Combinatorial Optimization of the piecewise constant Mumford-Shah functional with application to scalar/vector valued and volumetric image segmentation

Front propagation models represent an important category of image segmentation techniques in the current literature. These models are normally formulated in a continuous level sets framework and optimized using gradient descent methods. Such formulations result in very slow algorithms that get easily stuck in local solutions and are highly sensitive to initialization.In this paper, we reformulate one of the most influential front propagation models, the Chan-Vese model, in the discrete domain. The graph representability and submodularity of the discrete energy function is established and then max-flow/min-cut approach is applied to perform the optimization of the discrete energy function. Our results show that this formulation is much more robust than the level sets formulation. Our approach is not sensitive to initialization and provides much faster solutions than level sets. The results also depict that our segmentation approach is robust to topology changes, noise and ill-defined edges, i.e., it preserves all the advantages associated with level sets methods.     

Broadband Transmitarray for Satellite Applications in Ku Band

Wireless Personal Communications - This paper focuses on the user-pair association for multi-tier relay-based dual-hop heterogeneous networks (HetNets) and proposes a novel min–max user-pair...     

“StoryWeb”: A storytelling‐based knowledge‐sharing application among multiple stakeholders

The research objective of this paper is to develop a storytelling‐based knowledge‐sharing application that enables users to co‐create their own stories for both individuals and groups. To address this, a design science research methodology was applied for elucidating users' requirements. As empirical evidence, a case study was conducted on the children's book industry to synthesize a knowledge‐sharing design application named “StoryWeb”. Usability tests were conducted to reconfigure users' feedback and suggestions after two StoryWeb prototypes were developed. This study makes three main contributions. First, it empirically tests individual's or group's creativity and co‐creation by a view of knowledge sharing. Second, it methodologically applies a design thinking approach into a knowledge‐sharing study. Third, it also practically suggests feasible guidelines for the creativity and innovation research community on which features of storytelling‐based applications can be configured.     

Importance of the Long-Chain Fatty Acid Beta-Hydroxylating Cytochrome P450 Enzyme YbdT for Lipopeptide Biosynthesis in Bacillus subtilis Strain OKB105

Bacillus species produce extracellular, surface-active lipopeptides such as surfactin that have wide applications in industry and medicine. The steps involved in the synthesis of 3-hydroxyacyl-coenzyme A (CoA) substrates needed for surfactin biosynthesis are not understood. Cell-free extracts of Bacillus subtilis strain OKB105 synthesized lipopeptide biosurfactants in presence of l-amino acids, myristic acid, coenzyme A, ATP, and H(2)O(2), which suggested that 3-hydroxylation occurs prior to CoA ligation of the long chain fatty acids (LCFAs). We hypothesized that YbdT, a cytochrome P450 enzyme known to beta-hydroxylate LCFAs, functions to form 3-hydroxy fatty acids for lipopeptide biosynthesis. An in-frame mutation of ybdT was constructed and the resulting mutant strain (NHY1) produced predominantly non-hydroxylated lipopeptide with diminished biosurfactant and beta-hemolytic activities. Mass spectrometry showed that 95.6% of the fatty acids in the NHY1 biosurfactant were non-hydroxylated compared to only ～61% in the OKB105 biosurfactant. Cell-free extracts of the NHY1 synthesized surfactin containing 3-hydroxymyristic acid from 3-hydroxymyristoyl-CoA at a specific activity similar to that of the wild type (17 ± 2 versus 17.4 ± 6 ng biosurfactant min(-1)·ng·protein(-1), respectively). These results showed that the mutation did not affect any function needed to synthesize surfactin once the 3-hydroxyacyl-CoA substrate was formed and that YbdT functions to supply 3-hydroxy fatty acid for surfactin biosynthesis. The fact that YbdT is a peroxidase could explain why biosurfactant production is rarely observed in anaerobically grown Bacillus species. Manipulation of LCFA specificity of YbdT could provide a new route to produce biosurfactants with activities tailored to specific functions.     

