Lipid Droplet Motility Increases Following Viral Immune Stimulation

Lipid droplets (LDs) have traditionally been thought of as solely lipid storage compartments for cells; however, in the last decade, they have emerged as critical organelles in health and disease. LDs are highly dynamic within cells, and their movement is critical in organelle–organelle interactions. Their dynamics are known to change during cellular stress or nutrient deprivation; however, their movement during pathogen infections, especially at very early timepoints, is under-researched. This study aimed to track LD dynamics in vitro, in an astrocytic model of infection. Cells were either stimulated with a dsRNA viral mimic, poly I:C, or infected with the RNA virus, Zika virus. Individual LDs within infected cells were analysed to determine displacement and speed, and average LD characteristics for multiple individual cells calculated. Both LD displacement and mean speed were significantly enhanced in stimulated cells over a time course of infection with an increase seen as early as 2 h post-infection. With the emerging role for LDs during innate host responses, understanding their dynamics is critical to elucidate how these organelles influence the outcome of viral infection.     

Thermal analysis of polymer–water interactions and their relation to gas hydrate inhibition

Gas hydrates formed in oil production pipelines are crystalline solids where hydrocarbon gas molecules such as methane, propane, and their mixtures are trapped in a cagelike structure by hydrogen‐bonded water molecules to form undesirable plugs. Methanol and glycol are currently used to prevent these plugs via thermodynamic inhibition. Small amounts of water‐soluble polymers may provide an alternate approach for preventing gas hydrates. In this study, we expand the fundamental understanding of water–polymer systems with differential scanning calorimetry. Nonfreezable bound water was used to quantify polymer–water interactions and relate them to the chemical structure for a series of polymers, including acrylamides, cyclic lactams, and n‐vinyl amides. For good interactions, the water structure needs to be stabilized through hydrophobic interactions. An increased hydrophobicity of the pendant group also appears to favor polymer performance as a gas hydrate inhibitor. Good inhibitors, such as poly(diethyl acrylamide) and poly(N‐vinyl caprolactam), also show higher heat capacities, which indicate higher hydrophobicity, than poor performers such as polyzwitterions, in which hydrophilicity dominated. The phase behavior and thermodynamic properties of dilute polymer solutions were also evaluated through measurements of the heat of demixing and lower critical solution temperature. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2642–2653, 2007     

Numerical characterization and experimental verification of an in-plane MEMS-actuator with thin-film aluminum heater

In this paper, a novel concept of a thermo-mechanical MEMS actuator using aluminum thin-film heaters on a thermal oxide for electrical insulation is presented. The actuator is part of an universal tensile testing platform for thermo-mechanical material characterization of one dimensional materials on a micro- and nano-scopic scale under different environmental conditions, as varying temperatures, pressure, moisture or even vacuum and is realised in BDRIE technology. It is shown, that the actuator concept fulfills the requirements for the use in a tensile loading stage along with heterogeneously integrated nanofunctional elements, following a specimen centered approach in line with bottom-up self-assembly processes. Simulation and experiment agree very well in the thermal and mechanical domain and allow subsequent optimisation of the actuator performance.     

Total content and dialyzable iron and zinc in foods from northern and Southern Mexico

Developing countries diets are based on a variety of plant foods that often are the main suppliers of important amounts of iron (Fe) and zinc (Zn). The objectives of this study were 1) to measure the total and dialyzable amounts of Fe and Zn in foods from Northern Mexico (Sonora) and from Southern Mexico (Oaxaca) and 2) to evaluate the effect of meat content of diets on the dialyzable amount of Fe and Zn. Methods to calculate the total dialyzable amount of Fe and Zn, were those of the AOAC and of Shen et al. Total Fe in e northern Mexican foods went from 0.78 +/- 0.0 to 11.59 +/- to 0.03 mg/ 100g (dry weight, DW); in southern Mexican foods the same micronutrient amounts were 0.86 +/- 0.18 to 8.8 +/- 0.57 mg/100 g (BS). Total Zn values were 0.91 +/- 0.00 to 13.58 +/- 0.05 mg/100 g (DW) in Sonora, and 0.64 +/- 0.18 to 20.80 +/- 0.33 mg/100 g (DW) in Oaxaca. In northern Mexico, foods dialyzable Fe had values from 0.1 +/- 0.04% to 10.6 +/- 0.36% and for Zn from 4.0 +/- 0.21% to 55.32 +/- 0.14%. Meanwhile, the range of values of dialyzable Fe for foods from Oaxaca were from 0.22 +/- 0.06% to 9.40 +/- 0.14% for and from 2.41 +/- 0.26% to 54.27 +/- 1.49% for dialyzable Zn. The average value for dialyzable Fe was higher in the foods that contained meat or meat products (p= 0.001).     

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.     

Evaluation of messaging middleware for high-performance cloud computing

Cloud computing is posing several challenges, such as security, fault tolerance, access interface singularity, and network constraints, both in terms of latency and bandwidth. In this scenario, the performance of communications depends both on the network fabric and its efficient support in virtualized environments, which ultimately determines the overall system performance. To solve the current network constraints in cloud services, their providers are deploying high-speed networks, such as 10 Gigabit Ethernet. This paper presents an evaluation of high-performance computing message-passing middleware on a cloud computing infrastructure, Amazon EC2 cluster compute instances, equipped with 10 Gigabit Ethernet. The analysis of the experimental results, confronted with a similar testbed, has shown the significant impact that virtualized environments still have on communication performance, which demands more efficient communication middleware support to get over the current cloud network limitations.     

F-MPJ: scalable Java message-passing communications on parallel systems

This paper presents F-MPJ (Fast MPJ), a scalable and efficient Message-Passing in Java (MPJ) communication middleware for parallel computing. The increasing interest in Java as the programming language of the multi-core era demands scalable performance on hybrid architectures (with both shared and distributed memory spaces). However, current Java communication middleware lacks efficient communication support. F-MPJ boosts this situation by: (1) providing efficient non-blocking communication, which allows communication overlapping and thus scalable performance; (2) taking advantage of shared memory systems and high-performance networks through the use of our high-performance Java sockets implementation (named JFS, Java Fast Sockets); (3) avoiding the use of communication buffers; and (4) optimizing MPJ collective primitives. Thus, F-MPJ significantly improves the scalability of current MPJ implementations. A performance evaluation on an InfiniBand multi-core cluster has shown that F-MPJ communication primitives outperform representative MPJ libraries up to 60 times. Furthermore, the use of F-MPJ in communication-intensive MPJ codes has increased their performance up to seven times.     

Automatic mapping of parallel applications on multicore architectures using the Servet benchmark suite

Servet is a suite of benchmarks focused on detecting a set of parameters with high influence on the overall performance of multicore systems. These parameters can be used for autotuning codes to increase their performance on multicore clusters. Although Servet has been proved to detect accurately cache hierarchies, bandwidths and bottlenecks in memory accesses, as well as the communication overhead among cores, up to now the impact of the use of this information on application performance optimization has not been assessed. This paper presents a novel algorithm that automatically uses Servet for mapping parallel applications on multicore systems and analyzes its impact on three testbeds using three different parallel programming models: message-passing, shared memory and partitioned global address space (PGAS). Our results show that a suitable mapping policy based on the data provided by this tool can significantly improve the performance of parallel applications without source code modification.