Difference in Acquired Radioresistance Induction Between Repeated Photon and Particle Irradiation

In recent years, advanced radiation therapy techniques, including stereotactic body radiotherapy and carbon‑ion radiotherapy, have progressed to such an extent that certain types of cancer can be treated with radiotherapy alone. The therapeutic outcomes are particularly promising for early-stage lung cancer, with results that match those of surgical resection. Nevertheless, patients may still experience local tumor recurrence, which might be exacerbated by the acquisition of radioresistance after primary radiotherapy. Notwithstanding the risk of tumors acquiring radioresistance, secondary radiotherapy is used increasingly to treat recurrent tumors. In this context, it appears essential to comprehend the radiobiological effects of repeated photon and particle irradiation and their underlying cellular and molecular mechanisms to achieve the most favorable therapeutic outcome. However, to date, the mechanisms of acquisition of radioresistance in cancer cells have mainly been studied after repeated in vitro X-ray irradiation. In contrast, other critical aspects of radioresistance remain mostly unexplored, including the response to carbon‑ion irradiation of X-ray radioresistant cancer cells, the mechanisms of acquisition of carbon‑ion resistance, and the consequences of repeated in vivo X-ray or carbon‑ion irradiation. In this review, we discuss the underlying mechanisms of acquisition of X-ray and carbon‑ion resistance in cancer cells, as well as the phenotypic differences between X-ray and carbon‑ion resistant cancer cells, the biological implications of repeated in vivo X-ray or carbon‑ion irradiation, and the main open questions in the field

What has NMR taught us about stripes and inhomogeneity?

The purpose of this brief invited paper is to summarize what we have (not) learned from NMR on stripes and inhomogeneity in La{2-x}Sr{x}CuO{4}. We explain that the reality is far more complicated than generally accepted.Comment: Accepted for publication in the Proceedings of the LT-23 Conference (invited

On Blow-up Sets for the Parabolic Equation dt ∂tβ(u) = ⊿u+f(u) in a Ball

Radially symmetric solutions of nonlinear degenerate parabolic equations are considered in a ball, under some blow-up conditions on β(ξ), f(ξ) and the initial date. Blow-up sets of solutions are classified by the increasing order of the heat source f(ξ) as ξ → ∞.Article信州大学理学部紀要 25(2): 51-58(1991)departmental bulletin pape

Superconductivity and Spin Fluctuations in the Electron-Doped Infinitely-Layered High Tc Superconductor Sr0.9_0.9La0.1_0.1CuO2_2 (Tc=42K)

This paper describes the first 63-Cu NMR study of an electron-doped infinitely-layered high Tc superconductor Sr$_0.9$La$_0.1$CuO$_2$ (Tc=42K). The spin dynamics in the normal state above Tc exhibits qualitatively the same behavior as some hole-doped materials with significantly enhanced spin fluctuations. Below Tc, we observed no signature of a Hebel-Slichter coherence peak, suggesting an unconventional nature of the symmetry of the superconducting order parameter.Comment: Invited Paper to SNS-95 Conference (Spectroscopies on Novel Superconductors 1995 at Stanford). Also presented at Aspen Winter Conference on Superconductivity and Grenoble M^2S-HTSC in 199

Superconductivity of MI(MII0.5, Si0.5)2 (MI=Sr and Ba, MII=Al and Ga), ternary silicides with the AlB2-type structure

Ternary silicides MI(MII0.5, Si0.5)2 (MI=Sr and Ba, MII=Al and Ga) were prepared by Ar arc melting. Powder X-ray diffraction indicates that they have the AlB2-type structure, in which Si and MII atoms are arranged in honeycomb layers and MI atoms are intercalated between them. Electrical resistivity and dc magnetization measurements revealed that Sr(Al0.5, Si0.5)2 is superconductive, with a critical temperature for superconductivity (TC) of 4.2 K, while Ba(Al0.5, Si0.5)2 is not at temperatures ranging above 2.0 K. Sr(Ga0.5, Si0.5)2 and Ba(Ga0.5, Si0.5)2 are also superconductors, with TCs of 5.1 and 3.3 K, respectively.Comment: 5 pages, 3 tables, 4 figure

Statistical Estimation Model for Product Quality of Petroleum

Controls of the temperature, pressure and flowing quantity are important for the stable operation of the product quality in the distillation tower. The usual measuring way of product quality estimation is made by the off-line analysis. In this paper, online estimation method of product quality is studied for improving the product quality. The estimation method based on stochastic analysis was developed for online estimation. In this paper, the data of temperature, pressure and flow volum in the distillation tower are treated. As the estimation models, RNN (Recurrent Neural Net Work) and PLS (Partial Least Square Regression Method) were adopted. The actual plant data were used in the analysis. Both PLS and RNN models could compensate each other to improve the accuracy in estimation

Application of Neural Network to Fault Diagnosis ofElectro-Mechanical System

In this paper, neuro based intelligent diagnosis methods for electro-mechanical control system are proposed. A self organizing map neural network (SOM) is used to classify measured data of the target system as a qualitative diagnostic method. Besides of the above procedure, it is expected to attain more efficient maintenance by a quantitative estimation of failure. For the purpose, new method is proposed using a hierarchical neural network (HNN). In the method, classified results by SOM are processed for the quantitative diagnosis. Hierarchical neural network can identify inner structure of the relations between failure causes and its results that enables a quantitative diagnosis

Feature Extraction and Classification of Operational Data for Diagnosis of Hot Strip Mill Looper Control

In these days, mechanical systems are becoming more complex and highly automated. So, there exist wide variety of demands for reliable diagnostic technology. A reliable data analysis and quantitative diagnosis method of mechanical system is necessary for the purpose. In this paper a quantitative diagnosis method for looper height control system has been developed based on neural network technologies. The wavelet transformation is used for pre-processing to analyze characteristics of looper height control system. And, self organizing map neural network is used for the purpose of classification based on the pre-processed data. After that, the classified results are used for quantitative diagnosis in hierarchical neural network