Atmospheric Downscaling using Multi-Objective Genetic Programming

Numerical models are used to simulate and to understand the interplay of physical processes in the atmosphere, and to generate weather predictions and climate projections. However, due to the high computational cost of atmospheric models, discrepancies between required and available spatial resolution of modeled atmospheric data occur frequently. One approach to generate higher-resolution atmospheric data from coarse atmospheric model output is statistical downscaling. The present work introduces multi-objective Genetic Programming (MOGP) as a method for downscaling atmospheric data. MOGP is applied to evolve downscaling rules, i.e., statistical relations mapping coarse-scale atmospheric information to the point scale or to a higher-resolution grid. Unlike classical regression approaches, where the structure of the regression model has to be predefined, Genetic Programming evolves both model structure and model parameters simultaneously. Thus, MOGP can flexibly capture nonlinear and multivariate predictor-predictand relations. Classical linear regression predicts the expected value of the predictand given a realization of predictors minimizing the root mean square error (RMSE) but in general underestimating variance. With the multi-objective approach multiple cost/fitness functions can be considered which are not solely aimed at the minimization of the RMSE, but simultaneously consider variance and probability distribution based measures. Two areas of application of MOGP for atmospheric downscaling are presented: The downscaling of mesoscale near-surface atmospheric fields from 2.8 km to 400 m grid spacing and the downscaling of temperature and precipitation series from a global reanalysis to a set of local stations. (1) With growing computational power, integrated modeling platforms, coupling atmospheric models to land surface and hydrological/subsurface models are increasingly used to account for interactions and feedback processes between the different components of the soil-vegetation-atmosphere system. Due to the small-scale heterogeneity of land surface and subsurface, land surface and subsurface models require a small grid spacing, which is computationally unfeasible for atmospheric models. Hence, in many integrated modeling systems, a scale gap occurs between atmospheric model component and the land surface/subsurface components, which potentially introduces biases in the estimation of the turbulent exchange fluxes at the surface. Under the assumption that the near surface atmospheric boundary layer is significantly influenced by land surface heterogeneity, MOGP is used to evolve downscaling rules that recover high-resolution near-surface fields of various atmospheric variables (temperature, wind speed, etc.) from coarser atmospheric data and high-resolution land surface information. For this application MOGP does not significantly reduce the RMSE compared to a pure interpolation. However, (depending on the state variable under consideration) large parts of the spatial variability can be restored without any or only a small increase in RMSE. (2) Climate change impact studies often require local information while the general circulation models used to create climate projections provide output with a grid spacing in the order of approximately 100~km. MOGP is applied to estimate the local daily maximum, minimum and mean temperature and the daily accumulated precipitation at selected stations in Europe from global reanalysis data. Results are compared to standard regression approaches. While for temperature classical linear regression already achieves very good results and outperforms MOGP, the results of MOGP for precipitation downscaling are promising and outperform a standard generalized linear model. Especially the good representation of precipitation extremes and spatial correlation (with the latter not incorporated in the objectives) are encouraging.Numerische Modelle, welche für Wettervorhersagen und Klimaprojektionen verwendet werden, simulieren das Zusammenspiel physikalischer Prozesse in der Atmosphäre. Bedingt durch den hohen Rechenaufwand atmosphärischer Modelle treten jedoch häufig