Whole brain radiotherapy plus simultaneous in-field boost with image guided intensity-modulated radiotherapy for brain metastases of non-small cell lung cancer

BACKGROUND: Whole brain radiotherapy (WBRT) plus sequential focal radiation boost is a commonly used therapeutic strategy for patients with brain metastases. However, recent reports on WBRT plus simultaneous in-field boost (SIB) also showed promising outcomes. The objective of present study is to retrospectively evaluate the efficacy and toxicities of WBRT plus SIB with image guided intensity-modulated radiotherapy (IG-IMRT) for inoperable brain metastases of NSCLC. METHODS: Twenty-nine NSCLC patients with 87 inoperable brain metastases were included in this retrospective study. All patients received WBRT at a dose of 40 Gy/20 f, and SIB boost with IG-IMRT at a dose of 20 Gy/5 f concurrent with WBRT in the fourth week. Prior to each fraction of IG-IMRT boost, on-line positioning verification and correction were used to ensure that the set-up errors were within 2 mm by cone beam computed tomography in all patients. RESULTS: The one-year intracranial control rate, local brain failure rate, and distant brain failure rate were 62.9%, 13.8%, and 19.2%, respectively. The two-year intracranial control rate, local brain failure rate, and distant brain failure rate were 42.5%, 30.9%, and 36.4%, respectively. Both median intracranial progression-free survival and median survival were 10 months. Six-month, one-year, and two-year survival rates were 65.5%, 41.4%, and 13.8%, corresponding to 62.1%, 41.4%, and 10.3% of intracranial progression-free survival rates. Patients with Score Index for Radiosurgery in Brain Metastases (SIR) >5, number of intracranial lesions <3, and history of EGFR-TKI treatment had better survival. Three lesions (3.45%) demonstrated radiation necrosis after radiotherapy. Grades 2 and 3 cognitive impairment with grade 2 radiation leukoencephalopathy were observed in 4 (13.8%) and 4 (13.8%) patients. No dosimetric parameters were found to be associated with these late toxicities. Patients received EGFR-TKI treatment had higher incidence of grades 2–3 cognitive impairment with grade 2 leukoencephalopathy. CONCLUSIONS: WBRT plus SIB with IG-IMRT is a tolerable and effective treatment for NSCLC patients with inoperable brain metastases. However, the results of present study need to be examined by the prospective investigations

Topological structures of energy flow: Poynting vector skyrmions

Topological properties of energy flow of light are fundamentally interesting and have rich practical applications in optical manipulations. Here, skyrmion-like structures formed by Poynting vectors are unveiled in the focal region of a pair of counter-propagating cylindrical vector vortex beams in free space. A N\'eel-Bloch-N\'eel skyrmion type transformation of Poynting vectors is observed along the light propagating direction within a volume with subwavelength feature sizes. The corresponding skyrmion type can be determined by the phase singularities of the individual components of the coherently superposed electromagnetic field in the focal region. This work reveals a new family member of optical skyrmions and may introduce novel physical phenomena associated with light scattering and optical force

Development and validation of ferroptosis-related lncRNAs signature for hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors throughout the world. However, there is no research to establish a ferroptosis-related lncRNAs (FRlncRNAs) signature for the patients with HCC. Therefore, this study was designed to establish a novel FRlncRNAs signature to predict the survival of patients with HCC. Method The expression profiles of lncRNAs were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. FRlncRNAs co-expressed with ferroptosis-related genes were utilized to establish a signature. Cox regression was used to construct a novel three FRlncRNAs signature in the TCGA cohort, which was verified in the GEO validation cohort. Results Three differently expressed FRlncRNAs significantly associated with prognosis of HCC were identified, which composed a novel FRlncRNAs signature. According to the FRlncRNAs signature, the patients with HCC could be divided into low- and high-risk groups. Patients with HCC in the high-risk group displayed shorter overall survival (OS) contrasted with those in the low-risk group (P  1, P  1, P < 0.05). Meanwhile, it was also a useful tool in predicting survival among each stratum of gender, age, grade, stage, and etiology,etc. This signature was connected with immune cell infiltration (i.e., Macrophage, Myeloid dendritic cell, and Neutrophil cell, etc.) and immune checkpoint blockade targets (PD-1, CTLA-4, and TIM-3). Conclusion The three FRlncRNAs might be potential therapeutic targets for patients, and their signature could be utilized for prognostic prediction in HCC

Effects of Stress on Phase Transformations in Grinding by FE Modeling and Experimental Approaches

In the grinding process, the materials within the surface layer may undergo phase transformation and finally form a strengthened layer. It is of great significance to model the phase transformation and predict the characteristics of the strengthened layer accurately. The phase transformations occur under the varying temperature and high stress&ndash;strain in grinding, so the effects of stress on the transformations are inescapable. This paper focuses on revealing the effects of stress on phase transformations in grinding. For this purpose, a thermal&ndash;mechanical&ndash;metallurgical direct coupling finite element (FE) model of grinding was established in Abaqus. The coupling interactions such as the latent heat, the volume change strain caused by phase transformation, and the stress-induced phase transformation were considered in the modeling procedure. Grinding experiments were carried out and proved the model could accurately predict the microstructure distribution and thickness of the strengthened layer. Further, the evolution of the phase transformation was discussed, and the effects of stress on the transformations were revealed

