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Neste ensaio, demonstra-se que qualquer grupo de difeomorfismos que preserve orientação e aja na 2-esfera, estendendo propriamente o grupo conforme das transformações de Möbius, precisa ser ao menos 4 transitivo ou, mais precisamente, 4-transitivo por arcos. Isso significa que quaisquer duas listas ordenadas de quatro pontos distintos podem ser aplicadas uma sobre a outra por alguma transformação do grupo, isotópica à identidade. Argumenta-se, também, que tais grupos apresentam sempre um elemento de entropia topológica positiva, para o qual é dada uma descrição como isotópico a um homeomorfismo pseudo-Anosov relativo da esfera 4-perfurada. Além disso, apresenta-se uma caracterização elementar em termos de transitividade das transformações de Möbius dentro do grupo total de difeomorfismos. It is proven in this essay that any group of orientation preserving diffeomorphisms acting on the 2-sphere and properly extending the conformal group of Möbius transformations must be at least 4-transitive or, more precisely, arc 4-transitive. This means that any two ordered lists of four distinct points can be mapped one onto the other via a transformation in the group, isotopic to the identity. In addition, it is shown that any such group must always contain an element of positive topological entropy, for which a description as isotopic to a relative pseudo-Anosov homeomorphism of the 4-punctured sphere is provided. Furthermore, an elementary characterisation of the Möbius transformations within the full group of sphere diffeomorphisms is given in terms of transitivity.

This page details programming, registration and accessibility options, and volunteer efforts for the Open Science Special Interest Group's Open Science Room, a conference within a conference that is part of the Organization for Human Brain Mapping'd (OHBM) annual meeting.

Self-regulation, the ability to guide behavior according to one’s goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test–retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures’ construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks.

This graphic was created to share on Twitter to encourage people to submit compelling stories about open science practices in research to the TOPS team. This graphic was tweeted out on Dr. Chelle Gentemann's Twitter account on August 23, 2022. Link to tweet .

Microbes typically form highly complex and diverse communities that account for a significant portion of life's genetic diversity. Analysis of living systems, e.g. bacterial or cell population, plays a significant role in detecting and identifying pathogens, testing antibiotic susceptibility, and the fundamental research of population diversity and evolution. This work focuses on the analysis of bacterial communities using droplets based millifluidics. To monitor the bacteria growth, we designed an optofluidic system, combining the encapsulation of bacteria in numerous emulsion droplets to monitor their long-term behavior and relationship in a co-culture environment using fluorescent signals. In the first part of this work, we co-encapsulated and cultured two isogenic strains of Escherichia coli (E. coli) in numerous emulsion droplets to reveal their competition and cooperation relationship. Since two strains of E. coli express blue and yellow fluorescent proteins (BFP and YFP, respectively), we quantified their growth by integrating a fluorescence detection system. We analyzed the following parameters: doubling time, population yield, final biomass ratio, correlation map of doubling time and competition coefficient to characterize and compare the bacterial growth kinetics and behavior in mono and co-cultures. In addition, the experimental observations were compared with the predictions from a single growth model. Finally, we employed the millifluidic device to verify the appearance of cross-protection between antibiotic-sensitive bacteria and antibiotic-resistant bacteria. It is one of the mechanisms by which different bacteria, sharing the same environment, protect each other to survive in the presence of antibiotics. For this purpose, the E.coli YFP strain was chosen as an antibiotic-sensitive group. Simultaneously, the E.coli BFP strain with β-lactam and its mutations were selected as resistant strains. Combining the millifluidic droplet reactor method with other detection strategies, e.g. fluorescence microscopy, fluorescence flow cytometry, and plate reader, we proved the appearance of cross-protection by detecting the filamentary cells, the fluorescence of cell-free media, viable cell rates, cell shape and size, as well as β-lactamase activity. All these results obtained by millifluidic devices proved that this strategy could be used in a high-throughput bacterial coexistence study. In addition, the research of these general fields, such as bacterial community and antibiotic impact, can help us to reveal the interaction between microbial species and determine the right dose of antibiotics to inhibit bacterial growth in a co-existent environment efficiently.

