How to tell a data story

At Stadt.Geschichte.Basel, more than 70 historians research the history of Basel from the Celts to the present. The focus is on current and little-researched topics such as the industrial and commercial history of the 19th and 20th centuries or the history of migration. The city's history is not viewed in isolation, but is interwoven regionally and internationally at the economic, political and cultural levels. It is published in 10 printed volumes. In addition, selected historical aspects are presented in the form of data stories. To facilitate this process, the Stadt.Geschichte.Basel research data management team has developed a guide. It walks through the questions that led to the development, production and publication of the data story

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

The analysis of natural disasters in a timely manner often suffers from limited sensor data. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called “Volunteered Geographic Information (VGI)”. To save the analyst from manual inspection of all images posted online, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 results from 55% to 87% after 5 rounds of feedback

Predictive value of clinical and 18F-FDG-PET/CT derived imaging parameters in patients undergoing neoadjuvant chemoradiation for esophageal squamous cell carcinoma.

Aim of this study was to validate the prognostic impact of clinical parameters and baseline 18F-FDG-PET/CT derived textural features to predict histopathologic response and survival in patients with esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiation (nCRT) and surgery. Between 2005 and 2014, 38 ESCC were treated with nCRT and surgery. For all patients, the 18F-FDG-PET-derived parameters metabolic tumor volume (MTV), SUVmax, contrast and busyness were calculated for the primary tumor using a SUV-threshold of 3. The parameter uniformity was calculated using contrast-enhanced computed tomography. Based on histopathological response to nCRT, patients were classified as good responders (< 10% residual tumor) (R) or non-responders (≥ 10% residual tumor) (NR). Regression analyses were used to analyse the association of clinical parameters and imaging parameters with treatment response and overall survival (OS). Good response to nCRT was seen in 27 patients (71.1%) and non-response was seen in 11 patients (28.9%). Grading was the only parameter predicting response to nCRT (Odds Ratio (OR) = 0.188, 95% CI: 0.040-0.883; p = 0.034). No association with histopathologic treatment response was seen for any of the evaluated imaging parameters including SUVmax, MTV, busyness, contrast and uniformity. Using multivariate Cox-regression analysis, the heterogeneity parameters busyness (Hazard Ratio (HR) = 1.424, 95% CI: 1.044-1.943; p = 0.026) and contrast (HR = 6.678, 95% CI: 1.969-22.643; p = 0.002) were independently associated with OS, while no independent association with OS was seen for SUVmax and MTV. In patients with ESCC undergoing nCRT and surgery, baseline 18F-FDG-PET/CT derived parameters could not predict histopathologic response to nCRT. However, the PET/CT derived features busyness and contrast were independently associated with OS and should be further investigated

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Three-dimensional Geometric Models from Pictures

Denne masteroppgaven implementerer form deteksjon på bilder ved hjelp av programmet OpenCV og programmeringsspråket Python. OpenCV programmet har en god del hjelpefunksjoner, som gjør det enkelt gjenkjenne former på et bilde. Flere filter hjelper til med å separere former fra sin bakgrunn. I starten var det kun mulig å gjenkjenne veldig enkle omriss av former, som firkanter og femkanter. Per dags dato, ved hjelp av justering av de implementerte filtrene, er det mulig å gjenkjenne relativ komplekse former. Nivå av detalj kan selv velges av brukeren. Dersom en form har blitt gjenkjent, kan den bli portet over til programmet GeoMod som lever i et C++-miljø. Programmet GeoMod er et program laget av Profesor Sven Fjeldaas. Dette programmet har eksistert i mange år, og mange masterstudenter har jobbet med det tidligere. Programmet støtter tegning av figurer i en kameravisning. Former som ble gjenkjent i Python-miljøet blir lest inn i GeoMod programmet ved hjelp av tekst-filer. Når formen er lest fra fil, kan den modifiseres. En sentral del av masteroppgaven var å implementere en støtte for å gjenkjenne kuber i bilder og å gjøre disse kubene tre-dimensjonale i GeoMod kameravisningen. Dette ble gjort ved at ulike beregninger ble utført på nodene som ble funnet av Python programmet. En relativt nøyaktig modell av en kube kunne nå bli tegnet opp i tre dimensjoner og bli påvirket i kameravisningen. To metoder for å finne denne tredimensjonale modellen ble implementert. Ved siden av en generell metode, ble det implementert en metode som ble kalt for "Four-points" metode. Denne metoden tar kun utgangspunkt i fire noder som brukes til å beregne seg fram til en full kube. Fordelen med denne metoden var å minske sannsynligheten for feil med kanter, men en ulempe var at avstanden mellom enkelte noder kunne være litt lengre eller kortere enn på det originale bilde. En metode for å gi generelle former en dybde ble også implementert. Normalvektoren mellom tre punkter beregner retningen til dybden og gjør formen tredimensjonal

