Structure and development of eosinophilic granules (EG1) in eosinophils from larva (ammocoetes) of far eastern brook lamprey Lethenteron reissneri were speculated. EG1 is stratified granules (two-layer) and consists of inner eosinophilic layer (L0) and chromophobic outer layer (L1). Three subtypes of EG1 are identified based on the optical artificial image (OAI) of inclusion structure (IS) in L0: EG1a, EG1b, and EG1c. The EG1a had no OAI (probably no IS). The EG1b and EG1c contain IS in L0. The OAI of both EG1b and EG1c were larger than IS. The former was round or oval chromophobic area (OAI-1), and the latter was expanded and rugged (three-dimensional) image (OAI-2) surrounded with OAI-1. EG1a was thought to be a prototype of EG1, which would develop into EG1b and then EG1c.

Monocytes were observed in the blood of inshore hagfish Eptatretus burgei. The monocytes were round or oval, high nucleus/cytoplasm ratio, and have several round or oval eosinophilic granules, which show dark red or blackish red color when stained with May-Grünwald (MG), Giemsa, and MG-Giemsa stain. The granules were positive for alkaline phosphatase, acid phosphatase, and α-naphthyl butyrate esterase. Also, the granules show metachromatic (reddish purple) with toluidine blue. However, ꞵ-glucuronidase, α-naphthyl acetate esterase, naphthol AS-D chloroacetate esterase and peroxidase were not detected in the monocytes, and negative for periodic acid Schiff reaction, alcian blue, Sudan black B, Sudan III, and oil red O. The monocytes engulfed many yeast particles (zymosan).

Since the construction of class timetables in the university requires a great deal of effort and time, many studies have been conducted in Japan and abroad on the automatic construction of class timetables. Similarly, at the National Fisheries University, labor and time are devoted to the creation of class timetables. Therefore, author aims to conduct basic research on automatic timetable construction with the simulated annealing method in this study. This paper describes the proposed method and algorithm for generating timetables using the simulated annealing method. The generated timetables by the proposed method are also reported.

The Tiger Pufferfish (Takifugu rubripes) is a staple in Japanese cuisine, with over ten species of the Takifugu genus found in the surrounding seas. Given that certain parts of the pufferfish are toxic, they are predominantly prepared by trained professionals. Furthermore, species within the Takifugu genus are susceptible to hybridization, leading to an increase in hybrid numbers. However, identifying these hybrids is a challenging and time-consuming task, even for experts. To address this, we developed a transfer learning model using the pretrained VGG16 model to differentiate between pufferfish species. The VGG16 model, commonly used in image recognition, is built on convolutional neural networks. We also implemented Gradient-weighted Class Activation Mapping (Grad-CAM) for visual interpretation of the model. Grad-CAM generates a heat map that highlights the areas focused on by the AI model in the image, allowing us to identify factors contributing to misjudgment and make further improvements. We used seven species from the Takifugu genus (excluding hybrids), and approximately 15 colored images of each species were prepared for machine learning. The results showed that our model was able to distinguish between pufferfish species with relatively high accuracy, although some misclassification occurred among species with similar body patterns. The Grad-CAM results revealed that the model was able to distinguish body patterns, but some misclassifications occurred due to gravel and background objects being recognized as patterns.