Biofabrication of customized bone grafts by combination of additive manufacturing and bioreactor knowhow

This study reports on an original concept of additive manufacturing for the fabrication of tissue engineered constructs (TEC), offering the possibility of concomitantly manufacturing a customized scaffold and a bioreactor chamber to any size and shape. As a proof of concept towards the development of anatomically relevant TECs, this concept was utilized for the design and fabrication of a highly porous sheep tibia scaffold around which a bioreactor chamber of similar shape was simultaneously built. The morphology of the bioreactor/scaffold device was investigated by micro-computed tomography and scanning electron microscopy confirming the porous architecture of the sheep tibiae as opposed to the non-porous nature of the bioreactor chamber. Additionally, this study demonstrates that both the shape, as well as the inner architecture of the device can significantly impact the perfusion of fluid within the scaffold architecture. Indeed, fluid flow modelling revealed that this was of significant importance for controlling the nutrition flow pattern within the scaffold and the bioreactor chamber, avoiding the formation of stagnant flow regions detrimental for in vitro tissue development. The bioreactor/scaffold device was dynamically seeded with human primary osteoblasts and cultured under bi-directional perfusion for two and six weeks. Primary human osteoblasts were observed homogenously distributed throughout the scaffold, and were viable for the six week culture period. This work demonstrates a novel application for additive manufacturing in the development of scaffolds and bioreactors. Given the intrinsic flexibility of the additive manufacturing technology platform developed, more complex culture systems can be fabricated which would contribute to the advances in customized and patient-specific tissue engineering strategies for a wide range of applications.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Speeding up AdaBoost Classifier with Random Projection

The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.

Use of S1 nuclease and formamide in combination for the reassociation studies on GC-rich DNA

The optimal parameters in the use of nuclease S1 in DNA reassociation kinetics in the presence of formamide have been determined. The conditions are especially suitable for the study of DNA rich in mole percent GC. A 10-fold dilution of the reassociation samples leading to a decrease in both NaCl and formamide concentrations, consequently resulting in a lowering of Tm by only 1.5°C, and the S1 digestion at temperatures identical to the reassociation assay in order to retain the stability of the duplex, are two important aspects of this system. Under these conditions, the kinetics of reassociation followed the theoretically predicted pattern, while the earlier reported methods have shown lower values.

A Combination of Hexagonal and 12-Sided Polygonal Voltage Space Vector PWM Control for IM Drives Using Cascaded Two-Level Inverters

This paper proposes a multilevel inverter configuration which produces a hexagonal voltage space vector structure in the lower modulation region and a 12-sided polygonal space vector structure in the overmodulation region. A conventional multilevel inverter produces 6n plusmn 1 (n = odd) harmonics in the phase voltage during overmodulation and in the extreme square-wave mode of operation. However, this inverter produces a 12-sided polygonal space vector location, leading to the elimination of 6n plusmn 1 (n = odd) harmonics in the overmodulation region extending to a final 12-step mode of operation with a smooth transition. The benefits of this arrangement are lower losses and reduced torque pulsation in an induction motor drive fed from this converter at higher modulation indexes. The inverter is fabricated by using three conventional cascaded two-level inverters with asymmetric dc-bus voltages. A comparative simulation study of the harmonic distortion in the phase voltage and associated losses in conventional multilevel inverters and that of the proposed inverter is presented in this paper. Experimental validation on a prototype shows that the proposed converter is suitable for high-power applications because of low harmonic distortion and low losses.

Improved Approach for Analyzing Bromophenols in Seafood Using Stable Isotope Dilution Analysis in Combination with SPME

An analytical method for the measurement of five naturally occurring bromophenols of sensory relevance in seafood (barramundi and prawns) is presented. The method combines simultaneous distillation−extraction followed by alkaline back extraction of a hexane extract and subsequent acetylation of the bromophenols. Analysis of the bromophenol acetates was accomplished by headspace solid phase microextraction and gas chromatography−mass spectrometry using selected ion monitoring. The addition of 13C6 bromophenol stable isotope internal standards for each of the five congeners studied permitted the accurate quantitation of 2-bromophenol, 4-bromophenol, 2,6-dibromophenol, 2,4-dibromophenol, and 2,4,6-tribromophenol down to a limit of quantification of 0.05 ng/g of fish flesh. The method indicated acceptable precision and repeatability and excellent linearity over the typical concentration range of these compounds in seafood (0.5−50 ng/g). The analytical method was applied to determine the concentration of bromophenols in a range of farmed and wild barramundi and prawns and was also used to monitor bromophenol uptake in a pilot feeding trial.

