A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training

© 2019 IEEE. In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods

A method to enhance the deep learning in an aerial image

© 2017 IEEE. In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives

Association of the rs3743205 variant of DYX1C1 with dyslexia in Chinese children

<p>Abstract</p> <p><b>Background</b></p> <p>Dyslexia is a learning disability that is characterized by difficulties in the acquisition of reading and spelling skills independent of intelligence, motivation or schooling. Studies of western populations have suggested that <it>DYX1C1 </it>is a candidate gene for dyslexia. In view of the different languages used in Caucasian and Chinese populations, it is therefore worthwhile to investigate whether there is an association of <it>DYX1C1 </it>in Chinese children with dyslexia.</p> <p>Method and Results</p> <p>Eight single nucleotide polymorphisms (SNPs) were genotyped from three hundred and ninety three individuals from 131 Chinese families with two which have been reported in the literature and six tag SNPs at <it>DYX1C1</it>. Analysis for allelic and haplotypic associations was performed with the UNPHASED program and multiple testing was corrected using false discovery rates. We replicated the previously reported association of rs3743205 in Chinese children with dyslexia (<it>p</it>_{<it>corrected </it>}= 0.0072). This SNP was also associated with rapid naming, phonological memory and orthographic skills in quantitative trait analysis.</p> <p>Conclusion</p> <p>Our findings suggest that <it>DYX1C1 </it>is associated with dyslexia in people of Chinese ethnicity in Hong Kong.</p

Robust Facial Alignment for Face Recognition

© 2017, Springer International Publishing AG. This paper proposes a robust real-time face recognition system that utilizes regression tree based method to locate the facial feature points. The proposed system finds the face region which is suitable to perform the recognition task by geometrically analyses of the facial expression of the target face image. In real-world facial recognition systems, the face is often cropped based on the face detection techniques. The misalignment is inevitably occurred due to facial pose, noise, occlusion, and so on. However misalignment affects the recognition rate due to sensitive nature of the face classifier. The performance of the proposed approach is evaluated with four benchmark databases. The experiment results show the robustness of the proposed approach with significant improvement in the facial recognition system on the various size and resolution of given face images

A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

© 2016 IEEE. A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications

Robust Feature-Based Automated Multi-View Human Action Recognition System

© 2013 IEEE. Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets

A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large

Pan-RAF and MEK vertical inhibition enhances therapeutic response in non-V600 BRAF mutant cells

BACKGROUND: Currently, there are no available targeted therapy options for non-V600 BRAF mutated tumors. The aim of this study was to investigate the effects of RAF and MEK concurrent inhibition on tumor growth, migration, signaling and apoptosis induction in preclinical models of non-V600 BRAF mutant tumor cell lines. METHODS: Six BRAF mutated human tumor cell lines CRL5885 (G466 V), WM3629 (D594G), WM3670 (G469E), MDAMB231 (G464 V), CRL5922 (L597 V) and A375 (V600E as control) were investigated. Pan-RAF inhibitor (sorafenib or AZ628) and MEK inhibitor (selumetinib) or their combination were used in in vitro viability, video microscopy, immunoblot, cell cycle and TUNEL assays. The in vivo effects of the drugs were assessed in an orthotopic NSG mouse breast cancer model. RESULTS: All cell lines showed a significant growth inhibition with synergism in the sorafenib/AZ628 and selumetinib combination. Combination treatment resulted in higher Erk1/2 inhibition and in increased induction of apoptosis when compared to single agent treatments. However, single selumetinib treatment could cause adverse therapeutic effects, like increased cell migration in certain cells, selumetinib and sorafenib combination treatment lowered migratory capacity in all the cell lines. Importantly, combination resulted in significantly increased tumor growth inhibition in orthotropic xenografts of MDAMB231 cells when compared to sorafenib - but not to selumetinib - treatment. CONCLUSIONS: Our data suggests that combined blocking of RAF and MEK may achieve increased therapeutic response in non-V600 BRAF mutant tumors

Ex vivo modelling of drug efficacy in a rare metastatic urachal carcinoma

Background Ex vivo drug screening refers to the out-of-body assessment of drug efficacy in patient derived vital tumor cells. The purpose of these methods is to enable functional testing of patient specific efficacy of anti-cancer therapeutics and personalized treatment strategies. Such approaches could prove powerful especially in context of rare cancers for which demonstration of novel therapies is difficult due to the low numbers of patients. Here, we report comparison of different ex vivo drug screening methods in a metastatic urachal adenocarcinoma, a rare and aggressive non-urothelial bladder malignancy that arises from the remnant embryologic urachus in adults. Methods To compare the feasibility and results obtained with alternative ex vivo drug screening techniques, we used three different approaches; enzymatic cell viability assay of 2D cell cultures and image-based cytometry of 2D and 3D cell cultures in parallel. Vital tumor cells isolated from a biopsy obtained in context of a surgical debulking procedure were used for screening of 1160 drugs with the aim to evaluate patterns of efficacy in the urachal cancer cells. Results Dose response data from the enzymatic cell viability assay and the image-based assay of 2D cell cultures showed the best consistency. With 3D cell culture conditions, the proliferation rate of the tumor cells was slower and potency of several drugs was reduced even following growth rate normalization of the responses. MEK, mTOR, and MET inhibitors were identified as the most cytotoxic targeted drugs. Secondary validation analyses confirmed the efficacy of these drugs also with the new human urachal adenocarcinoma cell line (MISB18) established from the patient’s tumor. Conclusions All the tested ex vivo drug screening methods captured the patient’s tumor cells’ sensitivity to drugs that could be associated with the oncogenic KRASG12V mutation found in the patient’s tumor cells. Specific drug classes however resulted in differential dose response profiles dependent on the used cell culture method indicating that the choice of assay could bias results from ex vivo drug screening assays for selected drug classes

Gemcitabine/cannabinoid combination triggers autophagy in pancreatic cancer cells through a ROS-mediated mechanism

Gemcitabine (GEM, 2′,2′-difluorodeoxycytidine) is currently used in advanced pancreatic adenocarcinoma, with a response rate of < 20%. The purpose of our work was to improve GEM activity by addition of cannabinoids. Here, we show that GEM induces both cannabinoid receptor-1 (CB1) and cannabinoid receptor-2 (CB2) receptors by an NF-κB-dependent mechanism and that its association with cannabinoids synergistically inhibits pancreatic adenocarcinoma cell growth and increases reactive oxygen species (ROS) induced by single treatments. The antiproliferative synergism is prevented by the radical scavenger N-acetyl--cysteine and by the specific NF-κB inhibitor BAY 11-7085, demonstrating that the induction of ROS by GEM/cannabinoids and of NF-κB by GEM is required for this effect. In addition, we report that neither apoptotic nor cytostatic mechanisms are responsible for the synergistic cell growth inhibition, which is strictly associated with the enhancement of endoplasmic reticulum stress and autophagic cell death. Noteworthy, the antiproliferative synergism is stronger in GEM-resistant pancreatic cancer cell lines compared with GEM-sensitive pancreatic cancer cell lines. The combined treatment strongly inhibits growth of human pancreatic tumor cells xenografted in nude mice without apparent toxic effects. These findings support a key role of the ROS-dependent activation of an autophagic program in the synergistic growth inhibition induced by GEM/cannabinoid combination in human pancreatic cancer cells