広告分析における国際規格を用いた食品・飲料および健康強調情報・栄養強調情報の分類カテゴリの検討

本研究の目的は、健康の観点から、子どもを対象とした食品および飲料のテレビコマーシャルの特徴を明らかにするための分析の指標を開発することである。食品カテゴリについては、イギリスのOfcom（Office of Communication）が提示している不健康な食品のカテゴリの内容、オーストラリアのガイドラインなどを踏まえて、国際規格（Codex Alimentarius international food standards）の食品や飲料のカテゴリの分類を行った。食品カテゴリについての分類の結果、計13の指標を作成した。飲料カテゴリについては、アメリカのRudd Center が提示している飲料カテゴリ、日本の飲料の市場調査で用いられている飲料カテゴリなどを組み合わせ、計17の指標を作成した。健康強調情報および栄養強調情報の提示は広告の技法に含まれており、詳細なカテゴリとして、国際規格（Codex Alimentarius international food standards）のこれらの情報の下位カテゴリの内容に基づいて、それぞれ計4つの指標を作成した。これらの指標は体系的かつ網羅的であり、国際基準も踏まえていることから、日本だけでなく、国際的なテレビコマーシャルの内容分析研究への利用が期待される。The purpose of this research is to construct coding items to elucidate the characteristics, from a health-perspective, of television commercials for foods and beverages that are marketed to children. I categorized food and beverage products based on international standards (Codex Alimentarius international food standards), while also considering the unhealthy food categories identified by Ofcom in the UK, as well as Australian guidelines, and other sources. I created a total of 13 coding items based on this classification. I categorized beverages based on the beverage categories provided by the Rudd Center in the US, and combined them with the beverage categories used in Japanese market research, as well as international standards. This resulted in a total of 17 coding items. The presentation of health or nutrition claims was included in the analysis of advertising techniques, I also created four detailed coding items based on the subcategories included in international standards (Codex Alimentarius international food standards). Due to the systematic and comprehensive nature of these coding items, as well as their basis in international standards, it is expected that they will be useful not only for research and analysis into the content of television commercials in Japan, but also internationally

小学生の食品および飲料の摂取と関連情報の参照に関する検討

本研究では、小学生がよく摂取している食品や飲料の種類を調査し、これらの食品や飲料の選択にかかわるテレビコマーシャルの視聴状況、食品や飲料の選択の際に確認する情報の内容について検討を行った。調査対象者は小学校3校の5・6年生計177名であった。1か月間に摂取した食品、飲料について尋ねた結果、「果物」、「アイスクリーム」、「チョコレート」、「牛乳」、「お茶（甘くないもの）」、「炭酸の飲み物」を摂取した児童が多かった。また、「アイスクリーム」、「フレーバーウォーター」、「炭酸の飲み物」に対しては2～3割の児童がテレビコマーシャルを見て食べたと回答していた。テレビコマーシャルの視聴については、リアルタイムでの視聴のほうが録画での視聴よりもテレビコマーシャルをより頻繁に視聴していた。食品および飲料の選択の際には、「賞味期限」の情報を確認している児童が多かった。一方、栄養成分やカロリーオフなどの栄養強調表示を確認していた児童は2～3割であり、これらの情報に注意を向け、意味や健康への影響を理解するための取り組みを進めていく必要があることが示唆された。In this research, I investigated the types of foods and beverages which elementary school students frequently consumed, the viewing situation of television commercials affecting the selection of these foods and beverages, and the contents of information to be confirmed in selecting foods and beverages. The participants were 177 fifth and sixth grade elementary school students. The result indicated more children consumed food such as "fruit", "ice cream", "chocolate", "milk", "tea without sugar", and "carbonated drink" for the past month. Twenty to thirty percent of the students answered they decided to consume "ice cream", "flavored water" and "carbonated drink" by the exposure to commercials. They were exposed to commercials more frequently in the real-time viewing, compared to viewing recorded programs. In the selection, more children confirmed the information on "expiration date". Meanwhile, 20 to 30 percent of the students confirmed nutrition claims such as nutritional components and lowcalorie. The findings of this study suggested it would be necessary to develop the practices to promote more attention to the related information and understanding the meaning and the influence of the related information on health.本研究はJSPS科研費JP17K00459の助成を受けたものです

ヨウチエン ニオケル シドウ ケイカク ノ サクセイ ト ホイク ノ テンカイ : ヨソウサレル コドモ ノ スガタ ニ チャクモク シテ

幼稚園における指導計画の作成と保育の展開について、現場の実践を観察し、保育者へのインタビューをすることで詳細に検討した。絵画指導の場面において、あらかじめ予想した子どもの姿が実際はどのように現れてくるのか分析し、保育者が保育を振り返った内容と合わせて考察した。保育者は、指導計画を立案する際に予想した子どもの姿を考慮して多様な援助をしたり、予想外な子どもの活動にあわせて適時に判断して援助を行っており、子どもの姿を予想して計画を立てることの重要性が示唆された

Loss of axonal mitochondria promotes tau-mediated neurodegeneration and Alzheimer\u27s disease-related tau phosphorylation via PAR-1.

