Transcriptome Analysis of Skin Color Variation During and after Overwintering of Malaysian Red Tilapia

The commercial value of red tilapia is hampered by variations in skin color during overwintering. In this study, three types skin of red tilapia, including the skin remained pink color during and after overwintering (WP), the skin changed from pink color to black color during overwintering and remained black color after overwintering (PB), and the skin changed from pink color to black color during overwintering but recovered to pink color when the temperature rose after overwintering (BP), were used to analyze their molecular mechanisms of color variation. The transcriptome results revealed that the PB, WP, and BP libraries had 42, 43, and 43 million clean reads, respectively. The top 10 abundance mRNAs and specific mRNAs (specificity measure SPM > 0.9) were screened. After comparing intergroup gene expression levels, there were 2528, 1924 and 1939 differentially expressed genes (DEGs) between BP and PB, BP and WP, and PB and WP, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of color-related mRNAs showed that a number of DEGs, including tyrp1, tyr, pmel, mitf, mc1r, asip, tat, hpdb and foxd3, might play a potential role in pigmentation. Additionally, the co-expression patterns of genes were detected within the pigment-related pathways by PPI network from PB_WP group. Furthermore, DEGs from the apoptosis and autophagy pathways, such as baxα, beclin1, and atg7, might be involved in the fading of red tilapia melanocytes. The findings will aid in understanding the molecular mechanism underlying skin color variation in red tilapia during and after overwintering, as well as lay a foundation for future research aimed at improving red tilapia skin color characteristics.

Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

PurposeIn general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015; Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model.Design/methodology/approachThis paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO.FindingsThrough the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional classification methods.Originality/valueThis paper intends to propose an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN.

The lexical context in a style analysis: A word embeddings approach

This is a pilot study of usability of Context Specificity measure for stylometric purposes. Specifically, the word embedding Word2vec approach based on measuring lexical context similarity between lemmas is applied to the analysis of texts that belong to different styles. Three types of Czech texts are investigated: fiction, non-fiction, and journalism. Specifically, forty lemmas were observed (10 lemmas each for verbs, nouns, adjectives, and adverbs). The aim of the present study is to introduce a concept of the Context Specificity and to test whether this measurement is sensitive to different styles. The results show that the proposed method Closest Context Specificity (CCS) is a corpus size independent method which has a promising potential in analyzing different styles.

Fast Large Scale Oligonucleotide Selection Using the Longest Common Factor Approach

We present a fast method that selects oligonucleotide probes (such as DNA 25-mers) for microarray experiments on a truly large scale. For example, reliable oligos for human genes can be found within four days, a speedup of one to two orders of magnitude compared to previous approaches. This speed is attained by using the longest common substring as a specificity measure for candidate oligos. We present a space- and time-efficient algorithm, based on a suffix array with additional information, to compute matching statistics (lengths of longest matches) between all candidate oligos and all remaining sequences. With the matching statistics available, we show how to incorporate constraints such as oligo length, melting temperature, and self-complementarity into the selection process at a postprocessing stage. As a result, we can now design custom oligos for any sequenced genome, just as the technology for on-site chip synthesis is becoming increasingly mature.

A NON-SPECIFICITY MEASURE FOR CONVEX SETS OF PROBABILITY DISTRIBUTIONS

In belief functions, there are two types of uncertainty which are due to lack of knowledge: randomness and non-specificity. In this paper, we present a non-specificity measure for convex sets of probability distributions that generalizes Dubois and Prade's non-specificity measure in the Dempster-Shafer theory of evidence.