3D-Printed Enzyme-Embedded Plastics.

A simple and environmentally friendly approach toward the thermoplastic processing of rapidly degradable plastic-enzyme composites using three-dimensional (3D) printing techniques is described. Polycaprolactone/Amano lipase (PCL/AL) composite films (10 mm × 10 mm; height [h] = ∼400 µm) with an AL loading of 0.1, 1.0, and 5.0% were prepared via 3D printing techniques that entail direct mixing in the solid state and thermal layer-by-layer extrusion. It was found that AL can tolerate in situ processing temperatures up to 130 °C in the solid-state for 60 min without loss of enzymatic activity. The composites were degraded in phosphate buffer (8 mg/mL, composite to buffer) for 7 days at 37 °C and the resulting average percent total weight loss (WLavg %) was found to be 5.2, 92.9, and 100%, for the 0.1, 1.0, and 5.0% films, respectively. The degradation rates of PCL/AL composites were found to be faster than AL applied externally in the buffer. Thicker PCL/AL 1.0% films (10 mm × 10 mm; h = ∼500 µm) were also degraded over a 7 day period to examine how the weight loss occurs over time with 3.0, 18.1, 36.4, 46.4, and 70.2% weight loss for days 1, 2, 3, 4, and 7, respectively. Differential scanning calorimetry (DSC) analysis shows that the film's percent crystallinity (Dxtal%) increases over time with Dxtal% = 46.5 for day 0 and 53.1% for day 7. Scanning electron microscopy (SEM) analysis found that film erosion begins at the surface and that water can penetrate the interior via surface pores activating the enzymes embedded in the film. Controlled release experiments utilizing dye-loaded PCL/AL/dye (AL = 1.0%; dye = 0.1%) composites were degraded over a 7 day period with the bulk of the dye released by the fourth day. The PCL/AL multimaterial objects containing AL-resistant polylactic acid (PLA) were also printed and degraded to demonstrate the application of this material on more complex structures.

Pilot-Scale Production of Hemicellulose Ethers from Softwood Hemicelluloses Obtained from Compression Screw Pressate of a Thermo-Mechanical Pulping Plant.

Bio-derived materials are becoming increasingly sought-after as a sustainable alternative to petrochemical-derived polymers. In the present pilot-scale study, a hemicellulose-rich compression screw pressate, obtained from the pre-heating stage of thermo-mechanical pulping (TMP) of radiata pine, was purified by treatment with adsorbent resin (XAD7), then ultrafiltered and diafiltered at 10 kDa to isolate the high-molecular weight (MW) hemicellulose fraction (yield 18.4% on pressate solids), and, finally, reacted with butyl glycidyl ether to plasticise the hemicelluloses. The resulting light brown hemicellulose ethers (yield 102% on the isolated hemicelluloses) contained ca. 0.5 butoxy-hydroxypropyl side chains per pyranose unit and had weight- and number-average MWs of 13,000 Da and 7200 Da, respectively. The hemicellulose ethers may serve as raw material for bio-based products such as barrier films.

Rheological Behavior of High Cell Density Pseudomonas putida LS46 Cultures during Production of Medium Chain Length Polyhydroxyalkanoate (PHA) Polymers.

The rheology of high-cell density (HCD) cultures is an important parameter for its impact on mixing and sparging, process scale-up, and downstream unit operations in bioprocess development. In this work, time-dependent rheological properties of HCD Pseudomonas putida LS46 cultures were monitored for microbial polyhydroxyalkanoate (PHA) production. As the cell density of the fed-batch cultivation increased (0 to 25 g·L-1 cell dry mass, CDM), the apparent viscosity increased nearly nine-fold throughout the fed-batch process. The medium behaved as a nearly Newtonian fluid at lower cell densities, and became increasingly shear-thinning as the cell density increased. However, shear-thickening behavior was observed at shearing rates of approximately 75 rad·s-1 or higher, and its onset increased with viscosity of the sample. The supernatant, which contained up to 9 g·L-1 soluble organic material, contributed more to the observed viscosity effect than did the presence of cells. Owing to this behavior, the oxygen transfer performance of the bioreactor, for otherwise constant operating conditions, was reduced by 50% over the cultivation time. This study has shown that the dynamic rheology of HCD cultures is an important engineering parameter that may impact the final outcome in PHA cultivations. Understanding and anticipating this behavior and its biochemical origins could be important for improving overall productivity, yield, process scalability, and the efficacy of downstream processing unit operations.

