Spatial turn / Raumforschung

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Altered medial frontal feedback learning signals in anorexia nervosa

Background In their relentless pursuit of thinness, individuals with anorexia nervosa (AN) engage in maladaptive behaviors (restrictive food choices, over-exercising) which may originate in altered decision-making and learning. Methods In this fMRI study we employed computational modelling to elucidate the neural correlates of feedback learning and value-based decision making in 36 female AN patients and 36 age-matched healthy volunteers (12-24 years). Participants performed a decision task which required adaptation to changing reward contingencies. Data were analyzed within a hierarchical Gaussian filter model, which captures inter-individual variability in learning under uncertainty. Results Behaviorally, patients displayed an increased learning rate specifically after punishments. At the neural level, hemodynamic correlates for learning rate, expected value and prediction error did not differ between the groups. However, activity in the posterior medial frontal cortex was elevated in AN following punishment. Conclusion Our findings suggest that the neural underpinning of feedback learning is selectively altered for punishment in AN

How Can Academia Help Industry Reduce the Footprint of Chemicals Manufacture?

Industrial representatives from the Swiss chemistry ecosystem met to formulate unmet needs in the field of sustainability and share the content of the exchange. The aim is to spark inspiration and trigger ambitious and pre-competitive projects collectively at the interface of the academic and industrial worlds, with the hope to profoundly change the current practices and provide an answer to some of the most urgent environmental challenges.  

222 Circulating pentraxin3-specific B cells are decreased in lupus nephritis

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Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. Methods: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. Results: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). Conclusion: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery

Testing combinatorial transcription factor activities using multidimensional recruitment assays

Höhere eukaryotische Organismen bestehen aus einer Vielzahl von verschiedenen Zelltypen. All diese Zelltypen besitzen die gleiche Erbinformation und entstehen während der Embryonalentwicklung aus einer einzigen Zelle. Eine der interessantesten Fragen der Biologie zur Zeit ist, wie es möglich ist, dass eine so große Komplexität zustande kommen kann, obwohl jede Zelle das gleiche Genom innewohnt. Dies wird zu einem großen Teil durch die transkriptionelle Regulation von Genen erreicht. Enhancer sind cis-regulatorische Sequenzen die die korrekte zeitliche und räumliche Expression von Genen sicherstellen. Transkriptionsfaktoren binden an kurze Sequenzmotive im Enhancer und lesen somit die regulatorische Information die dort kodiert ist. Transkriptionsfaktoren sind essenziell für Enhancer Funktion. Mutiert man die Bindestellen für einen Transkriptionsfaktor in einem Enhancer so ist dieser nicht mehr in der Lage seine regulatorischen Funktionen korrekt auszuführen. Umgekehrt verliert der Enhancer auch seine Aktivität wenn der Transkriptionsfaktor nicht vorhanden ist. Dies deutet darauf hin, dass einzelne Transkriptionsfaktoren nicht ausreichend sind um einen Enhancer zu aktivieren, sondern dass ein Kollektiv von Transkritionsfaktoren zusammenkommen muss um die korrekte Aktivität sicherzustellen. In dieser Arbeit beschreiben wir einen Assay der es uns erlaubt mehrere Transkriptionsfaktoren an einen transkriptionellen Reporter zu rekrutieren, indem diese an verschiedene DNA Bindedomänen fusioniert warden die an distinkte DNASequenzen binden. Damit ist es uns möglich deren cooperative Aktivität zu untersuchen. Wir verwendeten kontext-spezifische Transkriptionsfaktoren um gezielt nach deren Partnerfaktoren zu suchen. Wir testeten 476 Drosophila melanogaster Transkriptionsfaktoren und fanden 42 cooperative Paare. Diese Paare bestätigten sich in zwei Kontrollexperimenten. Keines dieser Paare ist jedoch ausreichend für transkriptionelle Aktivierung wenn man sie aus dem Enhancer Kontext heraußnimmt, und in einem synthetischen Kontext rekrutieret der nur die Erkennungssequenzen der DNA Bindedomänen enthält. Aus diesem Grund testeten wir Tripletts von Transkriptionsfaktoren, sowohl in einem Enhnacer Kontext als auch im synthetischen Kontext, und fanden cooperative Transkriptionsfaktoren in beiden Experimenten. Die 5 Kooperativität der Transkriptionsfaktoren wurde verstärkt wenn wir die natürliche Anordnung der Motive beibehielten.The temporal and spatial expression of genes is regulated by transcription factors (TFs) that bind to enhancer regions in a combinatorial fashion. Even though we know the identity of many TFs and the genes they regulate, it is unclear how exactly TFs control enhancer activity and gene transcription. Here we probe the functional interdependencies of TFs and determine combinations of TFs that show synergistic activation. We co-recruit defined sets of TFs via different DNA-binding-domains (DBDs) to different positions within enhancer contexts. This multi-dimensional enhancer complementation assay revealed obligate combinatorial TFs and enabled the definition of pairs of TFs that strongly activate transcription when co-bound, even though each TF alone is inactive. Furthermore, we demonstrate that, even though both partner TFs are necessary for transcriptional activation, these cooperative TF pairs are not sufficient to reconstitute enhancer activity when co-recruited outside enhancer contexts. In contrast, enhancer function and reporter transcription can be achieved by recruiting three TFs simultaneously and is enhanced when they are recruited in an arrangement that reflects the binding site arrangement of an endogenous enhancer. The demonstration that TFs control transcription via combinations of (biochemically) distinct regulatory functions has important implications for our understanding of combinatorial enhancer control and gene expression (Reiter et al., 2016)