Correlating flow induced shear stress and chondrocytes activity in micro-porous scaffold using computational fluid dynamics and rapid prototyping

Flow induced shear stress plays an important role in regulating cell growth and distribution in scaffolds. This study sought to correlate wall shear stress and chondrocytes activity for engineering design of micro-porous osteochondral grafts based on the hypothesis that it is possible to capture and discriminate between the transmitted force and cell response at the inner irregularities. Unlike common tissue engineering therapies with perfusion bioreactors in which flow-mediated stress is the controlling parameter, this work assigned the associated stress as a function of porosity to influence in vitro proliferation of chondrocytes. D-optimality criterion was used to accommodate three pore characteristics for appraisal in a mixed level fractional design of experiment (DOE); namely, pore size (4 levels), distribution pattern (2 levels) and density (3 levels). Micro-porous scaffolds (n=12) were fabricated according to the DOE using rapid prototyping of an acrylic-based bio-photopolymer. Computational fluid dynamics (CFD) models were created correspondingly and used on an idealized boundary condition with a Newtonian fluid domain to simulate the dynamic microenvironment inside the pores. In vitro condition was reproduced for the 3D printed constructs seeded by high pellet densities of human chondrocytes and cultured for 72 hours. The results showed that cell proliferation was significantly different in the constructs (p<0.05). Inlet fluid velocity of 3×10-2mms-1 and average shear stress of 5.65×10-2 Pa corresponded with increased cell proliferation for scaffolds with smaller pores in hexagonal pattern and lower densities. Although the analytical solution of a Poiseuille flow inside the pores was found insufficient for the description of the flow profile probably due to the outside flow induced turbulence, it showed that the shear stress would increase with cell growth and decrease with pore size. This correlation demonstrated the basis for determining the relation between the induced stress and chondrocyte activity to optimize microfabrication of engineered cartilaginous constructs.

The correlation of pore morphology, interconnectivity and physical properties of 3D ceramic scaffolds with bone ingrowth

In the design of tissue engineering scaffolds, design parameters including pore size, shape and interconnectivity, mechanical properties and transport properties should be optimized to maximize successful inducement of bone ingrowth. In this paper we describe a 3D micro-CT and pore partitioning study to derive pore scale parameters including pore radius distribution, accessible radius, throat radius, and connectivity over the pore space of the tissue engineered constructs. These pore scale descriptors are correlated to bone ingrowth into the scaffolds. Quantitative and visual comparisons show a strong correlation between the local accessible pore radius and bone ingrowth; for well connected samples a cutoff accessible pore radius of approximately 100 microM is observed for ingrowth. The elastic properties of different types of scaffolds are simulated and can be described by standard cellular solids theory: (E/E(0))=(rho/rho(s))(n). Hydraulic conductance and diffusive properties are calculated; results are consistent with the concept of a threshold conductance for bone ingrowth. Simple simulations of local flow velocity and local shear stress show no correlation to in vivo bone ingrowth patterns. These results demonstrate a potential for 3D imaging and analysis to define relevant pore scale morphological and physical properties within scaffolds and to provide evidence for correlations between pore scale descriptors, physical properties and bone ingrowth.

Characterising anomalous events using change point correlation on unsolicited network traffic

Monitoring unused or dark IP addresses offers opportunities to extract useful information about both on-going and new attack patterns. In recent years, different techniques have been used to analyze such traffic including sequential analysis where a change in traffic behavior, for example change in mean, is used as an indication of malicious activity. Change points themselves say little about detected change; further data processing is necessary for the extraction of useful information and to identify the exact cause of the detected change which is limited due to the size and nature of observed traffic. In this paper, we address the problem of analyzing a large volume of such traffic by correlating change points identified in different traffic parameters. The significance of the proposed technique is two-fold. Firstly, automatic extraction of information related to change points by correlating change points detected across multiple traffic parameters. Secondly, validation of the detected change point by the simultaneous presence of another change point in a different parameter. Using a real network trace collected from unused IP addresses, we demonstrate that the proposed technique enables us to not only validate the change point but also extract useful information about the causes of change points.