Novel anticorrosive emulsion-type paints containing organic/inorganic nanohybrid particles

Novel anticorrosive emulsion-type paints were prepared by utilizing organic/inorganic nanohybrid particles (nHPs) of composition (1:1) based on polyaniline (PANI) and nanosilica. Polyaniline (PANI) and nanosilica were synthesized via chemical oxidative and sol–gel polymerization techniques respectively. The prepared materials were characterized via (gel permeation chromatography (GPC), infra red (FT-IR), transmission electron microscope (TEM) and differential scanning calorimetry (DSC)). The binder used in this context is polyvinyl acetate (PVAc). Nanohybrid particles/PVAc emulsion composites (nHPs ECs) were simply formed by individual mixing of the prepared neat PANI and nHPs of composition (1:1) with PVAc to produce the corresponding nanohybrid particles/PVAc emulsion composites EC₁ and EC₂ respectively. The chemical and basic properties including (acid and alkali resistance, adhesion, washability, gloss, opacity, whiteness, hardness, impact, and weathering) as well as the anticorrosion properties of the blank paint films and the paint films containing neat PANI and nHPs of different concentrations (5%, 10%, and 15%) were investigated and evaluated. The obtained experimental results revealed that the presence of nHPs in the blank paint are highly enhanced both basic and anticorrosion properties of the coated films.     

New insights in structural characterization of transparent ZnS ceramics hot-pressed from nanocrystalline powders synthesized by combustion method

Zinc sulfide transparent ceramics have been fabricated by hot pressing (HP) powders prepared by a newly developed combustion method. Chemical, structural and microstructural properties of powders and ceramics were characterized using different experimental techniques (XRD, SEM-EDS, laser granulometry, TEM, BET, FT-IR spectroscopy). ZnS powders were densified to full density by HP under vacuum atmosphere. The ceramics exhibit highly dense microstructure with mean grain size of 1 μm. TEM characterization identified, both in powders and ceramics, twins and simple stacking faults due to the aperiodic distribution of hexagonal domains. With optical transmission of the theoretical level (～75%), without absorption band (at 6 μm) and with negligible optical loss, in the 4–12 μm region, the ceramics exhibit better optical performances than standard grade CVD ZnS, and unprecedented performances for hot-pressed ZnS.     

An Improved DeepNN with Feature Ranking for Covid-19 Detection

The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients’ electronic medical records (EMR). MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance. Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy. Experiments were conducted using three different COVID-19 datasets and eight different models, including the proposed MLPFS. Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models. Additionally, it outperforms the other models in classification results as well as time.     

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of the EEG signals. Moreover, KELM model is used for the detection and classification of epileptic seizures utilizing EEG signal. Furthermore, the RCMO technique was utilized for the optimal parameter tuning of the KELM technique in such a way that the overall detection outcomes can be considerably enhanced. The experimental result analysis of the RCMO-KELM technique has been examined using benchmark dataset and the results are inspected under several aspects. The comparative result analysis reported the better outcomes of the RCMO-KELM technique over the recent approaches with the of 0.956.     

Association of MALAT1 and PVT1 Variants,Expression Profiles and Target miRNA-101 and miRNA-186 with Colorectal Cancer: Correlation with Epithelial-Mesenchymal Transition

The influence of PVT1 and MALAT1 variants on colorectal cancer (CRC) susceptibility and their impact on PVT1/miRNA-186/epithelial-mesenchymal transition (EMT) and MALAT1/miRNA-101/EMT axes in CRC are unknown. We investigated the influence of PVT1 rs13255292 and MALAT1 rs3200401 on the risk of CRC and adenomatous polyps (AP), their impact on the long noncoding RNAs PVT1 and MALAT1 expression and their target miRNA-186, miRNA-101/E-cadherin pathways, along with their potential as early CRC biomarkers. Overall, 280 individuals were recruited: 140 patients with CRC, 40 patients with AP, and 100 healthy volunteers. Genotyping and serum expression profiles were assessed using qPCR. The EMT biomarker, E-cadherin, was measured by ELISA. rs3200401 was associated with increased CRC risk, whereas rs13255292 was protective. Serum PVT1 and MALAT1 were upregulated in CRC and AP patients versus healthy controls, whereas, miRNA-186, miRNA-101 and E-cadherin were downregulated in CRC versus non-CRC groups. MALAT1 showed superior diagnostic potential for CRC and predicted CRC risk among non-CRC groups in the multivariate logistic analysis. PVT1, MALAT1, miRNA-186 and miRNA-101 levels were correlated with E-cadherin, tumor stage, lymph node and distant metastasis. E-cadherin was lost in metastatic vs. non-metastatic CRC. rs3200401CC genotype carriers showed higher E-cadherin levels than CC + CT carriers. rs3200401 was correlated with lymph node status. For the first time, rs13255292 and rs3200401 are potential genetic CRC predisposition markers, with rs3200401 possibly impacting the EMT process. Serum PVT1, MALAT1, miRNA-186 and miRNA-101 are novel non-invasive diagnostic biomarkers that could improve the clinical outcome of CRC.