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Dataset related to article: "Quantitative assessment of the quality of home sleep studies: A computer‐assisted approach"
<p>Abstract</p> <p>Home monitoring is the most practical means of collecting sleep data in large-scale research investigations. Because the portion of recording time with poor-quality data is higher than in attended polysomnography, a quantitative assessment of the quality of each signal should be recommended. Currently, only qualitative or semi-quantitative assessments are carried out, likely because of the lack of computer-based applications to carry out this task efficiently. This paper presents an innovative computer-assisted procedure designed to perform a quantitative quality assessment of standard respiratory signals recorded by Type 2 and Type 3 portable sleep monitors. The proposed system allows to assess the quality (good versus bad) of consecutive 1-min segments of thoraco-abdominal movements, oronasal, nasal airflow and oxygen saturation through an automatic classifier. The performance of the classifier was evaluated in a sample of 30 unattended polysomnography recordings, comparing the computer output with the consensus of two expert scorers. The difference (computer versus scorers) in the percentage of good-quality segments was on average very small, ranging from -3.1% (abdominal movements) to 0.8% (nasal flow), with an average total classification accuracy from 90.2 (oronasal flow) to 94.9 (nasal flow), a Sensitivity from 0.93 (oronasal flow) to 0.98 (nasal flow), and a Specificity from 0.74 (nasal flow) to 0.86 (abdominal movements). In practical applications, the scorer can run a check-and-edit procedure, further improving the classification accuracy. Considering a sample of 270 unattended polysomnography recordings (recording time: 545 ± 44 min), the average time taken for the check-and-edit procedure of each recording was 6.9 ± 2.1 min for all respiratory signals.</p>
Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress
<p>The goal of this study was to provide reliable quantitative analyses of psycho-physiological measures during acute mental stress. Acute, time-limited stressors are used extensively as experimental stimuli in psychophysiological research. In particular, the Stroop Color Word Task and the Arithmetical Task have been widely used in several settings as effective mental stressors. We collected psychophysiological data on blood volume pulse, thoracic respiration, and skin conductance from 60 participants at rest and during stressful situations. Subsequently, we used statistical univariate tests and multivariate computational approaches to conduct comprehensive studies on the discriminative properties of each condition in relation to psychophysiological correlates. The results showed evidence of a greater discrimination capability of the Arithmetical Task compared to the Stroop test. The best predictors were the short time Heart Rate Variability (HRV) indices, in particular, the Respiratory Sinus Arrhythmia index, which in turn could be predicted by other HRV and respiratory indices in a hierarchical, multi-level regression analysis. Thus, computational psychometrics analyses proved to be an effective tool for studying such complex variables. They could represent the first step in developing complex platforms for the automatic detection of mental stress, which could improve the treatment.</p>
Computational modelling of the cellular interplay in Rheumatoid Arthritis. Deciphering the role of innate and adaptive immunity in cartilage destruction and bone erosion
