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BACKGROUND AND AIM Microbiota have been associated with several diseases including colorectal cancer (CRC). This study aimed to evaluate the microbiota in early/invasive CRC utilizing stool and cytological brushes to determine differences in relative abundance (RA). METHODS Colonoscopy patients referred for endoscopic submucosal dissection or previous to CRC surgery were prospectively enrolled. Stool was collected pre-bowel preparation; and brush samples were taken during colonoscopy (three regions). DNA extraction, 16S rRNA next generation sequencing, and biostatistics (qiime and stamp software packages) followed. Primary outcome was the difference in RA of the Fusobacterium genus between the groups. Secondary outcomes included analyses of other microbiota. RESULTS Twenty-five patients were included, of which 14 had invasive cancer (≥ 1000 mm into the submucosa). The three major genera for invasive cancer were Bacterioides, Oribacterium, and Fusobacterium, whereas for early cancer were Oribacterium, Bacterioides, and Prevotella (decreasing order of RA). There was a significantly higher RA of Fusobacterium in the invasive cancer group (9.65% vs 0.95%, respectively, P

Mucosal healing is the main treatment goal for Crohn’s disease. In this situation, some patients have difficulty with endoscopic evaluation of the entire small intestine. Crohn’s disease is closely associated with the gut microbiota, but the relationship between the microbiome and disease activity in the small intestine remains unclear. We examined the association between the microbiome and endoscopic findings in the small intestine and determined whether the microbiome can predict mucosal healing. The patients with Crohn’s disease who were scheduled for capsule or balloon-assisted endoscopy were included in this prospective study. Patients whose entire small intestine was evaluated were divided into two groups based on ulcerative findings. The microbiomes in the fecal samples were analyzed using 16S rRNA sequencing. The 38 enrolled patients were divided into the ulcer group (24) and mucosal healing group (14). The ulcer group exhibited lower α diversity. Six genera, namely Faecalibacterium (P = 0.008), Lachnospira (P = 0.009), Paraprevotella (P = 0.01), Dialister (P = 0.012), Streptococcus (P = 0.025), and Clostridium (P = 0.028) were enriched in the mucosal healing group. A predictive score for mucosal healing was defined using these six genera. The area under the curve was 0.795 and the sensitivity and specificity for predicting mucosal healing were 0.643 and 0.917, respectively. Fecal microbiome is corelated with disease activity in the entire small intestine in Crohn’s disease patients. The predictive score proposed by microbiota characteristics was a potential biomarker for mucosal healing in the small intestine.

PBLs signifies both problem-based learning and project-based learning; we clearly distinguish between the two types of PBLs. The former PBL involves finding ways to solve problems under unfavorable conditions. In the curriculum at our institute, problem-based learning has been conducted since 2006. By contrast, project-based learning involves a student team undertaking an actual project, devised by a company. We did not adopt project-based learning in our curriculum. In 2016, our institute joined the Education Network for Practical Information Technology project of Japan’s Ministry of Education, Culture, Sports, Science and Technology. In that project, around 40 universities are engaged in developing a cooperative network. Each university develops practical education using PBL; its knowledge and resources obtained are shared among other universities in the network. For our part of the project, we added 3 practical exercises based on project-based learning. By combining the two different types of PBL, our curriculum provides exercises that introduce better problem-solving skills, instill a challenging spirit, and provide practical experience for students. In this paper, we report our exercises and practical results for 2017.

A 44-year-old woman was referred to our hospital because of a cystic lesion in the pancreatic body that was found by computed tomography (CT) as a result of a screening for impaired liver function after the patient presented with a high fever in 2011. Trans-abdominal ultrasonography (US) revealed a 33-mm unilocular cyst within the pancreatic body and a 5-mm hypoechoic mass in the pancreatic neck. Contrast-enhanced CT showed a slight enhancement around the cyst and a mild dilation of the main pancreatic duct, but neither septum nor nodule was detected inside. Contrast-enhanced endoscopic ultrasonography (CE-EUS) revealed a hyperechoic elevated lesion inside the cystic lesion without enhancement in the pancreatic body; CE-EUS also revealed a 5-mm homogeneous hypoechoic mass with a remarkable enhancement in the pancreatic neck with the use of Sonazoid(®) as a contrast medium. These lesions were diagnosed as a pancreatic pseudocyst and a neuroendocrine tumor (NET), respectively, and were followed up with periodic examinations. The cystic lesion showed contraction 6 months after the initial exam. However, US revealed an enlargement of the cystic lesion to 40 mm in diameter 2 years after the initial exam, and EUS showed irregular thickening of the wall with a cyst-in-cyst appearance. The diagnoses of a mucinous cystic neoplasm (MCN) and a concomitant small NET were made after a distal pancreatectomy. We herein report a rare case of MCN that showed various morphological changes over 2 years of observation.

