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opencc-by-4.0Dec 2020View details →

Fig. 5. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 5. A-D: Derivative melt curves showing mock mixed infections generated from plasmid clones containing the following DNA: (A) T. noyesi G8 and T. copemani; (B) T. vegrandis G7 and T. copemani; (C) T. vegrandis G7 and T. noyesi G8; (D) T. vegrandis G7, T. noyesi G8 and T. copemani.

opencc-by-4.0Dec 2020View details →

Fig. 1 in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)

Fig. 1. Multiple sequence alignment of a portion of the 18S rDNA of seven Trypanosome species and subsequent genotypes used to design the HRM-qPCR assays.

Fig. 3 in Comparison of the modified agglutination test and real-time PCR for detection of Toxoplasma gondii exposure in feral cats from Phillip Island, Australia, and risk factors associated with infection

Fig. 3. Predicted lines of fit for the multivariable logistic regression model plotted as probability of Toxoplasma gondii qPCR positivity in feral cats on Phillip Island (Victoria) versus body weight for each season. Dashed lines show 95% confidence intervals.

Fig. 1 in Comparison of the modified agglutination test and real-time PCR for detection of Toxoplasma gondii exposure in feral cats from Phillip Island, Australia, and risk factors associated with infection

Fig. 1. Location and Toxoplasma gondii infection status, as detected by real-time PCR (qPCR), of feral cats trapped on Phillip Island (Victoria) from July 2016 to December 2017. Map shows the distribution of different location types (Park, Agricultural, Residential) used in multivariable regression analysis. A circular spread of points around a location marked with 'x' indicates multiple animals were sampled at the same site (i.e. same GPS coordinates). Red = T. gondii qPCR positive, white = T. gondii qPCR negative. Map created using Quantum GIS, version 3.8. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in A novel quantitative real-time PCR diagnostic assay for fecal and nasal swab detection of an otariid lungworm, Parafilaroides decorus

Fig. 2. Standard curve based on sensitivity data. A 95% confidence interval for the linear regression model is shaded in grey.

Fig. 1. Repeat family selection for the P. decorus diagnostic assay. A in A novel quantitative real-time PCR diagnostic assay for fecal and nasal swab detection of an otariid lungworm, Parafilaroides decorus

Fig. 1. Repeat family selection for the P. decorus diagnostic assay. A. Number of sequencing reads for P. decorus compared to outgroup species reads for each repeat family (1–104) on a log scale. Arrows indicate repeat families with no reads from the outgroup species. Plot was made using Tableau Software, 2019. B. Within a cluster, reads with similar sequences are closer together. Edges connect a read with its closest match (creating a pair) and the length of this edge represents the amount of overlap between the reads. The mean edge width provides context for the lengths in the cluster, so in a cluster with a larger mean edge width the edges are actually longer than edges in a cluster with a smaller mean edge width. Reads therefore may be distant because of sequence divergence, or in the case of a long repeat (more than 150 base pairs), because of a lack of overlap between reads. However, because there will likely be continuous reads covering different regions of the repeat, these longer repeats should still appear as a tight, though possibly larger, cluster. Read dots that stray from the central cluster most likely represent sequence divergence. Higher density therefore indicates lower sequence divergence.

Fig. 4 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 4. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis detected by rapid antigen test. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia using rapid antigen test (RAT). The sample identity is located at the bottom of the graph with two labels (C20, C3) shaded indicating samples that were found as Giardia positive by real-time PCR. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (RAT+) and negative (RAT-) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 3. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis as detected by rapid antigen test and real-time PCR combined. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia. The sample identity is located at the bottom of the graph. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (+) and negative (‒) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 2. | Results of Giardia duodenalis rapid antigen test applied on faecal samples from chimpanzees. A positive result for the Giardia duodenalis rapid antigen test (RAT, Anigen Rapid Giardia AG Test Kit) is represented by the line in the 'T' position in the window along with the positive control line in the 'C' position.

