Yilmazhaugaard4603

This paper presents a geometric microcanonical ensemble perspective on two-dimensional truncated Euler flows, which contain a finite number of (Fourier) modes and conserve energy and enstrophy. We explicitly perform phase space volume integrals over shells of constant energy and enstrophy. Two applications are considered. In the first part, we determine the average energy spectrum for highly condensed flow configurations and show that the result is consistent with Kraichnan's canonical ensemble description, despite the fact that no thermodynamic limit is invoked. In the second part, we compute the probability density for the largest-scale mode of a free-slip flow in a square, which displays reversals. We test the results against numerical simulations of a minimal model and find excellent agreement with the microcanonical theory, unlike the canonical theory, which fails to describe the bimodal statistics. This article is part of the theme issue 'Mathematical problems in physical fluid dynamics (part 2)'.Interferon beta (IFNβ) is a well-known cytokine, belonging to the type I family, that exerts antiviral, immunomodulatory, and antiproliferative activity. It has been reported that the artificially deamidated form of recombinant IFNβ-1a at Asn25 position shows an increased biological activity. As a deepening of the previous study, the molecular mechanism underlying this biological effect was investigated in this work by combining experimental and computational techniques. Specifically, the binding to IFNAR1 and IFNAR2 receptors and the canonical pathway of artificially deamidated IFNβ-1a molecule were analyzed in comparison to the native form. As a result, a change in receptor affinity of deamidated IFNβ-1a with respect to the native form was observed, and to better explore this molecular interaction, molecular dynamics simulations were carried out. Results confirmed, as previously hypothesized, that the N25D mutation can locally change the interaction network of the mutated residue but also that this effect can be propagated throughout the molecule. In fact, many residues not involved in the interaction with IFNAR1 in the native form participate to the recognition in the deamidated molecule, enhancing the binding to IFNAR1 receptor and consequently an increase of signaling cascade activation. In particular, a higher STAT1 phosphorylation and interferon-stimulated gene expression was observed under deamidated IFNβ-1a cell treatment. In conclusion, this study increases the scientific knowledge of deamidated IFNβ-1a, deciphering its molecular mechanism, and opens new perspectives to novel therapeutic strategies.

To compare image quality and radiation dose of computed tomography angiography (CTA) of the head and neck in patients using two Gemstone Spectral Imaging (GSI) scanning protocols.

A total of 100 patients who underwent head-neck CTA were divided into two groups (A and B) according to the scanning protocols, with 50 patients in each group. The patients in group A underwent GSI scanning protocol 1 (GSI profile head and neck CTA), while those in group B underwent GSI scanning protocol 2 (GSI profile chest 80 mm). All images were reconstructed using 40% and 70% pre- and post-adaptive level statistical iterative reconstruction V (pre-ASiR-V and post-ASiR-V) algorithms, respectively. The CT dose index (CTDIvol) and dose-length (DLP) product were recorded and the mean value was calculated and converted to the effective dose. CT values, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of all images were calculated. Additionally, subjective image evaluation was conducted by two independent radiologists using a five-point scoring method. All data were statistically analyzed.

There were no significant differences in the CT values, SNR, CNR, and subjective score between groups A and B (p > 0.05); however, the mean effective dose (1.2±0.1 mSv) in group B was 45.5% lower than that in group A (2.2±0.2 mSv) (p < 0.05).

GSI scanning protocol 2 could more effectively reduce the radiation dose in head-neck CT angiography while maintaining image quality compared to GSI scanning protocol1.

GSI scanning protocol 2 could more effectively reduce the radiation dose in head-neck CT angiography while maintaining image quality compared to GSI scanning protocol 1.

To investigate the following hypotheses (1) ExacTrac X-ray Snap Verification (ET-SV) is an alternative to CBCT for positioning patients with esophageal carcinoma (EC), (2) ET-SV can detect displacement in EC patients during radiotherapy (RT) and (3) EC patients can be feasibly monitored in quasi-real-time with ET-SV during RT.

