integr_integrated_including_kataegis_counts.tsv (table of gene by patients, stating which mutations were observed, including the count of mutations falling into kataegis hotspots) <br> - Radke_et_al_supplementary_somatic_SNVs.tsv (somatic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_exonic_functional.tsv (somatic exonic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_rescued_by_TiNDA (mutations initially classified as germline, but likely tumor mutations based on VAF modelling by TiNDA)<br> - Radke_et_al_supplementary_somatic_SVs.tsv (somatic structural variations predicted by the DKFZ Sophia workflow)<br> - Radke_et_al_supplementary_RNAseq_numReads_CNSLs.tsv (RNAseq read counts calculated by the DKFZ RNAseq workflow)</p> <p>This repository contains the raw unedited images from the manuscript:</p> <p>- Radke_et_al_Main_Figure_1c_BCL6.tif (raw unedited image for Main Figure 1c - BCL6)<br> - Radke_et_al_Main_Figure_1c_CD10.tif (raw unedited image for Main Figure 1c - CD10)<br> - Radke_et_al_Main_Figure_1c_MUM1.tif (raw unedited image for Main Figure 1c - MUM1)<br> - Radke_et_al_Supplementary_Figure_1a_CD20.tif (raw unedited image for Supplementary Figure 1a - CD20)<br> - Radke_et_al_Supplementary_Figure_1a_EBV.tif (raw unedited image for Supplementary Figure 1a - EBV)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(FFPE).tif (raw unedited image for Supplementary Figure 1a - HE (FFPE))<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_1.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 1)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_2.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 2)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_3.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 3)<br> - Radke_et_al_Supplementary_Figure_1a_Ki67.tif (raw unedited image for Supplementary Figure 1a - Ki67)<br> - Radke_et_al_Supplementary_Figure_1b_EBV_PCR.pptx (raw unedited image for Supplementary Figure 1b - EBV PCR )<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_1.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 1)<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_2.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 2)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-033.tif (raw unedited image for Supplementary Figure 7h - PD-L1 LS-033)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-031.tif (raw unedited image for Supplemnetary Figure 7h - PD-L1 LS-031)</p>
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
Regional cytoarchitecture of the adult and developing mouse enteric nervous system
<p>The organization and cellular composition of tissues are key determinants of their biological function. In the mammalian gastrointestinal (GI) tract, the enteric nervous system (ENS) intercalates between muscular and epithelial layers of the gut wall and can control GI function independent of central nervous system (CNS) input. As in the CNS, distinct regions of the GI tract are highly specialized and support diverse functions, yet the regional and spatial organization of the ENS remains poorly characterized.<a href="https://www-sciencedirect-com.stanford.idm.oclc.org/science/article/pii/S0960982222013070?via%3Dihub#bib2"><sup>2</sup></a> Cellular arrangements, circuit connectivity patterns, and diverse cell types are known to underpin ENS functional complexity and GI function, but enteric neurons are most typically described only as a uniform meshwork of interconnected ganglia. Here, we present a bird’s eye view of the mouse ENS, describing its previously underappreciated cytoarchitecture and regional variation. We visually and computationally demonstrate that enteric neurons are organized in circumferential neuronal stripes. This organization emerges gradually during the perinatal period, with neuronal stripe formation in the small intestine (SI) preceding that in the colon. The width of neuronal stripes varies throughout the length of the GI tract, and distinct neuronal subtypes differentially populate specific regions of the GI tract, with stark contrasts between SI and colon as well as within subregions of each. This characterization provides a blueprint for future understanding of region-specific GI function and identifying ENS structural correlates of diverse GI disorders.</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
Expression of immunoglobulin constant domain genes in neurons of the mouse central nervous system
<p>Data related to the publication "Expression of immunoglobulin constant domain genes in neurons of the mouse central nervous system". </p> <p>The fasta file (.fa) contains the sequence of neuronal FC-Ighm</p> <p>The .pdb files contain the model of the two different protein versions of Ighm</p> <p>The excel file contains the data of the quantification of co-expression and the ATG prediction results.</p>
Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses
<p>This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are:</p> <p>- ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals.</p> <p>- ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.</p>
Figure 11. Nervous system and brain. A in Systematics, evolution and phylogeny of Annelida - a morphological perspective
Figure 11. Nervous system and brain. A. Nervous system of the trunk with longitudinal and segmental circular nerves exemplified by Parapodrilus psammophilus (Dorvilleidae). Ventral cord consists of unpaired median (mn) and main paired nerves (mvn). B-D. Anti α-tubulin immunoreactivity; dotted lines indicate segment borders. B. Polygordius appendiculatus (Polygordiidae), ventral nerve cord (green) comprising three closely apposed neurite bundles, serotonergic perikarya (red) in a repetitive pattern although distinct ganglia are absent (medullary cord). Note high number of segmental nerves. C-D. Brania clavata (Syllidae); depth coding images. C. Brain (b) and ventral nerve cord in ventral view, ventral cord consists of several closely apposed nerves forming 3 bundles behind 1st ganglion (g1), 4 segmental nerves (arrowheads, ppn) in each segment; brain gives rise to several stomatogastric nerves (sn). D. Ventral cord in the trunk region. F. General diagram of the cephalic nervous system in polychaetes, numerals refer to palp nerve roots, somata stippled. E-H. Nereis sp. (Nereididae). E Ventral nerve cord in basiepithelial position (arrowheads refer to epidermal extracellular matrix). F. Parasagittal section with mushroom bodies (mb), note subepithelial position of brain; arrowheads point to cerebral ganglia. H Enlargement of anterior part of mushroom body with stalks of globuli cells (gc). – br = brain, cc = circumoesophageal connective, dcdr = dorsal commissure of drcc, dcvr = dorsal commissure of vrcc, dlln = dorsolateral longitudinal nerve, drcc = dorsal root of cc, ecm = extracellular matrix, ep = epidermis, g1 = 1st ganglion, gc = globuli cell, in = intestine, lln = lateral longitudinal nerve, mb = mushroom body, mn = median nerve of ventral cord, mvn = main nerve of ventral cord, nla = nerve of lateral antenna, nma = nerve of median antenna, no = nuchal organ, np = neuropil, obm = oblique muscle, pn = palp nerve, ppn = parapodial nerve, sn = stomatogastric nerve, so = somata of neurites, sog = suboesophageal ganglion, vbv = ventral blood vessel, vcdr = ventral commissure of drcc, vcvr = ventral commissure of vrcc, vlm = ventral longitudinal muscle, vrcc = ventral root of cc. A, F: modified from Müller and Orrhage (2005). Micrographs; B C: Lehmacher, C, D: M. Kuper, Osnabrück.
Fig. 1 in Towards a ground pattern reconstruction of bivalve nervous systems: neurogenesis in the zebra mussel Dreissena polymorpha
Fig. 1 Development of Dreissena polymorpha from gastrula to early veliger stage. a, g, h, and i Scanning electron micrographs. b, c Confocal microscope Zprojection images. d, e, and f Single optical sections of c. Acetylated α-tubulin-lir (green), HCS CellMask (pink), and cell nuclei counter staining (blue). Apical is always up. Lateral views. Scale bars are 15 μm. a Ciliated gastrula stage (16 h post fertilization, hpf) with blastopore (bp) on the vegetal pole. b Elongated early trochophore (22 hpf) with prominent apical tuft (at) and prototroch (pt). c Early-trochophore (23 hpf) with apical tuft (at), prototroch (pt), and telotroch (tt). d Early trochophore (23 hpf). e, f Early trochophore (23 hpf) in different optical planes with foregut (fg) and shell field (sf) invagination. g Early veliger (39 hpf) with embryonic shell (s) and expanded velum (ve). h 46 hpf old veliger. i Late veliger larva (188 hpf)
Fig. 2 Tomopteris pacifica. Neurogenesis. Confocal maximum projections. A in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)
Fig. 2 Tomopteris pacifica. Neurogenesis. Confocal maximum projections. A Early developmental stages are characterized by a large amount of yolk and a prominent prototroch (pt). B At 5 days post-fertilization (dpf), the larval stages possess four pairs of well-developed trunk appendages and a distinct prototroch (pt). Note that the anterior-most appendage (I) is uniramous while all other appendages appear biramous. C Slightly older stages show a well-developed ventral nerve cord (vnc) with outgoing parapodial neurite bundles (pn) innervating the body appendages; serotonergic somata form serial clusters along the ventral nerve cord. D A closer examination of larvae at around 6–7 dpf shows the presence of a prominent nuchal nerve (nn) innervating the nuchal organs and originating from the dorsal part of the larval brain (br). The insert shows the innervation of the nuchal organ.
