scholarly journals Impact of Baseline Clinical and Radiological Features on Outcome of Chronic Rhinosinusitis in Granulomatosis with Polyangiitis

2020 ◽  
Author(s):  
Sigrun Skaar Holme ◽  
Karin Kilian ◽  
Heidi B. Eggesbø ◽  
Jon Magnus Moen ◽  
Øyvind Molberg
Keyword(s):  
Chronic Rhinosinusitis ◽  
Granulomatosis With Polyangiitis ◽  
Negative Impact ◽  
Renal Involvement ◽  
Ct Scans ◽  
Data Sets ◽  
Data Set ◽  
Mucosal Disease ◽  
Life Threatening ◽  
Observation Period

Abstract Background: Granulomatosis with polyangiitis (GPA) causes a recurring inflammation in nose and paranasal sinuses that clinically resembles chronic rhinosinusitis (CRS) of other aetiologies. While sinonasal inflammation is not among the life-threatening features of GPA, patients report it to have major negative impact on quality of life. A relatively large proportion of GPA patients have severe CRS with extensive damage to nose and sinus structures evident by CT, but risk factors for severe CRS development remain largely unknown. In this study, we aimed to identify clinical and radiological predictors of CRS-related damage in GPA. Methods: We included GPA patients who had clinical data sets from time of diagnosis, and two or more paranasal sinus CT scans obtained ≥ 12 months apart available for analysis. We defined time from first to last CT as the study observation period, and evaluated CRS development across this period by CT scores for inflammatory sinus bone thickening (osteitis), bone destructions and sinus opacifications (here defined as mucosal disease). In logistic regression, we applied osteitis as main outcome measure for CRS-related damage.Results: We evaluated 697 CT scans obtained over median 5 years observation from 116 GPA patients. We found that 39% (45/116) of the GPA patients remained free from CRS damage across the study observation period, while 33% (38/116) had progressive damage. By end of observation, 32% (37/116) of the GPA patients had developed severe osteitis. We identified mucosal disease at baseline as a predictor for osteitis (Odds Ratio 1.34), and we found that renal involvement at baseline was less common in patients with severe osteitis at last CT (41%, 15/37) than in patients with no osteitis (60%, 27/45). Conclusions: In this largely unselected GPA patient cohort, baseline sinus mucosal disease associated with CRS-related damage, as measured by osteitis at end of follow-up. We found no significant association with clinical factors, but the data set indicated an inverse relationship between renal involvement and severe sinonasal affliction.

scholarly journals Impact of baseline clinical and radiological features on outcome of chronic rhinosinusitis in granulomatosis with polyangiitis

2020 ◽  
Author(s):  
Sigrun Skaar Holme ◽  
Karin Kilian ◽  
Heidi B. Eggesbø ◽  
Jon Magnus Moen ◽  
Øyvind Molberg
Keyword(s):  
Chronic Rhinosinusitis ◽  
Granulomatosis With Polyangiitis ◽  
Negative Impact ◽  
Renal Involvement ◽  
Ct Scans ◽  
Data Sets ◽  
Data Set ◽  
Mucosal Disease ◽  
Life Threatening ◽  
Observation Period

Abstract Background: Granulomatosis with polyangiitis (GPA) causes a recurring inflammation in nose and paranasal sinuses that clinically resembles chronic rhinosinusitis (CRS) of other aetiologies. While sinonasal inflammation is not among the life-threatening features of GPA, patients report it to have major negative impact on quality of life. A relatively large proportion of GPA patients have severe CRS with extensive damage to nose and sinus structures evident by CT, but risk factors for severe CRS development remain largely unknown. In this study, we aimed to identify clinical and radiological predictors of CRS-related damage in GPA.Methods: We included GPA patients who had clinical data sets from time of diagnosis, and two or more paranasal sinus CT scans obtained ≥ 12 months apart available for analysis. We defined time from first to last CT as the study observation period, and evaluated CRS development across this period using CT scores for inflammatory sinus bone thickening (osteitis), bone destructions and sinus opacifications (here defined as mucosal disease). In logistic regression, we applied osteitis as main outcome measure for CRS-related damage.Results: We evaluated 697 CT scans obtained over median 5 years observation from 116 GPA patients. We found that 39% (45/116) of the GPA patients remained free from CRS damage across the study observation period, while 33% (38/116) had progressive damage. By end of observation, 32% (37/116) of the GPA patients had developed severe osteitis. We identified mucosal disease at baseline as a predictor for osteitis (Odds Ratio 1.33), and we found that renal involvement at baseline was less common in patients with severe osteitis at last CT (41%, 15/37) than in patients with no osteitis (60%, 27/45).Conclusions: In this largely unselected GPA patient cohort, baseline sinus mucosal disease associated with CRS-related damage, as measured by osteitis at end of follow-up. We found no significant association with clinical factors, but the data set indicated an inverse relationship between renal involvement and severe sinonasal affliction.

