Showing papers by "University of Mannheim published in 2020"

The ATLAS Experiment at the CERN Large Hadron Collider

Abstract: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper. A brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.

Acceptability of App-Based Contact Tracing for COVID-19: Cross-Country Survey Study.

Abstract: Background: The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. Objective: The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. Methods: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. Results: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. Conclusions: Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

Abstract: The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pre-training. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pre-trained multilingual model to a new language. MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. Our code and adapters are available at this http URL

From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers

Abstract: Massively multilingual transformers (MMTs) pretrained via language modeling (e.g., mBERT, XLM-R) have become a default paradigm for zero-shot language transfer in NLP, offering unmatched transfer performance. Current evaluations, however, verify their efficacy in transfers (a) to languages with sufficiently large pretraining corpora, and (b) between close languages. In this work, we analyze the limitations of downstream language transfer with MMTs, showing that, much like cross-lingual word embeddings, they are substantially less effective in resource-lean scenarios and for distant languages. Our experiments, encompassing three lower-level tasks (POS tagging, dependency parsing, NER) and two high-level tasks (NLI, QA), empirically correlate transfer performance with linguistic proximity between source and target languages, but also with the size of target language corpora used in MMT pretraining. Most importantly, we demonstrate that the inexpensive few-shot transfer (i.e., additional fine-tuning on a few target-language instances) is surprisingly effective across the board, warranting more research efforts reaching beyond the limiting zero-shot conditions.

Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions

Abstract: Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.

Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering

Abstract: Models for computer vision are commonly defined either w.r.t. low-level concepts such as pixels that are to be grouped, or w.r.t. high-level concepts such as semantic objects that are to be detected and tracked. Combining bottom-up grouping with top-down detection and tracking, although highly desirable, is a challenging problem. We state this joint problem as a co-clustering problem that is principled and tractable by existing algorithms. We demonstrate the effectiveness of this approach by combining bottom-up motion segmentation by grouping of point trajectories with high-level multiple object tracking by clustering of bounding boxes. We show that solving the joint problem is beneficial at the low-level, in terms of the FBMS59 motion segmentation benchmark, and at the high-level, in terms of the Multiple Object Tracking benchmarks MOT15, MOT16, and the MOT17 challenge, and is state-of-the-art in some metrics.

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

Abstract: The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer. However, due to limited model capacity, their transfer performance is the weakest exactly on such low-resource languages and languages unseen during pre-training. We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations. In addition, we introduce a novel invertible adapter architecture and a strong baseline method for adapting a pre-trained multilingual model to a new language. MAD-X outperforms the state of the art in cross lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning, and achieves competitive results on question answering. Our code and adapters are available at AdapterHub.ml.

You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings

Abstract: Knowledge graph embedding (KGE) models learn algebraic representations of the entities and relations in a knowledge graph. A vast number of KGE techniques for multi-relational link prediction has been proposed in the recent literature, often with state-of-the-art performance. These approaches differ along a number of dimensions, including different model architectures, different training strategies, and different approaches to hyperparameter optimization. In this paper, we take a step back and aim to summarize and quantify empirically the impact of each of these dimensions on model performance. We report on the results of an extensive experimental study with popular model architectures and training strategies across a wide range of hyperparameter settings. We found that when trained appropriately, the relative performance differences between various model architectures often shrinks and sometimes even reverses when compared to prior results. For example, RESCAL, one of the first KGE models, showed strong performance when trained with state-of-the-art techniques; it was competitive to or outperformed more recent architectures. We also found that good (and often superior to prior studies) model configurations can be found by exploring relatively few random samples from a large hyperparameter space. Our results suggest that many of the more advanced architectures and techniques proposed in the literature should be revisited to reassess their individual benefits.

