Neotyphodium endophytes often confer benefits to their host grasses and may enhance invasiveness of some grasses. The knowledge of infection frequencies of endophytes among invading weed populations is necessary to understand the relationships between endophyte infection and invasiveness. Here we present data on infection frequencies of Italian ryegrass (Lolium multiflorum Lam.), an important weed in some farmlands in Japan, persisting in contrasting farmlands: a terraced paddy field and a wheat‐soybean double‐cropped field in the western region of Shizuoka prefecture, Japan. The terraced paddy site is a mosaic of several landscape elements such as paddy fields, levees, fallow and abandoned fields, with a high percentage of non‐crop area. Rice (Oryza sativa L.) has been cultivated for more than a decade with no application of chemical fertilizers, pesticides and fungicides. The wheat‐soybean field is characterized by the aggregation of large‐scaled fields that were originally reconstructed paddy fields, showing a low percentage of non‐crop area. Wheat and soybean have been grown as winter and summer crops, respectively, using chemical fertilizers and herbicides. We examined the presence or absence of endophytes in a total of 1200 seeds sampled from the two Italian ryegrass populations. The terraced paddy population exhibited a markedly high infection frequency (91.0%), due possibly to selective feeding of non‐infected seeds by insects. In contrast, the wheat‐soybean farmland population showed almost no infection (1.1%), whereas the putative source of the invasion in the proximity exhibited a relatively high infection rate (64.4%). Such a micro‐scale variation in infection frequencies may be attributable to a loss in endophyte viability within the wheat‐soybean field. The findings suggest that endophyte infection frequency may markedly differ among the Italian ryegrass populations even within the same region, presumably depending on the abundance of the seed‐eating insects, farmland management regimes and/or environmental conditions such as soil humidity.
Abstract-With the deepening of aging and low birth rate in China, the solitary elderly or old couple living alone is becoming more and more, who has a higher risk of senile dementia caused by disuse of cognitive function because of loneliness without communication. Due to the shortage of care workers, the young volunteer is expected becoming communication partner for them. But it is difficult for the young volunteer without the experience of communication with the elderly, and for two generations to find common topics. However, Conversation Support System was proposed so that the elderly and the young volunteer can talk smoothly with common photo contents. In order to evaluate the utility of photo contents of the system in China, we did the conversation experiment by photos in China, to analyze the expression and stress of subjects during the conversation. As a result, the photos which made the elderly and the young volunteer feel easy and difficult for the conversation were found. Then we analyzed utterance data of subjects with protocol analysis method to discuss the common features of these photos.
Due to aging society, there has recently been an increasing percentage of people with serious cognitive decline and dementia around the world. Such patients often lose their diversity of facial expressions and even their ability to speak, rendering them unable to express their feelings to their caregivers. However, emotions and feelings are strongly correlated with physiological signals, detectable with EEG and ECG etc. Therefore, this research develops an emotion predicting system for people with dementia using bio-signals to support their interaction with their caregivers. In this paper, we focused on a previous study for binary classification of emotional changes using spectrograms of EEG and RRI by CNN, verifying the effectiveness of the method. Firstly, the participants were required to watch simulating videos while collecting their EEG and ECG data. Then, STFT was performed, processing the raw data signals by extracting the time-frequency domain features to get the spectrograms. Finally, deep learning was used to detect the emotional changes. CNN was used for arousal classification, with an accuracy of 90.00% with EEG spectrograms, 91.67% with RRI spectrograms, and 93.33% with EEG and RRI spectrograms.
Abstract-Conversation is a good preventative against behavioral problems in the elderly. However, caregivers are usually very busy tending to patients and lack the time to communicate extensively with them. Toward overcoming such problems actively listening volunteers have more opportunities to communicate with the elderly, but the number of skilled volunteers is limited. Therefore, we investigated conversational support systems for inexperienced volunteers; such systems usually include content such as photographs, videos, and music. We expected that the volunteers would feel less stress when using videos instead of photographs for conversational support because the former provided both volunteers and patients with richer information than the latter. On the other hand, photographs gave patients more chances to talk with volunteers. However, there has been no research to date on the effect of content type upon stress and conversational quality. In this paper, we compared using photographs with using video from such viewpoints.
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