Unexpectedly, in all the three analyzed batches from two out of the three producers Lactobacillus kefiri was also detected, thus representing an absolute novelty, which suggests the presence of bioactive compounds (e.g. exopolysaccharides) similar to those characterizing milk kefir beverage. Mycobiota population, studied for the very first time in Gioddu, revealed a more complex composition, with Kluyveromyces marxianus, Galactomyces candidum and Geotrichum galactomyces constituting the core species. Further research is needed to disclose the eventual occurence in Gioddu of probiotic cultures and bioactive compounds (e.g. exopolysaccharides, angiotensin-converting enzyme inhibitory peptides and antimicrobial compounds) with potential health-benefits for the consumers. Degradation of chlorinated ethenes (CEs) in low conductivity layers of aquifers reduces pollution plume tailing and accelerates natural attenuation timeframes. The degradation pathways involved are often different from those in the higher conductive layers and might go undetected when only highly conductive layers are targeted in site assessments. Reactive transport model simulations (PHT3D in FloPy) were executed to assess the performance of dual carbon and chlorine compound specific stable isotope analysis (CSIA) in degradation pathway identification and quantification in a coupled physical-chemical heterogeneous virtual aquifer. Degradation rate constants were assumed correlated to the hydraulic conductivity positively for oxidative transformation (higher oxygen availability in coarser sands) and negatively for chemical reduction (higher content of reducing solids in finer sediments). Predicted carbon isotope ratios were highly heterogeneous. They generally increased downgradient of the pollution source buimportant degradation pathway. V.OBJECTIVE The clinical features and incidence of benign paroxysmal positional vertigo (BPPV) are not well known in pediatric populations. The aim of this study was to describe the clinical characteristics of pediatric BPPV and to estimate the frequency of pediatric BPPV in the general population. METHODS We retrospectively reviewed the medical records of 20 children (6-14 years old) diagnosed with BPPV between 2007 and 2017. The age/sex distribution of BPPV for all ages at our hospital and in the Korean Health Insurance Review and Assessment Service-National Patient Sample (HIRA-NPS) database were compared. The annual incidence and proportion of children with BPPV were calculated. RESULTS BPPV occurred 1.86 times more frequently in girls than in boys. Nine children (45%) had associated comorbidities, such as inner ear disorders and recent head trauma. The posterior and lateral semicircular canals were most commonly involved (n = 9 for each), and recurrence was observed in two patients (10%). Children younger than 15 years accounted for approximately 1% of all BPPV cases. The annual incidence of BPPV was 171.5/100,000 for all ages and 9.5/100,000 in the pediatric population. CONCLUSIONS Our findings suggest that pediatric BPPV is a relatively uncommon cause of vertigo in children and that the rates of related illness and recurrence are high. Steganography is one of the approaches used in data hiding. Image steganography, is a type of steganography that the image is used as a covering object. Data hiding capacity and image quality of the cover object are important factors in image steganography. Because the deterioration of image quality can be noticed by the human vision system, it attracts the attention of attackers. Therefore, the purpose of this study is increasing the amount of data to be hidden and stego image is to ensure high image quality. In the study, a new optimization-based method has been proposed by making use of the similarities of the pixels. In order to test the performance of the proposed method has been used visual quality analysis metrics such as MSE, RMSE, PSNR, SSIM and UQI. As a cover object; different sizes medical images have been used that obtained from the open access Dicom library database. Doctor comments in different capacities have been hidden to the medical images. Experimental results show that the average PSNR value is 66.5374, 59.4420 and 56.3936, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 512 × 512 images. In addition, the average PSNR value is 60.4308, 53.3529 and 47.4113, respectively, when 1000 characters, 5000 characters and 10,000 characters data is hidden in 256 × 256 images. 10,000 characters of data have not been hidden in 256 × 256 images without using data compression techniques with classical similarity based LSB method. In the proposed method, 10,000 characters of data have been hidden in 256 × 256 size images without using data compression techniques. Primary trigeminal neuralgia is a common clinical refractory neuralgia characterized by an onset of excruciating pain that can severely affect patients' quality of life. Long-term suffering from this pain may lead to depression, anxiety, and suicide. Current treatments, however, are associated with high recurrent rates and severe complications. We hypothesize that both the applicability and efficacy of magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) treatment in primary trigeminal neuralgia can be achieved under the following conditions a specific target focus and incident channel, a temperature measurement system that does not incur damage to surrounding tissues, and an optimal radiation dose. Successful non-invasive treatment of primary trigeminal neuralgia by MR-HIFU systems could represent a breakthrough of this technology applied to the oral and maxillofacial region. Brain tumor is one of the dangerous and deadly cancer types seen in adults and children. Early and accurate diagnosis of brain tumor is important for the treatment process. It is an important step for specialists to detect the brain tumor using computer aided systems. find more These systems allow specialists to perform tumor detection more easily. However, mistakes made with traditional methods are also prevented. In this paper, it is aimed to diagnose the brain tumor using MRI images. CNN models, one of the deep learning networks, are used for the diagnosis process. Resnet50 architecture, one of the CNN models, is used as the base. The last 5 layers of the Resnet50 model have been removed and added 8 new layers. With this model, 97.2% accuracy value is obtained. Also, results are obtained with Alexnet, Resnet50, Densenet201, InceptionV3 and Googlenet models. Of all these models, the model developed with the highest performance has classified the brain tumor images. As a result, when analyzed in other studies in the literature, it is concluded that the developed method is effective and can be used in computer-aided systems to detect brain tumor.