The dynamic behaviour of epithelial cell sheets plays a central role during development, growth, disease and wound healing. These processes occur as a result of cell adhesion, migration, division, ...differentiation and death, and involve multiple processes acting at the cellular and molecular level. Computational models offer a useful means by which to investigate and test hypotheses about these processes, and have played a key role in the study of cell–cell interactions. However, the necessarily complex nature of such models means that it is difficult to make accurate comparison between different models, since it is often impossible to distinguish between differences in behaviour that are due to the underlying model assumptions, and those due to differences in the in silico implementation of the model. In this work, an approach is described for the implementation of vertex dynamics models, a discrete approach that represents each cell by a polygon (or polyhedron) whose vertices may move in response to forces. The implementation is undertaken in a consistent manner within a single open source computational framework, Chaste, which comprises fully tested, industrial-grade software that has been developed using an agile approach. This framework allows one to easily change assumptions regarding force generation and cell rearrangement processes within these models. The versatility and generality of this framework is illustrated using a number of biological examples. In each case we provide full details of all technical aspects of our model implementations, and in some cases provide extensions to make the models more generally applicable.
In the absence of widespread snowfall observations over the Arctic Ocean, reanalysis products provide a wide range of estimates of time‐evolving snowfall rates over Arctic sea ice, and it can be ...difficult to determine which product is most representative. In this work, Arctic snowfall rates retrieved from 2006 to 2016 CloudSat observations and snowfall products from three reanalyses are assessed. The products can be brought into encouraging agreement over the region on interannual time scales once differences in spatial representativeness and temporal sampling are accounted for. This motivates the use of CloudSat's snowfall product to calibrate reanalysis snowfall. The calibration is carried out for four Arctic quadrants and combined to produce regionally resolved and consistent estimates of interannually varying snowfall. Calibrated reanalysis snowfall inputs are then used to drive the NASA Eulerian Snow On Sea Ice Model, reducing the interproduct spread in the resulting simulated snow depths across the Arctic.
Plain Language Summary
Snow on Arctic sea ice impacts global climate in many ways. Because the Arctic is a remote region, we have few direct measurements of snow depth on Arctic sea ice. We can use snow models, which take input of snowfall rates from numerical model‐based products that incorporate observations, to estimate this snow depth. However, we are unsure which of these models best describes the actual amount of snowfall over the Arctic Ocean. In this study, we examine how well snowfall rates from satellite observations and model‐based snowfall products agree over the Arctic Ocean. We find that the snowfall rates are broadly well correlated, for different seasons and years, despite the differences between how the satellite snowfall and the model‐based snowfall are derived. We then calibrate the snowfall from each model‐based product to better match the satellite snowfall and apply this calibration to products used to predict snow depth on sea ice. This calibration provides a measurable reduction of uncertainty and gives us a more confident estimate of snow depth that can be compared directly to in situ observations and used to better estimate related quantities like sea ice thickness.
Key Points
Basin‐averaged snowfall rates from CloudSat and reanalysis products compare well over the Arctic Ocean
Reanalysis snowfall rates can be calibrated to CloudSat observations, reducing the interproduct spread
Applying the calibration to snowfall inputs for a snow‐on‐sea‐ice model significantly reduces the spread in simulated snow depths
Purpose:
To optimize radiation dose efficiency in CT while maintaining image quality, it is important to select the optimal tube potential. The selection of optimal tube potential, however, is highly ...dependent on patient size and diagnostic task. The purpose of this work was to develop a general strategy that allows for automatic tube potential selection for each individual patient and each diagnostic task.
Methods:
The authors propose a general strategy that allows automatic adaptation of the tube potential as a function of patient size and diagnostic task, using a novel index of image quality, “iodine contrast to noise ratio with a noise constraint (iCNR_NC),” to characterize the different image quality requirements by various clinical applications. The relative dose factor (RDF) at each tube potential to achieve a target image quality was then determined as a function of patient size and the noise constraint parameter. A workflow was developed to automatically identify the optimal tube potential that is both dose efficient and practically feasible, incorporating patient size and diagnostic task. An experimental study using a series of semianthropomorphic thoracic phantoms was used to demonstrate how the proposed general strategy can be implemented and how the radiation dose reduction achievable by the tube potential selection depends on phantom sizes and noise constraint parameters.
Results:
The proposed strategy provides a flexible and quantitative way to select the optimal tube potential based on the patient size and diagnostic task. The noise constraint parameter
α
can be adapted for different clinical applications. For example,
α
=
1
for noncontrast routine exams;
α
=
1.1
–
1.25
for contrast-enhanced routine exams; and
α
=
1.5
–
2.0
for CT angiography. For the five thoracic phantoms in the experiment, when
α
=
1
, the optimal tube potentials were 80, 100, 100, 120, 120, respectively. The corresponding RDFs (relative to 120 kV) were 78.0%, 90.9%, 95.2%, 100%, and 100%. When
α
=
1.5
, the optimal tube potentials were 80, 80, 80, 100, 100, respectively, with corresponding RDFs of 34.7%, 44.7%, 54.7%, 60.8%, and 89.5%.
Conclusions:
A general strategy to automatically select the most dose efficient tube potential for CT exams was developed that takes into account patient size and diagnostic task. Dependent on the patient size and the selection of noise constraint parameter for different diagnostic tasks, the dose reduction at each tube potential, quantified explicitly with the RDF, varies significantly.
Reanalysis products provide spatially homogeneous coverage for a variety of climate variables in regions such as the Arctic where observational data are limited. Soil temperatures are an important ...control of many land–atmosphere exchanges and hydrological processes, and permafrost soils are thawing as the climate warms. However, very little validation of reanalysis soil temperatures in the Arctic has been performed to date, because widespread in situ reference observations have historically been limited there. Here we validate pan-Arctic soil temperatures from eight reanalysis and land data assimilation system products, using a newly assembled database of in situ observations from diverse measurement networks across Eurasia and North America. We examine product performance across the extratropical Northern Hemisphere between 1982 and 2018, and find that most products have soil temperatures that are biased cold by 1–5 K, with an RMSE of 2–9 K, and that biases and RMSE are generally largest in the cold season. Monthly mean values from most products correlate well with in situ data (r>0.9) in the warm season but show lower correlations (r=0.55–0.85) in the cold season. Similarly, the magnitude of monthly variability in soil temperatures is well captured in summer but overestimated by 20 %–50 % for several products in winter. The suggestion is that soil temperatures in reanalysis products are subject to much higher uncertainty when the soil is frozen and/or when the ground is snow covered, suggesting that the representation of processes controlling snow cover in reanalysis systems should be urgently studied. We also validate the ensemble mean of all available products and find that, when all seasons and metrics are considered, the ensemble mean generally outperforms any individual product, in terms of its correlation and variability, while maintaining relatively low biases. As such, we recommend the ensemble mean soil temperature product for a wide range of applications, such as the validation of soil temperatures in climate models, and to inform models that require soil temperature inputs, such as hydrological models.
Both urinary tract infection (UTI) and asymptomatic bacteriuria (ASB) are common problems among elderly adults and represent a significant health care burden. Despite their frequency, differentiating ...between ASB and true UTI remains controversial among health care providers. Several challenges exist in the evaluation of urinary symptoms in the elderly patient. Symptoms of UTI are variable; problems are encountered in the collection, testing, and interpretation of urine specimens; and results of urinalysis are often misinterpreted and mishandled. Multiple studies have shown no morbidity or mortality benefit to antibiotic therapy in either community or long-term care facility residents with ASB.
In x-ray computed tomography (CT), materials having different elemental compositions can be represented by identical pixel values on a CT image (ie, CT numbers), depending on the mass density of the ...material. Thus, the differentiation and classification of different tissue types and contrast agents can be extremely challenging. In dual-energy CT, an additional attenuation measurement is obtained with a second x-ray spectrum (ie, a second "energy"), allowing the differentiation of multiple materials. Alternatively, this allows quantification of the mass density of two or three materials in a mixture with known elemental composition. Recent advances in the use of energy-resolving, photon-counting detectors for CT imaging suggest the ability to acquire data in multiple energy bins, which is expected to further improve the signal-to-noise ratio for material-specific imaging. In this review, the underlying motivation and physical principles of dual- or multi-energy CT are reviewed and each of the current technical approaches is described. In addition, current and evolving clinical applications are introduced.
