Civil Rights Bill Does Not Require Open Occupancy

The highly controversial Civil Rights Bill, a complex piece of legislation currently before the Senate, seems to be the object of some public misunderstanding

Efficient City Farming

Urbaner Pflanzenbau, insbesondere erwerbsmäßig betriebener Land- und Gartenbau in Städten hat in den vergangenen Jahren weltweit an Bedeutung gewonnen. In den Megacities der sogenannten Entwicklungsländer ist es aber auch eine Möglichkeit der Einkommens- und Nahrungsmittelsicherung für die ärmeren Teile der Bevölkerung. In den Städten der Industrieländer sind Ursachen für „City Farming“ unter anderem, ein gewachsenes Bewusstsein für Nachhaltigkeit, kurze Wege bei der Produktion und Distribution von Nahrungsmitteln sowie eine neue Art der Begrünung von Städten und der Beschäftigung mit der Natur. Es sind ganz unterschiedliche Gründe, weshalb sich in den letzten Jahren immer mehr Menschen mit den Themengebieten urbane Landwirtschaft, urbaner Gartenbau und Hydroponik beschäftigen. Zu diesen Gründen zählen neben dem aufkommenden Klimawandel, die Ressourcenverknappung, Fragen der globalen Gerechtigkeit und des globalen Konsums. Zu den unterschiedlichen Formen und Effekten des städtischem Land- und Gartenbaus gibt es allerdings erst wenig wissenschaftlich dokumentiertes Wissen. Um diesem Defizit Abhilfe zu verschaffen, wurde gemeinsam mit heranwachsenden WissenschaftlerInnen die Fragestellung nach der Gesundheit urban produzierter Nahrungsmittel (pflanzlich) geklärt. Zu diesem Zweck wurden an verschiedenen Standorten (urban, periurban und auf dem Land) Hochbeete errichtet, welche mit jeweils denselben Pflanzen bestückt wurden. Um das Ertragspotential mit dem System der ECF Containerfarm vergleichen zu können, wurden in der Hydroponikanlage dieselben Pflanzen angebaut. Zwischen dem System der Hydroponik und den Hochbeeten wurden die Erträge verglichen, wobei der Vergleich der Hochbeete untereinander anhand der eingetragenen Schwermetalle gezogen wurde

Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models

Introduction: A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture’s lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability and scalability of the DL models. Methods: In this work, we propose a technique for handling such varied numbers of EEG channels by splitting the EEG montages into distinct regions and merge the channels within the same region to a region representation. The solution is termed Region Based Pooling (RBP). The procedure of splitting the montage into regions is performed repeatedly with different region configurations, to minimize potential loss of information. As RBP maps a varied number of EEG channels to a fixed number of region representations, both current and future DL architectures may apply RBP with ease. To demonstrate and evaluate the adequacy of RBP to handle a varied number of EEG channels, sex classification based solely on EEG was used as a test example. The DL models were trained on 129 channels, and tested on 32, 65, and 129-channels versions of the data using the same channel positions scheme. The baselines for comparison were zero-filling the missing channels and applying spherical spline interpolation. The performances were estimated using 5-fold cross validation. Results: For the 32-channel system version, the mean AUC values across the folds were: RBP (93.34%), spherical spline interpolation (93.36%), and zero-filling (76.82%). Similarly, on the 65-channel system version, the performances were: RBP (93.66%), spherical spline interpolation (93.50%), and zero-filling (85.58%). Finally, the 129-channel system version produced the following results: RBP (94.68%), spherical spline interpolation (93.86%), and zero-filling (91.92%). Conclusion: In conclusion, RBP obtained similar results to spherical spline interpolation, and superior results to zero-filling. We encourage further research and development of DL models in the cross-dataset setting, including the use of methods such as RBP and spherical spline interpolation to handle a varied number of EEG channels.publishedVersio

The predictive value of depression in the years after heart transplantation for mortality during long-term follow-up

Objective Current understanding of the prognostic impact of depression on mortality after heart transplantation (HTx) is limited. We examined whether depression after HTx is a predictor of mortality during extended follow-up. Subsequently, we explored whether different symptom dimensions of depression could be identified and whether they were differentially associated with mortality. Methods Survival analyses were performed in a sample of 141 HTx recipients assessed for depression, measured by self-report of depressive symptoms (Beck Depression Inventory – version 1A [BDI-1A]), at median 5.0 years after HTx, and followed thereafter for survival status for up to 18.6 years. We used uni- and multivariate Cox proportional hazard models to examine the association of clinically significant depression (BDI-1A total score ≥10), as well as the cognitive-affective and the somatic subscales of the BDI-1A (resulting from principal component analysis) with mortality. In the multivariate analyses, we adjusted for relevant sociodemographic and clinical variables. Results Clinically significant depression was a significant predictor of mortality (hazard ratio = 2.088; 95% confidence interval = 1.366–3.192; p = .001). Clinically significant depression also was an independent predictor of mortality in the multivariate analysis (hazard ratio = 1.982; 95% confidence interval = 1.220–3.217; p = .006). The somatic subscale, but not the cognitive-affective subscale, was significantly associated with increased mortality in univariate analyses, whereas neither of the two subscales was an independent predictor of mortality in the multivariate analysis. Conclusions Depression measured by self-report after HTx is associated with increased mortality during extended follow-up. Clinical utility and predictive validity of specific depression components require further study.acceptedVersio

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual’s risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AIMind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind’s innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contribution.publishedVersio