Hydrogen Oxidation Influences Glycogen Accumulation in a Verrucomicrobial Methanotroph.

Metabolic flexibility in aerobic methane oxidizing bacteria (methanotrophs) enhances cell growth and survival in instances where resources are variable or limiting. Examples include the production of intracellular compounds (such as glycogen or polyhydroxyalkanoates) in response to unbalanced growth conditions and the use of some energy substrates, besides methane, when available. Indeed, recent studies show that verrucomicrobial methanotrophs can grow mixotrophically through oxidation of hydrogen and methane gases via respiratory membrane-bound group 1d [NiFe] hydrogenases and methane monooxygenases, respectively. Hydrogen metabolism is particularly important for adaptation to methane and oxygen limitation, suggesting this metabolic flexibility may confer growth and survival advantages. In this work, we provide evidence that, in adopting a mixotrophic growth strategy, the thermoacidophilic methanotroph, Methylacidiphilum sp. RTK17.1 changes its growth rate, biomass yields and the production of intracellular glycogen reservoirs. Under nitrogen-fixing conditions, removal of hydrogen from the feed-gas resulted in a 14% reduction in observed growth rates and a 144% increase in cellular glycogen content. Concomitant with increases in glycogen content, the total protein content of biomass decreased following the removal of hydrogen. Transcriptome analysis of Methylacidiphilum sp. RTK17.1 revealed a 3.5-fold upregulation of the Group 1d [NiFe] hydrogenase in response to oxygen limitation and a 4-fold upregulation of nitrogenase encoding genes (nifHDKENX) in response to nitrogen limitation. Genes associated with glycogen synthesis and degradation were expressed constitutively and did not display evidence of transcriptional regulation. Collectively these data further challenge the belief that hydrogen metabolism in methanotrophic bacteria is primarily associated with energy conservation during nitrogen fixation and suggests its utilization provides a competitive growth advantage within hypoxic habitats.

Mixotrophy drives niche expansion of verrucomicrobial methanotrophs.

Aerobic methanotrophic bacteria have evolved a specialist lifestyle dependent on consumption of methane and other short-chain carbon compounds. However, their apparent substrate specialism runs contrary to the high relative abundance of these microorganisms in dynamic environments, where the availability of methane and oxygen fluctuates. In this work, we provide in situ and ex situ evidence that verrucomicrobial methanotrophs are mixotrophs. Verrucomicrobia-dominated soil communities from an acidic geothermal field in Rotokawa, New Zealand rapidly oxidised methane and hydrogen simultaneously. We isolated and characterised a verrucomicrobial strain from these soils, Methylacidiphilum sp. RTK17.1, and showed that it constitutively oxidises molecular hydrogen. Genomic analysis confirmed that this strain encoded two [NiFe]-hydrogenases (group 1d and 3b), and biochemical assays revealed that it used hydrogen as an electron donor for aerobic respiration and carbon fixation. While the strain could grow heterotrophically on methane or autotrophically on hydrogen, it grew optimally by combining these metabolic strategies. Hydrogen oxidation was particularly important for adaptation to methane and oxygen limitation. Complementary to recent findings of hydrogenotrophic growth by Methylacidiphilum fumariolicum SolV, our findings illustrate that verrucomicrobial methanotrophs have evolved to simultaneously utilise hydrogen and methane from geothermal sources to meet energy and carbon demands where nutrient flux is dynamic. This mixotrophic lifestyle is likely to have facilitated expansion of the niche space occupied by these microorganisms, allowing them to become dominant in geothermally influenced surface soils. Genes encoding putative oxygen-tolerant uptake [NiFe]-hydrogenases were identified in all publicly available methanotroph genomes, suggesting hydrogen oxidation is a general metabolic strategy in this guild.

Physical interpretation of polarization-encoded images by color preview.

