Treatment of acute lateral ankle ligament ruptures. A prospective 9-13-year follow-up study of three different therapies in 79 patients

In a prospective study of 79 patients with arthrographically verified acute ruptured lateral ankle ligaments we have evaluated the long-term results of three different treatments: operation and walking-cast, walking-cast alone and elastic bandage. The average follow-up period was 11 (9-13) years, and 32 parameters concerning interview, clinical and radiographic examinations were statistically analysed. The three kinds of treatment were found to be equal, since only one parameter showed significant difference at a 5% level using Fisher's exact and the Mann-Whitney test, and no difference by using the Kruskal-Wallis test for homogeneity. Residual disabilities and late complications, such as instability, pain on activity, and the number of secondary ligament reconstructions and talocrural arthroses were low. It was concluded that nonoperative treatment provided adequate results even after a decade.     

Enzymatic biodiesel production: Technical and economical considerations

It is well documented in the literature that enzymatic processing of oils and fats for biodiesel is technically feasible. However, with very few exceptions, enzyme technology is not currently used in commercial‐scale biodiesel production. This is mainly due to non‐optimized process design and a lack of available cost‐effective enzymes. The technology to re‐use enzymes has typically proven insufficient for the processes to be competitive. However, literature data documenting the productivity of enzymatic biodiesel together with the development of new immobilization technology indicates that enzyme catalysts can become cost effective compared to chemical processing. This work reviews the enzymatic processing of oils and fats into biodiesel with focus on process design and economy.     

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.     

Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method

To date the design of structures using topology optimization methods has mainly focused on single-objective problems. Since real-world design problems typically involve several different objectives, most of which counteract each other, it is desirable to present the designer with a set of Pareto optimal solutions that capture the trade-off between these objectives, known as a smart Pareto set. Thus far only the weighted sums and global criterion methods have been incorporated into topology optimization problems. Such methods are unable to produce evenly distributed smart Pareto sets. However, recently the smart normal constraint method has been shown to be capable of directly generating smart Pareto sets. Therefore, in the present work, an updated smart Normal Constraint Method is combined with a Bi-directional Evolutionary Structural Optimization (SNC-BESO) algorithm to produce smart Pareto sets for multiobjective topology optimization problems. Two examples are presented, showing that the Pareto solutions found by the SNC-BESO method make up a smart Pareto set. The first example, taken from the literature, shows the benefits of the SNC-BESO method. The second example is an industrial design problem for a micro fluidic mixer. Thus, the problem is multi-physics as well as multiobjective, highlighting the applicability of such methods to real-world problems. The results indicate that the method is capable of producing smart Pareto sets to industrial problems in an effective and efficient manner.     

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.