The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report.

In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process

AAL-Onto: A formal representation of RAALI integration profiles

The integration and commissioning of Ambient Assisted Living (AAL) systems are time consuming and complicated. The lack of interoperability of available AAL system components has to be considered as an obstacle especially for innovative SMEs. In order to ease integration and commissioning of systems, knowledge based methods should be taken into account to enable innovative characteristics such as design automation, self-configuration and self-management. Semantic technologies are suitable instruments for mastering the problems of interoperability of heterogeneous and distributed systems. As an important prerequisite for the emergence of knowledge-based assistance functions a standard for an unambiguous representation of AAL relevant knowledge has to be developed. In this article, the development of an AAL ontology is proposed as a formal basis for knowledge-based system functions. A prototype of an AAL specific ontology engineering process is presented through the modeling example of a formal representation of a sensor block that is part of an AAL Integration Profile proposed by the RAALI project consortium

Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction.

IntroductionAn increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease's trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction.MethodsWe used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation.ResultsBoth SVM (AUC 0.84; 95% CI: 0.71-0.96) and aNN (AUC 0.82; 95% CI: 0.69-0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65-0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58-0.74; p ConclusionsThe ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units

Human cytomegalovirus seropositivity is associated with reduced patient survival during sepsis

$\bf Background$ Sepsis is one of the leading causes of death. Treatment attempts targeting the immune response regularly fail in clinical trials. As HCMV latency can modulate the immune response and changes the immune cell composition, we hypothesized that HCMV serostatus affects mortality in sepsis patients. $\bf Methods$ We determined the HCMV serostatus (i.e., latency) of 410 prospectively enrolled patients of the multicenter SepsisDataNet.NRW study. Patients were recruited according to the SEPSIS-3 criteria and clinical data were recorded in an observational approach. We quantified 13 cytokines at Days 1, 4, and 8 after enrollment. Proteomics data were analyzed from the plasma samples of 171 patients. $\bf Results$ The 30-day mortality was higher in HCMV-seropositive patients than in seronegative sepsis patients (38% vs. 25%, respectively; $\it p$ = 0.008; HR, 1.656; 95% CI 1.135–2.417). This effect was observed independent of age ($\it p$ = 0.010; HR, 1.673; 95% CI 1.131–2.477). The predictive value on the outcome of the increased concentrations of IL-6 was present only in the seropositive cohort (30-day mortality, 63% vs. 24%; HR 3.250; 95% CI 2.075–5.090; $\it p$ < 0.001) with no significant differences in serum concentrations of IL-6 between the two groups. Procalcitonin and IL-10 exhibited the same behavior and were predictive of the outcome only in HCMV-seropositive patients. $\bf Conclusion$ We suggest that the predictive value of inflammation-associated biomarkers should be re-evaluated with regard to the HCMV serostatus. Targeting HCMV latency might open a new approach to selecting suitable patients for individualized treatment in sepsis

The impact of the COVID-19 pandemic on non-COVID induced sepsis survival

$\bf Background:$ The COVID-19 pandemic has taken a toll on health care systems worldwide, which has led to increased mortality of different diseases like myocardial infarction. This is most likely due to three factors. First, an increased workload per nurse ratio, a factor associated with mortality. Second, patients presenting with COVID-19-like symptoms are isolated, which also decreases survival in cases of emergency. And third, patients hesitate to see a doctor or present themselves at a hospital. To assess if this is also true for sepsis patients, we asked whether non-COVID-19 sepsis patients had an increased 30-day mortality during the COVID-19 pandemic. $\bf Methods:$ This is a post hoc analysis of the SepsisDataNet.NRW study, a multicentric, prospective study that includes septic patients fulfilling the SEPSIS-3 criteria. Within this study, we compared the 30-day mortality and disease severity of patients recruited pre-pandemic (recruited from March 2018 until February 2020) with non-COVID-19 septic patients recruited during the pandemic (recruited from March 2020 till December 2020). $\bf Results:$ Comparing septic patients recruited before the pandemic to those recruited during the pandemic, we found an increased raw 30-day mortality in sepsis-patients recruited during the pandemic (33% vs. 52%, $\it p$ = 0.004). We also found a significant difference in the severity of disease at recruitment (SOFA score pre-pandemic: 8 (5 - 11) vs. pandemic: 10 (8 - 13); $\it p$ < 0.001). When adjusted for this, the 30-day mortality rates were not significantly different between the two groups (52% vs. 52% pre-pandemic and pandemic, $\it p$ = 0.798). $\bf Conclusions:$ This led us to believe that the higher mortality of non-COVID19 sepsis patients during the pandemic might be attributed to a more severe septic disease at the time of recruitment. We note that patients may experience a delayed admission, as indicated by elevated SOFA scores. This could explain the higher mortality during the pandemic and we found no evidence for a diminished quality of care for critically ill sepsis patients in German intensive care units

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report

Breznau N, Rinke EM, Wuttke A, et al. The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report. 2019

