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  • In this research, we developed a high throughput method to systematically map functional connections from the dorsal cortex to the thalamus in awake mice by combing optogenetic inactivation with multichannel recording. Here, we provide: 1. Raw data of multi-units and single-units obtained in this study. 2. MATLAB codes for data analysis.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • File Set
  • Data and images for surrogate-adjoint refine based global optimization method Surrogate-adjoint refine based global optimization method combining with multi-stage fuzzy clustering space reduction strategy for expensive problems are included in the attachment.
    Data Types:
    • Dataset
    • File Set
  • This repository includes a solidWorks (2019) CAD model of a research quality double pendulum. Additionally, we have provided video data with trackers recorded at 1000 FPS and Encoder data to validate the video data analysis.
    Data Types:
    • Software/Code
    • Image
    • Video
    • Dataset
    • Text
    • File Set
  • This dataset is a seamless, high resolution (5m cell size) bathymetry model of Lord Howe Island and Balls Pyramid. This dataset provides detailed depth information from the island shoreline to the shelf drop off. This bathymetry dataset integrates multibeam echo sounder data together with depth derived from satellite imagery. Multibeam echo sounder data were acquired aboard two voyages on the CSIRO Marine National Facility R.V. Southern Surveyor in 2008 and 2013. Shallow data was derived from World View II satellite imagery of Lord Howe Island collected in 2013. Inshore depth data around Balls Pyramid derived from Quickbird imagery (collected in 2009) was excluded from this dataset. Gaps in data coverage were interpolated using Natural Neighbor in ArcGIS 10.4. Data were clipped at 0 to 300 m depth. Full description of methods is outlined in the following open-access publication, accessible by the following link: http://www.mdpi.com/2076-3263/8/1/11/htm
    Data Types:
    • Dataset
    • File Set
  • Here we present the first demonstration of the use of eggshell membrane for research on endangered Maleo (Macrocephalon maleo). We used 24 post-hatched eggshell membranes collected from two different sites, Tambun and Tanjung Binerean, in North Sulawesi, 12 samples in each. Two different DNA extraction methods: alkaline lysis method and gSYNCTM DNA Extraction Kit were applied. To determine the sex of Maleo, we utilized PCR-based DN A sexing using CHD genes, with the primer set 2550F/2718R. We successfully extracted all samples; mean sample concentration was 267.5 ng/µl (range 47–510.5 ng/µl) and samples were of high purity (A 260/280 ratio 1.85±0.03). All samples were used to successfully identified sexes, 9 females and 15 males. Our research clearly illustrates that eggshell membranes can be used for DNA sexing and open the possibility to build noninvasive DNA collections over large spatial scales for population study of endangered birds. The data consist of A. Electrophoresis photos of (1). DNA extraction of Maleo from eggshell membrane, collected from Tambun and Tanjung Binerean, North Sulawesi, from 4th April until 1st May 2018. We used gSYNCTM DNA Extraction Kit (Genaid) followed the provided user manual with little modifications to isolate the DNA frm the eggshell membrane. The eluded DNA (1 µl) was quantified using NanoVue Plus™ (Biochrom, Harvard Bioscience, Inc), at A260 nm. The 260/280 nm absorbance ratio was measured to give an indication of purity of the DNA. (2). PCR products of molecular sexing, using lysate and extracted DNA. We applied PCR based DNA sexing by using CHD genes, with the primer set 2550F/2718R (28). PCR used a 10 µl total volume containing template DNA (genomic DNA or lysate), 1.2 µl sterile dH2O, 5 µl 2x PCR buffer KOD FX Neo, 2 µl dNTPs (2 mM), (TOYOBO Co. Ltd.), 0.3 µl Primer 2550F (10 µM; 5'-GTT ACT GAT TCG TCT ACG AGA-3'), and 0.3 µl Primer 2718R (10 µM; 5'-ATT GAA ATG ATC CAG TGC TTG-3', (28)), and 0.2 U KOD polymerase enzyme. PCR was carried out in a Veriti™ 96-well thermal cycler (Applied Biosystems™). For genomic DNA templates, the following profile was used: 1 cycle at 94℃ for 2 min followed by 35 cycles of 98℃ for 10 sec, 53℃ for 30 sec and 68℃ for 45 sec;, and a final extension at 68℃ for 7 minutes. For lysate as DNA template, the PCR profiles was the same for DNA genome, except that it was run for more cycles (40x). B. Sequences of CHD-W and CHD-Z genes of Maleo The electrophoresis gels were cut on upper and lower bands for female sample and single band for male sample, then purified for sequencing. The sequence reactions were carried for both direction in sequencing services laboratory provided by 1st BASE Laboratories (Apical Scientific Sdn Bhd), Malaysia.
    Data Types:
    • Image
    • Sequencing Data
    • Dataset
    • Document
  • bug data of open-source applications
    Data Types:
    • Dataset
    • File Set
  • This repository contains the image dataset and the manual annotations used to develop the HEPASS algorithm for automated liver steatosis quantification: - Salvi M., Molinaro M., Metovic J., Patrono D., Romagnoli R., Papotti M, and Molinari F., "Fully Automated Quantitative Assessment of Hepatic Steatosis in Liver Transplants", Computers in Biology and Medicine 2020 (DOI: 10.​1016/​j.​compbiomed.​2020.​103836) ABSTRACT Background: The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists’ visual evaluations on liver histology specimens. Method: The aim of this study was to develop and validate a fully automated algorithm, called HEPASS (HEPatic Adaptive Steatosis Segmentation), for both micro- and macro-steatosis detection in digital liver histological images. The proposed method employs a hybrid deep learning framework, combining the accuracy of an adaptive threshold with the semantic segmentation of a deep convolutional neural network. Starting from all white regions, the HEPASS algorithm was able to detect lipid droplets and classify them into micro- or macrosteatosis. Results: The proposed method was developed and tested on 385 hematoxylin and eosin (H&E) stained images coming from 77 liver donors. Automated results were compared with manual annotations and nine state-of-the-art techniques designed for steatosis segmentation. In the TEST set, the algorithm was characterized by 97.27% accuracy in steatosis quantification (average error 1.07%, maximum average error 5.62%) and outperformed all the compared methods. Conclusions: To the best of our knowledge, the proposed algorithm is the first fully automated algorithm for the assessment of both micro- and macrosteatosis in H&E stained liver tissue images. Being very fast (average computational time 0.72 seconds), this algorithm paves the way for automated, quantitative and real-time liver graft assessments.
    Data Types:
    • Dataset
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  • Market survey, lab experiment, field experiment, instructions, energy bills
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    • Dataset
    • File Set
  • Robot-based Facade Spatial Assembly Optimization
    Data Types:
    • Dataset
    • File Set
  • Mass Spectrometry Imaging datasets used as validation of the functionality of rMSIcleanup (https://github.com/gbaquer/rMSIcleanup). Acquired with silver-assisted LDI using MALDI TOF/TOF ultrafleXtreme. Referred to as Dataset 1-10 in the accompanying publication (https://doi.org/10.1101/2019.12.20.884957). Datasets 1 and 2: Mouse Pancreatic Tissue. Dataset 3: Mouse Kidney Tissue. Datasets 4-10: Mouse Brain Tissue.
    Data Types:
    • Dataset
    • Text
    • File Set
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