Large (Ti, V) Carbonitride in Nonquenched and Tempered Steel 38MnVS6

Large (Ti, V) carbonitrides with size even up to tens of microns in autoparts steel 38MnVS6 are studied in this work. A great number of micron-sized (Ti, V) carbonitrides are found in continuous casting billet, and the atomic ratios of V/Ti are mainly distributed in range 0.130–0.200 with an average value of 0.171. The large (Ti, V) carbonitrides have irregular morphologies, and some even have an obviously extending shape along the dendrite boundary. 3D morphologies of the large (Ti, V) carbonitride after being etched by AA solution present obvious long and flake shapes. The large (Ti, V) carbonitride has high thermal stability even at 1200°C, even though the atomic ratio of V/Ti has a decreasing tendency. There are still many large (Ti, V) carbonitrides in the rolled bar and partially broken in some which are clearly visible. According to the Thermo-Calc calculation result, the large (Ti, V) carbonitride precipitates in liquid steel during solidification. The chemical compositional characteristic is the result of subsequent mutual diffusion of elements Ti, V, C, and N. Simply reducing the content of Ti, even 13 ppm cannot eliminate the large (Ti, V) carbonitride for the nitrogen-containing, nonquenched, and tempered steel, but the quantity and size of large carbonitride are significantly reduced

A New <i>ϵ</i>-Adaptive Algorithm for Improving Weighted Compact Nonlinear Scheme with Applications

To improve the resolution and accuracy of the high-order weighted compact nonlinear scheme (WCNS), a new ϵ-adaptive algorithm based on local smoothness indicators is proposed. The new algorithm introduces a high-order global smoothness indicator to adjust the value of ϵ according to the local flow characteristics. Specifically, the algorithm increases ϵ in smooth regions, which can help cover up the disparity in smoothness indicators of sub-stencils and make the nonlinear scheme approach the background linear scheme. As a result, optimal order accuracy can be achieved in smooth regions, including critical points. While near discontinuities, the algorithm decreases ϵ, thereby strengthening the stencil selection mechanism and further attenuating spurious oscillations. Meanwhile, the new algorithm makes nonlinear schemes scale-invariant of flow variables. The results of approximate dispersion relation (ADR) show that the new algorithm can greatly reduce spectral errors of nonlinear schemes in the medium and low wavenumber range without inducing instability. Numerical results indicate that the new algorithm can significantly improve resolution of small-scale structures and suppress numerical oscillations near discontinuities with only a minor increment in computational cost

A New &#1013;-Adaptive Algorithm for Improving Weighted Compact Nonlinear Scheme with Applications

To improve the resolution and accuracy of the high-order weighted compact nonlinear scheme (WCNS), a new &#1013;-adaptive algorithm based on local smoothness indicators is proposed. The new algorithm introduces a high-order global smoothness indicator to adjust the value of &#1013; according to the local flow characteristics. Specifically, the algorithm increases &#1013; in smooth regions, which can help cover up the disparity in smoothness indicators of sub-stencils and make the nonlinear scheme approach the background linear scheme. As a result, optimal order accuracy can be achieved in smooth regions, including critical points. While near discontinuities, the algorithm decreases &#1013;, thereby strengthening the stencil selection mechanism and further attenuating spurious oscillations. Meanwhile, the new algorithm makes nonlinear schemes scale-invariant of flow variables. The results of approximate dispersion relation (ADR) show that the new algorithm can greatly reduce spectral errors of nonlinear schemes in the medium and low wavenumber range without inducing instability. Numerical results indicate that the new algorithm can significantly improve resolution of small-scale structures and suppress numerical oscillations near discontinuities with only a minor increment in computational cost

Ionic effect of NaCl and KCl on the flotation of diaspore and kaolinite using sodium oleate as collector

The major type of bauxite in China is low-grade diasporic bauxite, which is mainly composed of diaspore and kaolinite. Separation of silicate minerals by flotation technology can meet the requirements of Bayer process, but Na, K and Cl deriving from mineral dissolution and entrainment in flotation water will inevitably mix in the flotation slurry, which will affect the flotation of bauxite. The results of flotation show that NaCl and KCl have little effect on the flotation of diaspore, but NaCl has a beneficial effect on kaolinite flotation. It may be attributed to the ion size order Na\ua0&lt;\ua0K\ua0&lt;\ua0Cl. Na has the ability to enter the layer spacing of kaolinite due to its smaller size, which increases the zeta potential and the dipole-dipole force between kaolinite and sodium oleate, therefore improving the flotation of kaolinite. In addition, Na has “salt effect” on the anionic collector, which also promotes the flotation of kaolinite. This study may have some reference significance to the industry flotation of diasporic bauxite

Conditional random mapping for effective ELM feature representation

Extreme learning machine (ELM) has been extensively studied, due to its fast training and good generalization. Unfortunately, the existing ELM-based feature representation methods are uncompetitive with state-of-the-art deep neural networks (DNNs) when conducting some complex visual recognition tasks. This weakness is mainly caused by two critical defects: (1) random feature mappings (RFM) by ad hoc probability distribution is unable to well project various input data into discriminative feature spaces; (2) in the ELM-based hierarchical architectures, features from previous layer are scattered via RFM in the current layer, which leads to abstracting higher level features ineffectively. To address these issues, we aim to take advantage of label information for optimizing random mapping in the ELM, utilizing an efficient label alignment metric to learn a conditional random feature mapping (CRFM) in a supervised manner. Moreover, we proposed a new CRFM-based single-layer ELM (CELM) and then extended CELM to the supervised multi-layer learning architecture (ML-CELM). Extensive experiments on various widely used datasets demonstrate our approach is more effective than original ELM-based and other existing DNN feature representation methods with rapid training/testing speed. The proposed CELM and ML-CELM are able to achieve discriminative and robust feature representation, and have shown superiority in various simulations in terms of generalization and speed