Uncoupling tumor necrosis factor-α and interleukin-10 at tumor immune microenvironment of breast cancer through miR-17-5p/MALAT-1/H19 circuit

Triple Negative Breast Cancer (TNBC) immunotherapy has recently shown promising approach. However, some TNBC patients presented with resistance. One of the reasons was attributed to the excessive release of cytokines at the tumor microenvironment (TME) such as Tumor necrosis factor alpha (TNF-α) and Interleukin-10 (IL-10). Fine regulation of these cytokines’ levels via non-coding RNAs (ncRNAs) might alleviate the immune quiescent nature of TME at TNBC tumors. However, the extrapolation of ncRNAs as therapeutic tools is highly challenging. Therefore, disentanglement the nature for the isolation of natural compounds that could modulate the ncRNAs and their respective targets is an applicable translational therapeutic approach. Hence, this study aimed to targeting the chief immune suppressive cytokines at the TME (TNF-α and IL-10) via ncRNAs and to examine the effects of Rosemary aerial parts extract on the expression levels of these ncRNAs in TNBC. Results revealed miR-17-5p as a dual regulator of TNF-α and IL-10. Moreover, an intricate interaction has been shown between miR-17-5p and the oncogenic lncRNAs: MALAT1 and H19. Knocking down of MALAT1 and/or H19 caused an induction in miR-17-5p and reduction in TNF-α and IL-10 expression levels. miR-17-5p was found to be down-regulated, while TNF-α, IL-10, MALAT1 and H19 were up-regulated in BC patients. Forced expression of miR-17-5p in MDA-MB-231 cells reduced TNF-α, IL-10, MALAT1 and H19 expression levels, as well as several BC hallmarks. In a translational approach, ursolic acid (UA) isolated from rosemary induced the expression of miR-17-5p, MALAT1 and decreased H19 expression levels. In conclusion, this study suggests miR-17-5p as a tumor suppressor and an immune-activator miRNA in BC through tuning up the immunological targets TNF-α, IL-10 at the TME and the oncological mediators MALAT1 and H19 lncRNAs.     

Cavity cutting efficiency of a Bioglass<Superscript>TM</Superscript> and alumina powder combination utilized in an air abrasion system

This study investigated the attempt to replace alumina in the air abrasion system with an alternative material that is effective at cutting and also has remineralization potential. The powder samples were randomized into three groups: group 1—alumina (composed of aluminium and oxygen), group 2—45S5 (composed of 45% silica, 24.5% calcium oxide, 24.5% sodium oxide and 6% phosphorus pentoxide in weight percentage) and group 3—alumina + 45S5. Thirty human enamel blocks and microscope glass slides of 0.5 mm thickness were randomly divided into these three groups. The time taken to cut a hole through the glass slide and for the cutting of human enamel blocks was recorded, the cutting time was fixed at 15 s. The depths of the cavities were measured using a periodontal probe and the enamel blocks were then analysed by scanning electron microscope (SEM). The mean time taken to cut a hole through the microscope glass slide was 2.96, 23.01 and 3.02 s for groups 1, 2 and 3, respectively. After cutting the human enamel blocks, the mean cavity depths produced were measured to be 2.5, 1.0 and 2.0 mm for groups 1, 2 and 3, respectively. The SEM micrographs revealed that the cavities formed by 45S5 were more conical in shape, whereas cavities produced by alumina and alumina + 45S5 were more cylindrical. The combined use of alumina and 45S5 has demonstrated a promising cutting efficiency and it has the potential to achieve effective cutting with the possibility of the remineralization.     

FUZZY CLUSTERING MODELING AND EVALUATION OF SURFACE INTERACTIONS AND EMULSIONS OF SELECTED WHEAT PROTEIN COMBINED TO IOTA-CARRAGEENAN AND GUM ARABIC SOLUTIONS

Gums and proteins are valuable ingredients with a wide spectrum of applications. Surface properties (surface tension, interfacial tension, emulsion activity index [EAI] and emulsion stability index [ESI]) of 4% deamidated wheat protein (DWP) in a combination with iota-carrageenan (IC) (0.05, 0.1 and 0.5%) or gum arabic (GA) (0.5, 1 and 5%) were investigated. The results showed that the addition of IC to 4% DWP significantly increased the surface tension, interfacial tension, EAI and ESI, whereas the addition of GA to 4% DWP significantly increased the surface tension and decreased the interfacial tension and EAI (except at 5% of GA). In addition, a fuzzy-based clustering model was used to predict the surface properties of the DWP. The fuzzy model achieved accuracies of (97%, 90%, 97% and 75%) for predicting (EAI, ESI, surface tension and interfacial tension), respectively. This approach can be applied to predict many other parameters and properties in the food industry.

PRACTICAL APPLICATIONS

The surface properties (surface tension, interfacial tension, emulsion activity index and emulsion stability index) of wheat protein (DWP) in combination with iota-carrageenan or gum arabic were investigated. In addition, subtractive clustering-based fuzzy models for predicting the surface properties of DWP was constructed. This modeling can be used as an indicator of usefulness of fuzzy clustering in such a system which directly can be used as a tool by the food processors to produce a high quality beverage product.     

