First results obtained using the CENBG nanobeam line: performances and applications

A high resolution focused beam line has been recently installed on the AIFIRA (“Applications Interdisciplinaires des Faisceaux d’Ions en Région Aquitaine”) facility at CENBG. This nanobeam line, based on a doublet–triplet configuration of Oxford Microbeam Ltd. OM-50™ quadrupoles, offers the opportunity to focus protons, deuterons and alpha particles in the MeV energy range to a sub-micrometer beam spot. The beam optics design has been studied in detail and optimized using detailed ray-tracing simulations and the full mechanical design of the beam line was reported in the Debrecen ICNMTA conference in 2008. During the last two years, the lenses have been carefully aligned and the target chamber has been fully equipped with particle and X-ray detectors, microscopes and precise positioning stages. The beam line is now operational and has been used for its firstapplications to ion beam analysis. Interestingly, this set-up turned out to be a very versatile tool for a wide range of applications. Indeed, even if it was not intended during the design phase, the ion optics configuration offers the opportunity to work either with a high current microbeam (using the triplet only) or with a lower current beam presenting a sub-micrometer resolution (using the doublet–triplet configuration). The performances of the CENBGnanobeam line are presented for both configurations. Quantitative data concerning the beam lateral resolutions at different beam currents are provided. Finally, the firstresults obtained for different types of application are shown, including nuclear reaction analysis at the micrometer scale and the firstresults on biological samples

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.

An Architecture forRepresenting Biological Processes based on Networks of Bio-inspired Processors

n this paper we propose the use of Networks of Bio-inspired Processors (NBP) to model some biological phenomena within a computational framework. In particular, we propose the use of an extension of NBP named Network Evolutionary Processors Transducers to simulate chemical transformations of substances. Within a biological process, chemical transformations of substances are basic operations in the change of the state of the cell. Previously, it has been proved that NBP are computationally complete, that is, they are able to solve NP complete problems in linear time, using massively parallel computations. In addition, we propose a multilayer architecture that will allow us to design models of biological processes related to cellular communication as well as their implications in the metabolic pathways. Subsequently, these models can be applied not only to biological-cellular instances but, possibly, also to configure instances of interactive processes in many other fields like population interactions, ecological trophic networks, in dustrial ecosystems, etc.

Photovoltaic LiNbO3particles: Applications to Biomedicine/Biophotonics

Recently, a novel method to trap and pattern ensembles of nanoparticles has been proposed and tested. It relies on the photovoltaic (PV) properties of certain ferroelectric crystals such as LiNbO3 [1,2]. These crystals, when suitably doped, develop very high electric fields in response to illumination with light of suitable wavelength. The PV effect lies in the asymmetrical excitation of electrons giving rise to PV currents and associated space-charge fields (photorefractive effect). The field generated in the bulk of the sample propagates to the surrounding medium as evanescent fields. When dielectric or metal nanoparticles are deposited on the surface of the sample the evanescent fields give rise to either electrophoretic or dielectrophoretic forces, depending on the charge state of the particles, that induce the trapping and patterning effects [3,4]. The purpose of this work has been to explore the effects of such PV fields in the biology and biomedical areas. A first work was able to show the necrotic effects induced by such fields on He-La tumour cells grown on the surface of an illuminated iron-doped LiNbO3 crystal [5]. In principle, it is conceived that LiNbO3 nanoparticles may be advantageously used for such biomedical purposes considering the possibility of such nanoparticles being incorporated into the cells. Previous experiments using microparticles have been performed [5] with similar results to those achieved with the substrate. Therefore, the purpose of this work has been to fabricate and characterize the LiNbO3 nanoparticles and assess their necrotic effects when they are incorporated on a culture of tumour cells. Two different preparation methods have been used: 1) mechanical grinding from crystals, and 2) bottom-up sol-gel chemical synthesis from metal-ethoxide precursors. This later method leads to a more uniform size distribution of smaller particles (down to around 50 nm). Fig. 1(a) and 1(b) shows SEM images of the nanoparticles obtained with both method. An ad hoc software taking into account the physical properties of the crystal, particullarly donor and aceptor concentrations has been developped in order to estimate the electric field generated in noparticles. In a first stage simulations of the electric current of nanoparticles, in a conductive media, due to the PV effect have been carried out by MonteCarlo simulations using the Kutharev 1-centre transport model equations [6] . Special attention has been paid to the dependence on particle size and [Fe2+]/[Fe3+]. First results on cubic particles shows large dispersion for small sizes due to the random number of donors and its effective concentration (Fig 2). The necrotic (toxicity) effect of nanoparticles incorporated into a tumour cell culture subjected to 30 min. illumination with a blue LED is shown in Fig.3. For each type of nanoparticle the percent of cell survival in dark and illumination conditions has been plot as a function of the particle dilution factor. Fig. 1a corresponds to mechanical grinding particles whereas 1b and 1c refer to chemically synthesized particles with two oxidation states. The light effect is larger with mechanical grinding nanoparticles, but dark toxicity is also higher. For chemically synthesized nanoparticles dark toxicity is low but only in oxidized samples, where the PV effect is known to be larger, the light effect is appreciable. These preliminary results demonstrate that Fe:LiNbO· nanoparticles have a biological damaging effect on cells, although there are many points that should be clarified and much space for PV nanoparticles optimization. In particular, it appears necessary to determine the fraction of nanoparticles that become incorporated into the cells and the possible existence of threshold size effects. This work has been supported by MINECO under grant MAT2011-28379-C03.

