Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging

The automatic segmentation of the abnormality region from the head MRI is a challenging task in the medical science domain. The abnormality in the form of the tumor comprises the uncontrolled growth of the cells. The automatic identification of the affected cells using computerized software systems is demanding in the past several years to provide a second opinion to radiologists. In this paper, a new clustering approach is introduced based on the machine learning aspect that clusters the tumor region from the input MRI using disjoint tree generation followed by tree merging. Further, the proposed algorithm is improved by introducing the theory of joint probabilities and nearest neighbors. Later, the proposed algorithm is automated to find the number of clusters required with its nearest neighbors to do semantic segmentation of the tumor cells. The proposed algorithm provides good semantic segmentation results having the DB index-0.11 and Dunn index-13.18 on the SMS dataset. While the experimentation with BRATS 2015 dataset yields Dice complete=80.5 %, Dice core=73.2 %, and Dice enhanced=62.8 %. The comparative analysis of the proposed approach with benchmark models and algorithms proves the model's significance and its applicability to do semantic segmentation of the tumor cells with the average increment in the accuracy of around ±2.5 % with machine learning algorithms.

A lower bound for set-coloring Ramsey numbers

The set-coloring Ramsey number R r , s ( k ) is defined to be the minimum n such that if each edge of the complete graph K n is assigned a set of s colors from { 1 , … , r } , then one of the colors contains a monochromatic clique of size k . The case s = 1 is the usual r -color Ramsey number, and the case s = r - 1 was studied by Erdős, Hajnal and Rado in 1965, and by Erdős and Szemerédi in 1972. The first significant results for general s were obtained only recently, by Conlon, Fox, He, Mubayi, Suk and Verstraëte, who showed that R r , s ( k ) = 2 Θ ( k r ) if s / r is bounded away from 0 and 1. In the range s = r - o ( r ) , however, their upper and lower bounds diverge significantly. In this note we introduce a new (random) coloring, and use it to determine R r , s ( k ) up to polylogarithmic factors in the exponent for essentially all r , s , and k .

Probabilistic modeling and risk characterization of the chronic aflatoxin M1 exposure of Hungarian consumers

Aflatoxin contamination can appear in various points of the food chain. If animals are fed with contaminated feed, AFB1 is transformed-among others-to aflatoxin M1 (AFM1) metabolite. AFM1 is less toxic than AFB1, but it is still genotoxic and carcinogenic and it is present in raw and processed milk and all kinds of milk products. In this article, the chronic exposure estimation and risk characterization of Hungarian consumers are presented, based on the AFM1 contamination of milk and dairy products, and calculated with a probabilistic method, the two-dimensional Monte-Carlo model. The calculations were performed using the R plugin (mc2d package) integrated into the KNIME (Konstanz Information Miner) software. The simulations were performed using data from the 2018-2020 food consumption survey. The AFM1 analytical data were derived from the Hungarian monitoring survey and 1,985 milk samples were analyzed within the framework of the joint project of the University of Debrecen and the National Food Chain Safety Office of Hungary (NÉBIH). Limited AFM1 concentrations were available for processed dairy products; therefore, a database of AFM1 processing factors for sour milk products and various cheeses was produced based on the latest literature data, and consumer exposure was calculated with the milk equivalent of the consumed quantities of these products. For risk characterization, the calculation of hazard index (HI), Margin of Exposure, and the hepatocellular carcinoma incidence were used. The results indicate that the group of toddlers that consume a large amount of milk and milk products are exposed to a certain level of health risk. The mean estimated daily intake of toddlers is in the range of 0.008-0.221 ng kg-1 bw day-1; the 97.5th percentile exposure of toddlers is between 0.013 ng kg-1 bw day-1 and 0.379 ng kg-1 bw day-1, resulting in a HI above 1. According to our study, the exposure of older age groups does not pose an emergent health risk. Nevertheless, the presence of carcinogenic compounds should be kept to a minimum in the whole population.