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4,612 results for “Labels”
Figure 1 from: Drinkwater R, Cubey R, Haston E (2014) The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels. PhytoKeys 38: 15-30. https://doi.org/10.3897/phytokeys.38.7168
Figure 1 - Example labels: a Pre-printed label with handwritten details b and c mixed labels with pre-printed and typed information d Mainly handwritten label, with printers mark e Mainly handwritten label with unusual phrasing.
Label-Free Identification of Exosomes Using Raman Spectroscopy and Machine Learning
<p>Figures and datasets used in the manuscript "Label-Free Identification of Exosomes Using Raman Spectroscopy and Machine Learning". Codes that produce the figures are also included.</p> <p> </p> <p> </p> <div> </div>
RGB-LWIR Labeled Dataset for Ground-based platforms
<p>Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms. </p>
Supplementary material 1 from: Gjonov I (2022) An automated procedure ten times more efficient in cutting labels for entomological collections. Nota Lepidopterologica 45: 133-137. https://doi.org/10.3897/nl.45.81451
Template file
MAESTRO Real - Multi-Annotator Estimated Strong Labels
<p>The dataset was created for studying estimation of strong labels using crowdsourcing.</p> <p>It contains 49 real-life audio files from 5 different acoustic scenes, and the annotation outcome. Annotation was performed using Amazon Mechanical Turk. Total duration of the dataset is 189 minutes and 52 seconds</p> <p>Audio files are a subset from TUT Acoustic Scenes 2016 dataset, belonging to five acoustic scenes: cafe/restaurant, city center, grocery store, metro station and residential area. Each scene have 6 classes, some of them are common to all the scenes, resulting into 17 classes in total.</p> <p><br> The dataset contains:</p> <ul> <li>audio: the 49 real-life recordings, each from 3 to 5 min long.</li> <li>soft labels: estimated strong labels from the crowdsourced data, values between 0 and 1 indicates the uncertainty of the annotators.</li> </ul> <p>For more details about the real-life recordings, please see the following paper:</p> <p>A. Mesaros, T. Heittola and T. Virtanen, "TUT database for acoustic scene classification and sound event detection," <em>2016 24th European Signal Processing Conference (EUSIPCO)</em>, 2016, pp. 1128-1132.</p> <p> </p>
Figure 3 in Lost and found in Ireland; how a data label resulted in a postal delivery to Metriocnemus (Inermipupa) carmencitabertarum (Orthocladiinae)
Figure 3. Front of the Royal Mail envelope that contained the returned vial addressed to "Metriocnemus carmencitabertarum" and showing the An Post date stamp 13 March 2013.
Preparation of Deuterium-Labeled Armodafinil by Hydrogen–Deuterium Exchange and Its Application in Quantitative Analysis by LC-MS
<p>Armodafinil, the R enantiomer of modafinil, was approved in 2007 by the US Food and<br> Drug Administration as a wake-promoting agent for excessive sleepiness treatment. Due to its<br> abuse by students and athletes, there is a need of its quantification. Quantitative analysis by liquid<br> chromatography-mass spectrometry, however, though very common and sensitive, frequently cannot<br> be performed without isotopically labeled standards which usually have to be specially synthesized.<br> Here we reported our investigation on the preparation of deuterated standard of armodafinil based<br> on the simple and inexpensive hydrogen–deuterium exchange reaction at the carbon centers. The<br> obtained results clearly indicate the possibility of introduction of three deuterons into the armodafinil<br> molecule. The introduced deuterons do not undergo back exchange under neutral and acidic<br> conditions. Moreover, the deuterated and non-deuterated armodafinil isotopologues revealed coelution<br> during the chromatographic analysis. The ability to control the degree of deuteration using<br> different reaction conditions was determined. The proposed method of deuterated armodafinil<br> standard preparation is rapid, cost-efficient and may be successfully used in its quantitative analysis<br> by LC-MS.</p>
Labeled RGB and depth images for cattle body condition score prediction
