Comprehensive Identification of Immunodominant Proteins of Brucella abortus and Brucella melitensis Using Antibodies in the Sera from Naturally Infected Hosts

Brucellosis is a debilitating zoonotic disease that affects humans and animals. The diagnosis of brucellosis is challenging, as accurate species level identification is not possible with any of the currently available serology-based diagnostic methods. The present study aimed at identifying Brucella (B.) species-specific proteins from the closely related species B. abortus and B. melitensis using sera collected from naturally infected host species. Unlike earlier reported investigations with either laboratory-grown species or vaccine strains, in the present study, field strains were utilized for analysis. The label-free quantitative proteomic analysis of the naturally isolated strains of these two closely related species revealed 402 differentially expressed proteins, among which 63 and 103 proteins were found exclusively in the whole cell extracts of B. abortus and B. melitensis field strains, respectively. The sera from four different naturally infected host species, i.e., cattle, buffalo, sheep, and goat were applied to identify the immune-binding protein spots present in the whole protein extracts from the isolated B. abortus and B. melitensis field strains and resolved on two-dimensional gel electrophoresis. Comprehensive analysis revealed that 25 proteins of B. abortus and 20 proteins of B. melitensis were distinctly immunoreactive. Dihydrodipicolinate synthase, glyceraldehyde-3-phosphate dehydrogenase and lactate/malate dehydrogenase from B. abortus, amino acid ABC transporter substrate-binding protein from B. melitensis and fumarylacetoacetate hydrolase from both species were reactive with the sera of all the tested naturally infected host species. The identified proteins could be used for the design of serological assays capable of detecting pan-Brucella, B. abortus- and B. melitensis-specific antibodies.     

Toward an intelligent approach for predicting surface tension of binary mixtures containing ionic liquids

Knowledge of the surface tension of ionic liquids (ILs) and their related mixtures is of central importance and enables engineers to efficiently design new processes dealing with these fluids on an industrial scale. It’s obvious that experimental determination of surface tension of every conceivable IL and its mixture with other compounds would be a herculean task. Besides, experimental measurements are intrinsically laborious and expensive; therefore, accurate prediction of the property using a reliable technique would be overwhelmingly favorable. To do so, a modeling method based on artificial neural network (ANN) trained by Bayesian regulation back propagation training algorithm (trainbr) has been proposed to predict surface tension of the binary ILs mixtures. A total set of 748 data points of binary surface tension of IL systems within temperature range of 283.1-348.15 K was used to train and test the applied network. The obtained results indicated that the predictive values and experimental data are quite matching, representing reliability of the used ANN model for such purpose. Also, compared with other methods, such as SVM, GA-SVM, GA-LSSVM, CSA-LSSVM, GMDH-PNN and ANN trained with trainlm algorithm the proposed model was better in terms of accuracy.     

A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system

In a supply chain, scheduling plays a significant role in coordinating and cooperation. This article considers an integration of supplier and vehicle scheduling problems in terms of vehicle routing determination for transporting raw materials from the suppliers to some manufacturing centres. The aim is to minimize the total tardiness of all assigned orders to the suppliers and simultaneously minimize the total travelled distance of the vehicles. Most manufacturing companies, which have to manage their suppliers as an industrial unit, experience this problem. A new metaheuristic algorithm called the multiple league championship algorithm (MLCA), inspired by championship matches, is proposed to solve this problem. To show the efficiency of MLCA, it is compared with two different algorithms used for the problems in the literature that are closest to this problem and a soccer-based algorithm called golden ball. The experimental results prove that the proposed algorithm has better performance than these algorithms.     

Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality

Barriers to use health related quality of life measuring systems include the time needed to complete the forms and the need for staff to be trained to understand the results. An ideal system of health assessment needs to be clinically useful, timely, sensitive to change, culturally sensitive, low burden, low cost, involving for the patient and built into standard procedures. A new generation of short and easy-to-use tools to monitor patient outcomes on a regular basis has been recently proposed. These tools are quick, effective and easy to understand, as they are very structured and rigid. Such structuredness, however, leaves no space to those patients who would like to say something more. Patients, in fact, are usually willing to express their opinions and feelings in free text, rather than simply filling in a questionnaire, for either speaking out their satisfaction or for cathartic complaining. Sentic PROMs allow patients to evaluate their health status and experience in a semi-structured way and accordingly aggregate input data by means of sentic computing, while tracking patients’ physio-emotional sensitivity.     

Dielectric abnormality and high-permittivity microwave dielectric properties of SrO-TiO2-CeO2 solid solution

The SrO-TiO₂-CeO₂ (Sr_1?1.5×Ce_xTiO₃, SCTO, 0?≤?x?≤?0.2, sintered in N₂) solid solution exhibited the existence of dielectric abnormality/anomaly (for polished samples) and high-permittivity microwave dielectric properties (for unpolished samples). X-ray diffraction (XRD) and Rietveld refinement, along with high-resolution transmission electron microscopy (HRTEM), indicated the evidence of cubic like structure. The SEM-EDX maps demonstrated the formation of a complete solid solution, which further support the XRD results. X-ray photoelectron spectroscopy (XPS) analysis showed mixture of ion valence states upon lattice defects formation. The activation of the TO2/TiO4 polar bands usually described a relaxor-type-dielectric anomaly. The ε′-T curve, together with the polar nature measurements exhibited hysteresis loops, indicating that ceria ions induced weak relaxor behavior. The observed Q×f values were primarily dependent on the lattice defects and Ti³⁺ cations. The temperature coefficient of resonant frequency (${\tau }_f$) shifted gradually from more positive (+1321?ppm/°C) to less positive (+539?ppm/°C) values with a rise of Ce content (x). The unpolished sample with x?=?0.2 exhibited a high permittivity microwave dielectric properties with ε_r =?182, ${\tau }_f$?=?+539?ppm/°C, and Q×f =?668?GHz.     

Prediction of gas hydrate formation in the presence of methanol,ethanol, (ethylene,diethylene and triethylene) glycol thermodynamic inhibitors

Hydrate formation is a serious, fundamental problem in the blocking of process equipment and pipelines in gas transportation pipelines. The objective of the present work is the simulation of the pipelines in the 15th and 16th phases of South Pars and investigation of operating conditions for hydrate formation. The effects of injecting various thermodynamic inhibitors on the hydrate formation conditions have also been studied. According to the simulation results, investigation of the operational reasons and the cost of these compounds, pure methanol and monoethylene glycol were determined as the best inhibitors for gas flow rates of 500 and 1000 mmscfd.     

Hydrogen Production from Co‐Gasification of Coal and Biomass in the Presence of CaO as a Sorbent

Among the options for clean energy production, the gasification process is receiving increasing attention as it offers the best combination of investment and value of produced electricity compared to other methods. An Aspen Plus model of co‐gasification of biomass and coal with in situ CO₂ capture was developed to evaluate its potential for hydrogen production and cracking of organic impurities, i.e., tars. The effects of some critical operational variables on gas composition and yields of hydrogen gas and tar were investigated. The obtained results indicate that the fuel particle size plays a minor role in the process; smaller particles favor the conversion of tar and production of more hydrogen gas.     

Tunable optical,electronic and magnetic properties of semiconductor nanoparticles induced by magnetic and nonmagnetic dopants: A comparative experimental and theoretical study

While combining semiconductor and magnetic properties at the nanoscale provides dilute magnetic semiconductor (DMS) nanomaterials with a wide range of applications in next-generation electronic devices, tuning DMS properties still presents a challenge. Here, the synthesis of pure ZnO and transition metal (TM)-doped ZnO nanoparticles (NPs) with different magnetic (Fe and Co) and nonmagnetic (Mn) dopant concentrations (ranging from 2% to 10%) is reported using a co-precipitation method. Introducing the TM-dopants into ZnO NPs with 35?nm wurtzite structure causes crystallite and mean NP sizes to decrease, as characterized by X-ray diffraction and field-emission scanning electron microscopic analyses. Room-temperature magnetic measurements indicate coexistence of paramagnetic and ferromagnetic phases with tunability in the resulting TM-doped NPs. The maximum ferromagnetic coercivity and saturation magnetization are found to be 89?Oe and 0.074?emu/g for 10% Fe-doped ZnO NPs. UV–visible spectra showed a blue shift with increasing the dopant concentration, being in agreement with increasing trend in band gap energy calculated from band structure and density of state of TM-doped ZnO nanocrystal systems.     

Synthesis of nanosized SiC by low-temperature magnesiothermal reduction of nanocomposites of functionalized carbon nanotubes with MCM-48

Silicon carbide synthesis by a magnesiothermal method was investigated using MCM-48 as the silica source mechanically mixed with carbon nanotubes (CNTs) as the carbon source, and nanocomposites of MCM-48/functionalized CNTs (CNTF). SiC syntheses were carried out with different molar ratios of MCM-48, carbon and magnesium at 700?°C in argon. The MCM-48 and carbon nanotube starting materials and the SiC products were characterized by BET, XRD, FESEM, EDX and TEM. The effect of the carbon content and the type of CNTs (either functionalized or unfunctionalized) on the SiC synthesis was studied. The results show that an improved yield of SiC is obtained when the carbon nanotubes are functionalized, producing a better contact with the MCM-48. This improved contact between the reactants ensures a good degree of reaction in a stoichiometric mixture of silicon and carbon, with no improvement in product formation being achieved by the use of additional carbon. These findings suggest that the degree of contact between reactants is an important factor in the magnesiothermal synthesis of SiC. The SiC products from magnesiothermal synthesis of the functionalized nanocomposite precursors were shown by TEM and FESEM to have unusual nanofiber morphologies mimicking the morphology of the CNTF nanotubes.     

Modeling ductile to brittle transition temperature of functionally graded steels by fuzzy logic

In this article, a model based on fuzzy logic (FL) for predicting ductile to brittle transition temperature of functionally graded steels in both crack divider and crack arrester configurations has been presented. Functionally graded steels containing graded ferritic and austenitic regions together with bainite and martensite intermediate layers were produced by electroslag remelting. For purpose of building the model, training and testing using experimental results from 140 specimens produced from two basic composites were conducted. The used data as inputs in FL models are arranged in a format of six input parameters that cover the FGS type, the crack tip configuration, the thickness of graded ferritic region, the thickness of graded austenitic region, the distance of the notch from bainite or martensite intermediate layer, and temperature. According to these input parameters, in the FL, the ductile to brittle transition temperature of each FGS specimen was predicted. It has been found that FL model will be valid within the ranges of variables. The training and testing results in the FL model have shown a strong potential for predicting the ductile to brittle transition temperature of each FGS specimen.