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2,721 results for “CONNECTIVITY”
Data from: Integrating machine learning with otolith isoscapes: reconstructing connectivity of a marine fish over four decades
<p>Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., isoscapes) allow a probabilistic and continuous assignment of origin across space, although applications to marine organisms remain limited. We present a new framework that integrates nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous). We applied this integrated framework to predict the geographic origin of the Northwest Atlantic mackerel (<em>Scomber scombrus</em>), a migratory pelagic fish comprised of northern and southern components that have distinct spawning sites off Canada (northern contingent) and the US (southern contingent), and seasonally overlap in US fished regions. The nominal approach based on otolith carbon and oxygen stable isotopes (δ<sup>13</sup>C/δ<sup>18</sup>O) yielded high contingent classification accuracy (84.9%). Contingent assignment of unknown-origin samples revealed prevalent, yet highly varied contingent mixing levels (12.5–83.7%) within the US waters over four decades (1975–2019). Nominal predictions were integrated into mackerel-specific otolith oxygen isoscapes developed independently for Canadian and US waters. The combined approach identified geographic nursery hotspots in known spawning sites, but also detected geographic shifts over multi-decadal time scales. This framework can be applied to other marine species to understand migration and connectivity at high spatial resolution, relevant to management of unit stocks in fisheries and other conservation assessments.</p>
Data: Regularized and Opposite spin-scaled functionals from Møller-Plesset adiabatic connection -- higher accuracy at lower cost (arXiv.2307.02715)
<p>Data corresponding to the work <em>Regularized and Opposite spin-scaled functionals from Møller-Plesset adiabatic connection -- higher accuracy at lower cost (</em>https://arxiv.org/abs/2307.02715). </p> <p>The zip file contains:</p> <p>- The data of the S22, NCCE31, S66x8, B30, B30x8, x40 and water hexamers datasets.</p> <p>- the data_com and data_frag files contain E_HF, Ex, MP2-SS, MP2-OS, LDA-int, GEA-int for respectively the complex and sum of the fragments for all the complexes (.npy and .csv).</p> <p>- data_kappa_com and data_frag_com files contain [kappa_os 0.6, kappa_ss 0.6], [kappa_os 0.7, kappa_ss 0.7], ..., [kappa_os 1.7, kappa_ss 1.7] for respectively the complex and sum of the fragments for all the complexes (.npy and .csv).</p> <p>-See README.txt for more information</p>
Changes in patterns of age-related network connectivity are associated with risk for schizophrenia
<p>This is the online data repository accompanying the following manuscript:</p> <p><strong>Changes in patterns of age-related network connectivity are associated with risk for schizophrenia</strong></p> <p>Roberta Passiatore<sup>a,b,c</sup>, Linda A. Antonucci<sup>a</sup>, Thomas P. DeRamus<sup>b</sup>, Leonardo Fazio<sup>d</sup>, Giuseppe Stolfa<sup>a</sup>, Leonardo Sportelli<sup>a,e</sup>, Gianluca C. Kikidis<sup>a,e</sup>, Giuseppe Blasi<sup>a,f</sup>, Qiang Chen<sup>e</sup>, Juergen Dukart<sup>c,g</sup>, Aaron L. Goldman<sup>e</sup>, Venkata S. Mattay<sup>e,h</sup>, Teresa Popolizio<sup>i</sup>, Antonio Rampino<sup>a,f</sup>, Fabio Sambataro<sup>j</sup>, Pierluigi Selvaggi<sup>a,f</sup>, William Ulrich<sup>e</sup>, Apulian Network on Risk for Psychosis<sup>a,k,l,m,n,o</sup>, Daniel R. Weinberger<sup>e,h,p,q,r</sup>, Alessandro Bertolino<sup>a,f1</sup>, Vince D. Calhoun<sup>b1</sup>, Giulio Pergola<sup>a,e,p1</sup></p> <p><sup>a</sup> Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124 Bari, Italy</p> <p><sup>b</sup> Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303 Atlanta, GA</p> <p><sup>c</sup> Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428 Jülich, Germany</p> <p><sup>d</sup> Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010 Casamassima, Italy</p> <p><sup>e </sup>Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, MD</p> <p><sup>f</sup> Psychiatric Unit, University Hospital, 70124 Bari, Italy</p> <p><sup>g</sup> Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany</p> <p><sup>h</sup> Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287 Baltimore, MD</p> <p><sup>i</sup> Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Foggia, Italy</p> <p><sup>j</sup> Section of Psychiatry, Department of Neuroscience, University of Padova, 35121 Padova, Italy</p> <p><sup>k</sup> Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121 Foggia, Italy</p> <p><sup>l </sup>Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy</p> <p><sup>m </sup>Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123 Andria, Italy</p> <p><sup>n </sup>Department of Mental Health, Azienda Sanitaria Locale Bari, 70132 Bari, Italy</p> <p><sup>o </sup>Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100 Brindisi, Italy</p> <p><sup>p</sup> Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205 Baltimore, MD</p> <p><sup>q</sup> Department of Neuroscience, Johns Hopkins University School of Medicine, 21287 Baltimore, MD<br> <sup>r</sup> Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287 Baltimore, MD</p> <p> </p> <p>Members of the Apulian Network on Risk for Psychosis include Ileana Andriola, Lucia Mare, Nicolò Parente, Alessandra Raio, Veronica D. Toro, (Department of Translational Biomedicine and Neuroscience – University of Bari Aldo Moro, Bari, IT), Mario Altamura, Marimar Castrigno, Melania Difino (Department of Mental Health, ASL Foggia, Foggia, IT; Department of Clinical and Experimental Medicine, University of Foggia, Foggia, IT), Flora Brudaglio, Domenico S. Savino, Rossana Vista (Department of Mental Health, ASL Barletta-Andria-Trani, Andria, IT), Angela Carofiglio, Barbara Gelao, Marina Mancini (Department of Mental Health, ASL Bari, Bari, IT), Alessandro Saponaro, Anna Manzari, Domenico Suma (Department of Mental Health, ASL Brindisi, Brindisi, IT).</p> <p><strong>Abstract</strong></p> <p>Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single- session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age-stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlate with genetic risk for SCZ and are detectable with MRI in young participants.</p> <p><em><strong>DOI: 10.1073/pnas.2221533120</strong></em></p> <p> </p> <p>Files:</p> <p>- <strong>NeuroMark_1.0_template </strong></p> <ol> <li><strong><em>NeuroMark_1.0.nii</em>: </strong> 4D Nifti containing the 100 Independent Components (ICs) spatial maps that was used for individual IC extraction based on Du Y, Fu Z, Sui J, Gao S, Xing Y, Lin D, Salman M, Abrol A, Rahaman MA, Chen J, Hong LE, Kochunov P, Osuch EA, Calhoun VD; Alzheimer's Disease Neuroimaging Initiative. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. Neuroimage Clin. 2020;28:102375. doi: 10.1016/j.nicl.2020.102375. Epub 2020 Aug 11. PMID: 32961402; PMCID: PMC7509081.</li> <li><em><strong>NeuroMark_1.0_Label.doc</strong></em>: document containing labels for each IC extracted including brain regions, peak coordinates of ICs, corresponding functional network in which ICs are arranged, and the corresponding image number in the NeuroMark_1.0.nii file. NOTE</li> </ol> <p>- <strong>AveragedMatrix</strong></p> <ol> <li><em><strong>vectorized_FNC_LIBD.RData, </strong></em><strong><em>vectorized_FNC_UNIBA1.RData, </em></strong><strong><em>vectorized_FNC_UNIBA1.RData:</em></strong></li> <li><strong><em>FNC_all_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects averaged across groups, sessions and cohorts</li> <li><strong><em>FNC_all_rest (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during resting state fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during encoding (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during retrieval (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on all subjects during working memory (2-Back) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_all_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix across subjects during emotion recognition (Faces/FMT) fMRI averaged across groups and cohorts</li> <li><strong><em>FNC_yNC_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC averaged across sessions</li> <li><strong><em>FNC_yNC_rest (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during resting state fMRI</li> <li><strong><em>FNC_yNC_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during encoding (RISE/PEAR) fMRI</li> <li><strong><em>FNC_yNC_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during retrieval (RISE/PEAR) fMRI</li> <li><strong><em>FNC_yNC_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during working memory (2-Back) fMRI</li> <li><strong><em>FNC_yNC_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on yNC during emotion recognition (Faces/FMT) fMRI</li> <li><strong><em>FN_oNC_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC averaged across sessions</li> <li><strong><em>FNC_oNC_rest (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during resting state fMRI</li> <li><strong><em>FNC_oNC_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during encoding (RISE/PEAR) fMRI</li> <li><strong><em>FNC_oNC_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during retrieval (RISE/PEAR) fMRI</li> <li><strong><em>FNC_oNC_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during working memory (2-Back) fMRI</li> <li><strong><em>FNC_oNC_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oNC during emotion recognition (Faces/FMT) fMRI</li> <li><strong><em>FNC_ySIB_averaged (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB averaged across sessions</li> <li><strong><em>FNC_ySIB_rest (LIBD): </em></strong>mean FNC matrix on ySIB during resting state fMRI</li> <li><strong><em>FNC_ySIB_enc (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB during encoding (PEAR) fMRI</li> <li><strong><em>FNC_ySIB_ret (LIBD): </em></strong>mean FNC matrix on ySIB during retrieval (PEAR) fMRI</li> <li><strong><em>FNC_ySIB_nback (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB during working memory (2-Back) fMRI</li> <li><strong><em>FNC_ySIB_emo (UNIBA1, LIBD): </em></strong>mean FNC matrix on ySIB during emotion recognition (Faces/FMT) fMRI</li> <li><strong><em>FNC_oSIB_averaged (UNIBA1, LIBD): </em></strong>mean FNC matrix on oSIB averaged across sessions</li> <li><strong><em>FNC_oSIB_rest (LIBD): </em></strong>mean FNC matrix on oSIB during resting state fMRI</li> <li><strong><em>FNC_oSIB_enc (UNIBA1, LIBD): </em></strong>mean FNC matrix on oSIB during encoding (PEAR) fMRI</li> <li><strong><em>FNC_oSIB_ret (LIBD): </em></strong>mean FNC matrix on oSIB during retrieval (PEAR) fMRI</li> <li><strong><em>FNC_oSIB_nback (UNIBA1, LIBD): </em></strong>mean FNC matrix on oSIB during working memory (2-Back) fMRI</li> <li><strong><em>FNC_oSIB_emo (LIBD): </em></strong>mean FNC matrix on oSIB during emotion recognition (FMT) fMRI</li> <li><strong><em>FNC_yPSY_averaged (UNIBA2): </em></strong>mean FNC matrix on yPSY averaged across sessions</li> <li><strong><em>FNC_yPSY_rest (UNIBA2): </em></strong>mean FNC matrix on yPSY during resting state fMRI</li> <li><strong><em>FNC_yPSY_enc (UNIBA2): </em></strong>mean FNC matrix on yPSY during encoding (RISE) fMRI</li> <li><strong><em>FNC_yPSY_ret (UNIBA2): </em></strong>mean FNC matrix on yPSY during retrieval (RISE) fMRI</li> <li><strong><em>FNC_yPSY_nback (UNIBA2): </em></strong>mean FNC matrix on yPSY during working memory (2-Back) fMRI</li> <li><strong><em>FNC_yPSY_emo (UNIBA2): </em></strong>mean FNC matrix on yPSY during emotion recognition (Faces) fMRI</li> <li><strong><em>FNC_oSCZ_averaged (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ averaged across sessions</li> <li><strong><em>FNC_oSCZ_rest (UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during resting state fMRI</li> <li><strong><em>FNC_oSCZ_enc (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during encoding (PEAR/RISE) fMRI</li> <li><strong><em>FNC_oSCZ_ret (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during retrieval (PEAR/RISE) fMRI</li> <li><strong><em>FNC_oSCZ_nback (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during working memory (2-Back) fMRI</li> <li><strong><em>FNC_oSCZ_emo (UNIBA1, UNIBA2, LIBD): </em></strong>mean FNC matrix on oSCZ during emotion recognition (Faces/FMT) fMRI</li> <li><strong>PlotMatrixFNC.m (Script): </strong>code used to generate FNC plots. Example of FNC plots are included in Figure 1.</li> </ol> <p>- <strong>Connectogram</strong></p> <ol> <li><em><strong>MeanMatrix.mat: </strong></em>matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Matlab object.</li> <li><em><strong>MeanMatrix.txt: </strong></em>matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Text file.</li> <li><em><strong>ConnectogramNeuroMark_1.0.m (Script)</strong></em>: code used to generate Figure 2. Graphical and statistical options are provided. Please refer to <a href="https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf">https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf</a> for further details.</li> </ol> <p>- <strong>ResFNCAgeDifferences</strong></p> <ol> <li><em><strong>data_example.RData (Data): </strong></em>example data frame for <em>AgeDiff </em>code.</li> <li><strong><em>AgeDiff_real.R (Script)</em></strong>: code to test age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li><em><strong>AgeDiff_permutation.R (Script): </strong></em>code to permute age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li><strong><em>ResFNCAgeDifference.RData: </em></strong>summary statistics obtained on UNIBA1, UNIBA2, LIBD cohorts included in the manuscript.</li> <li><em><strong>boxplot_permutation_p_LIBD.pdf, boxplot_permutation_p_UNIBA1.pdf, boxplot_permutation_p_UNIBA2.pdf: </strong></em>error bars showing results obtained after permutation (10,000 iteration) across IC pairs.</li> </ol> <p><strong>- dataFNC</strong></p> <ol> <li><strong><em>data_final.RData: </em></strong>de-identified individuals' aggregate FNC data for each cohort (UNIBA1, UNIBA2, LIBD, PNC, ABCD, UKB)</li> <li><em><strong>plotData.R (Script): </strong></em>code used to plot FNC differences across groups (Figure 3). Code used to assess group differences - Wilcoxon Rank sum test - is included.</li> </ol> <p><strong>- ResFNCRiskDifferences</strong></p> <ol> <li><em><strong>res_cerebellar_occipitoparietal_ICs.csv: </strong></em>summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted<sub>FDR</sub><0.05) on the cerebellar-occipitoparietal FNC</li> <li><strong><em>res_dorsolateralPFC_sensorimotor_ICs.csv: </em></strong>summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted<sub>FDR</sub><0.05) on medial PFC-sensorimotor FNC</li> <li><strong><em>res_medialPFC_sensorimotor_ICs.csv: </em></strong>summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjusted<sub>FDR</sub><0.05) on dorsolateral PFC-sensorimotor FNC</li> </ol> <p>- <strong>ResFNCxPRS</strong></p> <ol> <li><strong><em>ResMetanalysisPRS_FNC_R.csv: </em></strong>summary statistics of metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups</li> <li><strong><em>ResMetanalysisPRS_FNC_children_R.csv: </em></strong>summary statistics of metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li><strong><em>t2r.R (Script):</em></strong> code for t to R<sup>2</sup> transform</li> <li><em><strong>MetanalysisAveragedFNC_PRS.R (Script): </strong></em>code used perform metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> <li><strong><em>MetanalysisAveragedFNC_PRS_children.R (Script): </em></strong>code used perform metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li><strong><em>MetanalysisPRS_FNC_sessions_R.R (Script): </em></strong>code used perform metanalysis on the session-specific FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> </ol> <p> </p> <p>The present work includes data from UK Biobank (ID #41655), Adolescence Brain Cognitive Development (ABCD; ID #12036) Study, and Philadelphia Neurodevelopmental Cohort (PNC; ID #20998). Data are publicly accessible under approved request at the following links:</p> <p>- UK Biobank: <a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a></p> <p>- ABCD: <a href="https://abcdstudy.org/">https://abcdstudy.org/</a></p> <p>- PNC: <a href="https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html">https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html</a></p> <p>The ABCD data used in this report came from NIMH Data Archive DOI: https://doi.org/10.15154/1528793.