Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

Effects of Switching from Treatment with Amlodipine and Atorvastatin Using Two Pills to an Equal Dose of Single-Pill Therapy in Japanese Outpatients

This study examined whether switching from amlodipine and atorvastatin treatment using two pills to an equal dose of single-pill therapy is useful in Japanese outpatients. We retrospectively reviewed data obtained from 94 outpatients for whom treatment with two pills, namely amlodipine and atorvastatin, was switched to an equal dose of single-pill therapy in 11 hospitals. The criterion for enrollment in this study was that patients had switched their medication without changing other anti-hypertensive or anti-cholesterol drugs. Neither systolic nor diastolic blood pressure changed significantly after switching to an equal dose of single-pill therapy, whereas low-density lipoprotein (LDL) cholesterol levels significantly decreased after the medication was switched from 94±24 mg/dl to 89±17 mg/dl (p=0.015). A switch from medication with two separate pills of amlodipine and atorvastatin to an equal dose of single-pill therapy resulted in an overall decrease in LDL cholesterol. The results indicated that the switch to single-pill therapy might be a useful treatment

Nucleolar integrity during interphase supports faithful Cdk1 activation and mitotic entry

The nucleolus is a dynamic nuclear body that has been demonstrated to disassemble at the onset of mitosis; the relationship between cell cycle progression and nucleolar integrity, however, remains poorly understood. We studied the role of nucleolar proteins in mitosis by performing a global analysis using small interfering RNAs specific to nucleolar proteins; we focused on nucleolar protein 11 (NOL11), with currently unknown mitotic functions. Depletion of NOL11 delayed entry into the mitotic phase owing to increased inhibitory phosphorylation of cyclin-dependent kinase 1 (Cdk1) and aberrant accumulation of Wee1, a kinase that phosphorylates and inhibits Cdk1. In addition to effects on overall mitotic phenotypes, NOL11 depletion reduced ribosomal RNA (rRNA) levels and caused nucleolar disruption during interphase. Notably, mitotic phenotypes found in NOL11-depleted cells were recapitulated when nucleolar disruption was induced by depletion of rRNA transcription factors or treatment with actinomycin D. Furthermore, delayed entry into the mitotic phase, caused by the depletion of pre-rRNA transcription factors, was attributable to nucleolar disruption rather than to G2/M checkpoint activation or reduced protein synthesis. Our findings therefore suggest that maintenance of nucleolar integrity during interphase is essential for proper cell cycle progression to mitosis via the regulation of Wee1 and Cdk1

Feature extraction from images of endoscopic large intestine

The 14th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2008), Poster ; Place : Beppu, Oita, Japan ; Date : January 23-26, 200

A consideration for condition analysis with pit pattern of endoscope image

医療の分野において,大腸の拡大内視鏡を用いた病状レベル推定を行うシステムが要望されている.大腸管腔内への腺管の開口部の形態をpit patternと呼び,これは内視鏡診断の際に組織診断を推定する基準とされている.そこで本論文ではpit patternを画像から抽出し,その特徴量を算出することで病状レベルの推定を行う手法について述べる.大腸観察の際には病変部を染色しpit patternを強調する.そこで,画像中の色エッジを抽出し,watershed法を用いて領域分割を行いpit patternを抽出する.各pit patternの特徴量を算出し,病状レベルとの相関について検討する.Diagnosis system of condition level with an endoscope of large intestines is demanded in the field of medical treatment. The form of opening of duct of the gland in a large intestines lumen is called pit pattern, and this is used for an organizational diagnosis with an endoscope. In this paper we consider a method for analysing pit pattern from endoscope image. Pit pattern is extracted by color edge of image, and watershed segmentation. Feature of the extracted pits are examined to find correlation between the condition level and the features

Feature extraction from images of endoscopic large intestine

In this paper, we propose feature extraction methods from two types of images of endoscopic large intestine taken by a colonoscopy for diagnosis of colon cancer. Today, there are two observation methods. One is staining surface of large intestine. The other is colonoscopy using Narrow Band Imaging (NBI) system, a new feature of endoscope. We describe extraction methods of features for each observation method so that the features may be used to estimate colon cancer staging from an observed image. Pit pattern is a texture that appears on the surface of stained intestine and they are categorized and used for diagnosis. Thus, we extract pits from an endoscope image to analyze patterns. First, color edge of the image is extracted, then watershed segmentation is applied. In the result, pits are roughly extracted. NBI system can observe vasucular structure under the surface of large intestine. The vascular structure can be used to estimate cancer staging. A vascular area is roughly extracted by adaptive binarization, then the fine shape of vascular area is extracted by the level set method