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Motivation mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed. Results In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA. [ABSTRACT FROM AUTHOR]

This letter presents a novel approach for extracting accurate outlines of individual buildings from very high-resolution (0.1–0.4 m) optical images. Building outlines are defined as polygons here. Our approach operates on a set of straight line segments that are detected by a line detector. It groups a subset of detected line segments and connects them to form a closed polygon. Particularly, a new grouping cost is defined first. Second, a weighted undirected graph $\textit {G(V,E)}$ is constructed based on the endpoints of those extracted line segments. The building outline extraction is then formulated as a problem of searching for a graph cycle with the minimal grouping cost. To solve the graph cycle searching problem, the bidirectional shortest path method is utilized. Our method is validated on a newly created data set that contains 123 images of various building roofs with different shapes, sizes, and intensities. The experimental results with an average intersection-over-union of 90.56% and an average alignment error of 6.56 pixels demonstrate that our approach is robust to different shapes of building roofs and outperforms the state-of-the-art method. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explore high-quality features from complex data. • Keep the critical features information. • The dimensionality reduction method is based on an ensemble strategy, overcoming the limitations of a single method. • Provide a variety of charts to help visualize data analysis. • User-friendly, allowing non-computing people to perform complex analyses of data. Dimensionality reduction is a hot topic in machine learning that can help researchers find key features from complex medical or biological data, which is crucial for biological sequence research, drug development, etc. However, when applied to specific datasets, different dimensionality reduction methods generate different results, which produces instability and makes tuning the parameters a time-consuming task. Exploring high quality features, genes, or attributes from complex data is an important task and challenge. To ensure the efficiency, robustness, and accuracy of experiments, in this work, we developed a dimensionality reduction tool MRMD3.0 based on the ensemble strategy of link analysis. It is mainly divided into two steps: first, the ensemble method is used to integrate different feature ranking algorithms to calculate feature importance, and then the forward feature search strategy combined with cross-validation is used to explore the proper feature combination. Compared with the previously developed version, MRMD3.0 has added more link-based ensemble algorithms, including PageRank, HITS, LeaderRank, and TrustRank. At the same time, more feature ranking algorithms have been added, and their effect and calculation speed have been greatly improved. In addition, the newest version provides an interface used by each feature ranking method and five kinds of charts to help users analyze features. Finally, we also provide an online webserver to help researchers analyze the data. Availability and implementation Webserver: http://lab.malab.cn/soft/MRMDv3/home.html. GitHub: https://github.com/heshida01/MRMD3.0. [ABSTRACT FROM AUTHOR]

Abstract: The rising commercial use and large-scale production of engineered nanoparticles (NPs) may lead to unintended exposure to humans. The central nervous system (CNS) is a potential susceptible target of the inhaled NPs, but so far the amount of studies on this aspect is limited. Here, we focus on the potential neurological lesion in the brain induced by the intranasally instilled titanium dioxide (TiO2) particles in rutile phase and of various sizes and surface coatings. Female mice were intranasally instilled with four different types of TiO2 particles (i.e. two types of hydrophobic particles in micro- and nano-sized without coating and two types of water-soluble hydrophilic nano-sized particles with silica surface coating) every other day for 30 days. Inductively coupled plasma mass spectrometry (ICP-MS) were used to determine the titanium contents in the sub-brain regions. Then, the pathological examination of brain tissues and measurements of the monoamine neurotransmitter levels in the sub-brain regions were performed. We found significant up-regulation of Ti contents in the cerebral cortex and striatum after intranasal instillation of hydrophilic TiO2 NPs. Moreover, TiO2 NPs exposure, in particular the hydrophilic NPs, caused obvious morphological changes of neurons in the cerebral cortex and significant disturbance of the monoamine neurotransmitter levels in the sub-brain regions studied. Thus, our results indicate that the surface modification of the NPs plays an important role on their effects on the brain. In addition, the difference in neurotoxicity of the two types of hydrophilic NPs may be induced by the shape differences of the materials. The present results suggest that physicochemical properties like size, shape and surface modification of the nanomaterials should be considered when evaluating their neurological effects. [Copyright &y& Elsevier]