In Situ,Fast, High‐Temperature Synthesis of Nickel Nanoparticles in Reduced Graphene Oxide Matrix

For the first time, a fast heating–cooling process is reported for the synthesis of carbon‐coated nickel (Ni) nanoparticles on a reduced graphene oxide (RGO) matrix (nano‐Ni@C/RGO) as a high‐performance H₂O₂ fuel catalyst. The Joule heating temperature can reach up to ≈2400 K and the heating time can be less than 0.1 s. Ni microparticles with an average diameter of 2 µm can be directly converted into nanoparticles with an average diameter of 75 nm. The Ni nanoparticles embedded in RGO are evaluated for electro‐oxidation performance as a H₂O₂ fuel in a direct peroxide–peroxide fuel cell, which exhibits an electro‐oxidation current density of 602 mA cm^?2 at 0.2 V (vs Ag/AgCl), ≈150 times higher than the original Ni microparticles embedded in the RGO matrix (micro‐Ni/RGO). The high‐temperature, fast Joule heating process also leads to a 4–5 nm conformal carbon coating on the surface of the Ni nanoparticles, which anchors them to the RGO nanosheets and leads to an excellent catalytic stability. The newly developed nano‐Ni@C/RGO composites by Joule heating hold great promise for a range of emerging energy applications, including the advanced anode materials of fuel cells.     

Toxicity of Artemisia annua (Asteraceae) essential oil on the tea mealy bug,Pseudococcus viburni Sigornet (Hemiptera: Pseudococcidae)

A combination of bioassay and biochemical approaches were used to determine toxicity of Artemisia annua essential oil (AaEO) Pseudococcus viburni. AaEO via leaf dipping bioassay showed LC₅₀ values of 0.693 and 0.419% after two time exposures. Different concentrations of AaEO caused deterrence index between 28.58 to 86.26% by the calculated ED₅₀ of 0.4%. Although, α-esterase activity using α-naphtyl acetate increased in the treated nymphs by AaEO after 24 hours but it showed the lower activity in the treated nymphs using β-naphtyl acetate. Glutathione S-transferase assayed by CDNB showed the higher activity in the treated nymphs than control after 24 hours while the adverse results gained not only after 48 hours but also after 24 hours by using DCNB. No significant differences were found in the activity of alanine aminotransferase versus control, but aspartate aminotransferase and γ-glutamyl transferase showed the statistically higher activities in the treated nymphs in comparison with control. Activities of aldolase and lactate dehydrogenase were significantly lower than those of control. Only acid phosphatase showed the significantly altered activity in the treated nymphs in comparison with control after 24 hours. Results of our study indicated significant toxicity, deterrence and physiological effects of AaEO on P. viburni.     

Cardioprotective role of extracellular vesicles: A highlight on exosome beneficial effects in cardiovascular diseases

Extracellular vesicles (EVs) are nano-sized vesicles, released from many cell types including cardiac cells, have recently emerged as intercellular communication tools in cell dynamics. EVs are an important mediator of signaling within cells that influencing the functional behavior of the target cells. In heart complex, cardiac cells can easily use EVs to transport bioactive molecules such as proteins, lipids, and RNAs to the regulation of neighboring cell function. Cross-talk between intracardiac cells plays pivotal roles in the heart homeostasis and in adaptive responses of the heart to stress. EVs were released by cardiomyocytes under baseline conditions, but stress condition such as hypoxia intensifies secretome capacity. EVs secreted by cardiac progenitor cells and cardiosphere-derived cells could be pinpointed as important mediators of cardioprotection and cardiogenesis. Furthermore, EVs from many different types of stem cells could potentially exert a therapeutic effect on the damaged heart. Recent evidence shows that cardiac-derived EVs are rich in microRNAs, suggesting a key role in the controlling of cellular processes. EVs harboring exosomes may be clinically useful in cell-free therapy approaches and potentially act as prognosis and diagnosis biomarkers of cardiovascular diseases.     

