Ｘ線吸収分光法による歪んだチタン酸化物のフォトルミネセンスの研究

広島大学(Hiroshima University)博士(理学)Doctor of Sciencedoctora

A Message Passing Detection based Affine Frequency Division Multiplexing Communication System

The next generation of wireless communication technology is anticipated to address the communication reliability challenges encountered in high-speed mobile communication scenarios. An Orthogonal Time Frequency Space (OTFS) system has been introduced as a solution that effectively mitigates these issues. However, OTFS is associated with relatively high pilot overhead and multiuser multiplexing overhead. In response to these concerns within the OTFS framework, a novel modulation technology known as Affine Frequency Division Multiplexing (AFDM) which is based on the discrete affine Fourier transform has emerged. AFDM effectively resolves the challenges by achieving full diversity through parameter adjustments aligned with the channel's delay-Doppler profile. Consequently, AFDM is capable of achieving performance levels comparable to OTFS. As the research on AFDM detection is currently limited, we present a low-complexity yet efficient message passing (MP) algorithm. This algorithm handles joint interference cancellation and detection while capitalizing on the inherent sparsity of the channel. Based on simulation results, the MP detection algorithm outperforms Minimum Mean Square Error (MMSE) and Maximal Ratio Combining (MRC) detection techniques.Comment: 8 pages, 7 figure

Crack-Net: Prediction of Crack Propagation in Composites

Computational solid mechanics has become an indispensable approach in engineering, and numerical investigation of fracture in composites is essential as composites are widely used in structural applications. Crack evolution in composites is the bridge to elucidate the relationship between the microstructure and fracture performance, but crack-based finite element methods are computationally expensive and time-consuming, limiting their application in computation-intensive scenarios. Here we propose a deep learning framework called Crack-Net, which incorporates the relationship between crack evolution and stress response to predict the fracture process in composites. Trained on a high-precision fracture development dataset generated using the phase field method, Crack-Net demonstrates a remarkable capability to accurately forecast the long-term evolution of crack growth patterns and the stress-strain curve for a given composite design. The Crack-Net captures the essential principle of crack growth, which enables it to handle more complex microstructures such as binary co-continuous structures. Moreover, transfer learning is adopted to further improve the generalization ability of Crack-Net for composite materials with reinforcements of different strengths. The proposed Crack-Net holds great promise for practical applications in engineering and materials science, in which accurate and efficient fracture prediction is crucial for optimizing material performance and microstructural design

Effectiveness of external Sanjierupi Gao on mastalgia caused by mammary gland hyperplasia: a placebo controlled trial

AbstractObjectiveTo evaluate the curative effect of external application of the Chinese drug, Sanjierupi Gao, on mastalgia caused by mammary gland hyperplasia.MethodsThis randomized, double-blinded, and placebo controlled study enrolled 260 patients with mammary gland hyperplasia from five hospitals. Patients were randomly and equally divided into a Sanjierupi Gao treatment group and a placebo control group. An adhesive plaster was applied to the most painful area on either breast for 7 h a day. Treatment lasted for two menstrual cycles without application during menstruation. Mastalgia was used as the main index of curative effect. The change before and after treatment in days of mastalgia, the time to alleviate pain, pain extent, and severe pain scores were observed.ResultsCompared to the control group, the treatment group had significantly fewer days of mastalgia (P<0.01), a significantly lower severe pain score (P<0.01), and significantly less subjective pain and tenderness (P<0.05 and P<0.01, respectively). Three days before the follow-up visit, the pain score in the treatment group was significantly lower than that in the control group (P<0.05). A non-parametric test was used to compare the time to alleviate mastalgia between the two groups and found no statistical difference (Z=−0.313, P=0.754).ConclusionApplication of Sanjierupi Gao can decrease mastalgia duration in patients with mammary gland hyperplasia during menstruation and alleviate the extent of mastalgia. The time to alleviate pain is psychologically influenced

Characterization of Non-heading Mutation in Heading Chinese Cabbage (Brassica rapa L. ssp. pekinensis)

Heading is a key agronomic trait of Chinese cabbage. A non-heading mutant with flat growth of heading leaves (fg-1) was isolated from an EMS-induced mutant population of the heading Chinese cabbage inbred line A03. In fg-1 mutant plants, the heading leaves are flat similar to rosette leaves. The epidermal cells on the adaxial surface of these leaves are significantly smaller, while those on the abaxial surface are much larger than in A03 plants. The segregation of the heading phenotype in the F2 and BC1 population suggests that the mutant trait is controlled by a pair of recessive alleles. Phytohormone analysis at the early heading stage showed significant decreases in IAA, ABA, JA and SA, with increases in methyl IAA and trans-Zeatin levels, suggesting they may coordinate leaf adaxial-abaxial polarity, development and morphology in fg-1. RNA-sequencing analysis at the early heading stage showed a decrease in expression levels of several auxin transport (BrAUX1, BrLAXs, and BrPINs) and responsive genes. Transcript levels of important ABA responsive genes, including BrABF3, were up-regulated in mid-leaf sections suggesting that both auxin and ABA signaling pathways play important roles in regulating leaf heading. In addition, a significant reduction in BrIAMT1 transcripts in fg-1 might contribute to leaf epinastic growth. The expression profiles of 19 genes with known roles in leaf polarity were significantly different in fg-1 leaves compared to wild type, suggesting that these genes might also regulate leaf heading in Chinese cabbage. In conclusion, leaf heading in Chinese cabbage is controlled through a complex network of hormone signaling and abaxial-adaxial patterning pathways. These findings increase our understanding of the molecular basis of head formation in Chinese cabbage

Prediction of Drought-Resistant Genes in Arabidopsis thaliana Using SVM-RFE

Background: Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits. Methodology: In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination) feature selection method for the prediction. To address small sample sizes, we developed a modified approach for SVM-RFE by using bootstrapping and leave-one-out cross-validation. We also expanded our study to predict genes involved in water susceptibility. Conclusions: We analyzed the top 10 genes predicted to be involved in water tolerance. Seven of them are connected to known biological processes in drought resistance. We also analyzed the top 100 genes in terms of their biological functions. Our study shows that the SVM-RFE method is a highly promising method in analyzing plant microarray data for studyin

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification

<p>Abstract</p> <p>Background</p> <p>Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.</p> <p>Results</p> <p>Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (<b>SVD</b>-based <b>P</b>attern <b>P</b>airing and <b>C</b>hart <b>S</b>plitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W¹¹⁸of <it>M. drosophila</it>. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering.</p> <p>Conclusions</p> <p>svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.</p