Este fichero contiene los códigos de la programación realizada en Arduino de los trabajos prácticos de Física y Química para el nivel de bachillerato.
Estos códigos son de software libre: puede redistribuirlo y/o modificarlo bajo los términos de la Licencia Pública General GNU publicada por la Free Software Foundation.
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)
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.
Contributors:Teresa López-Pellisa, Fernando Rodríguez-Gallego, Neus Rotger
This database provides the interviews done to the students at the end of the Teaching Innovation Project: "Writing an academic review in tele-collaborative settings" (PID 181960), carried out at the Universitat de les Illes Balears (UIB) with the collaboration of the Open University of Catalonia (UOC) during the academic year 2018-2019. We have used some of the information obtained from these interviews in the article: "Collaborative Writing at Work: Peer Feedback in a Blended Learning Environment"
Article Summary: This exploratory study aims to analyse the nature of peer feedback during a collaborative writing assignment, and to identify the possible effects feedback has on the revision of a text written by university students in a blended learning environment. Under analysis are two different graduate’s courses in academic writing, during which, over a period of a whole semester, the students (n = 85) were divided into 25 work groups to carry out a co-evaluation assignment with the support of a technology platform. The results obtained indicate that, when collaborative writing includes peer feedback, instead of unidirectional corrections from the teacher, the students respond more reflectively and constructively, they discuss the content they are working with, and, as a result, they effect significant changes in their own writing.
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.
Contributors:Rebecca L Hansen, Maria Emilia Dueñas, Young Jin Lee
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).
Contributors:Kerem Özkap, Ertan Peksen, Ismail Kaplanvural, Deniz Çaka
This data and code are associated with the article "3D Scanner Technology Implementation to Numerical Modeling of GPR" by the same authors. The 3D scanner data and Matlab code used in the article are provided with other necessary files. The Readme file comprises detailed descriptions of the data files and formats.
Please see the publication for more information about this data set.
Contributors:Javier Pastor-Galindo, Mattia Zago, Pantaleone Nespoli, Sergio Lopez, Alberto Huertas Celdrán, Manuel Gil Pérez, José A. Ruipérez-Valiente, Gregorio Martinez Perez, Felix Gomez Marmol
While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.
The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
Data have been exported in three formats to provide the maximum flexibility
- MongoDB Dump BSONs: To import these data, please refer to the official MongoDB documentation.
- JSON Exports: Both the users and the tweets collections have been exported as canonical JSON files.
- CSV Exports (only tweets): The tweet collection has been exported as plain CSV file with comma separators.