This paper introduces a new method to find the most important samples for classification in image sets to speed-up the classification phase and reduce the storage space for large-scale face recognition tasks that use image sets obtained from face videos. We approximate the image sets with the kernelized convex hulls and show that it is sufficient to use only the samples that participate to shape the image set boundaries in this setting. To find those important samples that form the image set boundaries in the feature space, we employed the kernelized Support Vector Data Description (SVDD) method which finds a compact hypersphere that fits the image set samples best. Then, we show that these kernelized hypersphere models can also be used to model image sets for classification purposes. Lastly, we introduce ESOGU-285 (ESkisehir OsmanGazi University) Face Videos database that includes 285 people since the most popular video datasets used for set based recognition methods include either a few amount of people or large amount of people with just a few (or single) video collections. The experimental results on small sized standard datasets and our new larger sized dataset show that the proposed method greatly improves the testing times of the classification system (we obtained speed-ups up to a factor of 10 in ESOGU Face Videos dataset) without a significant drop in accuracies.
On 11 March 2020, World Health Organization has made the assessment that COVID-19 can be characterized as a pandemic. COVID-19 is a usually self-limited infection but it can be present a severe and fatal disease in patients with comorbitidies and the elderly. The characteristics of the virus and general health condition of the host determine disease progression. Scientists have been investigating on the pathophysiology of this disease, diagnostic tools, effective treatment protocols, and the development a vaccine. Preventive strategies are as important as the treatment modalities. This review focuses on the pathophysiological mechanisms of COVID-19, in addition to the roles of exercise in the immunomodulatory mechanisms, host defense systems, and also prevention and treatment of COVID-19. Exercise exerts many important effects such as immunomodulation, augmented defense system on the immune system via mainly muscle derived myokines and increased cardiorespiratory endurance. CO-VID-19 pandemic once again demonstrated the importance of prophylactic approaches such as healthy life, maintaining and strengthening of health, and immune system modulation. This pandemic may start a period in which humanity prioritizes healthy living principles, functional foods, maintaining health and welfare approaches, and increased effort to develop and maintain them.
Bu çalışmada, Antalya ve Isparta illerinden temin edilen 112 piliç eti örneğinde yüksek seviyede aminoglikozit dirençli (YSAD) Enterococcus yaygınlığı araştırılmış ve izolatların antibiyotik direnç profilleri belirlenmiştir. Çalışmada toplam 32 YSAD Enterococcus suşu izole edilmiştir. Moleküler yöntemler ile izolatların 18'i E. faecium, 5'i E. faecalis, 5'i E. durans, 3'ü E. avium ve 1'i E. casseliflavus olarak tanımlanmıştır. Disk difüzyon testi sonucu, izolatların en duyarlı olduğu antibiyotiklerin ampisilin (%93.75), linezolid (%93.75), penisilin G (%90.62), teikoplanin (%90.62), nitrofurantoin (%78.12), vankomisin (%75) ve kloramfenikol (%68.75) olduğu belirlenmiştir. İzolatların en dirençli olduğu antibiyotiklerin ise eritromisin (%96.87), minosiklin (%96.87), streptomisin (%96.87) ve tetrasiklin (%96.87) olduğu tespit edilmiştir. İzolatların gentamisin ve streptomisin minimum inhibisyon konsantrasyonu (MİK) değerlerinin sırasıyla 16 ile >4096 ve 64 ile >4096 µg/mL arasında değiştiği belirlenmiştir. MİK testleri sonucu, 32 YSAD Enterococcus izolatının 18'inin hem yüksek seviyede streptomisin dirençli (YSSD) hem de yüksek seviyede gentamisin dirençli (YSGD) oldukları tespit edilmiştir.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.