Autonomous Trajectory Planning for External Ventricular Drain Placement

2018 ◽  
Vol 15 (4) ◽  
pp. 433-439
Author(s):  
Joel S Beckett ◽  
Bilwaj Gaonkar ◽  
Diana Babayan ◽  
Justin Mathew ◽  
David McArthur ◽  
...  
Keyword(s):  
Image Registration ◽  
Intracranial Hemorrhage ◽  
Sagittal Plane ◽  
External Ventricular Drain ◽  
Ct Scans ◽  
Data Sets ◽  
Foramen Of Monro ◽  
Data Set ◽  
Neurosurgical Procedure ◽  
Noncontrast Ct

Abstract BACKGROUND External ventricular drain (EVD) placement is the most frequently performed neurosurgical procedure for management of various conditions including hydrocephalus, traumatic brain injury, and stroke. State-of-the-art computational pattern recognition techniques could improve the safety and accuracy of EVD placement. Placement of the Kocher's point EVD is the most common neurosurgical procedure which is often performed in urgent conditions. OBJECTIVE To present the development of a novel computer algorithm identifying appropriate anatomy and autonomously plan EVD placement on clinical computed tomography (CT) scans. METHODS The algorithm was tested on 2 data sets containing 5-mm slice noncontrast CT scans. The first contained images of 300 patients without significant intracranial pathology (normal), the second of 43 patients with significant acute intracranial hemorrhage. Automated planning was performed by custom 2-tiered heuristic with run-time template selection in combination with refinement using nonlinear image registration. RESULTS Automated EVD planning was accurate in 297 of 300 normal and 41 of 43 patient cases. In the normal data set, mean distance between Kocher's point and the ipsilateral foramen of Monro was 63 ± 3.1 mm in women and 65 ± 6.5 mm in men (P = .0008). Trajectory angle with respect to the sagittal plane was 91 ± 6° in women and 90 ± 6° in men (obtuse posterior) (P = .15); to the coronal plane, 85 ± 6° and 86 ± 5° in women and men (P = .12), respectively (acute lateral). CONCLUSION A combination of linear and nonlinear image registration techniques accurately planned EVD trajectory in 99% of normal scans and 95% of scans with significant intracranial hemorrhage.

Development and Validation of Patient-Specific Finite Element Models of the Hemipelvis Generated From a Sparse CT Data Set

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Vickie B. Shim ◽  
Rocco P. Pitto ◽  
Robert M. Streicher ◽  
Peter J. Hunter ◽  
Iain A. Anderson
Keyword(s):  
Finite Element ◽  
Material Properties ◽  
Patient Data ◽  
Ct Scans ◽  
Patient Specific ◽  
Data Sets ◽  
Fe Model ◽  
Data Set ◽  
Spatially Varying ◽  
Ct Slices

To produce a patient-specific finite element (FE) model of a bone such as the pelvis, a complete computer tomographic (CT) or magnetic resonance imaging (MRI) geometric data set is desirable. However, most patient data are limited to a specific region of interest such as the acetabulum. We have overcome this problem by providing a hybrid method that is capable of generating accurate FE models from sparse patient data sets. In this paper, we have validated our technique with mechanical experiments. Three cadaveric embalmed pelves were strain gauged and used in mechanical experiments. FE models were generated from the CT scans of the pelves. Material properties for cancellous bone were obtained from the CT scans and assigned to the FE mesh using a spatially varying field embedded inside the mesh while other materials used in the model were obtained from the literature. Although our FE meshes have large elements, the spatially varying field allowed them to have location dependent inhomogeneous material properties. For each pelvis, five different FE meshes with a varying number of patient CT slices (8–12) were generated to determine how many patient CT slices are needed for good accuracy. All five mesh types showed good agreement between the model and experimental strains. Meshes generated with incomplete data sets showed very similar stress distributions to those obtained from the FE mesh generated with complete data sets. Our modeling approach provides an important step in advancing the application of FE models from the research environment to the clinical setting.

