Three dimensional structure from intensity correlations

We develop the analysis of x-ray intensity correlations from dilute ensembles of identical particles in a number of ways. First, we show that the 3D particle structure can be determined if the particles can be aligned with respect to a single axis having a known angle with respect to the incident beam. Second, we clarify the phase problem in this setting and introduce a data reduction scheme that assesses the integrity of the data even before the particle reconstruction is attempted. Finally, we describe an algorithm that reconstructs intensity and particle density simultaneously, thereby making maximal use of the available constraints.Comment: 17 pages, 9 figure

Scalable grid resource allocation for scientific workflows using hybrid metaheuristics

Grid infrastructure is a valuable tool for scientific users, but it is characterized by a high level of complexity which makes it difficult for them to quantify their requirements and allocate resources. In this paper, we show that resource trading is a viable and scalable approach for scientific users to consume resources. We propose the use of Grid resource bundles to specify supply and demand combined with a hybrid metaheuristic method to determine the allocation of resources in a market-based approach. We evaluate this through the application domain of scientific workflow execution on the Grid

Retroviral Danger from Within: TLR7 Is in Control

In this issue of Immunity, Yu et al. (2012) outline a fascinating model in which TLR7-mediated antibody production acts as a dominant immunosurveillance mechanism against endogenous retroviruses (ERVs), with additional support of TLR3 and TLR9 that function to prevent ERV-mediated malignancy

Using Open Data for Modeling and Simulation of the All Electrical Society in eASiMOV

The present study examines a future energy systems scenario, the so-called All Electrical Society (AES), which is defined by a very high number of active prosumers in the distribution grid in view of future 100% renewables-based energy systems. In this paper, we present data modeling methods that describe the power consumption behavior and power generation patterns via time series for 78 prosumers, each fully equipped with rooftop PV, two battery electrical vehicles and a heat pump. Quasi-dynamic simulations of a low voltage grid under stress conditions are performed using open data and free software. The simulatively determined increase in network utilization and congestion is also compared with the currently available grid capacity gained through extensive measurements in the examined distribution grid. The result is that in the AES scenario the current deployed electrical infrastructure of the distribution grid will be more than heavily overloaded, both the transformers and the respective power lines

The tax-inducible actin-bundling protein fascin is crucial for release and cell-to-cell transmission of human T-cell leukemia virus type 1 (HTLV-1)

The delta-retrovirus Human T-cell leukemia virus type 1 (HTLV-1) preferentially infects CD4(+) T-cells via cell-to-cell transmission. Viruses are transmitted by polarized budding and by transfer of viral biofilms at the virological synapse (VS). Formation of the VS requires the viral Tax protein and polarization of the host cytoskeleton, however, molecular mechanisms of HTLV-1 cell-to-cell transmission remain incompletely understood. Recently, we could show Tax-dependent upregulation of the actin-bundling protein Fascin (FSCN-1) in HTLV-1-infected T-cells. Here, we report that Fascin contributes to HTLV-1 transmission. Using single-cycle replication-dependent HTLV-1 reporter vectors, we found that repression of endogenous Fascin by short hairpin RNAs and by Fascin-specific nanobodies impaired gag p19 release and cell-to-cell transmission in 293T cells. In Jurkat T-cells, Tax-induced Fascin expression enhanced virus release and Fascin-dependently augmented cell-to-cell transmission to Raji/CD4(+) B-cells. Repression of Fascin in HTLV-1-infected T-cells diminished virus release and gag p19 transfer to co-cultured T-cells. Spotting the mechanism, flow cytometry and automatic image analysis showed that Tax-induced T-cell conjugate formation occurred Fascin-independently. However, adhesion of HTLV-1-infected MT-2 cells in co-culture with Jurkat T-cells was reduced upon knockdown of Fascin, suggesting that Fascin contributes to dissemination of infected T-cells. Imaging of chronically infected MS9 T-cells in co-culture with Jurkat T-cells revealed that Fascin's localization at tight cell-cell contacts is accompanied by gag polarization suggesting that Fascin directly affects the distribution of gag to budding sites, and therefore, indirectly viral transmission. In detail, we found gag clusters that are interspersed with Fascin clusters, suggesting that Fascin makes room for gag in viral biofilms. Moreover, we observed short, Fascin-containing membrane extensions surrounding gag clusters and clutching uninfected T-cells. Finally, we detected Fascin and gag in long-distance cellular protrusions. Taken together, we show for the first time that HTLV-1 usurps the host cell factor Fascin to foster virus release and cell-to-cell transmission

SIMULATION OF THE FLIGHT DISTANCES OF JAVELINS BASED ON A NEURAL NETWORK APPROACH

INTRODUCTION: The flight distances of javelins are determined by the release parameters as well as by the forces acting on the javelin during flight. The flight phase of the javelin has been under investigation by many researchers using engineering approaches to model the flight phase. The objective is to allow an optimization of the release parameters for maximizing the flight distance. The measurement of release parameters as well as wind influence is not very precise. This means that the models are based on already distorted data. Artificial neural networks (NNs, Haykin 1994) are powerful information processing tools that allow to construct a input-output model of a problem by learning from examples. They are able to generalize , i.e. to produce reasonable outputs for inputs that have not been encountered during learning. NNs handle imprecise data well and could be suitable for modeling the flight distance of javelins as a result of the release parameters. METHODS: Release parameters have been measured using three dimensional film and video analysis. Relevant parameters were determined: the angle of release, the angle of attack (seen from the side), the angle of side attack (seen from behind) as well as the velocity of release. The overall flight was measured as the distance between the throwing line and the athlete’s hand at the point of release plus the distance between the line and the point of touch down of the javelin. Other parameters such as javelin brand, wind speed, etc., were not considered in the model. Multi-Layer-Perceptron Neural Networks (MLPs) were used to construct a model with the release parameters as inputs and the overall distance as output. RESULTS: Several setups were used for the training of the MLPs and 40 sets of release parameters were processed. We used 37 sets for the training of the MLPs and 3 sets were kept for examining the MLPs’ generalization performance (crossvalidation). This was repeated with randomly selected sets for training and crossvalidation. Predictions of the total flight distance using the release parameters were exact up to 5 percent of the overall distance for the cross validation sets. CONCLUSIONS: The MLP simulation of the flight distance is a suitable instrument even though it uses only a small number of parameters. This can be helpful for coaching and provides an alternative to other models. Using more data sets may improve the quality of prediction, and further work will include recording more data sets as well as studies on optimal javelin release parameters. REFERENCES: Haykin, S. (1994). Neural Networks. Englewood Cliffs: Macmillan Publishing Company

Minimal information for studies of extracellular vesicles 2018 (MISEV2018) : a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines

The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, andmany other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points