Dexterous Orbital Servicing System (DOSS)

The Dexterous Orbiter Servicing System (DOSS) is a dexterous robotic spaceflight system that is based on the manipulator designed as part of the Flight Telerobotics Servicer program for the Space Station Freedom and built during a 'technology capture' effort that was commissioned when the FTS was cancelled from the Space Station Freedom program. The FTS technology capture effort yielded one flight manipulator and the 1 g hydraulic simulator that had been designed as an integrated test tool and crew trainer. The DOSS concept was developed to satisfy needs of the telerobotics research community, the space shuttle, and the space station. As a flight testbed, DOSS would serve as a baseline reference for testing the performance of advanced telerobotics and intelligent robotics components. For shuttle, the DOSS, configured as a movable dexterous tool, would be used to provide operational flexibility for payload operations and contingency operations. As a risk mitigation flight demonstration, the DOSS would serve the International Space Station to characterize the end to end system performance of the Special Purpose Dexterous Manipulator performing assembly and maintenance tasks with actual ISSA orbital replacement units. Currently, the most likely entrance of the DOSS into spaceflight is a risk mitigation flight experiment for the International Space Station

Determination of ππ\pi\pi scattering lengths from measurement of π+π−\pi^+\pi^- atom lifetime

The DIRAC experiment at CERN has achieved a sizeable production of $\pi^+\pi^-$ atoms and has significantly improved the precision on its lifetime determination. From a sample of 21227 atomic pairs, a 4% measurement of the S-wave $\pi\pi$ scattering length difference $|a_0-a_2| = (.0.2533^{+0.0080}_{-0.0078}|_\mathrm{stat}.{}^{+0.0078}_{-0.0073}|_\mathrm{syst})M_{\pi^+}^{-1}$ has been attained, providing an important test of Chiral Perturbation Theory.Comment: 6 pages, 6 figure

Characterization of mercury bioremediation by transgenic bacteria expressing metallothionein and polyphosphate kinase

<p>Abstract</p> <p>Background</p> <p>The use of transgenic bacteria has been proposed as a suitable alternative for mercury remediation. Ideally, mercury would be sequestered by metal-scavenging agents inside transgenic bacteria for subsequent retrieval. So far, this approach has produced limited protection and accumulation. We report here the development of a transgenic system that effectively expresses metallothionein (<it>mt-1</it>) and polyphosphate kinase (<it>ppk</it>) genes in bacteria in order to provide high mercury resistance and accumulation.</p> <p>Results</p> <p>In this study, bacterial transformation with transcriptional and translational enhanced vectors designed for the expression of metallothionein and polyphosphate kinase provided high transgene transcript levels independent of the gene being expressed. Expression of polyphosphate kinase and metallothionein in transgenic bacteria provided high resistance to mercury, up to 80 μM and 120 μM, respectively. Here we show for the first time that metallothionein can be efficiently expressed in bacteria without being fused to a carrier protein to enhance mercury bioremediation. Cold vapor atomic absorption spectrometry analyzes revealed that the <it>mt-1 </it>transgenic bacteria accumulated up to 100.2 ± 17.6 μM of mercury from media containing 120 μM Hg. The extent of mercury remediation was such that the contaminated media remediated by the <it>mt-1 </it>transgenic bacteria supported the growth of untransformed bacteria. Cell aggregation, precipitation and color changes were visually observed in <it>mt-1 </it>and <it>ppk </it>transgenic bacteria when these cells were grown in high mercury concentrations.</p> <p>Conclusion</p> <p>The transgenic bacterial system described in this study presents a viable technology for mercury bioremediation from liquid matrices because it provides high mercury resistance and accumulation while inhibiting elemental mercury volatilization. This is the first report that shows that metallothionein expression provides mercury resistance and accumulation in recombinant bacteria. The high accumulation of mercury in the transgenic cells could present the possibility of retrieving the accumulated mercury for further industrial applications.</p

Effect of oxygen on the expression of renin-angiotensin system components in a human trophoblast cell line

During the first trimester, normal placental development occurs in a low oxygen environment that is known to stimulate angiogenesis via upregulation of vascular endothelial growth factor (VEGF). Expression of the placental renin-angiotensin system (RAS) is highest in early pregnancy. While the RAS and oxygen both stimulate angiogenesis, how they interact within the placenta is unknown. We postulated that low oxygen increases expression of the proangiogenic RAS pathway and that this is associated with increased VEGF in a first trimester human trophoblast cell line (HTR-8/SVneo). HTR-8/SVneo cells were cultured in one of three oxygen tensions (1%, 5% and 20%). RAS and VEGF mRNA expression were determined by qPCR. Prorenin, angiotensin converting enzyme (ACE) and VEGF protein levels in the supernatant, as well as prorenin and ACE in cell lysates, were measured using ELISAs. Low oxygen significantly increased the expression of both angiotensin II type 1 receptor (AGTR1) and VEGF (both P < 0.05). There was a positive correlation between AGTR1 and VEGF expression at low oxygen (r = 0.64, P < 0.005). Corresponding increases in VEGF protein were observed with low oxygen (P < 0.05). Despite no change in ACE1 mRNA expression, ACE levels in the supernatant increased with low oxygen (1% and 5%, P < 0.05). Expression of other RAS components did not change. Low oxygen increased AGTR1 and VEGF expression, as well as ACE and VEGF protein levels, suggesting that the proangiogenic RAS pathway is activated. This highlights a potential role for the placental RAS in mediating the proangiogenic effects of low oxygen in placental development