<p>Immune dysregulation was first implicated in the pathogenesis of Rheumatoid Arthritis (RA) by the discovery of anti-immunoglobulin G (IgG) antibodies known as rheumatoid factors. However, concepts of how immune responses contribute to disease have evolved dramatically over the last 50 years. Many cells and their cytokines play critical roles in the development of RA. The synovial compartment is infiltrated by leukocytes and the synovial fluid is inundated with pro-inflammatory mediators that are produced to induce an inflammatory cascade, which is characterized by interactions of fibroblast-like synoviocytes with the cells of the innate immune system, including monocytes, macrophages, mast cells, dendritic cell as well as cells of adaptive immune system such as T cells and B cells. The fulminant stage contains hyperplastic synovium, cartilage damage, bone erosion, and systemic consequence.</p> <p>The objective of my project is to construct a computational model able to decipher the interplay between cells of the innate and adaptive immunity in RA, that eventually leads to bone and cartilage breakdown.</p> <p>To do so, we will start by creating separate maps for T cells, B cells, macrophages, fibroblasts, osteoblasts and osteoclasts. We will use different data mining tools, appropriate data bases such as KEGG (Kanehisa et al, 2000), REACTOME (Fabregat et al, 2018) as well as internal data generated within Sanofi. We will exploit the graph editor CellDesigner (Funahashi et al, 2003) and the platform Minerva (Gawron et al, 2016) for automatic annotation and reference of the cell specific maps. We will benefit greatly from a global, fully annotated RA specific map (Singh et al, 2018, Singh et al, 2020). This map features interactions implicated in RA coming from various cell types, but due to the extensive annotations the user can opt for cell specific interactions and extract the corresponding network. We are also going to use public datasets of expression data (microarrays, RNAseq, RNAseq single cell), data concerning metabolic pathways from MetaCyc and Sanofi’s proprietary datasets to enrich and expand existing pathway resources. These maps will be used to generate cell specific dynamic models using the tool CaSQ (Aghamiri et al, 2020). The next step is the creation of a multicellular model to understand how the different cells interact, contributing to the emergent behavior of the system. We will prioritize signature pathways for each cell type and combine them to build a logical model that will represent the intra- and intercellular relationships.</p> <p> </p>
Supplementary (MD): Repurposing Alzheimer's Therapeutics: A Computational Approach to Identifying Potential SARS-CoV-2 Mpro Inhibitors
<p>100 ns Molecular Dynamic Simulation Trajectories for Compound 2, 3, 6, 7, and 8 </p>
Revolutionising Insect Biodiversity Monitoring with Cutting-Edge Sensors, Deep Learning, and Computer Vision
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Design and Computational Fluid Dynamics (CFD) modelling of Biomimetic Voronoi lattice Scaffolds
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Computer-based prediction of coronary artery compression in the planning of transcatheter pulmonary valve implantation.
<p>Datset from Pluchinotta FR, Caimi A, Pilati M, Carminati M, Sturla F. Computer-based prediction of coronary artery compression in the planning of transcatheter pulmonary valve implantation. EuroIntervention. 2021 Sep 20;17(7):584-585. doi: 10.4244/EIJ-D-20-01114. PMID: 33433388.</p> <p> </p>
A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography
<p>Full dataset of segmentations for both the thoracic artery and the proximal pulmonary arteries (format: standard NIfTI, nii) from Saitta S, Sturla F, Caimi A, Riva A, Palumbo MC, Nano G, Votta E, Corte AD, Glauber M, Chiappino D, Marrocco-Trischitta MM, Redaelli A. A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography. J Digit Imaging. 2022 Jan 26. doi: 10.1007/s10278-021-00535-1. Epub ahead of print. PMID: 35083618.</p> <p>Abstract</p> <p>Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.</p> <p> </p>
Training data for computational counterselection manuscript
<p>Training datasets for currently unpublished manuscript titled: "Computational counterselection identifies nonspecific therapeutic biologic candidates". </p>
Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features
<p>Dataset from Schiaffino S, Codari M, Cozzi A, Albano D, Alì M, Arioli R, Avola E, Bnà C, Cariati M, Carriero S, Cressoni M, Danna PSC, Della Pepa G, Di Leo G, Dolci F, Falaschi Z, Flor N, Foà RA, Gitto S, Leati G, Magni V, Malavazos AE, Mauri G, Messina C, Monfardini L, Paschè A, Pesapane F, Sconfienza LM, Secchi F, Segalini E, Spinazzola A, Tombini V, Tresoldi S, Vanzulli A, Vicentin I, Zagaria D, Fleischmann D, Sardanelli F. Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features. J Pers Med. 2021 Jun 3;11(6):501. doi: 10.3390/jpm11060501. PMID: 34204911; PMCID: PMC8230339.</p> <p>Abstract</p> <p>Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney <em>U</em> test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, <em>p</em> < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, <em>p</em> < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.</p>
