Review of seasonal heat storage in large basins: water tanks and gravel-water pits

In order to respond to climatic change, many efforts have been made to reduce harmful gas emissions. According to energy policies, an important goal is the implementation of renewable energy sources, as well as electrical and oil combustion savings through energy conservation. This paper focuses on an extensive review of the technologies developed, so far, for central solar heating systems employing seasonal sensible water storage in artificial large scale basins. Among technologies developed since the late 70s, the use of underground spaces as an energy storage medium - Underground Thermal Energy Storage (UTES) - has been investigated and closely observed in experimental plants in many countries, most of them, as part of government programmes. These projects attempt to optimise technical and economic aspects within an international knowledge exchange; as a result, UTES is becoming a reliable option to save energy through energy conservation. Other alternatives to UTES include large water tanks and gravel-water pits, also called man-made or artificial aquifers. This implies developing this technology by construction and leaving natural aquifers untouched. The present article reviews most studies and results obtained in this particular area to show the technical and economical feasibility for each system and specifics problems occurred during construction and operation. Advantages and disadvantages are pointed out to compare both alternatives. The projects discussed have been carried out mainly in European states with some references to other countries

Evaluation of Mucociliary Clearance by Three Dimension Micro-CT-SPECT in Guinea Pig: Role of Bitter Taste Agonists

Different image techniques have been used to analyze mucociliary clearance (MCC) in humans, but current small animal MCC analysis using in vivo imaging has not been well defined. Bitter taste receptor (T2R) agonists increase ciliary beat frequency (CBF) and cause bronchodilation but their effects in vivo are not well understood. This work analyzes in vivo nasal and bronchial MCC in guinea pig animals using three dimension (3D) micro-CT-SPECT images and evaluates the effect of T2R agonists. Intranasal macroaggreggates of albumin-Technetium 99 metastable (MAA-Tc99m) and lung nebulized Tc99m albumin nanocolloids were used to analyze the effect of T2R agonists on nasal and bronchial MCC respectively, using 3D micro-CT-SPECT in guinea pig. MAA-Tc99m showed a nasal mucociliary transport rate of 0.36 mm/min that was increased in presence of T2R agonist to 0.66 mm/min. Tc99m albumin nanocolloids were homogeneously distributed in the lung of guinea pig and cleared with time-dependence through the bronchi and trachea of guinea pig. T2R agonist increased bronchial MCC of Tc99m albumin nanocolloids. T2R agonists increased CBF in human nasal ciliated cells in vitro and induced bronchodilation in human bronchi ex vivo. In summary, T2R agonists increase MCC in vivo as assessed by 3D micro-CT-SPECT analysis

Efficacy of the local endometrial injury in patients who had previous failed IVF-ICSI outcome

Background: The latest studies reported that local endometrial injury is a useful method to improve the success of IVF-ICSI outcome. Objective: To assess whether local endometrial injury occurred by Pipelle in the spontaneous cycle could improve implantation rate, cleavage rate, and pregnancy outcome in the subsequent IVF-ICSI cycle in patients who had recurrent IVF failure. Materials and Methods: An endometrial biopsy was performed on day 21st in 41 patients as intervention group in this retrospective cross-sectional study. The control group contained 42 women. Results: Implantation rate was 22.5% and 10.5% in intervention and control group, respectively and this difference was found to be statistically significant (p=001). Pregnancy rate was 43.9% in the intervention group and this parameter was significantly lower in control group (21.4%) (p=0.03). Conclusion: Local endometrial injury in the nontransfer cycle increases the implantation rate and pregnancy rate in the subsequent IVF-ICSI cycle in patients who had previous failed IVF-ICSI outcome

Reperfusion Injury to Skeletal Muscle on Bone Scan

We report the bone scan appearance of reperfusion injury involving the muscle groups of the lower extremities in a patient after undergoing thromboembolectomy of the bilaterally femoral artery. The patient was referred to our department with a history of colon adenocarcinoma and widespread bone pain. Bone scan demonstrated extraosseous accumulation of technetium-99m methylene diphosphonate in the muscle groups of the lower extremities, predominantly in the right extremity. Pathologic examination of the gastrocnemius muscle was suggestive of reperfusion injury and atrophic changes. There was no finding of rhabdomyolysis

Detection of breast cancer via deep convolution neural networks using MRI images

Ikizceli, Turkan/0000-0002-5683-0391; Erbay, Hasan/0000-0002-7555-541X; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466WOS:000538675900065Breast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655

Metabolic Imaging Based Sub-Classification of Lung Cancer

Lung cancer is one of the deadliest cancer types whose 84% is non-small cell lung cancer (NSCLC). In this study, deep learning-based classification methods were investigated comprehensively to differentiate two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The study used 1457 F-18-FDG PET images/slices with tumor from 94 patients (88 men), 38 of which were ADC and the rest were SqCC. Three experiments were carried out to examine the contribution of peritumoral areas in PET images on subtype classification of tumors. We assessed multilayer perceptron (MLP) and three convolutional neural network (CNN) models such as SqueezeNet, VGG16 and VGG19 using three kinds of images in these experiments: 1) Whole slices without cropping or segmentation, 2) cropped image portions (square subimages) that include the tumor and 3) segmented image portions corresponding to tumors using random walk method. Several optimizers and regularization methods were used to optimize each model for the diagnostic classification. The classification models were trained and evaluated by performing stratified 10-fold cross validation, and F-score and area-under-curve (AUC) metrics were used to quantify the performance. According to our results, it is possible to say that inclusion of peritumoral regions/tissues both contributes to the success of models and makes segmentation effort unnecessary. To the best of our knowledge, deep learning-based models have not been applied to the subtype classification of NSCLC in PET imaging, therefore, this study is a significant cornerstone providing thorough comparisons and evaluations of several deep learning models on metabolic imaging for lung cancer. Even simpler deep learning models are found promising in this domain, indicating that any improvement in deep learning models in machine learning community can be reflected well in this domain as well

A COMPARATIVE STUDY ON SEGMENTATION AND CLASSIFICATION IN BREAST MRI IMAGING

Cinarer, Gokalp/0000-0003-0818-6746; Erbay, Hasan/0000-0002-7555-541XWOS: 000455271800005Background: Breast cancer is the type of cancer that develops from cells in the breast tissue. The breast cancer is leading cancer in women. One in every eight to nine women has breast cancer at some point during their lifetime. Computer-Aided Diagnosis (CAD) Technology is getting more important to assist radiologists not only to detect breast cancer tumor but also to interpret lesioned regions. The CAD, as a second reader in the clinic, improves the classification of malignant and benign lesions. On the other hand, Magnetic Resonance Imaging (MRI) is a highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. In MRI image analysis, the segmentation images are important objective because accurate measurement of the delineation of the regions of interest (ROI) is critical for the breast cancer diagnosis and treatment. Herein, by using MRI scans, we propose a semi-automated CAD system prototype to assist radiologists in detecting breast cancer tumors and interpreting lesioned regions. The prototype, first, pre-processes the raw selected suspicious region to reduce the noises and to reveal the structure. Later, using Expectation Maximization (EM), the prototype segments the pre-processed region. After that, we use the Discrete Wavelet Transform (DWT) for providing efficient multi-resolution sub and decomposition of signals. Then Random Forest Algorithm is used for feature selection. Finally, Naive Bayes, Linear Discriminant Analysis and C4.5 Decision Tree Algorithms are used to classify the features of the ROI in the diagnosis analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. 80% of the images are allocated for training and 20% of images reserved for testing. The CAD classified 20 patients correctly in case of 5 fold cross-validation. Only one patient is misclassified. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis