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757 results for “twinning”
Bright correlated twin-beam generation and radiation shaping in high-gain parametric down-conversion with anisotropy
<p>Dataset of the publication “Bright correlated twin-beam generation and radiation shaping in high-gain parametric down-conversion with anisotropy“, M. Riabinin, P. R. Sharapova, and T. Meier, Optics Express 29, 21876 (2021) ( <a href="https://doi.org/10.1364/OE.424977">https://doi.org/10.1364/OE.424977</a> ). The zip file includes the data on which the plots shown in figures 2, 3, 4, 6, 7, and 8 are based.</p>
Data for: Using a digital twin of an electrical stimulation device to monitor and control the electrical stimulation of cells in vitro
<p>Replication data for "Using a digital twin of an electrical stimulation device to monitor and control the electrical stimulation of cells in vitro".</p>
Figure 2 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 2. Mesotarsus, Cholevinae: Ptomaphagini - Adelopsis leo, articulation between tarsomeres I (below) and II (above). A, lateral-external view. B, ventral view. C, lateral-internal view. bs = 'bachelor seta'; tw = 'twin spines'; arrow = additional slender setae; stars = periapical spines of the apical crown of spines.
Figure 8 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 8. Mesotarsus, Cholevinae: Anemadini. A, Nemadina – Nemadus colonoides, female. B, C, Paracatopina – Paracatops alacris. D, Anemadina – Anemadus italicus. A, C, ventro-lateral-external view. B, D, ventro-lateral-internal view. tI-tIV = first to fourth tarsomeres; bs = 'bachelor seta'.
Figure 9 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 9. Mesotarsus, Cholevinae: Cholevini: Catopina and Cholevina, and Oritocatopini. A, B, Catops fuliginosus. C, D, Catopsimorphus (s.s.) orientalis. E, F, Afrocatops sp. A, C, E, ventro-lateral-internal view; B, D, F, ventro-lateral-external view. tI-tIV = first to fourth tarsomeres.
Figure 1 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 1. Midleg, Cholevinae: Ptomaphagini - Adelopsis leo. A, dorsal/internal view. B, ventral/external view. C, a hypothetical cross-section of a tarsomere of the left leg, showing what we refer to as the dorsal ('df'), internal lateral ('ilf'), ventral ('vf') and external lateral ('elf') faces of the tarsomere, as well as the inner ('icv') and external ('ecv') 'corners' of the ventral face of the tarsomere. Fe = femur; Ta = tarsus; Ti = tibia.
Figure 12. Data from Table 1 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 12. Data from Table 1 mapped on taxonomic diagrams of the subfamily Cholevinae. A, the 'traditional' division of the Cholevinae (based on Newton, 1998). B, phylogenetic analysis of the Cholevinae [based on Antunes-Carvalho et al. (2019: fig. 25) – in our figure we represent non-monophyletic taxa with a double line connection]. Taxa without an assigned symbol were not studied here. 'Anemadini' as a taxon is not represented in (B), because it was considered non-monophyletic in that study; however, its subtribes are represented. The black squares denote taxa where the features studied here were observed – 'T' denotes presence of 'twin spines' and 'B' denotes presence of a 'bachelor seta'; the open squares with a 'X' denote taxa where the features studied were not observed in the specimens analysed.
Figure 4 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 4. Mesotarsus, Cholevinae: Ptomaphagini. A, B, Amplexella dimorpha. C, D, Ptomaphaminus chapmani. A, C, ventro-lateral-internal view. B, D, ventro-lateral-external view. tI-tV = first to fifth tarsomeres; bs = 'bachelor seta'; tw = 'twin spines'; arrow = pair of setae at the apical margin of mesotarsomere IV; stars = periapical spines of the apical crown of spines.