Positive correlation of male and female Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) catches in orange-ammonia traps

Populations of the Queensland fruit fly, Bactrocera tryoni, are routinely monitored using cue-lure, a male-only attractant. Such monitoring provides no information about females and there is little information available to show if male and female B. tryoni numbers are correlated in the field. Using a data set of 1 148 weekly clearances of orange-ammonia baited traps, which catch both males and females, the correlation between male and female numbers was tested for 48 weeks of the year (four weeks each month) and for the combined data set. Weekly male and female trap catches were almost entirely highly correlated, regardless of mean population size or time of year. For the whole year, the correlation between male and female numbers was r = 0.722, significant at p<0.001. Results suggest that changes in the number if male B. tryoni, as detected through cue-lure sampling, will reflect changes in numbers of female B. tryoni.

Compact arrays for mobile platforms : trade-off between size and performance for SDMA and MIMO applications

Smart antenna receiver and transmitter systems consist of multi-port arrays with an individual receiver channel (including ADC) and an individual transmitter channel (including DAC)at every of the M antenna ports, respectively. By means of digital beamforming, an unlimited number of simultaneous complex-valued vector radiation patterns with M-1 degrees of freedom can be formed. Applications of smart antennas in communication systems include space-division multiple access. If both stations of a communication link are equipped with smart antennas (multiple-input-multiple-output, MIMO). multiple independent channels can be formed in a "multi-path-rich" environment. In this article, it will be shown that under certain circumstances, the correlation between signals from adjacent ports of a dense array (M + ΔM elements) can be kept as low as the correlation between signals from adjacent ports of a conventional array (M elements and half-wavelength pacing). This attractive feature is attained by means of a novel approach which employs a RF decoupling network at the array ports in order to form new ports which are decoupled and associated with mutually orthogonal (de-correlated) radiation patterns.

Orthogonality defect and reduced search-space size for solving integer least-squares problems

In the context of ambiguity resolution (AR) of Global Navigation Satellite Systems (GNSS), decorrelation among entries of an ambiguity vector, integer ambiguity search and ambiguity validations are three standard procedures for solving integer least-squares problems. This paper contributes to AR issues from three aspects. Firstly, the orthogonality defect is introduced as a new measure of the performance of ambiguity decorrelation methods, and compared with the decorrelation number and with the condition number which are currently used as the judging criterion to measure the correlation of ambiguity variance-covariance matrix. Numerically, the orthogonality defect demonstrates slightly better performance as a measure of the correlation between decorrelation impact and computational efficiency than the condition number measure. Secondly, the paper examines the relationship of the decorrelation number, the condition number, the orthogonality defect and the size of the ambiguity search space with the ambiguity search candidates and search nodes. The size of the ambiguity search space can be properly estimated if the ambiguity matrix is decorrelated well, which is shown to be a significant parameter in the ambiguity search progress. Thirdly, a new ambiguity resolution scheme is proposed to improve ambiguity search efficiency through the control of the size of the ambiguity search space. The new AR scheme combines the LAMBDA search and validation procedures together, which results in a much smaller size of the search space and higher computational efficiency while retaining the same AR validation outcomes. In fact, the new scheme can deal with the case there are only one candidate, while the existing search methods require at least two candidates. If there are more than one candidate, the new scheme turns to the usual ratio-test procedure. Experimental results indicate that this combined method can indeed improve ambiguity search efficiency for both the single constellation and dual constellations respectively, showing the potential for processing high dimension integer parameters in multi-GNSS environment.

Grain size distributions of Magneli phases and metallic titanium in chondritic porous interplanetary dust particles

A mineralogical survey of chondritic interplanetary dust particles (IDPs)showed that these micrometeorites differ significantly in form and texture from components of carbonaceous chondrites and contain some mineral assemblages which do not occur in any meteorite class1. Models of chondritic IDP mineral evolution generally ignore the typical (ultra-) fine grain size of consituent minerals which range between 0.002-0.1µm in size2. The chondritic porous (CP) subset of chondritic IDPs is probably debris from short period comets although evidence for a cometary origin is still circumstantial3. If CP IDPs represent dust from regions of the Solar System in which comet accretion occurred, it can be argued that pervasive mineralogical evolution of IDP dust has been arrested due to cryogenic storage in comet nuclei. Thus, preservation in CP IDPs of "unusual meteorite minerals", such as oxides of tin, bismuth and titanium4, should not be dismissed casually. These minerals may contain specific information about processes that occurred in regions of the solar nebula, and early Solar System, which spawned the IDP parent bodies such as comets and C, P and D asteroids6. It is not fully appreciated that the apparent disparity between the mineralogy of CP IDPs and carbonaceous chondrite matrix may also be caused by the choice of electron-beam techniques with different analytical resolution. For example, Mg-Si-Fe distributions of Cl matrix obtained by "defocussed beam" microprobe analyses are displaced towards lower Fe-values when using analytical electron microscope (AEM)data which resolve individual mineral grains of various layer silicates and magnetite in the same matrix6,7. In general, "unusual meteorite minerals" in chondritic IDPs, such as metallic titanium, Tin01-n(Magneli phases) and anatase8 add to the mineral data base of fine-grained Solar System materials and provide constraints on processes that occurred in the early Solar System.

