Application of a high density ratio lattice-Boltzmann model for the droplet impingement on flat and spherical surfaces

In the current study, a 3-dimensional lattice Boltzmann model which can tolerate high density ratios is employed to simulate the impingement of a liquid droplet onto a flat and a spherical target. The four phases of droplet impact on a flat surface, namely, the kinematic, spreading, relaxation and equilibrium phase, have been obtained for a range of Weber and Reynolds numbers. The predicted maximum spread factor is in good agreement with experimental data published in the literature. For the impact of the liquid droplet onto a spherical target, the temporal variation of the film thickness on the target surface is investigated. The three different temporal phases of the film dynamics, namely, the initial drop deformation phase, the inertia dominated phase and the viscosity dominated phase are reproduced and studied. The effect of the droplet Reynolds number and the target-to-drop size ratio on the film flow dynamics is investigated

TITER: predicting translation initiation sites by deep learning.

MotivationTranslation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e.g. GTI-seq and QTI-seq, provides abundant data for systematically studying the general principles of translation initiation and the development of computational method for TIS identification.MethodsWe have developed a deep learning-based framework, named TITER, for accurately predicting TISs on a genome-wide scale based on QTI-seq data. TITER extracts the sequence features of translation initiation from the surrounding sequence contexts of TISs using a hybrid neural network and further integrates the prior preference of TIS codon composition into a unified prediction framework.ResultsExtensive tests demonstrated that TITER can greatly outperform the state-of-the-art prediction methods in identifying TISs. In addition, TITER was able to identify important sequence signatures for individual types of TIS codons, including a Kozak-sequence-like motif for AUG start codon. Furthermore, the TITER prediction score can be related to the strength of translation initiation in various biological scenarios, including the repressive effect of the upstream open reading frames on gene expression and the mutational effects influencing translation initiation efficiency.Availability and implementationTITER is available as an open-source software and can be downloaded from https://github.com/zhangsaithu/titer [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online

Local diffusion theory of localized waves in open media

We report a first-principles study of static transport of localized waves in quasi-one-dimensional open media. We found that such transport, dominated by disorder-induced resonant transmissions, displays novel diffusive behavior. Our analytical predictions are entirely confirmed by numerical simulations. We showed that the prevailing self-consistent localization theory [van Tiggelen, {\it et. al.}, Phys. Rev. Lett. \textbf{84}, 4333 (2000)] is valid only if disorder-induced resonant transmissions are negligible. Our findings open a new direction in the study of Anderson localization in open media.Comment: 4 pages, 2 figure