Figure 3 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 3. Meso- and metatarsus, Cholevinae: Ptomaphagini - Adelopsis leo. A, B, mesotarsus (part). C, D, metarsus (part). A, C, ventro-lateral-internal view. B, D, ventro-lateral-external view. tI-tV = first to fifth tarsomeres; bs = 'bachelor seta'; tw = 'twin spines'; circle = pointy apices of 'twin spines' facing opposite to each other; arrow = pair of setae at the apical margin of mesotarsomere IV; ellipse shows that the 'twin spines' are articulated to a single elevation on the integument.
Figure 7 in The 'twins' and the 'bachelor', new potential synapomorphies inside the Cholevinae (Coleoptera: Leiodidae)
Figure 7. Mesotarsus, Cholevinae: Anemadini. A-C, Eunemadina. A, B, Dissochaetus vanini. C, Eunemadus chilensis. D, Nemadina – Nemadus colonoides, female. A, C, D, ventro-lateral-internal view. B, ventro-lateral-external view. tI-tV = first to fifth tarsomeres; tw = 'twin spines'.
Twin pregnancy outcome following teriflunomide treatment in a relapsing-remitting multiple sclerosis patient
<p>Teriflunomide is a disease-modifying drug that has been approved for treatment of relapsing-remitting multiple sclerosis. Due to its teratogenic effect in animals, however, it is not recommended during pregnancy. For this reason, effective contraception must be used during its administration. When an unscheduled pregnancy occurs during therapy, patients must undergo a cholestyramine procedure for rapid flushing of the drug. We describe the case of a 35-year-old female patient suffering diagnosed with relapsing-remitting multiple sclerosis at the age of 20. The patient as a result of side effects of previous therapies started taking teriflunomide. Despite recommendations for the use of contraceptives, the patient became pregnant during drug therapy. Pregnancy occurred 12 months after initiating teriflunomide treatment. Therapy with teriflunomide was immediately suspended and cholestyramine was prescribed (8 g 3 times a day, for 11 days) to flush out any residual drug from the body. Despite an 8-week exposure to teriflumomide during gestation, the patient gave birth to healthy twin girls at 35<sup>th</sup> week. Controls carried out after birth did not reveal any malformation or genetic and chromosomal abnormality. At a 5-month pediatric specialist check both babies were healthy and growing regularly. This shows that even if there is evidence of teratogenic effects in animals, an 8-week exposure to teraflunomide >0.02 mg/L did not have effects on the newborn.</p>
Sporadic pseudohypoparathyroidism type 1B in monozygotic twins: insights into the pathogenesis of methylation defects
<p>Context: Sporadic pseudohypoparathyroidism type 1B (sporPHP1B) is an imprinting disease without a defined genetic cause, characterized by broad methylation changes in differentially methylated regions (DMRs) of the <i>GNAS</i> gene.</p> <p>Objective: This work aims to provide insights into the causative event leading to the <i>GNAS</i> methylation defects through comprehensive molecular genetic analyses of a pair of female monozygotic twins concordant for sporPHP1B who were conceived naturally i.e., without assisted reproductive techniques.</p> <p>Methods: Using the leukocyte genome of the twins and family members, we performed targeted bisulfite sequencing, methylation-sensitive restriction enzyme (MSRE)-qPCR, whole-genome sequencing (WGS), high-density SNP array, and Sanger sequencing.</p> <p>Results: Methylation analyses by targeted bisulfite sequencing and MSRE-qPCR revealed almost complete loss of methylation at the <i>GNAS</i> AS, XL, and A/B DMRs and gain of methylation at the NESP55 DMR in the twins, but not in other family members. Except for the <i>GNAS</i> locus, we did not find apparent methylation defects at other imprinted genome loci of the twins. WGS, SNP array, and Sanger sequencing did not detect the previously described genetic defects associated with familial PHP1B. Sanger sequencing also ruled out any novel genetic alterations in the entire NESP55/AS region. However, the analysis of 28 consecutive SNPs could not exclude the possibility of paternal heterodisomy in a span of 22 kb comprising exon NESP55 and AS exon 5.</p> <p>Conclusion: Our comprehensive analysis of a pair of monozygotic twins with sporPHP1B ruled out all previously described genetic causes. Twin concordance indicates that the causative event was an imprinting error earlier than the timing of monozygotic twinning.</p>