Concrete corrosion in wastewater systems: Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors. This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models. The models include three different types of extreme learning machines, including the standard, online sequential, and kernel extreme learning machines, in addition to the artificial neural network, classification and regression tree model, and statistical multiple linear regression model. The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models. The input variability was assessed based on two scenarios prior to the application of the predictive models. For the first assessment, the machine learning models were developed using all the available cement and concrete mixture input variables; the second assessment was conducted based on the gamma test approach, which is a sensitivity analysis technique. Subsequently, the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches. The adopted methodology attained optimistic and reliable modeling results. The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.     

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

Concrete compressive strength prediction is an essential process for material design and sustainability. This study investigates several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) evolutionary models, i.e., ANFIS–particle swarm optimization (PSO), ANFIS–ant colony, ANFIS–differential evolution (DE), and ANFIS–genetic algorithm to predict the foamed concrete compressive strength. Several concrete properties, including cement content (C), oven dry density (O), water-to-binder ratio (W), and foamed volume (F) are used as input variables. A relevant data set is obtained from open-access published experimental investigations and used to build predictive models. The performance of the proposed predictive models is evaluated based on the mean performance (MP), which is the mean value of several statistical error indices. To optimize each predictive model and its input variables, univariate (C, O, W, and F), bivariate (C–O, C–W, C–F, O–W, O–F, and W–F), trivariate (C–O–W, C–W–F, O–W–F), and four-variate (C–O–W–F) combinations of input variables are constructed for each model. The results indicate that the best predictions obtained using the univariate, bivariate, trivariate, and four-variate models are ANFIS–DE– (O) (MP= 0.96), ANFIS–PSO– (C–O) (MP= 0.88), ANFIS–DE– (O–W–F) (MP= 0.94), and ANFIS–PSO– (C–O–W–F) (MP= 0.89), respectively. ANFIS–PSO– (C–O) yielded the best accurate prediction of compressive strength with an MP value of 0.96.     

THE OPTIMAL AND STRAIGHTFORWARD DESIGN OF BANDPASS WAVE DIGITAL LATTICE FILTERS WITH ARBITRARY LOSS SPECIFICATIONS

An efficient, simple and reliable design method is introduced for bandpass wave digital lattice filters exhibiting arbitrary loss responses. The main feature of this method is that the approximation is straightforward. Thus it is carried out without the need to apply frequency transformation techniques. The method relies on employing the relationship between the amplitude and the two branch phase functions of the filter. As a consequence, the whole problem reduces finally to constructing a polynomial specified by its phase. The generation of this polynomial is achieved through the use of interpolation recurrence formulae. By factorizing this polynomial into its Hurwitz and non-Hurwitz parts, the two branch allpass functions are determined and realized. © 1997 by John Wiley & Sons, Ltd.     

ELECTROMAGNETIC FAULT CURRENT LIMITER (EMFCL)

ABSTRACT

Due to electrical power system expansion, the utility systems are faced with fault currents higher than the momentary and interrupting capabilities of the equipment installed. One method, to convert the problem, is to use a fault current limiter. The limiter must limit the peak current to a level below that of the momentary capabilities of the system components and limit the subsequent current peaks to a level which allows correct relay operation within the interrupting capabilities of the circuit breakers. This paper describe the design and modeling of a new device for limiting the fault currents. Tliis device is an electromagnetic circuit consisting of an iron core and armature with adjustable air gap. With load current, the device has minimum impedance at the supply frequency. During fault conditions, the fault current is used to give the operating force needed to change the device inductance to the maximum impedance. The fault current limiter is successfully modeled using a digital computer and validated in the laboratory.     

ON-LINE FAULT LEVEL MEASUREMENT

ABSTRACT

The fault level represents the maximum short circuit current that a power system causes to flow to the point of short circuit. Due to change of network configuration, the fault level at any point of the power system network changes accordingly

It is not practical to apply full short circuit on the system in order to determine the fault level. This paper describes scheme for continuous measuring of the fault level at any point of the power system network. This scheme is composed of an antiparallel thyristor pair. The thyristor current, which is controlled by the firing angle, can be used to perform the on-line fault level measurement

However, problems are associated with such measurement. These are mainly the complexity of the current waveform and its dependence on the system impedance. The effect of the system X/R ratio on the measurement is also analyzed.     

Thermal processing requirements of canned chickpea dip

The thermal process requirements in still retorts were determined for various processing conditions (two can sizes, two processing and four filling temperatures), based on a sterilization value, F_o= 2.78 min, and following both general and nomogram methods; the methods gave differences in the total processing time between 1.2 and 4.2 min. Viscosity decreased significantly but incubation, colour measurement and sensory evaluation of the products showed no statistically significant differences between any of the evaluated processing schedules; however for economic considerations the recommended process requirements for small and medium size cans were 32.9 and 57.2 min respectively at 121°C, with a filling temperature of 85°C.