Abnormal phosphorylation and toxicity of a microtubule-associated protein tau are involved in the pathogenesis of Alzheimer\u27s disease (AD); however, what pathological conditions trigger tau abnormality in AD is not fully understood. A reduction in the number of mitochondria in the axon has been implicated in AD. In this study, we investigated whether and how loss of axonal mitochondria promotes tau phosphorylation and toxicity in vivo. Using transgenic Drosophila expressing human tau, we found that RNAi-mediated knockdown of milton or Miro, an adaptor protein essential for axonal transport of mitochondria, enhanced human tau-induced neurodegeneration. Tau phosphorylation at an AD-related site Ser262 increased with knockdown of milton or Miro; and partitioning defective-1 (PAR-1), the Drosophila homolog of mammalian microtubule affinity-regulating kinase, mediated this increase of tau phosphorylation. Tau phosphorylation at Ser262 has been reported to promote tau detachment from microtubules, and we found that the levels of microtubule-unbound free tau increased by milton knockdown. Blocking tau phosphorylation at Ser262 site by PAR-1 knockdown or by mutating the Ser262 site to unphosphorylatable alanine suppressed the enhancement of tau-induced neurodegeneration caused by milton knockdown. Furthermore, knockdown of milton or Miro increased the levels of active PAR-1. These results suggest that an increase in tau phosphorylation at Ser262 through PAR-1 contributes to tau-mediated neurodegeneration under a pathological condition in which axonal mitochondria is depleted. Intriguingly, we found that knockdown of milton or Miro alone caused late-onset neurodegeneration in the fly brain, and this neurodegeneration could be suppressed by knockdown of Drosophila tau or PAR-1. Our results suggest that loss of axonal mitochondria may play an important role in tau phosphorylation and toxicity in the pathogenesis of AD

[Note] On current trends of a hometown tax in Yubari City

ふるさと納税制度に関しては、返礼品競争の過熱が大きな関心を寄せられている。本稿では、返礼品提供に過度に依存することなく、多くの寄附を集めてきた夕張市を取り上げて、その取り組みを夕張市でのヒヤリング調査にもとづき紹介した。本稿では、第1に返礼品の還元率、寄附総額に占める経費比率も他の地方団体に比べて低く、節度ある制度の利用をおこなっていること、第2に寄附者が直接支援する団体を選択できるという意味で寄附者の意思をより尊重する仕組みを構築していること、第3に情報公開の度合いについても先進的な地方団体であることがわかった。最後に、本稿で紹介した事例を参考にふるさと納税制度のあり方について考えると、税制の見直しに加えて返礼品規制の強化も検討すべき段階にきていると言える。返礼品規制の強化の方向性としては、返礼品の還元率が5割を超えるような地方団体や、寄附金の活用状況を公開していない地方団体については、地方税の特例控除を認めないといった措置を検討すべきだろう

Deep Learning Predicts Rapid Over-softening and Shelf Life in Persimmon Fruits

In contrast to the progress in the research on physiological disorders relating to shelf life in fruit crops, it has been difficult to non-destructively predict their occurrence. Recent high-tech instruments have gradually enabled non-destructive predictions for various disorders in some crops, while there are still issues in terms of efficiency and costs. Here, we propose application of a deep neural network (or simply deep learning) to simple RGB images to predict a severe fruit disorder in persimmon, rapid over-softening. With 1,080 RGB images of ‘Soshu’ persimmon fruits, three convolutional neural networks (CNN) were examined to predict rapid over-softened fruits with a binary classification and the date to fruit softening. All of the examined CNN models worked successfully for binary classification of the rapid over-softened fruits and the controls with > 80% accuracy using multiple criteria. Furthermore, the prediction values (or confidence) in the binary classification were correlated to the date to fruit softening. Although the features for classification by deep learning have been thought to be in a black box by conventional standards, recent feature visualization methods (or “explainable” deep learning) has allowed identification of the relevant regions in the original images. We applied Grad-CAM, Guided backpropagation, and layer-wise relevance propagation (LRP), to find early symptoms for CNNs classification of rapid over-softened fruits. The focus on the relevant regions tended to be on color unevenness on the surface of the fruit, especially in the peripheral regions. These results suggest that deep learning frameworks could potentially provide new insights into early physiological symptoms of which researchers are unaware