Computer simulation of the interplay between fractal structures and surrounding heterogeneous multifractal distributions. Applications

In a large number of physical, biological and environmental processes interfaces with high irregular geometry appear separating media (phases) in which the heterogeneity of constituents is present. In this work the quantification of the interplay between irregular structures and surrounding heterogeneous distributions in the plane is made For a geometric set image and a mass distribution (measure) image supported in image, being image, the mass image gives account of the interplay between the geometric structure and the surrounding distribution. A computation method is developed for the estimation and corresponding scaling analysis of image, being image a fractal plane set of Minkowski dimension image and image a multifractal measure produced by random multiplicative cascades. The method is applied to natural and mathematical fractal structures in order to study the influence of both, the irregularity of the geometric structure and the heterogeneity of the distribution, in the scaling of image. Applications to the analysis and modeling of interplay of phases in environmental scenarios are given.

Kabat Database and its applications: future directions

The Kabat Database was initially started in 1970 to determine the combining site of antibodies based on the available amino acid sequences. The precise delineation of complementarity determining regions (CDR) of both light and heavy chains provides the first example of how properly aligned sequences can be used to derive structural and functional information of biological macromolecules. This knowledge has subsequently been applied to the construction of artificial antibodies with prescribed specificities, and to many other studies. The Kabat database now includes nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules, and other proteins of immunological interest. While new sequences are continually added into this database, we have undertaken the task of developing more analytical methods to study the information content of this collection of aligned sequences. New examples of analysis will be illustrated on a yearly basis. The Kabat Database and its applications are freely available at http://immuno.bme.nwu.edu.

In vitro evolution of a highly replicating, doxycycline-dependent HIV for applications in vaccine studies

A major concern associated with the use of vaccines based on live-attenuated viruses is the possible and well documented reversion to pathogenic phenotypes. In the case of HIV, genomic deletions or mutations introduced to attenuate viral pathogenicity can be repaired by selection of compensating mutations. These events lead to increased virus replication rates and, eventually, disease progression. Because replication competence and degree of protection appear to be directly correlated, further attenuation of a vaccine virus may compromise the ability to elicit a protective immune response. Here, we describe an approach toward a safe attenuated HIV vaccine. The system is not based on permanent reduction of infectivity by alteration of important viral genomic sequences, but on strict control of replication through the insertion of the tetracycline (Tet) system in the HIV genome. Furthermore, extensive in vitro evolution was applied to the prototype Tet-controlled HIV to select for variants with optimized rather than diminished replication capacity. The final product of evolution has properties uniquely suited for use as a vaccine strain. The evolved virus is highly infectious, as opposed to a canonically attenuated virus. It replicates efficiently in T cell lines and in activated and unstimulated peripheral blood mononuclear cells. Most importantly, replication is strictly dependent on the nontoxic Tetanalogue doxycycline and can be turned on and off. These results suggest that this in vitro evolved, doxycycline-dependent HIV might represent a useful tool toward the development of a safer, live-attenuated HIV vaccine.

Study of interaction between Si(O,C) nanowires and the biological system

Nanomedicine is a new branch of medicine, based on the potentiality and intrinsic properties of nanomaterials. Indeed, the nanomaterials ( i.e. the materials with nano and under micron size) can be suitable to different applications in biomedicine. The nanostructures can be used by taking advantage of their properties (for example superparamagnetic nanoparticles) or functionalized to deliver the drug in a specific target, thanks the ability to cross biological barriers. The size and the shape of 1D-nanostructures (nanotubes and nanowires) have an important role on the cell fate: their morphology plays a key role on the interaction between nanostructure and the biological system. For this reason the 1D nanostructure are interesting for their ability to mime the biological system. An implantable material or device must therefore integrate with the surrounding extracellular matrix (ECM), a complex network of proteins with structural and signaling properties. Innovative techniques allow the generation of complex surface patterns that can resemble the structure of the ECM, such as 1D nanostructures. NWs based on cubic silicon carbide (3C-SiC), either bare (3C-SiC NWs) or surrounded by an amorphous shell (3C-SiC/SiO2 core/shell NWs), and silicon oxycarbide nanowires (SiOxCy NWs) can meet the chemical, mechanical and electrical requirements for tissue engineering and have a strong potential to pave the way for the development of a novel generation of implantable nano-devices. Silicon oxycarbide shows promising physical and chemical properties as elastic modulus, bending strength and hardness, chemical durability superior to conventional silicate glasses in aggressive environments and high temperature stability up to 1300 °C. Moreover, it can easily be engineered through functionalization and decoration with macro-molecules and nanoparticles. Silicon carbide has been extensively studied for applications in harsh conditions, as chemical environment, high electric field and high and low temperature, owing to its high hardness, high thermal conductivity, chemical inertness and high electron mobility. Also, its cubic polytype (3C) is highly biocompatible and hemocompatible, and some prototypes of biomedical applications and biomedical devices have been already realized starting from 3C-SiC thin films. Cubic SiC-based NWs can be used as a biomimetic biomaterial, providing a robust and novel biocompatible biological interface . We cultured in vitro A549 human lung adenocarcinoma epithelial cells and L929 murine fibroblast cells over core/shell SiC/SiO2, SiOxCy and bare 3C-SiC nanowire platforms, and analysed the cytotoxicity, by indirect and direct contact tests, the cell adhesion, and the cell proliferation. These studies showed that all the nanowires are biocompatible according to ISO 10993 standards. We evaluated the blood compatibility through the interaction of the nanowires with platelet rich plasma. The adhesion and activation of platelets on the nanowire bundles, assessed via SEM imaging and soluble P-selectin quantification, indicated that a higher platelet activation is induced by the core/shell structures compared to the bare ones. Further, platelet activation is higher with 3C-SiC/SiO2 NWs and SiOxCyNWs, which therefore appear suitable in view of possible tissue regeneration. On the contrary, bare 3C-SiC NWs show a lower platelet activation and are therefore promising in view of implantable bioelectronics devices, as cardiovascular implantable devices. The NWs properties are suitable to allow the design of a novel subretinal Micro Device (MD). This devices is based on Si NWs and PEDOT:PSS, though the well know principle of the hybrid ordered bulk heterojunction (OBHJ). The aim is to develop a device based on a well-established photovoltaic technology and to adapt this know-how to the prosthetic field. The hybrid OBHJ allows to form a radial p–n junction on a nanowire/organic structure. In addition, the nanowires increase the light absorption by means of light scattering effects: a nanowires based p-n junction increases the light absorption up to the 80%, as previously demonstrated, overcoming the Shockley-Queisser limit of 30 % of a bulk p-n junction. Another interesting employment of these NWs is to design of a SiC based epicardial-interacting patch based on teflon that include SiC nanowires. . Such contact patch can bridge the electric conduction across the cardiac infarct as nanowires can ‘sense’ the direction of the wavefront propagation on the survival cardiac tissue and transmit it to the downstream surivived regions without discontinuity. The SiC NWs are tested in terms of toxicology, biocompatibility and conductance among cardiomyocytes and myofibroblasts.

Using the R package Shiny to create web applications that facilitate quantitative gene expression analysis

Thesis (Master's)--University of Washington, 2016-06

A vulnerability analysis of the temperate forests of south central Chile A vulnerability analysis of the temperate forests of south central Chile. Biological Conservation, 122 2005: 9-21.