In the framework of Stokes parameters imaging, polarizationencoded images have four channels which makes physical interpretation of such multidimensional structures hard to grasp at once. Furthermore, the information content is intricately combined in the parameters channels which involve the need for a proper tool that allows the analysis and understanding this kind of images. In this paper we address the problem of analyzing polarization-encoded images and explore the potential of this information for classification issues and propose ad hoc color displays as an aid to the interpretation of physical properties content. The color representation schemes introduced hereafter employ a technique that uses novel Poincaré Sphere to color spaces mapping coupled with a segmentation map as an a priori information in order to allow, at best, a distribution of the information in the appropriate color space. The segmentation process relies on the fuzzy C-means clustering algorithms family where the used distances were redefined in relation with our images specificities. Local histogram equalization is applied to each class in order to bring out the intra-class's information smooth variations. The proposed methods are applied and validated with Stokes images of biological tissues.

Fuzzy Markov random fields versus chains for multispectral image segmentation.

This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

Vertebra segmentation based on two-step refinement.

Knowledge of vertebra location, shape, and orientation is crucial in many medical applications such as orthopedics or interventional procedures. Computed tomography (CT) offers a high contrast between bone and soft tissues, but automatic vertebra segmentation remains difficult. Hence, the wide range of shapes, aging, and degenerative joint disease alterations as well as the variety of pathological cases encountered in an aging population make automatic segmentation sometimes challenging. Besides, daily practice implies a need for affordable computation time. This paper aims to present a new automated vertebra segmentation method (using a first bounding box for initialization) for CT 3D data which tackles these problems. This method is based on two consecutive steps. The first one is a new coarse-to-fine method efficiently reducing the data amount to obtain a coarse shape of the vertebra. The second step consists in a hidden Markov chain (HMC) segmentation using a specific volume transformation within a Bayesian framework. Our method does not introduce any prior on the expected shape of the vertebra within the bounding box and thus deals with the most frequent pathological cases encountered in daily practice. We experiment this method on a set of standard lumbar, thoracic, and cervical vertebrae and on a public dataset, on pathological cases, and in a simple integration example. Quantitative and qualitative results show that our method is robust to changes in shapes and luminance and provides correct segmentation with respect to pathological cases.

Acetate production from lactose by Clostridium thermolacticum and hydrogen-scavenging microorganisms in continuous culture--effect of hydrogen partial pressure.

The effect of the addition of hydrogen-consuming microorganisms on the metabolism of Clostridium thermolacticum was studied. By growing this bacterium in continuous culture at 58 degrees C, on 29 mmol lactose l(-1) (10 gl(-1)) in the feed, with the H2-consuming microorganisms Methanothermobacter thermoautotrophicus and Moorella thermoautotrophica, the volumetric productivity of acetate was increased up to 3.9 mmol l(-1)h(-1) at a dilution rate of 0.058 h(-1). This was about three times higher than the maximal acetate volumetric productivity quantified when C. thermolacticum was cultivated alone. In the consortium, C. thermolacticum was the only species able to metabolize lactose; it produced not only acetate, but also hydrogen, carbon dioxide and lactate. The other species of the consortium were growing on these by-products. Meth. thermoautotrophicus played an important role as a very efficient hydrogen scavenger and decreased the hydrogen partial pressure drastically: hydrogen was converted to methane. Moor. thermoautotrophica converted lactate as well as hydrogen and carbon dioxide into acetate. As a consequence, lactose was efficiently consumed and the only organic product in the liquid phase was acetate.

Clustering of Mueller matrix images for skeletonized structure detection.

This paper extends and refines previous work on clustering of polarization-encoded images. The polarization-encoded images used in this work are considered as multidimensional parametric images where a clustering scheme based on Markovian Bayesian inference is applied. Hidden Markov Chains Model (HMCM) and Hidden Hierarchical Markovian Model (HHMM) show to handle effectively Mueller images and give very good results for biological tissues (vegetal leaves). Pretreatments attempting to reduce the image dimensionality based on the Principal Component Analysis (PCA) turns out to be useless for Mueller matrix images.

3D+t brain MRI segmentation using robust 4D Hidden Markov Chain.