This paper introduces the concept of Secure Spaces, one step ahead of Smart Spaces, on information security. We define Secure Spaces as physically isolated environments in which any resource is completely protected from its unauthorized objects with respect to information security. In other words, only if inside them, any information resource is completely protected from being accessed by its unauthorized visitors, and any visitor is completely protected from being exposed to her unwanted information resources. To build such a secure Smart Space, we propose a formalized model and architecture for space entry control based on its dynamically changing contents such as its visitors, physical information resources, and virtual information resources via its embedded output devices.

Various Web contents exist on the Internet in recent years, and are used by many users. But some people are so fascinated by and absorbed in Web contents that their everyday life is affected. Such a person yields to the temptation because of her/his weak will, even if s/he tries to control her/his Web access by self-control. Moreover, it is difficult for such a person to overcome the temptation because there are few tools to help her/his self-control. Then, this paper proposes a method that applies the filtering technology conventionally used for access restriction to self-control over Web access, and allows users to control their own Web access. Specifically, we create a prototype Web browser that has four functions to filter Web pages: a filtering based on their content, a filtering based on their ID (URL), a filtering based on their actions (buttons) such as purchasing and playing, and a time-based filtering.

Many researches on mining the Web, especially Social Networking Media such as web logs and microblogging sites which seem to store vast amounts of information about human societies, for knowledge about various phenomena and events in the physical world have been done actively, and Web applications with Web-mined knowledge have begun to be developed for the public. However, there is no detailed investigation on how accurately Web-mined data reflect real-world data. It must be problematic to idolatrously utilize the Web-mined data in public Web applications without ensuring their accuracy sufficiently. Therefore, this paper defines spatio-temporal Web Sensors by analyzing Twitter, Facebook, web logs, news sites, or the whole Web for a target natural phenomenon, and tries to validate the potential and reliability of the Web Sensors' spatio-temporal data by measuring the coefficient correlation with Japanese weather, earthquake, and influenza statistics per week by region as real-world data.

As next steps of Image Retrieval, it is very important to discriminate between “Typical Images” and “Peculiar Images” in the acceptable images, and moreover, to collect many different kinds of peculiar images exhaustively. As a solution to the 1st next step, my previous work has proposed a novel method to more precisely search the Web for peculiar images of a target object by its peculiar appearance descriptions (e.g., color-names) extracted from the Web and/or its peculiar image features (e.g., color-features) converted from them. This paper proposes a refined method equipped with cross-language (translation between Japanese and English) functions and validates its retrieval precision.

Many researches on mining the Web, especially CGM (Consumer Generated Media) such as Web logs, for knowledge about various phenomena and events in the physical world have been done actively, and Web services with the Web-mined knowledge have begun to be developed for the public. However, there is no detailed investigation on how accurately Web-mined data reflect real-world data. It must be problematic to idolatrously utilize the Web-mined data in public Web services without ensuring their accuracy sufficiently. Therefore, this paper defines the basic Web log Sensor with a neutral, positive, or negative description for a target phenomenon, and their linearly-combined Web log Sensors, and tries to validate the potential and reliability of these Web log Sensors' spatio-temporal data by measuring the correlation with weather (precipitation) and earthquake (maximum seismic intensity and number of felt quakes) statistics per day by region of Japan Meteorological Agency as real-world data.

This paper discusses PBL for first year software education, paying attention to aspects of holistic learning, and also proposes a new PLB course based on the discussion. The PBL course was given to high school students to confirm fundamental validity. The questionnaire method suggested that PBL might be fundamentally useful to learn holistically.

