The aim of this study was to illustrate the phenotypic modification of mitochondrion-rich (MR) cells and Na(+)/K(+)-ATPase (NKA) responses, including relative protein abundance, specific activity, and immunolocalization in gills of euryhaline tilapia exposed to deionized water (DW) for one week. The plasma osmolality was not significantly different between tilapia of the local fresh water (LFW) group and DW group. Remodeling of MR cells occurred in DW-exposed fish. After transfer to DW for one week, the relative percentage of subtype-I (wavy-convex) MR cells with apical size ranging from 3 to 9 microm increased and eventually became the dominant MR cell subtype. In DW tilapia gills, relative percentages of lamellar NKA immunoreactive (NKIR) cells among total NKIR cells increased to 29% and led to significant increases in the number of NKIR cells. In addition, the relative protein abundance and specific activity of NKA were significantly higher in gills of the DW-exposed fish. Our study concluded that tilapia require the development of subtype-I MR cells, the presence of lamellar NKIR cells, and enhancement of NKA protein abundance and activity in gills to deal with the challenge of an ion-deficient environment.

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Now a day, the tourists get used to take many photos in a journey and share these sightseeing spot images on album websites. The meaningful grouping of these images will become important and useful. In particular, sightseeing spot scenes are vary with different situations, such as weather conditions and seasons. Thus the categorization of different situations is expected to be beneficial for tourists to plan when to visit there. This paper proposes a hybrid approach which integrates contentbased image clustering with filtering based on tag information of image. Content-based image clustering categorizes sightseeing spot images into night, sunrise/sunset, cloudy, and shine situations based on color feature extraction from ROI (region of interest). By using geotag information, collected images can be limited to a reasonable boundary to eliminate outliers. Furthermore, by using the timestamp of images, the four situation categories constructed by content-based image clustering are further verified to increase the accuracy. Experimental results show that the hybrid approach of content-based image clustering and tag-based filtering is effective for obtaining clusters with high precision and recall.

Sharing traveling experience and photos on Social Network Service or Web albums is more and more popular recently. Good sightseeing photos in specific situation such as sunset and spring season can impress tourists well, and be clues for them to consider where and when to visit for sightseeing. Regarding situations to be identified, this paper focuses on season. Compared with situations relating with weather and time of day (e.g., sunrise/sunset), whether or not different seasons have different scenery depends on sightseeing spots. Therefore, classifying sightseeing spots into season-dependent/independent is required as preprocessing for season-based classification of sightseeing photos. This paper proposes a hybrid approach for identifying season-dependent sightseeing spots, of which the first phase applies machine learning with statistical features of sightseeing photos obtained from metadata. In order to improve precision, the second phase applies color-based classification to spots identified as season-dependent in the first phase. The experimental results show the effectiveness of the proposed method.

Nowadays, tourists take lots of photos and share them on album websites, so the meaningful grouping of images becomes important and useful. Specifically, sightseeing scenes vary with different situations such as weather and season. The categorization of different situations is thus expected to be beneficial to tourists planning when to visit different places. This paper proposes a hierarchical classification method based on local color feature extraction from the designed region of interest (ROI) andK-means clustering to categorize sightseeing images into several meaningful situations. Hierarchical organization consists of three stages and four situations. In the first stage, night-time images are discriminated from daytime images, then daytime images are divided into sunrise/sunset and other images in the second stage. Finally, cloudy images are separated from sunshiny images in other images obtained in the second stage. Experimental results show that the extraction of color features within the ROI is effective in obtaining clusters with high precision and recall.