Signal recognition particle binds to translating ribosomes before emergence of a signal anchor sequence.

The bacterial signal recognition particle (SRP) is part of the machinery that targets ribosomes synthesizing membrane proteins to membrane-embedded translocons co-translationally. Recognition of nascent membrane proteins occurs by virtue of a hydrophobic signal-anchor sequence (SAS) contained in the nascent chain, usually at the N terminus. Here we use fluorescence-based stopped-flow to monitor SRP-ribosome interactions with actively translating ribosomes while an SRP substrate is synthesized and emerges from the peptide exit tunnel. The kinetic analysis reveals that, at cellular concentrations of ribosomes and SRP, SRP rapidly binds to translating ribosomes prior to the emergence of an SAS and forms an initial complex that rapidly rearranges to a more stable engaged complex. When the growing peptide reaches a length of ∼50 amino acids and the SAS is partially exposed, SRP undergoes another conformational change which further stabilizes the complex and initiates targeting of the translating ribosome to the translocon. These results provide a reconciled view on the timing of high-affinity targeting complex formation, while emphasizing the existence of preceding SRP recruitment steps under conditions of ongoing translation

Modelling the economic efficiency of using different strategies to control Porcine Reproductive & Respiratory Syndrome at herd level

PRRS is among the diseases with the highest economic impact in pig production worldwide. Different strategies have been developed and applied to combat PRRS at farm level. The broad variety of available intervention strategies makes it difficult to decide on the most cost-efficient strategy for a given farm situation, as it depends on many farm-individual factors like disease severity, prices or farm structure. Aim of this study was to create a simulation tool to estimate the cost-efficiency of different control strategies at individual farm level. Baseline is a model that estimates the costs of PRRS, based on changes in health and productivity, in a specific farm setting (e.g. farm type, herd size, type of batch farrowing). The model evaluates different intervention scenarios: depopulation/repopulation (D/R), close & roll-over (C&R), mass vaccination of sows (MS), mass vaccination of sows and vaccination of piglets (MS + piglets), improvements in internal biosecurity (BSM), and combinations of vaccinations with BSM. Data on improvement in health and productivity parameters for each intervention were obtained through literature review and from expert opinions. The economic efficiency of the different strategies was assessed over 5 years through investment appraisals: the resulting expected value (EV) indicated the most cost-effective strategy. Calculations were performed for 5 example scenarios with varying farm type (farrow-to-finish – breeding herd), disease severity (slightly – moderately – severely affected) and PRRSV detection (yes – no). The assumed herd size was 1000 sows with farm and price structure as commonly found in Germany. In a moderately affected (moderate deviations in health and productivity parameters from what could be expected in an average negative herd), unstable farrow-to-finish herd, the most cost-efficient strategies according to their median EV were C&R (€1′126′807) and MS + piglets (€ 1′114′649). In a slightly affected farrow-to-finish herd, no virus detected, the highest median EV was for MS + piglets (€ 721′745) and MS (€ 664′111). Results indicate that the expected benefits of interventions and the most efficient strategy depend on the individual farm situation, e.g. disease severity. The model provides new insights regarding the cost-efficiency of various PRRSV intervention strategies at farm level. It is a valuable tool for farmers and veterinarians to estimate expected economic consequences of an intervention for a specific farm setting and thus enables a better informed decision

Shock and Release Temperatures in Molybdenum

Shock and release temperatures in Mo were calculated, taking account of heating from plastic flow predicted using the Steinberg-Guinan model. Plastic flow was calculated self-consistently with the shock jump conditions: this is necessary for a rigorous estimate of the locus of shock states accessible. The temperatures obtained were significantly higher than predicted assuming ideal hydrodynamic loading. The temperatures were compared with surface emission spectrometry measurements for Mo shocked to around 60GPa and then released into vacuum or into a LiF window. Shock loading was induced by the impact of a planar projectile, accelerated by high explosive or in a gas gun. Surface velocimetry showed an elastic wave at the start of release from the shocked state; the amplitude of the elastic wave matched the prediction to around 10%, indicating that the predicted flow stress in the shocked state was reasonable. The measured temperatures were consistent with the simulations, indicating that the fraction of plastic work converted to heat was in the range 70-100% for these loading conditions

Cost of porcine reproductive and respiratory syndrome virus at individual farm level – An economic disease model

Porcine reproductive and respiratory syndrome (PRRS) is reported to be among the diseases with the highest economic impact in modern pig production worldwide. Yet, the economic impact of the disease at farm level is not well understood as, especially in endemically infected pig herds, losses are often not obvious. It is therefore difficult for farmers and veterinarians to appraise whether control measures such as virus elimination or vaccination will be economically beneficial for their farm. Thus, aim of this study was to develop an epidemiological and economic model to determine the costs of PRRS for an individual pig farm. In a production model that simulates farm outputs, depending on farm type, farrowing rhythm or length of suckling period, an epidemiological model was integrated. In this, the impact of PRRS infection on health and productivity was estimated. Financial losses were calculated in a gross margin analysis and a partial budget analysis based on the changes in health and production parameters assumed for different PRRS disease severities. Data on the effects of endemic infection on reproductive performance, morbidity and mortality, daily weight gain, feed efficiency and treatment costs were obtained from literature and expert opinion. Nine different disease scenarios were calculated, in which a farrow-to-finish farm (1000 sows) was slightly, moderately or severely affected by PRRS, based on changes in health and production parameters, and either in breeding, in nursery and fattening or in all three stages together. Annual losses ranged from a median of € 75′724 (90% confidence interval (C.I.): € 78′885–€ 122′946), if the farm was slightly affected in nursery and fattening, to a median of € 650′090 (90% C.I. € 603′585–€ 698′379), if the farm was severely affected in all stages. Overall losses were slightly higher if breeding was affected than if nursery and fattening were affected. In a herd moderately affected in all stages, median losses in breeding were € 46′021 and € 422′387 in fattening, whereas costs were € 25′435 lower in nursery, compared with a PRRSV-negative farm. The model is a valuable decision-support tool for farmers and veterinarians if a farm is proven to be affected by PRRS (confirmed by laboratory diagnosis). The output can help to understand the need for interventions in case of significant impact on the profitability of their enterprise. The model can support veterinarians in their communication to farmers in cases where costly disease control measures are justified

