Metrics for Learning in Topological Persistence

Acknowledgments We gratefully acknowledge Roel Neggers for providing the DALES simulation data. JLS acknowledges support by the DFG-funded transregional research collaborative TR32 on Patterns in Soil–Vegetation–Atmosphere Systems.Peer reviewedPublisher PD

Topographic Wetness Index as a Proxy for Soil Moisture : The Importance of Flow-Routing Algorithm and Grid Resolution

The Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow-routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow-routing algorithms at DEM resolutions 1-30 m. We analyze the relationship between TWI and field-quantified soil moisture using statistical modeling methods and 5,200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow-routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest r(2) (23.7%) was reached using a multiple-flow-direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple-flow-direction algorithms were also more robust against DEM errors than single-flow-direction algorithms. The results provide field-evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow-routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture.Peer reviewe

Modelling soil moisture in a high-latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non-climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape-scale soil moisture variation by utilizing high-resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high-latitude landscape of mountain tundra in north-western Finland. We measured the plots three times during growing season 2016 with a hand-held time-domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R-2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R-2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R-2 = 0.47 and RMSE 9.34 VWC%, and for the latter R-2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high-resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1m(2) digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine-scale soil moisture variation. In the temporal variation models, the strongest predictor was the field-quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright (c) 2017 John Wiley & Sons, Ltd.Peer reviewe

Response style and severity and chronicity of depressive disorders in primary health care

Background: Response styles theory of depression postulates that rumination is a central factor in occurrence, severity and maintaining of depression. High neuroticism has been associated with tendency to ruminate. We investigated associations of response styles and neuroticism with severity and chronicity of depression in a primary care cohort study. Methods: In the Vantaa Primary Care Depression Study, a stratified random sample of 1119 adult patients was screened for depression using the Prime-MD. Depressive and comorbid psychiatric disorders were diagnosed using SCID-I/P and SCID-II interviews. Of the 137 patients with depressive disorders, 82% completed the prospective five-year follow-up with a graphic life chart enabling evaluation of the longitudinal course of episodes. Neuroticism was measured with the Eysenck Personality Inventory (EPI-Q). Response styles were investigated at five years using the Response Styles Questionnaire (RSQ-43). Results: At five years, rumination correlated significantly with scores of Hamilton Depression Rating Scale (r = 0.54), Beck Depression Inventory (r = 0.61), Beck Anxiety Inventory (r = 0.50), Beck Hopelessness Scale (r = 0.51) and Neuroticism (r = 0.58). Rumination correlated also with proportion of follow-up time spent depressed (r = 0.38). In multivariate regression, high rumination was significantly predicted by current depressive symptoms and neuroticism, but not by anxiety symptoms or preceding duration of depressive episodes. Conclusions: Among primary care patients with depression, rumination correlated with current severity of depressive symptoms, but the association with preceding episode duration remained uncertain. The association between neuroticism and rumination was strong. The findings are consistent with rumination as a state-related phenomenon, which is also strongly intertwined with traits predisposing to depression. (C) 2015 Elsevier Masson SAS. All rights reserved.Peer reviewe

Topographic Wetness Index as a Proxy for Soil Moisture : The Importance of Flow-Routing Algorithm and Grid Resolution

The Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow-routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow-routing algorithms at DEM resolutions 1-30 m. We analyze the relationship between TWI and field-quantified soil moisture using statistical modeling methods and 5,200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow-routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest r(2) (23.7%) was reached using a multiple-flow-direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple-flow-direction algorithms were also more robust against DEM errors than single-flow-direction algorithms. The results provide field-evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow-routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture.Peer reviewe

Rheological studies of the kinetics of gelation.

The Theological characteristics of the kinetics of gelation of a single extraction calf ekin derived gelatin were studied as a function of shear rate, shear rate history, concentration, pH, and temperature. Kinetic as well as molecular interpretations are provided* Changes in any of these variables alters the rate as well as the structure formed during gelation under shear* Optical rotatory dispersion studies failed to indicate an increase in the chain helix content during gelation, however, it is suspected that interference may be masking this rotation

Health-related quality of life of primary care patients with depressive disorders

Background: Depressive disorders are known to impair health-related quality of life (HRQoL) both in the short and long term. However, the determinants of long-term HRQoL outcomes in primary care patients with depressive disorders remain unclear. Methods: In a primary care cohort study of patients with depressive disorders, 82% of 137 patients were prospectively followed up for five years. Psychiatric disorders were diagnosed with SCID-I/P and SCID-II interviews; clinical, psychosocial and socio-economic factors were investigated by rating scales and questionnaires plus medical and psychiatric records. HRQoL was measured with the generic 15D instrument at baseline and five years, and compared with an age-standardized general population sample (n = 3707) at five years. Results: Depression affected the 15D total score and almost all dimensions at both time points. At the end of follow-up, HRQoL of patients in major depressive episode (MDE) was particularly low, and the association between severity of depression (Beck Depression Inventory [BDI]) and HRQoL was very strong (r = -0.804). The most significant predictors for change in HRQoL were changes in BDI and Beck Anxiety Inventory (BAI) scores. The mean 15D score of depressive primary care patients at five years was much worse than in the age-standardized general population, reaching normal range only among patients who were in clinical remission and had virtually no symptoms. Conclusions: Among depressive primary care patients, presence of current depressive symptoms markedly reduces HRQoL, with symptoms of concurrent anxiety also having a marked impact. For HRQoL to normalize, current depressive and anxiety symptoms must be virtually absent. (C) 2016 Elsevier Masson SAS. All rights reserved.Peer reviewe