Abstract
Aviation is an important contributor to the global economy, satisfying society’s mobility needs. It contributes to climate change through CO
2
and non-CO
2
effects, including contrail-cirrus ...and ozone formation. There is currently significant interest in policies, regulations and research aiming to reduce aviation’s climate impact. Here we model the effect of these measures on global warming and perform a bottom-up analysis of potential technical improvements, challenging the assumptions of the targets for the sector with a number of scenarios up to 2100. We show that although the emissions targets for aviation are in line with the overall goals of the Paris Agreement, there is a high likelihood that the climate impact of aviation will not meet these goals. Our assessment includes feasible technological advancements and the availability of sustainable aviation fuels. This conclusion is robust for several COVID-19 recovery scenarios, including changes in travel behaviour.
In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich ...geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.
•Nearest neighbor spatial models show relationships between tumor and immune cells.•Tumor and T regulatory cell spatial proximity predicts worse lung cancer survival.•Spatial proximity of CD8 to T ...regulatory cells predicts better lung cancer survival.•Tumor-immune cell spatial modelling helps our understanding of individual responses.
To determine the prognostic significance of spatial proximity of lung cancer cells and specific immune cells in the tumor microenvironment.
We probed formalin-fixed, paraffin-embedded (FFPE) tissue microarrays using a novel tyramide signal amplification multiplexing technique labelling CD8, CD4, Foxp3, and CD68+ cells. Each multiplex stained immunohistochemistry slide was digitally processed by Vectra INFORMS software, and an X- and Y-coordinate assigned to each labeled cell type. The abundance and spatial location of each cell type and their proximity to one another was analyzed using a novel application of the G-cross spatial distance distribution method which computes the probability of finding at least one immune cell of any given type within a rμm radius of a tumor cell. Cox proportional hazards multiple regression was used for multivariate analysis of the influence of proximity of lymphocyte types.
Pathologic tumor specimens from 120 NSCLC patients with pathologic tumor stage I–III disease were analyzed. On univariate analysis, age (P = .0007) and number of positive nodes (P = .0014) were associated with overall survival. Greater area under the curve (AUC) of the G-cross function for tumor cell-Treg interactions was significantly associated with worse survival adjusting for age and number of positive nodes (HR 1.52 (1.11–2.07), P = .009). Greater G-cross AUC for T-reg-CD8 was significantly associated with better survival adjusting for age and number of positive lymph nodes (HR 0.96 (0.92–0.99), P = .042).
Increased infiltration of regulatory T cells into core tumor regions is an independent predictor of worse overall survival in NSCLC. However, increased infiltration of CD8+ cytotoxic T cells among regulatory T cells seems to mitigate this effect and was significantly associated with better survival. Validation of the G-cross method of measuring spatial proximity between tumor and immune cell types and exploration of its use as a prognostic factor in lung cancer treatment is warranted.
Since its discovery, the Flameless Combustion (FC) regime has been seen as a promising alternative combustion technique to reduce pollutant emissions of gas turbine engines. This combustion mode is ...often characterized by well-distributed reaction zones, which can potentially decrease temperature gradients, acoustic oscillations and, consequently NOx emission. However, the application of FC to gas turbines is still not a reality due to the inherent difficulties faced in attaining the regime while meeting all the engine requirements. Over the past years, investigations related to FC have been focused on understanding the fundamentals of this combustion regime, the regime boundaries, its computational modelling, and combustor design attempts. This article reviews the progress achieved so far, discusses the various definitions of the FC regime, and attempts to point the directions for future research. The review suggests that modelling of the FC regime is still not capable of predicting intermediate species and pollutant emissions. Comprehensive experimental databases with conditions relevant to gas turbine combustors are not available, and moreover, many of the current experiments do not necessarily represent the FC regime. By analysing the latest developments in computational modelling, the review points to the most promising approaches for the prediction of reaction zones and pollutant emissions in FC. The lessons learned from previous design attempts provide valuable insights into the design of a successful gas turbine engine operating under the FC regime. The review concludes with some examples where the gas turbine architecture has been exploited to advance the possibilities of FC in gas turbines.
In patients with triple-negative breast cancer (TNBC), tumor-infiltrating lymphocytes (TILs) are associated with improved survival. Lehmann et al. identified 4 molecular subtypes of TNBC basal-like ...(BL) 1, BL2, mesenchymal (M), and luminal androgen receptor (LAR), and an immunomodulatory (IM) gene expression signature indicates the presence of TILs and modifies these subtypes. The association between TNBC subtype and TILs is not known. Also, the association between inflammatory breast cancer (IBC) and the presence of TILs is not known. Therefore, we studied the IM subtype distribution among different TNBC subtypes. We retrospectively analyzed patients with TNBC from the World IBC Consortium dataset. The molecular subtype and the IM signature positive (IM+) or negative (IM-) were analyzed. Fisher's exact test was used to analyze the distribution of positivity for the IM signature according to the TNBC molecular subtype and IBC status. There were 88 patients with TNBC in the dataset, and among them 39 patients (44%) had IBC and 49 (56%) had non-IBC. The frequency of IM+ cases differed by TNBC subtype (p = 0.001). The frequency of IM+ cases by subtype was as follows: BL1, 48% (14/29); BL2, 30% (3/10); LAR, 18% (3/17); and M, 0% (0/21) (in 11 patients, the subtype could not be determined). The frequency of IM+ cases did not differ between patients with IBC and non-IBC (23% and 33%, respectively; p = 0.35). In conclusion, the IM signature representing the underlying molecular correlate of TILs in the tumor may differ by TNBC subtype but not by IBC status.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative ...imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
Purpose:
Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer. Four molecular subtypes of GBM have been described but can only be determined by an invasive brain ...biopsy. The goal of this study is to evaluate the utility of texture features extracted from magnetic resonance imaging (MRI) scans as a potential noninvasive method to characterize molecular subtypes of GBM and to predict 12‐month overall survival status for GBM patients.
Methods:
The authors manually segmented the tumor regions from postcontrast T1 weighted and T2 fluid‐attenuated inversion recovery (FLAIR) MRI scans of 82 patients with de novo GBM. For each patient, the authors extracted five sets of computer‐extracted texture features, namely, 48 segmentation‐based fractal texture analysis (SFTA) features, 576 histogram of oriented gradients (HOGs) features, 44 run‐length matrix (RLM) features, 256 local binary patterns features, and 52 Haralick features, from the tumor slice corresponding to the maximum tumor area in axial, sagittal, and coronal planes, respectively. The authors used an ensemble classifier called random forest on each feature family to predict GBM molecular subtypes and 12‐month survival status (a dichotomized version of overall survival at the 12‐month time point indicating if the patient was alive or not at 12 months). The performance of the prediction was quantified and compared using receiver operating characteristic (ROC) curves.
Results:
With the appropriate combination of texture feature set, image plane (axial, coronal, or sagittal), and MRI sequence, the area under ROC curve values for predicting different molecular subtypes and 12‐month survival status are 0.72 for classical (with Haralick features on T1 postcontrast axial scan), 0.70 for mesenchymal (with HOG features on T2 FLAIR axial scan), 0.75 for neural (with RLM features on T2 FLAIR axial scan), 0.82 for proneural (with SFTA features on T1 postcontrast coronal scan), and 0.69 for 12‐month survival status (with SFTA features on T1 postcontrast coronal scan).
Conclusions:
The authors evaluated the performance of five types of texture features in predicting GBM molecular subtypes and 12‐month survival status. The authors’ results show that texture features are predictive of molecular subtypes and survival status in GBM. These results indicate the feasibility of using tumor‐derived imaging features to guide genomically informed interventions without the need for invasive biopsies.
The exact nature and dynamics of pancreatic ductal adenocarcinoma (PDAC) immune composition remains largely unknown. Desmoplasia is suggested to polarize PDAC immunity. Therefore, a comprehensive ...evaluation of the composition and distribution of desmoplastic elements and T-cell infiltration is necessary to delineate their roles. Here we develop a novel computational imaging technology for the simultaneous evaluation of eight distinct markers, allowing for spatial analysis of distinct populations within the same section. We report a heterogeneous population of infiltrating T lymphocytes. Spatial distribution of cytotoxic T cells in proximity to cancer cells correlates with increased overall patient survival. Collagen-I and αSMA
fibroblasts do not correlate with paucity in T-cell accumulation, suggesting that PDAC desmoplasia may not be a simple physical barrier. Further exploration of this technology may improve our understanding of how specific stromal composition could impact T-cell activity, with potential impact on the optimization of immune-modulatory therapies.
The potential environmental benefits of hybrid electric regional turboprop aircraft in terms of fuel consumption are investigated. Lithium–air batteries are used as energy source in combination with ...conventional fuel. A validated design and analysis framework is extended with sizing and analysis modules for hybrid electric propulsion system components. In addition, a modified Bréguet range equation, suitable for hybrid electric aircraft, is introduced. The results quantify the limits in range and performance for this type of aircraft as a function of battery technology level. A typical design for 70 passengers with a design range of 1528 km, based on batteries with a specific energy of 1000 Wh/kg, providing 34% of the shaft power throughout the mission, yields a reduction in emissions by 28%.
To review outcomes of locally advanced pancreatic cancer (LAPC) patients treated with dose-escalated intensity modulated radiation therapy (IMRT) with curative intent.
A total of 200 patients with ...LAPC were treated with induction chemotherapy followed by chemoradiation between 2006 and 2014. Of these, 47 (24%) having tumors >1 cm from the luminal organs were selected for dose-escalated IMRT (biologically effective dose BED >70 Gy) using a simultaneous integrated boost technique, inspiration breath hold, and computed tomographic image guidance. Fractionation was optimized for coverage of gross tumor and luminal organ sparing. A 2- to 5-mm margin around the gross tumor volume was treated using a simultaneous integrated boost with a microscopic dose. Overall survival (OS), recurrence-free survival (RFS), local-regional and distant RFS, and time to local-regional and distant recurrence, calculated from start of chemoradiation, were the outcomes of interest.
Median radiation dose was 50.4 Gy (BED = 59.47 Gy) with a concurrent capecitabine-based (86%) regimen. Patients who received BED >70 Gy had a superior OS (17.8 vs 15.0 months, P=.03), which was preserved throughout the follow-up period, with estimated OS rates at 2 years of 36% versus 19% and at 3 years of 31% versus 9% along with improved local-regional RFS (10.2 vs 6.2 months, P=.05) as compared with those receiving BED ≤70 Gy. Degree of gross tumor volume coverage did not seem to affect outcomes. No additional toxicity was observed in the high-dose group. Higher dose (BED) was the only predictor of improved OS on multivariate analysis.
Radiation dose escalation during consolidative chemoradiation therapy after induction chemotherapy for LAPC patients improves OS and local-regional RFS.
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