<p>This dataset contains a collection of preclassified Criollo cow RGB+depth videos, as well as processed depth, grayscale, and edge images. The dataset was previously used to train convolutional neural networks and vision transformers to estimate body condition scores of cattle, and will be useful to other researchers in need a high quaility visual dataset that incorporates depth for the purposes of three dimensional representations of cattle. The availability of compatible software and image processing packages makes this dataset very robust and applicable to other areas of both agricultural and machine learning research. This dataset has several features that make it a good test of machine learning algorithms such as low sample uniqueness and a slightly subjective metric.</p>
Synthesis and preclinical evaluation of a novel fluorine-18 labeled small-molecule PET radiotracer for imaging of CXCR3 receptor in mouse models of atherosclerosis
<p>Background CXCR3 is a chemokine receptor and is expressed in innate and adaptive immune cells. It promotes<br> the recruitment of T-lymphocytes and other immune cells to the inflammatory site in response to the binding of cognate<br> chemokines. Upregulation of CXCR3 and its chemokines has been found during atherosclerotic lesion formation.<br> Therefore, detection of CXCR3 by positron emission tomography (PET) radiotracer can be a useful tool for detecting the development of atherosclerosis in a noninvasive manner. Herein, we report the synthesis, radiosynthesis, and characterization of a novel fluorine-18 (F-18, <sup>18</sup>F) labeled small-molecule radiotracer for the imaging of the CXCR3 receptor in mouse models of atherosclerosis.<br> Results The reference standard <strong>1</strong> and its precursor <strong>9</strong> were synthesized over 5 steps from starting materials in good to moderate yields. The measured K<sub>i</sub> values of CXCR3A and CXCR3B were 0.81 ± 0.02 nM and 0.31 ± 0.02 nM, respectively. [<sup>18</sup>F]<strong>1</strong> was prepared by a two-step radiosynthesis with a decay-corrected radiochemical yield of 13 ± 2%, radiochemical purity > 99%, and specific activity of 44.4 ± 3.7 GBq/μmol at the end of synthesis (n = 6). The baseline studies showed that [<sup>18</sup>F]<strong>1</strong> displayed high uptake in the atherosclerotic aorta and brown adipose tissue in Apolipoprotein E (ApoE) knockout (KO) mice fed with a high-fat diet over 12 weeks. The uptake of [<sup>18</sup>F]<strong>1</strong> in these regions was reduced significantly in self-blocking studies, demonstrating CXCR3 binding specificity. Contrary to this, no significant differences in uptake of [<sup>18</sup>F]<strong>1</strong> in the abdominal aorta of C57BL/6 control mice fed with a normal diet were observed in both baseline and blocking studies, indicating increased CXCR3 expression in atherosclerotic lesions. Immunohistochemistry studies demonstrated that [<sup>18</sup>F]<strong>1</strong>-positive regions were correlated with CXCR3 expression, but some<br> atherosclerotic plaques with significant size were not detected by [<sup>18</sup>F]<strong>1</strong>, and their CXCR3 expressions were minimal.<br> Conclusion [<sup>18</sup>F]<strong>1</strong> was synthesized with good radiochemical yield and high radiochemical purity. In PET imaging<br> studies, [<sup>18</sup>F]<strong>1</strong> displayed CXCR3-specific uptake in the atherosclerotic aorta in ApoE KO mice. [<sup>18</sup>F]<strong>1</strong> visualized CXCR3<br> expression in different regions in mice aligned with the tissue histology studies. Taken together, [<sup>18</sup>F]<strong>1</strong> is a potential<br> PET radiotracer for imaging CXCR3 in atherosclerosis.</p>
Tracing low-CO2 fluxes in soil incubation and 13C labeling experiments: a simplified gas sampling system for respiration and photosynthesis measurements
<p>Data set containing data from feature tests (1-3) as well as photosynthesis and respiration measurements.</p>
Fig. 5. Type specimens and original labels. A–B in A taxonomic review of the endemic Mexican genus Ceratotrupes Jekel, 1865 (Coleoptera: Geotrupidae: Ceratotrupini), with the description of an unexpected new species from the Sierra Madre del Sur
Fig. 5. Type specimens and original labels. A–B. Ceratotrupes sturmi (Jekel, 1865), holotype, ♂ (NHML). A. Dorsal habitus. B. Labels. C–D. Ceratotrupes mniszechi (Jekel, 1865), holotype, ♂ (MNHN). C. Dorsal habitus. D. Labels.
