Evolving locomotion for a 12-DOF quadruped robot in simulated environments

We demonstrate the power of evolutionary robotics (ER) by comparing to a more traditional approach its performance and cost on the task of simulated robot locomotion. A novel quadruped robot is introduced, the legs of which – each having three non-coplanar degrees of freedom – are very maneuverable. Using a simplistic control architecture and a physics simulation of the robot, gaits are designed both by hand and using a highly parallel evolutionary algorithm (EA). It is found that the EA produces, in a small fraction of the time that takes to design by hand, gaits that travel at two to four times the speed of the hand-designed one. The flexibility of this approach is demonstrated by applying it across a range of differently configured simulators.     

Interactive fitting augmented by force-feedback and virtual reality

The synthesis of low-resolution electron microscopy data with high-resolution molecular structures has become a common routine in the modeling of biomolecular assemblies. In contrast to algorithmic "black box" solutions, the interactive "fitting by eye" takes advantage of an expert's structural or biochemical knowledge and can be used with very noisy experimental data. In the solution proposed in this paper, we support the expert user in an interactive fitting session by haptic rendering and virtual reality. The quantitative and tactile feedback facilitates and objectifies the otherwise unrestrained modeling. We introduce a highly accurate reduced representation of the gradient of the cross-correlation coefficient that sustains force updates for haptic rendering at sufficiently high refresh rates.     

Path formation in a robot swarm

We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. The second mechanism is called vectorfield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location. We test each controller on a task that consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our simulation experiments show promising results. All the controllers are able to form paths in complex obstacle environments and exhibit very good scalability, robustness, and fault tolerance characteristics. Additionally, we observe that chains perform better for small robot group sizes, while vectorfield performs better for large groups.     

Evaluation of the effect of stent strut profile on shear stress distribution using statistical moments

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.     

Touch Interacts with Vision during Binocular Rivalry with a Tight Orientation Tuning

Multisensory integration is a common feature of the mammalian brain that allows it to deal more efficiently with the ambiguity of sensory input by combining complementary signals from several sensory sources. Growing evidence suggests that multisensory interactions can occur as early as primary sensory cortices. Here we present incompatible visual signals (orthogonal gratings) to each eye to create visual competition between monocular inputs in primary visual cortex where binocular combination would normally take place. The incompatibility prevents binocular fusion and triggers an ambiguous perceptual response in which the two images are perceived one at a time in an irregular alternation. One key function of multisensory integration is to minimize perceptual ambiguity by exploiting cross-sensory congruence. We show that a haptic signal matching one of the visual alternatives helps disambiguate visual perception during binocular rivalry by both prolonging the dominance period of the congruent visual stimulus and by shortening its suppression period. Importantly, this interaction is strictly tuned for orientation, with a mismatch as small as 7.5° between visual and haptic orientations sufficient to annul the interaction. These results indicate important conclusions: first, that vision and touch interact at early levels of visual processing where interocular conflicts are first detected and orientation tunings are narrow, and second, that haptic input can influence visual signals outside of visual awareness, bringing a stimulus made invisible by binocular rivalry suppression back to awareness sooner than would occur without congruent haptic input.     

A program system for interactive measurements on digitized cell images.

Using a high precision image scanner and a PDP-8/F minicomputer, we have developed a program system for interactive measurements on microscopic images. By giving simple keyboard commands, the operator can run the image scanner and manipulate the digitized images. The interface between the operator and the microscope-computer system is a Tektronix 4010 graphic terminal. The system allows objects to be isolated and parameters to be calculated from each object, e.g., parameters characterizing shape of the object, irregularity in light transmission over the object, area, integrated light transmission, etc. Objects are isolated and parameters are calculated under complete operator control using interactive computer graphics technique. Calculated parameters may be stored in dedicated data records, which are stored in files for later statistical analysis. The system also includes a statistical evaluation part. Technically, the system consists of a command scanner, which translates commands into internal representation, a parser, which checks the syntax of the commands, and an interpreter, which executes the commands. The system is designed so that new commands can be added easily.     