Complete genome sequence of the industrial bacterium Bacillus licheniformis and comparisons with closely related Bacillus species

BACKGROUND: Bacillus licheniformis is a Gram-positive, spore-forming soil bacterium that is used in the biotechnology industry to manufacture enzymes, antibiotics, biochemicals and consumer products. This species is closely related to the well studied model organism Bacillus subtilis, and produces an assortment of extracellular enzymes that may contribute to nutrient cycling in nature. RESULTS: We determined the complete nucleotide sequence of the B. licheniformis ATCC 14580 genome which comprises a circular chromosome of 4,222,336 base-pairs (bp) containing 4,208 predicted protein-coding genes with an average size of 873 bp, seven rRNA operons, and 72 tRNA genes. The B. licheniformis chromosome contains large regions that are colinear with the genomes of B. subtilis and Bacillus halodurans, and approximately 80% of the predicted B. licheniformis coding sequences have B. subtilis orthologs. CONCLUSIONS: Despite the unmistakable organizational similarities between the B. licheniformis and B. subtilis genomes, there are notable differences in the numbers and locations of prophages, transposable elements and a number of extracellular enzymes and secondary metabolic pathway operons that distinguish these species. Differences include a region of more than 80 kilobases (kb) that comprises a cluster of polyketide synthase genes and a second operon of 38 kb encoding plipastatin synthase enzymes that are absent in the B. licheniformis genome. The availability of a completed genome sequence for B. licheniformis should facilitate the design and construction of improved industrial strains and allow for comparative genomics and evolutionary studies within this group of Bacillaceae

Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/ARMA is 14.9% compared to 26.2% for the na\"ive persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposedComment: Energy (2012)

Neurodegenerative influence of oxidative stress in the retina of a murine model of diabetes

Aims/hypothesis: Diabetic retinopathy is a progressive neuro-degenerative disease, but the underlying mechanism is still obscure. Here, we focused on oxidative stress in the retina, and analysed its influence on retinal neurodegeneration, using an antioxidant, lutein. Methods: C57BL/6 mice with streptozotocin-induced diabetes were constantly fed either a lutein-supplemented diet or a control diet from the onset of diabetes, and their metabolic data were recorded. In 1-month-diabetic mice, reactive oxygen species (ROS) in the retina were measured using dihydroethidium and visual function was evaluated by electroretinograms. Levels of activated extracellular signal-regulated kinase (ERK), synaptophysin and brain-derived neurotrophic factor (BDNF) were also measured by immunoblotting in the retina of 1-month-diabetic mice. In the retinal sections of 4-month-diabetic mice, histological changes, cleaved caspase-3 and TUNEL staining were analysed. Results: Lutein did not affect the metabolic status of the diabetic mice, but it prevented ROS generation in the retina and the visual impairment induced by diabetes. ERK activation, the subsequent synaptophysin reduction, and the BDNF depletion in the diabetic retina were all prevented by lutein. Later, in 4-month-diabetic mice, a decrease in the thickness of the inner plexiform and nuclear layers, and ganglion cell number, together with increase in cleaved caspase-3- and TUNEL-positive cells, were avoided in the retina of lutein-fed mice. Conclusions/interpretation: The results indicated that local oxidative stress that has a neurodegenerative influence in the diabetic retina is prevented by constant intake of a lutein-supplemented diet. The antioxidant, lutein may be a potential therapeutic approach to protect visual function in diabetes

Tools and data services registry: a community effort to document bioinformatics resources.

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools

S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures

With numerous new quantum chemistry methods being developed in recent years and the promise of even more new methods to be developed in the near future, it is clearly critical that highly accurate, well-balanced, reference data for many different atomic and molecular properties be available for the parametrization and validation of these methods. One area of research that is of particular importance in many areas of chemistry, biology, and material science is the study of noncovalent interactions. Because these interactions are often strongly influenced by correlation effects, it is necessary to use computationally expensive high-order wave function methods to describe them accurately. Here, we present a large new database of interaction energies calculated using an accurate CCSD(T)/CBS scheme. Data are presented for 66 molecular complexes, at their reference equilibrium geometries and at 8 points systematically exploring their dissociation curves; in total, the database contains 594 points: 66 at equilibrium geometries, and 528 in dissociation curves. The data set is designed to cover the most common types of noncovalent interactions in biomolecules, while keeping a balanced representation of dispersion and electrostatic contributions. The data set is therefore well suited for testing and development of methods applicable to bioorganic systems. In addition to the benchmark CCSD(T) results, we also provide decompositions of the interaction energies by means of DFT-SAPT calculations. The data set was used to test several correlated QM methods, including those parametrized specifically for noncovalent interactions. Among these, the SCS-MI-CCSD method outperforms all other tested methods, with a root-mean-square error of 0.08 kcal/mol for the S66 data set