S8 - PVT Analysis for RRAM and STT-MRAM-based Logic Computation-in-Memory

Emerging non-volatile resistive memories like Spin- Transfer Torque Magnetic Random Access Memory (STTMRAM) and Resistive RAM (RRAM) are in the focus of today’s research. They offer promising alternative computing architectures such as computation-in-memory (CiM) to reduce the transfer overhead between CPU and memory, usually referred to as the memory wall, which is present in all von Neumann architectures. A multitude of architectures with CiM capabilities are based on these devices, due to their inherent resistive behavior and thus their ability to perform calculation directly within the memory, and thus without invoking the CPU at all. However, emerging memories are sensitive to Process, Voltage and Temperature (PVT) variations. This sensitivity has an even larger impact on CiM architectures. In this paper, we analyze and compare the impact of PVT variations on STT-MRAM and RRAM-based CiM architectures. We perform a sensitivity analysis to identify which parts of the CiM structure are most susceptible to PVT variations, for each technology. Based on these analyses, we recommend that STT-MRAM is used in highperformance CiM, while RRAM is used for edge CiM

Neoadjuvant versus definitive chemoradiation in patients with squamous cell carcinoma of the esophagus

Abstract Background Multimodal treatment with neoadjuvant chemoradiation followed by surgery (nCRT + S) is the treatment of choice for patients with locally advanced or node-positive esophageal squamous cell carcinoma (E-SCC). Those who are unsuitable or who decline surgery can be treated with definitive chemoradiation (dCRT). This study compares the oncologic outcome of nCRT + S and dCRT in E-SCC patients. Methods Between 2011 and 2017, 95 patients with E-SCC were scheduled for dCRT or nCRT+ S with IMRT at our department. Patients undergoing dCRT received at least 50 Gy and those undergoing nCRT + S received at least 41.4 Gy. All patients received simultaneous chemotherapy with either carboplatin and paclitaxel or cisplatin and 5-fluoruracil. We retrospectively compared baseline characteristics and oncologic outcome including overall survival (OS), progression-free survival (PFS) and site of failure between both treatment groups. Results Patients undergoing dCRT were less likely to have clinically suspected lymph node metastases (85% vs. 100%, p = 0.019) than patients undergoing nCRT + S and had more proximally located tumors (median distance from dental arch to cranial tumor border 20 cm vs. 26 cm, p < 0.001). After a median follow up of 25.6 months for surviving patients, no significant differences for OS and PFS were noticed comparing nCRT + S and dCRT. However, the rate of local tumor recurrence was significantly higher in patients treated with dCRT than in those treated with nCRT + S (38% vs. 10%, p = 0.002). Within a multivariate Cox regression model, age, tumor location, and tumor grading were the only independent parameters affecting OS and PFS. In addition to that, proximal tumor location was the only parameter independently associated with an increased risk for local treatment failure. Conclusion In E-SCC patients treated with either dCRT or nCRT + S, a higher rate of local tumor recurrence was seen in patients treated with dCRT than in patients treated with nCRT + S. There was at least a trend towards an improved OS and PFS in patients undergoing nCRT + S. However, this should be interpreted with caution, because proximal tumor location was the only parameter independently affecting the risk of local tumor recurrence