This Time it's Different: The Role of Women's Employment in a Pandemic Recession

Abstract: From the Abstract: "In recent US recessions, employment losses have been much larger for men than for women Yet, in the current recession caused by the Covid-19 [coronavirus disease 2019] pandemic, the opposite is true: unemployment is higher among women In this paper, we analyze the causes and consequences of this phenomenon We argue that women have experienced sharp employment losses both because their employment is concentrated in heavily affected sectors such as restaurants, and due to increased childcare needs caused by school and daycare closures, preventing many women from working We analyze the repercussions of this trend using a quantitative macroeconomic model featuring heterogeneity in gender, marital status, childcare needs, and human capital Our quantitative analysis suggests that a pandemic recession will i) feature a strong transmission from employment to aggregate demand due to diminished within-household insurance;ii) result in a widening of the gender wage gap throughout the recovery;and iii) contribute to a weakening of the gender norms that currently produce a lopsided distribution of the division of labor in home work and childcare "

Probing Pretrained Language Models for Lexical Semantics

Abstract: The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture. While prior research focused on morphosyntactic, semantic, and world knowledge, it remains unclear to which extent LMs also derive lexical type-level knowledge from words in context. In this work, we present a systematic empirical analysis across six typologically diverse languages and five different lexical tasks, addressing the following questions: 1) How do different lexical knowledge extraction strategies (monolingual versus multilingual source LM, out-of-context versus in-context encoding, inclusion of special tokens, and layer-wise averaging) impact performance? How consistent are the observed effects across tasks and languages? 2) Is lexical knowledge stored in few parameters, or is it scattered throughout the network? 3) How do these representations fare against traditional static word vectors in lexical tasks 4) Does the lexical information emerging from independently trained monolingual LMs display latent similarities? Our main results indicate patterns and best practices that hold universally, but also point to prominent variations across languages and tasks. Moreover, we validate the claim that lower Transformer layers carry more type-level lexical knowledge, but also show that this knowledge is distributed across multiple layers.

When the whole is greater than the sum of its parts: On the conceptualization and measurement of populist attitudes and other multidimensional constructs

Abstract: Multidimensional concepts are non-compensatory when higher values on one component cannot offset lower values on another. Thinking of the components of a multidimensional phenomenon as non-compensatory rather than substitutable can have wide-ranging implications, both conceptually and empirically. To demonstrate this point, we focus on populist attitudes that feature prominently in contemporary debates about liberal democracy. Given similar established public opinion constructs, the conceptual value of populist attitudes hinges on its unique specification as an attitudinal syndrome, which is characterized by the concurrent presence of its non-compensatory concept subdimensions. Yet this concept attribute is rarely considered in existing empirical research. We propose operationalization strategies that seek to take the distinct properties of non-compensatory multidimensional concepts seriously. Evidence on five populism scales in 12 countries reveals the presence and consequences of measurement-concept inconsistencies. Importantly, in some cases, using conceptually sound operationalization strategies upsets previous findings on the substantive role of populist attitudes.

The association between weight stigma and mental health: A meta-analysis.

Abstract: In recent years, there has been considerable research on the relation between weight stigma and mental health, but no quantitative synthesis of the empirical evidence is available to date. This meta-analysis (105 studies, 59 172 participants, and 497 effect sizes) fills this gap by quantifying the association between weight stigma and mental health. Age, gender, and factors presumed to exert a protective role (i.e., adaptive coping strategies and perceived social support) were tested as potential moderators. The three-level meta-analytic model estimated under a random effects assumption revealed a medium to large negative association between weight stigma and mental health (r = -0.35). The overall association remained significant when controlling for publication year, education, and body weight. There was substantial heterogeneity in effect sizes between studies (I2 = 43%) and within studies (I2 = 56%). Surprisingly, all moderator hypotheses had to be rejected. Body weight was a significant moderator, indicating a stronger association between weight stigma and diminished mental health with increasing body mass index. Future research might focus on explaining the heterogeneity of findings and on testing causality as well as potential underlying mechanisms.

Addiction Research Consortium: Losing and regaining control over drug intake (ReCoDe)-From trajectories to mechanisms and interventions.

Abstract: One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12-year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism-based interventions. These goals will be achieved by: (i) using mobile health (m-health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real-life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal-directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake.

