@article{ChristakoudiTsilidisMulleretal.2020, author = {Christakoudi, Sofa and Tsilidis, Konstantinos K. and Muller, David C. and Freisling, Heinz and Weiderpass, Elisabete and Overvad, Kim and S{\"o}derberg, Stefan and H{\"a}ggstr{\"o}m, Christel and Pischon, Tobias and Dahm, Christina C. and Zhang, Jie and Tj{\o}nneland, Anne and Schulze, Matthias Bernd}, title = {A Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity: results from a large European cohort}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, number = {1}, publisher = {Springer Nature}, address = {Berlin}, pages = {15}, year = {2020}, abstract = {Abdominal and general adiposity are independently associated with mortality, but there is no consensus on how best to assess abdominal adiposity. We compared the ability of alternative waist indices to complement body mass index (BMI) when assessing all-cause mortality. We used data from 352,985 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) and Cox proportional hazards models adjusted for other risk factors. During a mean follow-up of 16.1 years, 38,178 participants died. Combining in one model BMI and a strongly correlated waist index altered the association patterns with mortality, to a predominantly negative association for BMI and a stronger positive association for the waist index, while combining BMI with the uncorrelated A Body Shape Index (ABSI) preserved the association patterns. Sex-specific cohort-wide quartiles of waist indices correlated with BMI could not separate high-risk from low-risk individuals within underweight (BMI<18.5 kg/m(2)) or obese (BMI30 kg/m(2)) categories, while the highest quartile of ABSI separated 18-39\% of the individuals within each BMI category, which had 22-55\% higher risk of death. In conclusion, only a waist index independent of BMI by design, such as ABSI, complements BMI and enables efficient risk stratification, which could facilitate personalisation of screening, treatment and monitoring.}, language = {en} } @misc{ChristakoudiTsilidisMulleretal.2020, author = {Christakoudi, Sofa and Tsilidis, Konstantinos K. and Muller, David C. and Freisling, Heinz and Weiderpass, Elisabete and Overvad, Kim and S{\"o}derberg, Stefan and H{\"a}ggstr{\"o}m, Christel and Pischon, Tobias and Dahm, Christina C. and Zhang, Jie and Tj{\o}nneland, Anne and Schulze, Matthias Bernd}, title = {A Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity: results from a large European cohort}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1}, issn = {1866-8372}, doi = {10.25932/publishup-52582}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-525827}, pages = {17}, year = {2020}, abstract = {Abdominal and general adiposity are independently associated with mortality, but there is no consensus on how best to assess abdominal adiposity. We compared the ability of alternative waist indices to complement body mass index (BMI) when assessing all-cause mortality. We used data from 352,985 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) and Cox proportional hazards models adjusted for other risk factors. During a mean follow-up of 16.1 years, 38,178 participants died. Combining in one model BMI and a strongly correlated waist index altered the association patterns with mortality, to a predominantly negative association for BMI and a stronger positive association for the waist index, while combining BMI with the uncorrelated A Body Shape Index (ABSI) preserved the association patterns. Sex-specific cohort-wide quartiles of waist indices correlated with BMI could not separate high-risk from low-risk individuals within underweight (BMI<18.5 kg/m(2)) or obese (BMI30 kg/m(2)) categories, while the highest quartile of ABSI separated 18-39\% of the individuals within each BMI category, which had 22-55\% higher risk of death. In conclusion, only a waist index independent of BMI by design, such as ABSI, complements BMI and enables efficient risk stratification, which could facilitate personalisation of screening, treatment and monitoring.}, language = {en} } @misc{CiemerRehmKurthsetal.2020, author = {Ciemer, Catrin and Rehm, Lars and Kurths, J{\"u}rgen and Donner, Reik Volker and Winkelmann, Ricarda and Boers, Niklas}, title = {An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic sea surface temperatures}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {9}, issn = {1866-8372}, doi = {10.25932/publishup-52586}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-525863}, pages = {12}, year = {2020}, abstract = {Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months.