Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science

Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team

Exploring the cost and performance benefits of AWS Step Functions using a data processing pipeline

In traditional cloud computing, dedicated hardware is substituted by dynamically allocated, utility-oriented resources such as virtualized servers. While cloud services are following the pay-as-you-go pricing model, resources are billed based on instance allocation and not on the actual usage, leading the customers to be charged needlessly. In serverless computing, as exemplified by the Function-as-a-Service (FaaS) model where functions are the basic resources, functions are typically not allocated or charged until invoked or triggered. Functions are not applications, however, and to build compelling serverless applications they frequently need to be orchestrated with some kind of application logic. A major issue emerging by the use of orchestration is that it complicates further the already complex billing model used by FaaS providers, which in combination with the lack of granular billing and execution details offered by the providers makes the development and evaluation of serverless applications challenging. Towards shedding some light into this matter, in this work we extensively evaluate the state-of-the-art function orchestrator AWS Step Functions (ASF) with respect to its performance and cost. For this purpose we conduct a series of experiments using a serverless data processing pipeline application developed as both ASF Standard and Express workflows. Our results show that Step Functions using Express workflows are economical when running short-lived tasks with many state transitions. In contrast, Standard workflows are better suited for long-running tasks, offering in addition detailed debugging and logging information. However, even if the behavior of the orchestrated AWS Lambda functions influences both types of workflows, Step Functions realized as Express workflows get impacted the most by the phenomena affecting Lambda functions

Were last glacial climate events simultaneous between Greenland and France? A quantitative comparison using non-tuned chronologies

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Quaternary Science 25 (2010): 387-394, doi:10.1002/jqs.1330.Several large abrupt climate fluctuations during the last glacial have been recorded in Greenland ice cores and archives from other regions. Often these Dansgaard-Oeschger events are assumed to have been synchronous over wide areas, and then used as tie-points to link chronologies between the proxy archives. However, it has not yet been tested independently whether or not these events were indeed synchronous over large areas. Here, we compare Dansgaard-Oeschgertype events in a well-dated record from southeastern France with those in Greenland ice cores. Instead of assuming simultaneous climate events between both archives, we keep their age models independent. Even these well-dated archives possess large chronological uncertainties, that prevent us from inferring synchronous climate events at decadal to multi-centennial time scales. If possible, comparisons between proxy archives should be based on independent, non-tuned time-scales.BW acknowledges support from the Swedish Research Council (VR)

Individual differences in the temporal relationship between sleep and agitation:a single-subject study in nursing home residents with dementia experiencing sleep disturbance and agitation

OBJECTIVES: Previous studies on the interrelationship between sleep and agitation relied on group-aggregates and so results may not be applicable to individuals. This proof-of-concept study presents the single-subject study design with time series analysis as a method to evaluate the association between sleep and agitation in individual nursing home residents using actigraphy. METHOD: To record activity, three women and two men (aged 78-89 years) wore the MotionWatch 8© (MW8) for 9 consecutive weeks. Total sleep time and agitation were derived from the MW8 data. We performed time series analysis for each individual separately. To gain insight into the experiences with the actigraphy measurements, care staff filled out an investigator-developed questionnaire on their and participants' MW8 experiences. RESULTS: A statistically significant temporal association between sleep and agitation was present in three out of five participants. More agitation was followed by more sleep for participant 1, and by less sleep for participant 4. As for participants 3 and 4, more sleep was followed by more agitation. Two-thirds of the care staff members (16/24) were positive about the use of the MW8. Acceptability of the MW8 was mixed: two residents refused to wear the MW8 thus did not participate, one participant initially experienced the MW8 as somewhat unpleasant, while four participants seemed to experience no substantial problems. CONCLUSION: A single-subject approach with time series analysis can be a valuable tool to gain insight into the temporal relationship between sleep and agitation in individual nursing home residents with dementia experiencing sleep disturbance and agitation

The u-can-act Platform:A Tool to Study Intra-individual Processes of Early School Leaving and Its Prevention Using Multiple Informants

We present the u-can-act platform, a tool that we developed to study the individual processes of early school leaving and the preventative actions that mentors take to steer these processes in the right direction. Early school leaving is a significant problem, particularly in vocational education, and can have severe consequences for both the individual and society. However, the prevention of early school leaving is hampered by a mismatch between research and practice: research tends to focus on identifying risk factors using group averages and cross-sectional studies, while practitioners focus on intervening in individual processes. We aim to help solve this mismatch with our project u-can-act. In this project we have developed a platform that helps to gain insight into both the individual processes that precede early school leaving as well as the actions that mentors take to prevent it. In this paper we introduce the u-can-act platform, which consists of three technology-based, reusable methodological innovations. Specifically, our innovations concern: (i) an open source web application for longitudinal personalized data-collection, (ii) an automated study protocol that optimizes adherence in a difficult target group (adolescents at risk for early school leaving), and (iii) a technologically assisted coupling between mentor and student that allows us to study dyadic interactions over time. We present performance results of our platform, including participant adherence, the behavior of the questionnaire items over time, and the way that our web application is experienced by the participants. We conclude that our innovative platform is successful in collecting multi-informant time-series data on intervention processes among students in vocational education, both for at-risk students and control students, and for their mentors. Moreover, our platform is suitable for broader applications: it can be used to study any malleable individual process including the efforts of a second individual who aims to influence this process. Because of the unique insights that the u-can-act platform is able to generate, the platform may ultimately contribute to solving the mismatch between research and practice, and to more effective interventions in individual processes

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology:The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient’s ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0–16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation

Dysregulated innate and adaptive immune responses discriminate disease severity in COVID-19

The clinical spectrum of COVID-19 varies and the differences in host response characterizing this variation have not been fully elucidated. COVID-19 disease severity correlates with an excessive pro-inflammatory immune response and profound lymphopenia. Inflammatory responses according to disease severity were explored by plasma cytokine measurements and proteomics analysis in 147 COVID-19 patients. Furthermore, peripheral blood mononuclear cell cytokine production assays and whole blood flow cytometry were performed. Results confirm a hyperinflammatory innate immune state, while highlighting hepatocyte growth factor and stem cell factor as potential biomarkers for disease severity. Clustering analysis reveals no specific inflammatory endotypes in COVID-19 patients. Functional assays reveal abrogated adaptive cytokine production (interferon-gamma, interleukin-17 and interleukin-22) and prominent T cell exhaustion in critically ill patients, whereas innate immune responses were intact or hyperresponsive. Collectively, this extensive analysis provides a comprehensive insight into the pathobiology of severe to critical COVID-19 and highlight potential biomarkers of disease severity

Neodymium isotope constraints on provenance, dispersal, and climate-driven supply of Zambezi sediments along the Mozambique Margin during the past ∼45,000 years

Marine sediments deposited off the Zambezi River that drains a considerable part of the southeast African continent provide continuous records of the continental climatic and environmental conditions. Here we present time series of neodymium (Nd) isotope signatures of the detrital sediment fraction during the past ~45,000 years, to reconstruct climate-driven changes in the provenance of clays deposited along the Mozambique Margin. Coherent with the surface current regime, the Nd isotope distribution in surface sediments reveals mixing of the alongshore flowing Zambezi suspension load with sediments supplied by smaller rivers located further north. To reconstruct past changes in sediment provenances, Nd isotope signatures of clays that are not significantly fractionated during weathering processes have been obtained from core 64PE304-80, which was recovered just north of the Zambezi mouth at 1329 m water depth. Distinctly unradiogenic clay signatures (ENd values <214.2) are found during the Last Glacial Maximum, Heinrich Stadial 1, and Younger Dryas. In contrast, the Nd isotope record shows higher, more radiogenic isotope signatures during Marine Isotope Stage 3 and between ~15 and ~5 ka BP, the latter coinciding with the timing of the northern hemisphere African Humid Period. The clay-sized sediment fraction with the least radiogenic Nd isotope signatures was deposited during the Holocene, when the adjacent Mozambique Shelf became completely flooded. In general, the contribution of the distinctly unradiogenic Zambezi suspension load has followed the intensity of precession-forced monsoonal precipitation and enhanced during periods of increased southern hemisphere insolation and high-latitude northern hemispheric climate variability

Widespread drying of European peatlands in recent centuries

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this record Climate warming and human impacts are thought to be causing peatlands to dry,potentially converting them from sinks to sources of carbon. However, it is unclear whether the hydrological status of peatlands has moved beyond their natural envelope. Here we show that European peatlands have undergone substantial, widespread drying during the last ~300 years. We analyse testate amoeba-derived hydrological reconstructions from 31 peatlands across Britain, Ireland, Scandinavia and continental Europe to examine changes in peatland surface wetness during the last 2000 years. 60% of our study sites were drier during the period CE 1800-2000 than they have been for the last 600 years; 40% of sites were drier than they have been for 1000 years; and 24% of sites were drier than they have been for 2000 years. This marked recent transition in the hydrology of European peatlands is concurrent with compound pressures including climatic drying, warming and direct human impacts on peatlands, although these factors vary between regions and individual sites. Our results suggest that the wetness of many European peatlands may now be moving away from natural baselines. Our findings highlight the need for effective management and restoration of European peatlands.Natural Environment Research Council (NERC

Scientific drilling projects in ancient lakes: integrating geological and biological histories

Sedimentary sequences in ancient or long-lived lakes can reach several thousands of meters in thickness and often provide an unrivalled perspective of the lake's regional climatic, environmental, and biological history. Over the last few years, deep drilling projects in ancient lakes became increasingly multi- and interdisciplinary, as, among others, seismological, sedimentological, biogeochemical, climatic, environmental, paleontological, and evolutionary information can be obtained from sediment cores. However, these multi- and interdisciplinary projects pose several challenges. The scientists involved typically approach problems from different scientific perspectives and backgrounds, and setting up the program requires clear communication and the alignment of interests. One of the most challenging tasks, besides the actual drilling operation, is to link diverse datasets with varying resolution, data quality, and age uncertainties to answer interdisciplinary questions synthetically and coherently. These problems are especially relevant when secondary data, i.e., datasets obtained independently of the drilling operation, are incorporated in analyses. Nonetheless, the inclusion of secondary information, such as isotopic data from fossils found in outcrops or genetic data from extant species, may help to achieve synthetic answers. Recent technological and methodological advances in paleolimnology are likely to increase the possibilities of integrating secondary information, e.g., through molecular dating of molecular phylogenies. Some of the new approaches have started to revolutionize scientific drilling in ancient lakes, but at the same time, they also add a new layer of complexity to the generation and analysis of sediment core data. The enhanced opportunities presented by new scientific approaches to study the paleolimnological history of these lakes, therefore, come at the expense of higher logistic, communication, and analytical efforts. Here we review types of data that can be obtained in ancient lake drilling projects and the analytical approaches that can be applied to empirically and statistically link diverse datasets for creating an integrative perspective on geological and biological data. In doing so, we highlight strengths and potential weaknesses of new methods and analyses, and provide recommendations for future interdisciplinary deep drilling projects