A review of conceptual approaches and empirical evidence on probability and nonprobability sample survey research

Abstract: There is an ongoing debate in the survey research literature about whether and when probability and nonprobability sample surveys produce accurate estimates of a larger population. Statistical theory provides a justification for confidence in probability sampling as a function of the survey design, whereas inferences based on nonprobability sampling are entirely dependent on models for validity. This article reviews the current debate about probability and nonprobability sample surveys. We describe the conditions under which nonprobability sample surveys may provide accurate results in theory and discuss empirical evidence on which types of samples produce the highest accuracy in practice. From these theoretical and empirical considerations, we derive best-practice recommendations and outline paths for future research.

COVID-19 policies in Germany and their social, political, and psychological consequences

Abstract: Many policy analyses on COVID-19 have been focusing on what kind of policies are implemented to contain the spread of COVID-19 What seems equally important to explore are the social and political consequences of the confinement policies Does the public support strict confinement policies? What are the social, political, and psychological consequences of the confinement policies? The question of how legitimate a policy is among the public is at the core of democratic theory Its relevance also stems from the expected consequences of public support on behavior: The more someone supports a policy, the more someone is likely to follow the policy even if the policy is not strictly enforced In this paper, we will focus on Germany, briefly summarize the main policies during the first 6 weeks of confinement and then explore political attitudes, risk perceptions, and the social consequences of the lockdown

Survival Outcomes Associated With 3 Years vs 1 Year of Adjuvant Imatinib for Patients With High-Risk Gastrointestinal Stromal Tumors: An Analysis of a Randomized Clinical Trial After 10-Year Follow-up.

Abstract: Importance Adjuvant imatinib is associated with improved recurrence-free survival (RFS) when administered after surgery to patients with operable gastrointestinal stromal tumor (GIST), but its influence on overall survival (OS) has remained uncertain. Objective To evaluate the effect of adjuvant imatinib on OS of patients who have a high estimated risk for GIST recurrence after macroscopically complete surgery. Design, Setting, and Participants In this open-label, randomized (1:1), multicenter phase 3 clinical trial conducted in Finland, Germany, Norway, and Sweden, 400 patients who had undergone macroscopically complete surgery for GIST with a high estimated risk for recurrence according to the modified National Institutes of Health Consensus Criteria were enrolled between February 2004 and September 2008. Data for this follow-up analysis were analyzed from September to November, 2019. Interventions Imatinib 400 mg/d administered orally for either 12 months or 36 months after surgery. Main Outcomes And Measures The primary end point was RFS; the secondary objectives included OS and treatment safety. Results The intention-to-treat cohort consisted of 397 patients (12-month group, 199; 36-month group, 198; 201 men and 196 women; median [IQR] age, 62 (51-69) years and 60 (51-67) years, during a median follow-up time of 119 months after the date of randomization, 194 RFS events and 96 OS events were recorded in the intention-to-treat population. Five-year and 10-year RFS was 71.4% and 52.5%, respectively, in the 36-month group and 53.0% and 41.8% in the 12-month group (hazard ratio [HR], 0.66; 95% CI, 0.49-0.87;P = .003). In the 36-month group, 5-year OS and 10-year OS rates were 92.0% and 79.0%, respectively, and in the 12-month group 85.5% and 65.3% (HR, 0.55; 95% CI, 0.37-0.83;P = .004). The results were similar in the efficacy population, from which 15 patients who did not have GIST in central pathology review and 24 patients who had intra-abdominal metastases removed at surgery were excluded (36-month group, 10-year OS 81.6%; 12-month group, 66.8%; HR, 0.50; 95% CI, 0.32-0.80;P = .003). No new safety signals were detected. Conclusions and Relevance Three years of adjuvant imatinib is superior in efficacy compared with 1 year of imatinib. Approximately 50% of deaths may be avoided during the first 10 years of follow-up after surgery with longer adjuvant imatinib treatment. Trial Registration ClinicalTrials.gov Identifier:NCT00116935

ConveRT: Efficient and Accurate Conversational Representations from Transformers

Abstract: General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train. We pretrain using a retrieval-based response selection task, effectively leveraging quantization and subword-level parameterization in the dual encoder to build a lightweight memory- and energy-efficient model. We show that ConveRT achieves state-of-the-art performance across widely established response selection tasks. We also demonstrate that the use of extended dialog history as context yields further performance gains. Finally, we show that pretrained representations from the proposed encoder can be transferred to the intent classification task, yielding strong results across three diverse data sets. ConveRT trains substantially faster than standard sentence encoders or previous state-of-the-art dual encoders. With its reduced size and superior performance, we believe this model promises wider portability and scalability for Conversational AI applications.