}, language = {en} } @article{CiemerRehmKurthsetal.2020, author = {Ciemer, Catrin and Rehm, Lars and Kurths, J{\"u}rgen and Donner, Reik Volker and Winkelmann, Ricarda and Boers, Niklas}, title = {An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic sea surface temperatures}, series = {Environmental Research Letters}, volume = {15}, journal = {Environmental Research Letters}, number = {9}, publisher = {IOP - Institute of Physics Publishing}, address = {Bristol}, pages = {10}, year = {2020}, abstract = {Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months.}, language = {en} } @misc{Korup2020, author = {Korup, Oliver}, title = {Bayesian geomorphology}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1}, issn = {1866-8372}, doi = {10.25932/publishup-53989}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-539892}, pages = {24}, year = {2020}, abstract = {The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.}, language = {en} } @article{Korup2020, author = {Korup, Oliver}, title = {Bayesian geomorphology}, series = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group}, volume = {46}, journal = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group}, number = {1}, publisher = {Wiley}, address = {Hoboken}, issn = {0197-9337}, doi = {10.1002/esp.4995}, pages = {151 -- 172}, year = {2020}, abstract = {The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.}, language = {en} } @article{Rabovsky2020, author = {Rabovsky, Milena}, title = {Change in a probabilistic representation of meaning can account for N400 effects on articles}, series = {Neuropsychologia : an international journal in behavioural and cognitive neuroscience}, volume = {143}, journal = {Neuropsychologia : an international journal in behavioural and cognitive neuroscience}, publisher = {Elsevier}, address = {Oxford}, issn = {0028-3932}, doi = {10.1016/j.neuropsychologia.2020.107466}, pages = {7}, year = {2020}, abstract = {Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles' cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.}, language = {en} } @article{Rabovsky2020, author = {Rabovsky, Milena}, title = {Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model}, series = {Neuropsychologia}, volume = {143}, journal = {Neuropsychologia}, publisher = {Elsevier}, address = {Amsterdam}, pages = {7}, year = {2020}, abstract = {Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles' cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.}, language = {en} } @misc{Rabovsky2020, author = {Rabovsky, Milena}, title = {Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, issn = {1866-8364}, doi = {10.25932/publishup-52698}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-526988}, pages = {9}, year = {2020}, abstract = {Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles' cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.}, language = {en} } @phdthesis{Stone2020, author = {Stone, Kate}, title = {Predicting long-distance lexical content in German verb-particle constructions}, doi = {10.25932/publishup-47679}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-476798}, school = {Universit{\"a}t Potsdam}, year = {2020}, abstract = {A large body of research now supports the presence of both syntactic and lexical predictions in sentence processing. Lexical predictions, in particular, are considered to indicate a deep level of predictive processing that extends past the structural features of a necessary word (e.g. noun), right down to the phonological features of the lexical identity of a specific word (e.g. /kite/; DeLong et al., 2005). However, evidence for lexical predictions typically focuses on predictions in very local environments, such as the adjacent word or words (DeLong et al., 2005; Van Berkum et al., 2005; Wicha et al., 2004). Predictions in such local environments may be indistinguishable from lexical priming, which is transient and uncontrolled, and as such may prime lexical items that are not compatible with the context (e.g. Kukona et al., 2014). Predictive processing has been argued to be a controlled process, with top-down information guiding preactivation of plausible upcoming lexical items (Kuperberg \& Jaeger, 2016). One way to distinguish lexical priming from prediction is to demonstrate that preactivated lexical content can be maintained over longer distances. In this dissertation, separable German particle verbs are used to demonstrate that preactivation of lexical items can be maintained over multi-word distances. A self-paced reading time and an eye tracking experiment provide some support for the idea that particle preactivation triggered by a verb and its context can be observed by holding the sentence context constant and manipulating the predictabilty of the particle. Although