Utilizing the Metallic Nano-Rods in Hexagonal Configuration to Enhance Sensitivity of the Plasmonic Racetrack Resonator in Sensing Application

A high sensitive plasmonic refractive index sensor based on metal-insulator-metal (MIM) waveguides with embedding metallic nano-rods in racetrack resonator has been proposed. The refractive index changes of the dielectric material inside the resonator together with temperature changes can be acquired from the detection of the resonance wavelength, based on their linear relationship. With optimum design and considering a tradeoff among detected power, structure size, and sensitivity, the finite difference time domain simulations show that the refractive index and temperature sensitivity values can be obtained as high as 2610 nm per refractive index unit (RIU) and 1.03 nm/°C, respectively. In addition, resonance wavelengths of resonator are obtained experimentally by using the resonant conditions. The effects of nano-rods radius and refractive index of racetrack resonator are studied on the sensing spectra, as well. The proposed structure with such high sensitivity will be useful in optical communications that can provide a new possibility for designing compact and high-performance plasmonic devices.     

Testing the Equality of Dependent Variances

The likelihood ratio test for testing equality of vgE;2 correlated variables is developed. In general, evaluation of the test statistic involves the iterative optimization of a likelihood function with 1 + v(v – 1)/2 parameters. The explicit form of the test statistic is derived in the bivariate case, and an iterative algorithm for determining the maximum likelihood estimates is suggested. A limited Monte Carlo study determines the behavior of the proposed procedure under the null hypothesis and variety of parameter values.     

Study of the Effect of Surfactants on Extraction and Determination of Polyphenolic Compounds and Antioxidant Capacity of Fruits Extracts

Micelle/water mixed solutions of different surface active agents were studied for their effectiveness in the extraction of polyphenolic compounds from various varieties of apples from west Azerbaijan province in Iran. The total content of polyphenolic compound in fruit extracts were determined using ferrous tartrate and Folin–Ciocalteu assays methods and chromatographic methods and compared with theme. High performance liquid chromatography is one of the most common and important methods in biochemical compound identification. The effect of pH, ionic strength, surfactant type, surfactant concentration, extraction time and common organic solvent in the apple polyphenolics extractions was studied using HPLC-DAD. Mixtures of surfactants, water and methanol at various ratios were examined and micellar-water solutions of Brij surfactant showed the highest polyphenol extraction efficiency. Optimum conditions for the extraction of polyphenolic compounds from apple occurred at 7 mM Brij35, pH 3. Effect of ionic strength on extraction was determined and 2% (W/V) potassium Chloride was determined to be the optimum salt concentration. The procedure worked well with an ultrasound bath. Total antioxidant capacity also was determined in this study. The method can be safely scaled up for pharmaceutical applications.     

Targeting age‐specific changes in CD4+ T cell metabolism ameliorates alloimmune responses and prolongs graft survival

Age impacts alloimmunity. Effects of aging on T‐cell metabolism and the potential to interfere with immunosuppressants have not been explored yet. Here, we dissected metabolic pathways of CD4⁺ and CD8⁺ T cells in aging and offer novel immunosuppressive targets. Upon activation, CD4⁺ T cells from old mice failed to exhibit adequate metabolic reprogramming resulting into compromised metabolic pathways, including oxidative phosphorylation (OXPHOS) and glycolysis. Comparable results were also observed in elderly human patients. Although glutaminolysis remained the dominant and age‐independent source of mitochondria for activated CD4⁺ T cells, old but not young CD4⁺ T cells relied heavily on glutaminolysis. Treating young and old murine and human CD4⁺ T cells with 6‐diazo‐5‐oxo‐l‐norleucine (DON), a glutaminolysis inhibitor resulted in significantly reduced IFN‐γ production and compromised proliferative capacities specifically of old CD4⁺ T cells. Of translational relevance, old and young mice that had been transplanted with fully mismatched skin grafts and treated with DON demonstrated dampened Th1‐ and Th17‐driven alloimmune responses. Moreover, DON diminished cytokine production and proliferation of old CD4⁺ T cells in vivo leading to a significantly prolonged allograft survival specifically in old recipients. Graft prolongation in young animals, in contrast, was only achieved when DON was applied in combination with an inhibition of glycolysis (2‐deoxy‐d‐glucose, 2‐DG) and OXPHOS (metformin), two alternative metabolic pathways. Notably, metabolic treatment had not been linked to toxicities. Remarkably, immunosuppressive capacities of DON were specific to CD4⁺ T cells as adoptively transferred young CD4⁺ T cells prevented immunosuppressive capacities of DON on allograft survival in old recipients. Depletion of CD8⁺ T cells did not alter transplant outcomes in either young or old recipients. Taken together, our data introduce an age‐specific metabolic reprogramming of CD4⁺ T cells. Targeting those pathways offers novel and age‐specific approaches for immunosuppression.     

An Evidence-Based Combining Classifier for Brain Signal Analysis

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.