Synergism of microwaves and ultrasound for advanced biorefineries

AbstractConventional energy sources are limited and non-renewable and their consumption contributes to greenhouse gas emissions. The world is in need of advanced biorefineries to meet ever growing energy demands associated with population growth and economic development. An advanced biorefinery should use renewable and sustainable (both in quality and quantity) feedstock that gives rise to higher energy gains with minimum non-renewable energy and resource consumption. Development of advanced biorefineries is currently encircled by two major issues. The first issue is to ensure adequate biofuel feedstock supplies while the second issue is to develop resource-efficient technologies for the feedstock conversion to maximize energy and economic and environmental benefits. While microalgae, microbial derived oils, and agricultural biomass and other energy crops show great potential for meeting current energy demands in a sustainable manner, process intensification and associated synergism can improve the resource utilization efficiency. Synergism of process intensification tools is important to increase energy efficiency, reduce chemical utilization and associated environmental impacts, and finally process economics. Among the many process intensification methods, this commentary provides a perspective on the essential role of MWs and US and their synergy in biofuel production. Individual, sequential, and simultaneous applications of MWs and US irradiations can be utilized for process intensification of various biofuels production and selective recovery of high value bioproducts. Process related barriers, namely mass and heat transfer limitations, can be eliminated by this synergism while improving the reaction efficiency and overall process economics significantly. In this article, a brief review focused on recent developments in MW and US mediated process intensification for biofuel synthesis and associated issues in their synergism followed by a discussion on current challenges and future prospective is presented

Проблемы процесса управления в техносферной безопасности

В статье будет рассмотрены проблемы техносферной безопасности через интеграционный подход, как самый эффективный в сфере управления природопользования. Самые важные проблемы будут обобщены в вопросах государственного регулирования, также будут выявлены факторы влияния на эффективность управления данного процесса

On the complexity of computing the kk-restricted edge-connectivity of a graph

The \emph{$k$-restricted edge-connectivity} of a graph $G$, denoted by $\lambda_k(G)$, is defined as the minimum size of an edge set whose removal leaves exactly two connected components each containing at least $k$ vertices. This graph invariant, which can be seen as a generalization of a minimum edge-cut, has been extensively studied from a combinatorial point of view. However, very little is known about the complexity of computing $\lambda_k(G)$. Very recently, in the parameterized complexity community the notion of \emph{good edge separation} of a graph has been defined, which happens to be essentially the same as the $k$-restricted edge-connectivity. Motivated by the relevance of this invariant from both combinatorial and algorithmic points of view, in this article we initiate a systematic study of its computational complexity, with special emphasis on its parameterized complexity for several choices of the parameters. We provide a number of NP-hardness and W[1]-hardness results, as well as FPT-algorithms.Comment: 16 pages, 4 figure

Status of Flat Electron Beam Production

Last year at LINAC2000 [1] we reported our initial verification of the round beam (comparable transverse emittances) to flat beam (high transverse emittance ratio) transformation described by Brinkmann, Derbenev, and Flöttmann [2]. Further analysis of our data has confirmed that a transverse emittance ratio of approximately 50 was observed. Graphics representing observational detail are included here, and future plans outlined

Superbeams versus Neutrino Factories

We compare the physics potential of planned superbeams with the one of neutrino factories. Therefore, the experimental setups as well as the most relevant uncertainties and errors are considered on the same footing as much as possible. We use an improved analysis including the full parameter correlations, as well as statistical, systematical, and degeneracy errors. Especially, degeneracies have so far not been taken into account in a numerical analysis. We furthermore include external input, such as improved knowledge of the solar oscillation parameters from the KamLAND experiment. This allows us to determine the limiting uncertainties in all cases. For a specific comparison, we choose two representatives of each class: For the superbeam, we take the first conceivable setup, namely the JHF to SuperKamiokande experiment, as well as, on a longer time scale, the JHF to HyperKamiokande experiment. For the neutrino factory, we choose an initially conceivable setup and an advanced machine. We determine the potential to measure the small mixing angle sin^2 2 theta_{13}, the sign of Delta m^2_{31}, and the leptonic CP phase \deltacp, which also implies that we compare the limitations of the different setups. We find interesting results, such as the complete loss of the sensitivity to the sign of Delta m^2_{31} due to degeneracies in many cases.Comment: Revised version with JHF energy resolution corrected, discussion of detector issues added (App. B), and references added. Summary and conclusions unchanged. 51 pages, 28 figures, 4 table