Self-assembled hyperbranched polymer - gold nanoparticle hybrids : understanding the effect of polymer coverage on assembly size and SERS performance

In the past few years, remarkable progress has been made in unveiling novel and unique optical properties of strongly coupled plasmonic nanostructures. However, application of such plasmonic nanostructures in biomedicine remains challenging due to the lack of facile and robust assembly methods for producing stable nanostructures. Previous attempts to achieve plasmonic nano-assemblies using molecular ligands were limited due to the lack of flexibility that could be exercised in forming them. Here, we report the utilization of tailor-made hyperbranched polymers (HBP) as linkers to assemble gold nanoparticles (NPs) into nano-assemblies. The ease and flexibility in tuning the particle size and number of branch ends of a HBP makes it an ideal candidate as a linker, as opposed to DNA, small organic molecules and linear or dendrimeric polymers. We report a strong correlation of polymer (HBP) concentration with the size of the hybrid nano-assemblies and “hot-spot” density. We have shown that such solutions of stable HBP-gold nano-assemblies can be barcoded with various Raman tags to provide improved surface-enhanced Raman scattering (SERS) compared with non-aggregated NP systems. These Raman barcoded hybrid nano-assemblies, with further optimization of NP shape, size and “hot-spot” density, may find application as diagnostic tools in nanomedicine.

Managing memory and reducing I/O cost for correlation matrix calculation in bioinformatics

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

Quantifying spatial structure in experimental observations and agent-based simulations using pair-correlation functions

We define a pair-correlation function that can be used to characterize spatiotemporal patterning in experimental images and snapshots from discrete simulations. Unlike previous pair-correlation functions, the pair-correlation functions developed here depend on the location and size of objects. The pair-correlation function can be used to indicate complete spatial randomness, aggregation or segregation over a range of length scales, and quantifies spatial structures such as the shape, size and distribution of clusters. Comparing pair-correlation data for various experimental and simulation images illustrates their potential use as a summary statistic for calibrating discrete models of various physical processes.

Staurosporine augments EGF-mediated EMT in PMC42-LA cells through actin depolymerisation, focal contact size reduction and Snail1 induction : a model for cross-modulation

Background A feature of epithelial to mesenchymal transition (EMT) relevant to tumour dissemination is the reorganization of actin cytoskeleton/focal contacts, influencing cellular ECM adherence and motility. This is coupled with the transcriptional repression of E-cadherin, often mediated by Snail1, Snail2 and Zeb1/δEF1. These genes, overexpressed in breast carcinomas, are known targets of growth factor-initiated pathways, however it is less clear how alterations in ECM attachment cross-modulate to regulate these pathways. EGF induces EMT in the breast cancer cell line PMC42-LA and the kinase inhibitor staurosporine (ST) induces EMT in embryonic neural epithelial cells, with F-actin de-bundling and disruption of cell-cell adhesion, via inhibition of aPKC. Methods PMC42-LA cells were treated for 72 h with 10 ng/ml EGF, 40 nM ST, or both, and assessed for expression of E-cadherin repressor genes (Snail1, Snail2, Zeb1/δEF1) and EMT-related genes by QRT-PCR, multiplex tandem PCR (MT-PCR) and immunofluorescence +/- cycloheximide. Actin and focal contacts (paxillin) were visualized by confocal microscopy. A public database of human breast cancers was assessed for expression of Snail1 and Snail2 in relation to outcome. Results When PMC42-LA were treated with EGF, Snail2 was the principal E-cadherin repressor induced. With ST or ST+EGF this shifted to Snail1, with more extreme EMT and Zeb1/δEF1 induction seen with ST+EGF. ST reduced stress fibres and focal contact size rapidly and independently of gene transcription. Gene expression analysis by MT-PCR indicated that ST repressed many genes which were induced by EGF (EGFR, CAV1, CTGF, CYR61, CD44, S100A4) and induced genes which alter the actin cytoskeleton (NLF1, NLF2, EPHB4). Examination of the public database of breast cancers revealed tumours exhibiting higher Snail1 expression have an increased risk of disease-recurrence. This was not seen for Snail2, and Zeb1/δEF1 showed a reverse correlation with lower expression values being predictive of increased risk. Conclusion ST in combination with EGF directed a greater EMT via actin depolymerisation and focal contact size reduction, resulting in a loosening of cell-ECM attachment along with Snail1-Zeb1/δEF1 induction. This appeared fundamentally different to the EGF-induced EMT, highlighting the multiple pathways which can regulate EMT. Our findings add support for a functional role for Snail1 in invasive breast cancer.