The Crowdsourced Replication Initiative

Crowdsourced Research on Immigration and Social Policy Preference

How Many Replicators Does It Take to Achieve Reliability? Investigating Researcher Variability in a Crowdsourced Replication

The paper reports findings from a crowdsourced replication. Eighty-four replicator teams attempted to verify results reported in an original study by running the same models with the same data. The replication involved an experimental condition. A “transparent” group received the original study and code, and an “opaque” group received the same underlying study but with only a methods section and description of the regression coefficients without size or significance, and no code. The transparent group mostly verified the original study (95.5%), while the opaque group had less success (89.4%). Qualitative investigation of the replicators’ workflows reveals many causes of non-verification. Two categories of these causes are hypothesized, routine and non-routine. After correcting non-routine errors in the research process to ensure that the results reflect a level of quality that should be present in ‘real-world’ research, the rate of verification was 96.1 in the transparent group and 92.4 in the opaque group. Two conclusions follow: (1) Although high, the verification rate suggests that it would take a minimum of three replicators per study to achieve replication reliability of at least 95 confidence assuming ecological validity in this controlled setting, and (2) like any type of scientific research, replication is prone to errors that derive from routine and undeliberate actions in the research process. The latter suggests that idiosyncratic researcher variability might provide a key to understanding part of the “reliability crisis” in social and behavioral science and is a reminder of the importance of transparent and well documented workflows

Early stage litter decomposition across biomes

Through litter decomposition enormous amounts of carbon is emitted to the atmosphere. Numerous large-scale decomposition experiments have been conducted focusing on this fundamental soil process in order to understand the controls on the terrestrial carbon transfer to the atmosphere. However, previous studies were mostly based on site-specific litter and methodologies, adding major uncertainty to syntheses, comparisons and meta-analyses across different experiments and sites. In the TeaComposition initiative, the potential litter decomposition is investigated by using standardized substrates (Rooibos and Green tea) for comparison of litter mass loss at 336 sites (ranging from −9 to +26 °C MAT and from 60 to 3113 mm MAP) across different ecosystems. In this study we tested the effect of climate (temperature and moisture), litter type and land-use on early stage decomposition (3 months) across nine biomes. We show that litter quality was the predominant controlling factor in early stage litter decomposition, which explained about 65% of the variability in litter decomposition at a global scale. The effect of climate, on the other hand, was not litter specific and explained <0.5% of the variation for Green tea and 5% for Rooibos tea, and was of significance only under unfavorable decomposition conditions (i.e. xeric versus mesic environments). When the data were aggregated at the biome scale, climate played a significant role on decomposition of both litter types (explaining 64% of the variation for Green tea and 72% for Rooibos tea). No significant effect of land-use on early stage litter decomposition was noted within the temperate biome. Our results indicate that multiple drivers are affecting early stage litter mass loss with litter quality being dominant. In order to be able to quantify the relative importance of the different drivers over time, long-term studies combined with experimental trials are needed.This work was performed within the TeaComposition initiative, carried out by 190 institutions worldwide. We thank Gabrielle Drozdowski for her help with the packaging and shipping of tea, Zora Wessely and Johannes Spiegel for the creative implementation of the acknowledgement card, Josip Dusper for creative implementation of the graphical abstract, Christine Brendle for the GIS editing, and Marianne Debue for her help with the data cleaning. Further acknowledgements go to Adriana Principe, Melanie Köbel, Pedro Pinho, Thomas Parker, Steve Unger, Jon Gewirtzman and Margot McKleeven for the implementation of the study at their respective sites. We are very grateful to UNILEVER for sponsoring the Lipton tea bags and to the COST action ClimMani for scientific discussions, adoption and support to the idea of TeaComposition as a common metric. The initiative was supported by the following grants: ILTER Initiative Grant, ClimMani Short-Term Scientific Missions Grant (COST action ES1308; COST-STSM-ES1308-36004; COST-STM-ES1308-39006; ES1308-231015-068365), INTERACT (EU H2020 Grant No. 730938), and Austrian Environment Agency (UBA). Franz Zehetner acknowledges the support granted by the Prometeo Project of Ecuador's Secretariat of Higher Education, Science, Technology and Innovation (SENESCYT) as well as Charles Darwin Foundation for the Galapagos Islands (2190). Ana I. Sousa, Ana I. Lillebø and Marta Lopes thanks for the financial support to CESAM (UID/AMB/50017), to FCT/MEC through national funds (PIDDAC), and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020. The research was also funded by the Portuguese Foundation for Science and Technology, FCT, through SFRH/BPD/107823/2015 (A.I. Sousa), co-funded by POPH/FSE. Thomas Mozdzer thanks US National Science Foundation NSF DEB-1557009. Helena C. Serrano thanks Fundação para a Ciência e Tecnologia (UID/BIA/00329/2013). Milan Barna acknowledges Scientific Grant Agency VEGA (2/0101/18). Anzar A Khuroo acknowledges financial support under HIMADRI project from SAC-ISRO, India