A rare cause of recurrent spontaneous pneumothorax: Birt-hogg-dube syndrome

Birt-Hogg-Dube (BHD) syndrome is an unusual disorder characterized by the triad of cutaneous lesions, renal tumors and lung cysts. In cases with BHD syndrome, the frequency of recurrent pneumothorax is increased due to presence of multiple lung cysts. It is important to evaluate the BHD syndrome in differential diagnosis of recurrent pneumothorax especially with multiple lung cysts predominating in the lung base. In these patients, the presence of accompanying kidney and other tumors should be investigated. Herein, we report a case of BHD syndrome presenting with recurrent pneumothorax. © 2018 by Turkish Thoracic Society

Analysis of the volatile components of five Turkish Rhododendron species by headspace solid-phase microextraction and GC-MS (HS-SPME-GC-MS)

Volatile constituents of various solvent extracts (n-hexane, CH2Cl2, H2O) of 15 different organs (leaves, flowers, fruits) of five Rhododendron species (Ericaceae) growing in Turkey were trapped with headspace solid-phase microextraction (HS-SPME) technique and analyzed by GC-MS. A total of 200 compounds were detected and identified from organic extracts, while the water extracts contained only traces of few volatiles. The CH2Cl2 extract of the R. luteum flowers was found to exhibit the most diverse composition: 34 compounds were identified, with benzyl alcohol (16.6%), limonene (14.6%) and p-cymene (8.4%) being the major compounds. The CH2Cl2-solubles of R. x sochadzeae leaves contained only phenyl ethyl alcohol. This study indicated appreciable intra-specific variations in volatile compositions within the genus. Different anatomical parts also showed altered volatile profiles. This is the first application of HS-SPME-GC-MS on the volatiles of Rhododendron species

Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach

Suspended sediment estimation is important to the water resources management and water quality problem. In this article, artificial neural networks (ANN), M5tree (M5T) approaches and statistical approaches such as Multiple Linear Regression (MLR), Sediment Rating Curves (SRC) are used for estimation daily suspended sediment concentration from daily temperature of water and streamflow in river. These daily datas were measured at Iowa station in US. These prediction aproaches are compared to each other according to three statistical criteria, namely, mean square errors (MSE), mean absolute relative error (MAE) and correlation coefficient (R). When the results are compared ANN approach have better forecasts suspended sediment than the other estimation methods

Modeling of dam reservoir volume using adaptive neuro fuzzy method.

Dam reservoir capacity estimation are important for dam structures, operation, design and safety assessments. Predictions of reservoir volumes must be considered as one of the main part of water resources management. As it is known in water resources management, reservoir capacity has direct effects on choosing irrigation systems, energy production, water supply systems etc. in a study region. In this study, the reservoir capacity of the Stony Brook dam in the USA state of Massachusetts, was tried to be estimated. Data set is taken by U.S. Geological Survey Institute (USGS) website. Reservoir capacity was estimated by Adaptive Neuro Fuzzy (NF) and Multilinear Linear Regression Analysis (MLR). NF model results was compared with MLR results. For the comparison, Mean Square Error (MSE), Mean Absolute Error (MAE) and correlation coefficient statistics were used

Groundwater Level Predıctıon Usıng Artıfıcıal Neural Network and M5 Tree Models

Most of the fresh water resources in our world consist of underground water reserves. Estimation of fluctuations of groundwater level (GWL) is very important in the management of water resources. In this study, groundwater level (GWL) was investigated using artificial neural networks (ANN), M5tree (M5T) approaches in Reyhanlı region in Turkey. Total 196 data from 2000-2015 taken from 1 observation station belonging to Reyhanlı sub-basin located in Asi basin were used in the study. Using the monthly average precipatation and temperature, the change in GWL is modeled by artificial neural networks (ANN), M5tree (M5T) approaches. The results showed that (ANN) and M5tree (M5T) models were found to be very close to each other

Attention deficit hyperactivity symptoms predict problematic mobile phone use

Attention-deficit-hyperactivity disorder (ADHD) is the most commonly diagnosed childhood disorder characterised by inattention, hyperactivity/impulsivity, or both. Some of the key traits of ADHD have previously been linked to addictive and problematic behaviours. The aim of the present study was to examine the relationship between problematic mobile phone use, smartphone addiction risk and ADHD symptoms in an adult population. A sample of 273 healthy adult volunteers completed the Adult ADHD Self-Report Scale (ASRS), the Mobile Phone Problem Usage Scale (MPPUS), and the Smartphone Addiction Scale (SAS). A significant positive correlation was found between the ASRS and both scales. More specifically, inattention symptoms and age predicted smartphone addiction risk and problematic mobile phone use. Our results suggest that there is a positive relationship between ADHD traits and problematic mobile phone use. In particular, younger adults with higher level of inattention symptoms could be at higher risk of developing smartphone addiction. The implication of our findings for theoretical frameworks of problematic mobile phone use and clinical practice are discussed