This article examines a series of dialogues by Pedro de Ribadeneyra (1527-1611) that recount stories of men who left the Society of Jesus only to endure a wide variety of misfortunes thereafter. ...These dialogues reveal a certain anxiety within the Society of Jesus concerning men who abandoned their vocation. When compared with Jesuit hagiographies, the stories of these men who left the Society show that the Jesuits were concerned that proximity to family could present temptations too poweful to overcome for many Jesuits. Ultimately, the rhetorical and propagandistic nature of the text presents the defectors as foils to showcase the holiness of the Society and its saints.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Fullerenes are an allotrope of carbon that create polyhedral cages. Their bond structures match the sole pentagon and hexagonal-faced planar cubic graphs. Several chemical properties of fullerenes ...can be studied using its graph structure. Any graph that models a particular molecular structure can be given a topological index or molecular descriptor. Based on the molecular descriptor, it is easy to assess mathematical data and conduct further research on a molecule's physicochemical characteristics. It is a beneficial technique to replace time-consuming, expensive, and labour-intensive laboratory experiments. Molecular descriptors play a significant role in molecular structural analysis by investigating quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs). In this study, some novel degree-based topological indices, multiplicative degree-based topological indices, and entropy versions for fullerene cages
and
have been computed and derived formula for them. Also, we have obtained the numerical computation and graphical representation of degree-based topological indices and entropy values of
and
. Understanding the topology of precursor fullerenes is undoubtedly aided by the results of our computations.
The adipose tissue plays a crucial role in metabolism and physiology, affecting animal lifespan and susceptibility to disease. In this study, we present evidence that adipose Dicer1 (Dcr-1), a ...conserved type III endoribonuclease involved in miRNA processing, plays a crucial role in the regulation of metabolism, stress resistance, and longevity. Our results indicate that the expression of Dcr-1 in murine 3T3L1 adipocytes is responsive to changes in nutrient levels and is subject to tight regulation in the
fat body, analogous to human adipose and hepatic tissues, under various stress and physiological conditions such as starvation, oxidative stress, and aging. The specific depletion of Dcr-1 in the
fat body leads to changes in lipid metabolism, enhanced resistance to oxidative and nutritional stress, and is associated with a significant increase in lifespan. Moreover, we provide mechanistic evidence showing that the JNK-activated transcription factor FOXO binds to conserved DNA-binding sites in the
promoter, directly repressing its expression in response to nutrient deprivation. Our findings emphasize the importance of FOXO in controlling nutrient responses in the fat body by suppressing Dcr-1 expression. This mechanism coupling nutrient status with miRNA biogenesis represents a novel and previously unappreciated function of the JNK-FOXO axis in physiological responses at the organismal level.
Leishmania infantum is a protozoan parasite that is phagocytized by human macrophages. The host macrophages kill the parasite by generating oxidative compounds that induce DNA damage. We have ...identified, purified and biochemically characterized a DNA polymerase θ from L. infantum (LiPolθ), demonstrating that it is a DNA-dependent DNA polymerase involved in translesion synthesis of 8oxoG, abasic sites and thymine glycol lesions. Stably transfected L. infantum parasites expressing LiPolθ were significantly more resistant to oxidative and interstrand cross-linking agents, e.g. hydrogen peroxide, cisplatin and mitomycin C. Moreover, LiPolθ-overexpressing parasites showed an increased infectivity toward its natural macrophage host. Therefore, we propose that LiPolθ is a translesion synthesis polymerase involved in parasite DNA damage tolerance, to confer resistance against macrophage aggression.
Purpose
The aim of this pilot study was to explore heterogeneity in the temporal behavior of intratumoral
18
Ffluorodeoxyglucose (FDG) accumulation at a regional scale in patients with cervical ...cancer undergoing chemoradiotherapy.
Methods
Included in the study were 20 patients with FIGO stages IB1 to IVA cervical cancer treated with combined chemoradiotherapy. Patients underwent FDG PET/CT before treatment, during weeks 2 and 4 of treatment, and 12 weeks after completion of therapy. Patients were classified based on response to therapy as showing a complete metabolic response (CMR), a partial metabolic response (PMR), or residual disease and the development of new disease (NEW). Based on the presence of residual primary tumor following therapy, patients were divided into two groups, CMR and PMR/NEW. Temporal profiles of intratumoral FDG heterogeneity as characterized by textural features at a regional scale were assessed and compared with those of the standardized uptake value (SUV) indices (SUV
max
and SUV
mean
) within the context of differentiating response groups.