Epithelial ovarian cancer (EOC) is a lethal gynecologic malignancy, with poor long-term survival. Early symptoms lack specificity, and currently there are no sufficient early detection strategies to identify this disease before it progresses to an advanced stage. In Germany the 5-year relative survival is approximately 43%. Ovarian cancer is a heterogeneous disease with disease histotypes originating from different cells and tissues of origin, and with different somatic mutations, progression profiles, and treatment responses and there is a need for population-based ovarian cancer survival analyses stratified by disease histology and stage at diagnosis. The most recent German cancer registry study was completed in 2013 by Chen et al. A updated analysis in the context of current clinical practice and contemporary classification of disease subtype is needed. Through the utilization of data from the ZfKD, we will be able to provide an updated characterization of ovarian cancer survival by histotype and stage in Germany, drawing upon the pooling of data from national registries. The study population will comprise of women with a diagnosis of invasive epithelial ovarian (ICD-O-3: C569), fallopian tube (C570) or primary peritoneal (C480, C481, C482, C488) in Germany from 1997 - present (most recent case data requested). Subjects will be followed until the earliest of: date of death, date of last follow-up, or December 31, 2022 (or last date available for survival outcomes). The major objectives of this project are to characterize survival over time by histotype and stage, and to assess the overall and relative 1-, 3-, and 5-year survival following an ovarian cancer diagnosis, with analyses stratified by disease stage (FIGO where available) and histotype. Please see the attached statistical methods section for a more detailed description of the analysis plan. Epitheliales Ovarialkarzinom (EOC) ist eine tödlich verlaufende gynäkologische Krebsart mit einer schlechten Langzeitüberlebensrate. Die Frühsymptome sind nicht spezifisch, und derzeit gibt es keine ausreichenden Früherkennungsstrategien, um diese Krankheit zu identifizieren, bevor sie ein fortgeschrittenes Stadium erreicht hat. In Deutschland liegt die relative 5-Jahres-Überlebensrate bei etwa 43 %. Das Ovarialkarzinom ist eine heterogene Erkrankung mit verschiedenen Histotypen, die aus unterschiedlichen Zellen und Ursprungsgeweben stammen, mit unterschiedlichen somatischen Mutationen, Verlaufsprofilen und Behandlungsreaktionen. Daher besteht ein Bedarf an bevölkerungsbasierten Überlebensanalysen des Ovarialkarzinoms, die nach der Histologie der Erkrankung und dem Stadium bei der Diagnose stratifiziert sind. Die jüngste Studie des deutschen Krebs Registers wurde 2013 von Chen et al. abgeschlossen. Eine aktualisierte Analyse im Kontext der aktuellen klinischen Praxis und einer Klassifizierung des Krankheitssubtyps ist erforderlich. Indem wir uns auf die Zusammenführung von Daten aus nationalen Registern stützen, werden wir durch die Nutzung der Daten des ZfKD in der Lage sein, eine aktualisierte Charakterisierung des Überlebens von Ovarialkarzinomen nach Histotyp und Stadium in Deutschland zu liefern.

On Thursday 12th March 2020, the Taoiseach announced a number of significant measures designed to contain the spread of Coronavirus (COVID-19) and lessen the risk of infection in the population, especially to vulnerable groups. These measures have placed significant restrictions on the normal operation of business and society. Many accommodations have had to be made to ensure the operation of critical services during this time, including both front-line health services and other services which support the continued functioning of society and business. Planning authorities have responded through implementation of business continuity plans and are ensuring, insofar as possible, and having regard to public health advice, that necessary provision is made to allow for the appropriate functioning of the planning service. This is an evolving situation, which will be kept under review by the Department through engagement with the City and County Management Association (CCMA), Local Government Management Agency (LGMA), An Bord Pleanála, the Office of the Planning Regulator and other Government Departments. This planning guidance addresses potential uncertainty which has arisen where there is an interaction between the public health measures that have been implemented, and operational restrictions imposed by planning condition, for example where essential retail operators, in an effort to ensure that food and other supplies can be delivered in a safe and timely manner, may be subject to planning conditions that restrict delivery times.

Late-stage functionalization of natural products offers an elegant route to create novel entities in a relevant biological target space. In this context, enzymes capable of halogenating sp3 carbons with high stereo- and regiocontrol under benign conditions have attracted particular attention. Enabled by a combination of smart library design and machine learning, we engineer the iron/α-ketoglutarate dependent halogenase WelO5* for the late-stage functionalization of the complex and chemically difficult to derivatize macrolides soraphen A and C, potent anti-fungal agents. While the wild type enzyme WelO5* does not accept the macrolide substrates, our engineering strategy leads to active halogenase variants and improves upon their apparent kcat and total turnover number by more than 90-fold and 300-fold, respectively. Notably, our machine-learning guided engineering approach is capable of predicting more active variants and allows us to switch the regio-selectivity of the halogenases facilitating the targeted analysis of the derivatized macrolides’ structure-function activity in biological assays.

Diffusion Models have achieved state-of-the-art results in image generating tasks, yet face different challenges when used in different domains. We first give a brief overview of the Diffusion Models architecture. Then, we present a new model and architecture called SVGFusion that applies the principles of Diffusion Models to generate Vector Graphics. Vector Graphics have a complex structure and are vastly different than pixel images, and thus the main challenge when working with Vector Graphics is how to represent their complex structure in a way that a Diffusion Model can effectively process. We will explain this and the further challenges that we encountered during the process and how we successfully addressed some of them. We demonstrate the effectiveness of our approach by training a sample model on a decently sized dataset as well as running valuable experiments. Furthermore, we offer useful insights, recommendations and code to researchers who wish to further explore this topic.