We have become able to get enough approvable images of a target object just by submitting its object-name to a conventional keyword-based Web image search engine. However, because the search results rarely include its uncommon images, we can often get only its common images and cannot easily get exhaustive knowledge about its appearance (look and feel). As next steps of image searches in the Web, it is very important to discriminate between “Typical Images” and “Peculiar Images” in the approvable images, and moreover, to collect many different kinds of peculiar images as exhaustively as possible. This paper proposes a novel method to precisely retrieve peculiar images of a target object by its typical/peculiar appearance descriptions (e.g., color-names) extracted from the Web and/or its typical/peculiar image features (e.g., color-features) converted from them, as a solution to the 1st next step of image retrievals in the Web.

Automatic knowledge extraction from such a very large document corpus as the Web is one of the hottest research topics in the domain of Artificial Intelligence and Database technologies. This chapter introduces my object-oriented and the existing methods to extract semantic (e.g., hyponymy and meronymy) and sensory (e.g., visual and aural) knowledge from the Web, and compares them by showing several experimental results. My object-oriented semantic knowledge extraction is based on property inheritance(s) and property aggregation, and repeatedly improves the extracted results of both hyponymy and meronymy relations. Meanwhile, my object-oriented sensory knowledge extraction is improved by utilizing the extracted hyponymy and meronymy relations. Finally, this chapter introduces my Sense-based Object-name Search (SOS) to enable users to identify the concrete name of a target object which they do not know only by inputting its hyponym (class-name) and some sensory descriptions, as an application system to utilize the Web-extracted semantic and sensory knowledge.

Concept hierarchy knowledge, such as hyponymy and meronymy, is very important for various natural language processing systems. While WordNet and Wikipedia are being manually constructed and maintained as lexical ontologies, many researchers have tackled how to extract concept hierarchies from very large corpora of text documents such as the Web not manually but automatically. However, their methods are mostly based on lexico-syntactic patterns as not necessary but sufficient conditions of hyponymy and meronymy, so they can achieve high precision but low recall when using stricter patterns or they can achieve high recall but low precision when using looser patterns. Therefore, we need necessary conditions of hyponymy and meronymy to achieve high recall and not low precision. In this paper, not only "Property Inheritance'' from a target concept to its hyponyms but also "Property Aggregation'' from its hyponyms to the target concept is assumed to be necessary and sufficient conditions of hyponymy, and we propose a method to extract concept hierarchy knowledge from the Web based on property inheritance and property aggregation.

Concept hierarchies, such as hyponymy and meronymy relations, are very important for various natural language processing systems. Many researchers have tackled how to mine very large corpora of documents such as the Web for them not manually but automatically. However, their methods are mostly based on lexico-syntactic patterns as not necessary but sufficient conditions of concept hierarchies, so they can achieve high precision but low recall when using stricter patterns or they can achieve high recall but low precision when using looser patterns. In this paper, property inheritance from a concept to its hyponyms is assumed to be necessary and sufficient conditions of hyponymy relations to achieve high recall and not low precision, and we propose a method to acquire hyponymy relations from the Web based on property inheritance.

This paper presents a method to extract appearance descriptions for a given set of objects. Conversion between an object name and its appearance descriptions is useful for various applications, such as searching for an unknown object, memory recall support, and car/walk navigation. The method is based on text mining applied to Web search results. Using a manually constructed dictionary of visual modifiers, our system obtains a set of pairs of a visual modifier and a component/class for a given name of object, which best describe its appearance. The experimental results have demonstrated the effectiveness of our method in discovering appearance descriptions of various types of objects.

In public places, information can be accessed by its unauthorized users, while we are sometimes forced to access our unwanted information unexpectedly. We have introduced the concept of Secure Spaces, physical environments where any information is always protected from its unauthorized users, and have proposed a content-based entry control model and an architecture for Secure Spaces which protect their contents' freedom of information delivery but do not protect their visitors' freedom of information access. This paper aims at building truly Secure Spaces which protect both freedoms and enhances our proposed model.

We define "Secure Space" as a physical space in which any resource is always protected from its unauthorized users in terms of enforcing its authorization policies assuredly. Aiming to build such secure spaces, this paper proposes an architecture and a model for space entry control based on its dynamically changing contents, such as users, physical resources and virtual resources outputted by some embedded devices. We first describe the architecture and then formalize the model and mechanism for secure spaces.