TY - JOUR A1 - De Freitas, Jessica K. A1 - Johnson, Kipp W. A1 - Golden, Eddye A1 - Nadkarni, Girish N. A1 - Dudley, Joel T. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin S. A1 - Miotto, Riccardo T1 - Phe2vec BT - Automated disease phenotyping based on unsupervised embeddings from electronic health records JF - Patterns N2 - Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts. Y1 - 2021 U6 - https://doi.org/10.1016/j.patter.2021.100337 SN - 2666-3899 VL - 2 IS - 9 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Klippert, Monika A1 - Stolpmann, Robert A1 - Grum, Marcus A1 - Thim, Christof A1 - Gronau, Norbert A1 - Albers, Albert T1 - Knowledge transfer quality improvement BT - the quality enhancement of knowledge transfers in product engineering T2 - Procedia CIRP N2 - Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect. KW - knowledge transfer KW - product generation engineering KW - improvement KW - quality KW - intervention Y1 - 2023 U6 - https://doi.org/10.1016/j.procir.2023.02.171 SN - 2212-8271 VL - 119 SP - 919 EP - 925 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Panzer, Marcel A1 - Gronau, Norbert T1 - Enhancing economic efficiency in modular production systems through deep reinforcement learning T2 - Procedia CIRP N2 - In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios. KW - modular production KW - production control KW - multi-agent system KW - deep reinforcement learning KW - discrete event simulation Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2023.09.229 SN - 2212-8271 VL - 121 SP - 55 EP - 60 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Grdseloff, Nastasja A1 - Boulday, Gwenola A1 - Roedel, Claudia J. A1 - Otten, Cecile A1 - Vannier, Daphne Raphaelle A1 - Cardoso, Cecile A1 - Faurobert, Eva A1 - Dogra, Deepika A1 - Tournier-Lasserve, Elisabeth A1 - Abdelilah-Seyfried, Salim T1 - Impaired retinoic acid signaling in cerebral cavernous malformations JF - Scientific reports N2 - The capillary-venous pathology cerebral cavernous malformation (CCM) is caused by loss of CCM1/Krev interaction trapped protein 1 (KRIT1), CCM2/MGC4607, or CCM3/PDCD10 in some endothelial cells. Mutations of CCM genes within the brain vasculature can lead to recurrent cerebral hemorrhages. Pharmacological treatment options are urgently needed when lesions are located in deeply-seated and in-operable regions of the central nervous system. Previous pharmacological suppression screens in disease models of CCM led to the discovery that treatment with retinoic acid improved CCM phenotypes. This finding raised a need to investigate the involvement of retinoic acid in CCM and test whether it has a curative effect in preclinical mouse models. Here, we show that components of the retinoic acid synthesis and degradation pathway are transcriptionally misregulated across disease models of CCM. We complemented this analysis by pharmacologically modifying retinoic acid levels in zebrafish and human endothelial cell models of CCM, and in acute and chronic mouse models of CCM. Our pharmacological intervention studies in CCM2-depleted human umbilical vein endothelial cells (HUVECs) and krit1 mutant zebrafish showed positive effects when retinoic acid levels were increased. However, therapeutic approaches to prevent the development of vascular lesions in adult chronic murine models of CCM were drug regiment-sensitive, possibly due to adverse developmental effects of this hormone. A treatment with high doses of retinoic acid even worsened CCM lesions in an adult chronic murine model of CCM. This study provides evidence that retinoic acid signaling is impaired in the CCM pathophysiology and suggests that modification of retinoic acid levels can alleviate CCM phenotypes. KW - Developmental biology KW - Molecular medicine Y1 - 2023 U6 - https://doi.org/10.1038/s41598-023-31905-0 SN - 2045-2322 VL - 13 IS - 1 PB - Nature Portfolio CY - Berlin ER - TY - JOUR A1 - Lewkowicz, Daniel A1 - Wohlbrandt, Attila A1 - Böttinger, Erwin T1 - Economic impact of clinical decision support interventions based on electronic health records JF - BMC Health Services Research N2 - Background Unnecessary healthcare utilization, non-adherence to current clinical guidelines, or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to improve quality of care and thereby yield substantial effects on reducing healthcare expenditure. In this article, we evaluate the economic impact of clinical decision support (CDS) interventions based on electronic health records (EHR). Methods We searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registry databases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practice application areas and categorized the investigated interventions according to an existing taxonomy of front-end CDS tools. Results and discussion Twenty-seven studies are investigated in this review. Of those, twenty-two studies indicate a reduction of healthcare expenditure after implementing an EHR based CDS system, especially towards prevalent application areas, such as unnecessary laboratory testing, duplicate order entry, efficient transfusion practice, or reduction of antibiotic prescriptions. On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance costs of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance of further high-quality economic evaluations for these CDS systems. Conclusion Current research studies lack consideration of comparative cost-outcome metrics as well as detailed cost components in their analyses. Nonetheless, the positive economic impact of EHR based CDS interventions is highly promising, especially with regard to reducing waste in healthcare. KW - Economic evaluation KW - Electronic health record KW - Clinical decision support KW - Behavioral economics Y1 - 2020 U6 - https://doi.org/10.1186/s12913-020-05688-3 SN - 1472-6963 VL - 20 PB - BioMed Central CY - London ER - TY - JOUR A1 - Wachs, Sebastian A1 - Wettstein, Alexander A1 - Bilz, Ludwig A1 - Gamez-Guadix, Manuel T1 - Motivos del discurso de odio en la adolescencia y su relación con las normas sociales BT - Adolescents' motivations to perpetrate hate speech and links with social norms JF - Comunicar : revista científica de comunicación y educación N2 - Hate speech has become a widespread phenomenon, however, it remains largely unclear why adolescents engage in it and which factors are associated with their motivations for perpetrating hate speech. To this end, we developed the multidimensional "Motivations for Hate Speech Perpetration Scale" (MHATE) and evaluated the psychometric properties. We also explored the associations between social norms and adolescents' motivations for hate speech perpetration. The sample consisted of 346 adolescents from Switzerland (54.6% boys; Mage=14; SD=0.96) who reported engagement in hate speech as perpetrators. The analyses revealed good psychometric properties for the MHATE, including good internal consistency. The most frequently endorsed subscale was revenge, followed by ideology, group conformity, status enhancement, exhilaration, and power. The results also showed that descriptive norms and peer pressure were related to a wide range of different motivations for perpetrating hate speech. Injunctive norms, however, were only associated with power. In conclusion, findings indicate that hate speech fulfills various functions. We argue that knowing the specific motivations that underlie hate speech could help us derive individually tailored prevention strategies (e.g., anger management, promoting an inclusive classroom climate). Furthermore, we suggest that practitioners working in the field of hate speech prevention give special attention to social norms surrounding adolescents. KW - Hate speech KW - cyberhate KW - motives KW - social norms KW - injunctive norms KW - peer KW - pressure Y1 - 2022 U6 - https://doi.org/10.3916/C71-2022-01 SN - 1134-3478 SN - 1988-3293 VL - 30 IS - 71 SP - 9 EP - 20 PB - Grupo Comunicar CY - Huelva ER - TY - JOUR A1 - Smith, Taylor A1 - Boers, Niklas T1 - Global vegetation resilience linked to water availability and variability JF - Nature Communications N2 - Quantifying the resilience of vegetated ecosystems is key to constraining both present-day and future global impacts of anthropogenic