Mayumi Ueda

Skeletal affinity of Tc(V)-DMS is bone cell mediated and pH dependent

In spite of recent advances in bone cellular and molecular biology, there is still a poor correlation between these parameters and data obtained from bone scintigraphy. Diphosphonate derivatives radiolabelled with technetium-99m (Tc-BPs) have long been recognised as bone-seeking agents with an affinity for areas of active mineralisation. However, during clinical trials with a pH-sensitive tumour agent, the pentavalent technetium complex of dimercaptosuccinic acid [Tc(V)-DMS] showed a noticeable osteotropic character only in bone pathologies (bone metastases, Paget’s diseases) and lacked accumulation in normal mature bone. To decipher the osteotropic character of Tc(V)-DMS, a study at the cellular level was considered necessary. Moreover, to learn more about the role of Tc bone agents, acid-base regulation by bone tissue or cells was studied. First, biological parameters in body fluid were measured under systemic acidosis, induced by glucose administration, in normal and Ehrlich ascites tumour (EAT)-bearing mice. Then, in vivo biodistribution studies using Tc(V)-DMS or a conventional Tc-BP agent were carried out. The effect of glucose-mediated acidification on the skeletal distribution of the Tc agents in the mice provided valuable hints regarding the differential mediation of bone cells in skeletal tissue affinity for the agents. Thereafter, in vitro studies on osteoblast and osteoclast cells were performed and the comparative affinity of Tc(V)-DMS and Tc-BP was screened under diverse acidification conditions. Moreover, studies were also carried out on acid-base parameters related to the cellular uptake mechanism. Very specific pH-sensitive Tc(V)-DMS accumulation only in the osteoclastic system was detected, and use of Tc(V)-DMS in the differential detection of osteoblastic and osteoclastic metastases is discussed.

Cooking Ingredient Recognition Based on the Load on a Chopping Board during Cutting

This paper presents a method for recognizing recipe ingredients based on the load on a chopping board when ingredients are cut. The load is measured by four sensors attached to the board. Each chop is detected by indentifying a sharp falling edge in the load data. The load features, including the maximum value, duration, impulse, peak position, and kurtosis, are extracted and used for ingredient recognition. Experimental results showed a precision of 98.1% in chop detection and 67.4% in ingredient recognition with a support vector machine (SVM) classifier for 16 common ingredients.

Recognizing ingredients at cutting process by integrating multimodal features

We propose a method for recognizing ingredients in food preparing activity. The research for object recognition mainly focuses on only visual information; however, ingredients are difficult to recognize only by visual information because of their limited color variations and larger within-class difference than inter-class difference in shapes. In this paper, we propose a method that involves some physical signals obtained in a cutting process by attaching load and sound sensors to the chopping board. The load may depend on an ingredients hardness. The sound produced when a knife passes through an ingredient reflects the structure of the ingredient. Hence, these signals are expected to facilitate more precise recognition. We confirmed the effectiveness of the integration of the three modalities (visual, auditory, and load) through experiments in which the developed method was applied to 23 classes of ingredients.

Cooking Recipe Recommendation Method Focusing on the Relationship Between User Preference and Ingredient Quantity

There are numerous websites on the Internet that recommend cooking recipes. However, these websites order recipes according to date of submission, access frequency, or user ratings for recipes, and therefore, they do not reflect a user’s personal preferences. In this paper, we propose a recipe recommendation method based on the user’s culinary preferences. We employ the user’s recipe browsing and cooking history in order to determine his/her preferences in food. In our previous study on the subject, we considered only the presence of certain ingredients in cooking recipes in order to determine user preferences. However, in order to ascertain them more accurately, we propose a scoring method for cooking recipes based on users’ food preferences and the quantity of the ingredients used.

Developing a Real-Time System for Measuring the Consumption of Seasoning

In this paper, we propose a real-time system for measuring the consumption of various types of seasonings. In our system, all seasonings are placed on a scale, and we continuously take images of these items using a camera. Our system estimates the consumption of each condiment by calculating the difference between the weight when the seasoning was picked up and the weight when it was placed back on the scale. Our system identifies the type of seasoning that was used by determining whether or not the seasoning was present on the scale. By using our system, users can automatically log their usage of seasoning. Then, they can adjust the seasoning according to their desired taste.

Mining Attribute-Specific Ratings from Reviews of Cosmetic Products

In the cosmetics domain, many online sellers support user-provided product reviews. It has been shown that reviews have a profound effect on product conversion rates. Reviews of cosmetic products carry particular importance in purchasing decisions because of their personal nature, and particularly because of the potential for irritation with unsuitable products. In this paper, we propose a method for automatic scoring of various aspects of cosmetic item review texts based on a curated dictionary of expressions from a corpus of real world online reviews. Results and discussion of a user experiment to evaluate the approach are presented. In particular, we find that a co-occurrence approach improved coverage of reviews, and that our automated approach predicted attributes in manually annotated ground truth with an accuracy of 79%.

How to Find Similar Users in Order to Develop a Cosmetics Recommender System

Most shopping web sites allow users to provide product reviews. It has been observed that reviews have a profound effect on item conversion rates. In particular, reviews of cosmetic products have significant impact on purchasing decisions because of the personal nature of such products, and also because of the potential for skin irritation caused by unsuitable items, which is a major consumer concern. In this study, we develop a method for user similarity calculation for a cosmetic review recommender system. To realize such a recommender system, we propose a method for the automatic scoring of various aspects of cosmetic item review texts based on an evaluation expression dictionary curated from a corpus of real-world online reviews. Furthermore, we consider how to calculate user similarity of cosmetic review sites.

Finding similar users based on their preferences against cosmetic item clusters

Portal sites supporting online purchases provide commercial items and reviews for them. In the case of purchasing cosmetic items, in particular, reviews have important roles in purchasing decisions, allowing purchasers to avoid becoming annoyed with unsuitable items. Thus, we are trying to develop a recommender system for cosmetic items and analyzing reviews. General recommender systems basically identify similar users based on their preferences against common items. However, owing to the huge number of cosmetic items, it is not easy to use preferences for common items because of the data sparsity problem. Therefore, we propose a method for finding similar users based on their preferences against cosmetic item clusters. Moreover, we evaluate and discuss the proposed method for finding similar users based on experimental evaluations.

Utilizing Various User Moods for Automatic Recipe-Metadata Generation

In recent times, numerous cooking websites that recommend recipes have been launched. For example, Cookpad [1] and Rakuten Recipe [2] are very popular in Japan. Cookpad contains 2.2 million recipes and 50 million monthly access users, and Rakuten Recipe contains one million recipes. These statistics reflect the high demand for recipe-providing services. We believe that the addition of various metadata to the recipes is effective in improving the accuracy of the recipe recommendation system. For example, if the recipe has metadata such as “good for a bedtime snack”, the system can effectively provide recipes for a specific user purpose, as shown in Fig.1. Open image in new window Fig. 1 Advantage of the recipe with metadata

Continuity of approximate reasoning using fuzzy number under Łukasiewicz t-norm

In this study, we analyzed the fuzzy approximate reasoning using t-norm calculation. We apply theoretical results to fuzzy optimal control. The input of the IF-THEN rules is conducted using fuzzy numbers instead of crisp numbers. We concluded that there is continuity in approximate reasoning, as well as compactness in the set of membership functions for fuzzy inference. In addition, the existence of a minimum value of the function that evaluates non-linear control is presented.