Synthesis of Tris[2‐(dimethylamino)ethyl]amine with Regiospecific Deuterium Labels
<p>Simple synthetic routes to regioselectively deuterated tris[2-(dimethylamino)ethyl]amine (Me6TREN) variants are described. Imine formation with formaldehyde-d2 from tris(2-aminoethyl)amine (TREN) and subsequent reductions with NaBD4 afforded N[CH2CH2N(CD3)2]3 or d18-Me6TREN in 79 % yield. A trisubstitution protocol from 2-bromo-N,N-dimethylacetamide and ammonium carbonate and subsequent reduction of the N(CH2CONMe2)3 intermediate by lithium aluminum deuteride has afforded N[CH2CD2N(CH3)2]3 or (d6-arm)-Me6TREN in three steps and 52 % overall yield. A similar protocol from 2-bromo-N,N-dimethyl-d2-acetamide, obtained in two steps from d4-acetic acid, with reduction of the N(CD2CONMe2)3 intermediate by lithium aluminum hydride has afforded N[CD2CH2N(CH3)2]3 or (d6-cap)-Me6TREN in four steps and 13 % overall yield from CD3COOD.</p>
Deuterium Magnetic Resonance Imaging Using Deuterated Water-Induced 2H-Tissue Labeling Allows Monitoring Cancer Treatment at Clinical Field Strength
<p>Raw 2H-NMR Data of deuterium content in tumor tissues. </p>
Multi-Spectral Reflection Matrix for Ultra-Fast 3D Label-Free Microscopy
<p>Optical data associated with the paper "Multi-Spectral Reflection Matrix for Ultra-Fast 3D Label-Free Microscopy<strong>". </strong></p> <p><br> </p>
Robust model training strategy via hard negative mining in the weakly labeled dataset for lymphatic invasion in gastric cancer
<p>Gastric cancer poses a significant public health concern, emphasizing the need for accurate evaluation of lymphatic invasion (LI) for determining prognosis and treatment options. However, this task is time-consuming, labor-intensive, and prone to intra- and inter-observer variability. Furthermore, the scarcity of annotated data presents a challenge, particularly in the field of digital pathology. Therefore, there is a demand for an accurate and objective method to detect LI using a small dataset, benefiting pathologists. In this study, we trained convolutional neural networks (CNNs) to classify LI using a four-step training process: (1) weak model training; (2) identification of false positives; (3) hard negative mining in a weakly labeled dataset; and (4) strong model training. To overcome the lack of annotated datasets, we applied a hard negative mining approach in a weakly labeled dataset, which contained only final diagnostic information, resembling the typical data found in hospital databases, and improved classification performance. Ablation studies were performed to simulate the lack of datasets and severely unbalanced datasets, further confirming the effectiveness of our proposed approach. Notably, our results demonstrated that despite the small number of annotated datasets, efficient training was achievable, with the potential to extend to other image classification approaches used in the field of medicine.</p><p> </p><p><strong>Key words: Artificial intelligence, Computational pathology, Gastric cancer, Lymphatic invasion, Hard negative mining</strong></p>
Micro-CT tomographic data set of 38 mummy labels from the BNU in Strasbourg (1/2)
<p><strong>Summary</strong></p> <p>This submission contains a tomographic dataset of 38 mummy labels from the BNU in Strasbourg used to perceive the anatomical identification possibilities of the woods used for mummy labels and to carry out ring width measurements. The data will be made available as part of [Blondel et al., 2024].</p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the EasyTom 150/160 X-ray tomograph (RX Solutions). This tomograph is equipped with a sealed X-ray generator with a compact tube and an interchangeable-plane sensor fitted with a CsI scintillator. The CT scanner parameters for the session carried out on the mummy labels were set at 90 Kv with an intensity of 195 mA for an acquisition resolution varying between 11 and 42 µm with 2016 projections (that is about 20 images on average per projection) with a frame rate of 12,5 and a temperature of 28°C. Each image was then reconstructed by filtered retroprojection using the XAct software (RX Solutions).