The stability and unfolding of an IgG binding protein based upon the B domain of protein A from Staphylococcus aureus probed by tryptophan substitution and fluorescence spectroscopy

The stability and unfolding of an immunoglobulin (Ig) G bindingprotein based upon the B domain of protein A (SpAB) from Staphylococcusaureus were studied by substituting tryptophan residues at strategiclocations within each of the three a-helical regions (al-a3)of the domain. The role of the C-terminal helix, a3, was investigatedby generating two protein constructs, one corresponding to thecomplete SpAB, the other lacking a part of ct3; the Trp substitutionswere made in both one-and two-domain versions of each of theseconstructs. The fluorescence properties of each of the single-tryptophanmutants were studied in the native state and as a function ofguanidine-HCl-mediated unfolding, and their IgG binding activitieswere determined by a competitive enzyme-linked immunosorbentassay. The free energies of folding and of binding to IgG foreach mutant were compared with those for the native domains.The effect of each substitution upon the overall structure andupon the IgG binding interface was modelled by molecular graphicsand energy minimization. These studies indicate that (i) 3 contributesto the overall stability of the domain and to the formationof the IgG binding site in l and 2, and (ii) al unfolds first,followed by 2 and 3 together.     

Robustness of ISS systems to inputs with limited moving average: Application to spacecraft formations

We provide a theoretical framework that fits realistic challenges related to spacecraft formation with disturbances. We show that the input‐to‐state stability of such systems guarantees some robustness with respect to a class of signals with bounded average‐energy, which encompasses the typical disturbances acting on spacecraft formations. Solutions are shown to converge to the desired formation, up to an offset, which is somewhat proportional to the considered moving average of disturbances. In the presence of fast peaking perturbations, the approach provides a tighter evaluation of the disturbances' influence, which allows for the use of more parsimonious control gains. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

Prediction of the binding mode of biarylpropylsulfonamide allosteric AMPA receptor modulators based on docking, GRID molecular interaction fields and 3D-QSAR analysis

A novel approach of combining flexible molecular docking, GRID molecular interaction fields, analysis of ligand-protein hydrogen bond interactions, conformational energy penalties and 3D-QSAR analysis was used to propose a binding mode in the dimer interface of the iGluR2 receptor for the biarylpropylsulfonamide class of positive allosteric AMPA modulators. Possible binding poses were generated by flexible molecular docking. GRID molecular interaction fields of the binding site, ligand-protein hydrogen bonding interactions and conformational energy penalties were used to select the most likely binding mode. The selected binding poses were subjected to a 3D-QSAR analysis using previously published activity data. The resulting model (2 LVs, R2=0.89, q2=0.61) predicted the activities of the compounds in the test set with a standard deviation on error of prediction of 0.17. The proposed binding mode was validated by interpretation of the PLS-coefficient regions from the 3D-QSAR analysis in terms of interactions between the receptor and the modulators.     

Incorporation of a Probabilistic Monotonic Strain Energy Analysis to a Lifting Method

The proposed work analyzes the possibility of improving the capabilities of an energy-based fatigue life prediction method. The improvement being addressed is regarding the variation of empirical monotonic strain energy density calculations and the effects on the energy-based fatigue life prediction capability. Since the prediction method was developed from the concept that the strain energy accumulated during both monotonic failure and an entire fatigue process are equal, meaning the strain energy accumulated during monotonic failure is a physical damage quantity, it was important to understand the variation of monotonic strain energy density. The process for incorporating this variation into the prediction method explores a probabilistic, Three-Sigma analysis that is applicable for all deterministic methods of measuring experimental monotonic strain energy density. The accuracy of the probabilistic energy-based lifing method was admirably assessed by comparison with experimental fatigue life results, between 10³ and 10⁵ cycles, conducted on Titanium 6Al–4V specimens at room temperature.     