Virtual Reality Based Support System for Layout Planning and Programming of an Industrial Robotic Work Cell

Traditional robotic work cell design and programming are considered inefficient and outdated in current industrial and market demands. In this research, virtual reality (VR) technology is used to improve human-robot interface, whereby complicated commands or programming knowledge is not required. The proposed solution, known as VR-based Programming of a Robotic Work Cell (VR-Rocell), consists of two sub-programmes, which are VR-Robotic Work Cell Layout (VR-RoWL) and VR-based Robot Teaching System (VR-RoT). VR-RoWL is developed to assign the layout design for an industrial robotic work cell, whereby VR-RoT is developed to overcome safety issues and lack of trained personnel in robot programming. Simple and user-friendly interfaces are designed for inexperienced users to generate robot commands without damaging the robot or interrupting the production line. The user is able to attempt numerous times to attain an optimum solution. A case study is conducted in the Robotics Laboratory to assemble an electronics casing and it is found that the output models are compatible with commercial software without loss of information. Furthermore, the generated KUKA commands are workable when loaded into a commercial simulator. The operation of the actual robotic work cell shows that the errors may be due to the dynamics of the KUKA robot rather than the accuracy of the generated programme. Therefore, it is concluded that the virtual reality based solution approach can be implemented in an industrial robotic work cell.     

Second generation registry framework

Background

Information management systems are essential to capture data be it for public health and human disease, sustainable agriculture, or plant and animal biosecurity. In public health, the term patient registry is often used to describe information management systems that are used to record and track phenotypic data of patients. Appropriate design, implementation and deployment of patient registries enables rapid decision making and ongoing data mining ultimately leading to improved patient outcomes. A major bottleneck encountered is the static nature of these registries. That is, software developers are required to work with stakeholders to determine requirements, design the system, implement the required data fields and functionality for each patient registry. Additionally, software developer time is required for ongoing maintenance and customisation. It is desirable to deploy a sophisticated registry framework that can allow scientists and registry curators possessing standard computing skills to dynamically construct a complete patient registry from scratch and customise it for their specific needs with little or no need to engage a software developer at any stage.

Results

This paper introduces our second generation open source registry framework which builds on our previous rare disease registry framework (RDRF). This second generation RDRF is a new approach as it empowers registry administrators to construct one or more patient registries without software developer effort. New data elements for a diverse range of phenotypic and genotypic measurements can be defined at any time. Defined data elements can then be utilised in any of the created registries. Fine grained, multi-level user and workgroup access can be applied to each data element to ensure appropriate access and data privacy. We introduce the concept of derived data elements to assist the data element standards communities on how they might be best categorised.

Conclusions

We introduce the second generation RDRF that enables the user-driven dynamic creation of patient registries. We believe this second generation RDRF is a novel approach to patient registry design, implementation and deployment and a significant advance on existing registry systems.

     

Mantisbot is a robotic model of visually guided motion in the praying mantis

Insects use highly distributed nervous systems to process exteroception from head sensors, compare that information with state-based goals, and direct posture or locomotion toward those goals. To study how descending commands from brain centers produce coordinated, goal-directed motion in distributed nervous systems, we have constructed a conductance-based neural system for our robot MantisBot, a 29 degree-of-freedom, 13.3:1 scale praying mantis robot. Using the literature on mantis prey tracking and insect locomotion, we designed a hierarchical, distributed neural controller that establishes the goal, coordinates different joints, and executes prey-tracking motion. In our controller, brain networks perceive the location of prey and predict its future location, store this location in memory, and formulate descending commands for ballistic saccades like those seen in the animal. The descending commands are simple, indicating only 1) whether the robot should walk or stand still, and 2) the intended direction of motion. Each joint's controller uses the descending commands differently to alter sensory-motor interactions, changing the sensory pathways that coordinate the joints' central pattern generators into one cohesive motion. Experiments with one leg of MantisBot show that visual input produces simple descending commands that alter walking kinematics, change the walking direction in a predictable manner, enact reflex reversals when necessary, and can control both static posture and locomotion with the same network.     

The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins

Background

The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins.

Results

We propose a new method for the prediction of CaM-binding proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding proteins and 193 mitochondrial proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF).

Conclusions

Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding proteins.