</p> <p>Additional code for MRI data processing and Independent Component Analysis are publicly available at the following link: <a href="https://trendscenter.org/software/">https://trendscenter.org/software/</a></p> <p> </p> <p>Please, check https://doi.org/10.5281/zenodo.7853030 for future updates.</p> <p>For any data inquiries please contact:</p> <ul> <li><strong>Roberta Passiatore: </strong>Roberta.Passiatore@uniba.it</li> <li><strong>Giulio Pergola: </strong>Giulio.Pergola@uniba.it - Giulio.Pergola@libd.org</li> <li><strong>Vince D Calhoun:</strong> VCalhoun@gsu.edu</li> <li><strong>Alessandro Bertolino:</strong> Alessandro.Bertolino@uniba.it</li> </ul> <p> </p> <p><strong>Funding</strong></p> <p>This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 798181 awarded to G.P. (PI) and A.B., D.R.W.; from the European Union funding within the MUR PNRR Extended Partnership initiative on Neuroscience and Neuropharmacology (Project no. PE00000006 CUP H93C22000660006 “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) to G.B., A.R., A.B., and G.P.; from the funding initiative Horizon Europe Seeds 2021 (Next Generation 8 EU–MUR D.M. 737/2021) for the project S68 CUP: H99J21017550006 to G.P. (PI) and L.A.A., G.B., A.R., A.B.; from the Apulian regional government for the project: “Early Identification of Psychosis Risk” to A.B.; from the Research Projects of National Relevance (PRIN) 2017 Prot. 2017K2NEF4 awarded to G.P.; from a Collaboration Grant from Exprivia Spa to G.P. under the ministerial decree D.M. n. 352/22 and A.B. under a collaboration agreement; from a Collaboration Grant from ITEL Telecomunicazioni Srl awarded to A.B.; from the 2021 Helmholtz Information and Data Science Academy Grant No. 12429 awarded to R.P. and J.D.; and from the NIH grant #R01MH118695 awarded to V.D.C. The collection of the MRI and genetic data for the LIBD cohort was supported by direct funding from the Intramural Research Program of the NIMH to the Clinical Brain Disorders Branch (PI: D.R.W., protocol 95-M-0150).</p> <p> </p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
Dataset for: Burma terrane amber fauna shows connections to Gondwana and transported Gondwanan lineages to the northern hemisphere (Araneae: Palpimanoidea)
<p class="MsoNormal">Burmese amber is a significant source of fossils that documents the mid-Cretaceous biota. This deposit was formed around 99 Ma on the Burma Terrane, which broke away from Gondwana and later collided with Asia, although the timing is disputed. Palpimanoidea is a dispersal-limited group that was a dominant element of the Mesozoic spider fauna, and has an extensive fossil record, particularly from Burmese amber. Using morphological and molecular data, evolutionary relationships of living and fossil Palpimanoidea are examined. Divergence dating with fossils as terminal tips, followed by ancestral range estimations, shows timing of diversification is contemporaneous with continental break-up and that widespread ancestral ranges divide into lineages that inherit different Pangean fragments, consistent with vicariance. Our results suggest that the Burmese amber fauna has ties to Gondwana due to a historical connection in the Early Cretaceous, and that the Burma Terrane facilitated biotic exchange by transporting lineages from Gondwana into the Holarctic in the Cretaceous.</p>
Tunneled antibiotic-impregnated versus bolt-connected non-coated external ventricular drainages: a comparison of complications.
