A preliminary assessment of water partitioning and ecohydrological coupling in northern headwaters using stable isotopes and conceptual runoff models

Funded by European Research Council ERC. Grant Number: GA 335910 VEWA Swedish Science Foundation (SITES) Future Forest Formas (ForWater) SKB the Kempe foundation Environment Canada the Garfield Weston Foundation the Natural Sciences and Engineering Research Council of Canada (NSERC) the Northwest Territories Cumulative Impacts Monitoring ProgramPeer reviewedPublisher PD

New approaches to mapping and managing palaeochannel resources in the light of future environmental change : a case study from the Trent Valley, UK

Abandoned river channels may provide rich primary sources of palaeoenvironmental and cultural information elucidating landscape evolution, climate change, vegetation history and human impact, especially since the beginning of the Holocene epoch. However, although potentially an important resource, palaeochannels are not often recorded systematically and only rarely enjoy robust statutory protection (in the UK as Sites of Special Scientific Interest). In consequence, it is challenging to mitigate and manage this important geoarchaeological resource effectively within the UK planning framework. Whilst palaeochannels have long been recognised on aerial photographs and historic maps, the advent of airborne laser scanning (Lidar) and other remote-sensing technologies has provided a hitherto unforeseen opportunity to record such landforms and related features at a catchment scale. This paper provides a case study from the Nottinghamshire reach of the Trent Valley, where a desk-based methodology that is now being extended across the entire catchment has been developed for recording, geospatially locating and defining the attributes of observed palaeochannels. After outlining the methodology, we consider how this approach to resource management can aid archaeological research and future heritage management, especially in the light of predicted climate and environmental change

Can consumers learn to ask three questions to improve shared decision making? A feasibility study of the ASK (AskShareKnow) Patient–Clinician Communication Model® intervention in a primary health-care setting

Funded by Informed Medical Decisions Foundation. Grant Number: #0175-1 National Health and Medical Research Council Public Health Training Fellowship. Grant Number: 568962Peer reviewedPublisher PD

Non-invasive Liver Fibrosis Screening on CT Images using Radiomics

Objectives: To develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver. Methods: For this retrospective, single-centre study, radiomic features were extracted from Regions of Interest (ROIs) on CT images of patients who underwent simultaneous liver biopsy and CT examinations. Combinations of contrast, normalization, machine learning model, and feature selection method were determined based on their mean test Area Under the Receiver Operating Characteristic curve (AUC) on randomly placed ROIs. The combination and selected features with the highest AUC were used to develop a final liver fibrosis screening model. Results: The study included 101 male and 68 female patients (mean age = 51.2 years $\pm$ 14.7 [SD]). When averaging the AUC across all combinations, non-contrast enhanced (NC) CT (AUC, 0.6100; 95% CI: 0.5897, 0.6303) outperformed contrast-enhanced CT (AUC, 0.5680; 95% CI: 0.5471, 0.5890). The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1.5 (AUC, 0.7833; 95% CI: 0.7821, 0.7845), (sensitivity, 0.9091; 95% CI: 0.9091, 0.9091). Conclusions: Radiomics-based machine learning models allow for the detection of liver fibrosis with reasonable accuracy and high sensitivity on NC CT. Thus, these models can be used to non-invasively screen for liver fibrosis, contributing to earlier detection of the disease at a potentially curable stage

Inter-Comparison of Lightning Trends from Ground-Based Networks During Severe Weather: Applications Toward GLM

The planned GOES-R Geostationary Lightning Mapper (GLM) will provide total lightning data on the location and intensity of thunderstorms over a hemispheric spatial domain. Ongoing GOES-R research activities are demonstrating the utility of total flash rate trends for enhancing forecasting skill of severe storms. To date, GLM total lightning proxy trends have been well served by ground-based VHF systems such as the Northern Alabama Lightning Mapping Array (NALMA). The NALMA (and other similar networks in Washington DC and Oklahoma) provide high detection efficiency (> 90%) and location accuracy (< 1 km) observations of total lightning within about 150 km from network center. To expand GLM proxy applications for high impact convective weather (e.g., severe, aviation hazards), it is desirable to investigate the utility of additional sources of continuous lightning that can serve as suitable GLM proxy over large spatial scales (order 100 s to 1000 km or more), including typically data denied regions such as the oceans. Potential sources of GLM proxy include ground-based long-range (regional or global) VLF/LF lightning networks such as the relatively new Vaisala Global Lightning Dataset (GLD360) and Weatherbug Total Lightning Network (WTLN). Before using these data in GLM research applications, it is necessary to compare them with LMAs and well-quantified cloud-to-ground (CG) lightning networks, such as Vaisala s National Lightning Detection Network (NLDN), for assessment of total and CG lightning location accuracy, detection efficiency and flash rate trends. Preliminary inter-comparisons from these lightning networks during selected severe weather events will be presented and their implications discussed

The bulk-edge correspondence for the quantum Hall effect in Kasparov theory

We prove the bulk-edge correspondence in $K$-theory for the quantum Hall effect by constructing an unbounded Kasparov module from a short exact sequence that links the bulk and boundary algebras. This approach allows us to represent bulk topological invariants explicitly as a Kasparov product of boundary invariants with the extension class linking the algebras. This paper focuses on the example of the discrete integer quantum Hall effect, though our general method potentially has much wider applications.Comment: 16 pages. Minor corrections and introduction expanded. To appear in Letters in Mathematical Physic

Relationships Between Long-Range Lightning Networks and TRMM/LIS Observations

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. The present study intercompares long-range lightning data with observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study examines network detection efficiency and location accuracy relative to LIS observations, describes spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by the long-range networks. Improved knowledge of relationships between these datasets will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM)