A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes

Triazole–Au(I) complex as chemoselective catalyst in promoting propargyl ester rearrangements

Triazole–Au (TA–Au) catalysts were employed in several transformations involving propargyl ester rearrangement. Good chemoselectivity was observed, which allowed the effective activation of the alkyne without affecting the reactivity of the allene ester intermediates. These results led to the investigation of the preparation of allene ester intermediates with TA–Au catalysts under anhydrous conditions. As expected, the desired 3,3-rearrangement products were obtained in excellent yields (generally \u3e90% yields with 1% loading). Besides the typical ester migrating groups, carbonates and carbamates were also found to be suitable for this transformation, which provided a highly efficient, practical method for the preparation of substituted allenes

Ethyne Reducing Metal-Organic Frameworks to Control Fabrications of Core/shell Nanoparticles as Catalysts

An approach using cobalt metal-organic frameworks (Co-MOF) as precursors is established for the fabrication of cobalt nanoparticles in porous carbon shells (core/shell Co@C). Chemical vapor deposition of ethyne is used for controlling the reduction of cobalt nanoclusters in the MOF and the spontaneous formation of the porous carbon shells. The metallic cobalt cores formed are up to 4 - 6 nm with the crystal phase varying between hexagonally-close-packed (hcp) and face-centre-packed (fcc). The porous carbon shells change from amorphous to graphene with the ethyne deposition temperature increasing from 400 to 600 oC. The core/shell Co@C nanoparticles exhibit high catalytic activity in selectively converting syngas (CTY: 254.1 - 312.1 μmolCO·gCo-1·s-1) into hydrocarbons (4.0 - 5.2 gHC·g-cat-1·h-1) at 260 oC. As well as the crystal size and phase, the coordination numbers of the cobalt to oxygen and to other cobalt atoms on the surface of the cobalt nanoparticles, and the permeability of the porous carbon shell have been related to the catalytic performance in FTS reactions

SFSDAF: an enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion

Spatio-temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both fine spatial and temporal resolution. Accurate prediction of reflectance change is difficult, especially when the change is caused by both phenological change and land cover class changes. Although several spatio-temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion (FSDAF) directly derive land cover phenological change information (such as endmember change) at different dates, the direct derivation of land cover class change information is challenging. In this paper, an enhanced FSDAF that incorporates sub-pixel class fraction change information (SFSDAF) is proposed. By directly deriving the sub-pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change. In particular, SFSDAF directly derives fine spatial resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction, which can help identify image reflectance change resulting from different sources. SFSDAF predicts a fine resolution image at the time of acquisition of coarse resolution images using only one prior coarse and fine resolution image pair, and accommodates variations in reflectance due to both natural fluctuations in class spectral response (e.g. due to phenology) and land cover class change. The method is illustrated using degraded and real images and compared against three established spatio-temporal methods. The results show that the SFSDAF produced the least blurred images and the most accurate predictions of fine resolution reflectance values, especially for regions of heterogeneous landscape and regions that undergo some land cover class change. Consequently, the SFSDAF has considerable potential in monitoring Earth surface dynamics

Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer

Purpose Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT. Methods and Materials For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LETd) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve. Results An overall statistically significant RAIC association with dose and LETd was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LETd, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LETd effect demonstrated increased risk of RAIC with increasing LETd for the majority of patients. Estimated probability of RAIC with LETd = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD15 were estimated to be 63.6 and 50.1 Gy with LETd equal to 2 and 5 keV/µm, respectively. Conclusions Our results suggest that the LETd effect could be of clinical significance for some patients; LETd assessment in clinical treatment plans should therefore be taken into consideration.publishedVersio

IEA-Task 31 WAKEBENCH: Towards a protocol for wind farm flow model evaluation. Part 1: Flow-over-terrain models

The IEA Task 31 Wakebench is setting up a framework for the evaluation of wind farm flow models operating at microscale level. The framework consists on a model evaluation protocol integrated on a web-based portal for model benchmarking (www.windbench.net). This paper provides an overview of the building-block validation approach applied to flow-over-terrain models, including best practices for the benchmarking and data processing procedures for the analysis and qualification of validation datasets from wind resource assessment campaigns. A hierarchy of test cases has been proposed for flow-over-terrain model evaluation, from Monin-Obukhov similarity theory for verification of surface-layer properties, to the Leipzig profile for the near-neutral atmospheric boundary layer, to flow over isolated hills (Askervein and Bolund) to flow over mountaneous complex terrain (Alaiz). A summary of results from the first benchmarks are used to illustrate the model evaluation protocol applied to flow-over-terrain modeling in neutral conditions

Genome Expression Profile Analysis of the Immature Maize Embryo during Dedifferentiation

Maize is one of the most important cereal crops worldwide and one of the primary targets of genetic manipulation, which provides an excellent way to promote its production. However, the obvious difference of the dedifferentiation frequency of immature maize embryo among various genotypes indicates that its genetic transformation is dependence on genotype and immature embryo-derived undifferentiated cells. To identify important genes and metabolic pathways involved in forming of embryo-derived embryonic calli, in this study, DGE (differential gene expression) analysis was performed on stages I, II, and III of maize inbred line 18-599R and corresponding control during the process of immature embryo dedifferentiation. A total of ∼21 million cDNA tags were sequenced, and 4,849,453, 5,076,030, 4,931,339, and 5,130,573 clean tags were obtained in the libraries of the samples and the control, respectively. In comparison with the control, 251, 324 and 313 differentially expressed genes (DEGs) were identified in the three stages with more than five folds, respectively. Interestingly, it is revealed that all the DEGs are related to metabolism, cellular process, and signaling and information storage and processing functions. Particularly, the genes involved in amino acid and carbohydrate transport and metabolism, cell wall/membrane/envelope biogenesis and signal transduction mechanism have been significantly changed during the dedifferentiation. To our best knowledge, this study is the first genome-wide effort to investigate the transcriptional changes in dedifferentiation immature maize embryos and the identified DEGs can serve as a basis for further functional characterization

A COVID-19 Risk Score Combining Chest CT Radiomics and Clinical Characteristics to Differentiate COVID-19 Pneumonia From Other Viral Pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective

Distribution, Bioaccumulation, and Risks of Pharmaceutical Metabolites and Their Parents: A Case Study in an Yunliang River, Nanjing City

The occurrence, bioaccumulation, and risks of 11 pairs of pharmaceutical metabolites and their respective parents were investigated in the water, sediment, and fish of an urban river in Nanjing city, China. The results showed that most of the target metabolites and their parents were detected in all water samples, with concentrations ranging from 0.1 ng/L to 72.9 ng/L. In some cases, the concentrations of metabolites in water were significantly higher than their parents, with fold changes reaching up 4.1 in the wet season and 6.6 in the dry season, while in sediment and fish, a lower concentration was observed in most cases. A lowered concentration of detected pharmaceuticals was observed in the dry season when compared to the wet season due to the seasonal variation in pharmaceutical consumption and overflow effluent. The bioaccumulation of pharmaceuticals in different fish tissues were detected with a descending order of overall concentration as gill > brain > muscle > gonad > intestine > liver > blood. In addition, the concentrations of both metabolites and their parents also decreased along the river in two seasons. However, the concentration rates of metabolites and their parents were significantly altered along the river in both water and sediment. The relatively high concentration proportions of the detected pharmaceuticals in water suggested that pharmaceuticals were more likely to apportion in water than in sediment, especially for the metabolites. Meanwhile, the rates of the metabolite/parent pairs between fish and water/sediment were generally lower, indicating the higher excretion capacity of metabolites from fish than their parents. Most of the detected pharmaceuticals had no impact on aquatic organisms. However, the presence of ibuprofen posed a medium risk to fish. Compared to the parents, metabolites showed a relatively low risk value but a high contribution to the total risk. It highlights that metabolites in the aquatic environments cannot be ignored

Aircraft engine gas path diagnostic methods: public benchmarking results

peer reviewedRecent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a publicly available gas path diagnostic benchmarking problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by individuals within the aircraft EHM community. It provides a standard benchmarking problem enabling users to develop, evaluate and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind test case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed