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修士論文要

Electromagnetic wave absorbing properties of aligned amorphous carbon nanotube/BaFe12O19 nanorod composite

Aligned amorphous carbon nanotube (AACNT)/BaFe12O19 nanorod (BNR) composite was prepared by chemical vapor deposition and ball-milling methods. Raman and XRD tests were performed to investigate the microstructures, and the microwave absorbing properties of the as prepared composite were characterized using a vector network analyzer. The experimental results indicated that the mean length of as-prepared ACNT arrays was about 24 μm and the average length of BNRs were about 50 nm. The maximum absorbing peak of AACNTs/BNR composite is −21.5 dB at the frequency of 9.3 GHz. The frequency bandwidth of the reflectivity loss below −10 dB is about 2.5 GHz. AACNTs have both features of amorphous CNTs which have multiple-reflective path inside the tube-wall and crystalline CNTs which have high conductivity.Institute of Textiles and Clothin

Neuroprotective Effects of Jitai Tablet, a Traditional Chinese Medicine, on the MPTP-Induced Acute Model of Parkinson’s Disease: Involvement of the Dopamine System

Jitai tablet (JTT) is a traditional Chinese medicine used to treat neuropsychiatric disorders. We previously demonstrated that JTT treatment led to increased level of dopamine transporter (DAT) in the striatum, thus indicating that JTT might have therapeutic potential for Parkinson’s disease (PD), which is characterized by dysregulated dopamine (DA) transmission and decreased striatal DAT expression. The aim of this study was to investigate the neuroprotective effect of JTT on MPTP-induced PD mice. Using locomotor activity test and rotarod test, we evaluated the effects of JTT (0.50, 0.15, or 0.05 g/kg) on MPTP-induced behavioral impairments. Tyrosine hydroxylase TH-positive neurons in the substantia nigra and DAT and dopamine D2 receptor (D2R) levels in the striatum were detected by immunohistochemical staining and/or autoradiography. Levels of DA and its metabolites were determined by HPLC. In MPTP-treated mice, behavioral impairments were alleviated by JTT treatment. Moreover, JTT protected against impairment of TH-positive neurons and attenuated the MPTP-induced decreases in DAT and D2R. Finally, high dose of JTT (0.50 g/kg) inhibited the MPTP-induced increase in DA metabolism rate. Taken together, results from our present study provide evidence that JTT offers neuroprotective effects against the neurotoxicity of MPTP and thus might be a potential treatment for PD

Sensitivity of joint atmospheric-terrestrial water balance simulations to soil representation: convection-permitting coupled WRF-Hydro simulations for southern Africa

Regional weather and climate models play a crucial role in understanding and representing the regional water cycle, yet the accuracy of soil data significantly affects their reliability. In this study, we employ the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) to assess how soil hydrophysical properties influence regional land-atmosphere coupling and the water cycle over the southern Africa region. We utilize four widely-used global soil datasets, including default soil data for model from the Food and Agriculture Organization, and alternative datasets from the Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids. By conducting convection-permitting coupled WRF-Hydro simulations with the Noah-MP land surface model using each of the aforementioned soil datasets, our benchmark analysis reveals substantial differences in soil hydrophysical properties and their significant impact on the simulated regional water cycle during the austral summer. Alterations in soil datasets lead to both spatial and temporal variations in surface water and energy fluxes, which in turn profoundly influence the atmospheric thermodynamic structure. Reduced soil water-holding capacity leads to subsequent reduction in soil moisture and latent heat, resulting in significant decreases in convective available potential energy and convective inhibition, signaling potential effects on precipitation distributions. In arid interior regions of southern Africa, shifts towards drier and warmer surface conditions due to soil data discrepancies are found to enhance atmospheric moisture convergence, suggesting a possible localized negative feedback of soil moisture on precipitation. Overall, the results for southern Africa indicate that soil data discrepancies exert more pronounced impact on terrestrial fields in dry subregions and on atmospheric fields in temperate subregions, highlighting the broad uncertainties in the regional water cycle reproduced within the model

A high-resolution regional climate model physics ensemble for Northern sub-Saharan Africa

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary e.g., from region to region. Besides land-surface processes, the most crucial processes to be parameterized in RCMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model for the period 2006–2010 in a horizontal resolution of approximately 9 km. Based on different evaluation strategies including traditional (Taylor diagram, probability densities) and more innovative validation metrics (ensemble structure-amplitude-location (eSAL) analysis, Copula functions) and by means of different observation data for precipitation (P) and temperature (T), the impact of different physics combinations on the representation skill of P and T has been analyzed and discussed in the context of subsequent impact modeling. With the specific experimental setup, we found that the selection of the CU scheme has resulted in the highest impact with respect to the representation of P and T, followed by the RA parameterization scheme. Both, PBL and MP schemes showed much less impact. We conclude that a multi-facet evaluation can finally lead to better choices about good physics scheme combinations

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection

Ultrasound-Assisted Enzymatic Extraction and Bioactivity Analysis of Polypeptides from Cordyceps militaris

Cordyceps militaris is rich in protein, polysaccharide, cordycepin, and other active components, with anticancer and antioxidation functions. In order to improve the economic value of C. militaris, the protein was extracted from its fruiting body by alkali-soluble acid precipitation process, and the extraction technology was optimized by orthogonal test. The polypeptide was obtained by digesting those proteins with a complex enzyme. And the antimicrobial and anticancer activities of those polypeptides were evaluated by measuring inhibitory zone and cytotoxicity. The results showed that the optimal extraction conditions of protein were as follows: pH of 8.5, material-to-water ratio of 1 : 28, extraction time of 3.5 h, extraction three times, and the highest protein yield was 45.06%. The optimum enzymatic hydrolysis process of C. militaris polypeptide solution was as follows: the ratio of alkaline protease to papain was 4 : 3, the optimum temperature was 55°C, pH was 7.2, the enzyme dosage was 7000 U/mL, the enzymolysis time was 3.5 h, and the highest yield of peptide was 16.73%. Under those conditions, the polypeptides prepared from C. militaris (<3000 Da) showed good antibacterial activity against Escherichia coli, Bacillus subtilis, and Staphylococcus aureus, with inhibitory zones of (12.08 ± 0.22), (6.67 ± 0.12), and (10.32 ± 0.23) mm, respectively. The results showed that the SAO-S (IC50 = 0.49 mg/L) and T24 (IC50 = 0.23 mg/L) were significantly inhibited by C. militaris polypeptide. Results from this study suggest that polypeptides can be utilized as a new approach for bioactive compounds production from C. militaris

Network Analyses of Integrated Differentially Expressed Genes in Papillary Thyroid Carcinoma to Identify Characteristic Genes

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC