Investigating the in vitro photothermal effect of green synthesized apigenin‐coated gold nanoparticle on colorectal carcinoma

Applying toxic chemical to the synthesis of stable gold nanoparticles is one of the limitations of gold nanoparticles for therapeutic applications such as photothermal therapy. Plant compounds such as apigenin (API) with therapeutic potential can be applied in the synthesis of gold nanoparticles. API‐coated gold nanoparticles (Api@AuNPs) with an average size of 19.1 nm and a surface charge of −4.3 mV have been synthesized by a simple and efficient technique. The stability of Api@AuNPs in the biological environment was verified through UV‐Vis spectroscopy. Based on Raman and FTIR spectroscopy analysis, chemical binding of API on the surface of Api@AuNPs through hydroxyl and carbonyl functional groups was found to be the main reason for the stability of the Api@AuNPs in comparison with citrate‐coated gold nanoparticles (Cit@AuNPs). The synthesized Api@AuNPs do not cause major toxic effects up to 128 ppm. Api@AuNP‐mediated photothermal therapy leads to the indiscriminate eradication of almost half of both mouse fibroblastic (L929) and colorectal cancer (CT26) cells. Flow‐cytometry analysis revealed that the cell death mechanism is mainly apoptosis. In the apoptosis triggered cell death in photothermal treatment, Api@AuNPs are preferred over commonly used gold nanoparticles in photothermal treatments which mostly trigger the necrosis cell death pathway.     

A New Method for Clustering Based on Development of Imperialist Competitive Algorithm

Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters.K-means is one of the popular clustering algorithms that,despite its inherent simplicity,has also some major problems.One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering.In this study,the Imperialist Competitive Algorithm（ICA） is developed and then used in the clustering process.Clustering of IRIS,Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA,GA and PSO algorithms,demonstrate the improvement of Imperialist competitive algorithm.     

Classification of normal/abnormal PCG recordings using a time–frequency approach

The early and accurate diagnosis of cardiovascular diseases (CVDs) are of great importance as they allow early and proper medical treatment and therefore result in reducing the chance of the CVDs being developed to an acute level. In medical procedures, the first step in examining the cardiovascular function is the auscultation of the heart. However, the correct medical diagnosis based on the heart sounds through a stethoscope requires a lot of expertise and, in some cases, needs referral of the patient to a cardiologist. This is not only time-consuming but also imposes a financial burden on the medical system. Thus, automated detection and analysis of the recorded heart sound auscultation has received a lot of attentions in recent years. This study presents a new time–frequency (T–F) based approach for classifying phonocardiogram (PCG) signals into normal and abnormal. In the proposed methodology, each PCG recording is first segmented into the 4 fundamental heart cycles, i.e. S1, systole, S2, and diastole. From each state, a set of T–F features are extracted with the aim of identifying their characteristics in the T–F domain. The features are then applied to a support vector machine to classify the PCG signal into normal or abnormal. The performance of the proposed method is evaluated using the 2016 PhysioNet challenge database and compared with that of the best performing existing methods. The experimental results using tenfold cross-validation show that the proposed method outperforms the existing methods in terms of sensitivity, specificity, and accuracy.

     

Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions

This paper discusses the use of artificial neural network (ANN) models for predicting daily flows from Khosrow Shirin watershed located in the northwest part of Fars province in Iran. A Multi-Layer Perceptron (MLP) neural network was developed using five input vectors leading to five ANN models: MLP1, MLP2, MLP3, MLP4, and MLP5. Two activation functions were used and they were logistic sigmoid and tangent sigmoid. The MLP_Levenberg–Marquardt (LM) algorithm was used for the training of ANN models. A 5-year data record, selected randomly, was used for ANN training and testing. The predicted outflow showed that the tangent sigmoid activation function performed better than did the logistic sigmoid activation function. The values of R ² and RMSE for MLP4 with the tangent sigmoid activation function for the validation period were equal to 0.89 and 1.7 m³/s, respectively. Appropriate input vectors for MLPs were determined by correlation analysis. It was found that antecedent precipitation and discharge with 1 day time lag as an input vector best predicted daily flows. Also, comparison of MLPs showed that an increase in input data was not always useful.     

Modeling groundwater nitrate concentrations using spatial and non-spatial regression models in a semi-arid environment

Nitrate nitrogen (NO₃^--N) from agricultural activities and in industrial wastewater has become the main source of groundwater pollution, which has raised widespread concerns, particularly in arid and semi-arid river basins with little water that meets relevant standards. This study aimed to investigate the performance of spatial and non-spatial regression models in modeling nitrate pollution in a semi-intensive farming region of Iran. To perform the modeling of the groundwater's NO₃^--N concentration, both natural and anthropogenic factors affecting groundwater NO₃^--N were selected. The results of Moran's I test showed that groundwater nitrate concentration had a significant spatial dependence on the density of wells, distance from streams, total annual precipitation, and distance from roads in the study area. This study provided a way to estimate nitrate pollution using both natural and anthropogenic factors in arid and semi-arid areas where only a few factors are available. Spatial regression methods with spatial correlation structures are effective tools to support spatial decision-making in water pollution control.     

A Selective,Hydrogel-Based Prodrug Delivery System Efficiently Activates a Suicide Gene to Remove Undifferentiated Human Stem Cells Within Neural Grafts

The directed differentiation of human pluripotent stem cells (hPSCs) into defined populations has advanced regenerative medicine, especially for Parkinson's disease where clinical trials are underway. Despite this, tumorigenic risks associated with incompletely patterned and/or quiescent proliferative cells within grafts remain. Addressing this, donor stem cells carrying the suicide gene, thymidine kinase (activated by the prodrug ganciclovir, GCV), are employed to enable the programmed ablation of proliferative cells within neural grafts. However, coinciding the short half-life of GCV with the short S-phase of neural progenitors is a key challenge. To overcome this, a smart hydrogel delivery matrix is fabricatedto prolong GCV presentation. Following matrix embedment, GCV retains its functionality, demonstrated by ablation of hPSCs and proliferating neural progenitors in vitro. A prolonged GCV release is measured by mass spectrometry following the injection of a GCV-functionalized hydrogel into mouse brains. Compared to suboptimal, daily systemic GCV injections, the intracerebral delivery of the functionalized hydrogel, as a “one-off treatment”, reduce proliferative cells in both hPSC-derived teratomas and neural grafts, without affecting the graft's functional unit (i.e., neurons). It is demonstrated that a functionalized biomaterial can enhance prodrug delivery and address safety concerns associated with the use of hPSCs for brain repair.     

Mapping out switching techniques

Several techniques can be used to minimize Boolean functions; some are more useful than others. This article deals with a computer program designed for two such techniques: the Karnaugh map and Quine-McCluskey (1952). The program demonstrates 24 switching variables. It provides a visual representation of the Karnaugh map technique for the user who wishes it. The second technique, Quine-McCluskey, provides the user with a list of the prime implicants. The architecture of digital computers, especially those for microcomputers, uses a design technique essential to any digital machine. This technique is referred to as digital logic. It includes the mathematics of Boolean algebra that generates Boolean equations, the topology and interaction of logic circuits, and the methods of minimizing logical equations. Boolean algebra is considered the basis of nearly all design and analysis methods in digital systems. The Boolean function can be described using various models. A few of these methods may be considered successful, but most have severe limitations. They all become unmanageable as the number of switching variables becomes larger     

Soft computing and fuzzy logic

Discusses soft computing, a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind     

Computer program for the mass and heat balance calculations for the wet process phosphoric acid (hemi-hydrate) and its applications to Saudi phosphate rock

A computer program is developed for the wet phosphoric acid process using Saudi phosphate ore. The program concentrates on the heart of the process that is the acidulation crystallization step. The key parameters of the program are obtained from micropilot plant continuous experiments performed at our laboratory using different types of Saudi phosphate ores with different chemical, physical and minerological properties. The program is used to correlate the main operating and design parameters with the ore measurable characteristics.     

Phase equilibrium data and liquidus for the system “MnO”-CaO-(Al2O3 + SiO2) at Al2O3/SiO2 = 0.41

Phase-equilibrium data and the liquidus for the system “MnO”-CaO-(Al₂O₃-SiO₂) at a manganese-rich alloy saturation have been determined in the temperature range from 1423 to 1723 K. The results are presented in the form of a pseudoternary section “MnO”-CaO-(Al₂O₃ + SiO₂) with an Al₂O₃/SiO₂ weight ratio of 0.41. The following primary phases are present in the range of conditions investigated: 3Al₂O₃·2SiO₂; SiO₂; MnO·Al₂O₃·2SiO₂; (Mn,Ca)O·SiO₂; 2(Mn,Ca)O·SiO₂; MnO·Al₂O₃; (Mn,Ca)O; α-2CaO·SiO₂; α′-2CaO·SiO₂; 2CaO·Al₂O₃·SiO₂; CaO·SiO₂, and CaO·Al₂O₃·2SiO₂. The presence of alumina in this system is shown to have a significant effect on the liquidus compared to the system “MnO”-CaO-SiO₂, leading to the stabilization of the anorthite and gehlenite phases.