Zwei Unbekannte Briefe Thomas Manns

Paper by Hans Eichner of the University of Toronto

Comment on "Scaling of atmosphere and ocean temperature correlations in observations and climate models"

In a recent letter [K. Fraedrich and R. Blender, Phys. Rev. Lett. 90, 108501 (2003)], Fraedrich and Blender studied the scaling of atmosphere and ocean temperature. They analyzed the fluctuation functions F(s) ~ s^alpha of monthly temperature records (mostly from grid data) by using the detrended fluctuation analysis (DFA2) and claim that the scaling exponent alpha over the inner continents is equal to 0.5, being characteristic of uncorrelated random sequences. Here we show that this statement is (i) not supported by their own analysis and (ii) disagrees with the analysis of the daily observational data from which the grid monthly data have been derived. We conclude that also for the inner continents, the exponent is between 0.6 and 0.7, similar as for the coastline-stations.Comment: 1 page with 2 figure

Gene Expression Signatures of a Preclinical Mouse Model during Colorectal Cancer Progression under Low-Dose Metronomic Chemotherapy

Simple Summary Colorectal cancer is one of the most frequent types of cancer world-wide, leading to over 500,000 cancer-related deaths each year. Although many primary colorectal cancer patients can be cured by surgery, up to 60% will develop metastases. Chemotherapeutic strategies are well-established, but finally often lead to chemo-resistance and tumor relapse. A specific chemotherapeutic approach is low dose metronomic (LDM) therapy, which is based on a constant administration of low doses of a chemotherapeutic compound instead of high-dose pulses, which are often a huge burden for patients and also may induce rapid resistance. However, the molecular mechanism of LDM chemotherapy is not fully understood. Our study therefore aims at identifying gene signatures of colorectal cancer progression under LDM chemotherapy which eventually provides new potential biomarkers for therapeutic interventions. Abstract Understanding the molecular signatures of colorectal cancer progression under chemotherapeutic treatment will be crucial for the success of future therapy improvements. Here, we used a xenograft-based mouse model to investigate, how whole transcriptome signatures change during metastatic colorectal cancer progression and how such signatures are affected by LDM chemotherapy using RNA sequencing. We characterized mRNAs as well as non-coding RNAs such as microRNAs, long non-coding RNAs and circular RNAs in colorectal-cancer bearing mice with or without LDM chemotherapy. Furthermore, we found that circZNF609 functions as oncogene, since over-expression studies lead to an increased tumor growth while specific knock down results in smaller tumors. Our data represent novel insights into the relevance of non-coding and circRNAs in colorectal cancer and provide a comprehensive resource of gene expression changes in primary tumors and metastases. In addition, we present candidate genes that could be important modulators for successful LDM chemotherapy

Corona Health -- A Study- and Sensor-based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July, 2020) in 8 languages and attracted 7,290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures

The influenza pandemic preparedness planning tool InfluSim

BACKGROUND: Planning public health responses against pandemic influenza relies on predictive models by which the impact of different intervention strategies can be evaluated. Research has to date rather focused on producing predictions for certain localities or under specific conditions, than on designing a publicly available planning tool which can be applied by public health administrations. Here, we provide such a tool which is reproducible by an explicitly formulated structure and designed to operate with an optimal combination of the competing requirements of precision, realism and generality. RESULTS: InfluSim is a deterministic compartment model based on a system of over 1,000 differential equations which extend the classic SEIR model by clinical and demographic parameters relevant for pandemic preparedness planning. It allows for producing time courses and cumulative numbers of influenza cases, outpatient visits, applied antiviral treatment doses, hospitalizations, deaths and work days lost due to sickness, all of which may be associated with economic aspects. The software is programmed in Java, operates platform independent and can be executed on regular desktop computers. CONCLUSION: InfluSim is an online available software which efficiently assists public health planners in designing optimal interventions against pandemic influenza. It can reproduce the infection dynamics of pandemic influenza like complex computer simulations while offering at the same time reproducibility, higher computational performance and better operability