Quantitative analysis of survivin in colorectal adenocarcinoma : increased expression and correlation with telomerase activity

The aims of the present study are to quantitatively analyze survivin expression, its clinicopathologic roles, and correlation with telomerase activity in a large cohort of patients with colorectal adenocarcinoma. Real-time polymerase chain reaction was used to quantitate expression level of survivin messenger RNA and human telomerase reverse transcriptase messenger RNA (telomerase activity) in 51 patients with colorectal adenocarcinomas. The findings were correlated with the clinicopathologic features of patients, which were prospectively collected into a computerized database. Survivin messenger RNA was expressed in all tumor samples. The level of expression in tumor tissues was increased in comparison with matched nontumor mucosa in the same patient (P = .01). The level of expression of survivin was significantly correlated with the level of human telomerase reverse transcriptase expression (P = .008) and size of the colorectal adenocarcinomas (P = .004). Survival of the patients with colorectal adenocarcinoma was associated with the TNM stages (P = .001) and not with the level of expression of survivin. Thus, survivin activity was altered in colorectal adenocarcinoma. The high prevalence of survivin expression and correlation with telomerase activity are important factors for consideration in gene targeting therapy for colorectal adenocarcinoma.

Optimizing I/O cost and managing memory for composition vector method based on correlation matrix calculation in bioinformatics

The generation of a correlation matrix for set of genomic sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. Each sequence may be millions of bases long and there may be thousands of such sequences which we wish to compare, so not all sequences may fit into main memory at the same time. Each sequence needs to be compared with every other sequence, so we will generally need to page some sequences in and out more than once. In order to minimize execution time we need to minimize this I/O. This paper develops an approach for faster and scalable computing of large-size correlation matrices through the maximal exploitation of available memory and reducing the number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different bioinformatics problems with different correlation matrix sizes. The significant performance improvement of the approach over previous work is demonstrated through benchmark examples.

Spectral analysis of pair-correlation bandwidth: Application to cell biology images

Images from cell biology experiments often indicate the presence of cell clustering, which can provide insight into the mechanisms driving the collective cell behaviour. Pair-correlation functions provide quantitative information about the presence, or absence, of clustering in a spatial distribution of cells. This is because the pair-correlation function describes the ratio of the abundance of pairs of cells, separated by a particular distance, relative to a randomly distributed reference population. Pair-correlation functions are often presented as a kernel density estimate where the frequency of pairs of objects are grouped using a particular bandwidth (or bin width), Δ>0. The choice of bandwidth has a dramatic impact: choosing Δ too large produces a pair-correlation function that contains insufficient information, whereas choosing Δ too small produces a pair-correlation signal dominated by fluctuations. Presently, there is little guidance available regarding how to make an objective choice of Δ. We present a new technique to choose Δ by analysing the power spectrum of the discrete Fourier transform of the pair-correlation function. Using synthetic simulation data, we confirm that our approach allows us to objectively choose Δ such that the appropriately binned pair-correlation function captures known features in uniform and clustered synthetic images. We also apply our technique to images from two different cell biology assays. The first assay corresponds to an approximately uniform distribution of cells, while the second assay involves a time series of images of a cell population which forms aggregates over time. The appropriately binned pair-correlation function allows us to make quantitative inferences about the average aggregate size, as well as quantifying how the average aggregate size changes with time.