Most noise reduction methods involve nonlinear processes, and objective evaluation of image quality can be challenging, since image noise cannot be fully characterized on the sole basis of the noise ...level at computed tomography (CT). Noise spatial correlation (or noise texture) is closely related to the detection and characterization of low-contrast objects and may be quantified by analyzing the noise power spectrum. High-contrast spatial resolution can be measured using the modulation transfer function and section sensitivity profile and is generally unaffected by noise reduction. Detectability of low-contrast lesions can be evaluated subjectively at varying dose levels using phantoms containing low-contrast objects. Clinical applications with inherent high-contrast abnormalities (eg, CT for renal calculi, CT enterography) permit larger dose reductions with denoising techniques. In low-contrast tasks such as detection of metastases in solid organs, dose reduction is substantially more limited by loss of lesion conspicuity due to loss of low-contrast spatial resolution and coarsening of noise texture. Existing noise reduction strategies for dose reduction have a substantial impact on lowering the radiation dose at CT. To preserve the diagnostic benefit of CT examination, thoughtful utilization of these strategies must be based on the inherent lesion-to-background contrast and the anatomy of interest. The authors provide an overview of existing noise reduction strategies for low-dose abdominopelvic CT, including analytic reconstruction, image and projection space denoising, and iterative reconstruction; review qualitative and quantitative tools for evaluating these strategies; and discuss the strengths and limitations of individual noise reduction methods.
Background & Aims Crohn's disease often involves the terminal ileum (TI), but skipping of the distal TI can occur. This can lead to negative results from ileocolonoscopy. We analyzed advanced ...cross-sectional images to determine how frequently this occurs. Methods We analyzed data from 189 consecutive patients (55% women) with Crohn's disease, evaluated in 2009 by computed tomography enterography (CTE) and ileocolonoscopy. The discharge impression of the gastroenterologist who treated the patients was used as the reference standard for Crohn's disease activity. Results Of the patients evaluated, 153 underwent TI intubation during endoscopy; 67 of these (43.8%) had normal results from ileoscopy, based on endoscopic appearance. Despite their normal results from ileoscopy, 36 of these patients (53.7%) had active, small-bowel Crohn's disease. The ileum appeared normal at ileoscopy because the disease had skipped the distal ileum of 11 patients (30.6%), developed only in the intramural and mesenteric distal ileum of 23 patients (63.9%), and appeared only in the upper gastrointestinal region of 2 patients (5.6%). These patients had a shorter duration of disease (61.1% for less than 5 years) compared with those found to have Crohn's disease based on ileoscopy (41.1% for less than 5 years; P < .05). CTE detected extracolonic Crohn's disease in 26% of patients; 14% of patients were found to have disorders unrelated to inflammatory bowel disease that warranted further investigation or consultation (including 4 cancers). Conclusions Ileoscopy examination can miss Crohn's disease of the TI because the disease can skip the distal ileum or is confined to the intramural portion of the bowel wall and the mesentery. CTE complements ileocolonoscopy in assessing disease activity in patients with Crohn's disease.
Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a ...snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.
Soil moisture is a critical indicator for climate change and agricultural drought, but its measurement is challenging due to large variability with land cover, soil type, time, space and depth. ...Satellite estimates of soil moisture are highly desirable and have become more widely available over the past decade. This study investigates and compares the performance of four surface soil moisture satellite datasets over Canada, namely, Soil Moisture and Ocean Salinity Level 3 (SMOS L3), versions 3.3 and 4.2 of European Space Agency Climate Change Initiative (ESA CCI) soil moisture product and a recent product called SMOS-INRA-CESBIO (SMOS-IC) that contains corrections designed to reduce several known sources of uncertainty in SMOS L3. These datasets were evaluated against in situ networks located in mostly agricultural regions of Canada for the period 2012 to 2014. Two statistical comparison methods were used, namely, metrics for mean soil moisture and median of metrics. The results suggest that, while both methods show similar comparisons for regional networks, over large networks, the median of metrics method is more representative of the overall correlation and variability and is therefore a more appropriate method for evaluating the performance of satellite products. Overall, the SMOS products have higher daily temporal correlations, but larger biases, against in situ soil moisture than the ESA CCI products, with SMOS-IC having higher correlations and smaller variability than SMOS L3. The SMOS products capture daily wetting and drying events better than the ESA CCI products, with the SMOS products capturing at least 75% of observed drying as compared to 55% for the ESA CCI products. Overall, for periods during which there are sufficient observations, both SMOS products are more suitable for agricultural applications over Canada than the ESA CCI products, even though SMOS-IC is able to capture soil moisture variability more accurately than SMOS L3.
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