Anthropomorphic phantoms and 13 patients were included in this study. CBCT and ET-SV were both implemented before treatment delivery to detect displacement, and their correction results were compared. For the patient tests, positional correction in 3 translational directions and the yaw direction were applied using the ET-SV correction results. The residual error was detected immediately using ET-SV. Finally, to acquire the intrafractional motion, ET-SV was implemented when the gantry was at 0°, 90°, 180° and 270°, respectively.

In phantom tests, the maximum value of the difference in displacement between the CBCT and ET systems was 1.16 mm for translation and 0.31° for yaw. According to Bland-Altman analysis of the patient test results, 5% (5/98), 5% (5/98), 5% (5/98), and 4% (4/98) of points were beyond the upper and lower limits of agreement in the AP, SI, LR and yaw directions, respectively. The mean residual error was -0.482 mm, 1.215 mm, 1.0 mm, -0.487°, 0.105°, and 0.003° in the AP, SI, LR, pitch, roll and yaw directions, respectively. The intrafractional displacement ranged from -0.21 mm to 0 mm for translation and from -0.63° to 0.21° for rotation. The mean total translational error for intrafractional motion increased from 0.47 mm to 1.14 mm during the treatment.

The accuracy of ET-SV for EC RT positional correction is comparable to that of CBCT. Thus, Quasi-real-time intrafractional monitoring can be used to detect EC patient displacement during radiotherapy.

The accuracy of ET-SV for EC RT positional correction is comparable to that of CBCT. Thus, Quasi-real-time intrafractional monitoring can be used to detect EC patient displacement during radiotherapy.In computed tomography (CT), the total variation (TV) constrained algebraic reconstruction technique (ART) can obtain better reconstruction quality when the projection data are sparse and noisy. However, the ART-TV algorithm remains time-consuming since it requires large numbers of iterations, especially for the reconstruction of high-resolution images. In this work, we propose a fast algorithm to calculate the system matrix for line intersection model and apply this algorithm to perform the forward-projection and back-projection operations of the ART. Then, we utilize the parallel computing techniques of multithreading and graphics processing units (GPU) to accelerate the ART iteration and the TV minimization, respectively. Numerical experiments show that our proposed parallel implementation approach is very efficient and accurate. For the reconstruction of a 2048 × 2048 image from 180 projection views of 2048 detector bins, it takes about 2.2 seconds to perform one iteration of the ART-TV algorithm using our proposed approach on a ten-core platform. Experimental results demonstrate that our new approach achieves a speedup of 23 times over the conventional single-threaded CPU implementation that using the Siddon algorithm.

To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis.

This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7  3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset.

Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). ACY-738 Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72.

The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

Head computed tomography (CT) is a commonly used imaging modality in radiology facilities. Since multiplanar reconstruction (MPR) processing can produce different results depending on the medical staff in charge, there is a possibility that the antemortem and postmortem images of the same person could be assessed and identified differently.

To propose and test a new automatic MPR method in order to address and overcome this limitation.

Head CT images of 108 cases are used. We employ the standardized transformation of statistical parametric mapping 8. The affine transformation parameters are obtained by standardizing the captured CT images. Automatic MPR processing is performed by using this parameter. The sphenoidal sinus of the orbitomeatal cross section of the automatic MPR processing of this study and the conventional manual MPR processing are cropped with a matrix size of 128×128, and the value of zero mean normalized correlation coefficient is calculated.

The computed zero mean normalized cross-correlation coefficient (Rzncc) of≥0.9, 0.8≤Rzncc < 0.9 and 0.7≤Rzncc < 0.8 are achieved in 105 cases (97.2%), 2 cases (1.9%), and 1 case (0.9%), respectively. The average Rzncc was 0.96±0.03.

Using the proposed new method in this study, MPR processing with guaranteed accuracy is efficiently achieved.

Using the proposed new method in this study, MPR processing with guaranteed accuracy is efficiently achieved.

The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease.

To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI).

This scheme includes the following stages; (i) Image acquisition and resizing, (ii) Gaussian filter-based pre-processing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with Mayfly Algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256×256×1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research.