An artificial muscle actuator for biomimetic underwater propulsors

In this paper, we introduce the analytical framework of the modeling dynamic characteristics of a soft artificial muscle actuator for aquatic propulsor applications. The artificial muscle used for this underwater application is an ionic polymer-metal composite (IPMC) which can generate bending motion in aquatic environments. The inputs of the model are the voltages applied to multiple IPMCs, and the output can be either the shape of the actuators or the thrust force generated from the interaction between dynamic actuator motions and surrounding water. In order to determine the relationship between the input voltages and the bending moments, the simplified RC model is used, and the mechanical beam theory is used for the bending motion of IPMC actuators. Also, the hydrodynamic forces exerted on an actuator as it moves relative to the surrounding medium or water are added to the equations of motion to study the effect of actuator bending on the thrust force generation. The proposed method can be used for modeling the general bending type artificial muscle actuator in a single or segmented form operating in the water. The segmented design has more flexibility in controlling the shape of the actuator when compared with the single form, especially in generating undulatory waves. Considering an inherent nature of large deformations in the IPMC actuator, a large deflection beam model has been developed and integrated with the electrical RC model and hydrodynamic forces to develop the state space model of the actuator system. The model was validated against existing experimental data.     

A biomimetic platform for studying root-environment interaction

Aims

Microstructure plays an important role in biological systems. Microstructural features are critical in the interaction between two biological organisms, for example, a microorganism and the surface of a plant. However, isolating the structural effect of the interaction from all other parameters is challenging when working directly with the natural system. Replicating microstructure of leaves was recently shown to be a powerful research tool for studying leaf-environment interaction. However, no such tool exists for roots. Roots present a special challenge because of their delicacy (specifically of root hairs) and their 3D structure. We aim at developing such a tool for roots.

Methods

Biomimetics use synthetic systems to mimic the structure of biological systems, enabling the isolation of structural function. Here we present a method which adapts tools from leaf microstructure replication to roots. We introduce new polymers for this replication.

Results

We find that Polyurethane methacrylate (PUMA) with fast UV curing gives a reliable replication of the tomato root surface microstructure. We show that our system is compatible with the pathogenic soilborne bacterium Ralstonia solanacearum.

Conclusions

This newly developed tool may be used to study the effect of microstructure, isolated from all other effects, on the interaction of roots with their environment.

     

Micromachined biomimetic artificial haircell sensors

The biological haircell is a modular building block of a rich variety of biological sensors. Using micro- and nanofabrication technology, an engineering equivalent artificial haircell sensor can be developed, imitating the structure and transfer function of the biological haircell. The artificial haircells can be made of hybrid semiconductor, metal and polymers. This paper discusses a number of strategies, using representative material systems, for building artificial haircell sensors and briefly outlines their fabrication method and performance. The motivation for imitating the biological haircell is also discussed to provide a background for this work.     

An experimental assessment of artificial within-family selection for fitness in conservation programs

Equalizing familiar contributions is the simplest recommended strategy to maintain genetic diversity in conservation programs. However, this method implies a relaxation of natural selection and the possibility of accumulation of deleterious mutations. Computer simulations have shown that performing selection within families for fitness traits in a conservation program can be useful to alleviate such problems. We thus carried out an experiment with the model species Drosophila melanogaster in order to assess whether or not selection for fitness traits can be useful. We considered a fitness trait (pupa productivity) that was first checked to perform as a typical fitness component. The trait showed an inbreeding depression of 1.2 per 1 % increase in inbreeding and an asymmetrical response to selection with average realized heritabilities of about 0.04 in the upward direction and an order of magnitude larger (0.36) in the downward direction. The management experiment indicated that artificial within-family selection for fitness had only a marginal success for two reasons. First, there was not an appreciable decline in fitness across the experiment despite the low population sizes assumed (N = 10 or 20), even in the population not subjected to selection. This result is compatible with fitness models which imply the segregation of few deleterious mutations of large effect. Second, artificial selection within families had a limited impact on the trait, as one expects for a typical fitness component with very low heritability.     

