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  • Repository of design files for the HardwareX article titled "A low-cost and customizable alternative for commercial implantable cannula for intracerebral administration in mice". Contributing authors: Rogneda B Kazanskaya, Alexander V Lopachev, Tatiana N Fedorova, Raul R Gainetdinov, Anna B Volnova. Abstract: Stereotaxic intracerebral cannula implantation for neuroactive agent administration is a wide-spread method for chronic experiments requiring bypassing the blood-brain barrier in rodents. However, commercially available cannula are bulky and may interfere with animal movement or lead to their dislodging during grooming. As the number of cannula needed in one experiment, and the accompanying costs can be high, it is in the interest of researchers to produce them on their own. Custom cannula manufacturing also offers the flexibility of different cannula lengths, which is required for agent delivery to various brain structures. In this article we present a protocol for making guide cannula along with the accompanying systems required for injection, which are small, cost-effective, light, easy to make, reusable, and can be made from easily procured materials.
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  • bug data of open-source applications
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  • 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.
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  • Market survey, lab experiment, field experiment, instructions, energy bills
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  • A systematic review was performed to summarize the maternal, fetal, and neonatal outcomes in pregnant women with COVID-19.
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  • Supplemental figures of TRICHOSCOPIC FINDINGS OF DISCOID LUPUS ERYTHEMATOSUS ALOPECIA: A CROSS-SECTIONAL STUDY (an article in JAAD Journal). Brownish discolotation and follicular plugging in lupus alopecia.
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  • Robot-based Facade Spatial Assembly Optimization
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  • 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.
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  • Mass Spectrometry Imaging dataset of B73 inbred root used in the validation of of rMSIcleanup (https://github.com/gbaquer/rMSIcleanup). Acquired with silver-assisted LDI using Thermo Finnigan™ MALDI-LTQ-Orbitrap Discovery. The datasets are referred to as Dataset 13 and Dataset 14 in the accompanying publication (https://doi.org/10.1101/2019.12.20.884957).
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  • Supplementary Figure 1. 3-mm punch biopsy specimen taken from affected scalp showing diffuse miniaturization of hair follicles with adjacent full-sized sebaceous glands [hematoxylin and eosin, original magnification (a) x 100; (b) x 200].
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