Fig. 9 in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)
Fig. 9 Tomopteris helgolandica. Nuchal organ, details of receptor cells, juvenile, TEM. A Outer part of the olfactory chamber (oc), showing that the epithelium is primarily formed by monociliary sensory dendrites (sd), some with basal bodies (arrows). The olfactory chamber has numerous sensory processes (spr), and vesicle-like structures appearing empty (asterisks). The apical region of dendrites often contains dense cored (arrowheads) and other vesicles. B Sensory dendrite (sd) with very short sensory cilium and shaft branches into microvillus-like structures (arrowhead). C Sensory dendrite sending out a microvillus
Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)
Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A Spherical trochophore ca. 48–72-h post-fertilization. B Elongated embryo at ca. 5 days post-fertilization (dpf) with four segments and rudiments of parapodia, Roman numerals refer to segment numbers. C Dorsal view of larva/juvenile at 6–7 dpf showing parapodia formation; note first cirruslike appendage. Nuchal organs (no) visible as cilia semicircles in front of the prototroch (pt). D Ventral view of larva/juvenile at ca. 8–10 dpf. E
Fig. 5 in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)
Fig. 5 Tomopteris helgolandica. Tentacular cirrus of juvenile. TEM. A Longitudinal section of the intracellular skeletal element (se) with regular cross striation pattern. B Skeletal element, periodicity, and substructure of the striation pattern. C Process of rod-bearing cells reaching the epithelial surface of the epidermis (arrow). D Gland cell opening at the base of cirrus with a circle of microvilli (arrow). E Group of distal gland cell processes close to opening at the base of cirrus. F Central part of the cirrus formed by numerous neurites cov-
Fig. 7 in Loss of complexity from larval towards adult nervous systems in Chaetopteridae (Chaetopteriformia, Annelida) unveils evolutionary patterns in Annelida
Fig. 7 The anterior adult nervous system of Phyllochaetopterus sp. revealed by immunohistochemistry. a, b confocal maximum projections of anti-5HT-staining. a: The brain (br) consists of a compact neuropil without prominent commissures. The palp nerves (pn1, pn2)
Fig. 8 in Loss of complexity from larval towards adult nervous systems in Chaetopteridae (Chaetopteriformia, Annelida) unveils evolutionary patterns in Annelida
Fig. 8 Representative larval and juvenile stages of Chaetopterus variopedatus. Light microscopic images. Stages are shown in hours (hpf) or days past fertilization (dpf). a: 48 hpf, the larvae are still spherical and possess a prominent apical tuft (at) at the anterior end. b: 13 dpf, the larvae exhibit an elongated body, with prominent apical eyespots (ey) and distinct chaetal bundles (ch). mo: mouth opening. c:> 50 dpf,
Fig. 4 in Loss of complexity from larval towards adult nervous systems in Chaetopteridae (Chaetopteriformia, Annelida) unveils evolutionary patterns in Annelida
Fig. 4 Adult ultrastructure of the nervous system. a: The brain is intraepidermal; i.e., the neurites (ne) are located dorsal to the basal lamina (bl). Intermediate filaments (if) are located inside radial glial cell processes (gcp) which cross the neuropil perpendicularly. mu: musculature. b: Somata (so) of neurons are located dorsal to the neurites (ne). Nuclei (nu) of neuronal somata are spherical. Somata of glial cells (sogc) are interspersed between the neuronal somata (so).
Fig. 2 in Loss of complexity from larval towards adult nervous systems in Chaetopteridae (Chaetopteriformia, Annelida) unveils evolutionary patterns in Annelida
Fig. 2 Adult histology of the cns. Azan, 5 µm, sections of anterior body region A (according to chaetopterid nomenclature). a, c, e: Spiochaetopterus costarum; b, d, f: Chaetopterus norvegicus. a: the brain (br) is located inside the epidermis (ep). It is composed of a neuropil (np) and dorsally located neuronal somata (so). Lateral of the brain, the lateral medullary cords (lmc) branch of. bl: basal lamina; eso: esophagus. b: The brain (br) is intraepidermal. The somata (so) layer is located dorsally to the neuropil (np). An esophageal plexus (epl) connects both medullary cords continuously. bl: basal lamina; ep: epidermis; eso: esophagus; c: somata (so) of the neuro-
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1708">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-NAM</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5764">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
ScienceDex guides
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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.