Shuttle-launch triangular space station

A triangular space station deployable in orbit is described. The framework is comprized of three trusses, formed of a pair of generally planar faces consistine of foldable struts. The struts expand and lock into rigid structural engagement forming a repetition of equilater triangles and nonfolding diagonal struts interconnecting the two faces. The struts are joined together by node fittings. The framework can be packaged into a size and configuration transportable by a space shuttle. When deployed, the framework provides a large work/construction area and ample planar surface area for solar panels and thermal radiators. A plurity of modules are secured to the framework and then joined by tunnels to make an interconnected modular display. Thruster units for the space station orientation and altitude maintenance are provided

The Impact of Task Load on the Integration of Explicit Contextual Priors and Visual Information during Anticipation

© 2020 The Authors. There is limited knowledge about the impact of task load on experts’ integration of contextual priors and visual information during dynamic and rapidly evolving anticipation tasks. We examined how experts integrate contextual priors––specifically, prior information regarding an opponent's action tendencies––with visual information such as movement kinematics, during a soccer‐specific anticipation task. Furthermore, we combined psychophysiological measures and retrospective self‐reports to gain insight into the cognitive load associated with this integration. Players were required to predict the action of an oncoming opponent, with and without the explicit provision of contextual priors, under two different task loads. In addition to anticipation performance, we compared continuous electroencephalography (EEG) and self‐reports of cognitive load across conditions. Our data provide tentative evidence that increased task load may impair performance by disrupting the integration of contextual priors and visual information. EEG data suggest that cognitive load may increase when contextual priors are explicitly provided, whereas self‐report data suggested a decrease in cognitive load. The findings provide insight into the processing demands associated with integration of contextual priors and visual information during dynamic anticipation tasks, and have implications for the utility of priors under cognitively demanding conditions. Furthermore, our findings add to the existing literature, suggesting that continuous EEG may be a more valid measure than retrospective self‐reports for in‐task assessment of cognitive load

Analysis of Stochastic Strategies in Bacterial Competence: A Master Equation Approach

Competence is a transiently differentiated state that certain bacterial cells reach when faced with a stressful environment. Entrance into competence can be attributed to the excitability of the dynamics governing the genetic circuit that regulates this cellular behavior. Like many biological behaviors, entrance into competence is a stochastic event. In this case cellular noise is responsible for driving the cell from a vegetative state into competence and back. In this work we present a novel numerical method for the analysis of stochastic biochemical events and use it to study the excitable dynamics responsible for competence in Bacillus subtilis. Starting with a Finite State Projection (FSP) solution of the chemical master equation (CME), we develop efficient numerical tools for accurately computing competence probability. Additionally, we propose a new approach for the sensitivity analysis of stochastic events and utilize it to elucidate the robustness properties of the competence regulatory genetic circuit. We also propose and implement a numerical method to calculate the expected time it takes a cell to return from competence. Although this study is focused on an example of cell-differentiation in Bacillus subtilis, our approach can be applied to a wide range of stochastic phenomena in biological systems

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

Convergence of Free Energy Profile of Coumarin in Lipid Bilayer

Atomistic molecular dynamics (MD) simulations of druglike molecules embedded in lipid bilayers are of considerable interest as models for drug penetration and positioning in biological membranes. Here we analyze partitioning of coumarin in dioleoylphosphatidylcholine (DOPC) bilayer, based on both multiple, unbiased 3 μs MD simulations (total length) and free energy profiles along the bilayer normal calculated by biased MD simulations (∼7 μs in total). The convergences in time of free energy profiles calculated by both umbrella sampling and z-constraint techniques are thoroughly analyzed. Two sets of starting structures are also considered, one from unbiased MD simulation and the other from “pulling” coumarin along the bilayer normal. The structures obtained by pulling simulation contain water defects on the lipid bilayer surface, while those acquired from unbiased simulation have no membrane defects. The free energy profiles converge more rapidly when starting frames from unbiased simulations are used. In addition, z-constraint simulation leads to more rapid convergence than umbrella sampling, due to quicker relaxation of membrane defects. Furthermore, we show that the choice of RESP, PRODRG, or Mulliken charges considerably affects the resulting free energy profile of our model drug along the bilayer normal. We recommend using z-constraint biased MD simulations based on starting geometries acquired from unbiased MD simulations for efficient calculation of convergent free energy profiles of druglike molecules along bilayer normals. The calculation of free energy profile should start with an unbiased simulation, though the polar molecules might need a slow pulling afterward. Results obtained with the recommended simulation protocol agree well with available experimental data for two coumarin derivatives

Investigation of Indazole Unbinding Pathways in CYP2E1 by Molecular Dynamics Simulations

Human microsomal cytochrome P450 2E1 (CYP2E1) can oxidize not only low molecular weight xenobiotic compounds such as ethanol, but also many endogenous fatty acids. The crystal structure of CYP2E1 in complex with indazole reveals that the active site is deeply buried into the protein center. Thus, the unbinding pathways and associated unbinding mechanisms remain elusive. In this study, random acceleration molecular dynamics simulations combined with steered molecular dynamics and potential of mean force calculations were performed to identify the possible unbinding pathways in CYP2E1. The results show that channel 2c and 2a are most likely the unbinding channels of CYP2E1. The former channel is located between helices G and I and the B-C loop, and the latter resides between the region formed by the F-G loop, the B-C loop and the β1 sheet. Phe298 and Phe478 act as the gate keeper during indazole unbinding along channel 2c and 2a, respectively. Previous site-directed mutagenesis experiments also supported these findings

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)

Novel Biochemical Markers of Psychosocial Stress in Women

Background: Prolonged psychosocial stress is a condition assessed through self-reports. Here we aimed to identify biochemical markers for screening and early intervention in women

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