Dataset related to the article "Computed tomography predictors of structural valve degeneration in patients undergoing transcatheter aortic valve implantation with balloon-expandable prostheses"
<p>This record contains raw data related to the article "Computed tomography predictors of structural valve degeneration in patients undergoing transcatheter aortic valve implantation with balloon-expandable prostheses"</p> <p><strong>Objectives: </strong>Computed tomography (CT) provides excellent anatomy assessment of the aortic annulus (AoA) and is utilized for pre-procedural planning of transcatheter aortic valve implantation (TAVI). We sought to investigate if geometrical characteristics of the AoA determined by CT may represent predictors of structural valve degeneration (SVD) in patients undergoing TAVI with balloon-expandable valves.</p> <p><strong>Methods: </strong>Retrospective study on 124 consecutive patients (mean age:79±7 years; female: 61%) undergoing balloon-expandable TAVI prospectively enrolled in a registry<strong>. </strong>AoA maximum diameter (D<sub>max</sub>), minimum diameter (D<sub>min</sub>), and area were assessed using pre-procedural CT. SVD was identified during follow-up with transthoracic echocardiography documenting structural prosthetic valve abnormalities with or without hemodynamic changes. </p> <p><strong>Results: </strong>The mean follow up was 5.9±1.7 years. SVD was found in 48 out of 124 patients (38%). AoA D<sub>max</sub>, D<sub>min</sub> and area were significantly smaller in patients with SVD compared to patients without SVD (25.6±2.2 mm vs. 27.1±2.8 mm , p=0.012; 20.5±2.1 mm vs. 21.8±2.1 mm ,p=0.001 and 419±77 mm<sup>2 </sup>vs. 467±88 mm<sup>2</sup>,p=0.002, respectively). At univariable analysis, female sex, BSA, 23-mm prosthetic valve size, D<sub>max</sub> <27.1 mm and a D<sub>min</sub> < 19.9 mm were associated with SVD whereas at multivariable analysis, only D<sub>min</sub> <19.9 mm (OR=2.873, 95% CI: 1.191-6.929,p=0.019) and female sex (OR=2.659, 95% CI: 1.095-6.458, p=0.031) were independent predictors of SVD.</p> <p><strong>Conclusions: </strong>Female sex and AoA D<sub>min</sub> < 19.9 mm are associated with SVD in patients undergoing TAVI with balloon expandable valves. When implanting large prostheses in order to avoid paraprosthetic regurgitation, caution should be observed due to the risk of excessive stretching of the AoA D<sub>min,</sub> which may play a role in SVD.</p>
Met4DX: A mass spectrum-oriented computational framework for ion mobility-resolved untargeted metabolomics
<p>Supplementary data</p>
Neuromorphic nanowire netoworks for conformable reservoir computing
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lexibank/bowernpny: Computational Phylogenetics and the Internal Structure of Pama-Nyungan
<p>Cite the lexibank dataset as DOI.</p> <p>Cite the source dataset as</p> <blockquote> <p>Bowern, Claire, & Atkinson, Quentin. (2012). Computational Phylogenetics and the Internal Structure of Pama-Nyungan: Dataset [Data set]. Language. <a href="http://doi.org/10.1353/lan.2012.0081">http://doi.org/10.1353/lan.2012.0081</a></p> </blockquote> <p>Compile from the dataset available at <a href="https://zenodo.org/record/1005671">https://zenodo.org/record/1005671</a> with Concepticon's conceptlist <a href="http://concepticon.clld.org/contributions/Bowern-2017-348">Bowern-2017-348</a></p>
HTTP Dataset 01 for Experimenting on Service-Oriented Computing
<p>The dataset was generated based on recording the traffic targeted to Slack REST service.</p>
Computational characterization of Chronic Lymphocytic Leukemia Immunoglobulin Rearrangements from Partial Read Sequencing
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Computational Fluid Dynamics Modeling of Proximal Landing Zones for Thoracic Endovascular Aortic Repair in the Bovine Arch Variant.