Utilising Learning Analytics to Support Study Success in Higher Education: A Systematic Review.

Abstract: This study examined the utilisation of learning analytics to support study success in higher education. The main research question was to identify whether there is a link between learning analytics and the respective intervention measures to increase study success at higher education institutions. The systematic review included empirical studies conducted during the past five years. Search terms identified 6,220 articles from various scientific sources. After duplicated articles were removed, there were 3,163 articles remaining. Each of the articles were screened and the inclusion criteria (e.g., peer-reviewed, rigorous research findings) limited the key studies to 41 articles. This paper presents an overview of the results of this systematic review. It is concluded that evidence can be found supporting the use of learning analytics to support study success in higher education. However, study success may not be exclusively the result of the use of learning analytics but also some additional means of technological or institutional support. The findings also suggest a wider adoption of learning analytics systems as well as work towards standardisation of learning analytics procedures which can be integrated into existing digital learning environments.

Dealing With the COVID-19 Infodemic: Distress by Information, Information Avoidance, and Compliance With Preventive Measures.

Abstract: In the ongoing coronavirus disease 2019 (COVID-19) pandemic, media reports have caused anxiety and distress in many. In some individuals, feeling distressed by information may lead to avoidance of information, which has been shown to undermine compliance with preventive health behaviors in many health domains (e.g., cancer screenings). We set out to examine whether feeling distressed by information predicts higher avoidance of information about COVID-19 (avoidance hypothesis), and whether this, in turn, predicts worse compliance with measures intended to prevent the spread of COVID-19 (compliance hypothesis). Thus, we conducted an online survey with a convenience sample (N = 1,059, 79.4% female) and assessed distress by information, information avoidance, and compliance with preventive measures. Furthermore, we inquired about participants' information seeking behavior and media usage, their trust in information sources, and level of eHealth literacy, as well as generalized anxiety. We conducted multiple linear regression analyses to predict distress by information, information avoidance, and compliance with preventive measures. Overall, distress by information was associated with better compliance. However, distress was also linked with an increased tendency to avoid information (avoidance hypothesis), and this reduced compliance with preventive measures (compliance hypothesis). Thus, distress may generally induce adaptive behavior in support of crisis management, unless individuals respond to it by avoiding information. These findings provide insights into the consequences of distress by information and avoidance of information during a global health crisis. These results underscore that avoiding information is a maladaptive response to distress by information, which may ultimately interfere with effective crisis management. Consequently, we emphasize the need to develop measures to counteract information avoidance.

Exercise-based cardiac rehabilitation in patients with reduced left ventricular ejection fraction: The Cardiac Rehabilitation Outcome Study in Heart Failure (CROS-HF): A systematic review and meta-analysis.

Abstract: BackgroundIn heart failure with reduced left ventricular ejection fraction (HFrEF) patients the effects of exercise-based cardiac rehabilitation on top of state-of-the-art pharmacological and devic...

Collecting Survey and Smartphone Sensor Data With an App: Opportunities and Challenges Around Privacy and Informed Consent:

Abstract: The new European General Data Protection Regulation (GDPR) imposes enhanced requirements on digital data collection. This article reports from a 2018 German nationwide population-based probability ...

Data-driven smart charging for heterogeneous electric vehicle fleets

Abstract: The ongoing electrification of mobility comes with the challenge of charging electric vehicles (EVs) sufficiently while charging infrastructure capacities are limited. Smart charging algorithms produce charge plans for individual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet. In practice, EV charging processes follow nonlinear charge profiles such as constant-current, constant-voltage (CCCV). Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power consumption. Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available. In this work we propose a data-driven approach for integrating a machine learning model to predict arbitrary charge profiles into a smart charging algorithm. We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models. Each charging process includes the time series of charging power. After preprocessing, the dataset contains 10.595 charging processes leading to 1.2 million data points in total. We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error (MAE) of 126W and a relative MAE of 0.06. Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21% more energy charged compared to smart charging without considering charge profiles. Furthermore, an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions. However, charging features are required including the number of phases used for charging.

