4 results
Effects of metformin on epicardial adipose tissue and atrial electromechanical delay of obese children with insulin resistance
- Hatice Güneş, Hakan Güneş, Şebnem Özmen, Enes Çelik, Fatih Temiz
-
- Journal:
- Cardiology in the Young / Volume 30 / Issue 10 / October 2020
- Published online by Cambridge University Press:
- 27 July 2020, pp. 1429-1432
-
- Article
- Export citation
-
Introduction:
Obesity is usually related to insulin resistance and glucose metabolism disorders. The relationship between insulin resistance and epicardial adipose tissue and atrial electromechanical delay has been described in previous studies.
Aim:This study aims to demonstrate the effects of metformin on epicardial adipose tissue and electromechanical delay in patients using metformin for insulin resistance.
Materials and methods:A total of 30 patients using metformin for insulin resistance were included in the study. Pre-treatment and post-treatment epicardial adipose tissue and electromechanical delay were evaluated.
Results:There was a statistically significant decrease in epicardial adipose tissue thickness after 3 months of metformin therapy (6.4 ± 2.1 versus 4.7 ± 2.0; p = 0.008). Furthermore, the inter-atrial and intra-atrial electromechanical delay also significantly decreased after 3 months of metformin monotherapy (23.6 ± 8.2 versus 18.1 ± 5.8; p < 0.001, 9.1 ± 2.9 versus 6.3 ± 3.6; p = 0.003, respectively).
Conclusion:In this study, we show that metformin monotherapy significantly decreases epicardial adipose tissue thickness and electromechanical delay in obese children.
14 - Automatic Analysis of Aesthetics: Human Beauty, Attractiveness, and Likability
- from Part II - Machine Analysis of Social Signals
-
- By Hatice Gunes, University of Cambridge, Björn Schüller, Imperial College London and Technical University Munich
- Edited by Judee K. Burgoon, University of Arizona, Nadia Magnenat-Thalmann, Université de Genève, Maja Pantic, Imperial College London, Alessandro Vinciarelli, University of Glasgow
-
- Book:
- Social Signal Processing
- Published online:
- 13 July 2017
- Print publication:
- 08 May 2017, pp 183-201
-
- Chapter
- Export citation
-
Summary
According to the Oxford English Dictionary the definition of aesthetics is “concerned with beauty or the appreciation of beauty.” Despite the continuous interest and extensive research in cognitive, evolutionary, and social sciences, modeling and analysis of aesthetic canons remain open.
Contemporary theories of aesthetics emphasize critical thinking about objects, things, and people as well as experience, interaction, and value. In this regard, aesthetic norms have become more relevant to the context of interaction between humans and objects, human and computers (human–computer interaction or HCI), and between humans themselves (human–human interaction or HHI) (Kelly, 2013).
When interested readers look up the phrases aesthetics and computing on the web, they will likely encounter three main areas that appear to be related: aesthetic computing (note the missing “s” at the end), aesthetics in human–computer interaction, and computational aesthetics. Although there appears to be a close link between these three, they refer to inherently different fields of research. Aesthetic computing can be broadly defined as “applying the philosophical area of aesthetics to the field of computing” linked principally to formal languages and design of programs or products (Fishwick, 2013). Driven by design concerns, aesthetics in HCI focuses on the question of how to make computational artifacts more aesthetically pleasing (Norman, 2004). This concern has recently shifted toward aesthetics of interaction, moving the focus from ease of use to enjoyable and emotionally rewarding experience (Ahmed, Mahmud, & Bergaust, 2009). Although this question has significant theoretical and practical implications, there exists another relevant, yet largely unexplored question of whether computational approaches can be useful in understanding aesthetic judgment and affect in the context of HHI and HCI mainly given its highly subjective nature and often highly different “taste” and perception. Computational aesthetics is the research of computational methods that can make applicable aesthetic decisions in a similar way to humans (Hoeing, 2005). In other words, can human aesthetic perception and judgment be quantified computationally, and can we make machines and systems aware of aesthetics similarly to humans?
16 - Automatic Analysis of Social Emotions
- from Part II - Machine Analysis of Social Signals
-
- By Hatice Gunes, University of Cambridge, Björn Schüller, Imperial College London and Technical University Munich
- Edited by Judee K. Burgoon, University of Arizona, Nadia Magnenat-Thalmann, Université de Genève, Maja Pantic, Imperial College London, Alessandro Vinciarelli, University of Glasgow
-
- Book:
- Social Signal Processing
- Published online:
- 13 July 2017
- Print publication:
- 08 May 2017, pp 213-224
-
- Chapter
- Export citation
-
Summary
Automatic emotion recognition has widely focused on analysing and inferring the expressions of six basic emotions – happiness, sadness, fear, anger, surprise, and disgust. Little attention has been paid to social emotions such as kindness, unfriendliness, jealousy, guilt, arrogance, shame, and understanding the consequent social behaviour. Social context plays an important factor on labeling and recognizing social emotions, which are difficult to recognise out of context.
Social emotions are emotions that have a social component such as rage arising from a perceived offense (Gratch, Mao, & Marsella, 2006), or embarrassment deflecting undue attention from someone else (Keltner & Buswell, 1997). Such emotions are crucial for what we call social intelligence and they appear to arise from social explanations involving judgments of causality as well as intention and free will (Shaver, 1985).
To date, most of the automatic affect analysers in the literature have performed one-sided analysis by looking only at one party irrespective of the other party with which they interact (Gunes & Schuller, 2013). This assumption is unrealistic for automatic analysis of social emotions due to the inherent social aspect and bias that affect the expressiveness of the emotions in a social context or group setting. Therefore, the recent interest in analysing and understanding group expressions (e.g., Dhall & Goecke, 2012) will potentially contribute to the progress in automatic analysis of social emotions.
Recent developments in social media and social websites have opened up new avenues for the employment of user-driven and user-generated emotional and affective tone such as amused, touched, and empathy in social interactions. Accordingly, a number of researchers refer to automatic analysis of social emotions as ‘social affective analysis’ (e.g., social affective text mining) (Bao et al., 2012). Such works have focused on automatic prediction of social emotions from text content by attempting to establish a connection between affective terms and social emotions (Bao et al., 2012).
Homofermentative lactic acid bacteria of a traditional cheese, Comlek peyniri from Cappadocia region
- Cisem Bulut, Hatice Gunes, Burcu Okuklu, Sebnem Harsa, Sevda Kilic, Hatice Sevgi Coban, Ali Fazil Yenidunya
-
- Journal:
- Journal of Dairy Research / Volume 72 / Issue 1 / February 2005
- Published online by Cambridge University Press:
- 14 January 2005, pp. 19-24
- Print publication:
- February 2005
-
- Article
- Export citation
-
Comlek peyniri is a typical artisanal cheese in Central Anatolia. This type of cheese was made by using the indigenous lactic acid bacteria (LAB) flora of cow or ewes' milk. Majority of the samples were taken from fresh cheese because the aim was to isolate homofermentative LAB. Initially 661 microbial isolates were obtained from 17 cheese samples. Only 107 were found to be homofermentative LAB. These isolates were selected and identified by using both phenotypic and molecular methods. Phenotypic identification included curd formation from skim milk, catalase test, Gram staining and light microscopy, growth at different temperatures and salt concentrations, arginine hydrolysis, gas production from glucose, and carbohydrate fermentation. Molecular identification was based on the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) of the 16S rRNA gene-ITS (internally transcribed spacer) region. By combining the phenotypic and molecular identification results, isolates belonging to each of the following genera were determined at species or subspecies level: 54 Lactococcus lactis subsp. lactis, 21 Enterococcus faecium, 3 Ec. faecalis, 2 Ec. durans, 10 Ec. sp., 15 Lactobacillus paracasei subsp. paracasei, and 2 Lb. casei strains. Technological characterisation was also performed by culturing each of the strains in UHT skim milk, and by monitoring pH change and lactic acid production at certain time intervals through the 24 h incubation. Results of the technological characterisation indicated that 33% of the isolates (35 strains) were capable of lowering the pH of UHT milk below 5·3 after 6 h incubation at 30 °C. Thirty four of these strains were Lc. lactis subsp. lactis, and only one was an Ec. faecium strain.