climate change. Here we apply both empirical and theoretical resilience metrics to remotely-sensed vegetation data in order to examine the role of water availability and variability in controlling vegetation resilience at the global scale. We find a concise global relationship where vegetation resilience is greater in regions with higher water availability. We also reveal that resilience is lower in regions with more pronounced inter-annual precipitation variability, but find less concise relationships between vegetation resilience and intra-annual precipitation variability. Our results thus imply that the resilience of vegetation responds differently to water deficits at varying time scales. In view of projected increases in precipitation variability, our findings highlight the risk of ecosystem degradation under ongoing climate change. Vegetation dynamics depend on both the amount of precipitation and its variability over time. Here, the authors show that vegetation resilience is greater where water availability is higher and where precipitation is more stable from year to year. Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-36207-7 SN - 2041-1723 VL - 14 IS - 1 PB - Springer Nature CY - London ER - TY - JOUR A1 - Falkenhagen, Undine A1 - Knöchel, Jane A1 - Kloft, Charlotte A1 - Huisinga, Wilhelm T1 - Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models BT - an application to warfarin JF - CPT: Pharmacometrics & Systems Pharmacology N2 - Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications. Y1 - 2023 U6 - https://doi.org/10.1002/psp4.12903 SN - 2163-8306 VL - 12 IS - 4 SP - 432 EP - 443 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Moreno-Romero, Jordi A1 - Probst, Aline V. A1 - Trindade, Inês A1 - Kalyanikrishna, A1 - Engelhorn, Julia A1 - Farrona, Sara T1 - Looking At the Past and Heading to the Future BT - Meeting Summary of the 6th European Workshop on Plant Chromatin 2019 in Cologne, Germany JF - Frontiers in Plant Science N2 - In June 2019, more than a hundred plant researchers met in Cologne, Germany, for the 6th European Workshop on Plant Chromatin (EWPC). This conference brought together a highly dynamic community of researchers with the common aim to understand how chromatin organization controls gene expression, development, and plant responses to the environment. New evidence showing how epigenetic states are set, perpetuated, and inherited were presented, and novel data related to the three-dimensional organization of chromatin within the nucleus were discussed. At the level of the nucleosome, its composition by different histone variants and their specialized histone deposition complexes were addressed as well as the mechanisms involved in histone post-translational modifications and their role in gene expression. The keynote lecture on plant DNA methylation by Julie Law (SALK Institute) and the tribute session to Lars Hennig, honoring the memory of one of the founders of the EWPC who contributed to promote the plant chromatin and epigenetic field in Europe, added a very special note to this gathering. In this perspective article we summarize some of the most outstanding data and advances on plant chromatin research presented at this workshop. KW - EWPC2019 KW - chromatin KW - epigenetics KW - transcription KW - nucleus Y1 - 2020 U6 - https://doi.org/10.3389/fpls.2019.01795 SN - 1664-462X VL - 10 IS - 1795 SP - 1 EP - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Dellepiane, Sergio A1 - Vaid, Akhil A1 - Jaladanki, Suraj K. A1 - Coca, Steven A1 - Fayad, Zahi A. A1 - Charney, Alexander W. A1 - Böttinger, Erwin A1 - He, John Cijiang A1 - Glicksberg, Benjamin S. A1 - Chan, Lili A1 - Nadkarni, Girish T1 - Acute kidney injury in patients hospitalized with COVID-19 in New York City BT - Temporal Trends From March 2020 to April 2021 JF - Kidney medicine Y1 - 2021 U6 - https://doi.org/10.1016/j.xkme.2021.06.008 SN - 2590-0595 VL - 3 IS - 5 SP - 877 EP - 879 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Datta, Suparno A1 - Sachs, Jan Philipp A1 - Freitas da Cruz, Harry A1 - Martensen, Tom A1 - Bode, Philipp A1 - Morassi Sasso, Ariane A1 - Glicksberg, Benjamin S. A1 - Böttinger, Erwin T1 - FIBER BT - enabling flexible retrieval of electronic health records data for clinical predictive modeling JF - JAMIA open N2 - Objectives: The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. Materials and Methods: FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. Results: Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. Conclusion: FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process. KW - databases KW - factual KW - electronic health records KW - information storage and KW - retrieval KW - workflow KW - software/instrumentation Y1 - 2021 U6 - https://doi.org/10.1093/jamiaopen/ooab048 SN - 2574-2531 VL - 4 IS - 3 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Cope, Justin L. A1 - Baukmann, Hannes A. A1 - Klinger, Jörn E. A1 - Ravarani, Charles N. J. A1 - Böttinger, Erwin A1 - Konigorski, Stefan A1 - Schmidt, Marco F. T1 - Interaction-based feature selection algorithm outperforms polygenic risk score in predicting Parkinson’s Disease status JF - Frontiers in genetics N2 - Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem. KW - epistasis KW - machine learning KW - feature selection KW - parkinson's disease KW - PPMI (parkinson's progression markers initiative) Y1 - 2021 U6 - https://doi.org/10.3389/fgene.2021.744557 SN - 1664-8021 VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Zenner, Alexander M. A1 - Böttinger, Erwin A1 - Konigorski, Stefan T1 - StudyMe BT - a new mobile app for user-centric N-of-1 trials JF - Trials N2 - N-of-1 trials are multi-crossover self-experiments that allow individuals to systematically evaluate the effect of interventions on their personal health goals. Although several tools for N-of-1 trials exist, there is a gap in supporting non-experts in conducting their own user-centric trials. In this study, we present StudyMe, an open-source mobile application that is freely available from https://play.google.com/store/apps/details?id=health.studyu.me and offers users flexibility and guidance in configuring every component of their trials. We also present research that informed the development of StudyMe, focusing on trial creation. Through an initial survey with 272 participants, we learned that individuals are interested in a variety of personal health aspects and have unique ideas on how to improve them. In an iterative, user-centered development process with intermediate user tests, we developed StudyMe that features an educational part to communicate N-of-1 trial concepts. A final empirical evaluation of StudyMe showed that all participants were able to create their own trials successfully using StudyMe and the app achieved a very good usability rating. Our findings suggest that StudyMe provides a significant step towards enabling individuals to apply a systematic science-oriented approach to personalize health-related interventions and behavior modifications in their everyday lives. Y1 - 2022 U6 - https://doi.org/10.1186/s13063-022-06893-7 SN - 1745-6215 VL - 23 PB - BioMed Central CY - London ER - TY - JOUR A1 - Lewkowicz, Daniel A1 - Böttinger, Erwin A1 - Siegel, Martin T1 - Economic evaluation of digital therapeutic care apps for unsupervised treatment of low back pain BT - Monte Carlo Simulation JF - JMIR mhealth and uhealth N2 - Background: Digital therapeutic care (DTC) programs are unsupervised app-based treatments that provide video exercises and educational material to patients with nonspecific low back pain during episodes of pain and functional disability. German statutory health insurance can reimburse DTC programs since 2019, but evidence on efficacy and reasonable pricing remains scarce. This paper presents a probabilistic sensitivity analysis (PSA) to evaluate the efficacy and cost-utility of a DTC app against treatment as usual (TAU) in Germany. Objective: The aim of this study was to perform a PSA in the form of a Monte Carlo simulation based on the deterministic base case analysis to account for model assumptions and parameter uncertainty. We also intend to explore to what extent the results in this probabilistic analysis differ from the results in the base case analysis and to what extent a shortage of outcome data concerning quality-of-life (QoL) metrics impacts the overall results. Methods: The PSA builds upon a state-transition Markov chain with a 4-week cycle length over a model time horizon of 3 years from a recently published deterministic cost-utility analysis. A Monte Carlo simulation with 10,000 iterations and a cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Quality-adjusted life years (QALYs) were derived from Veterans RAND 6-Dimension (VR-6D) and Short-Form 6-Dimension (SF-6D) single utility scores. Finally, we also simulated reducing the price for a 3-month app prescription to analyze at which price threshold DTC would result in being the dominant strategy over TAU in Germany. Results: The Monte Carlo simulation yielded on average a euro135.97 (a currency exchange rate of EUR euro1=US $1.069 is applicable) incremental cost and 0.004 incremental QALYs per person and year for the unsupervised DTC app strategy compared to in-person physiotherapy in Germany. The corresponding incremental cost-utility ratio (ICUR) amounts to an additional euro34,315.19 per additional QALY. DTC yielded more QALYs in 54.96% of the iterations. DTC dominates TAU in 24.04% of the iterations for QALYs. Reducing the app price in the simulation from currently euro239.96 to euro164.61 for a 3-month prescription could yield a negative ICUR and thus make DTC the dominant strategy, even though the estimated probability of DTC being more effective than TAU is only 54.96%. Conclusions: Decision-makers should be cautious when considering the reimbursement of DTC apps since no significant treatment effect was found, and the probability of cost-effectiveness remains below 60% even for an infinite willingness-to-pay threshold. More app-based studies involving the utilization of QoL outcome parameters are urgently needed to account for the low and limited precision of the available QoL input parameters, which are crucial to making profound recommendations concerning the cost-utility of novel apps. KW - cost-utility analysis KW - cost KW - probabilistic sensitivity analysis KW - Monte Carlo simulation KW - low back pain KW - pain KW - economic KW - cost-effectiveness KW - Markov model KW - digital therapy KW - digital health app KW - mHealth KW - mobile health KW - health app KW - mobile app KW - orthopedic KW - QUALY KW - DALY KW - quality-adjusted life years KW - disability-adjusted life years KW - time horizon KW - veteran KW - statistics Y1 - 2023 U6 - https://doi.org/10.2196/44585 SN - 2291-5222 VL - 11 PB - JMIR Publications CY - Toronto ER - TY - JOUR A1 - Konigorski, Stefan A1 - Wernicke, Sarah A1 - Slosarek, Tamara A1 - Zenner, Alexander M. A1 - Strelow, Nils A1 - Ruether, Darius F. A1 - Henschel, Florian A1 - Manaswini, Manisha A1 - Pottbäcker, Fabian A1 - Edelman, Jonathan A. A1 - Owoyele, Babajide A1 - Danieletto, Matteo A1 - Golden, Eddye A1 - Zweig, Micol A1 - Nadkarni, Girish N. A1 - Böttinger, Erwin T1 - StudyU: a platform for designing and conducting innovative digital N-of-1 trials JF - Journal of medical internet research N2 - N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice. KW - digital interventions KW - N-of-1 trial KW - SCED KW - single-case experimental design KW - web application KW - mobile application KW - app KW - digital health Y1 - 2022 U6 - https://doi.org/10.2196/35884 SN - 1439-4456 SN - 1438-8871 VL - 24 IS - 7 PB - Healthcare World CY - Richmond, Va. ER - TY - JOUR A1 - Barbolini, Natasha A1 - Woutersen, Amber A1 - Dupont-Nivet, Guillaume A1 - Silvestro, Daniele A1 - Tardif-Becquet, Delphine A1 - Coster, Pauline M. C. A1 - Meijer, Niels A1 - Chang, Cun A1 - Zhang, Hou-Xi A1 - Licht, Alexis A1 - Rydin, Catarina A1 - Koutsodendris, Andreas A1 - Han, Fang A1 - Rohrmann, Alexander A1 - Liu, Xiang-Jun A1 - Zhang, Y. A1 - Donnadieu, Yannick A1 - Fluteau, Frederic A1 - Ladant, Jean-Baptiste A1 - Le Hir, Guillaume A1 - Hoorn, M. Carina T1 - Cenozoic evolution of the steppe-desert biome in Central Asia JF - Science Advances N2 - The origins and development of the arid and highly seasonal