Fig. 1 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling

Fig. 1. Captive chimpanzees and their enclosure in Sydney, Australia. (A) Main chimpanzee open air exhibit with multiple climbing structures. (B) View from the other direction showing entry to the indoor area at the end of the exhibit. (C) smaller exhibit with mesh covering and more climbing and sleeping structures. (D) Members of the chimpanzee troop at the Taronga Zoo.

opencc-by-4.0Aug 2024View details →

Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning

The dataset titled "Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning" was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.

opencc-by-4.0Feb 2012View details →

GTSRB - German Traffic Sign Recognition Benchmark by Real-Time Computer Vision at Ruhr-Universität Bochum

<div> <div>The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011.  Our benchmark has the following properties: - Single-image, multi-class classification problem - More than 40 classes - More than 50,000 images in total - Large, lifelike database Acknowledgements: [INI Benchmark Website][1] [1]: http://benchmark.ini.rub.de/</div> </div>

opencc-by-4.0Aug 2022View details →

GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation

The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at  (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.

opencc-by-4.0Oct 2024View details →

Real-time monitoring of a 3D blood-brain barrier model maturation and integrity with a sensorized microfluidic device

A significant challenge in the treatment of central nervous system (CNS) disorders is represented by the presence of the blood-brain barrier (BBB), a highly selective membrane that regulates molecular transport and restricts the passage of pathogens and therapeutic compounds. Traditional in vivo models are constrained by high costs, lengthy experimental timelines, ethical concerns, and interspecies variations. In vitro models, particularly microfluidic BBB-on-a-chip devices, have been developed to address these limitations. These advanced models aim to more accurately replicate human BBB conditions by incorporating human cells and physiological flow dynamics. In this framework, here we developed an innovative microfluidic system that integrates thin-film electrodes for non-invasive, real-time monitoring of BBB integrity using electrochemical impedance spectroscopy (EIS). EIS measurements showed frequency-dependent impedance changes, indicating BBB integrity and distinguishing well-formed from non-mature barriers. The data from EIS monitoring was confirmed by permeability assays performed with a fluorescence tracer. The model incorporates human endothelial cells in a vessel-like arrangement to mimic the vascular component and three-dimensional cell distribution of human astrocytes and microglia to simulate the parenchymal compartment. By modeling the BBB-on-a-chip with an equivalent circuit, a more accurate trans-endothelial electrical resistance (TEER) value was extracted. The device demonstrated successful BBB formation and maturation, confirmed through live/dead assays, immunofluorescence and permeability assays. Computational fluid dynamics (CFD) simulations confirmed that the device mimics in vivo shear stress conditions. Drug crossing assessment was performed with two chemotherapy drugs: doxorubicin, with a known poor BBB penetration, and temozolomide, conversely specific drug for CNS disorders and able to cross the BBB, to validate the model predictive capability for drug crossing behavior. The proposed sensorized microfluidic device represents a significant advancement in BBB modeling, offering a versatile platform for CNS drug development, disease modeling, and personalized medicine.

opencc-by-4.0Feb 2023View details →

Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D

This dataset contains experimental data related: "Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D", T.Craig, A. Kadu, K.J. Batenburg and S. Bals, Nanoscale, 2023,15, 5391-5402 (2023).

opencc-by-4.0Oct 2022View details →

Mp4-Version of the supplementary Movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"

Videos in 'mp4'-format of the 8 supplementary movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"

opencc-zeroNov 2022View details →

Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection

5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.

opencc-by-4.0Nov 2022View details →

Real-time Automatic Temperature Regulation During In Vivo MRI-guided Laser Induced Thermotherapy (MR-LITT)

This dataset is associated with the article. For each example (in vitro/in vivo), two matrices are provided, each containing : - coefficients estimation curves ; - automatic regulation curves and recontructed data (thermometry, phase, module) of the MRI sequence.

opencc-zeroJan 2023View details →

Data for: Real time g(2) monitoring with 100 kHz sampling rate

Data for the publication C. Lüders, J. Thewes, and M. Assmann, Real time g(2) monitoring with 100 kHz sampling rate, Opt. Express 26, 24854-24863 (2018). We introduce a technique to determine photon correlations of optical light fields in real time. The method is based on ultrafast phase-randomized homodyne detection and allows us to follow the temporal evolution of the second-order correlation function g(2)(0) of a light field. We demonstrate the capabilities of our approach by applying it to a laser diode operated in the threshold region. In particular, we are able to monitor the emission dynamics of the diode switching back and forth between lasing and spontaneous emission with a g(2)(0)-sampling rate of 100 kHz. Funded by German Science Foundation (DFG) (AS 459/1-2).