Trans-differentiation of human adipose-derived mesenchymal stem cells into cardiomyocyte-like cells on decellularized bovine myocardial extracellular matrix-based films

In this study, we aimed at fabricating decellularized bovine myocardial extracellular matrix-based films (dMEbF) for cardiac tissue engineering (CTE). The decellularization process was carried out utilizing four consecutive stages including hypotonic treatment, detergent treatment, enzymatic digestion and decontamination, respectively. In order to fabricate the dMEbF, dBM were digested with pepsin and gelation process was conducted. dMEbF were then crosslinked with N-hydroxysuccinimide/1-Ethyl-3-(3-dimethylaminopropyl)-carbodiimide (NHS/EDC) to increase their durability. Nuclear contents of native BM and decellularized BM (dBM) tissues were determined with DNA content analysis and agarose-gel electrophoresis. Cell viability on dMEbF for 3rd, 7th, and 14th days was assessed by MTT assay. Cell attachment on dMEbF was also studied by scanning electron microscopy. Trans-differentiation capacity of human adipose-derived mesenchymal stem cells (hAMSCs) into cardiomyocyte-like cells on dMEbF were also evaluated by histochemical and immunohistochemical analyses. DNA contents for native and dBM were, respectively, found as 886.11?±?164.85 and 47.66?±?0.09?ng/mg dry weight, indicating a successful decellularization process. The results of glycosaminoglycan and hydroxyproline assay, and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), performed in order to characterize the extracellular matrix (ECM) composition of native and dBM tissue, showed that the BM matrix was not damaged during the proposed method. Lastly, regarding the histological study, dMEbF not only mimics native ECM, but also induces the stem cells into cardiomyocyte-like cells phenotype which brings it the potential of use in CTE.     

Alcoholism detection based on Hu moment invariants of event-related potentials

Diagnostic efforts to identify alcoholism are frequently subjective; however, recent technological advances have allowed the automated assessment of objective signals. To improve the performance of the automated classification of patients with alcohol dependence (AD) and controls, we analyzed features of event-related potentials (ERPs) in 72 subjects with AD and 42 controls while the subjects underwent three different visual oddball tasks. In addition to such commonly used morphological features (MFs) as time-domain amplitude and latency components as well as discrete wavelet transform coefficient entropies (WEs), we analyzed Hu moment invariants (HMIs) in this study. Smaller P3 and P2 amplitudes, longer latencies, lower WE values, and lower HMI values were observed for AD subjects relative to those observed for controls. We used a Naïve Bayes algorithm to compare classification accuracies among MFs, WEs, HMIs, combinations of MFs/WEs, and combinations of all three characteristics. We found that HMIs improved the classification accuracy by 17%. Our findings demonstrated that a combined feature analysis of ERPs obtained during a visual oddball task can be a useful and discriminative tool for diagnostic purposes in alcoholism and that HMIs show promise for improving the objective diagnosis of alcoholism and similar diseases.     

Efficient selective replication of critical code regions for SDC mitigation leveraging redundant multithreading

The Journal of Supercomputing - Redundant multithreading (RMT) is an effective reliability solution that provides thread-level replication; however, it imposes additional overheads in terms of...     

Extracellular Vesicles and Asthma—More Than Just a Co-Existence

Extracellular vesicles (EVs) are membranous structures, which are secreted by almost every cell type analyzed so far. In addition to their importance for cell-cell communication under physiological conditions, EVs are also released during pathogenesis and mechanistically contribute to this process. Here we summarize their functional relevance in asthma, one of the most common chronic non-communicable diseases. Asthma is a complex persistent inflammatory disorder of the airways characterized by reversible airflow obstruction and, from a long-term perspective, airway remodeling. Overall, mechanistic studies summarized here indicate the importance of different subtypes of EVs and their variable cargoes in the functioning of the pathways underlying asthma, and show some interesting potential for the development of future therapeutic interventions. Association studies in turn demonstrate a good diagnostic potential of EVs in asthma.     

Recent developments in catalyst synthesis using DBD plasma for reforming applications

The catalyst has a significant role in gas processing applications such as reforming technologies for H₂ and syngas production. The stable catalyst is requisite for any industrial catalysis application to make it commercially viable. Several methods are employed to synthesize the catalysts. However, there is still a challenge to achieve a controlled morphology and pure catalyst which majorly influences the catalytic activity in reforming applications. The conventional methods are expansive, and the removal of the impurities are major challenges. Nevertheless, it is not straightforward to achieve the desired structure and stability. Therefore, significant interest has been developed on the advanced techniques to take control of the physicochemical properties of the catalyst through non-thermal plasma (NTP) techniques. In this review, the systematic evolution of the catalyst synthesis using NTP technique is elucidated. The emerging DBD plasma to synthesized and effective surface treatment is reviewed. DBD plasma synthesized catalyst performance in reforming application for H₂ and syngas production is summarised. Furthermore, the status of DBD plasma for catalyst synthesis and proposed future avenues to design environmentally suitable and cost-effective synthesis techniques are discussed.     

Boosting the activity of FeOOH via integration of ZIF-12 and graphene to efficiently catalyze the oxygen evolution reaction

Transition metal oxyhydroxides have been used as promising electrocatalysts for water splitting however, their catalytic activity is restricted due to low surface area and poor conductivity. Herein, we report novel composite FeOOH@ZIF-12/graphene composite as electrocatalyst for water oxidation, whereby ZIF-12 provide extra surface for the FeOOH dispersion whilst graphene act as excellent electron mediator. The composite shows a low overpotential value of 291 mV to attain a current density of 10 mA cm^?2 and a low Tafel slope value of 78 mV dec^?1. The catalyst offers a maximum current density of 101 mA cm^?2, while it gives a turnover frequency (TOF) value of 0.031 s^?1 at an overpotential of 291 mV only. The excellent activity and remarkable stability of composite is attributed to highly conductive and porous support.     

Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

Wide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power system by Phasor Measurement Units (PMUs) are presented. The obtained results show that the temporal patterns can be used for extracting and identifying the low-frequency oscillation modes and the spatial patterns can be used for identifying modes with the most contribution in original data. Compared to other BSS approaches, the proposed method has shown to be better for the analysis of real ambient data.