Validation-Based Sparse Gaussian Process Classifier Design

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.

Automatic ICD-10 classification of cancers from free-text death certificates

Objective Death certificates provide an invaluable source for cancer mortality statistics; however, this value can only be realised if accurate, quantitative data can be extracted from certificates – an aim hampered by both the volume and variable nature of certificates written in natural language. This paper proposes an automatic classification system for identifying cancer related causes of death from death certificates. Methods Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates. These features were used to train Support Vector Machine classifiers (one classifier for each cancer type). The classifiers were deployed in a cascaded architecture: the first level identified the presence of cancer (i.e., binary cancer/nocancer) and the second level identified the type of cancer (according to the ICD-10 classification system). A held-out test set was used to evaluate the effectiveness of the classifiers according to precision, recall and F-measure. In addition, detailed feature analysis was performed to reveal the characteristics of a successful cancer classification model. Results The system was highly effective at identifying cancer as the underlying cause of death (F-measure 0.94). The system was also effective at determining the type of cancer for common cancers (F-measure 0.7). Rare cancers, for which there was little training data, were difficult to classify accurately (F-measure 0.12). Factors influencing performance were the amount of training data and certain ambiguous cancers (e.g., those in the stomach region). The feature analysis revealed a combination of features were important for cancer type classification, with SNOMED CT concept and oncology specific morphology features proving the most valuable. Conclusion The system proposed in this study provides automatic identification and characterisation of cancers from large collections of free-text death certificates. This allows organisations such as Cancer Registries to monitor and report on cancer mortality in a timely and accurate manner. In addition, the methods and findings are generally applicable beyond cancer classification and to other sources of medical text besides death certificates.

CSSI-PRO: a method for secondary structure type editing, assignment and estimation in proteins using linear combination of backbone chemical shifts

Estimation of secondary structure in polypeptides is important for studying their structure, folding and dynamics. In NMR spectroscopy, such information is generally obtained after sequence specific resonance assignments are completed. We present here a new methodology for assignment of secondary structure type to spin systems in proteins directly from NMR spectra, without prior knowledge of resonance assignments. The methodology, named Combination of Shifts for Secondary Structure Identification in Proteins (CSSI-PRO), involves detection of specific linear combination of backbone H-1(alpha) and C-13' chemical shifts in a two-dimensional (2D) NMR experiment based on G-matrix Fourier transform (GFT) NMR spectroscopy. Such linear combinations of shifts facilitate editing of residues belonging to alpha-helical/beta-strand regions into distinct spectral regions nearly independent of the amino acid type, thereby allowing the estimation of overall secondary structure content of the protein. Comparison of the predicted secondary structure content with those estimated based on their respective 3D structures and/or the method of Chemical Shift Index for 237 proteins gives a correlation of more than 90% and an overall rmsd of 7.0%, which is comparable to other biophysical techniques used for structural characterization of proteins. Taken together, this methodology has a wide range of applications in NMR spectroscopy such as rapid protein structure determination, monitoring conformational changes in protein-folding/ligand-binding studies and automated resonance assignment.

An unusual combination in papaya ( Carica papaya): the good (glucosinolates) and the bad (cyanogenic glycosides)

Glucosinolates are a group of sulphur-containing glycosides found in the plant order Brassicales which includes the Brassica vegetables such as broccoli, cabbage and cauliflower. When brought into contact with the plant enzymes, myrosinases, the glucosinolates break down releasing glucose and other products which serve principally in plant defence against herbivores. The most important of the products from a human nutritional viewpoint, are the isothiocyanates. These potent inducers of detoxifying enzymes bestow the distinct anti-cancer properties on these plants. Unique among tropical fruits, papaya is known to contain an abundance of one particular glucosinolate, glucotropaeolin. Other compounds that play a pivotal role in the chemical defence system of many plants are the cyanogenic glycosides. Cyanogenic glycosides are activated by plant enzymes in the event of pest attack, releasing the deterrent: toxic hydrogen cyanide. Papaya, in addition to glucosinolates, also contains low levels of cyanogenic glycosides, an unusual occurrence because it was assumed that the two classes of metabolites were mutually exclusive. Studies measuring the levels of both in the edible parts of the papaya fruit and other utilised tissues are discussed and considered in the context of potential human health ramifications. All rights reserved, Elsevier.