Watching Players: An Exploration of Media Enjoyment on Twitch:

Abstract: Video game streaming platforms have reached high popularity within the last years. As one of these popular platforms, Twitch provides users with the opportunity to participate in several gaming situations: They can simultaneously watch in-game actions, the streamer playing the game, and additionally, they can interact with the streamer and other viewers by using the chat. In an online survey, the current study explored how individuals (N = 548) experience media enjoyment when using Twitch. Findings indicate that social aspects of using Twitch predominantly contribute to enjoyment. Approaches toward the phenomenon of video game streaming as well as implications for research on the usage of second screens and Social TV are discussed.

XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning

Abstract: In order to simulate human language capacity, natural language processing systems must complement the explicit information derived from raw text with the ability to reason about the possible causes and outcomes of everyday situations. Moreover, the acquired world knowledge should generalise to new languages, modulo cultural differences. Advances in machine commonsense reasoning and cross-lingual transfer depend on the availability of challenging evaluation benchmarks. Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We benchmark a range of state-of-the-art models on this novel dataset, revealing that current methods based on multilingual pretraining and zero-shot fine-tuning transfer suffer from the curse of multilinguality and fall short of performance in monolingual settings by a large margin. Finally, we propose ways to adapt these models to out-of-sample resource-lean languages where only a small corpus or a bilingual dictionary is available, and report substantial improvements over the random baseline. XCOPA is available at this http URL.

EAU/ESPU guidelines on the management of neurogenic bladder in children and adolescent part I diagnostics and conservative treatment.

Abstract: Background In childhood, the most common reason for a neurogenic bladder is related to spinal dysraphism, mostly myelodysplasia. Aims Herein, we present the EAU/ESPU guidelines in respect to the diagnostics, timetable for investigations and conservative management including clean intermittent catheterization (CIC). Material and methods After a systematic literature review covering the period 2000 to 2017, the ESPU/EUAU guideline for neurogenic bladder underwent an update. Results The EAU/ESPU guideline panel advocates a proactive approach. In newborns with spina bifida, CIC should be started as soon as possible after birth. In those with intrauterine closure of the defect, urodynamic studies are recommended be performed before the patient leaves the hospital. In those with closure after birth urodynamics should be done within the next 3 months. Anticholinergic medication (oxybutynin is the only well-investigated drug in this age group-dosage 0.2-0.4 mg/kg weight per day) should be applied, if the urodynamic study confirmed detrusor overactivity. Close follow-up including ultrasound, bladder diary, urinalysis, and urodynamics are necessary within the first 6 years and after that the time intervals can be prolonged, depending on the individual risk and clinical course. In all other children with the suspicion of a neurogenic bladder due to various reasons as tethered cord, inflammation, tumors, trauma, or other reasons as well as those with anorectal malformations, urodynamics-preferable video-urodynamics, should be carried out as soon as there is a suspicion of a neurogenic bladder and conservative treatment should be started soon after confirmation of the diagnosis of neurogenic bladder. With conservative treatment the upper urinary tract is preserved in up to 90%, urinary tract infections are common, but not severe, complications of CIC are quite rare and continence can be achieved at adolescence in up to 80% without further treatment. Discussion and conclusions The transition into adulthood is a complicated time for both patients, their caregivers and doctors, as the patient wants to become independent from caregivers and treatment compliance is reduced. Also, transition to adult clinics for patients with neurogenic bladders is often not well-established.

