パラジウム触媒を用いる還元的新規グリコシル化反応の開発

要約のみTohoku University正田晋一郎課

Quantitative estimating size of deep defects in multi-layered structures from eddy current NDT signals using improved ant colony algorithm

Detection and quantitative estimation of deep defects in multi-layered structures is an essential task in a range of technological applications, such as maintaining the integrity of structures, enhancing the safety of aging aircraft, and assuring the quality of products. A novel approach to accurately quantify the two-dimensional axisymmetric deep defect size from eddy current nondestructive testing (NDT) signals is presented here. The method uses a finite element forward model to simulate the underlying physical process and an improved ant colony algorithm (IACA) to solve the inverse problem. Experiments are carried out. The performance comparison between the IACA method and the least square method is shown. The comparison results demonstrate the feasibility and validity of the IACA method. Between them, the IACA method gives a better estimation performance than the least square method at present

The distinct binding properties between avian/human influenza A virus NS1 and Postsynaptic density protein-95 (PSD-95), and inhibition of nitric oxide production

<p>Abstract</p> <p>Background</p> <p>The NS1 protein of influenza A virus is able to bind with many proteins that affect cellular signal transduction and protein synthesis in infected cells. The NS1 protein consists of approximately 230 amino acids and the last 4 amino acids of the NS1 C-terminal form a PDZ binding motif. Postsynaptic Density Protein-95 (PSD-95), which is mainly expressed in neurons, has 3 PDZ domains. We hypothesise that NS1 binds to PSD-95, and this binding is able to affect neuronal function.</p> <p>Result</p> <p>We conducted a yeast two-hybrid analysis, GST-pull down assays and co-immunoprecipitations to detect the interaction between NS1 and PSD-95. The results showed that NS1 of avian influenza virus H5N1 (A/chicken/Guangdong/1/2005) is able to bind to PSD-95, whereas NS1 of human influenza virus H1N1 (A/Shantou/169/2006) is unable to do so. The results also revealed that NS1 of H5N1 significantly reduces the production of nitric oxide (NO) in rat hippocampal neurons.</p> <p>Conclusion</p> <p>In summary, our study indicates that NS1 of influenza A virus can bind with neuronal PSD-95, and the avian H5N1 and human H1N1 influenza A viruses possess distinct binding properties.</p

Exploiting Transformations of the Galois Configuration to Improve Guess-and-Determine Attacks on NFSRs

Guess-and-determine attacks are based on guessing a subset of internal state bits and subsequently using these guesses together with the cipher\u27s output function to determine the value of the remaining state. These attacks have been successfully employed to break NFSR-based stream ciphers. The complexity of a guess-and-determine attack is directly related to the number of state bits used in the output function. Consequently, an opportunity exits for efficient cryptanalysis of NFSR-based stream ciphers if NFSRs used can be transformed to derive an equivalent stream cipher with a simplified output function. In this paper, we present a new technique for transforming NFSRs. We show how we can use this technique to transform NFSRs to equivalent NFSRs with simplified output functions. We explain how such transformations can assist in cryptanalysis of NFSR-based ciphers and demonstrate the application of the technique to successfully cryptanalyse the lightweight cipher Sprout. Our attack on Sprout has a time complexity of 2^70.87, which is 2^3.64 times better than any published non-TMD attack, and requires only 164 bits of plaintext-ciphertext pairs

Induction of cytopathic effect and cytokines in coxsackievirus B3-infected murine astrocytes

BACKGROUND: Coxsackievirus commonly infects children and occasionally causes severe meningitis and/or encephalitis in the newborn. The underlying mechanism(s) behind the central nervous system pathology is poorly defined. METHODS: It is hypothesized that astrocytes may be involved in inflammatory response induced by CVB3 infection. Here we discuss this hypothesis in the context of CVB3 infection and associated inflammatory response in primary mouse astrocytes. RESULTS: The results showed that coxsackievirus receptor (CAR) was distributed homogeneously on the astrocytes, and that CVB3 could infect and replicate in astrocytes, with release of infectious virus particles. CVB3 induced cytopathic effect and production of proinflammatory cytokines IL-1β, TNF-α, IL-6, and chemokine CXCL10 from astrocytes. CONCLUSION: These data suggest that direct astrocyte damage and cytokines induction could be a mechanism of virus-induced meningitis and/or encephalitis

Mean platelet volume: A controversial marker of disease activity in Crohn’s disease

Background: We investigated and compared the capacity of mean platelet volume (MPV) and other inflammatory markers in detecting Crohn’s disease (CD) activity and differentiating CD patients from healthy controls. Methods: MPV, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and white blood cells were measured in 61 CD patients and 50 healthy subjects. Disease activity was assessed by the Crohn’s Disease Activity Index. Results: A significant decrease in MPV was noted in patients with CD compared with healthy controls (P <0.0001), but statistical difference was not found between active and inactive CD groups. In CD, no significant correlation was found between MPV and other inflammatory markers. The overall accuracy of MPV (cutoff: 10.35 fl), CRP (cutoff: 4.85 mg/dl) and ESR (cutoff: 8.5 mm/hour) in differentiating CD patients from healthy controls was 76.6%, 65.8% and 72.1% respectively. The overall accuracy of CRP (cutoff: 4.95 mg/dl) and ESR (cutoff: 16.5 mm/hour) in determination of active CD was 80.3% and 73.8%. Conclusions: MPV declined in CD patients compared with healthy subjects. MPV had the best accuracy in determination of CD patients and healthy controls. MPV did not show a discriminative value in disease activity

Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability