A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

The Journal of Supercomputing - Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields...     

Model-based test case generation and prioritization: a systematic literature review

Software and Systems Modeling - Model-based test case generation (MB-TCG) and prioritization (MB-TCP) utilize models that represent the system under test (SUT) for test generation and...     

Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs

Oxidation of raw water with chlorine results in formation of trihalomethanes (THM) and haloacetic acids (HAA). Factors affecting their concentrations have been found to be organic matter type and concentration, pH, temperature, chlorine dose, contact time and bromide concentration, but the mechanisms of their formation are still under investigation. Within this scope, chlorination experiments have been conducted with water reservoirs from Terkos, Buyukcekmece and Omerli lakes, Istanbul, with different water quality regarding bromide concentration and organic matter content. The factors studied were pH, contact time, chlorine dose, and specific ultraviolet absorbance (SUVA). The determination of disinfection by-products (DBP) was carried out by gas chromatography techniques. Statistical analysis of the results was focused on the development of multiple regression models for predicting the concentrations of total THM and total HAA based on the use of pH, contact time, chlorine dose, and SUVA. The developed models provided satisfactory estimations of the concentrations of the DBP and the model regression coefficients of THM and HAA are 0.88 and 0.61, respectively. Further, the Durbin-Watson values confirm the reliability of the two models. The results indicate that under these experimental conditions which indicate the variations of pH, chlorine dosages, contact time, and SUVA values, the formation of THM and HAA in water can be described by the multiple linear regression technique.     

Seasonal variations of disinfection by-product precursors profile and their removal through surface water treatment plants

A sampling program has been undertaken to investigate the variations of disinfection by-products (DBPs) formation and nature and fate of natural organic matter (NOM) through water treatment plants in Istanbul. Specific focus has been given to the effect seasonal changes on the formation of DBPs and organic precursors levels. Water samples were collected from the three reservoirs inlet and within three major water treatment plants of Istanbul, Turkey. Changes in the dissolved organic carbon (DOC), ultraviolet absorbance at 254 nm (UV(254)), specific ultraviolet absorbance (SUVA), trihalomethane formation potential (THMFP), and haloacetic acids formation potential (HAAFP) were measured for both the treated and raw water samples. The variations of THM and HAA concentrations within treatment processes were monitored and also successfully assessed. The reactivity of the organic matter changed throughout the year with the lowest reactivity (THMFP and HAAFP) in winter, increasing in spring and reaching a maximum in fall season. This corresponded to the water being easier to treat in fall and an increase in the proportion of hydrophobic content. Understanding the seasonal changes in organic matter character and their reactivity with treatment chemicals should lead to a better optimization of the treatment processes and a more consistent water quality.     

Stabilization and Encapsulation of Gold Nanostars Mediated by Dithiols

Surface chemistry plays a pivotal role in regulating the morphology of nanoparticles, maintaining colloidal stability, and mediating the interaction with target analytes toward practical applications such as surface‐enhanced Raman scattering (SERS)‐based sensing and imaging. The use of a binary ligand mixture composed of 1,4‐benzenedithiol (BDT) and hexadecyltrimethylammonium chloride (CTAC) to provide gold nanostars with long‐term stability is reported. This is despite BDT being a bifunctional ligand, which usually leads to bridging and loss of colloidal stability. It is found however that neither BDT nor CTAC alone are able to provide sufficient colloidal and chemical stability. BDT‐coated Au nanostars are additionally used as seeds to direct the encapsulation with a gold outer shell, leading to the formation of unusual nanostructures including semishell‐coated gold nanostars, which are characterized by high‐resolution electron microscopy and electron tomography. Finally, BDT is exploited as a probe to reveal the enhanced local electric fields in the different nanostructures, showing that the semishell configuration provides significantly high SERS signals as compared to other core–shell configurations obtained during seeded growth, including full shells.     

Vital parameters for high gamma-aminobutyric acid (GABA) production by an industrial soy sauce koji Aspergillus oryzae NSK in submerged-liquid fermentation

Food Science and Biotechnology - In submerged-liquid fermentation, seven key parameters were assessed using one-factor-at-a-time to obtain the highest GABA yield using an industrial soy sauce koji...     

Investigation of the Thermal Behavior of Polypyrrole/Carbon Nanotube Composites and Utilization as Capacitive Material or Support for Catalysts

In this study, polypyrrole (PPy)/carbon nanotube (CNT) composites were synthesized by in situ chemical oxidative polymerization of a pyrrole monomer on CNT. Two different types of CNT having different structural properties were used. The composites were characterized using BET surface area analysis, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA) techniques. Thermal decomposition kinetics of PPy/CNT composites was studied by thermal gravimetric analysis techniques (TG/DTG (differential thermal gravimetric)) at different heating rates (2.5, 5, 7.5, and 10?K min^?1). Kinetic parameters of the composites were obtained from the TG and DTG curves using the Kissinger–Akahira–Sunose (KAS) and Flynn–Wall–Ozawa (FWO) models. The electrochemical capacitive properties of the composites were investigated by the cyclic voltammetry (CV) technique. Pt nanoparticles were decorated on the plain CNTs and composite materials via the microwave irradiation method.     

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.