Weeksrobles3342

High-frequency oscillations >80 Hz (HFOs) have unique features distinguishing them from spikes and artifactual components that can be well-evidenced in the time-frequency representations. We introduce an unsupervised HFO detector that uses computer-vision algorithms to detect HFO landmarks on two-dimensional (2D) time-frequency maps. To validate the detector, we introduce an analytical model of the HFO based on a sinewave having a Gaussian envelope, for which analytical equations in time-frequency space can be derived, allowing us to establish a direct correspondence between common HFO detection criteria in the time domain with the ones in the frequency domain, used by the computer-vision detection algorithm. The detector identifies potential HFO events on the time-frequency representation, which are classified as true HFOs if criteria regarding the HFO's frequency, amplitude, and duration are met. The detector is validated on simulated HFOs according to the analytical model, in the presence of noise, with ditter than the most used HFO detectors. Copyright © 2020 Donos, Mîndruţă and Barborica.The segmentation of brain region contours in three dimensions is critical for the analysis of different brain structures, and advanced approaches are emerging continuously within the field of neurosciences. With the development of high-resolution micro-optical imaging, whole-brain images can be acquired at the cellular level. However, brain regions in microscopic images are aggregated by discrete neurons with blurry boundaries, the complex and variable features of brain regions make it challenging to accurately segment brain regions. Manual segmentation is a reliable method, but is unrealistic to apply on a large scale. Here, we propose an automated brain region segmentation framework, DeepBrainSeg, which is inspired by the principle of manual segmentation. DeepBrainSeg incorporates three feature levels to learn local and contextual features in different receptive fields through a dual-pathway convolutional neural network (CNN), and to provide global features of localization by image registration and domain-condition constraints. Validated on biological datasets, DeepBrainSeg can not only effectively segment brain-wide regions with high accuracy (Dice ratio > 0.9), but can also be applied to various types of datasets and to datasets with noises. It has the potential to automatically locate information in the brain space on the large scale. Copyright © 2020 Tan, Guan, Feng, Ni, Zhang, Wang, Li, Yuan, Gong, Luo and Li.This study explored whether a Brief Form of the California Odor Learning Test 3 (COLT), an olfactory analog of the newly released Brief Form of the California Verbal Learning Test (CVLT 3), could retain the ability of the COLT to detect odor memory dysfunctions observed in normal aging. 52 participants, 28 young (18-30 years old) and 24 old (65 years of age and older), were administered the Brief Forms of the CVLT 3 and the COLT 3. Results indicated poorer performance in immediate and delayed odor recall in older than in younger adults. Poorer odor recognition memory performance in older adults than in younger adults was detected. This study suggests that the Brief Form of the COLT can detect differential odor learning and memory between young and older adults. Thus, the current brief test holds promise as a measure that can be incorporated into studies that demand a brief, non-invasive test capable of detecting impairment in olfactory function. Copyright © 2020 Frank and Murphy.INTRODUCTION Lung cancer remains a leading cause of cancer incidence, yet, in Greece, country-level registry-based data are limited. We have thus investigated the epidemiology of lung cancer and its trends in the George Papanikolaou Hospital, Northern Greece over 18 years (2000-2018). METHODS We analyzed all the cases reported in the Bronchoscopy Unit of the Hospital for the period 2000-2018. In total, 15131 subjects (12300 males and 2831 females) that presented with a mass in the imaging, were submitted to bronchoscopy. Characteristics of patients such as age, sex, smoking history and occupation were collected. Statistical analysis was performed with SPSS 21.0 software package. RESULTS Among all subjects, a total of 5628 (37.2%; mean age 65.85 ± 9.6 years) cases of primary lung cancer were identified with a male to female ratio of 21 (41.1% to 20.4%) (p less then 0.001). find more Squamous cell lung cancer was the most common type of lung cancer identified in this population (44%) with a higher proportion in males compared to females (p less then 0.001). Furthermore, adenocarcinoma was mostly observed in female non-smokers compared to males (p less then 0.001). The majority of lung cancer cases were identified in patients occupied with agriculture and livestock breeding (41.1%). The mean age at lung cancer diagnosis was 66.13 ± 9.19 years for the whole study population. Lung cancer cases observed with a higher mean of 43.93 ± 10.84 years of smoking compared to cancer-free patients with 39.64 ± 13.23 years of smoking (p less then 0.001). CONCLUSIONS Apart from smoking, demographic characteristics including age, sex and occupation appear to have an impact on lung cancer development in this population. Smoking history alone could not predict the development of lung cancer in the studied northern Greek population. © 2020 Domvri K. et al.INTRODUCTION The All Causes of Death Surveillance (ACDS) system was used to measure smoking-attributed mortality by inserting questions on smoking on death certificates. Smoking status information of the deceased has been routinely collected in death certificates since 2010. We describe a death registry-based case-control study using smoking and cause-of-death data for the period 2010-15. METHODS From 2010, three questions about the smoking status of the deceased were inserted in a revised death certificate 1) Smoking status (current smoker, quit smoking, never smoker); 2) Number of cigarettes per day smoked; and 3) Number of years of smoking. A data-accuracy survey of 1788 telephone interviews of the family of the deceased was also conducted. Smoking habits (current/ex-smoker vs non-smoker) were compared in study cases (persons who died of lung cancer and other diseases known to be caused by smoking) and the controls (never smokers). Multivariate logistic regression analysis was conducted to estimate relative risks, RR (odds ratios) for smoking-attributed mortality, for lung cancer and all causes of death related to smoking, adjusted for 5-year interval age groups, education, marital status, and year of death.