Association of Vegetable and Animal Flesh Intake with Inflammation in Pregnant Women from India

In pregnant women, studies are lacking on the relationship of vegetable and animal flesh (poultry, red meat and seafood) intake with inflammation, especially in low- and middle-income countries. We conducted a cohort study of pregnant women receiving antenatal care at BJ Medical College in Pune, India. The dietary intake of pregnant women was queried in the third trimester using a validated food frequency questionnaire. Twelve inflammatory markers were measured in plasma samples using immunoassays. Only 12% of the study population were vegetarians, although animal flesh intake levels were lower compared to Western populations. In multivariable models, higher intakes of total vegetables were associated with lower levels of the T-helper (Th) 17 cytokine interleukin (IL)-17a (p = 0.03) and the monocyte/macrophage activation marker soluble CD163 (sCD163) (p = 0.02). Additionally, higher intakes of poultry were negatively associated with intestinal fatty-acid binding protein (I-FABP) levels (p = 0.01), a marker of intestinal barrier dysfunction and Th2 cytokine IL-13 (p = 0.03), and higher seafood was associated with lower IL-13 (p = 0.005). Our data from pregnant women in India suggest that a higher quality diet emphasizing vegetables and with some animal flesh is associated with lower inflammation. Future studies should confirm these findings and test if modulating vegetables and animal flesh intake could impact specific aspects of immunity and perinatal health

Liquid marbles: topical context within soft matter and recent progress

The study of particle stabilized interfaces has a long history in terms of emulsions, foams and related dry powders. The same underlying interfacial energy principles also allow hydrophobic particles to encapsulate individual droplets into a stable form as individual macroscopic objects, which have recently been called "Liquid Marbles". Here we discuss conceptual similarities to superhydrophobic surfaces, capillary origami, slippery liquids-infused porous surfaces (SLIPS) and Leidenfrost droplets. We provide a review of recent progress on liquid marbles, since our earlier Emerging Area article (Soft Matter, 2011, 7, 5473–5481), and speculate on possible future directions from new liquid-infused liquid marbles to microarray applications. We highlight a range of properties of liquid marbles and describe applications including detecting changes in physical properties (e.g. pH, UV, NIR, temperature), use for gas sensing, synthesis of compounds/composites, blood typing and cell culture

Socio-spatial influence maximization in location-based social networks

Identifying influential nodes in social networks is a key issue in many domains such as sociology, economy, biology, and marketing. A common objective when studying such networks is to find the minimum number of nodes with the highest influence. One might for example, maximize information diffusion in social networks by selecting some appropriate nodes. This is known as the Influence Maximization Problem (IMP). Considering the social aspect, most of the current works are based on the number, intensity, and frequency of node relations. On the spatial side, the maximization problem is denoted as the Location-Aware Influence Maximization Problem (LAIMP). When advertising for a new product, having access to people who have the highest social status and their neighbors are distributed evenly across a given region is often a key issue to deal with. Another valuable issue is to inform the maximum number of users located around an event, denoted as a query point, as quickly as possible. The research presented in this paper, along with a new measure of centrality that both considers network and spatial properties, extends the influence maximization problem to the locationbased social networks and denotes it hereafter as the Socio-Spatial Influence Maximization Problem (SSIMP). The focus of this approach is on the neighbor nodes and the concept of line graph as a possible framework to reach and analyze these neighbor nodes. Furthermore, we introduce a series of local and global indexes that take into account both the graph and spatial properties of the nodes in a given network. Moreover, additional semantics are considered in order to represent the distance to a query point as well as the measure of weighted farness. Overall, these indexes act as the components of the feature vectors and using k-nearest neighbors, the closest nodes to the ‘ideal’ node are determined as top-k nodes. The node with maximum values for feature vectors is considered as the ‘ideal’ node. The experimental evaluation shows the performance of the proposed method in determining influential nodes to maximize the socio-spatial influence in location-based social networks