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High-dimensional pathological images produced by Immunohistochemistry (IHC) methods consist of many pathological indexes, which play critical roles in cancer treatment planning. However, these indexes currently cannot be utilized in survival prediction because joining them with patients' clinicopathological features (e.g., age and tumor size) is challenging due to their high dimension and sparse features. To address this problem, we propose a novel two-stage survival prediction model named ICSPM to join the IHC images and clinicopathological features. For the first stage, our proposed SDAE-GAN compresses high-dimensional IHC images to flat, compact and representative feature vectors by compressing and reconstructing them. For the first time, SDAE-GAN integrates dense blocks, the stacked auto-encoder and the GAN architecture to maximize the ability to detect patterns in IHC images. In addition, we propose a novel policy gradient based data augmentation method to involve the diversity in IHC images without breaking patterns inside them. For the second stage, ICSPM adopts a DenseNet to join feature vectors and clinicopathological features for survival prediction. Experimental results demonstrate that ICSPM reached a state-of-the-art prediction accuracy of 0.72 on the five-year survival. ICSPM is the first work to enable high-dimensional IHC images in cancer survival prediction. We prove that high-dimensional IHC images and clinicopathological features provide valuable and complementary information in survival prediction., Competing Interests: Declaration of Competing Interest Conflicts of interest: none, (Copyright © 2020 Elsevier B.V. All rights reserved.)

We study the problem of three-dimensional localization of the underwater mobile sensor networks using only range measurements without GPS devices. This problem is challenging because sensor nodes often drift with unknown water currents. Consequently, the moving direction and speed of a sensor node cannot be predicted. Moreover, the motion devices of the sensor nodes are not accurate in underwater environments. Therefore, we propose an adaptive localization scheme, ProLo, taking these uncertainties into consideration. This scheme applies the rigidity theory and maintains a virtual rigid structure through projection. We have proved the correctness of this three-dimensional localization scheme and also validated it using simulation. The results demonstrate that ProLo is promising for real mobile underwater sensor networks with various noises and errors.

Repetitive transcranial magnetic stimulation (rTMS) at sub-threshold intensity is a viable clinical strategy to enhance the sensory and motor functions of extremities by increasing or decreasing motor cortical excitability. Despite this, it remains unclear how sub-threshold rTMS modulates brain cortical excitability and connectivity. In this study, we applied functional near-infrared spectroscopy (fNIRS) to investigate the alterations in hemodynamic responses and cortical connectivity patterns that are induced by high-frequency rTMS at a sub-threshold intensity. Forty high-frequency (10 Hz) trains of rTMS at 90% resting motor threshold (RMT) were delivered through a TMS coil placed over 1-2 cm lateral from the vertex. fNIRS signals were acquired from the frontal and bilateral motor areas in healthy volunteers ( n = 20) during rTMS administration and at rest. A significant reduction in oxygenated hemoglobin (HbO) concentration was observed in most defined regions of interest (ROIs) during the stimulation period ( p < 0.05). Decreased functional connectivity within prefrontal areas as well as between symmetrical ROI-pairs was also observed in most participants during the stimulation ( p < 0.05). Results suggest that fNIRS imaging is able to provide a reliable measure of regional cortical brain activation that advances our understanding of the manner in which sub-threshold rTMS affects cortical excitability and brain connectivity.

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.

This paper proposes a flexible ant colony (FAC) algorithm for solving protein folding problems based on the hydrophobic-polar square lattice model. Collaborations of novel pheromone and heuristic strategies in the proposed algorithm make it more effective in predicting structures of proteins compared with other state-of-the-art algorithms.