Diskrepanzen zwischen benötigter und verfügbarer Auflösung atmosphärischer Daten auf. Ein möglicher Ansatz, höher aufgelöste atmosphärische Daten aus vergleichsweise grobem Modelloutput zu generieren, ist statistisches Downscaling. Die vorliegende Arbeit stellt multi-objektives Genetic Programming (MOGP) als Methode für das Downscaling atmosphärischer Daten vor. MOGP wird verwendet, um Downscaling Regeln (statistische Beziehungen) zu generieren, welche grobskalige atmosphärische Daten auf die Punktskala oder ein höher aufgelöstes Gitter abbilden. Im Gegensatz zu klassischen Regressionsansätzen, in welchen die Struktur des Regressionsmodells vorgegeben wird, entwickelt MOGP Modellstruktur und Modellparameter simultan. Dieses erlaubt es, auch nicht lineare und multivariate Beziehungen zwischen Prädiktoren und Prädiktand zu berücksichtigen. Ein klassisches lineares Regressionsmodel schätzt den Erwartungswert des Prädiktanden, eine Realisierung von Prädiktoren gegeben, und minimiert somit den mittleren quadratischen Fehler (root mean square error, RMSE), aber unterschätzt im Allgemeinen die Varianz. Mit einem multi-objektiven Ansatz können multiple Kostenfunktionen berücksichtigt werden, welche nicht ausschließlich auf die Minimierung des RMSE ausgelegt sind, sondern simultan auch Varianz und Wahrscheinlichkeitsverteilung berücksichtigen. In dieser Arbeit werden zwei verschiedene Anwendungen von MOGP für atmosphärisches Downscaling präsentiert: Das Downscaling mesoskaliger oberflächennaher atmosphärischer Felder von einem 2.8km auf ein 400 m Gitter und das Downscaling von Temperatur- und Niederschlagszeitreihen von globalen Reanalysedaten auf lokale Stationen. (1) Mit wachsender Rechenleistung werden integrierte Modellplattformen, welche Atmosphären-modelle mit Landoberflächenmodellen und hydrologischen Bodenmodellen koppeln, immer häufiger verwendet, um auch die Interaktionen und Feedbacks zwischen den Komponenten des Boden-Vegetations-Atmosphären Systems zu berücksichtigen. Aufgrund kleinskaliger Heterogenitäten in Landoberfläche und Boden benötigen die Landoberflächen- und Bodenmodelle eine hohe Gitterauflösung. Für atmosphärische Modelle hingegen ist eine solch hohe Auflösung rechnerisch nicht praktikabel. Daher findet sich typischerweise ein Skalenunterschied zwischen atmosphärischer und Landoberflächen-/hydrologischer Modellkomponente. Solch ein Skalensprung kann jedoch zu Problemen bei der Schätzung der turbulenten Flüsse zwischen Atmosphäre und Boden führen, da die turbulenten Flüsse in nichtlinearer Weise vom Zustand des Bodens und der bodennahen Atmosphäre abhängen. Die mit MOGP entwickelten Downscaling Regeln verwenden grob aufgelöste atmosphärische Daten und hoch aufgelöste Landoberflächen-Informationen, um hoch aufgelöste Felder verschiedener bodennaher atmosphärischer Variablen (Temperatur, Windgeschwindigkeit etc.) generieren. Die Regeln basieren somit auf der Annahme, dass die bodennahe atmosphärische Grenzschicht signifikant von der Heterogenität der Landoberfläche beeinflusst wird. Zwar erreicht MOGP für diese Anwendung nur selten eine signifikante Reduktion des RMSE gegenüber einer reinen Interpolation, jedoch kann, abhängig von der betrachteten atmosphärischen Variablen, ein großer Teil der räumlichen Variabilität wiederhergestellt werden ohne oder mit nur sehr geringem Anstieg des RMSE. (2) Studien zur Auswirkung des Klimawandels benötigen oft hochaufgelöste oder lokale atmosphärische Daten. Der Output globaler Klimamodelle, mit Hilfe derer Klimaprojektionen erstellt werden, ist gemeinhin zu grob. MOGP wird verwendet, um Tagesmaximum, -minimum und -mittel der Temperatur sowie den täglich akkumulierten Niederschlag an lokalen Stationen in Europa zu schätzen. Die Resultate werden mit linearen Regressionsmethoden verglichen. Für das Downscaling von Temperatur liefert eine klassische lineare Regression bereits sehr gute Resultate, welche MOGP im Allgemeinen an Qualität übertreffen. Für Niederschlag hingegen sind die MOGP Resultate vielversprechend, auch im Vergleich zu generalisierten linearen Modellen. Insbesondere die Repräsentation von Niederschlagsextremen und räumlicher Korrelation (letzteres ist nicht Bestandteil der Kostenfunktionen) sind vielversprechend

