Hirobumi Yamamoto

Malonylation is a key reaction in the metabolism of xenobiotic phenolic glucosides in Arabidopsis and tobacco

Tobacco cells (Nicotiana tabacum L.) accumulate harmful naphthols in the form of malonylated glucosides (Taguchi etxa0al., 2005). Here, we showed that the malonylation of glucosides is a system to metabolize xenobiotics and is common to higher plants. Moreover, some plantlets including Arabidopsis thaliana excreted some of the incorporated naphthols into the culture media as their glucosides. In order to analyze the function of malonylation in the metabolism of these xenobiotics, we identified a malonyltransferase gene (At5g39050) responsible for the malonylation of these compounds in A.xa0thaliana. The recombinant enzyme had malonyltransferase activity toward several phenolic glucosides including naphthol glucosides. A knockout mutant of At5g39050 (pmat1) exposed to naphthols accumulated only a few malonylglucosides in the cell, and released larger amounts of simple glucosides into the culture medium. In contrast, forced expression of At5g39050 in the pmat1 mutant resulted in increased malonylglucoside accumulation and decreased glucoside excretion to the media. The results provided clear evidence of whether the release of glucosides or the storage of malonylglucosides was determined by the At5g39050 expression level. A similar event in naphthol metabolism was observed in the tobacco mutant with a suppressed malonyltransferase gene (NtMaT1). These results suggested that malonylation could be a key reaction to separate the way of xenobiotics disposition, that is, release from cell surface or storage in vacuoles.

Initial Design of Distributed Identification System for Geographical Origin by Trace Element Analysis

In Japan, districts where agricultural products are cultivated have recently begun to make their locality into a brand. In order to take advantage of this brand creation, some retailer and distributor camouflage agricultural products cultivated in non-branded districts to be sold as the branded districts goods with its attendant higher brand values; thus fraudulently raising their own prices. In order to prevent camouflaging of products from non-branded districts, traceability systems that mark an ID on the packaging have been proposed. However, these traceability systems cannot prevent fraud within the systems themselves, such as the use of illegally acquired IDs. Therefore, we proposed a system that distinguishes the various places of cultivation of green groceries by analyzing trace elements, which means extremely small quantities of elements, in the cultivated products and then storing this information in distributed databases. In this paper, firstly we show that discrimination is possible by correlation analysis on the results of our trace elements analysis, and we describe the outline and design of a system which identifies cultivated places of green groceries

Target selection by similarity preserve hash in distributed system for geographical origin identification of vegetables

Recently, camouflaging geographical origin of agricultural products is a major problem in Japan. Now we are developing a distributed system which identifies geographical origin of vegetables by analyzing, accumulating and comparing trace element compositions of vegetables. This system stores compositions of trace, or very small quantities of elements into database which located on farming districts, and compares them to trace element compositions of vegetables which gathered from distribution channel such as markets, food factories. Objective of this system is verifying geographical origin information by food traceability system and deter camouflaging geographical origin. However, in this system, all sites in farming districts must be searched to identify geographical origin. In this paper, we describe a method to reduce the number of targets to identify geographical origin using similarity preserve hash (SPH).

Efficient Target Selection in Similarity Preserve Hash for Distributed Geographical Origin Identification System of Vegetables

Recent years, especially in Japan, camouflaging geographical origin of agricultural products is a big problem. Therefore, we introduced a distributed system to identify their geographical origin using their differences of trace elements, or very small quantities of elements. Vegetables grown in farms absorb metals form the soil. Since compositions of trace metal elements differ from geographical places, this can be utilized to identify geographical origin of vegetables. In proposing system, trace element compositions of vegetables are measured when they are shipped from a farm, and the data is stored in databases which are located in farming districts. When a doubtful vegetable is found in food distribution channel, its trace element compositions are measured and compared by calculating correlation coefficients to ones accumulated in databases. This system can be used to verify geographical origin data by food traceability system. Because correlation coefficients are not known when they are once calculated, so correlation coefficients between all accumulated data in databases and doubtful vegetable. This means that proposing system is not scalable when the number of accumulated data is increased. Therefore, we introduced a method to reduce the number of target to calculate correlation coefficients using similarity preserve hash (SPH) which gives similar output for similar input. This could reduce time to calculation itself, however, computation time including picking out target data for calculation of correlation coefficients from database. Therefore, we introduce a method to accelerate picking up data form database by grouping value of SPH

A discrimination system for identification of cultivated products by trace elements analysis - using Broccoli as an example

In Japan, places where agricultural products are cultivated or fishery products produced have recently begun to make their locality into a brand. To take advantage of this brand creation, some other producing districts camouflage their agricultural or fishery products to be sold as the branded districts goods with its attendant higher brand values; thus fraudulently raising their own prices. To prevent camouflaging of produce from non-branded producing districts, traceability systems that mark an ID on the packaging have been proposed. However, these traceability systems cannot prevent fraud within the systems themselves, such as the use of illegally acquired IDs. Therefore, we proposed a system that distinguishes the various places of cultivation of green groceries by analyzing very small quantities of trace elements in the cultivated products and then storing this information in a database. In this paper, we describe the outline and design of a system that discriminates the place of cultivation. We show that discrimination is possible by correlation analysis on the results of our trace elements analysis.

Evaluations on classified selection of dense vectors for vegetable geographical origin identification system using trace elements

Recently, in Japan, some farming districts established their locality as brands, and prices of agricultural products differs from their grown places. This induced some agricultural food origin forgery cases. Food traceability systems are introduced and some are now in operation to solve this problem. However, food traceability systems have vulnerabilities in their nature because they traces only artificially attached IDs. So there are possibility to forge ID and packages, and switching the vegetables in packages. So, we developed a geographical origin identification system for vegetables by using their trace element compositions. Trace element means very small quantities of elements. This system gathers trace element data of vegetables when shipping from farms, and stores them into databases located in farming districts. In case of a vegetable which has doubtful geographical origin is found in markets, their trace element compositions are measured and compared with data in databases to find its actual geographical origin. Our system judges geographical origin by whether correlation coefficient. This requires calculating correlation coefficients for identifying one and all stored data. However, this is not scalable for the number of data. In this paper, we describe a method to limit the number of data to be used to calculate correlation coefficients before calculating them, and realize scalability.

Improvement and Evaluation of SPH on High Dimensional Vector for Geographical Origin Identification System for Vegetables by Trace Elements

Recently, as the result of establishment of brand of lobately of farming district, many geographical origin camouflaging cases have being detected in Japan. Therefore, as co-research project with agricultural chemical area, we are developing a vegetable geographical origin identification system by using differences of vegetables trace element compositions. If doubtful vegetable is found, its trace element data is compared to data in database. If it overcomes a certain threshold, the system judges that geographical origin of two is the same. This process is not efficient because it requires calculation against all data in databases. Therefore, we developed a method to select calculation target beforehand by employing SPH (similarity preserve hash) before. In this paper, we will describe its improvement and evaluations.

Effective Retrieval of High Dimensional Vector by Class Coding in Vegetable Geographical Origin Identification System

We proposed a vegetable geographical origin identification system using difference of trace element composition by cultivated places to prevent camouflaging geographical origin. The system compared trace element compositions by calculating correlation coefficient. This is one-to-one comparison and makes system response time to identify slow. To solve this problem, we employed Similarity Preserve Hash (SPH) to reduce the number of target of one-to-one comparison and realized efficient identification to some degree. Adlthough SPH makes identification itself faster, however, SPH wastes long time to retrieve trace element data for one-to-one comparison from database. In this paper, we discuss on coding of trace element composition to efficient retrieval from databases and realize efficient identification.

On Rejecting Outliers by Smirnov Test for Massive Data Input System in Vegetable Geographical Origin Identification System

As co-work with agricultural chemists, we developed a vegetable geographical origin identification system using trace element composition to solve cultivated place forging problems on vegetables in Japan. Vegetables cultivated in farm fields absorb trace metal elements from soil. Here, trace element means that very small quantity elements in chemistry. Since trace element compositions in soil differ from districts, trace element composition of cultivated vegetables also differs from districts, even farms. Our system can identify geographical origin of vegetables in short time by comparing trace element composition of doubtful vegetable and genuine vegetables. Therefore, trace element compositions of vegetables must be measured when they are shipped from farms. Normally, since fresh vegetables are sold in the same day of shipping, trace element compositions must be measured and recorded into databases in few hours, before they will arrive at market, sold to general consumers. In this paper, we describe a massive data gather system for our system which rejects outliers. In addition, we describe an interface between our system and measurement instruments that are PC/WS based but closed system.

Outlier Rejection in Massive Trace Element Data for Geographical Origin Identification System of Vegetables

As the result of establishment of brands for some farming districts in Japan, it is a big social issue that agricultural products of which geographical origins are camouflaged. To solve this problem, we have proposed a distributed system that identifies cultivated places of vegetables by using their trace element content as coresearch with agricultural chemists. This system records trace element content of vegetables when shipping from farms into databases which located in various farming districts. In case of a vegetable of which geographical origin is doubtful was found in markets, this system estimates real geographical origin by comparison between its trace element content and trace element content stored in databases. This system requires to measure and store trace element content in short time. Because amount of trace element data is relatively numerous, commitment of humans’ works should be avoided as possible as. On the other hand, since a lot of outliers are contained in trace element data, it is important that not to store trace element data which contains outliers to realize correct geographical origin identification in this system. In this paper, we describe a method for our geographical origin identification system to find and reject outliers in massive quantity of trace element content data.