<p><strong>Background: </strong>External ventricular drainage (EVD) is a common emergency neurosurgical procedure, but it is not free of adverse events. The aim of this study is to compare the complication rate of two frequently used EVD types: tunneled antibiotic-impregnated catheters (BactisealÓ) and bolt-connected non-coated devices (CaminoÓ).</p> <p><strong>Methods:</strong> All EVDs placed between March 1<sup>st</sup>, 2015 and December 31<sup>st</sup>, 2017 were registered. Procedures performed with any catheter different from BactisealÓ or CaminoÓ EVD, with incomplete follow-up and those EVDs placed due to an infectious disease were excluded. Demographic and clinical variables were collected, as well as overall complication rate (infection, hemorrhage, obstruction, malposition of the catheter, involuntary pull-out of the device) and need of replacement of the EVD. </p> <p><strong>Results: </strong>A total of 77 EVDs were finally considered for analysis (40 Bactiseal®, 37 Camino®). There was a statistically significant difference in diagnosis and also in the location of the procedure, as more bolt-connected EVD were place outside the operating room (97.3% vs 23.5%, <em>p</em><0.001) due to emergent pathologies like vascular diseases and spontaneous hemorrhages. In the univariate analysis, a statistically significant higher rate of catheter involuntary pull-out (29.7% vs 7.5%, <em>p</em>=0.012) and need of EVD replacement (32.4% vs 12.5%, <em>p</em>=0.035) was found in the Camino cohort. However, those differences could not be confirmed with multivariable analysis, which showed no association between the type of catheter and any of the studied complications. Ventriculostomy duration was identified as a risk factor for infection (OR 1.09, CI 95% 1.02-1.18).</p> <p><strong>Conclusion: </strong>No significant differences were observed regarding infection, hemorrhage, obstruction, malposition, involuntary catheter pull-out, and need for EVD replacement when comparing non-impregnated bolt-connected EVDs (Camino®) with tunneled antibiotic-impregnated catheters (Bactiseal®). Duration of EVD was associated with an increased risk of infection. </p>
Fig. 2 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance
Fig. 2. GCxGC–MS chromatogram of silylated free sterols from S. perennans (A) and projection of the GCxGC–MS chromatogram down to two dimensions (B). Peak numbers are as used in Fig. 1. The x-axis is the ZB-5ms column retention time (min), and the y-axis is the Rxi-17 column retention time (s).
Fig. 3. Ordination diagram for discriminant analysis using the P in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance
Fig. 3. Ordination diagram for discriminant analysis using the P <0.05 chisquare subset; 21 cases were used to develop a model to discriminate among the 3 types of halophytes. Six predictor variables were entered: campesterol, β-sitosterol, stigmasterol, and the sum of Δ5 -, Δ7-and Δ0-STs. Eu – euhalophytes (n = 11), Re – recretophytes (n = 5), Ex – salt excluders (n = 5).
Fig. 1 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance
Fig. 1. Representative GC–MS chromatograms of silylated free sterols from C. crassa (A) and S. linifolia (B). Peaks: cholesterol (1), lathosterol (cholest-7-en- 3β-ol) (2), campesterol (3), campestanol (4), stigmasterol (5), lophenol (4- methylcholest-7-en-3β-ol) (6), 22-stigmastenol (stigmast-22-en-3β-ol) (7), unknown sterol X1 with m/z 484 (8), β-sitosterol (9), spinasterol (stigmasta-7,22- dien-3β-ol) (10), sitostanol (11), β-amyrin (12), Δ5-avenasterol (13), unknown sterol X2 with m/z 484 (14), α-amyrin (15), Δ7-stigmastenol (=Δ7-sitosterol) (16), Δ7-avenasterol (17). * – nonsteroid component.
Fig. 5 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance
Fig. 5. Principal component analysis of the relationship between the composition of ST and soil characteristics of Chenopodiaceae. Vector designations: M – mineralization, Н О – moisture, pH – soil acidity; Δ5, Δ7 and Δ0 are the sums of 2 Δ5 -, Δ7-and Δ0-STs, respectively; Сhol, Camp, β-sito, Stig are the contents of cholesterol, campesterol, β-sitosterol, and stigmasterol. The dots indicate the names of the species: A. aphyla – I, C. arenarius – II, C. crassa – III, H. strobilaceum – IV, B. prostrata – V, S sedoides – VI, S. perennans – VII, S. eltonica –VIII, S. linifolia – IX, S. physophora – X, S. salsa –XI, A. cana – XII, H. verucifera – XIII. The content of mineral residue, moisture and acidity of the soil in the root zone of plants of the Chenopodiaceae are shown in Table 3.
Fig. 4 in Balance of Δ -and Δ -sterols and stanols in halophytes in connection with salinity tolerance
Fig. 4. The content of molecular types of sterols (% of the total) in the halophyte groups: Ex – excluders (n = 5), Re – recretophytes (n = 5), Eu – euhalophytes (n 11); A – sum of Δ5-STs, B – sum of Δ7-STs, C – sum of Δ0-STs; D – = stigmast-5-en-3β-ol, E stigmast-7-en-3β-ol, F – stigmastan-3β-ol, G – stigmasta-5,22-dien-3β-ol, H – stigmasta- 7,22-dien-3β-ol, I – stigmast-22-en-3β-ol. Data are presented as the mean values in each group and the error of the mean (mean ± SE).
Supplementary Dataset 1 for: High-frequency variability dominates potential connectivity between remote coral reefs
<p>Supplementary Dataset 1 (revision 1) for High-frequency variability dominates potential connectivity between remote coral reefs, containing scripts and data required to reproduce figures in the preprint.</p>
FIGURES 1A–D in Connecting the dots: DNA barcoding and lectotype designation shedding light on Labrundinia longipalpis (Goetghebuer, 1921), an intriguing non-biting midge (Chironomidae, Tanypodinae)
FIGURES 1A–D. Labrundinia longipalpis (Goetghebuer). A. Lectotype. B. Labels of Lectotype. C. Detailed views of the lectotype hypopygium, left: ventral aspect, right: dorsal aspect. D. Type series (Lectotype + Paralectotypes).
FIGURE 3 in Connecting the dots: DNA barcoding and lectotype designation shedding light on Labrundinia longipalpis (Goetghebuer, 1921), an intriguing non-biting midge (Chironomidae, Tanypodinae)
FIGURE 3. Neighbor joining tree for species of the genus Labrundinia (Chironomidae, Tanypodinae) based on partial COI sequences (DNA barcodes) and the Kimura-2-parameter. Bootstrap values are indicated on the branches. The North American and European populations of Labrundinia longipalpis (Goetghebuer, 1921) are highlighted within the red dotted box, indicating their clustering.
FIGURES 2A–B in Connecting the dots: DNA barcoding and lectotype designation shedding light on Labrundinia longipalpis (Goetghebuer, 1921), an intriguing non-biting midge (Chironomidae, Tanypodinae)
FIGURES 2A–B. Map illustrating the known occurrence records of Labrundinia longipalpis (Goetghebuer). A. Nearctic and Caribbean regions. (B) Palearctic region.
Datasets for Arctic weather variability and connectivity
<p>We provide the source data and Python codes for our paper "Arctic weather variability and connectivity".</p> <p> </p>
Si/SiGe QuBus for single electron information-processing devices with memory and micron-scale connectivity function
<p>Dataset and analysis scripts for the arXiv paper: Si/SiGe QuBus for single electron information-processing devices with memory and micron-scale connectivity function (submitted for peer-review)</p>
Data and source code of "Refining intra-patch connectivity measures in landscape fragmentation and connectivity indices"
<p>Data and source code related to the article "Refining intra-patch connectivity measures in landscape fragmentation and connectivity indices".</p> <ul> <li>The script "generate_artificial_landscapes.R" was used to produce the artificial landscapes located in the folder "ARTIFICAL_LANDSCAPES". The package rflsgen was used to produce these landscapes.</li> <li>The script "evaluation_artificial_landscapes.R" was used to evaluate these artificial landscapes, in accordance with what is presented in the article. It relies on the intra R package also introduced in the article.</li> <li>The script "evaluation_real_landscape.R" was used to evaluate the Koniambo massif landscape (New Caledonia), according to what is presented in the article. It also relies on the intra R package.</li> </ul>
Tunnel Technique With Emdogain® in Addition to Connective Tissue Graft Compared With Connective Tissue Graft Alone for the Treatment of Gingival Recessions: a Randomized Clinical Trial.
ClinicalTrials.gov study NCT03354104. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Enhancing Function in Later Life: Exercise and Functional Network Connectivity
ClinicalTrials.gov study NCT02068612. IPD Sharing: Not stated. Countries: 1. Publications: 4.
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.