Effect of nitrogen on the antibacterial behavior of oxynitride glasses

The main drawback of bioglasses is their restricted use in load bearing applications and the consequent need to develop stronger glassy materials. This has led to the consideration of oxynitride glasses for numerous biomedical applications. This paper investigated two different types of glasses at a constant cationic ratio, with and without nitrogen (a N containing and a N-free glass composition) to better understand the effect of N on the biological properties of glasses. The results revealed that the addition of N increased the glass transition temperature, isoelectric point (IEP) and slightly increased wettability. Moreover, compared to N including glass, N-free glass exhibited better anti-bacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), two key bacteria that infect implants. In summary, these in vitro results indicated that amine functional groups existing in N containing glasses which are missing in N-free glasses, caused a slight difference in wetting behavior and a more obvious change in isoelectric point and in bacterial response. N-free glasses exhibited better inhibitory results both against E. coli and S. aureus compared to N including glass suggesting that oxygen rich glasses should be further studied for their novel antibacterial properties.     

Design and Characterization of a New pVII Combinatorial Phage Display Peptide Library for Protease Substrate Mining Using Factor VII Activating Protease (FSAP) as Model

We describe a novel, easy and efficient combinatorial phage display peptide substrate-mining method to map the substrate specificity of proteases. The peptide library is displayed on the pVII capsid of the M13 bacteriophage, which renders pIII necessary for infectivity and efficient retrieval, in an unmodified state. As capture module, the 3XFLAG was chosen due to its very high binding efficiency to anti-FLAG mAbs and its independency of any post-translational modification. This library was tested with Factor-VII activating protease (WT-FSAP) and its single-nucleotide polymorphism variant Marburg-I (MI)-FSAP. The WT-FSAP results confirmed the previously reported Arg/Lys centered FSAP cleavage site consensus as dominant, as well as reinforcing MI-FSAP as a loss-of-function mutant. Surprisingly, rare substrate clones devoid of basic amino acids were also identified. Indeed one of these peptides was cleaved as free peptide, thus suggesting a broader range of WT-FSAP substrates than previously anticipated.     

On the design considerations of solid-state power amplifiers for satellite communications: A systems perspective

Conventional solid-state power amplifier (SSPA) design approach isolates radio frequency (RF) design from communication theory. In this paper, a unified SSPA design approach is proposed, which optimizes SSPA parameters (bias voltage and input RF signal power) to minimize total DC power consumption while satisfying received SNR constraint specified by the link budget. The effect of SSPA nonlinearity is quantified by the error vector magnitude measured at its output and the corresponding received SNR degradation is analyzed. Using the quantitative metrics for received SNR, it is possible to evaluate highly nonlinear SSPA classes such as Class-B or deep-Class AB, which are normally not considered in conventional SSPA design approach to be used in satellite communication applications.     

In Situ Electrical Network Activation and Deactivation in Short Carbon Fiber Composites via 3D Printing

Prior studies on carbon-filler based, conductive polymer composites have mainly investigated how conductive filler morphology and concentration can tailor a material's electrical conductivity and overlooks the effects of filler alignment due to the difficulty to control and quickly quantify the filler alignment. Here, direct ink write 3D printing's unique ability is utilized to control carbon fiber alignment with a single process parameter, velocity ratio, to instantaneously activate or deactivate the electrical network in composites. Maximum electrical conductivity is achieved by randomly aligning carbon fibers that enhances the chance of direct fiber-to-fiber contact and, thus, activating the electrical network. However, aligning the fibers by increasing the velocity ratio disrupts the electrical network by minimizing fiber-to-fiber contact that resulted in a drastic decrease in electrical conductivity by as much as five orders of magnitude in both short and long carbon fiber composites. With this study, this study demonstrates that electrically conductive or insulative composites can be fabricated sequentially with a single ink. This novel ability to instantaneously control the electrical conductivity of carbon fiber reinforced composites allow to directly embed conductive pathways into designs to 3D print multifunctional composites that are capable of localized heating and self-sensing.     

Nigella sativa oil entrapped polycaprolactone nanoparticles for leishmaniasis treatment

This study is the first to investigate the antileishmanial activities of Nigella sativa oil (NSO) entrapped poly‐ɛ ‐caprolactone (PCL) nanoparticles on Leishmania infantum promastigotes and amastigotes in vitro. NSO molecules with variable initial doses of 50, 100, 150, and 200 mg were successfully encapsulated into PCL nanoparticles identified as formulations NSO1, NSO2, NSO3, and NSO4, respectively. This process was characterised by scanning electron microscope, dynamic light scattering, Fourier transform infrared, encapsulation efficiency measurements, and release profile evaluations. The resulting synthetised nanoparticles had sizes ranging between 200 and 390 nm. PCL nanoparticles encapsulated 98% to 80% of initial doses of NSO and after incubation released approximately 85% of entrapped oil molecules after 288 h. All investigated formulations demonstrated strong antileishmanial effects on L. infantum promastigotes by inhibiting up to 90% of parasites after 192 h. The tested formulations decreased infection indexes of macrophages in a range between 2.4‐ and 4.1‐fold in contrast to control, thus indicating the strong anti‐amastigote activities of NSO encapsulated PCL nanoparticles. Furthermore, NSO‐loaded PCL nanoparticles showed immunomodulatory effects by increasing produced nitric oxide amounts within macrophages by 2–3.5‐fold in contrast to use of free oil. The obtained data showed significant antileishmanial effects of NSO encapsulated PCL nanoparticles on L. infantum promastigotes and amastigotes.Inspec keywords: antibacterial activity, drug delivery systems, nanofabrication, nitrogen compounds, nanomedicine, microorganisms, cellular biophysics, diseases, scanning electron microscopy, oils, polymers, biomedical materials, nanoparticles, encapsulation, Fourier transform infrared spectraOther keywords: encapsulation efficiency measurements, entrapped oil molecules, investigated formulations, NSO‐loaded PCL nanoparticles, Nigella sativa oil entrapped polycaprolactone nanoparticles, antileishmanial activities, poly‐ε‐caprolactone nanoparticles, scanning electron microscope, DLS, Fourier transform infrared, release profile evaluations, Leishmania infantum promastigotes, Leishmania infantum amastigotes, parasites, infection, infection indexes, macrophages, immunomodulatory effects, time 288.0 hour, time 192.0 hour, mass 50.0 mg, mass 100.0 mg, mass 150.0 mg, mass 200.0 mg, size 200.0 nm to 390.0 nm     

Modelling of residual stresses in the shot peened material C-1020 by artificial neural network

This study consists of two cases: (i) The experimental analysis: Shot peening is a method to improve the resistance of metal pieces to fatigue by creating regions of residual stress. In this study, the residual stresses induced in steel specimen type C-1020 by applying various strengths of shot peening, are investigated using the electrochemical layer removal method. The best result is obtained using 0.26 mm A peening strength and the stress encountered in the shot peened material is ?276 MPa, while the maximum residual stress obtained is ?363 MPa at a peening strength of 0.43 mm A. (ii) The mathematical modelling analysis: The use of ANN has been proposed to determine the residual stresses based on various strengths of shot peening using results of experimental analysis. The back-propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R² values are 0.996112 and 0.99896 for annealed before peening and shot peened only, respectively. Similarly, these values for testing data are 0.995858 and 0.999143, respectively. As seen from the results of mathematical modelling, the calculated residual stresses are obviously within acceptable uncertainties.     

Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques

The purpose of this article is to demonstrate the use of feedforward neural networks (FFNNs), adaptive neural fuzzy inference systems (ANFIS), and probabilistic neural networks (PNNs) to discriminate between earthquakes and quarry blasts in Istanbul and vicinity (the Marmara region). The tectonically active Marmara region is affected by the Thrace-Eski?ehir fault zone and especially the North Anatolian fault zone (NAFZ). Local MARNET stations, which were established in 1976 and are operated by the Kandilli Observatory and Earthquake Research Institute (KOERI), record not only earthquakes that occur in the region, but also quarry blasts. There are a few quarry-blasting areas in the Gaziosmanpa?a, Çatalca, Ömerli, and Hereke regions. Analytical methods were applied to a set of 175 seismic events (2001-2004) recorded by the stations of the local seismic network (ISK, HRT, and CTT stations) operated by the KOERI National Earthquake Monitoring Center (NEMC). Out of a total of 175 records, 148 are related to quarry blasts and 27 to earthquakes. The data sets were divided into training and testing sets for each region. In all the models developed, the input vectors consist of the peak amplitude ratio (S/P ratio) and the complexity value, and the output is a determination of either earthquake or quarry blast. The success of the developed models on regional test data varies between 97.67% and 100%.     

Estimation of target station data using satellite data and deep learning algorithms

In this study, an innovative model has been developed for wind speed estimation through the Deep Learning method using hourly wind speed data from the measurement stations of the General Directorate of Meteorology in Van and Hakkari provinces in Turkey in conjunction with simultaneous satellite images from Eumetsat. Obtained satellite images were used during the introduction of the model, while wind speed data were used at the output stage. As a result of the findings, it was found that 85% accuracy performance could be achieved to provide sufficient insight for systems that are widely established worldwide. The model, developed as a result of the study, eliminates the need to install wind measuring stations for any region on earth within the satellite field in terms of determining wind potential. Since the field of view of the Meteosat 7 satellite covers the whole of Eastern Europe, it was determined that it could predict a high rate of up to 6 hours later by the method used in image analysis. The systems to be controlled with this method will be able to examine the weather events instantly at each point in the satellite field of view and make more accurate decisions. Also, companies will be able to perform a more detailed and rapid field scan compared to existing limited methods, and reduce initial investment costs and operating costs in terms of renewable energy resources investments.     

Comparison on efficiency factors of F and C types of fly ashes

Fly ashes are obtained from thermal power plants and they are pozzolanic materials, which can act as partial replacement material for both portland cement and fine aggregate. With their economical advantages and potential for improving fresh and hardened concrete performance, they have some benefits for using in concrete industry. In this study, the objective was to find the efficiency factors of Turkish C and F-type fly ashes and to compare their properties. Three different cement dosages were used (260, 320, 400 kg/m³), two different ratios (10% and 17%) of cement reduced from the control concretes and three different ratios (depending on cement reduction ratio) of fly ash were added into the mixtures. At the ages of 28 and 90 days, compressive strength, modulus of elasticity and ultrasound velocity tests were carried out. From the compressive strength results, the k efficiency factors of C and F-type fly ashes were obtained. As a result, it is seen that efficiency factors of the concrete produced by the replacement of F and C type fly ashes with cement increase with the increase in cement dosage and concrete age.