Location-Based Analyses for Electronic Monitoring of Parolees

This study analyses the spatio-temporal pattern of parolees using electronic monitoring, where the developed spatial framework supports the Environmental Criminology concepts such as crime patterns or crime attractive locations. A grid-based solution for spatio-temporal analyses is introduced to ensure the anonymity of the parolees. In order to test these developed concepts, the Istanbul Metropolitan Area was selected as the pilot study area. Following the developed concepts of the Crime Pattern Theory, a spatial framework was designed. A novel grid-based weighted algorithm for the most attractive areas was generated via spatial point-of-interest data and a conducted survey among police officers. The designed framework and the spatio-temporal analyses were carried out for 77 parolees using geostatistical methods. The major findings of the study are (a) 24-hour trajectories of each parolee was monitored; (b) the most attractive grids within the city were defined; and (c) for each parolee, the entrance time to the grid and the time spent within that grid were reported and analyzed. This study is a preliminary study for spatio-temporal detection of parolees’ trajectories, where location-based analyses serve well. This study aims to aid decision-makers to better monitor the parolees and justify the benefits of surveillance

Outbreaks of Tularemia in Turkey

Tularemia, casued by Francisella tularensis, is a zoonotic disease presenting variousclinical forms. In the present study, three outbreaks of tularemia occurred fromJanuary to March and September in 2004 (first and second) and January to March in2005 (third) are reported from the north-eastern part of Turkey. All cases originatedfrom the same geographical location. In total, 56 patients having complaints of fever,malaise, chills and shivering, painful sore throat with swollen tonsils and enlargedcervical lymph nodes were affected and the patients were different in all cases.Forty-four, 7 and 5 people were affected in the first, second and third outbreak,respectively. The sera from all patients were analysed for the presence of F. tularensisantibodies using a microagglutination assay. Overall, of the 56 sera analysed, 39 (33, 3and 3 were from the first, second and third outbreak, respectively) showed antibodytitres of 1/160 and/or more against F. tularensis. The current report suggests thattularemia exists in north-eastern part of Turkey. The clinical manifestation of thecurrent cases were similar to those of oropharyngeal form of tularemia. It is consideredthat this region should be accepted as an endemic area for tularemia and kept undercontrol for a long period

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

Evaluation of Roseola Infantum Cases in Terms of Demographic Properties and Laboratory Values

Objective: To share our experince from demographic and labaratory results of 46 patients who applied to our policlinic with complaints of fever and rash and diagnosed as roseola infantum between 2012-2016

Suspected ALPS with clinical and laboratory findings: Three patients-three different diagnoses

Autoimmune lymphoproliferative syndrome is a rare genetic disorder characterized by dysregulation of the immune system due to defective Fas mediated lymphocyte apoptosis. The clinical spectrum includes lymphoproliferative disease with lymphadenopathy, hepatomegaly, splenomegaly and an increased risk of lymphoma, as well as autoimmune disease typically involving blood cells. Definitive diagnosis is made by demonstrating infectious/non-malignant chronic lymphoproliferation for more than six months, high CD3+CD4-CD8- T Cell and defective lymphocyte apoptosis or one of the FAS, FASL, CASP10 mutations. Since clinical and laboratory findings may overlap with other immune dysregulation or autoimmune diseases, differential diagnosis of autoimmune lymphoproliferative syndrome remains essential. Here, we present three cases of suspected autoimmune lymphoproliferative syndrome with clinical and laboratory findings, which resulted in three different diagnoses (chronic idiopathic thrombocytopenic purpura, ALPS-like and ALPS) after diagnostic evaluations. For all three cases, next-generation sequencing, flow cytometric analysis, protein expression and Fas mediated lymphocyte apoptosis with functional assays were performed