     

Differentiation of eosinophilic granulocytes of carp (Cyprinus carpio L.).

Electron-microscopic studies were conducted to observe ultrastructural changes during differentiation of eosinophilic granulocytes in carp (Cyprinus carpio L.). Differentiation at the myelocyte stage was found to relate to specific granules made of dense and light fields. By maturation they assume a mosaic-like texture and in each granule of mature granulocytes, a light, central "internum" and a peripheral dense wrapper can be distinguished. The activity peroxidase and acid phosphatase is located in the internum and of peroxidase in the wrapper of the granules.     

Ebstein's Anomaly: Anatomo-echocardiographic correlation

Background

Carotid intima-media thickness (CIMT) measured by B-mode ultrasonography is a marker of atherosclerosis and is commonly used as an outcome in intervention trials. We have developed DICOM-based software that measures CIMT rapidly on multiple end-diastolic image frames. The aims of this study were to compare the performance of our new software with older bitmap-based CIMT measurement software and to determine whether a ten-fold increase in the number of measurements used to calculate mean CIMT would improve reproducibility.

Methods

Two independent sonographers recorded replicate carotid scans in thirty volunteers and two blinded observers measured CIMT off-line using the new DICOM-based software and older bitmap-based software. A Bland-Altman plot was used to compare CIMT results from the two software programs and t-tests were used to compare analysis times. F-tests were used to compare the co-efficients of variation (CVs) from a standard six-frame measurement protocol with CVs from a sixty-frame measurement protocol. Ordinary least products (OLP) regression was used to test for sonographer and observer biases.

Results

The new DICOM-based software was much faster than older bitmap-based software (average measurement time for one scan 3.4 ± 0.6 minutes versus 8.4 ± 1.8 minutes, p < 0.0001) but CIMT measurements were larger than those made using the alternative software (+0.02 mm, 95%CI 0.01–0.03 mm). The sixty-frame measurement protocol had worse reproducibility than the six-frame protocol (inter-observer CV 5.1% vs 3.5%, p = 0.004) and inter and intra-observer biases were more pronounced in the sixty-frame than the six-frame results.

Conclusion

While the use of DICOM-based software significantly reduced analysis time, a ten-fold increase in the number of measurements used to calculate CIMT did not improve reproducibility. In addition, we found that observer biases caused differences in mean CIMT of a magnitude commonly reported as significant in intervention trials. Our results highlight the importance of good study design with concurrent controls and the need to ensure that no observer drift occurs between baseline and follow-up measurements when CIMT is used to monitor the effect of an intervention.     

AutoMoDe: A novel approach to the automatic design of control software for robot swarms

We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias–variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules—the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe-Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe-Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe-Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable.     

Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis

Background

Pyrosequencing Allele Quantification (AQ) is a cost-effective DNA sequencing method that can be used for detecting somatic mutations in formalin-fixed paraffin-embedded (FFPE) samples. The method displays a low turnaround time and a high sensitivity. Pyrosequencing suffers however from two main drawbacks including (i) low specificity and (ii) difficult signal interpretation when multiple mutations are reported in a hotspot genomic region.

Results

Using a constraint-based regression method, the new AdvISER-PYRO-SMQ algorithm was developed in the current study and implemented into an R package. As a proof-of-concept, AdvISER-PYRO-SMQ was used to identify a set of 9 distinct point mutations affecting codon 61 of the NRAS oncogene. In parallel, a pyrosequencing assay using the Qiagen software and its AQ module was used to assess selectively the presence of a single point mutation (NRAS \(c.182A>G\) - Q61R-1) among the set of codon 61 mutations, and to analyze related pyrosequencing signals. AdvISER-PYRO-SMQ produced a lower limit of blank (0 %) than the AQ module of Qiagen software (5.1 %) and similar limit of detection were obtained for both software (5.6 vs 4.8 %). AdvISER-PYRO-SMQ was able to screen for the presence of 9 distinct mutations with a single pyrosequencing reaction whereas the AQ module was limited to screen a single mutation per reaction.

