Table_1_iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition.docx

Summary: Identification of replication origins is playing a key role in understanding the mechanism of DNA replication. This task is of great significance in DNA sequence analysis. Because of its importance, some computational approaches have been introduced. Among these predictors, the iRO-3wPseKNC predictor is the first discriminative method that is able to correctly identify the entire replication origins. For further improving its predictive performance, we proposed the Pseudo k-tuple GC Composition (PsekGCC) approach to capture the “GC asymmetry bias” of yeast species by considering both the GC skew and the sequence order effects of k-tuple GC Composition (k-GCC) in this study. Based on PseKGCC, we proposed a new predictor called iRO-PsekGCC to identify the DNA replication origins. Rigorous jackknife test on two yeast species benchmark datasets (Saccharomyces cerevisiae, Pichia pastoris) indicated that iRO-PsekGCC outperformed iRO-3wPseKNC. It can be anticipated that iRO-PsekGCC will be a useful tool for DNA replication origin identification.Availability and implementation: The web-server for the iRO-PsekGCC predictor was established, and it can be accessed at http://bliulab.net/iRO-PsekGCC/.</p

Guest Editorial: AI and Machine Learning Solution Cyber Intelligence Technologies: New Methodologies and Applications

Guest Editorial: AI and Machine Learning Solution Cyber Intelligence Technologies: New Methodologies and Application

A Facile Method for Delaying the Migration of Antifogging Agents in Polyethylene Films

Antifog polyethylene (PE) films are widely used as covers for greenhouse structures because they can eliminate the multiple adverse effects of the fogging phenomenon. Herein, we propose a way to enhance the shelf life of antifog PE films by delaying the migration of antifogging agents using polymer microspheres. Cross-linked poly­(maleic anhydride-styrene-divinylbenzene) microspheres (PMSD) were prepared by self-stabilized precipitation polymerization (2SP) and then converted into PMSD grafted with methoxy polyethylene glycol (mPEG) of different chain lengths (PMSD-g-mPEG). PMSD-g-mPEG microspheres were incorporated into PE films with an antifogging agent, and the properties of the prepared antifog PE films were investigated in detail. Scanning electron microscopy displayed the good dispersion state of PMSD-g-mPEG at low loadings and its positive correlation with mPEG chain length. Accelerated dripping experiments and contact angle tests showed that PMSD-g-mPEG2000 microspheres can exert the best effect in delaying the migration of antifogging agents, giving rise to a 36% improvement in dripping duration under optimized conditions. In addition, PMSD-g-mPEG2000 microspheres do not affect the optical, mechanical, and thermal properties of antifog PE films obviously

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, <i>f</i>₁₀, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, f10, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.</p

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, <i>f</i>₁₅, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, f15, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.</p

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, <i>f</i>₆, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, f6, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.</p

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO,<i>f</i>₁₈, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO,f18, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.</p

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, <i>f</i>₁₉, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, f19, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.</p

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, <i>f</i>₂₃, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, f23, (a) iteration 0-6000, (b) iteration 6000-10000 the unit is 100 iteration.</p

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, <i>f</i>₁₃, (a) iteration 0-6000, (b) iteration 6000-10000, the unit is 100 iteration.

Comparison of convergence speed between PSO, PBBPSO, DA-BBPSO, DLS-BBPSO, FBBPSO and ETBBPSO, f13, (a) iteration 0-6000, (b) iteration 6000-10000, the unit is 100 iteration.</p