Experimental investigation of interaction of strong shock heated oxygen gas on the surface of ZrO2 - a novel method to understand re-combination heating

Interaction of shock heated test gas in the free piston driven shock tube with bulk and thin film of cubic zirconium dioxide (ZrO2) prepared by combustion method is investigated. The test samples before and after exposure to the shock wave are analyzed by X-ray diffraction (XRD), X-Ray Photoelectron Spectroscopy (XPS) and Scanning Electron Microscope (SEM). The study shows transformation of metastable cubic ZrO2 to stable monoclinic ZrO2 phase after interacting with shock heated oxygen gas due to the heterogeneous catalytic recombination surface reaction.

A combination of local inflammation and central memory T cells potentiates immunotherapy in the skin

Adoptive T cell therapy uses the specificity of the adaptive immune system to target cancer and virally infected cells. Yet the mechanism and means by which to enhance T cell function are incompletely described, especially in the skin. In this study, we use a murine model of immunotherapy to optimize cell-mediated immunity in the skin. We show that in vitro - derived central but not effector memory-like T cells bring about rapid regression of skin-expressing cognate Ag as a transgene in keratinocytes. Local inflammation induced by the TLR7 receptor agonist imiquimod subtly yet reproducibly decreases time to skin graft rejection elicited by central but not effector memory T cells in an immunodeficient mouse model. Local CCL4, a chemokine liberated by TLR7 agonism, similarly enhances central memory T cell function. In this model, IL-2 facilitates the development in vivo of effector function from central memory but not effector memory T cells. In a model of T cell tolerogenesis, we further show that adoptively transferred central but not effector memory T cells can give rise to successful cutaneous immunity, which is dependent on a local inflammatory cue in the target tissue at the time of adoptive T cell transfer. Thus, adoptive T cell therapy efficacy can be enhanced if CD8+ T cells with a central memory T cell phenotype are transferred, and IL-2 is present with contemporaneous local inflammation. Copyright © 2012 by The American Association of Immunologists, Inc.

Diffusion of Pure CH4 and Its Binary Mixture with CO2 in Faujasite NaY: A Combination of Neutron Scattering Experiments and Molecular Dynamics Simulations

The self-diffusion properties of pure CH4 and its binary mixture with CO2 within MY zeolite have been investigated by combining an experimental quasi-elastic neutron scattering (QENS) technique and classical Molecular dynamics simulations. The QENS measurements carried out at 200 K led to an unexpected self-diffusivity profile for Pure CH4 with the presence of a maximum for a loading of 32 CH4/unit cell, which was never observed before for the diffusion of apolar species in azeolite system With large windows. Molecular dynamics simulations were performed using two distinct microscopic models for representing the CH4/NaY interactions. Depending on the model, we are able to fairly reproduce either the magnitude or the profile of the self-diffusivity.Further analysis allowed LIS to provide some molecular insight into the diffusion mechanism in play. The QENS measurements report only a slight decrease of the self-diffusivity of CH4 in the presence of CO2 when the CO2 loading increases. Molecular dynamics simulations successfully capture this experimental trend and suggest a plausible microscopic diffusion mechanism in the case of this binary mixture.

Metabolomic and proteomic analysis of breast cancer patient samples suggests that glutamate and 12-HETE in combination with CA15-3 may be useful biomarkers reflecting tumour burden

Evaluation of protein and metabolite expression patterns in blood using mass spectrometry and high-throughput antibody-based screening platforms has potential for the discovery of new biomarkers for managing breast cancer patient treatment. Previously identified blood-based breast cancer biomarkers, including cancer antigen 15.3 (CA15-3) are useful in combination with imaging (computed tomography scans, magnetic resonance imaging, X-rays) and physical examination for monitoring tumour burden in advanced breast cancer patients. However, these biomarkers suffer from insufficient levels of accuracy and with new therapies available for the treatment of breast cancer, there is an urgent need for reliable, non-invasive biomarkers that measure tumour burden with high sensitivity and specificity so as to provide early warning of the need to switch to an alternative treatment. The aim of this study was to identify a biomarker signature of tumour burden using cancer and non-cancer (healthy controls/non-malignant breast disease) patient samples. Results demonstrate that combinations of three candidate biomarkers from Glutamate, 12-Hydroxyeicosatetraenoic acid, Beta-hydroxybutyrate, Factor V and Matrix metalloproteinase-1 with CA15-3, an established biomarker for breast cancer, were found to mirror tumour burden, with AUC values ranging from 0.71 to 0.98 when comparing non-malignant breast disease to the different stages of breast cancer. Further validation of these biomarker panels could potentially facilitate the management of breast cancer patients, especially to assess changes in tumour burden in combination with imaging and physical examination.