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From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this article demonstrates a novel method and framework to synthesize an artificial brain with cognitive abilities by taking advantage of the same process responsible for the growth of the biological brain called ``neuroembryogenesis. This framework shares some of the key behavioral aspects of the biological brain, such as spiking neurons, neuroplasticity, neuronal pruning, and excitatory and inhibitory interactions between neurons, together making it capable of learning and memorizing. One of the highlights of the proposed design is its potential to incrementally improve itself over generations based on system performance, using genetic algorithms. A proof of concept at the end of this article demonstrates how a simplified implementation of the human visual cortex using the proposed framework is capable of character recognition. Our framework is open source, and the code is shared with the scientific community at www.feagi.org.Recently, the dynamical behaviors of coupled neural networks (CNNs) with and without reaction-diffusion terms have been widely researched due to their successful applications in different fields. This article introduces some important and interesting results on this topic. First, synchronization, passivity, and stability analysis results for various CNNs with and without reaction-diffusion terms are summarized, including the results for impulsive, time-varying, time-invariant, uncertain, fuzzy, and stochastic network models. In addition, some control methods, such as sampled-data control, pinning control, impulsive control, state feedback control, and adaptive control, have been used to realize the desired dynamical behaviors in CNNs with and without reaction-diffusion terms. In this article, these methods are summarized. Finally, some challenging and interesting problems deserving of further investigation are discussed.Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technique used for the treatment of a great variety of neurological disorders. The technique involves applying a magnetic field in certain areas of the cerebral cortex in order to modify neuronal excitability outside the skull. However, the exact brain mechanisms underlying rTMS effects are not completely elucidated. For that purpose, and in order to generate a pulsed magnetic field, a half-bridge converter controlled by a microcontroller has been designed to apply rTMS in small animals. Moreover, the small size of the rodent head makes it necessary to design a magnetic transducer, with the aim of focusing the magnetic field on selected brain areas using a specific and a small magnetic head. Using such devices, our purpose was to compare the effects of five different rTMS dosages on rat brain metabolic activity. The experimental results showed that one day of stimulation leads to an enhancement of brain metabolic activity in cortical areas, meanwhile with three days of stimulation it is possible to also modify subcortical zones, results that were not found when extending the number of rTMS applications up to seven days. In consequence, the number of pulses delivered might be an important parameter in rTMS protocols, highlighting its importance in rTMS impact.Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact that most of the existing datasets are imbalanced, i.e. the number of instances of a particular class far exceeds that of the others. In this paper, we propose a framework based on the notion of transfer learning to address this issue and focus our efforts on histopathological and imbalanced image classification. We use the popular VGG-19 as the base model and complement it with several state-of-the-art techniques to improve the overall performance of the system. With the ImageNet dataset taken as the source domain, we apply the learned knowledge in the target domain consisting of histopathological images. With experimentation performed on a large-scale dataset consisting of 277,524 images, we show that the framework proposed in this paper gives superior performance than those available in the existing literature. Through numerical simulations conducted on a supercomputer, we also present guidelines for work in transfer learning and imbalanced image classification.To understand the underlying biological mechanisms of gene expression data, it is important to discover the groups of genes that have similar expression patterns under certain subsets of conditions. Biclustering algorithms have been effective in analyzing large-scale gene expression data. Recently, traditional biclustering has been improved by introducing biological knowledge along with the expression data during the biclustering process. In this paper, we propose the Pathway-based Order Preserving Biclustering (POPBic) algorithm by incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG) based on the hypothesis that two genes sharing similar pathways are likely to be similar. The basic principle of the POPBic approach is to apply the concept of longest common subsequence between a pair of genes which have a high number of common pathways. The algorithm identifies the expression patterns from data using two major steps (i) selection of significant seed genes, and (ii) extraction of biclusters. We have performed exhaustive experimentation with the POPBic algorithm using synthetic dataset to evaluate the bicluster model, finding its robustness in the presence of noise and identifying overlapping biclusters. We demonstrate that POPBic discovers biologically significant biclusters for four cancer microarray gene expression datasets. POPBic has been found to perform consistently well in comparison to its closest competitors.Graph models often give us a deeper understanding of real-world networks. In the case of biological networks they help in predicting the evolution and history of biomolecule interactions, provided we map properly real networks into the corresponding graph models. In this paper, we show that for biological graph models many of the existing parameter estimation techniques overlook the critical property of graph symmetry (also known formally as graph automorphisms), thus the estimated parameters give statistically insignificant results concerning the observed network. To demonstrate it and to develop accurate estimation procedures, we focus on the biologically inspired duplication-divergence model, and the up-to-date data of protein-protein interactions of seven species including human and yeast. Using exact recurrence relations of some prominent graph statistics, we devise a parameter estimation technique that provides the right order of symmetries and uses phylogenetically old proteins as the choice of seed graph nodes. We also find that our results are consistent with the ones obtained from maximum likelihood estimation (MLE). AZD2811 cost However, the MLE approach is significantly slower than our methods in practice.Identifying motifs in promoter regions is crucial to our understanding of transcription regulation. Researchers commonly use known promoter features in a variety of species to predict promoter motifs. However the results are not particularly useful. Different species rarely have similar features in promoter binding sites. In this study, we adopt sequence analysis techniques to find the possible promoter binding sites among all species. We sought to improve the existing algorithm to suit the task of mining sequential patterns with specific number of gaps. Moreover, we discuss the implementation of proposed method in a distributed environment. The proposed method finds the transcription start sites (TSS) and extracts possible promoter regions from DNA sequences according to TSS. We derived the motifs in the possible promoter regions, while taking into account the number of gaps in the motifs to deal with unimportant nucleotides. The motifs generated from promoter regions using the proposed methodology were shown to tolerate unimportant nucleotides. A comparison with known promoter motifs verified the efficacy of the proposed method.Segmenting small retinal vessels with width less than 2 pixels in fundus images is a challenging task. In this paper, in order to effectively segment the vessels, especially the narrow parts, we propose a local regression scheme to enhance the narrow parts, along with a novel multi-label classification method based on this scheme. We consider five labels for blood vessels and background in particular the center of big vessels, the edge of big vessels, the center as well as the edge of small vessels, the center of background, and the edge of background. We first determine the multi-label by the local de-regression model according to the vessel pattern from the ground truth images. Then, we train a convolutional neural network (CNN) for multi-label classification. Next, we perform a local regression method to transform the previous multi-label into binary label to better locate small vessels and generate an entire retinal vessel image. Our method is evaluated using two publicly available datasets and compared with several state-of-the-art studies. The experimental results have demonstrated the effectiveness of our method in segmenting retinal vessels.Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information. Thus, an intelligent transformation from MR to CT is of great interest. To address this need and using combined MR UTE and modified Dixon (mDixon) data, we propose the SCT-PK-PS method that jointly leverages prior knowledge and partial supervision. link2 Two key machine learning techniques KL-TFCM and LapSVM are used in SCT-PK-PS. The significance of our effort is threefold 1) Via KL-TFCM, SCT-PK-PS can group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. From the initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent LapSVM classification; 2) Exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM can obtain multiple desired tissue-recognizers; 3) Jointly using KL-TFCM and LapSVM, and assisted by the edge detector based feature extraction, SCT-PK-PS features good recognition accuracy, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis.! OBJECTIVE Brain-computer interface (BCI) based communication remains a challenge for people with later-stage amyotrophic lateral sclerosis (ALS) who lose all voluntary muscle control. Although recent studies have demonstrated the feasibility of functional near-infrared spectroscopy (fNIRS) to control BCIs primarily for healthy cohorts, these systems are yet inefficient for people with severe motor disabilities like ALS. METHODS In this study, we developed a new fNIRS-based BCI system in concert with a single-trial Visuo-Mental (VM) paradigm to investigate the feasibility of enhanced communication for ALS patients, particularly those in the later stages of the disease. In the first part of the study, we recorded data from six ALS patients using our proposed protocol (fNIRS-VM) and compared the results with the conventional electroencephalography (EEG)-based multi-trial P3Speller (P3S). link3 In the second part, we recorded longitudinal data from a patient in the late locked-in state (LIS) who had fully lost eye-gaze control.