Spatially resolved measurement of helium atom emission line spectrum in scrape-off layer of Heliotron J by near-infrared Stokes spectropolarimetry

1視線の観測のみで核融合プラズマ中のヘリウム近赤外輝線の発光分布を推定. 京都大学プレスリリース. 2022-09-26.For plasma spectroscopy, Stokes spectropolarimetry is used as a method to spatially invert the viewing-chord-integrated spectrum on the basis of the correspondence between the given magnetic field profile along the viewing chord and the Zeeman effect appearing on the spectrum. Its application to fusion-related toroidal plasmas is, however, limited owing to the low spatial resolution as a result of the difficulty in distinguishing between the Zeeman and Doppler effects. To resolve this issue, we increased the relative magnitude of the Zeeman effect by observing a near-infrared emission line on the basis of the greater wavelength dependence of the Zeeman effect than of the Doppler effect. By utilizing the increased Zeeman effect, we are able to invert the measured spectrum with a high spatial resolution by Monte Carlo particle transport simulation and by reproducing the measured spectra with the semiempirical adjustment of the recycling condition at the first walls. The inversion result revealed that when the momentum exchange collisions of atoms are negligible, the velocity distribution of core-fueling atoms is mainly determined by the initial distribution at the time of recycling. The inversion result was compared with that obtained using a two-point emission model used in previous studies. The latter approximately reflects the parameters of atoms near the emissivity peak

Overview of transport and MHD stability study: focusing on the impact of magnetic field topology in the Large Helical Device

The progress in the understanding of the physics and the concurrent parameter extension in the large helical device since the last IAEA-FEC, in 2012 (Kaneko O et al 2013 Nucl. Fusion 53 095024), is reviewed. Plasma with high ion and electron temperatures (Ti(0) ~ Te(0) ~ 6 keV) with simultaneous ion and electron internal transport barriers is obtained by controlling recycling and heating deposition. A sign flip of the nondiffusive term of impurity/momentum transport (residual stress and convection flow) is observed, which is associated with the formation of a transport barrier. The impact of the topology of three-dimensional magnetic fields (stochastic magnetic fields and magnetic islands) on heat momentum, particle/impurity transport and magnetohydrodynamic stability is also discussed. In the steady state operation, a 48 min discharge with a line-averaged electron density of 1 × 1019 m−3 and with high electron and ion temperatures (Ti(0) ~ Te(0) ~ 2 keV), resulting in 3.36 GJ of input energy, is achieved

A Genome-Wide Association Study Predicts the Onset of Dysgeusia Due to Anti-cancer Drug Treatment

Dysgeusia is a major side effect of anti-cancer drug treatment. Since dysgeusia significantly lowers the patient’s QOL, predicting and avoiding its onset in advance is desirable. Accordingly, aims of the present study were to use a genome-wide association study (GWAS) to identify genes associated with the development of dysgeusia in patients taking anti-cancer drugs and to predict the development of dysgeusia using associated single nucleotide polymorphisms (SNPs). GWAS was conducted on 76 patients admitted to the Department of Hematology, Tokushima University Hospital. Using Sanger sequencing for 23 separately collected validation samples, the top two SNPs associated with the development of dysgeusia were determined. GWAS identified rs73049478 and rs41396146 SNPs on the retinoic acid receptor beta (RARB) gene associated with dysgeusia development due to the administration of anti-cancer drugs. Evaluation of the two SNPs using 23 validation samples indicated that the accuracy rate of rs73049478 was relatively high (87.0%). Thus, the findings of the present study suggest that the rs73049478 SNP of RARB can be used to predict the onset of dysgeusia caused by the administration of anti-cancer drugs

Association analysis between adverse drug reactions to cytarabine therapy and single nucleotide polymorphisms in cytarabine metabolic genes in patients with hematopoietic tumor

