Evolutionary conservation and over-representation of functionally enriched network patterns in the yeast regulatory network

BACKGROUND: Localized network patterns are assumed to represent an optimal design principle in different biological networks. A widely used method for identifying functional components in biological networks is looking for network motifs – over-represented network patterns. A number of recent studies have undermined the claim that these over-represented patterns are indicative of optimal design principles and question whether localized network patterns are indeed of functional significance. This paper examines the functional significance of regulatory network patterns via their biological annotation and evolutionary conservation. RESULTS: We enumerate all 3-node network patterns in the regulatory network of the yeast S. cerevisiae and examine the biological GO annotation and evolutionary conservation of their constituent genes. Specific 3-node patterns are found to be functionally enriched in different exogenous cellular conditions and thus may represent significant functional components. These functionally enriched patterns are composed mainly of recently evolved genes suggesting that there is no evolutionary pressure acting to preserve such functionally enriched patterns. No correlation is found between over-representation of network patterns and functional enrichment. CONCLUSION: The findings of functional enrichment support the view that network patterns constitute an important design principle in regulatory networks. However, the wildly used method of over-representation for detecting motifs is not suitable for identifying functionally enriched patterns

Facebook, Being Cool, and Your Brain: What Science Tells Us

What happens in your brain when you find out that someone thinks you’re cool? Neuroscientists have recently started to investigate this by looking into how our brains process information concerning our reputation. Just a few years ago, it was discovered that when we learn that other people think highly of us, a key part of the brain’s reward system is activated [1]. The reward system is a set of interconnected brain structures that gives us a pleasurable feeling when we obtain or do things with a positive value. Getting a compliment feels good, so it makes sense that the reward system might be involved

The effect of environmental conditions on the physiological response during a stand-up paddle surfing session

Stand Up Paddleboard (SUP) surfing entails riding breaking waves and maneuvering the board on the wave face in a similar manner to traditional surfing. Despite some scientific investigations on SUP, little is known about SUP surfing. The aim of this study was to investigate the physiological response during SUP surfing sessions and to determine how various environmental conditions can influence this response. Heart rate (HR) of an experienced male SUP surfer aged 43 was recorded for 14.9 h during ten surfing sessions and synced with on board video footage to enable the examination of the effect of different surfing modes and weather conditions on exercise intensity. Results indicated that the SUP surfer’s HR was above 70% of HRmax during 85% of each session, with the greatest heart rates found during falls off the board (~85% HRmax) and while paddling back to the peak (~83% HRmax). Total time surfing a wave was less than 5%, with the majority of time spent paddling back into position. Wind speed positively correlated with HR (r = 0.75, p < 0.05) and wave height negatively correlated with wave caching frequency (r = 0.73, p < 0.05). The results highlight the aerobic fitness for SUP surfing, where wave riding, paddling back to the peak, and falls appear to be associated with the greatest cardiovascular demand and demonstrate that environmental conditions can have an effect on the physiological response during SUP surfing sessions

Train and test tightness of LP relaxations in structured prediction

This is the author accepted manuscript. The final version is available from Microtome Publishing via http://www.jmlr.org/proceedings/papers/v48/meshi16.htmlStructured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typically require the use of approximate solvers. We propose a theoretical explanation to the striking observation that approximations based on linear programming (LP) relaxations are often tight on real-world instances. In particular, we show that learning with LP relaxed inference encourages integrality of training instances, and that tightness generalizes from train to test data