Online cognitive monitoring technology for people with Parkinson’s disease and REM sleep behavioural disorder

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson’s Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices

Motor complications in Parkinson’s disease:results from 3,343 patients followed for up to 12 years

Background: Motor complications are well recognised in Parkinson’s disease (PD), but their reported prevalence varies and functional impact has not been well studied. Objectives: To quantify the presence, severity, impact and associated factors for motor complications in PD.Methods: Analysis of 3 large prospective cohort studies of recent-onset PD patients followed for up to 12 years. The MDS-UPDRS part 4 assessed motor complications and multivariable logistic regression tested for associations. Genetic risk score (GRS) for Parkinson’s was calculated from 79 single nucleotide polymorphisms. Results: 3,343 cases were included (64.7% male). Off periods affected 35.0% (95% CI 33.0, 37.0) at 4-6 years and 59.0% (55.6, 62.3) at 8-10 years. Dyskinesia affected 18.5% (95% CI 16.9, 20.2) at 4-6 years and 42.1% (38.7, 45.5) at 8-10 years. Dystonia affected 13.4% (12.1, 14.9) at 4-6 years and 22.8% (20.1, 25.9) at 8-10 years. Off periods consistently caused greater functional impact than dyskinesia. Motor complications were more common among those with higher drug doses, younger age at diagnosis, female gender, and greater dopaminergic responsiveness (in challenge tests), with associations emerging 2 to 4 years post-diagnosis. Higher Parkinson’s GRS was associated with early dyskinesia (0.026 ≤ P ≤ 0.050 from 2 to 6 years).Conclusions: Off periods are more common and cause greater functional impairment than dyskinesia. We confirm previously reported associations between motor 4 complications with several demographic and medication factors. Greater dopaminergic responsiveness and a higher genetic risk score are two novel and significant independent risk factors for the development of motor complications

Multi‐objective downscaling of precipitation time series by genetic programming

We use symbolic regression to estimate daily precipitation amounts at six stations in the Alpine region from a global reanalysis. Symbolic regression only prescribes the set of mathematical expressions allowed in the regression model, but not its structure. The regression models are generated by genetic programming (GP) in analogy to biological evolution. The two conflicting objectives of a low root‐mean‐square error (RMSE) and consistency in the distribution between model and observations are treated as a multi‐objective optimization problem. This allows us to derive a set of downscaling models that represents different achievable trade‐offs between the two conflicting objectives, a so‐called Pareto set. Our GP setup limits the size of the regression models and uses an analytical quotient instead of a standard or protected division operator. With this setup we obtain models that have a generalization performance comparable with generalized linear regression models (GLMs), which are used as a benchmark. We generate deterministic and stochastic downscaling models with GP. The deterministic downscaling models with low RMSE outperform the respective stochastic models. The stochastic models with low IQD, however, perform slightly better than the respective deterministic models for the majority of cases. No approach is uniquely superior. The stochastic models with optimal IQD provide useful distribution estimates that capture the stochastic uncertainty similar to or slightly better than the GLM‐based downscaling.We have fitted deterministic and stochastic empirical‐statistical downscaling models that represent different possible compromises between two conflicting objectives: (a) a low RMSE and (b) consistency in the distribution between downscaled series and reference observations. The graphic shows the skill of our downscaling models w.r.t. the two objectives (larger is better) for the station Sonnblick.CRC/TR32: Patterns in Soil‐Vegetation‐Atmosphere Systems: Monitoring, Modelling and Data Assimilation; funded by the German Research Foundation (Deutsche Forschungsgemeinschaft DFG) http://dx.doi.org/10.13039/50110000165

Probabilistic predictions of SIS epidemics on networks based on population-level observations

We predict the future course of ongoing susceptible–infected–susceptible (SIS) epidemics on regular, Erdős–Rényi and Barabási–Albert networks. It is known that the contact network influences the spread of an epidemic within a population. Therefore, observations of an epidemic, in this case at the population-level, contain information about the underlying network. This information, in turn, is useful for predicting the future course of an ongoing epidemic. To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model. The surrogate model is based on a birth-and-death process; the effect of the underlying network is described by a parametric model for the birth rates. We demonstrate empirically that the surrogate model captures the intrinsic stochasticity of the epidemic once it reaches a point from which it will not die out. Bayesian parameter inference allows for uncertainty about the model parameters and the class of the underlying network to be incorporated directly into probabilistic predictions. An evaluation of a number of scenarios shows that in most cases the resulting prediction intervals adequately quantify the prediction uncertainty. As long as the population-level data is available over a long-enough period, even if not sampled frequently, the model leads to excellent predictions where the underlying network is correctly identified and prediction uncertainty mainly reflects the intrinsic stochasticity of the spreading epidemic. For predictions inferred from shorter observational periods, uncertainty about parameters and network class dominate prediction uncertainty. The proposed method relies on minimal data at population-level, which is always likely to be available. This, combined with its numerical efficiency, makes the proposed method attractive to be used either as a standalone inference and prediction scheme or in conjunction with other inference and/or predictive models

