Deducing topology of protein-protein interaction networks from experimentally measured sub-networks.

BackgroundProtein-protein interaction networks are commonly sampled using yeast two hybrid approaches. However, whether topological information reaped from these experimentally-measured sub-networks can be extrapolated to complete protein-protein interaction networks is unclear.ResultsBy analyzing various experimental protein-protein interaction datasets, we found that they are not random samples of the parent networks. Based on the experimental bait-prey behaviors, our computer simulations show that these non-random sampling features may affect the topological information. We tested the hypothesis that a core sub-network exists within the experimentally sampled network that better maintains the topological characteristics of the parent protein-protein interaction network. We developed a method to filter the experimentally sampled network to result in a core sub-network that more accurately reflects the topology of the parent network. These findings have fundamental implications for large-scale protein interaction studies and for our understanding of the behavior of cellular networks.ConclusionThe topological information from experimental measured networks network as is may not be the correct source for topological information about the parent protein-protein interaction network. We define a core sub-network that more accurately reflects the topology of the parent network

The drivers and impacts of Amazon forest degradation

BACKGROUND: Most analyses of land-use and land-cover change in the Amazon forest have focused on the causes and effects of deforestation. However, anthropogenic disturbances cause degradation of the remaining Amazon forest and threaten their future. Among such disturbances, the most important are edge effects (due to deforestation and the resulting habitat fragmentation), timber extraction, fire, and extreme droughts that have been intensified by human-induced climate change. We synthesize knowledge on these disturbances that lead to Amazon forest degradation, including their causes and impacts, possible future extents, and some of the interventions required to curb them. ADVANCES: Analysis of existing data on the extent of fire, edge effects, and timber extraction between 2001 and 2018 reveals that 0.36 ×106 km2 (5.5%) of the Amazon forest is under some form of degradation, which corresponds to 112% of the total area deforested in that period. Adding data on extreme droughts increases the estimate of total degraded area to 2.5 ×106 km2, or 38% of the remaining Amazonian forests. Estimated carbon loss from these forest disturbances ranges from 0.05 to 0.20 Pg C year−1 and is comparable to carbon loss from deforestation (0.06 to 0.21 Pg C year−1). Disturbances can bring about as much biodiversity loss as deforestation itself, and forests degraded by fire and timber extraction can have a 2 to 34% reduction in dry-season evapotranspiration. The underlying drivers of disturbances (e.g., agricultural expansion or demand for timber) generate material benefits for a restricted group of regional and global actors, whereas the burdens permeate across a broad range of scales and social groups ranging from nearby forest dwellers to urban residents of Andean countries. First-order 2050 projections indicate that the four main disturbances will remain a major threat and source of carbon fluxes to the atmosphere, independent of deforestation trajectories. OUTLOOK: Whereas some disturbances such as edge effects can be tackled by curbing deforestation, others, like constraining the increase in extreme droughts, require additional measures, including global efforts to reduce greenhouse gas emissions. Curbing degradation will also require engaging with the diverse set of actors that promote it, operationalizing effective monitoring of different disturbances, and refining policy frameworks such as REDD+. These will all be supported by rapid and multidisciplinary advances in our socioenvironmental understanding of tropical forest degradation, providing a robust platform on which to co-construct appropriate policies and programs to curb it

The drivers and impacts of Amazon forest degradation

Approximately 2.5 × 10 6 square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year −1 ), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year −1 ). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest

Supporting distributed collaborative work with multi-versioning

The multi-version approach is useful in both synchronous and asynchronous groupware systems. This paper discusses the implementation of a real-time group editor that embodies our approaches and algorithms based on multi-versioning, which can preserve individual users' concurrent conflicting intentions in a consistent way. To highlight the distinct contributions of our work, we also present a detailed description of some novel features of the system.6 page(s

Machine learning phases in swarming systems

Recent years have witnessed a growing interest in using machine learning to predict and identify phase transitions (PTs) in various systems. Here we adopt convolutional neural networks (CNNs) to study the PTs of Vicsek model, solving the problem that traditional order parameters are insufficiently able to do. Within the large-scale simulations, there are four phases, and we confirm that all the PTs between two neighboring phases are first-order. We have successfully classified the phase by using CNNs with a high accuracy and identified the PT points, while traditional approaches using various order parameters fail to obtain. These results indicate the great potential of machine learning approach in understanding the complexities in collective behaviors, and in related complex systems in general

Obstacle optimization for panic flow--reducing the tangential momentum increases the escape speed.

A disastrous form of pedestrian behavior is a stampede occurring in an event involving a large crowd in a panic situation. To deal with such stampedes, the possibility to increase the outflow by suitably placing a pillar or some other shaped obstacles in front of the exit has been demonstrated. We present a social force based genetic algorithm to optimize the best design of architectural entities to deal with large crowds. Unlike existing literature, our simulation results indicate that appropriately placing two pillars on both sides but not in front of the door can maximize the escape efficiency. Human experiments using 80 participants correspond well with the simulations. We observed a peculiar property named tangential momentum, the escape speed and the tangential momentum are found to be negatively correlated. The idea to reduce the tangential momentum has practical implications in crowd architectural design