Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks

The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using considering the focus measurement in the spatial domain. However, the multi-focus image fusion processing is very time-saving and appropriate in discrete cosine transform (DCT) domain, especially when JPEG images are used in visual sensor networks (VSN). So the most of the researchers are interested in focus measurements calculation and fusion processes directly in DCT domain. Accordingly, many researchers developed some techniques which are substituting the spatial domain fusion process with DCT domain fusion process. Previous works in DCT domain have some shortcomings in selection of suitable divided blocks according to their criterion for focus measurement. In this paper, calculation of two powerful focus measurements, energy of Laplacian (EOL) and variance of Laplacian (VOL), are proposed directly in DCT domain. In addition, two other new focus measurements which work by measuring correlation coefficient between source blocks and artificial blurred blocks are developed completely in DCT domain. However, a new consistency verification method is introduced as a post-processing, improving the quality of fused image significantly. These proposed methods reduce the drawbacks significantly due to unsuitable block selection. The output images quality of our proposed methods is demonstrated by comparing the results of proposed algorithms with the previous algorithms

Understanding the undelaying mechanism of HASubtyping in the level of physic-chemal characteristics of protein

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.Mansour Ebrahimi, Parisa Aghagolzadeh, Narges Shamabadi, Ahmad Tahmasebi, Mohammed Alsharifi, David L. Adelson, Farhid Hemmatzadeh, Esmaeil Ebrahimi

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics

Corrigendum: Low-Dimensional Motor Cortex Dynamics Preserve Kinematics Information During Unconstrained Locomotion in Nonhuman Primates.

[This corrects the article DOI: 10.3389/fnins.2019.01046.]

A novel fluorescent probe-based flow cytometric assay for mineral-containing nanoparticles in serum

Calciprotein particles, nanoscale aggregates of insoluble mineral and binding proteins, have emerged as potential mediators of phosphate toxicity in patients with Chronic Kidney Disease. Although existing immunochemical methods for their detection have provided compelling data, these approaches are indirect, lack specificity and are subject to a number of other technical and theoretical shortcomings. Here we have developed a rapid homogeneous fluorescent probe-based flow cytometric method for the detection and quantitation of individual mineral-containing nanoparticles in human and animal serum. This method allows the discrimination of membrane-bound from membrane-free particles and different mineral phases (amorphous vs. crystalline). Critically, the method has been optimised for use on a conventional instrument, without the need for manual hardware adjustments. Using this method, we demonstrate a consistency in findings across studies of Chronic Kidney Disease patients and commonly used uraemic animal models. These studies demonstrate that renal dysfunction is associated with the ripening of calciprotein particles to the crystalline state and reveal bone metabolism and dietary mineral as important modulators of circulating levels. Flow cytometric analysis of calciprotein particles may enhance our understanding of mineral handling in kidney disease and provide a novel indicator of therapeutic efficacy for interventions targeting Chronic Kidney Disease-Mineral Bone Disorder

The conserved long non-coding RNA CARMA regulates cardiomyocyte differentiation.

Production of functional cardiomyocytes from pluripotent stem cells requires tight control of the differentiation process. Long non-coding RNAs (lncRNAs) exert critical regulatory functions in cell specification during development. In this study, we designed an integrated approach to identify lncRNAs implicated in cardiogenesis in differentiating human embryonic stem cells (ESCs). We identified CARMA (CARdiomyocyte Maturation-Associated lncRNA), a conserved lncRNA controlling cardiomyocyte differentiation and maturation in human ESCs. CARMA is located adjacent to MIR-1-1HG, the host gene for two cardiogenic miRNAs: MIR1-1 and MIR-133a2, and transcribed in an antisense orientation. The expression of CARMA and the miRNAs are negatively correlated, and CARMA knockdown increases MIR1-1 and MIR-133a2 expression. In addition, CARMA possesses MIR-133a2 binding sites, suggesting the lncRNA could be also a target of miRNA action. Upon CARMA down-regulation, MIR-133a2 target protein-coding genes are coordinately down-regulated. Among those, we found RBPJ, the gene encoding the effector of the NOTCH pathway. NOTCH has been shown to control a binary cell fate decision between the mesoderm and the neuroectoderm lineages, and NOTCH inhibition leads to enhanced cardiomyocyte differentiation at the expense of neuroectodermal derivatives. Interestingly, two lncRNAs, linc1230 and linc1335, which are known repressors of neuroectodermal specification, were found up-regulated upon Notch1 silencing in ESCs. Forced expression of either linc1230 or linc1335 improved ESC-derived cardiomyocyte production. These two lncRNAs were also found up-regulated following CARMA knockdown in ESCs. Altogether, these data suggest the existence of a network, implicating three newly identified lncRNAs, the two myomirs MIR1-1 and MIR-133a2 and the NOTCH signalling pathway, for the coordinated regulation of cardiogenic differentiation in ESCs