Prevalence of SARS-CoV-2 Infection in Children and Their Parents in Southwest Germany

Abstract: Background: School and day-care closures were enforced as measures to confine the COVID-19 pandemic based on the assumption that young children may play a key role in SARS-CoV-2 spreading. However, infection prevalence in children under 10 years of age is not very well analysed. Methods: The COVID-19 BaWu study is a large-scale multicentre cross-sectional investigation of children aged 1–10 years and one of their parents, both not diagnosed with COVID-19 before, in southwest Germany. We tested for SARS-CoV-2 RNA from nasopharyngeal swabs by RT-PCR and for SARS-CoV-2 specific IgG antibodies in serum by ELISA and immunofluorescence. Discordant results were clarified by ECLIA, a second ELISA or an in-house Luminex-based assay. We used mixed effects logistic regression to estimate the seroprevalence and to analyse the association between SARS-CoV-2 seropositivity and covariates. Findings: Between April 22nd and May 15th, 2020, we enrolled 4964 subjects, 2482 children and 2482 corresponding parents. 0•04% tested positive for SARS-CoV-2 RNA. The estimated SARS-CoV-2 seroprevalence was low in parents (1•8%; 95% CI, 1•2–2•4%) and 3-fold lower in children (0•6%; 95% CI, 0•3–1•0%). We observed virus-neutralizing activity for 66 of 70 IgG-positive sera (94•3%). Interpretation: The spread of SARS-CoV-2 infection during a period of lock-down in southwest Germany was particularly low in children aged 1–10 years. Accordingly, it is unlikely that children have boosted the pandemic. This largest reported SARS-CoV-2 prevalence study focussing on children is instructive for how ad hoc mass testing provides the basis for rational political decision making in a pandemic setting. Funding: Grant from the Federal State of Baden-Wurttemberg, Germany Declaration of Interests: All authors state no conflict of interest. Ethics Approval Statement: The study protocol was approved by the independent Ethics committees of each centre. The study was conducted according to the Declaration of Helsinki. Written informed consent was obtained from all parents/guardians, with assent from children when appropriate for their age.

Quality of Surgery and Outcome in Localized Gastrointestinal Stromal Tumors Treated Within an International Intergroup Randomized Clinical Trial of Adjuvant Imatinib.

Abstract: Importance The association between quality of surgery and overall survival in patients affected by localized gastrointestinal stromal tumors (GIST) is not completely understood. Objective To assess the risk of death with and without imatinib according to microscopic margins status (R0/R1) using data from a randomized study on adjuvant imatinib. Design, Setting, and Participants This is a post hoc observational study on patients included in the randomized, open-label, phase III trial, performed between December 2004 and October 2008. Median follow-up was 9.1 years (IQR, 8-10 years). The study was performed at 112 hospitals in 12 countries. Inclusion criteria were diagnosis of primary GIST, with intermediate or high risk of relapse; no evidence of residual disease after surgery; older than 18 years; and no prior malignancies or concurrent severe/uncontrolled medical conditions. Data were analyzed between July 17, 2017, and March 1, 2020. Interventions Patients were randomized after surgery to either receive imatinib (400 mg/d) for 2 years or no adjuvant treatment. Randomization was stratified by center, risk category (high vs intermediate), tumor site (gastric vs other), and quality of surgery (R0 vs R1). Tumor rupture was included in the R1 category but also analyzed separately. Main Outcomes and Measures Primary end point of this substudy was overall survival (OS), estimated using Kaplan-Meier method and compared between R0/R1 using Cox models adjusted for treatment and stratification factors. Results A total of 908 patients were included; 51.4% were men (465) and 48.6% were women (440), and the median age was 59 years (range, 18-89 years). One hundred sixty-two (17.8%) had an R1 resection, and 97 of 162 (59.9%) had tumor rupture. There was a significant difference in OS for patients undergoing an R1 vs R0 resection, overall (hazard ratio [HR], 2.05; 95% CI, 1.45-2.89) and by treatment arm (HR, 2.65; 95% CI, 1.37-3.75 with adjuvant imatinib and HR, 1.86; 95% CI, 1.16-2.99 without adjuvant imatinib). When tumor rupture was excluded, this difference in OS between R1 and R0 resections disappeared (HR, 1.05; 95% CI, 0.54-2.01). Conclusions and Relevance The difference in OS by quality of surgery with or without imatinib was associated with the presence of tumor rupture. When the latter was excluded, the presence of R1 margins was not associated with worse OS. Trial Registration ClinicalTrials.gov Identifier:NCT00103168