A new target preparation facility for high precision AMS measurements and strategies for efficient 14CO2 sampling

The aim of this thesis was to allow for the processing of large-scale atmospheric 14CO2 samples into graphite targets for high-precision analysis on an accelerator mass spectrometer (AMS) and to further develop the sampling itself, which takes place throughout Europe within the Integrated Carbon Observation System (ICOS) network. For the first part, a largely automated Extraction and Graphitisation Line (EGL) was developed at the ICOS Central Radiocarbon Laboratory(CRL); the construction at the institute workshop was guided and process behaviour and target quality was characterised. Process fractionation in δ13C from the whole-air sample flask to the graphite target cannot be distinguished from zero with (0.04 ± 0.09)‰. The deviation from the absolute canonical Δ14C scale was determined to (0.7 ± 0.5)‰. It was shown that the reproducibility of Δ14C results from processed air samples is at ±1.9‰ or below for the final graphitisation parameters. Compatibility tests provided a deviation of the results of samples processed with EGL and analysed by the AMS at the Curt-Engelhorn-Centre Archaeometry (CEZ) from the results of the CRL Low-Level Counting (LLC) laboratory of (2.2 ± 0.9)‰. The reason for the deviation is currently unknown. For the further development of 14CO2 sampling, a new trajectory-triggered strategy was simulated in an atmospheric forward modelling system. It was shown that weak fossil CO2 signals from emission hotspots at four German ICOS stations can be amplified by a factor of up to 7, while the signal background is estimated with parallely taken samples

Weiterbildungsstatistik im Verbund: Ergebnisse für das Berichtsjahr 2018

Das Deutsche Institut für Erwachsenenbildung erhebt jedes Jahr die Daten zu öffentlich finanzierten Weiterbildungsangeboten in Deutschland. Die Statistiken enthalten aktuelle Informationen zu institutionellen Merkmalen, zu Personal und Finanzen, zum Leistungsspektrum durchgeführter Veranstaltungen und umfangreiche Daten zu den Teilnehmenden. Kooperationspartner:innen der Statistik sind der Bundesarbeitskreis Arbeit und Leben (BAK AL), die Deutsche Evangelische Arbeitsgemeinschaft für Erwachsenenbildung (DEAE), die Katholische Bundesarbeitsgemeinschaft Erwachsenenbildung (KEB) sowie der Deutsche Volkshochschul-Verband e.V. (DVV)

Weiterbildungsstatistik im Verbund

Die Langzeitstatistik präsentiert Daten zu öffentlich finanzierten Weiterbildungsangeboten in Deutschland, die jährlich vom Deutschen Institut für Erwachsenenbildung (DIE) erhoben und ausgewertet werden. Die Statistiken informieren über die Entwicklung in Institutionen, bei Personal und Finanzen, zum Leistungsspektrum durchgeführter Veranstaltungen sowie zu den Teilnehmenden. Die Statistik wird in Kooperation mit dem Bundesarbeitskreis Arbeit und Leben (BAK AL), der Deutschen Evangelischen Arbeitsgemeinschaft für Erwachsenenbildung (DEAE), der Katholischen Bundesarbeitsgemeinschaft Erwachsenenbildung (KEB) sowie dem Deutschen Volkshochschul-Verband e.V. (DVV) erstellt

Weiterbildungsstatistik im Verbund 2017 - Kompakt

Das Deutsche Institut für Erwachsenenbildung erhebt jedes Jahr die Daten zu öffentlich finanzierten Weiterbildungsangeboten in Deutschland. Die Statistiken enthalten aktuelle Informationen zu institutionellen Merkmalen, zu Personal und Finanzen, zum Leistungsspektrum durchgeführter Veranstaltungen und umfangreiche Daten zu den Teilnehmenden. Kooperationspartner:innen der Statistik sind der Bundesarbeitskreis Arbeit und Leben (BAK AL), die Deutsche Evangelische Arbeitsgemeinschaft für Erwachsenenbildung (DEAE), die Katholische Bundesarbeitsgemeinschaft Erwachsenenbildung (KEB) sowie der Deutsche Volkshochschul-Verband e.V. (DVV)

Transdisciplinary Sustainability Research and Citizen Science: Options for Mutual Learning.

Both citizen science and transdisciplinary sustainability research involve non-academic actors in the production of knowledge while seeking to contribute to sustainability transitions, albeit in different ways. From citizen science, transdisciplinary researchers can learn about the multiple ways of engaging knowledge holders, and producing and sharing knowledge

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Nuclear Reprogramming: Kinetics of Cell Cycle and Metabolic Progression as Determinants of Success

Establishment of totipotency after somatic cell nuclear transfer (NT) requires not only reprogramming of gene expression, but also conversion of the cell cycle from quiescence to the precisely timed sequence of embryonic cleavage. Inadequate adaptation of the somatic nucleus to the embryonic cell cycle regime may lay the foundation for NT embryo failure and their reported lower cell counts. We combined bright field and fluorescence imaging of histone H2b-GFP expressing mouse embryos, to record cell divisions up to the blastocyst stage. This allowed us to quantitatively analyze cleavage kinetics of cloned embryos and revealed an extended and inconstant duration of the second and third cell cycles compared to fertilized controls generated by intracytoplasmic sperm injection (ICSI). Compared to fertilized embryos, slow and fast cleaving NT embryos presented similar rates of errors in M phase, but were considerably less tolerant to mitotic errors and underwent cleavage arrest. Although NT embryos vary substantially in their speed of cell cycle progression, transcriptome analysis did not detect systematic differences between fast and slow NT embryos. Profiling of amino acid turnover during pre-implantation development revealed that NT embryos consume lower amounts of amino acids, in particular arginine, than fertilized embryos until morula stage. An increased arginine supplementation enhanced development to blastocyst and increased embryo cell numbers. We conclude that a cell cycle delay, which is independent of pluripotency marker reactivation, and metabolic restraints reduce cell counts of NT embryos and impede their development

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14