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FIGURE. RAxML tree based on a combined dataset of partial LSU and ITS sequence analyses. Bootstrap support values for ML equal to or greater than 60 %, Bayesian posterior probabilities (BYPP) equal to or greater than 0.95 are shown as ML/ BYPP above the nodes. New isolates are in red bold. The tree is rooted to Conioscypha lignicola and Conioschypha minutispora (FMR11245) and Conioscyphascus varius. The scale bar represents the expected number of nucleotide substitutions per site. in Yunnan-Guizhou Plateau: a mycological hotspot
FIGURE. RAxML tree based on a combined dataset of partial LSU and ITS sequence analyses. Bootstrap support values for ML equal to or greater than 60 %, Bayesian posterior probabilities (BYPP) equal to or greater than 0.95 are shown as ML/ BYPP above the nodes. New isolates are in red bold. The tree is rooted to Conioscypha lignicola and Conioschypha minutispora (FMR11245) and Conioscyphascus varius. The scale bar represents the expected number of nucleotide substitutions per site.
FIGURE. Phylogram of Panus generated from Maximum likelihood analysis of ITS sequence data. Lentinus crinitus (MK408650) was selected as the outgroup taxon. Maximum likelihood bootstrap values greater than 60% are indicated above the nodes. The new record Panus similis (HKAS 121668) is in black bold. in Yunnan-Guizhou Plateau: a mycological hotspot
FIGURE. Phylogram of Panus generated from Maximum likelihood analysis of ITS sequence data. Lentinus crinitus (MK408650) was selected as the outgroup taxon. Maximum likelihood bootstrap values greater than 60% are indicated above the nodes. The new record Panus similis (HKAS 121668) is in black bold.
Data of the Paper: Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage
<p>This is the online artifact containing the code, data and evaluation log of the experiment performed for the research paper accepted at IEEE QRS 2022 with the title: </p> <p><strong>Stateful Depletion and Scheduling of Containers on Cloud Nodes for Efficient Resource Usage</strong></p> <p><strong>Abstract:</strong> Container scheduling is a fundamental part of today’s service and cloud-based applications. Schedulers operate at different levels depending on how much control the system developers have. On the one hand, container orchestration managers such as Google Kubernetes manage the scheduling of containers to different nodes. On the other hand, serverless managers, such as Google Autopilot, take care of the underlying infrastructure automatically, and developers do not need to manage the nodes. However, when it comes to container depletion, i.e., removing the assigned cloud resources to an idle container, current scheduling technologies have limitations. In this paper, we propose our approach to managing cloud resource usage when containers are idle efficiently. For this purpose, we deplete idle containers statefully, i.e., propose a novel manager that monitors idle containers, saves their state, and efficiently depletes them. This manager reconstructs a depleted container using the saved state when reconstruction is needed. In our approach, we suggest an Infrastructure as Code component to automate the creation of new nodes if a depleted container cannot be scheduled on the same node, e.g., because of being overloaded. We provide an analytical model for the stateful depletion of containers and their rescheduling and empirically evaluate the accuracy of our model. For this purpose, we ran an experiment on a private cloud infrastructure and Google Cloud Platform. Our model has a low error rate of 4.28% averaged over public and private clouds.</p>
Data Table - Digital Scholarship, PhD project "Bridging Data Science and Intellectual History: Computing the Nodes and Edges in the Old University of Louvain (1425-1797)"
<p>How were academic networks configured in the premodern world, and how did they change? This doctoral project seeks to offer a data-driven answer by computing networks at and around the Old University of Louvain (1425-1797), a crucial hub for the transfer of knowledge in late medieval and early modern Europe. Drawing upon datasets under construction from the teams of the PIs at KU Leuven and UCLouvain, this project sets out to plot and visualize networks of students, scholars and their ‘books’ over almost four centuries, thus integrating data from demographic, prosopographical and book historical datasets. This will lead to a better understanding of how academic communities evolved in the past, and it will help to assess how their organization and structure promoted or hindered the creation and transfer of knowledge in premodern Europe. Hence, the doctoral research project offers an innovative test case to develop novel understandings of networks (e.g.. new tested ways to define nodes and edges) in datasets on scholars and ‘literati’, and it will be able to compare these new results to interpretations of human capital indices in the past. As such, the project creates a pioneering pilot for integrating data science into the field of intellectual and early modern history. This enhanced collaboration between KU Leuven and UCLouvain on a theme related to their common past is timely in view of 600 years Leuven/Louvain in 2025.</p>
FIGURE 1. Parahellenia trongduyii. A. Rhizome with a terminal bud. B. Leafy shoot. C. Nodes with leaf bases. D in Taxonomic studies on Parahellenia (Costaceae) in Vietnam: a new species, P. trongduyii, and a new combination, P. candida
FIGURE 1. Parahellenia trongduyii. A. Rhizome with a terminal bud. B. Leafy shoot. C. Nodes with leaf bases. D. Node with an axillary bud. E. Inflorescence with flowers. F. Bract, bracteole, ovary and calyx. G. Flower. H. Flower (calyx, corolla lobes and labellum removed). I. Longitudinal section of a flower. J. Corolla lobes (abaxial and adaxial views). K. Stamen and labellum. L. Longitudinal sections of a fruit. M. Stamen (abaxial and adaxial views). N. Stamen (lateral view). O. Stigma (from left to right: lateral view, abaxial view and adaxial view). Drawn by Mr. D.H. Cui from the type V.C. Nguyen & V.K. Nguyen CD 01.
Figure 1. A, molecular phylogeny from MrBayes. Node labels are posterior probability support values. B in A new lineage of deep-reef gobies from the Caribbean, including two new species and one new genus (Teleostei: Gobiidae: Gobiosomatini)
Figure 1. A, molecular phylogeny from MrBayes. Node labels are posterior probability support values. B, time-calibrated tree from BEAST. Node bars are 95% highest posterior densities for age estimates. Coloured taxa are new species. Colored circles in B refer to the placement of fossil calibration points 1 through 4.
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the continuation of dataset 10.5281/zenodo.7494413 and 10.5281/zenodo.7494509.</p> <p>The file tiff_stacks_masks.zip contains the IMC image stacks and single-cell masks as tiff files.</p> <p>The IHC_TMAs.zip contains the scans of the IHC stains of ZTMA25 and the QuPATH projects used to extract the single-cell data (incl. the single-cell measurements as csv files).</p>
Trained nnUnet models for total lymph node segmentation
<p>The trained nnUnet models for Whole Regional Lymph Node Area Delineation with Deep Learning Model for Total Marrow and Lymphoid Irradiation.</p> <p>We are submitting the article, and we will update the information.</p> <p> </p> <p><strong>******** Abstract </strong><strong>********</strong></p> <p> </p> <p><strong><em>Purpose</em></strong></p> <p>Total body irradiation (TBI) has been performed for conditioning before hematopoietic stem cell transplantation. However, TBI can be related to diverse adverse events including radiation pneumonitis and cataract. Efforts to reduce these events include the total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI). Compared to TMI, TMLI requires more target delineations with lymph nodes which can be labor-intensive and time-consuming. However, with the TMI plans, the coverage to lymph node might be lower than TMLI and its clinical significance is unknown. In the current study, we aimed to develop a deep learning model for automatic delineation of whole regional lymph nodes and assess the dose coverage of lymph nodes with TMI plans using the model.</p> <p><strong><em>Materials and Methods</em></strong></p> <p>Whole regional lymph nodes (cervical, axillary, mediastinal, para-aortic, common iliac, external iliac, internal iliac, obturator, presacral, inguinal lymph nodes) were manually contoured and confirmed by 3 radiation oncologists in 26 patients having whole body computed tomography (CT) images. Twenty patients were designated as the training/validation set and 6 patients as the testing set, and model was developed using the 'nnUNET' framework. The trained model was evaluated with dice similiary coefficient (DSC), precision, recall and Hausdorff distance 95 (HD95). In addition, dose coverage of the manually delineated lymph nodes in TMI plans was calculated.