Results
Textural features at a regional scale with emphasis on characterizing contiguous regions of high uptake in tumors decreased significantly with time (
P
< 0.001) in the CMR group, while features describing contiguous regions of low uptake along with those measuring the nonuniformity of contiguous isointense regions in tumors exhibited significant temporal changes in the PMR/NEW group (
P
< 0.03) but showed no persistent trends with time. Both SUV indices showed significant changes during the course of the disease in both patient groups (
P
< 0.001 for SUV
max
and SUV
mean
in the CMR group;
P
= 0.0109 and 0.0136, respectively, for SUV
max
and SUV
mean
in the PMR/NEW group), and also decreased at a constant rate in the CMR group and decreased up to the 4th week of treatment and then increased in the PMR/NEW group.
Conclusion
The temporal changes in the heterogeneity of intratumoral FDG distribution characterized at a regional scale using image-based textural features may provide an adjunctive or alternative option for understanding tumor response to chemoradiotherapy and interpreting FDG accumulation dynamics in patients with malignant cervical tumors during the course of the disease.
Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the ...angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem. Here, we show that intelligent machines for unsupervised recognition and visualization of similarities in big data can also infer the network angular coordinates of the hyperbolic model according to a geometrical organization that we term "angular coalescence." Based on this phenomenon, we propose a class of algorithms that offers fast and accurate "coalescent embedding" in the hyperbolic circle even for large networks. This computational solution to an inverse problem in physics of complex systems favors the application of network latent geometry techniques in disciplines dealing with big network data analysis including biology, medicine, and social science.
To use magnetic resonance image guided radiation therapy (MR-IGRT) for accelerated partial-breast irradiation (APBI) to (1) determine intrafractional motion of the breast surgical cavity; and (2) ...assess delivered dose versus planned dose.
Thirty women with breast cancer (stages 0-I) who underwent breast-conserving surgery were enrolled in a prospective registry evaluating APBI using a 0.35-T MR-IGRT system. Clinical target volume was defined as the surgical cavity plus a 1-cm margin (excluding chest wall, pectoral muscles, and 5 mm from skin). No additional margin was added for the planning target volume (PTV). A volumetric MR image was acquired before each fraction, and patients were set up to the surgical cavity as visualized on MR imaging. To determine the delivered dose for each fraction, the electron density map and contours from the computed tomography simulation were transferred to the pretreatment MR image via rigid registration. Intrafractional motion of the surgical cavity was determined by applying a tracking algorithm to the cavity contour as visualized on cine MR.
Median PTV volume was reduced by 52% when using no PTV margin compared with a 1-cm PTV margin used conventionally. The mean (± standard deviation) difference between planned and delivered dose to the PTV (V95) was 0.6% ± 0.1%. The mean cavity displacement in the anterior-posterior and superior-inferior directions was 0.6 ± 0.4 mm and 0.6 ± 0.3 mm, respectively. The mean margin required for at least 90% of the cavity to be contained by the margin for 90% of the time was 0.7 mm (5th-95th percentile: 0-2.7 mm).
Minimal intrafractional motion was observed, and the mean difference between planned and delivered dose was less than 1%. Assessment of efficacy and cosmesis of this MR-guided APBI approach is under way.
Background
For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor‐collected data) and the data on which the model is deployed in the clinic are typically ...not the same, potentially impacting the performance of these models by a process called domain shift. Tools to routinely monitor and predict segmentation performance are needed for quality assurance. Here, we develop an approach to perform such monitoring and performance prediction for cardiac substructure segmentation.
Purpose
To develop a quality assurance (QA) framework for routine or continuous monitoring of domain shift and the performance of cardiac substructure autosegmentation algorithms.
Methods
A benchmark dataset consisting of computed tomography (CT) images along with manual cardiac substructure delineations of 241 breast cancer radiotherapy patients were collected, including one “normal” image domain of clean images and five “abnormal” domains containing images with artifact (metal, contrast), pathology, or quality variations due to scanner protocol differences (field of view, noise, reconstruction kernel, and slice thickness). The QA framework consisted of an image domain shift detector which operated on the input CT images and a shape quality detector on the output of an autosegmentation model, and a regression model for predicting autosegmentation model performance. The image domain shift detector was composed of a trained denoising autoencoder (DAE) and two hand‐engineered image quality features to detect normal versus abnormal domains in the input CT images. The shape quality detector was a variational autoencoder (VAE) trained to estimate the shape quality of the auto‐segmentation results. The output from the image domain shift and shape quality detectors was used to train a regression model to predict the per‐patient segmentation accuracy, measured by Dice coefficient similarity (DSC) to physician contours. Different regression techniques were investigated including linear regression, Bagging, Gaussian process regression, random forest, and gradient boost regression. Of the 241 patients, 60 were used to train the autosegmentation models, 120 for training the QA framework, and the remaining 61 for testing the QA framework. A total of 19 autosegmentation models were used to evaluate QA framework performance, including 18 convolutional neural network (CNN)‐based and one transformer‐based model.
