Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L 1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L 1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited supe- *

This work suggests how a global trade-off between energy consumption and encryption strength allows for optimal security mode selection in optical wireless, with respect to energy consumption. An adaptive security scheme is proposed, that involves the minimum energy needed to achieve a desired security level. The subsequent security approach is twofold. First, encryption strength is adjusted according to the severity of the requested service. This helps save energy, while preserving the encryption strength. Second, for battery-powered devices, data can be encrypted according to a specified threshold, or even based on the battery level itself. The reliability function, which is the backbone of the proposed adaptive security scheme, serves as a quality factor that describes all encryption parameters and their impact on energy consumption and therefore as a global indicator of the overall security with respect to the energy consumption.

Abstract:3 Floral transition is a crucial event in the reproductive cycle of a flowering plant during which many 4 genes are expressed that govern the transition phase and regulate the expression and functions 5 of several other genes involved in the process. Identification of additional genes connected to 6 flowering genes is vital since they may regulate flowering genes and vice versa. Through our 7 study, expression values of these additional genes has been found similar to flowering genes FLC 8and LFY in the transition phase. The presented approach plays a crucial role in this discovery. An 9 RNA-Seq computational pipeline was developed for identification of novel genes involved in floral 10 transition from A. thaliana apical shoot meristem time-series data.

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