Conclusion

Using a constraint-based regression method enables to analyze pyrosequencing signal and to detect multiple mutations within a hotspot genomic region with an optimal compromise between sensitivity and specificity. The AdvISER-PYRO-SMQ R package provides a generic tool which can be applied on a wide range of somatic mutations. Its implementation in a Shiny web interactive application (available at https://ucl-irec-ctma.shinyapps.io/Pyrosequencing-NRAS-61/) enables its use in research or clinical routine applications.

     

I-Ak polymorphisms define a functionally dominant region for the presentation of hen egg lysozyme peptides

The class II molecules of the MHC not only bind processed antigenic peptides but also interact with the TCR. This latter interaction is thought to be the basis for allele specific "restriction" of Ag presentation to T cells. The specificity of this interaction is likely due to amino acid differences in a small number of polymorphic or "hypervariable" regions located in the amino terminal domains of the alpha- and beta-chains. We have explored the functional significance of these polymorphic regions in an I-Ak-restricted, hen egg lysozyme specific Ag presentation system in which the measurement of IL-2 production by T cell hybridomas was used as the indicator of TCR recognition of the I-A/Ag complex. Chimeric I-A molecules, in which b allelic residues were substituted in one or more of the polymorphic regions of the A alpha k chain or in which d allelic residues were substituted in one or more of the polymorphic regions of the A beta k chain, were used to examine the contribution of each polymorphic region of the molecule to its function. The results obtained demonstrate that the regions between residues 69 to 76 of the A alpha k chain and the regions between residues 63 to 67 and 75 to 78 of the A beta k-chain exert a dominant effect on the presentation of lysozyme peptides by I-Ak to the T cell hybridomas in our panel. These observations were confirmed and extended by the analysis of Ag presentation by seven serologically selected mutants, all of which have amino acid interchanges in or around the dominant polymorphic regions. The results suggest that the serologically selected mutants fail to present Ag not because they fail to bind the peptide Ag but because the amino acid substitutions destabilize the interaction between the Ia/peptide complex and the TCR. Use of the recently published hypothetical model for class II structure to interpret the Ag presentation results suggests that the dominant polymorphic regions lie across from one another near one end of the alpha-helices that form the two walls of the proposed Ag-binding cleft located on the top surface of the class II molecule. Furthermore, the majority of the amino acids which have been changed in the serologically selected mutants have side chains which are postulated to point up toward the exterior of the molecule and would, therefore, be potential contact residues for the TCR.     

EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought

Background

The Brain Computer Interfaces (BCI) are devices allowing direct communication between the brain of a user and a machine. This technology can be used by disabled people in order to improve their independence and maximize their capabilities such as finding an object in the environment. Such devices can be realized by the non-invasive measurement of information from the cortex by electroencephalography (EEG).

Simplifying a life cycle assessment of a mobile phone

Purpose

The possibilities for full life cycle assessment (LCA) of new Information and Communication Technology (ICT) products are often limited, so simplification approaches are needed. The aim of this paper is to investigate possible simplifications in LCA of a mobile phone and to use the results to discuss the possibilities of LCA simplifications for ICT products in a broader sense. Another aim is to identify processes and data that are sensitive to different methodological choices and assumptions related to the environmental impacts of a mobile phone.

Methods

Different approaches to a reference LCA of a mobile phone was tested: (1) excluding environmental impact categories, (2) excluding life cycle stages/processes, (3) using secondary process data from generic databases, (4) using input-output data and (5) using a simple linear relationship between mass and embodied emissions.

Results and discussion

It was not possible to identify one or a few impact categories representative of all others. If several impact categories would be excluded, information would be lost. A precautionary approach of not excluding impact categories is therefore recommended since impacts from the different life cycle stages vary between impact categories. Regarding use of secondary data for an ICT product similar to that studied here, we recommend prioritising collection of primary (specific) data on energy use during production and use, key component data (primarily integrated circuits) and process-specific data regarding raw material acquisition of specific metals (e.g. gold) and air transport. If secondary data are used for important processes, the scaling is crucial. The use of input-output data can be a considerable simplification and is probably best used to avoid data gaps when more specific data are lacking.

Conclusions

Further studies are needed to provide for simplified LCAs for ICT products. In particular, the end-of-life treatment stage need to be further addressed, as it could not be investigated here for all simplifications due to data gaps.