Doubly biomimetic catecholic phosphorylcholine copolymer: a platform strategy for fabricating antifouling surfaces

A doubly biomimetic PMNC polymer bearing cell antifouling phosphorylcholine and mussel adhesive protein catechol groups is synthesized. The polymer can be deposited onto a variety of substrates by dip-coating in an aqueous solution, adhering to surfaces via the catechol functional group while at the same time forming a cell outer membrane mimetic antifouling surface. Contact angle, ATR-FTIR and XPS measurements confirm polymer coating formation on a variety of inorganic and organic substrates. BSA and bovine plasma fibrinogen protein adsorption on PMNC coated surfaces are reduced significantly compared to unmodified substrates, and platelet adhesion from human serum onto the PMNC coated substrate surfaces is highly suppressed in this study.     

An artificial receptor for glycoproteins

We describe the rational design, synthesis and development of a sterilizable biomimetic ligand for the affinity purification of glycoproteins. Based on mimicking the principles of natural carbohydrate recognition, a putative library of 196 glycoprotein-binding synthetic ligands was designed and synthesized on a polymeric support. Ligand 11/11, based on a triazine scaffold and immobilized on a hydrophilic support, was identified as the "lead" ligand. The carbohydrate recognizing the potential of the "lead" ligand was revealed by reduced binding of a periodate oxidized model glycoprotein, and by "sharp" elution profiles achieved with borate buffer eluents. Specific elution and competitive binding experiments determined the monosaccharide specificity of 11/11 in the order mannoside > glucoside > galactoside. The diastereo-selective performance of ligand 11/11 was quantified and reaffirmed by analytical affinity chromatography and (1)H-NMR, in the order galactoside < glucoside < mannoside with binding affinities (K(a), M(-1)) in the 63-214 and 20-83 M(-1) range, respectively. Partition coefficient analysis revealed binding constants towards glycoproteins in the 10(4) M(-1) range, that compared favourably with the affinities of carbohydrate binding lectins for glycoproteins, such as concanavalin A. Molecular modelling studies of ligand 11/11 revealed the formation of a pre-organized apolar "tweezer-like" cavity, containing complementary nitrogenous hydrogen bond donor and acceptor groups that formed selective interactions with the equatorial 3- and 4-hydroxyl groups of saccharides.     

PEPPeR, a platform for experimental proteomic pattern recognition

Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed "Landmark Matching" and "Peak Matching" that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery.     

EUROCarbDB: An open-access platform for glycoinformatics

The EUROCarbDB project is a design study for a technical framework, which provides sophisticated, freely accessible, open-source informatics tools and databases to support glycobiology and glycomic research. EUROCarbDB is a relational database containing glycan structures, their biological context and, when available, primary and interpreted analytical data from high-performance liquid chromatography, mass spectrometry and nuclear magnetic resonance experiments. Database content can be accessed via a web-based user interface. The database is complemented by a suite of glycoinformatics tools, specifically designed to assist the elucidation and submission of glycan structure and experimental data when used in conjunction with contemporary carbohydrate research workflows. All software tools and source code are licensed under the terms of the Lesser General Public License, and publicly contributed structures and data are freely accessible. The public test version of the web interface to the EUROCarbDB can be found at http://www.ebi.ac.uk/eurocarb.     

An artificial riboswitch for controlling pre-mRNA splicing

Riboswitches, as previously reported, are natural RNA aptamers that regulate the expression of numerous bacterial metabolic genes in response to small molecule ligands. It has recently been shown that these RNA genetic elements are also present near the splice site junctions of plant and fungal introns, thus raising the possibility of their involvement in regulating mRNA splicing. Here it is shown for the first time that a riboswitch can be engineered to regulate pre-mRNA splicing in vitro. We show that insertion of a high-affinity theophylline binding aptamer into the 3' splice site (3' ss) region of a model pre-mRNA (AdML-Theo29AG) enables its splicing to be repressed by the addition theophylline. Our results indicate that the location of 3' ss AG within the aptamer plays a crucial role in conferring theophylline-dependent control of pre-mRNA splicing. We also show that theophylline-mediated control of pre-mRNA splicing is highly specific by first demonstrating that a small molecule ligand similar in shape and size to theophylline had no effect on the splicing of AdML-Theo29AG pre-mRNA. Second, theophylline failed to exert any influence on the splicing of a pre-mRNA that does not contain its binding site. Third, theophylline specifically blocks the step II of the splicing reaction. Finally, we provide evidence that theophylline-dependent control of pre-mRNA splicing is functionally relevant.     

An artificial immune system for data analysis

We present a simplified view of those parts of the human immune system which can be used to provide the basis for a data analysis tool. The motivation for and reasoning behind such a model is given and the desire for a 'transparent' model and meaningful visualization and interpretation techniques is noted. A minimalist formulation of an artificial immune system and some of its behaviour is described. A simple implementation and a suitable visualization technique are demonstrated using some trivial data and the famous 'iris' data set.     

An in vivo platform for tumor biomarker assessment

Tumor biomarkers provide a quantitative tool for following tumor progression and response to therapy. However, investigations of clinically useful tumor biomarkers are time-consuming, costly, and limited by patient and tumor heterogeneity. In addition, assessment of biomarkers as indicators of therapy response is confounded by the concomitant use of multiple therapeutic interventions. Herein we report our use of a clinically relevant orthotopic animal model of malignant pleural mesothelioma for investigating tumor biomarkers. Utilizing multi-modality imaging with correlative histopathology, we demonstrate the utility and accuracy of the mouse model in investigating tumor biomarkers--serum soluble mesothelin-related peptide (SMRP) and osteopontin (OPN). This model revealed percentage change in SMRP level to be an accurate biomarker of tumor progression and therapeutic response--a finding consistent with recent clinical studies. This in vivo platform demonstrates the advantages of a validated mouse model for the timely and cost-effective acceleration of human biomarker translational research.     

An experimental study of vascular dynamics by an acceleration plethysmogram using artificial circulation devices

Plethysmogram has been widely known as a conventional non-invasive simple method to obtain information on peripheral circulatory kinetics. We made a quantitative assessment of the relationships between the various circulatory parameters, using the simple conventional measurement method of treating acceleration plethysmograms (the second derivatives of digital plethysmograms) as indicators of peripheral circulation kinetics. We designed a prototype model peripheral circulatory circuit, using an artificial heart, for the purpose of clarifying the relationship between physical factors and plethysmograms. In our models of the peripheral circulatory system, we made use of various types of matching silicon tubes, as well as canine carotid arteries, as substitutes for human arteries. We evaluated the mechanical characteristics, such as the pressure and volume characteristics of the silicon tubes and canine carotid arteries. Plethysmogram alterations were measured using a photoelectric digital plethysmogram. When we examined the acceleration plethysmogram b/a values, which indicate blood vessel extensibility and hardness, and the d/a values, which we were able to use as an index of peripheral circulatory kinetics, it was clear from out model testing that canine carotid arteries displayed b/a values that were closest to those found in the human fingertip; among artificial tubes, the closest was a tube with a diameter of 4 mm (0.18 mm in thickness). The b/a values of all types of tubes declined when the compliance of the arterial system was increased, and it was confirmed through model testing that b/a values are an indicator of blood vessel extensibility.     

An artificial neural network for estimating haplotype frequencies

The problem of estimating haplotype frequencies from population data has been considered by numerous investigators, resulting in a wide variety of possible algorithmic and statistical solutions. We propose a relatively unique approach that employs an artificial neural network (ANN) to predict the most likely haplotype frequencies from a sample of population genotype data. Through an innovative ANN design for mapping genotype patterns to diplotypes, we have produced a prototype that demonstrates the feasibility of this approach, with provisional results that correlate well with estimates produced by the expectation maximization algorithm for haplotype frequency estimation. Given the computational demands of estimating haplotype frequencies for 20 or more single-nucleotide polymorphisms, the ANN approach is promising because its design fits well with parallel computing architectures.