Consumption of very hot beverages and foods increases the incidence of oral and esophageal cancer but the mechanisms are not known and the critical temperature is not well defined. We realized a study with exfoliated cells from the oral cavity of individuals (n = 73) that live in an area in Iran which has the highest incidence of EC worldwide. Consumption of beverages at very high temperatures is a characteristic feature of this population. We analyzed biomarkers which are (i) indicative for genetic instability (micronuclei that are formed as a consequence of chromosomal damage, nuclear buds which are a consequence of gene amplifications and binucleated cells which reflect mitotic disturbances), (ii) markers that reflect cytotoxic effects (condensed chromatin, karyorrhectic, karyolitic and pyknotic cells), (iii) furthermore, we determined the number of basal cells which is indicative for the regenerative capacity of the buccal mucosa. The impact of the drinking temperature on the frequencies of these parameters was monitored with thermometers. We found no evidence for induction of genetic damage but an increase of the cytotoxic effects with the temperature was evident. This effect was paralleled by an increase of the cell division rate of the mucosa which was observed when the temperature exceeded 60 °C. Our findings indicate that cancer in the upper digestive tract in drinkers of very hot beverages is not caused by damage of the genetic material but by an increase of the cell division rate as a consequence of cytotoxic effects which take place at temperatures over 60 °C. It is known from earlier experiments with rodents that increased cell divisions lead to tumor promotion in the esophagus. Our findings provide a mechanistic explanation and indicate that increased cancer risks can be expected when the drinking temperature of beverages exceeds 60 °C.
Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5-15% and 5-17% compared with the GP model, 12-21% and 10-22% compared with the M5T model, and 7-15% and 5-18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.