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1,890 results for “Defects”
Theoretical study of stability of halogen defective trihalide monolayers: cases of AlI3, AsI3 and IrBr3
<p>Please see ref:</p> <p>Lu, L.; Botella, R.; Cao, W. <em>Phys. Status Solidi B</em> <strong>2023</strong>, 2300001</p> <p>For more information about the dataset.</p>
Children, Heart Defects , Surgeons and God - Dr KS IYER
<p><strong>Dr Sadasivam oration by Dr KS Iyer delivered in IACTSCON 2012</strong></p>
CT Data of Battery Pouch Cell with Defects
<p>These are CT slices as TIF files.</p> <p>Data: cubic voxels of (22 µm)³</p> <p>You may also open the included project file in VGStudio or the free myVGL viewer (https://www.volumegraphics.com/en/download-viewer.html) or other compatible software.</p> <p> </p> <p>The sample was prepared by Johannes Münch. For further details see the following paper:</p> <p><a href="https://doi.org/10.1002/ente.202300323">https://doi.org/10.1002/ente.202300323</a></p>
PI3K block restores age-dependent neurovascular coupling defects associated with cerebral small vessel disease
<p class="MsoNormal">Neurovascular coupling (NVC), a vital physiological process that rapidly and precisely directs localized blood flow to the most active regions of the brain, is accomplished in part by the vast network of cerebral capillaries acting as a sensory web capable of detecting increases in neuronal activity and orchestrating the dilation of upstream parenchymal arterioles. Here, we report a <em>Col4a1</em> mutant mouse model of cerebral small vessel disease (cSVD) with age-dependent defects in capillary-to-arteriole dilation, functional hyperemia in the brain, and memory. The fundamental defect in aged mutant animals was the depletion of the minor membrane phospholipid phosphatidylinositol 4,5 bisphosphate (PIP<sub>2</sub>) in brain capillary endothelial cells, leading to the loss of inwardly rectifier K<sup>+</sup> (Kir2.1) channel activity. Blocking phosphatidylinositol-3-kinase (PI3K), an enzyme that diminishes the bioavailability of PIP<sub>2</sub> by converting it to phosphatidylinositol (3,4,5)-trisphosphate (PIP<sub>3</sub>), restored Kir2.1 channel activity, capillary-to-arteriole dilation, and functional hyperemia. In longitudinal studies, chronic PI3K inhibition also improved the memory function of aged <em>Col4a1</em> mutant mice. Our data suggest that PI3K inhibition is a viable therapeutic strategy for treating defective NVC and cognitive impairment associated with cSVD.</p>
Hemostasis defects underlying the hemorrhagic syndrome caused by Mammarenaviruses in a cynomolgus macaque model
<p>Abstract: Viral hemorrhagic fevers (HF) are a group of acute febrile diseases with high mortality rates. While hemostatic dysfunction appears to be a major determinant of the severity of the disease, it is still unclear what pathogenic mechanisms lead to it. In clinical studies, arenaviruses such as Lassa, Machupo and Guanarito viruses caused HF that vary in symptoms and biological alterations.</p> <p>In this study we aimed to characterize the hemostatic dysfunction induced by arenaviral HF to determine its implication in the severity of the disease and to elucidate the origin of this syndrome.</p> <p>We found that lethal infection with Machupo, Guanarito and Lassa viruses is associated with cutaneo-mucosal, cerebral, digestive and pulmonary hemorrhages. The affected animals developed a severe alteration of the coagulation system, which was concomitant with acute hepatitis, minor deficit of hepatic factor synthesis, presence of a plasmatic inhibitor of coagulation and dysfunction of the fibrinolytic system. Despite signs of increased vascular permeability, endothelial cell infection was not a determinant factor of the hemorrhagic syndrome. There were also alterations of the primary hemostasis during lethal infection, with moderate to severe thrombocytopenia and platelet dysfunction. Finally, we show that lethal infection is accompanied by a reduced hematopoietic potential of the bone marrow. This study provides an unprecedented characterization of the hemostasis defects induced by several highly pathogenic Arenaviruses.</p> <p>Transcriptomics data presented in Figure 3 and in Supp Figure 2</p>
Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials
<p><strong>Our paper has been published on Phys. Rev. Res. (doi: 10.1103/PhysRevResearch.6.013259)</strong></p> <p><strong>Our preprint paper is also avilable at arXiv(https://arxiv.org/abs/2310.06406), here is the abstract of our paper:</strong></p> <p>Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of ±1/2 defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay–Berne particles. MED performs faster than conventional defect detection method and can achieve over 90% accuracy on recognizing ±1/2 defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED can provide correct structural information about ±1 defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.</p> <p> </p> <p><strong>Repository Organization</strong></p> <p><strong>Trained Models.zip</strong></p> <p>This directory is integral for model deployment and houses all relevant pre-trained models.</p> <ul> <li><strong>plus_vit_vecUV.pt</strong>: Pre-trained model for the Plus Transformer variant.</li> <li><strong>minus_vit_theR.pt</strong>: Pre-trained model for the Minus Transformer variant.</li> <li><strong>nanodet-plus-m_416-halfenhance</strong>: A sub-directory containing all files associated with the trained Nanodet-Plus model.</li> <li><strong>configs</strong>: Configuration files for training procedures.</li> </ul> <p><strong>Training Data.zip</strong></p> <p>This directory contains all datasets used for the training of Nanodet-Plus, Plus Transformer, and Minus Transformer models.</p> <p><strong>Code.zip</strong></p> <p>This directory features the implementation details and example use-cases showcased in Figure 2 and Figure 3c of our associated paper. The directory also includes code corresponding to the specific versions of Nanodet-Plus and SAM models cited in our study.</p> <ul> <li><strong>nanodet</strong>: Code in this folder is adapted from <a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet). We have included the exact version used for compatibility.</li> <li><strong>segment_anything</strong>: Code sourced from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything). The specific version used is included for compatibility.</li> <li><strong>Fig2</strong>: Code for predicting topological defects in tissue cell images, citing the following reference: T. B. Saw et al., Nature 544, 212 (2017).</li> <li><strong>Fig3c</strong>: Code for predicting topological defects in microtubules images, citing the following reference: M. Golden et al., Sci. Adv. 9, eabq6120 (2023).</li> </ul> <p><strong>Initialization Steps</strong></p> <p>Before executing any code, please ensure the following:</p> <ul> <li>All files in the <strong>Trained Models</strong> directory must be available.</li> <li>Download the checkpoint <strong>sam_vit_l_0b3195.pth</strong> from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a>. (https://github.com/facebookresearch/segment-anything)</li> </ul> <p><strong>Acknowledgments</strong></p> <ul> <li><a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet)</li> <li><a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything)</li> </ul> <p>For further inquiries or issue reporting, you may contact us via email.</p> <p><strong>Contact Information</strong>: <a href="mailto:hrenae@connect.ust.hk">hrenae@connect.ust.hk</a></p>
BrUOG 337: Olaparib Prior to Radical Prostatectomy For Advanced Prostate Cancer Defects in DNA Repair Genes
ClinicalTrials.gov study NCT03432897. IPD Sharing: NO. Countries: 1. Publications: 1.
Diagnostic Test Validity of Structural Vertebral Endplate Defects
ClinicalTrials.gov study NCT04808960. IPD Sharing: YES. Countries: 1. Publications: 9.
Superiority of MACI® Versus Microfracture Treatment in Patients With Symptomatic Articular Cartilage Defects in the Knee
ClinicalTrials.gov study NCT00719576. IPD Sharing: NO. Countries: 7. Publications: 1.
Enhanced Secondary Intention Healing vs. Standard Secondary Intention Healing in Mohs Surgical Defects on the Head and Distal Lower Extremities
ClinicalTrials.gov study NCT04545476. IPD Sharing: NO. Countries: 1. Publications: 13.
Repair of Infective Wound Associated With Nerve Defect in the Finger Using A Bipedicled Nerve Flap
ClinicalTrials.gov study NCT01707654. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Olaparib in Treating Patients With Relapsed or Refractory Advanced Solid Tumors, Non-Hodgkin Lymphoma, or Histiocytic Disorders With Defects in DNA Damage Repair Genes (A Pediatric MATCH Treatment Tri
ClinicalTrials.gov study NCT03233204. IPD Sharing: UNDECIDED. Countries: 2. Publications: 2.
Treatment of Alveolar Bone Defects Using Aastrom Biosciences Autologous Tissue Repair Cell Therapy
ClinicalTrials.gov study NCT00755911. IPD Sharing: Not stated. Countries: 1. Publications: 44.
StrataGraft™ Skin Tissue (Human Donor Skin) In The Surgical Management Of Complex Skin Defects
ClinicalTrials.gov study NCT00618839. IPD Sharing: Not stated. Countries: 1. Publications: 2.
RCT of ChondroCelect® (in an ACI Procedure) vs Microfracture in the Repair of Cartilage Defects of the Knee
ClinicalTrials.gov study NCT00414700. IPD Sharing: Not stated. Countries: 4. Publications: 8.
Effectiveness of Modified-free Gingival Graft for Treatment of Localized Gingival Recession Defects
ClinicalTrials.gov study NCT04718545. IPD Sharing: NO. Countries: 1. Publications: 6.
StrataGraft® Skin Tissue in the Promotion of Autologous Skin Regeneration of Complex Skin Defects Due to Thermal Burns That Contain Intact Dermal Elements
ClinicalTrials.gov study NCT03005106. IPD Sharing: NO. Countries: 1. Publications: 1.
The Natural History of Severe Viral Infections and Characterization of Immune Defects in Patients Without Known Immunocompromise
ClinicalTrials.gov study NCT01011712. IPD Sharing: YES. Countries: 1. Publications: 3.
Niraparib Before Surgery in Treating Patients With High Risk Localized Prostate Cancer and DNA Damage Response Defects
ClinicalTrials.gov study NCT04030559. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Ocrelizumab on B-cell Tolerance Defect in Relapsing Multiple Sclerosis
ClinicalTrials.gov study NCT04261790. IPD Sharing: NO. Countries: 1. Publications: 1.
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OpenNeuro
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