Reach-scale bankfull channel types can exist independently of catchment hydrology

© 2020 John Wiley & Sons, Ltd. Reach-scale morphological channel classifications are underpinned by the theory that each channel type is related to an assemblage of reach- and catchment-scale hydrologic, topographic, and sediment supply drivers. However, the relative importance of each driver on reach morphology is unclear, as is the possibility that different driver assemblages yield the same reach morphology. Reach-scale classifications have never needed to be predicated on hydrology, yet hydrology controls discharge and thus sediment transport capacity. The scientific question is: do two or more regions with quantifiable differences in hydrologic setting end up with different reach-scale channel types, or do channel types transcend hydrologic setting because hydrologic setting is not a dominant control at the reach scale? This study answered this question by isolating hydrologic metrics as potential dominant controls of channel type. Three steps were applied in a large test basin with diverse hydrologic settings (Sacramento River, California) to: (1) create a reach-scale channel classification based on local site surveys, (2) categorize sites by flood magnitude, dimensionless flood magnitude, and annual hydrologic regime type, and (3) statistically analyze two hydrogeomorphic linkages. Statistical tests assessed the spatial distribution of channel types and the dependence of channel type morphological attributes by hydrologic setting. Results yielded 10 channel types. Nearly all types existed across all hydrologic settings, which is perhaps a surprising development for hydrogeomorphology. Downstream hydraulic geometry relationships were statistically significant. In addition, cobble-dominated uniform streams showed a consistent inverse relationship between slope and dimensionless flood magnitude, an indication of dynamic equilibrium between transport capacity and sediment supply. However, most morphological attributes showed no sorting by hydrologic setting. This study suggests that median hydraulic geometry relations persist across basins and within channel types, but hydrologic influence on geomorphic variability is likely due to local influences rather than catchment-scale drivers. © 2020 John Wiley & Sons, Ltd

Night sky brightness at sites from DMSP-OLS satellite measurements

We apply the sky brightness modelling technique introduced and developed by Roy Garstang to high-resolution DMSP-OLS satellite measurements of upward artificial light flux and to GTOPO30 digital elevation data in order to predict the brightness distribution of the night sky at a given site in the primary astronomical photometric bands for a range of atmospheric aerosol contents. This method, based on global data and accounting for elevation, Earth curvature and mountain screening, allows the evaluation of sky glow conditions over the entire sky for any site in the World, to evaluate its evolution, to disentangle the contribution of individual sources in the surrounding territory, and to identify main contributing sources. Sky brightness, naked eye stellar visibility and telescope limiting magnitude are produced as 3-dimensional arrays whose axes are the position on the sky and the atmospheric clarity. We compared our results to available measurements.Comment: 14 pages, 12 figures, accepted for publication in MNRAS, 17 june 200

Patterns of cervical and masticatory impairment in subgroups of people with temporomandibular disorders–an explorative approach based on factor analysis

Objectives: To identify clinical patterns of impairment affecting the cervical spine and masticatory systems in different subcategories of temporomandibular disorder (TMD) by an explorative data-driven approach. Methods: For this observational study, 144 subjects were subdivided according to Research Diagnostic Criteria for TMDs into: Healthy controls, temporomandibular joint (TMJ) signs without symptoms, TMJ affected, temporomandibular muscles affected, or TMJ and muscles affected. Factor analysis and linear regression were applied to cervical spine and masticatory data to identify and characterize clinical patterns in subgroups. Results: Factor analysis identified five clinical dimensions, which explained 59% of all variance: Mechanosensitivity, cervical movement, cervical and masticatory dysfunction, jaw movement, and upper cervical movement. Regression analysis identified different clinical dimensions in each TMD subgroup. Conclusion: Distinct clinical patterns of cervical spine and masticatory function were found among subgroups of TMD, which has clinical implications for therapeutic management

Digital Elevation Models: Terminology and Definitions

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same backgroun

Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among∼20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred-meter reaches. Performance in statistical learning is reasonable with a 61% median cross-validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large-scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy-based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors

Local Spatial and Temporal Processes of Influenza in Pennsylvania, USA: 2003–2009

Background: Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health's influenza surveillance system. Methodology and Findings: We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003-2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic. Conclusions: These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs. © 2012 Stark et al

Exploring the sensitivity of coastal inundation modelling to DEM vertical error

© 2018 Informa UK Limited, trading as Taylor & Francis Group. As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover

Maximum occlusal force and medial mandibular flexure in relation to vertical facial pattern: a cross-sectional study

BACKGROUND: Vertical facial pattern may be related to the direction of pull of the masticatory muscles, yet its effect on occlusal force and elastic deformation of the mandible still is unclear. This study tested whether the variation in vertical facial pattern is related to the variation in maximum occlusal force (MOF) and medial mandibular flexure (MMF) in 51 fully-dentate adults. METHODS: Data from cephalometric analysis according to the method of Ricketts were used to divide the subjects into three groups: Dolichofacial (n = 6), Mesofacial (n = 10) and Brachyfacial (n = 35). Bilateral MOF was measured using a cross-arch force transducer placed in the first molar region. For MMF, impressions of the mandibular occlusal surface were made in rest (R) and in maximum opening (O) positions. The impressions were scanned, and reference points were selected on the occlusal surface of the contralateral first molars. MMF was calculated by subtracting the intermolar distance in O from the intermolar distance in R. Data were analysed by ANCOVA (fixed factors: facial pattern, sex; covariate: body mass index (BMI); alpha = 0.05). RESULTS: No significant difference of MOF or MMF was found among the three facial patterns (P = 0.62 and P = 0.72, respectively). BMI was not a significant covariate for MOF or MMF (P > 0.05). Sex was a significant factor only for MOF (P = 0.007); males had higher MOF values than females. CONCLUSION: These results suggest that MOF and MMF did not vary as a function of vertical facial pattern in this Brazilian sample

Signs and symptoms of temporomandibular disorders and oral parafunctions in urban Saudi arabian adolescents: a research report

BACKGROUND: The aim of this study was to evaluate the prevalence of signs and symptoms of temporomandibular disorders (TMD) and oral parafunction habits among Saudi adolescents in the permanent dentition stage. METHODS: A total of 385 (230 females and 155 males) school children age 12–16, completed a questionnaire and were examined clinically. A stratified selection technique was used for schools allocation. RESULTS: The results showed that 21.3% of the subjects exhibited at least one sign of TMD and females were generally more affected than males. Joint sounds were the most prevalent sign (13.5%) followed by restricted opening (4.7%) and opening deviation (3.9%). The amplitude of mouth opening, overbite taken into consideration, was 46.5 mm and 50.2 mm in females and males respectively. TMJ pain and muscle tenderness were rare (0.5%). Reported symptoms were 33%, headache being the most frequent symptom 22%, followed by pain during chewing 14% and hearing TMJ noises 8.7%. Difficulty during jaw opening and jaw locking were rare. Lip/cheek biting was the most common parafunction habit (41%) with females significantly more than males, followed by nail biting (29%). Bruxism and thumb sucking were only 7.4% and 7.8% respectively. CONCLUSION: The prevalence of TMD signs were 21.3% with joint sounds being the most prevalent sign. While TMD symptoms were found to be 33% as, with headache being the most prevalent. Among the oral parafunctions, lip/cheek biting was the most prevalent 41% followed by nail biting 29%