Automated bird counting with deep learning for regional bird distribution mapping

A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.No sponso

TimeInspector: a static analysis approach for detecting timing attacks

We present a static analysis approach to detect malicious binaries that are capable of carrying out a timing attack. The proposed approach is based on a simple observation that the timing attacks typically operate by measuring the execution times of short sequences of instructions. Consequently, given a binary, we first construct the control flow graph of the binary and then determine the paths between the pairs of time readings, on which a suspiciously low number of instructions might be executed. In the presence of such a path, we mark the binary as potentially malicious and report all the suspicious paths identified. In the experiments, where a collection of benign and malicious binaries were used, the proposed approach correctly detected all the malicious binaries with an accuracy up to 99.5% and without any false negatives

Mental status and physical activity in patients with homozygous familial hypercholesterolemia: A subgroup analysis of a nationwide survey (A-HIT1 registry)

BACKGROUND: Homozygous familial hypercholesterolemia (HoFH) is a rare, life-threatening disease due to high serum low-density lipoprotein (LDL) cholesterol levels. LDL cholesterol-lowering interventions are fundamental for patients with HoFH

Turkish Real Life Atrial Fibrillation in Clinical Practice: TRAFFIC Study

Background: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide and is associated with an increased risk of thromboembolism, ischemic stroke, impaired quality of life, and mortality. The latest research that shows the prevalence and incidence of AF patients in Türkiye was the Turkish Adults’ Heart Disease and Risk Factors study, which included 3,450 patients and collected data until 2006/07.The Turkish Real Life Atrial Fibrillation in Clinical Practice (TRAFFIC) study is planned to present current prevalence data, reveal the reflection of new treatment and risk approaches in our country, and develop new prediction models in terms of outcomes. Methods: The TRAFFIC study is a national, prospective, multicenter, observational registry. The study aims to collect data from at least 1900 patients diagnosed with atrial fibrillation, with the participation of 40 centers from Türkiye. The following data will be collected from patients: baseline demographic characteristics, medical history, vital signs, symptoms of AF, ECG and echocardiographic findings, CHADS2-VASC2 and HAS-BLED (1-year risk of major bleeding) risk scores, interventional treatments, antithrombotic and antiarrhythmic medications, or other medications used by the patients. For patients who use warfarin, international normalized ratio levels will be monitored. Follow-up data will be collected at 6, 12, 18, and 24 months. Primary endpoints are defined as systemic embolism or major safety endpoints (major bleeding, clinically relevant nonmajor bleeding, and minor bleeding as defined by the International Society on Thrombosis and Hemostasis). The main secondary endpoints include major adverse cardiovascular events (systemic embolism, myocardial infarction, and cardiovascular death), all-cause mortality, and hospitalizations due to all causes or specific reasons. Results: The results of the 12-month follow-up of the study are planned to be shared by the end of 2023. Conclusion: The TRAFFIC study will reveal the prevalence and incidence, demographic characteristics, and risk profiles of AF patients in Türkiye. Additionally, it will provide insights into how current treatments are reflected in this population. Furthermore, risk prediction modeling and risk scoring can be conducted for patients with AF