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Cell-level systems biology model to study inflammatory bowel diseases and their treatment options
(2023)
To help understand the complex and therapeutically challenging inflammatory bowel diseases (IBDs), we developed a systems biology model of the intestinal immune system that is able to describe main aspects of IBD and different treatment modalities thereof. The model, including key cell types and processes of the mucosal immune response, compiles a large amount of isolated experimental findings from literature into a larger context and allows for simulations of different inflammation scenarios based on the underlying data and assumptions. In the context of a large and diverse virtual IBD population, we characterized the patients based on their phenotype (in contrast to healthy individuals, they developed persistent inflammation after a trigger event) rather than on a priori assumptions on parameter differences to a healthy individual. This allowed to reproduce the enormous diversity of predispositions known to lead to IBD. Analyzing different treatment effects, the model provides insight into characteristics of individual drug therapy. We illustrate for anti-TNF-alpha therapy, how the model can be used (i) to decide for alternative treatments with best prospects in the case of nonresponse, and (ii) to identify promising combination therapies with other available treatment options.
Der Band widmet sich im Schwerpunkt aktuellen Fragestellungen an der Schnittmenge vom allgemeinen Teil des Strafrechts und der spezifischen Materie des Wirtschaftsstrafrechts. Er enthält die Vorträge, die Hans Achenbach, Jörg Eisele, Christian Schröder und Detlev Sternberg-Lieben im Juli 2022 auf dem Kolloquium zu dem Thema „Der Allgemeine Teil des Wirtschaftsstrafrechts“ zu Ehren von Wolfgang Mitsch und Uwe Hellmann gehalten haben. Sie werden ergänzt um weitere Beiträge aus dem wissenschaftlichen Umfeld der zu Ehrenden.
The long term relationship between Medicaid expansion and adult life-threatening chronic conditions
(2023)
We test whether the expansions of children's Medicaid eligibility in the 1980s–1990s resulted in long-term health benefits in terms of severe chronic conditions. Still relatively rare in the field, we use prospective individual-level panel data from the Panel Study of Income Dynamics (PSID) along with the higher quality income measures from the Cross-National Equivalent File (adjusting for taxes, transfers and household size). We observe severe chronic conditions (high blood pressure/heart disease, cancer, diabetes, or lung disease) at ages 30–56 (average age 43.1) for 4670 respondents who were also prospectively observed during childhood (i.e., at ages 0–17). Our analysis exploits within-region temporal variation in childhood Medicaid eligibility and adjusts for state- and individual-level controls. We uniquely concentrate attention on adjusting for childhood income. A standard deviation greater childhood Medicaid eligibility significantly reduces the probability of severe chronic conditions in adulthood by 0.05 to 0.12 (16%–37.5% reduction from mean 0.32). Across the range of observed childhood Medicaid eligibility, the probability is approximately cut in half. Greater childhood Medicaid eligibility also substantially reduces childhood income disparities in severe chronic conditions. At higher levels of childhood Medicaid eligibility, we find no significant childhood income disparities in adult severe chronic conditions.
Fetal alcohol-spectrum disorder (FASD) is underdiagnosed and often misdiagnosed as attention-deficit/hyperactivity disorder (ADHD). Here, we develop a screening tool for FASD in youth with ADHD symptoms. To develop the prediction model, medical record data from a German University outpatient unit are assessed including 275 patients aged 0-19 years old with FASD with or without ADHD and 170 patients with ADHD without FASD aged 0-19 years old. We train 6 machine learning models based on 13 selected variables and evaluate their performance. Random forest models yield the best prediction models with a cross-validated AUC of 0.92 (95% confidence interval [0.84, 0.99]). Follow-up analyses indicate that a random forest model with 6 variables - body length and head circumference at birth, IQ, socially intrusive behaviour, poor memory and sleep disturbance - yields equivalent predictive accuracy. We implement the prediction model in a web-based app called FASDetect - a user-friendly, clinically scalable FASD risk calculator that is freely available at https://fasdetect.dhc-lab.hpi.de.
Purpose
Due to the increasing application of genome analysis and interpretation in medical disciplines, professionals require adequate education. Here, we present the implementation of personal genotyping as an educational tool in two genomics courses targeting Digital Health students at the Hasso Plattner Institute (HPI) and medical students at the Technical University of Munich (TUM).
Methods
We compared and evaluated the courses and the students ' perceptions on the course setup using questionnaires.
Results
During the course, students changed their attitudes towards genotyping (HPI: 79% [15 of 19], TUM: 47% [25 of 53]). Predominantly, students became more critical of personal genotyping (HPI: 73% [11 of 15], TUM: 72% [18 of 25]) and most students stated that genetic analyses should not be allowed without genetic counseling (HPI: 79% [15 of 19], TUM: 70% [37 of 53]). Students found the personal genotyping component useful (HPI: 89% [17 of 19], TUM: 92% [49 of 53]) and recommended its inclusion in future courses (HPI: 95% [18 of 19], TUM: 98% [52 of 53]).
Conclusion
Students perceived the personal genotyping component as valuable in the described genomics courses. The implementation described here can serve as an example for future courses in Europe.
In real-world scene perception, human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here, we propose a scan-path model for both fixation positions and fixation durations, which include influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan-path statistics across individual observers.
Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is CSV. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard CSV formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic lpollutionz process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.
The Gutenberg-Richter (GR) and the Omori-Utsu (OU) law describe the earthquakes' energy release and temporal clustering and are thus of great importance for seismic hazard assessment. Motivated by experimental results, which indicate stress-dependent parameters, we consider a combined global data set of 127 main shock-aftershock sequences and perform a systematic study of the relationship between main shock-induced stress changes and associated seismicity patterns. For this purpose, we calculate space-dependent Coulomb Stress (& UDelta;CFS) and alternative receiver-independent stress metrics in the surrounding of the main shocks. Our results indicate a clear positive correlation between the GR b-value and the induced stress, contrasting expectations from laboratory experiments and suggesting a crucial role of structural heterogeneity and strength variations. Furthermore, we demonstrate that the aftershock productivity increases nonlinearly with stress, while the OU parameters c and p systematically decrease for increasing stress changes. Our partly unexpected findings can have an important impact on future estimations of the aftershock hazard.
Core-collapse supernova remnants are structures of the interstellar medium (ISM) left behind the explosive death of most massive stars ( ?40 M-?). Since they result in the expansion of the supernova shock wave into the gaseous environment shaped by the star's wind history, their morphology constitutes an insight into the past evolution of their progenitor star. Particularly, fast-mo ving massiv e stars can produce asymmetric core-collapse superno va remnants. We inv estigate the mixing of materials in core-collapse supernova remnants generated by a moving massive 35 M-? star, in a magnetized ISM. Stellar rotation and the wind magnetic field are time-dependently included into the models which follow the entire evolution of the stellar surroundings from the zero-age main-sequence to 80 kyr after the supernova explosion. It is found that very little main-sequence material is present in remnants from moving stars, that the Wolf-Rayet wind mixes very efficiently within the 10 kyr after the explosion, while the red supergiant material is still unmixed by 30 per cent within 50 kyr after the supernova. Our results indicate that the faster the stellar motion, the more complex the internal organization of the supernova remnant and the more ef fecti ve the mixing of ejecta therein. In contrast, the mixing of stellar wind material is only weakly affected by progenitor motion, if at all.