Dataset for image-based geometric digital twinning for stone masonry elements
<p>This is the dataset used to assess the performance of the geometrical digital twinning algorithm proposed by Pantoja-Rosero et, al (2023) in the article "Image-based geometric digital twinning for stone masonry elements" (<a href="https://doi.org/10.1016/j.autcon.2022.104632">https://doi.org/10.1016/j.autcon.2022.104632</a>)</p>
Dataset for image-based geometric digital twinning for stone masonry elements - part 2
<p>This is the dataset used to assess the performance of the geometrical digital twinning algorithm proposed by Pantoja-Rosero et, al (2023) in the article "Image-based geometric digital twinning for stone masonry elements" (<a href="https://doi.org/10.1016/j.autcon.2022.104632">https://doi.org/10.1016/j.autcon.2022.104632</a>)</p>
Dataset for Digital Twin paper
<p>Description for each folder:</p> <p>1) The "raw_signal_data" folder contains original sensor data. The origigital sensor data is named as "ACF-x-x.xlsx". In each excel document, three directions of vibration, current and force sensor data is included. Naming rules is as follows: "ACF-1-2.xlsx" represents the orginal sensor data from first layler's second slot in. "ACF-2-3.xlsx" represents the orginal sensor data from second layler's third slot.</p> <p>2) The original real measured surface roughness is in the "Ra.xlsx". The corresponding cutting parameters for every raw signal data of each slot are shown in "Ra.xlsx" document as well. <br> 3) The prediction results from different models are save in "prediction_results". The detalies are listed below.</p> <p> "BPNN_all_signal.xlsx" is the prediction result from BPNN used the combination of all sensors data as the model input <br> "BPNN_current_signal.xlsx" is the prediction result from BPNN used only the current sensor data as the model input <br> "BPNN_force_signal.xlsx" is the prediction results from BPNN used only the force sensor data as the model input <br> "BPNN_vibration_signal.xlsx" is the prediction result from BPNN used only the vibration sensor data as the model input<br> "ELM.xlsx" is the prediction result from ELM used the combination of all sensors data as the model input <br> "GPR.xlsx" is the prediction result from GPR used the combination of all sensors data as the model input<br> "LASSO.xlsx" is the prediction result from LASSO used the combination of all sensors data as the model input<br> "MLR.xlsx" is the prediction result from MLR used the combination of all sensors data as the model input<br> "SVR.xlsx" is the prediction result from SVR used the combination of all sensors data as the model input<br> "cnn_all_signal.xlsx" is the prediction result from CNN used the combination of all sensors data as the model input <br> "cnn_current.xlsx" is the prediction result from CNN used only the current sensor data as the model input <br> "cnn_force.xlsx" is the prediction results from CNN used only the force sensor data as the model input <br> "cnn_vibration.xlsx" is the prediction result from CNN used only the vibration sensor data as the model input<br> "real_ytest.xlsx" is the real-measured surface roughness data for testing models.</p>
Risk and Equity Metrics for the NYC Flood Risk Digital Twin
<p>These datasets contain the Risk and Equity metrics used to quantify the impact of pluvial flooding in NYC. They are obtained combining several sources (US Census data, New York State Traffic data, etc.) with the NYC Stormwater Flood Map corresponding to an extreme rain event.</p>
Supplementary Information for "Evolution upon leather manufacturing: comparison of the two Halobacterium salinarum strains isolated in the early 20-th century with the twin laboratory strains NRC-1 and R1"