steppe-desert biome in Central Asia, the largest of its kind in the world, remain largely unconstrained by existing records. It is unclear how Cenozoic climatic, geological, and biological forces, acting at diverse spatial and temporal scales, shaped Central Asian ecosystems through time. Our synthesis shows that the Central Asian steppe-desert has existed since at least Eocene times but experienced no less than two regime shifts, one at the Eocene-Oligocene Transition and one in the mid-Miocene. These shifts separated three successive "stable states," each characterized by unique floral and faunal structures. Past responses to disturbance in the Asian steppe-desert imply that modern ecosystems are unlikely to recover their present structures and diversity if forced into a new regime. This is of concern for Asian steppes today, which are being modified for human use and lost to desertification at unprecedented rates. Y1 - 2020 U6 - https://doi.org/10.1126/sciadv.abb8227 SN - 2375-2548 VL - 6 IS - 41 PB - American Association for the Advancement of Science CY - Washington ER - TY - JOUR A1 - Woutersen, Amber A1 - Jardine, Phillip E. A1 - Giovanni Bogota-Angel, Raul A1 - Zhang, Hong-Xiang A1 - Silvestro, Daniele A1 - Antonelli, Alexandre A1 - Gogna, Elena A1 - Erkens, Roy H. J. A1 - Gosling, William D. A1 - Dupont-Nivet, Guillaume A1 - Hoorn, Carina T1 - A novel approach to study the morphology and chemistry of pollen in a phylogenetic context, applied to the halophytic taxon Nitraria L.(Nitrariaceae) JF - PeerJ N2 - Nitraria is a halophytic taxon (i.e., adapted to saline environments) that belongs to the plant family Nitrariaceae and is distributed from the Mediterranean, across Asia into the south-eastern tip of Australia. This taxon is thought to have originated in Asia during the Paleogene (66-23 Ma), alongside the proto-Paratethys epicontinental sea. The evolutionary history of Nitraria might hold important clues on the links between climatic and biotic evolution but limited taxonomic documentation of this taxon has thus far hindered this line of research. Here we investigate if the pollen morphology and the chemical composition of the pollen wall are informative of the evolutionary history of Nitraria and could explain if origination along the proto-Paratethys and dispersal to the Tibetan Plateau was simultaneous or a secondary process. To answer these questions, we applied a novel approach consisting of a combination of Fourier Transform Infrared spectroscopy (FTIR), to determine the chemical composition of the pollen wall, and pollen morphological analyses using Light Microscopy (LM) and Scanning Electron Microscopy (SEM). We analysed our data using ordinations (principal components analysis and non-metric multidimensional scaling), and directly mapped it on the Nitrariaceae phylogeny to produce a phylomorphospace and a phylochemospace. Our LM, SEM and FTIR analyses show clear morphological and chemical differences between the sister groups Peganum and Nitraria. Differences in the morphological and chemical characteristics of highland species (Nitraria schoberi, N. sphaerocarpa, N. sibirica and N. tangutorum) and lowland species (Nitraria billardierei and N. retusa) are very subtle, with phylogenetic history appearing to be a more important control on Nitraria pollen than local environmental conditions. Our approach shows a compelling consistency between the chemical and morphological characteristics of the eight studied Nitrariaceae species, and these traits are in agreement with the phylogenetic tree. Taken together, this demonstrates how novel methods for studying fossil pollen can facilitate the evolutionary investigation of living and extinct taxa, and the environments they represent. KW - FTIR KW - LM KW - SEM KW - Paratethys KW - Tibet KW - Sporopollenin KW - Mediterranean KW - Steppe-desert KW - Australia KW - Palynology Y1 - 2018 U6 - https://doi.org/10.7717/peerj.5055 SN - 2167-8359 VL - 6 PB - PeerJ Inc. CY - London ER - TY - JOUR A1 - Vaid, Akhil A1 - Somani, Sulaiman A1 - Russak, Adam J. A1 - De Freitas, Jessica K. A1 - Chaudhry, Fayzan F. A1 - Paranjpe, Ishan