Enhanced O2 Flux of CaTi0.85Fe0.15O3−δ Based Membranes by Mn Doping

Dense symmetric membranes of CaTi_0.85?xFe_0.15Mn_xO_3?δ (x = 0.1, 0.15, 0.25, 0.4) are investigated in order to determine the optimal Mn dopant content with respect to highest O₂ flux. O₂ permeation measurements are performed as function of temperature between 700°C–1000°C and as function of the feed side ranging between 0.01 and 1 bar. X‐ray photoelectron spectroscopy is utilized to elucidate the charge state of Mn, and synchrotron radiation X‐ray powder diffraction (SR‐XPD) is employed to investigate the structure symmetry and cell volume of the perovskite phase at temperatures up to 800°C. The highest O₂ permeability is found for x = 0.25 over the whole temperature and ranges, followed by x = 0.4 above 850°C. The O₂ permeability for x = 0.25 reaches 0.01 mL(STP) min^?1 cm^?1 at 925°C with 0.21 bar feed side and Ar sweep gas. X‐ray photoelectron spectroscopy indicates that the charge state of Mn changes from approx. +3 to +4 when x > 0.1, which implies that Mn mainly improves electronic conductivity for x > 0.1. The cell volume is found to decrease linearly with Mn content, which coincides with an increase in the activation energy of O₂ permeability. These results are consistent with the interpretation of the temperature and dependency of O₂ permeation. The sintering behavior and thermal expansion properties are investigated by dilatometry, which show improved sinterability with increasing Mn content and that the thermal expansion coefficient decreases from 12.4 to 11.9 × 10^?6 K^?1 for x = 0 and x = 0.25, respectively.     

The Effects of Exercise and Activity-Based Physical Therapy on Bone after Spinal Cord Injury

Spinal cord injury (SCI) produces paralysis and a unique form of neurogenic disuse osteoporosis that dramatically increases fracture risk at the distal femur and proximal tibia. This bone loss is driven by heightened bone resorption and near-absent bone formation during the acute post-SCI recovery phase and by a more traditional high-turnover osteopenia that emerges more chronically, which is likely influenced by the continual neural impairment and musculoskeletal unloading. These observations have stimulated interest in specialized exercise or activity-based physical therapy (ABPT) modalities (e.g., neuromuscular or functional electrical stimulation cycling, rowing, or resistance training, as well as other standing, walking, or partial weight-bearing interventions) that reload the paralyzed limbs and promote muscle recovery and use-dependent neuroplasticity. However, only sparse and relatively inconsistent evidence supports the ability of these physical rehabilitation regimens to influence bone metabolism or to increase bone mineral density (BMD) at the most fracture-prone sites in persons with severe SCI. This review discusses the pathophysiology and cellular/molecular mechanisms that influence bone loss after SCI, describes studies evaluating bone turnover and BMD responses to ABPTs during acute versus chronic SCI, identifies factors that may impact the bone responses to ABPT, and provides recommendations to optimize ABPTs for bone recovery.     

Plastic-containing materials as alternative reductants for base metal production

Shredder residue materials are produced after the removal of ferrous and non-ferrous fractions from end-of-life electronic equipment. Despite the high plastic content and metal value in the ash, high percentages of these materials are currently sent to landfills. In this study, the potential of utilising shredder residue material and other plastic-containing materials as reducing agents was studied. Plastic-containing materials were co-injected with coal into a zinc-fuming furnace in Boliden-Rönnskär smelter. The data obtained from the trial, such as the data from the chemical analysis of the slag and the steam production, are discussed. The observations indicate that plastic-containing material can replace up to 1?ton?h^?1 of coal without a significant decrease in the zinc reduction rate.     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.     

Molecular cloning and characterization of a thermostable α-amylase exhibiting an unusually high activity

An α-amylase gene was cloned from the thermophilic bacterium Bacillus subtilis isolated from Indonesian oil palm shell waste. The gene expressed an extracellular enzyme. Optimal hydrolysis conditions for the enzyme were 70°C and pH 6.0. The specific activity of the enzyme was 16.0 kU per mg of protein, which was higher than for other thermostable amylases. Hydrolytic products of the enzyme using starch and glycogen were mainly maltohexaose and maltopentaose. The enzyme had a K _m value of 0.099 mg/mL for amylopectin, more than 10 times lower than for amylose. The catalytic efficiency of the enzyme using amylopectin was 39,200 mL/mg·s and was 3,270 mL/mg·s using amylose. The enzyme liquefied corn starch at pH 5.0, which was successfully converted to glucose using commercial glucoamylase and pullulanase without pH adjustment. The enzyme has advantages for industrial applications.