Direct measurements of meltwater runoff on the Greenland ice sheet surface

Meltwater runoff from the Greenland ice sheet surface influences surface mass balance (SMB), ice dynamics, and global sea level rise, but is estimated with climate models and thus difficult to validate. We present a way to measure ice surface runoff directly, from hourly in situ supraglacial river discharge measurements and simultaneous high-resolution satellite/drone remote sensing of upstream fluvial catchment area. A first 72-h trial for a 63.1-km2 moulin-terminating internally drained catchment (IDC) on Greenland?s midelevation (1,207?1,381 m above sea level) ablation zone is compared with melt and runoff simulations from HIRHAM5, MAR3.6, RACMO2.3, MERRA-2, and SEB climate/SMB models. Current models cannot reproduce peak discharges or timing of runoff entering moulins but are improved using synthetic unit hydrograph (SUH) theory. Retroactive SUH applications to two older field studies reproduce their findings, signifying that remotely sensed IDC area, shape, and supraglacial river length are useful for predicting delays in peak runoff delivery to moulins. Applying SUH to HIRHAM5, MAR3.6, and RACMO2.3 gridded melt products for 799 surrounding IDCs suggests their terminal moulins receive lower peak discharges, less diurnal variability, and asynchronous runoff timing relative to climate/SMB model output alone. Conversely, large IDCs produce high moulin discharges, even at high elevations where melt rates are low. During this particular field experiment, models overestimated runoff by +21 to +58%, linked to overestimated surface ablation and possible meltwater retention in bare, porous, low-density ice. Direct measurements of ice surface runoff will improve climate/SMB models, and incorporating remotely sensed IDCs will aid coupling of SMB with ice dynamics and subglacial systemspublishersversionPeer reviewe

Cancer risk in hospitalised asthma patients

Asthma is an increasingly common disorder, affecting 5–10% of the population. It involves a dysregulated immune function, which may predispose to subsequent cancer. We examined cancer risk among Swedish subjects who had hospital admission once or multiple times for asthma. An asthma research database was created by identifying asthma patients from the Swedish Hospital Discharge Register and by linking them with the Cancer Registry. A total of 140 425 patients were hospitalised for asthma during 1965–2004, of whom 7421 patients developed cancer, giving an overall standardised incidence ratio (SIR) of 1.36. A significant increase was noted for most sites, with the exception of breast and ovarian cancers and non-Hodgkin's lymphoma and myeloma. Patients with multiple hospital admissions showed a high risk, particularly for stomach (SIR 1.70) and colon (SIR 1.99) cancers. A significant decrease was noted for endometrial cancer and skin melanoma. Oesophageal and lung cancers showed high risks throughout the study period, whereas stomach cancer increased towards the end of the period. The relatively stable temporal trends suggest that the asthmatic condition rather than its medication is responsible for the observed associations

Eosinophils in glioblastoma biology

Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. The development of this malignant glial lesion involves a multi-faceted process that results in a loss of genetic or epigenetic gene control, un-regulated cell growth, and immune tolerance. Of interest, atopic diseases are characterized by a lack of immune tolerance and are inversely associated with glioma risk. One cell type that is an established effector cell in the pathobiology of atopic disease is the eosinophil. In response to various stimuli, the eosinophil is able to produce cytotoxic granules, neuromediators, and pro-inflammatory cytokines as well as pro-fibrotic and angiogenic factors involved in pathogen clearance and tissue remodeling and repair. These various biological properties reveal that the eosinophil is a key immunoregulatory cell capable of influencing the activity of both innate and adaptive immune responses. Of central importance to this report is the observation that eosinophil migration to the brain occurs in response to traumatic brain injury and following certain immunotherapeutic treatments for GBM. Although eosinophils have been identified in various central nervous system pathologies, and are known to operate in wound/repair and tumorstatic models, the potential roles of eosinophils in GBM development and the tumor immunological response are only beginning to be recognized and are therefore the subject of the present review

Learning Adjustable Reduced Downsampling Network for Small Object Detection in Urban Environments

Detecting small objects (e.g., manhole covers, license plates, and roadside milestones) in urban images is a long-standing challenge mainly due to the scale of small object and background clutter. Although convolution neural network (CNN)-based methods have made significant progress and achieved impressive results in generic object detection, the problem of small object detection remains unsolved. To address this challenge, in this study we developed an end-to-end network architecture that has three significant characteristics compared to previous works. First, we designed a backbone network module, namely Reduced Downsampling Network (RD-Net), to extract informative feature representations with high spatial resolutions and preserve local information for small objects. Second, we introduced an Adjustable Sample Selection (ADSS) module which frees the Intersection-over-Union (IoU) threshold hyperparameters and defines positive and negative training samples based on statistical characteristics between generated anchors and ground reference bounding boxes. Third, we incorporated the generalized Intersection-over-Union (GIoU) loss for bounding box regression, which efficiently bridges the gap between distance-based optimization loss and area-based evaluation metrics. We demonstrated the effectiveness of our method by performing extensive experiments on the public Urban Element Detection (UED) dataset acquired by Mobile Mapping Systems (MMS). The Average Precision (AP) of the proposed method was 81.71%, representing an improvement of 1.2% compared with the popular detection framework Faster R-CNN

Proglacial river dataset from the Akuliarusiarsuup Kuua River northern tributary, Southwest Greenland, 2008 - 2010, version 1.0

Pressing scientific questions concerning the Greenland ice sheet's climatic sensitivity, hydrology, and contributions to current and future sea level rise require hydrological datasets to resolve. While direct observations of ice sheet meltwater losses can be obtained in terrestrial rivers draining the ice sheet and from lake levels, few such datasets exist. We present a new dataset of meltwater river discharge for the vicinity of Kangerlussuaq, Southwest Greenland. The dataset contains measurements of river stage and discharge for three sites along the Akuliarusiarsuup Kuua (Watson) River's northern tributary, with 30 minute temporal resolution between June 2008 and August 2010. Additional data of water temperature, air pressure, and lake water depth and temperature are also provided. Discharge data were measured at sites with near-ideal properties for such data collection. Regardless, high water bedload and turbulent flow introduce considerable uncertainty. These were constrained and quantified using statistical techniques, thereby providing a high quality dataset from this important site. The greatest data uncertainties are associated with streambed elevation change and measurements. Large portions of stream channels deepened according to statistical tests, but poor precision of streambed depth measurements also added uncertainty. Quality checked data are freely available for scientific use as supplementary online material

Proglacial river dataset from the Akuliarusiarsuup Kuua River northern tributary, Southwest Greenland, 2008 - 2013, Version 2.0

The dataset contains measurements of river stage and discharge for one sites along the Akuliarusiarsuup Kuua River's northern tributary, with 30 minute temporal resolution between June 2008 and August 2013 This river is a tributary to the Watson River discharging into Kangerlussuaq Fjord by the town of Kangerlussuaq, Southwest Greenland. Additional data of water temperature, air pressure are also provided. Compared to version 1.0 of the dataset, this dataset used a total of 36 in situ discharge observations collected between 2008 and 2012 to construct the rating curve. Furthermore, data of Station AK-004-001 between 2010-09-06T11:30 to 2010-09-07T13:30 have been removed from version 2.0 because these values were likely caused by backflow when a jokulhlaup from a large glacier dammed lake caused increased water levels in the downstreams lake. Thus, data measured at AK-004-001 between 2010-09-06T11:30 to 2010-09-07T13:30 are not representative for the AK-004 catchment