Results
When tested on the benchmark dataset, all abnormal domains resulted in a significant DSC decrease relative to the normal domain for CNN models (p<0.001$p < 0.001$), but only for some domains for the transformer model. No significant relationship was found between the performance of an autosegmentation model and scanner protocol parameters (p=0.42$p = 0.42$) except noise (p=0.01$p = 0.01$). CNN‐based autosegmentation models demonstrated a decreased DSC ranging from 0.07 to 0.41 with added noise, while the transformer‐based model was not significantly affected (ANOVA, p=0.99$p=0.99$). For the QA framework, linear regression models with bootstrap aggregation resulted in the highest mean absolute error (MAE) of 0.041±0.002$0.041 \pm 0.002$, in predicted DSC (relative to true DSC between autosegmentation and physician). MAE was lowest when combining both input (image) detectors and output (shape) detectors compared to output detectors alone.
Conclusions
A QA framework was able to predict cardiac substructure autosegmentation model performance for clinically anticipated “abnormal” domain shifts.
Purpose
To develop and evaluate deep learning‐based autosegmentation of cardiac substructures from noncontrast planning computed tomography (CT) images in patients undergoing breast cancer ...radiotherapy and to investigate the algorithm sensitivity to out‐of‐distribution data such as CT image artifacts.
Methods
Nine substructures including aortic valve (AV), left anterior descending (LAD), tricuspid valve (TV), mitral valve (MV), pulmonic valve (PV), right atrium (RA), right ventricle (RV), left atrium (LA), and left ventricle (LV) were manually delineated by a radiation oncologist on noncontrast CT images of 129 patients with breast cancer; among them 90 were considered in‐distribution data, also named as “clean” data. The image/label pairs of 60 subjects were used to train a 3D deep neural network while the remaining 30 were used for testing. The rest of the 39 patients were considered out‐of‐distribution (“outlier”) data, which were used to test robustness. Random rigid transformations were used to augment the dataset during training. We investigated multiple loss functions, including Dice similarity coefficient (DSC), cross‐entropy (CE), Euclidean loss as well as the variation and combinations of these, data augmentation, and network size on overall performance and sensitivity to image artifacts due to infrequent events such as the presence of implanted devices. The predicted label maps were compared to the ground‐truth labels via DSC and mean and 90th percentile symmetric surface distance (90th‐SSD).
Results
When modified Dice combined with cross‐entropy (MD‐CE) was used as the loss function, the algorithm achieved a mean DSC = 0.79 ± 0.07 for chambers and 0.39 ± 0.10 for smaller substructures (valves and LAD). The mean and 90th‐SSD were 2.7 ± 1.4 and 6.5 ± 2.8 mm for chambers and 4.1 ± 1.7 and 8.6 ± 3.2 mm for smaller substructures. Models with MD‐CE, Dice‐CE, MD, and weighted CE loss had highest performance, and were statistically similar. Data augmentation did not affect model performances on both clean and outlier data and model robustness was susceptible to network size. For a certain type of outlier data, robustness can be improved via incorporating them into the training process. The execution time for segmenting each patient was on an average 2.1 s.
Conclusions
A deep neural network provides a fast and accurate segmentation of large cardiac substructures in noncontrast CT images. Model robustness of two types of clinically common outlier data were investigated and potential approaches to improve them were explored. Evaluation of clinical acceptability and integration into clinical workflow are pending.
Purpose
The superior soft‐tissue contrast achieved using magnetic resonance imaging (MRI) compared to x‐ray computed tomography (CT) has led to the popularization of MRI‐guided radiation therapy ...(MR‐IGRT), especially in recent years with the advent of first and second generation MRI‐based therapy delivery systems for MR‐IGRT. The expanding use of these systems is driving interest in MRI‐only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient’s MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI‐to‐CT image‐to‐image translation task and compare its performance to the well established U‐Net architecture in a generative adversarial network (GAN) framework.
Methods
Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi‐scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large‐scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal‐to‐noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method.
Results
Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7 ± 4.3 HU, 0.9995 ± 0.0003, and 71.7 ± 2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2 mm criterion.
Conclusions
The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image‐to‐image translation task of sCT generation. Adopting the method is a first step toward an MRI‐only RT workflow that enables widespread clinical applications for MR‐IGRT including online adaptive therapy.
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