Dynamic Hormone Control of Stress and Fertility

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData Availability Statement: The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author/s.Neuroendocrine axes display a remarkable diversity of dynamic signaling processes relaying information between the brain, endocrine glands, and peripheral target tissues. These dynamic processes include oscillations, elastic responses to perturbations, and plastic long term changes observed from the cellular to the systems level. While small transient dynamic changes can be considered physiological, larger and longer disruptions are common in pathological scenarios involving more than one neuroendocrine axes, suggesting that a robust control of hormone dynamics would require the coordination of multiple neuroendocrine clocks. The idea of apparently different axes being in fact exquisitely intertwined through neuroendocrine signals can be investigated in the regulation of stress and fertility. The stress response and the reproductive cycle are controlled by the Hypothalamic-Pituitary-Adrenal (HPA) axis and the Hypothalamic-Pituitary-Gonadal (HPG) axis, respectively. Despite the evidence surrounding the effects of stress on fertility, as well as of the reproductive cycle on stress hormone dynamics, there is a limited understanding on how perturbations in one neuroendocrine axis propagate to the other. We hypothesize that the links between stress and fertility can be better understood by considering the HPA and HPG axes as coupled systems. In this manuscript, we investigate neuroendocrine rhythms associated to the stress response and reproduction by mathematically modeling the HPA and HPG axes as a network of interlocked oscillators. We postulate a network architecture based on physiological data and use the model to predict responses to stress perturbations under different hormonal contexts: normal physiological, gonadectomy, hormone replacement with estradiol or corticosterone (CORT), and high excess CORT (hiCORT) similar to hypercortisolism in humans. We validate our model predictions against experiments in rodents, and show how the dynamic responses of these endocrine axes are consistent with our postulated network architecture. Importantly, our model also predicts the conditions that ensure robustness of fertility to stress perturbations, and how chronodisruptions in glucocorticoid hormones can affect the reproductive axis’ ability to withstand stress. This insight is key to understand how chronodisruption leads to disease, and to design interventions to restore normal rhythmicity and health.Medical Research Council (MRC)Biotechnology and Biological Sciences Research Council (BBSRC)Engineering and Physical Sciences Research Council (EPSRC)Wellcome Trus

Online cognitive monitoring technology for people with Parkinson’s disease and REM sleep behavioural disorder

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson’s Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices

Online cognitive monitoring technology for people with Parkinson’s disease and REM sleep behavioural disorder

Automated online cognitive assessments are set to revolutionise clinical research and healthcare. However, their applicability for Parkinson’s Disease (PD) and REM Sleep Behavioural Disorder (RBD), a strong PD precursor, is underexplored. Here, we developed an online battery to measure early cognitive changes in PD and RBD. Evaluating 19 candidate tasks showed significant global accuracy deficits in PD (0.65 SD, p = 0.003) and RBD (0.45 SD, p = 0.027), driven by memory, language, attention and executive underperformance, and global reaction time deficits in PD (0.61 SD, p = 0.001). We identified a brief 20-min battery that had sensitivity to deficits across these cognitive domains while being robust to the device used. This battery was more sensitive to early-stage and prodromal deficits than the supervised neuropsychological scales. It also diverged from those scales, capturing additional cognitive factors sensitive to PD and RBD. This technology offers an economical and scalable method for assessing these populations that can complement standard supervised practices.</p

Motor complications in Parkinson's disease: results from 3343 patients followed for up to 12 years

Background Motor complications are well recognized in Parkinson's disease (PD), but their reported prevalence varies and functional impact has not been well studied. Objectives To quantify the presence, severity, impact and associated factors for motor complications in PD. Methods Analysis of three large prospective cohort studies of recent-onset PD patients followed for up to 12 years. The MDS-UPDRS part 4 assessed motor complications and multivariable logistic regression tested for associations. Genetic risk score (GRS) for Parkinson's was calculated from 79 single nucleotide polymorphisms. Results 3343 cases were included (64.7% male). Off periods affected 35.0% (95% CI 33.0, 37.0) at 4–6 years and 59.0% (55.6, 62.3) at 8–10 years. Dyskinesia affected 18.5% (95% CI 16.9, 20.2) at 4–6 years and 42.1% (38.7, 45.5) at 8–10 years. Dystonia affected 13.4% (12.1, 14.9) at 4–6 years and 22.8% (20.1, 25.9) at 8–10 years. Off periods consistently caused greater functional impact than dyskinesia. Motor complications were more common among those with higher drug doses, younger age at diagnosis, female gender, and greater dopaminergic responsiveness (in challenge tests), with associations emerging 2–4 years post-diagnosis. Higher Parkinson's GRS was associated with early dyskinesia (0.026 ≤ P ≤ 0.050 from 2 to 6 years). Conclusions Off periods are more common and cause greater functional impairment than dyskinesia. We confirm previously reported associations between motor complications with several demographic and medication factors. Greater dopaminergic responsiveness and a higher genetic risk score are two novel and significant independent risk factors for the development of motor complications