Purpose: Cytarabine arabinoside (Ara-C) is an anti-metabolite that is commonly used as a therapeutic agent for acute leukemia; however, it can cause adverse drug reactions, such as digestive disorders, rashes, and fever. Therefore, identification of gene markers that can accurately predict the development of adverse drug reactions is useful for selecting effective drugs for therapy. After entering the cells, Ara-C is metabolized to Ara-C triphosphate, which inhibits DNA synthesis and exhibits antitumor activity. Therefore, we conducted an association study between the adverse reactions to cytarabine therapy and single nucleotide polymorphisms (SNPs) in cytarabine metabolic genes. Methods: Among the patients treated with cytarabine at the Department of Hematology at Tokushima University Hospital, 46 patients provided informed consent and were included in this study. We selected 14 tag SNPs located in nine genes that are involved in the cytarabine metabolic pathway; these SNPs were genotyped using the polymerase chain reaction (PCR) restriction fragment length polymorphism (RFLP) technique. Association analyses between adverse reactions to Ara-C therapy and SNPs were performed using logistic regression analysis. Results: The rs9394992 polymorphism in the SLC29A1 gene and rs3886768 polymorphism in the DCTD gene were associated with the development of rash after Ara-C therapy. The rs7277 polymorphism in the DCTD gene was associated with fever, and the rs16945930 polymorphism in the ABCC11 gene was associated with sore throat. Conclusions: Our findings suggest that SNPs in the Ara-C metabolic genes influence the development of adverse reactions to Ara-C, and the results suggest that these genes can be predictive of adverse reactions to Ara-C therapy

Pulse Pressure is a Stronger Predictor Than Systolic Blood Pressure for Severe Eye Diseases in Diabetes Mellitus

Background: Evidence of the role of systolic blood pressure (SBP) in development of severe diabetic retinopathy is not strong, although the adverse effect of low diastolic blood pressure has been a partial explanation. We assessed the predictive ability of incident severe diabetic retinopathy between pulse pressure (PP) which considers both SBP and diastolic blood pressure, compared with SBP. Methods and Results: Eligible patients (12 242, 83% men) aged 19 to 72 years from a nationwide claims database were analyzed for a median observational 4.8‐year period. Severe diabetic retinopathy was defined as vision‐threatening treatment‐required diabetic eye diseases. Multivariate Cox regression analysis revealed that hazard ratios (95% CI) of treatment‐required diabetic eye diseases for 1 increment of standard deviation and the top tertile compared with the bottom tertile were 1.39 (1.21–1.60) and 1.72 (1.17–2.51), respectively, for PP and 1.22 (1.05–1.41) and 1.43 (0.97–2.11), respectively, for SBP adjusted for age, sex, body mass index, hemoglobin A1c, fasting plasma glucose, lipids, and smoking status. In a model with SBP and PP simultaneously as covariates, the hazard ratios of only PP (hazard ratios [95% CI], 1.57 [1.26–1.96]) but not SBP (0.85 [0.68–1.07]) were statistically significant. Delong test revealed a significant difference in the area under the receiver operating characteristic curve between PP and SBP (area under the receiver operating characteristic curve [95% CI], 0.58 [0.54–0.63] versus 0.54 [0.50–0.59]; P=0.03). The strongest predictor remained as hemoglobin A1c (area under the receiver operating characteristic curve [95% CI], 0.80 [0.77–0.84]). Conclusions: After excluding the significant impact of glycemic control, PP in comparison with SBP is a better predictor of severe diabetic retinopathy, suggesting a role of diastolic blood pressure and arterial stiffness in pathology

Machine learning and GWAS for peripheral neuropathy

Vincristine treatment may cause peripheral neuropathy. In this study, we identified the genes associated with the development of peripheral neuropathy due to vincristine therapy using a genome-wide association study (GWAS) and constructed a predictive model for the development of peripheral neuropathy using genetic information-based machine learning. The study included 72 patients admitted to the Department of Hematology, Tokushima University Hospital, who received vincristine. Of these, 56 were genotyped using the Illumina Asian Screening Array-24 Kit, and a GWAS for the onset of peripheral neuropathy caused by vincristine was conducted. Using Sanger sequencing for 16 validation samples, the top three single nucleotide polymorphisms (SNPs) associated with the onset of peripheral neuropathy were determined. Machine learning was performed using the statistical software R package “caret.” The 56 GWAS and 16 validation samples were used as the training and test sets, respectively. Predictive models were constructed using random forest, support vector machine, naive Bayes, and neural network algorithms. According to the GWAS, rs2110179, rs7126100, and rs2076549 were associated with the development of peripheral neuropathy on vincristine administration. Machine learning was performed using these three SNPs to construct a prediction model. A high accuracy of 93.8% was obtained with the support vector machine and neural network using rs2110179 and rs2076549. Thus, peripheral neuropathy development due to vincristine therapy can be effectively predicted by a machine learning prediction model using SNPs associated with it