Crime, corruption and the role of institutions

2014 ◽  
Vol 7 (1) ◽  
pp. 73-95 ◽  
Author(s):  
Ishita Chatterjee ◽  
Ranjan Ray
Keyword(s):  
Negative Impact ◽  
Probit Model ◽  
Data Sets ◽  
Older Individuals ◽  
Crime Victim ◽  
Data Set ◽  
Content Type ◽  
Country Level ◽  
Level Data

Purpose – There have been very few attempts in the economics literature to empirically study the link between criminal and corrupt behaviour due to lack of data sets on simultaneous information on both types of illegitimate activities. The paper aims to discuss these issues. Design/methodology/approach – The present study uses a large cross-country data set containing individual responses to questions on crime and corruption along with information on the respondents' characteristics. These micro-level data are supplemented by country-level macro and institutional indicators. A methodological contribution of this study is the estimation of an ordered probit model based on outcomes defined as combinations of crime and bribe victimisation. Findings – The authors find that: a crime victim is more likely to face bribe demands, males are more likely victims of corruption while females are of serious crime, older individuals and those living in the smaller towns are less exposed to crime and corruption, increasing levels of income and education increase the likelihood of crime and bribe victimisation to be reported and a stronger legal system and a happier society reduce both crime and corruption. However, the authors find no evidence of a strong and uniformly negative impact of either crime or corruption on a country's growth rate. Originality/value – This paper is, to the authors' knowledge, the first in the literature to explore the nexus between crime and corruption, their magnitudes, determinants and their effects on growth rates.

scholarly journals Excluding loci with substitution saturation improves inferences from phylogenomic data

2021 ◽  
Author(s):  
David A. Duchêne ◽  
Niklas Mather ◽  
Cara Van Der Wal ◽  
Simon Y.W. Ho
Keyword(s):  
Negative Impact ◽  
Effective Means ◽  
Phylogenetic Inference ◽  
Nucleotide Sequences ◽  
Data Sets ◽  
Data Set ◽  
Deep Time ◽  
Substitution Saturation ◽  
Genome Scale ◽  
Scale Data

AbstractThe historical signal in nucleotide sequences becomes eroded over time by substitutions occurring repeatedly at the same sites. This phenomenon, known as substitution saturation, is recognized as one of the primary obstacles to deep-time phylogenetic inference using genome-scale data sets. We present a new test of substitution saturation and demonstrate its performance in simulated and empirical data. For some of the 36 empirical phylogenomic data sets that we examined, we detect substitution saturation in around 50% of loci. We found that saturation tends to be flagged as problematic in loci with highly discordant phylogenetic signals across sites. Within each data set, the loci with smaller numbers of informative sites are more likely to be flagged as containing problematic levels of saturation. The entropy saturation test proposed here is sensitive to high evolutionary rates relative to the evolutionary timeframe, while also being sensitive to several factors known to mislead phylogenetic inference, including short internal branches relative to external branches, short nucleotide sequences, and tree imbalance. Our study demonstrates that excluding loci with substitution saturation can be an effective means of mitigating the negative impact of multiple substitutions on phylogenetic inferences.

scholarly journals Excluding Loci With Substitution Saturation Improves Inferences From Phylogenomic Data

2021 ◽  
Author(s):  
David A Duchêne ◽  
Niklas Mather ◽  
Cara Van Der Wal ◽  
Simon Y W Ho
Keyword(s):  
Negative Impact ◽  
Effective Means ◽  
Model Performance ◽  
Phylogenetic Inference ◽  
Nucleotide Sequences ◽  
Data Sets ◽  
Data Set ◽  
Deep Time ◽  
Substitution Saturation ◽  
Genome Scale

Abstract The historical signal in nucleotide sequences becomes eroded over time by substitutions occurring repeatedly at the same sites. This phenomenon, known as substitution saturation, is recognized as one of the primary obstacles to deep-time phylogenetic inference using genome-scale data sets. We present a new test of substitution saturation and demonstrate its performance in simulated and empirical data. For some of the 36 empirical phylogenomic data sets that we examined, we detect substitution saturation in around 50% of loci. We found that saturation tends to be flagged as problematic in loci with highly discordant phylogenetic signals across sites. Within each data set, the loci with smaller numbers of informative sites are more likely to be flagged as containing problematic levels of saturation. The entropy saturation test proposed here is sensitive to high evolutionary rates relative to the evolutionary timeframe, while also being sensitive to several factors known to mislead phylogenetic inference, including short internal branches relative to external branches, short nucleotide sequences, and tree imbalance. Our study demonstrates that excluding loci with substitution saturation can be an effective means of mitigating the negative impact of multiple substitutions on phylogenetic inferences. [Phylogenetic model performance; phylogenomics; substitution model; substitution saturation; test statistics.]

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

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