This paper aims at proposing a new system to estimate the emotional value content for short sentences. The proposed system utilizes the co-occurrence strength between adjectives and nouns based on similarity measurements and semantic relationships to explore the possibility of finding the semantic association between adjectives and an input sentence. At first, keywords extracted from the input sentence are used to query adjectives from Google N-gram corpus using keywords-based templates. The dataset for the step of association measurement is continually collected using templates created from each keyword. Co-occurrence frequencies of the adjectives and keywords are obtained; however, to improve the efficiency of this task, patterns showing the semantic relationships between them are also considered. The semantic similarity scores computed by several modified computational measurements and the pattern frequencies are used for training not only to classify adjectives into two classes-association and non-association, but also to get the association scores. For each keyword, the lists of adjectives and keyword are then sorted in the decreasing order by their association scores. Finally, a rank aggression method -Borda's method which is used to generate an acceptable ranking for a given set of rankings is considered and the top n a adjectives (in this paper n a is 5) are chosen according to the estimated values. The main contribution of this method is to design an effective method for the adjective selection task of the input sentence of the impression estimation system. We evaluated our approach using two tasks: the first one is the quality of the association measurement and the second one is the efficiency of the proposed method. The evaluation for association classification on 4,500 pairs of words shows that the average accuracy is 87.0 %. And for the performance of the proposed method, we carried out subjective experiments and obtained fairly good results.

The genus Streptomyces is not only known as a natural producer of antibiotics but also a prolific source of chitinolytic enzymes that digest recalcitrant chitin to chitooligosaccharides. However, only a few reports have used whole-genome sequencing to study chitin degradation of Streptomyces to date. In the present study, out of 22 Streptomyces strains, Streptomyces parvulus VCCM 22513 produced the highest chitinase activity. Time courses of incubation revealed that the maximum chitinase (0.91 ± 0.04 U/mL) of this strain was observed after 96 hours in the yeast extract salts medium supplemented with 10.0 g/L colloidal chitin. Additional genomic analysis of VCCM 22513 was also conducted to discover the genomic information related to chitin degradation. The VCCM 22513 genome consists of 341 CAZy genes divided into 6 families including glycoside hydrolase (134 genes), carbohydrate-binding module (88 genes), glycosyl transferase (87 genes), carbohydrate esterase (18 genes), polysaccharide lyase (7 genes), and auxiliary activity (7 genes). Further genome mining revealed the presence of 10 chitinases, 4 lytic polysaccharide monooxygenases, and 14 β-N-acetylhexosaminidases, which mainly contribute to the degradation of chitin polymers. This is the first report revealing the mechanism underlying the chitin degradation of S. parvulus. Further investigations are required to characterize chitinolytic enzymes found in this study for the bioeconomic production of high-quality chitooligosaccharides from chitin food wastes.

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This paper addresses the challenge of creating a new system to estimate the impression of an image. The proposed system combines the human annotated tags of images and an image classification method to discover "showing a photo, what are people looking at?". Then, to tackle the challenge "what are they thinking about the one they look at?", the semantic association strengths between adjectives and image keywords are computed by pointwise mutual information (PMI) and the pattern frequencies using a machine learning approach. To select the output, we use a rank aggregation method, Borda's method, to generate an acceptable ranking for a given set of rankings and the top n a adjectives (in this paper n a is 5) are chosen according to the estimated values. The main contribution of this method is to design an effective method for estimating the association of the impression adjectives with images. We evaluated the proposed approach using two tasks: the first one is the performance of the task of keyword extraction and the second one is the efficiency of the proposed method. For the performance of the proposed method, we carried out subjective experiments and obtained fairly good results.