</p> <p><strong><em>Results</em></strong></p> <p>The median value of DCS, precision, recall and HD95 of the trained model was 0.79 (IQR, 0.70-0.84), 0.83 (IQR, 0.75-0.89), 0.76 (IQR, 0.68-0.85), and 2.63 (IQR, 2.00-4.58) respectively. Dose parameters for manually delineated lymph nodes in previously treated TMI plans showed the median value of V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% to be 46.0%, 62.1%, and 73.5%, respectively. The highest V100% was observed in presacral (71.1%), axillary (61.2%), obturator (60.3%), and internal iliac lymph nodes (84.67%). In contrast, the lowest V100% was identified in inguinal (13.3%), external iliac (21.8%), and cervical lymph nodes (42.1%).</p> <p><strong><em>Conclusion</em></strong></p> <p>Automatic delineation of lymph node using deep learning showed the potential to reduce the labor-intensive process of TMLI. When treated with TMI, the coverage of inguinal, external iliac, cervical lymph nodes was lower than expected.</p> <p> </p>
Supplementary data to 'Coupling between cohesive element method and Node-to-segment contact algorithm : Implementation and application'
<p>This archive contains the data and the scripts for the simulations presented in the paper</p> <p>Pundir, M., Guillaume, A. "Coupling between cohesive element method and Node-to-segment contact algorithm : Implementation and application'" (2020)</p>
Figure 4. ITS1 gene tree. Terminal tips have been collapsed for visualization purposes. Nodes are labelled with ultrafast bootstrap support. A in Hidden in the hills: phylogeny of the freshwater mussel genus Alasmidonta (Bivalvia: Unionidae) and description of a new species
Figure 4. ITS1 gene tree. Terminal tips have been collapsed for visualization purposes. Nodes are labelled with ultrafast bootstrap support. A full tree is available in the Supporting Information (File S1). The scale is in substitutions per site.
Methylene Blue Sentinel Lymph Node Biopsy for Breast Cancer Learning Curve in Covid-19 era: How many cases are enough?
<p>Methylene Blue Sentinel Lymph Node Biopsy for Breast Cancer Learning Curve in Covid-19 era: How many cases are enough?</p>
FIGURE4. Maximum-likelihood tree inferred from 694 bp of COI using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE4. Maximum-likelihood tree inferred from 694 bp of COI using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes.
FIGURE3. Maximum-likelihood tree inferred from 998 bp of CR using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes. in --Molecular--and--acoustic--evidence--support--the--species--status--of--Anthus rubescens rubescens and--Anthus [rubescens] japonicus--(Passeriformes:--Motacillidae)
FIGURE3. Maximum-likelihood tree inferred from 998 bp of CR using a HKY+G substitution model implemented in MEGAX (Kumar et al. 2018). Bootstrap values are indicated on the nodes.
Dose-Dense Docetaxel Before or After Doxorubicin/Cyclophosphamide in Axillary Node-Positive Breast Cancer
ClinicalTrials.gov study NCT00201708. IPD Sharing: Not stated. Countries: 1. Publications: 1.
To Accurately Assess Lymph Node Response to NACT by Wire Localization of Clip-marked Lymph Nodes
ClinicalTrials.gov study NCT03715686. IPD Sharing: NO. Countries: 1. Publications: 1.
Mesorectal Excision (ME) Versus ME With Lateral Node Dissection for Stage II, III Lower Rectal Cancer (JCOG0212)
ClinicalTrials.gov study NCT00190541. IPD Sharing: Not stated. Countries: 1. Publications: 3.
No Axillary Surgical Treatment in Clinically Lymph Node Negative Patients on Ultrasonography After Neoadjuvant Chemotherapy
ClinicalTrials.gov study NCT06704945. IPD Sharing: YES. Countries: 1. Publications: 2.
RecurIndex Guided Avoidance of Regional Nodal Irradiation for Node Positive Breast Cancer
ClinicalTrials.gov study NCT04069884. IPD Sharing: YES. Countries: 1. Publications: 10.
Sentinel Node Biopsy Vs Observation After Axillary PET
ClinicalTrials.gov study NCT04072653. IPD Sharing: NO. Countries: 1. Publications: 0.
Gastric Cancer Sentinel Lymph Node Mapping
ClinicalTrials.gov study NCT03049345. IPD Sharing: NO. Countries: 1. Publications: 3.
ScienceDex guides
Understand access before you commit
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.