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

SNAC-tag for sequence-specific chemical protein cleavage.

Site-specific protein cleavage is essential for many protein-production protocols and typically requires proteases. We report the development of a chemical protein-cleavage method that is achieved through the use of a sequence-specific nickel-assisted cleavage (SNAC)-tag. We demonstrate that the SNAC-tag can be inserted before both water-soluble and membrane proteins to achieve fusion protein cleavage under biocompatible conditions with efficiency comparable to that of enzymes, and that the method works even when enzymatic cleavages fail

Vibrations of a plate on a two-parameter foundation subjected to moving rectangular loads of varying velocities

The vibrational characteristics of a plate on a two-parameter foundation under moving rectangular loads with variable velocities are investigated, and the general solution for the dynamic deflection of the plate is derived using the double Fourier transform. Employing the fast Fourier Transform, a rigid pavement is chosen to obtain numerical results, which are consistent with those from the classical solution. The effects of initial load velocity, load acceleration, load deceleration and horizontal resistance at the plate bottom on the dynamic deflection are discussed. An expression to predict the critical velocity is derived, and the results from this formula show very good agreement with those from the numerical analysis. The numerical analysis indicates that the maximum dynamic deflection occurs when the load velocity reaches the critical velocity for the plate. The initial velocity, the acceleration and the deceleration of the rectangular load influence the dynamic response, and the dynamic deflection of the plate at the critical velocity decreases significantly as they increases

Dynamic response of a pavement-subgrade-soft ground system subjected to moving traffic load

This paper introduces a three-dimensional model for the steady-state response of a pavement-subgrade-soft ground system subjected to moving traffic load. A semi-analytical wave propagation model is introduced which is subjected to four rectangular moving loads and based on a calculation method of the dynamic stiffness matrix of the ground. In order to model a complete road system, the effect of a simple road model is taken into account including pavement, subgrade and soft subsoil. The pavement and the subgrade are regarded as two elastic layers resting on a poroelastic half-space soil medium. The priority has been given to a simple formulation based on the principle of spatial Fourier transforms compatible with good numerical efficiency and yet providing quick solutions. The frequency wave-number domain solution of the road system is obtained by the compatibility condition at the interface of the structural layers. By introducing FFT (Fast Fourier Transform) algorithm, the numerical results are derived and the influences of the observation coordinates, the load speed and excitation frequency, the permeability of the soft subsoil, and the rigidity of the subgrade on the response of the pavement-subgrade-soft ground system are investigated. The numerical results show that the influences of these parameters on the dynamic response of the road system are significant

Less is More: Focus Attention for Efficient DETR

DETR-like models have significantly boosted the performance of detectors and even outperformed classical convolutional models. However, all tokens are treated equally without discrimination brings a redundant computational burden in the traditional encoder structure. The recent sparsification strategies exploit a subset of informative tokens to reduce attention complexity maintaining performance through the sparse encoder. But these methods tend to rely on unreliable model statistics. Moreover, simply reducing the token population hinders the detection performance to a large extent, limiting the application of these sparse models. We propose Focus-DETR, which focuses attention on more informative tokens for a better trade-off between computation efficiency and model accuracy. Specifically, we reconstruct the encoder with dual attention, which includes a token scoring mechanism that considers both localization and category semantic information of the objects from multi-scale feature maps. We efficiently abandon the background queries and enhance the semantic interaction of the fine-grained object queries based on the scores. Compared with the state-of-the-art sparse DETR-like detectors under the same setting, our Focus-DETR gets comparable complexity while achieving 50.4AP (+2.2) on COCO. The code is available at https://github.com/huawei-noah/noah-research/tree/master/Focus-DETR and https://gitee.com/mindspore/models/tree/master/research/cv/Focus-DETR.Comment: 8 pages, 6 figures, accepted to ICCV202