evidence of an effect of particle predictability was only seen in eye tracking, this is consistent with previous evidence suggesting that predictive processing facilitates only some eye tracking measures to which the self-paced reading modality may not be sensitive (Staub, 2015; Rayner1998). Interestingly, manipulating the distance between the verb and the particle did not affect reading times, suggesting that the surprisal-predicted faster reading times at long distance may only occur when the additional distance is created by information that adds information about the lexical identity of a distant element (Levy, 2008; Grodner \& Gibson, 2005). Furthermore, the results provide support for models proposing that temporal decay is not major influence on word processing (Lewandowsky et al., 2009; Vasishth et al., 2019). In the third and fourth experiments, event-related potentials were used as a method for detecting specific lexical predictions. In the initial ERP experiment, we found some support for the presence of lexical predictions when the sentence context constrained the number of plausible particles to a single particle. This was suggested by a frontal post-N400 positivity (PNP) that was elicited when a lexical prediction had been violated, but not to violations when more than one particle had been plausible. The results of this study were highly consistent with previous research suggesting that the PNP might be a much sought-after ERP marker of prediction failure (DeLong et al., 2011; DeLong et al., 2014; Van Petten \& Luka, 2012; Thornhill \& Van Petten, 2012; Kuperberg et al., 2019). However, a second experiment in a larger sample experiment failed to replicate the effect, but did suggest the relationship of the PNP to predictive processing may not yet be fully understood. Evidence for long-distance lexical predictions was inconclusive. The conclusion drawn from the four experiments is that preactivation of the lexical entries of plausible upcoming particles did occur and was maintained over long distances. The facilitatory effect of this preactivation at the particle site therefore did not appear to be the result of transient lexical priming. However, the question of whether this preactivation can also lead to lexical predictions of a specific particle remains unanswered. Of particular interest to future research on predictive processing is further characterisation of the PNP. Implications for models of sentence processing may be the inclusion of long-distance lexical predictions, or the possibility that preactivation of lexical material can facilitate reading times and ERP amplitude without commitment to a specific lexical item.}, language = {en} } @article{VaidSomaniRussaketal.2020, author = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.}, title = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation}, series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, volume = {22}, journal = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, number = {11}, publisher = {Healthcare World}, address = {Richmond, Va.}, issn = {1439-4456}, doi = {10.2196/24018}, pages = {19}, year = {2020}, abstract = {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.}, language = {en} } @article{ZoellerHainzlTilmannetal.2020, author = {Z{\"o}ller, Gert and Hainzl, Sebastian and Tilmann, Frederik and Woith, Heiko and Dahm, Torsten}, title = {Comment on: Wikelski, Martin; M{\"u}ller, Uschi; Scocco, Paola; Catorci, Andrea; Desinov, Lev V.; Belyaev, Mikhail Y.; Keim, Daniel A.; Pohlmeier, Winfried; Fechteler, Gerhard; Mai, Martin P. : Potential short-term earthquake forecasting by farm animal monitoring. - Ethology. - 126 (2020), 9. - S. 931 - 941. -ISSN 0179-1613. - eISSN 1439-0310. - doi 10.1111/eth.13078}, series = {Ethology}, volume = {127}, journal = {Ethology}, number = {3}, publisher = {Wiley}, address = {Hoboken}, issn = {0179-1613}, doi = {10.1111/eth.13105}, pages = {302 -- 306}, year = {2020}, abstract = {Based on an analysis of continuous monitoring of farm animal behavior in the region of the 2016 M6.6 Norcia earthquake in Italy, Wikelski et al., 2020; (Seismol Res Lett, 89, 2020, 1238) conclude that animal activity can be anticipated with subsequent seismic activity and that this finding might help to design a "short-term earthquake forecasting method." We show that this result is based on an incomplete analysis and misleading interpretations. Applying state-of-the-art methods of statistics, we demonstrate that the proposed anticipatory patterns cannot be distinguished from random patterns, and consequently, the observed anomalies in animal activity do not have any forecasting power.}, language = {en} }