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Motivation Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank. Results We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. Availability and implementation https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. Supplementary information Supplementary data are available at Bioinformatics online.

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An attempt has been made to characterize the colour spectrum of methane flame under various burning conditions using RGB and HSV colour models instead of resolving the real physical spectrum. The results demonstrate that each type of flame has its own characteristic distribution in both the RGB and HSV space. It has also been observed that the averaged B and G values in the RGB model represent well the CH * and C * 2 emission of methane premixed flame. Theses features may be utilized for flame measurement and monitoring. The great advantage of using a conventional camera for monitoring flame properties based on the colour spectrum is that it is readily available, easy to interface with a computer, cost effective and has certain spatial resolution. Furthermore, it has been demonstrated that a conventional digital camera is able to image flame not only in the visible spectrum but also in the infrared. This feature is useful in avoiding the problem of image saturation typically encountered in capturing the very bright sooty flames. As a result, further digital imaging processing and quantitative information extraction is possible. It has been identified that an infrared image also has its own distribution in both the RGB and HSV colour space in comparison with a flame image in the visible spectrum.

Accurate and timely forecasting of traffic parameters is crucial for effective management of intelligent transportation systems. Travel time index (TTI) is a fundamental measure in transportation. In this article, a non-parametric technique called least squares support vector machines (LS-SVMs) is proposed to forecast TTI. To the best of our knowledge, it is the first time to cooperate the rising computational intelligence technique with state space approach in traffic forecasting. Five other baseline predictors are selected for comparison purposes because of their proved effectiveness. Having good generalisation ability and guaranteeing global minima, LS-SVMs perform better than the others. Experimental results demonstrate that our predictor can significantly reduce mean absolute percentage errors and variance of absolute percentage errors, especially for predicting traffic data with weak regularity. Persuasive comparisons clearly show that it provides a large improvement in stability and robustness, which reveals that it is a promising approach in traffic forecasting and time series analysis.

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.

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