In recent years many automatic methods have been developed to help physicians diagnose brain disorders, but the problem remains complex. In this paper we propose a method to segment brain structures on two 3D multi-modal MR images taken at different times (longitudinal acquisition). A bias field correction is performed with an adaptation of the Hidden Markov Chain (HMC) allowing us to take into account the temporal correlation in addition to spatial neighbourhood information. To improve the robustness of the segmentation of the principal brain structures and to detect Multiple Sclerosis Lesions as outliers the Trimmed Likelihood Estimator (TLE) is used during the process. The method is validated on 3D+t brain MR images.

Hyperconnections and hierarchical representations for grayscale and multiband image processing.

Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.

Segmentation of respiratory signals by evidence theory.

This paper presents an evidential segmentation scheme of respiratory signals for the detection of the wheezing sounds. The segmentation is based on the modeling of the data by evidence theory which is well suited to represent such uncertain and imprecise data. In this paper, we particularly focus on the modelization of the data imprecision using the fuzzy theory. The modelization result is then used to define the mass function. The effectiveness of the method is demonstrated on synthetic and real signals.

Content uniformity of pharmaceutical solid dosage forms by near infrared hyperspectral imaging: A feasibility study.

Near-infrared imaging systems simultaneously record spectral and spatial information. Each measurement generates a data cube containing several thousand spectra. Chemometric methods are therefore required to extract qualitative and quantitative information. The aim of this study was to determine the feasibility of quantifying active pharmaceutical ingredient (API) and excipient content in pharmaceutical formulations using hyperspectral imaging. Two kinds of tablets with a range of API content were analysed: a binary mixture of API and cellulose, and a pharmaceutical formulation with seven different compounds. Two pixel sizes, 10mum/pixel and 40mum/pixel, were compared, together with two types of spectral pretreatment: standard normal variate (SNV) normalization and Savitzky-Golay smoothing. Two methods of extracting concentrations were compared: the partial least squares 2 (PLS2) algorithm, which predicts the content of several compounds simultaneously, and the multivariate classical least squares (CLS) algorithm based on pure compound reference spectra without calibration. Best content predictions were achieved using 40mum/pixel resolution and the PLS2 method with SNV normalized spectra. However, the CLS method extracted distribution maps with higher contrast and was less sensitive to noisy spectra and outliers; its API predictions were also highly correlated to real content, indicating the feasibility of predicting API content using hyperspectral imaging without calibration.

Metabolism of lactose by Clostridium thermolacticum growing in continuous culture.

The objective of the present study was to characterize the metabolism of Clostridium thermolacticum, a thermophilic anaerobic bacterium, growing continuously on lactose (10 g l(-1)) and to determine the enzymes involved in the pathways leading to the formation of the fermentation products. Biomass and metabolites concentration were measured at steady-state for different dilution rates, from 0.013 to 0.19 h(-1). Acetate, ethanol, hydrogen and carbon dioxide were produced at all dilution rates, whereas lactate was detected only for dilution rates below 0.06 h(-1). The presence of several key enzymes involved in lactose metabolism, including beta-galactosidase, glyceraldehyde-3-phosphate dehydrogenase, pyruvate:ferredoxin oxidoreductase, acetate kinase, ethanol dehydrogenase and lactate dehydrogenase, was demonstrated. Finally, the intracellular level of NADH, NAD+, ATP and ADP was also measured for different dilution rates. The production of ethanol and lactate appeared to be linked with the re-oxidation of NADH produced during glycolysis, whereas hydrogen produced should come from reduced ferredoxin generated during pyruvate decarboxylation. To produce more hydrogen or more acetate from lactose, it thus appears that an efficient H2 removal system should be used, based on a physical (membrane) or a biological approach, respectively, by cultivating C. thermolacticum with efficient H2 scavenging and acetate producing microorganisms.

Clustering of polarization-encoded images.

Polarization-encoded imaging consists of the distributed measurements of polarization parameters for each pixel of an image. We address clustering of multidimensional polarization-encoded images. The spatial coherence of polarization information is considered. Two methods of analysis are proposed: polarization contrast enhancement and a more-sophisticated image-processing algorithm based on a Markovian model. The proposed algorithms are applied and validated with two different Mueller images acquired by a fully polarimetric imaging system.