<p>This set of files contains Supplementary Data associated with the emerging manuscript "Evolution upon leather manufacturing: comparison of the two <em>Halobacterium salinarum</em> strains isolated in the early 20-th century with the twin laboratory strains NRC-1 and R1." by Friedhelm Pfeiffer and Mike Dyall-Smith.</p> <p>The manuscript provides a detailed comparison of the complete genome sequences from four strains of <em>Halobacterium salinarum</em>:<br> 91-R6 (NRC 34002, the type strain of <em>Halobacterium salinarum</em>);<br> 63-R2 (NRC 34001, a strain originally reported as 'cutirubra');<br> NRC-1 and R1 (two laboratory strains).</p> <p>“Supplementary Methods” extends the Methods section of the manuscript, providing additional detail.</p> <p>“Supplementary Text S1” provides additional detail for the comparison of the chromosome from strain 63-R2 to those from strains NRC-1 and R1.</p> <p>“Supplementary Text S2” provides additional details for the comparison of the chromosomes from strains 63-R2 and 91-R6.</p> <p>“Supplementary Text S3” provides additional detail for the comparison of the plasmids from strain 63-R2 to those from strains NRC-1 and R1.</p> <p> </p> <p>“Supplementary Table S4” correlates positions from “core” sequences (devoid of strain-specific mobile genetic elements) to the original genome sequences.</p>
Dataset for damage-augmented digital twins towards the automated inspection of buildings
<p>This repository contains the dataset used for computing damage augmented digital twins for buildings via image-based approach. The method that uses this data set was presented in the paper "Damage-augmented digital twins towards the automated inspection of buildings" by Pantoja-Rosero et., al. (2023)" https://doi.org/10.1016/j.autcon.2023.104842</p> <p> </p> <p> </p> <p> </p>
Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies
<pre>We introduce a new bimodal dataset recorded during affect elicitation by means of audio-visual stimuli for human emotion recognition based on facial and corporal expressions. Our dataset was collected using three devices: an RGB camera, Kinect 1, and Kinect 2. The Kinect 1 and Kinect 2 sensors provide 121 and 1347 face key points, respectively, offering a more comprehensive analysis of facial expressions. Additionally, for the 2D RGB sequences, we utilized the feature points provided by the open-source OpenFace, which includes 2D 68 facial landmarks. From these landmarks, we selected 26 facial points that were most relevant for our emotion recognition task. To gather the data, we conducted experiments involving 17 participants. We captured both facial and skeleton keypoints, allowing for a comprehensive understanding of the participants' emotional expressions. By combining the RGB and RGB-D data from the various devices, our dataset provides a rich and diverse set of information for human emotion recognition research. This new dataset not only expands the available resources for studying human emotions but also offers a more detailed analysis with the increased number of facial keypoints provided by the Kinect sensors. Researchers can leverage this dataset to develop and evaluate more accurate and robust models for human emotion recognition, ultimately advancing our understanding of how emotions are expressed through facial and corporal cues. Please cite as: K. Amara, O. Kerdjidj and N. Ramzan, "Emotion Recognition for Affective human digital twin by means of virtual reality enabling technologies," in <em>IEEE Access</em>, doi: 10.1109/ACCESS.2023.3285398. </pre> <p> </p> <p>Please state your name, contact details (e-mail), institution, and position, as well as the reason for requesting access to our database.</p> <p>For additional info contact:</p> <p>kahina.amara88@gmail.com or kamara@cdta.dz</p> <p>Naeem.Ramzan@uws.ac.uk</p> <p>okerdjidj@ud.ac.ae</p>
Physics-driven universal twin-image removal network for digital in-line holographic microscopy - dataset
<p>Dataset (Matlab files) containing holograms and reference reconstructions employed in:<br> <br> M. Rogalski, P. Arcab, L. Stanaszek, V. Micó, C. Zuo and M. Trusiak, "Physics-driven universal twin-image removal network for digital in-line holographic microscopy", Submitted 2023<br> <br> This dataset should be used together with the codes present at:<br> https://github.com/MRogalski96/UTIRnet</p>
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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.