Data

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder with monogenic forms representing prototypes of the underlying molecular pathology and reproducing to variable degrees the sporadic forms of the disease. Using a patient-based in vitro model of PARK7-linked PD, we identified a U1-dependent splicing defect causing a drastic reduction in DJ-1 protein and, consequently, mitochondrial dysfunction. Targeting defective exon skipping with genetically engineered U1-snRNA recovered DJ-1 protein expression in neuronal precursor cells and differentiated neurons.

On May 18, I took part in a very interesting workshop organized by the Malach Centre for Visual History at the Faculty of Mathematics and Physics of the Charles University in Prague. This exceptional association provides local access to the extensive digital archives of the USC Shoah Foundation - the Institute for Visual history and Education (USC), the Refugee Voices archive of the Association of Jewish Refugees and the testimony collection of the Jewish Holocaust Centre in Melbourne.

We simulate the late July/early August re-emergence and spread of COVID-19 in Aotearoa New Zealand. We use a stochastic, individual-based network model of all ≈5 million individuals in Aotearoa, and run simulations for a period of 30 days. Based on these simulations, we calculate: the expected time to detection of the first case after initial seed cases; the number of cases at the time of detection; the time until detection of a first case outside of Auckland; and how the overall number of cases increases without intervention.

My contribution attempts to find answers to the following questions, among others: How did Luxembourg officials deal with Jewish refugees from Eastern European countries? Did the Luxembourgish authorities distinguish or give priority to citizens of particular nations? Who was involved in the approval process? Who played the most active role and collected the most controversial information about Jewish immigrants?

This paper discusses the added value of applying machine learning (ML) to contextually enrich digital collections. In this study, we employed ML as a method to geographically enrich historical datasets. Specifically, we used a sequence tagging tool (Riedl and Padó 2018) which implements TensorFlow to perform NER on a corpus of historical immigrant newspapers. Afterwards, the entities were extracted and geocoded. The aim was to prepare large quantities of unstructured data for a conceptual historical analysis of geographical references.

The location choice of Stone Age settlements has been long considered to be influenced by environmental conditions. Proximity to water and sandy soils are most typical examples of those conditions. The notion of the influence resulted from the evidence from a relatively small amount of sites. During the recent decades the number of known settlements has increased to a level where statistical assessment of relation between environmental characteristics and settlement location choice is possible.