A1 - Johnson, Kipp W. A1 - Lee, Samuel J. A1 - Miotto, Riccardo A1 - Richter, Felix A1 - Zhao, Shan A1 - Beckmann, Noam D. A1 - Naik, Nidhi A1 - Kia, Arash A1 - Timsina, Prem A1 - Lala, Anuradha A1 - Paranjpe, Manish A1 - Golden, Eddye A1 - Danieletto, Matteo A1 - Singh, Manbir A1 - Meyer, Dara A1 - O'Reilly, Paul F. A1 - Huckins, Laura A1 - Kovatch, Patricia A1 - Finkelstein, Joseph A1 - Freeman, Robert M. A1 - Argulian, Edgar A1 - Kasarskis, Andrew A1 - Percha, Bethany A1 - Aberg, Judith A. A1 - Bagiella, Emilia A1 - Horowitz, Carol R. A1 - Murphy, Barbara A1 - Nestler, Eric J. A1 - Schadt, Eric E. A1 - Cho, Judy H. A1 - Cordon-Cardo, Carlos A1 - Fuster, Valentin A1 - Charney, Dennis S. A1 - Reich, David L. A1 - Böttinger, Erwin A1 - Levin, Matthew A. A1 - Narula, Jagat A1 - Fayad, Zahi A. A1 - Just, Allan C. A1 - Charney, Alexander W. A1 - Nadkarni, Girish N. A1 - Glicksberg, Benjamin S. T1 - Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation JF - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR N2 - Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes. KW - machine learning KW - COVID-19 KW - electronic health record KW - TRIPOD KW - clinical KW - informatics KW - prediction KW - mortality KW - EHR KW - cohort KW - hospital KW - performance Y1 - 2020 U6 - https://doi.org/10.2196/24018 SN - 1439-4456 SN - 1438-8871 VL - 22 IS - 11 PB - Healthcare World CY - Richmond, Va. ER - TY - JOUR A1 - Döll, Stefanie A1 - Djalali Farahani-Kofoet, Roxana A1 - Zrenner, Rita A1 - Henze, Andrea A1 - Witzel, Katja T1 - Tissue-specific signatures of metabolites and proteins in asparagus roots and exudates JF - Horticulture research N2 - Comprehensive untargeted and targeted analysis of root exudate composition has advanced our understanding of rhizosphere processes. However, little is known about exudate spatial distribution and regulation. We studied the specific metabolite signatures of asparagus root exudates, root outer (epidermis and exodermis), and root inner tissues (cortex and vasculature). The greatest differences were found between exudates and root tissues. In total, 263 non-redundant metabolites were identified as significantly differentially abundant between the three root fractions, with the majority being enriched in the root exudate and/or outer tissue and annotated as 'lipids and lipid-like molecules' or 'phenylpropanoids and polyketides'. Spatial distribution was verified for three selected compounds using MALDI-TOF mass spectrometry imaging. Tissue-specific proteome analysis related root tissue-specific metabolite distributions and rhizodeposition with underlying biosynthetic pathways and transport mechanisms. The proteomes of root outer and inner tissues were spatially very distinct, in agreement with the fundamental differences between their functions and structures. According to KEGG pathway analysis, the outer tissue proteome was characterized by a high abundance of proteins related to 'lipid metabolism', 'biosynthesis of other secondary metabolites' and 'transport and catabolism', reflecting its main functions of providing a hydrophobic barrier, secreting secondary metabolites, and mediating water and nutrient uptake. Proteins more abundant in the inner tissue related to 'transcription', 'translation' and 'folding, sorting and degradation', in accord with the high activity of cortical and vasculature cell layers in growth- and development-related processes. In summary, asparagus root fractions accumulate specific metabolites. This expands our knowledge of tissue-specific plant cell function. Y1 - 2021 U6 - https://doi.org/10.1038/s41438-021-00510-5 SN - 2052-7276 SN - 2662-6810 VL - 8 IS - 1 PB - Nanjing Agricultural Univ. CY - Nanjing ER - TY - CHAP A1 - Löw, Martina A1 - Sayman, Volkan A1 - Schwerer, Jona A1 - Wolf, Hannah ED - Löw, Martina ED - Sayman, Volkan ED - Schwerer, Jona ED - Wolf, Hannah T1 - Am Ende der Globalisierung BT - über die Refiguration von Räumen T2 - Am Ende der Globalisierung Y1 - 2021 SN - 978-3-8394-5402-2 SN - 978-3-8376-5402-8 U6 - https://doi.org/10.14361/9783839454022 SP - 9 EP - 22 PB - transcript CY - Bielefeld ER -