</p> <p><strong>Information on placing mummy labels in the tomograph</strong></p> <p>The installation of the mummy labels was the same for all the different labels, some of which varied in size. They were attached to a plastic clamping vice-type support covered in expanded foam to prevent the labels from being marked during clamping, before being placed on the tomograph's rotating platform.</p> <p><strong>Issues relating to the data collected</strong></p> <p>The data collected for this study were carried out to perceive the possibilities of anatomical identification from tomographic images in the transverse plane. The tangential and radial planes were not of sufficiently high resolution due to the dimensions of the mummy labels, see details in [Blondel et al., 2024]. The other objective was to use tomographic imagery to facilitate the acquisition of ring widths in the transverse plane of mummy labels. The mummy labels were not tomographed in their entirety. Only the central part, a few centimetres high, was tomographed to maximise resolution. The number of projections and the resolution per label are specified in table form in [Blondel et al., 2024], as they vary according to the width and thickness of the mummy labels. All raw tomography image data (i.e. without corrections) are available in .tif format. The post-processing steps are described in the methodology of [Blondel et al., 2024].</p> <p><strong>List of Contents</strong></p> <p>The content of the submission is divided into 38 data sets corresponding to the 38 mummy labels. Each set is labelled with the inventory number of the BNU mummy label and its resolution. Each set contains:<br>- All the images of the transverse plane in .tif format, the number of projections of which varies from one label to another depending on the resolution of the acquisitions, see details in [Blondel et al., 2024].<br>- The .xls file containing a summary of the scanner metadata for each of the mummy labels.<br>- The three images processed in the transverse plane for each label, including those used to measure ring width for the 7 labels for which ring width measurement was possible, as presented in [Blondel et al., 2024].<br>- Colour photographs of the front and back of each tomographed mummy label including those on which ring width measurements were taken on their surface, unless otherwise stated<a title="" href="#_ftn1" name="_ftnref1">[1]</a>. All these photographs are marked: Coll._et_photogr._BNU_Strasbourg_OpenLicence, accompanied by the inventory number.</p> <p><strong>Acknowledgments</strong></p> <p>We would also like to thank engineers Damien Favier and Antoine Egele from the Charles Sadron Institute for their work on the tomographic acquisitions carried out on the 38 mummy labels.</p> <div><br> <div> <p><a title="" href="#_ftnref1" name="_ftn1">[1]</a> The photographs of the front and back of mummy label HO255 are not available, as they are currently being studied.</p> </div> </div>
Long-term, Open-label Safety Extension Study of Retigabine/Ezogabine in Pediatric Subjects (>= 12 Years Old) With POS or LGS
ClinicalTrials.gov study NCT01668654. IPD Sharing: NO. Countries: 1. Publications: 0.
Open Label Extension Study to Assess the Safety and Long-Term Immunogenicity of ARCT-021
ClinicalTrials.gov study NCT04728347. IPD Sharing: NO. Countries: 1. Publications: 0.
A Phase III Open-Label Extension Study Of Gabapentin As Adjunctive Therapy In Japanese Pediatric Patients With Partial Seizures
ClinicalTrials.gov study NCT00620555. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Absorption, Distribution, Metabolism and Excretion of [14C]-Labeled BIA 9-1067 and Metabolites
ClinicalTrials.gov study NCT01515891. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.