On Distance Preserving and Sequentially Distance Preserving Graphs

Preprin

Modular Decomposition of Graphs and the Distance Preserving Property

Peer reviewedPreprin

Assessment of Chemical Inhibitor Addition to Improve the Gas Production from Biowaste

The coexistence of sulphate-reducing bacteria and methanogenic archaea in the reactors during the anaerobic digestion from sulphate-containing waste could favor the accumulation of sulfide on the biogas, and therefore reduce its quality. In this study, the effect of sulphate-reducing bacteria inhibitor (MoO−2 4 ) addition in a two phase system from sulphate-containing municipal solid waste to improve the quality of the biogas has been investigated. The results showed that although SRB and sulphide production decreased, the use of inhibitor was not effective to improve the anaerobic digestion in a two phase system from sulphate-containing waste, since a significant decrease on biogas and organic matter removal were observed. Before MoO−2 4 addition the average values of volatile solid were around 12 g/kg, after 5 days of inhibitor use, those values did exceed to 28 g/kg. Molybdate caused acidification in the reactor and it was according to decrease in the pH values. In relation to microbial consortia, the effect of inhibitor was a decrease in Bacteria (44%; 60% in sulphate-reducing bacteria) and Archaea (38%) population

The potential of fractional diagonal chromatography strategies for the enrichment of post-translational modifications

More than 450 post-translational modifications (PTMs) are known, however, currently only some of those can be enriched and analyzed from complex samples such as cell lysates. Therefore, we need additional methods and concepts to improve our understanding about the dynamic crosstalk of PTMs and the highly context-dependent regulation of protein function by so-called ‘PTM codes’. The mere focus on affinity-based enrichment techniques may not be sufficient to achieve this ambitious goal. However, the complementary use of two-dimensional chromatography-based strategies such as COFRADIC and ChaFRADIC might open new avenues for enriching a variety of so far inaccessible PTMs for large-scale proteome studies

Monitoring the suitability of the fit of a lower-limb prosthetic socket using artificial neural network in commonly encountered walking conditions

Prosthetic sockets are still routinely designed without the aid of quantitative measurement, relying instead on the experience and skill of clinicians. Sockets remain the most common cause for complaint regarding the suitability of a prosthesis, and poor pressure distribution is implicated in many forms of unacceptable care outcomes. Monitoring pressure distribution has been effectively restricted to laboratory settings, and only limited work has examined conditions other than flat walking. In this work, a transtibial amputee completed static and dynamic tasks on flat ground, on slopes and with changes to prosthetic materials and alignment. This was achieved using a set of wireless measurement nodes and custom LabView and MATLAB code, using external strain measurements and a neural network to understand the internal pressure distribution. Future work will focus on modifying the software to be more user-friendly for a clinical operator, and in simplifying the required hardware. Although the system in its current form facilitated the desired measurements effectively, it required engineering support to function accurately. Improving the reliability and stability of the system will be necessary before routine use is possible

Cut finite element methods for coupled bulk–surface problems

We develop a cut finite element method for a second order elliptic coupled bulk-surface model problem. We prove a priori estimates for the energy and L2L2 norms of the error. Using stabilization terms we show that the resulting algebraic system of equations has a similar condition number as a standard fitted finite element method. Finally, we present a numerical example illustrating the accuracy and the robustness of our approach

Applying Ensemble Neural Networks to an Inverse Problem Solution to Prosthetic Socket Pressure Measurement

Ensemble neural networks are commonly used as a method to boost performance of artificial intelligence applications. By collating the response of multiple networks with differences in composition or training and hence a range of estimation error, an overall improvement in the appraisal of new problem data can be made. In this work, artificial neural networks are used as an inverse-problem solver to calculate the internal distribution of pressures on a lower limb prosthetic socket using information on the deformation of the external surface of the device. Investigation into the impact of noise injection was studied by changing the maximum noise alteration parameter and the differences in network composition by altering the variance around this maximum noise value. Results indicate that use of ensembles of networks provides a meaningful improvement in overall performance. RMS error expressed as a percentage of the total applied load was 3.86% for the best performing ensemble, compared to 5.32% for the mean performance of the networks making up that ensemble. Although noise injection resulted in an improvement in typical network estimates of load distribution, ensembles performed better with low noise and low variance between network training patterns. These results mean that ensembles have been implemented in the research tool under developmen

High spatial and temporal resolution cell manipulation techniques in microchannels

The advent of microfluidics has enabled thorough control of cell manipulation experiments in so called lab on chips. Lab on chips foster the integration of actuation and detection systems, and require minute sample and reagent amounts. Typically employed microfluidic structures have similar dimensions as cells, enabling precise spatial and temporal control of individual cells and their local environments. Several strategies for high spatio-temporal control of cells in microfluidics have been reported in recent years, namely methods relying on careful design of the microfluidic structures (e.g. pinched flow), by integration of actuators (e.g. electrodes or magnets for dielectro-, acousto- and magneto-phoresis), or integrations thereof. This review presents the recent developments of cell experiments in microfluidics divided into two parts: an introduction to spatial control of cells in microchannels followed by special emphasis in the high temporal control of cell-stimulus reaction and quenching. In the end, the present state of the art is discussed in line with future perspectives and challenges for translating these devices into routine applications

Identification of cleavage sites and substrate proteins for two mitochondrial intermediate peptidases in Arabidopsis thaliana

Most mitochondrial proteins contain an N-terminal targeting signal that is removed by specific proteases following import. In plant mitochondria, only mitochondrial processing peptidase (MPP) has been characterized to date. Therefore, we sought to determine the substrates and cleavage sites of the Arabidopsis thaliana homologues to the yeast Icp55 and Oct1 proteins, using the newly developed ChaFRADIC method for N-terminal protein sequencing. We identified 88 and seven putative substrates for Arabidopsis ICP55 and OCT1, respectively. It was determined that the Arabidopsis ICP55 contains an almost identical cleavage site to that of Icp55 from yeast. However, it can also remove a far greater range of amino acids. The OCT1 substrates from Arabidopsis displayed no consensus cleavage motif, and do not contain the classical –10R motif identified in other eukaryotes. Arabidopsis OCT1 can also cleave presequences independently, without the prior cleavage of MPP. It was concluded that while both OCT1 and ICP55 were probably acquired early on in the evolution of mitochondria, their substrate profiles and cleavage sites have either remained very similar or diverged completely

A Stabilized Cut Streamline Diffusion Finite Element Method for Convection-Diffusion Problems on Surfaces

We develop a stabilized cut finite element method for the stationary convection diffusion problem on a surface embedded in R d . The cut finite element method is based on using an embedding of the surface into a three dimensional mesh consisting of tetrahedra and then using the restriction of the standard piecewise linear continuous elements to a piecewise linear approximation of the surface. The stabilization consists of a standard streamline diffusion stabilization term on the discrete surface and a so called normal gradient stabilization term on the full tetrahedral elements in the active mesh. We prove optimal order a priori error estimates in the standard norm associated with the streamline diffusion method and bounds for the condition number of the resulting stiffness matrix. The condition number is of optimal order for a specific choice of method parameters. Numerical examples supporting our theoretical results are also included