Contributors:Massalha, Hassan, Bahar Halpern, Keren, Abu Gazala, Samir, Jana, Tamar, E. Massasa, Efi, E. Moor, Andreas, Pikarsky, Eli, Amit, Ido, Zamir, Gideon, Itzkovitz, Shalev
Matlab scripts used to analyze data associated with the manuscript entitled "A single cell atlas of the human liver tumor microenvironment".
*please used Matlab 2019b to run the following m files.
inputData.mat: mat contains all raw and preprocessed data used in the study
Create_Interactions_Network.m: Matlab script used to calculate Ligand-Receptor interaction score between different cell types. The script creates panels of Figure 3 and Table S5.
Hepatocytes_Reconstruction.m: Matlab script used to reconstruct human hepatocytes zonation along the lobule axis. The script creates panel 'c' of Figure 4, Figure S4, and Table S7.
Cancer_Cells_Spatial_Analysis.m: Matlab script used to calculate differential gene expression between malignant cells found at different zones (malignant border, malignant core, and fibrotic zone) captured by laser microdissection. The script creates panel 'd' of Figure 4
helperFunctions.zip: This folder contains required functions used by the m files.
Integrating data from multiple sources with the aim to identify records that correspond to the same entity is required in many real-world applications including healthcare, national security, and businesses. However, privacy and confidentiality concerns impede the sharing of personal identifying values to conduct linkage across different organizations. Privacy-preserving record linkage (PPRL) techniques have been developed to tackle this problem by performing clustering based on the similarity between encoded record values, such that each cluster contains (similar) records corresponding to one single entity. When employing PPRL on databases from multiple parties, one major challenge is the prohibitively large number of similarity comparisons required for clustering, especially when the number and size of databases are large. While there have been several private blocking methods proposed to reduce the number of comparisons, they fall short in providing an efficient and effective solution for linking multiple large databases. Further, all of these methods are largely dependent on data. In this paper, we propose a novel private blocking method for efficiently linking multiple databases by exploiting the data characteristics in the form of probabilistic signatures and introduce a local blocking evaluation step for validating blocking methods without knowing the ground-truth. Experimental results show the efficacy of our method in comparison to several state-of-the-art methods.
This are the R codes used to generate the research clusters and its respective plots in the paper "What are the ingredients for food systems change towards sustainability? - Insights from the literature" by Weber, Hanna; Poeggel, Karoline; Eakin, Hallie; Fischer, Daniel; Lang, Daniel; von Wehrden, Henrik; Wiek, Arnim, which is currently under revision. The code groups publications into different clusters based on co-abundancy of words. The main code is the file “using_ginko_explained”, which sources the codes “statistics” and “scopus”, as well as the csv-file "Conceptual_Vocabulary".
Snapshot of peer-evaluated artifact corresponding to the published conference paper .
 Sandeep Dasgupta, Sushant Dinesh, Deepan Venkatesh, Vikram S. Adve, and
Christopher W. Fletcher 2020. Scalable Validation of Binary Lifters. In
Proceedings of the 2020 ACM SIGPLAN Conference on Programming Language Design
and Implementation. ACM. https://doi.org/10.1145/3385412.3385964
Contributors:Eduardo, Jose Ángel, Ángel, Cristina, Estefanía, José Manuel
Early detection of infectious diseases is the most cost-effective strategy in disease
surveillance for reducing the risk of outbreaks. Latest deep learning and computer
vision improvements are powerful tools that open up a new field of research in
epidemiology and disease control. In this work, these techniques were employed to
develop an algorithm aimed to track and compute individual animal motion in real time.
This algorithm was used in experimental trials in order to assess African swine fever
(ASF) infection course in Eurasian wild boar. Overall, the outcomes showed a strong
correlation between motion reduction and fever caused by ASF infection. In addition,
infected animals computed significant low movements compared to uninfected animals.
The obtained results suggest that a motion monitoring system based on artificial
intelligence may be used to trigger suspicions of fever. It would help farmers and animal
health services to early detect clinical signs compatible with infectious diseases. This
technology shows a promising start up for implementing non-intrusive, economic and
real time solutions in the livestock industry with especial interest in ASF, considering
the current concern in the world pig industry.