<p>Dataset from the article Marrocco-Trischitta MM, Romarowski RM, Alaidroos M, Sturla F, Glauber M, Nano G. Computational Fluid Dynamics Modeling of Proximal Landing Zones for Thoracic Endovascular Aortic Repair in the Bovine Arch Variant. Ann Vasc Surg. 2020 Nov;69:413-417. doi: 10.1016/j.avsg.2020.05.024. Epub 2020 May 29. PMID: 32479874.</p> <p><strong>Abstract</strong></p> <p><strong>Background: </strong>To assess the endograft displacement forces (DF), which quantify the forces exerted by the pulsatile blood flow on the vessel wall and transmitted on the terminal fixation site of the endograft after its deployment in proximal landing zones (PLZs) of the bovine aortic arch variant.</p> <p><strong>Methods: </strong>Thirty healthy aortic computed tomographic angiographies of subjects with bovine arch configuration (10 per type of arch, I-III) were selected for the purpose of the study. A 3-dimensional model of the aortic arch lumen was reconstructed. Computational fluid dynamics modeling was then used to compute DF magnitude and orientation (i.e., x, y, and z axes) in PLZs of each case. DF values were normalized to the corresponding aortic wall area to estimate equivalent surface traction (EST).</p> <p><strong>Results: </strong>DF were highest in zone 0, consistently with the greater surface area. DF in zone 3 were much greater than in zone 2 because of a 3-fold greater upward component (z axis) (P < 0.001), being therefore mainly oriented orthogonally to the aortic blood flow and to the vessel longitudinal axis in that zone. EST progressively increased from zone 0 toward more distal PLZs, with EST in zone 3 being much greater than that in zone 2 (P < 0.001). The same pattern was observed after stratification by type of arch.</p> <p><strong>Conclusions: </strong>The bovine arch is associated with a consistent fluid dynamic pattern, which identifies in zone 3 an unfavorable biomechanical environment for endograft deployment.</p>
Thermal decomposition of hexamethylenetetramine: mechanistic study and identification of reaction intermediates via a computational and NMR approach
<p>FID of the NMR spectra of the manuscript entitled: Thermal decomposition of hexamethylenetetramine: mechanistic study and identification of reaction intermediates via a computational and NMR approach</p>
DATASET An evidence-based study on teaching computer aided design in higher education during the COVID-19 pandemic
<p>Course survey using google form. In Swedish, contact the authors for translation.</p> <p>The pandemic has had a major effect on engineering education, transforming both current and future teaching practices. The physical meetings between students and teachers have during the pandemic been replaced by online contact and recordings of lectures and demonstrations. In this paper, the focus is on computer-aided design (CAD) teaching for first-year engineering students. CAD is a topic usually characterized by close contact by students and teachers, with hands-on instruction at the computer using the CAD software. In the paper, the experiences and learnings from the rapid shift to online teaching in CAD are summarized and discussed, and learnings and takeaways for a redesign of future CAD teaching are discussed. Both students’ learning and mental wellbeing are evaluated. The findings are based on a student survey, existing literature, and the authors' own teaching practices during the pandemic. </p>
Dataset related to the article "Prediction of myocardial blood flow under stress conditions by means of a computational model"
<p>This record contains raw data related to the article “Prediction of myocardial blood flow under stress conditions by means<br> of a computational model”</p> <p><strong>Purpose. </strong>Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps.</p> <p><strong>Methods. </strong>A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBF<sub>CTP</sub>) maps. The relative error between the computational MBF under stress conditions (MBF<sub>COMP</sub>) and MBF<sub>CTP</sub> was evaluated to assess the accuracy of the proposed computational model.</p> <p><strong>Results.</strong> Applying our method to 9 patients (4 control subjects without ischemica vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBF<sub>COMP</sub> and MBF<sub>CTP</sub>. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%.</p> <p><strong>Conclusion. </strong>The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e. the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.</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.