Areas of the landscape that are priorities for conservation should be those that are both vulnerable to threatening processes and that if lost or degraded, will result in conservation targets being compromised. While much attention is directed towards understanding the patterns of biodiversity, much less is given to determining the areas of the landscape most vulnerable to threats. We assessed the relative vulnerability of remaining areas of native forest to conversion to plantations in the ecologically significant temperate rainforest region of south central Chile. The area of the study region is 4.2 million ha and the extent of plantations is approximately 200000 ha. First, the spatial distribution of native forest conversion to plantations was determined. The variables related to the spatial distribution of this threatening process were identified through the development of a classification tree and the generation of a multivariate. spatially explicit, statistical model. The model of native forest conversion explained 43% of the deviance and the discrimination ability of the model was high. Predictions were made of where native forest conversion is likely to occur in the future. Due to patterns of climate, topography, soils and proximity to infrastructure and towns, remaining forest areas differ in their relative risk of being converted to plantations. Another factor that may increase the vulnerability of remaining native forest in a subset of the study region is the proposed construction of a highway. We found that 90% of the area of existing plantations within this region is within 2.5 km of roads. When the predictions of native forest conversion were recalculated accounting for the construction of this highway, it was found that: approximately 27000 ha of native forest had an increased probability of conversion. The areas of native forest identified to be vulnerable to conversion are outside of the existing reserve network. (C) 2004 Elsevier Ltd. All tights reserved.

Discovery, structure and biological activities of the cyclotides

The cyclotides are a family of small disulfide rich proteins that have a cyclic peptide backbone and a cystine knot formed by three conserved disulfide bonds. The combination of these two structural motifs contributes to the exceptional chemical, thermal and enzymatic stability of the cyclotides, which retain bioactivity after boiling. They were initially discovered based on native medicine or screening studies associated with some of their various activities, which include uterotonic action, anti-HIV activity, neurotensin antagonism, and cytotoxicity. They are present in plants from the Rubiaceae, Violaceae and Cucurbitaccae families and their natural function in plants appears to be in host defense: they have potent activity against certain insect pests and they also have antimicrobial activity. There are currently around 50 published sequences of cyclotides and their rate of discovery has been increasing over recent years. Ultimately the family may comprise thousands of members. This article describes the background to the discovery of the cyclotides, their structural characterization, chemical synthesis, genetic origin, biological activities and potential applications in the pharmaceutical and agricultural industries. Their unique topological features make them interesting from a protein folding perspective. Because of their highly stable peptide framework they might make useful templates in drug design programs, and their insecticidal activity opens the possibility of applications in crop protection.

Multivariate monitoring of a biological wastewater treatment process: a case study at Melbourne Water's Western Treatment Plant

Biological wastewater treatment is a complex, multivariate process, in which a number of physical and biological processes occur simultaneously. In this study, principal component analysis (PCA) and parallel factor analysis (PARAFAC) were used to profile and characterise Lagoon 115E, a multistage biological lagoon treatment system at Melbourne Water's Western Treatment Plant (WTP) in Melbourne, Australia. In this study, the objective was to increase our understanding of the multivariate processes taking place in the lagoon. The data used in the study span a 7-year period during which samples were collected as often as weekly from the ponds of Lagoon 115E and subjected to analysis. The resulting database, involving 19 chemical and physical variables, was studied using the multivariate data analysis methods PCA and PARAFAC. With these methods, alterations in the state of the wastewater due to intrinsic and extrinsic factors could be discerned. The methods were effective in illustrating and visually representing the complex purification stages and cyclic changes occurring along the lagoon system. The two methods proved complementary, with each having its own beneficial features. (C) 2003 Elsevier B.V. All rights reserved.

Synthesis of gadolinium-labeled shell-crosslinked nanoparticles for magnetic resonance imaging applications

Shell-crosslinked knedel-like nanoparticles (SCKs; knedel is a Polish term for dumplings) were derivatized with gadolinium Shell chelates and studied as robust magnetic-resonance-imaging-active structures with hydrodynamic diameters of 40 +/- 3 nm. SCKs possessing an amphiphilic core-shell morphology were produced from the aqueous assembly of diblock copolymers of poly(acrylic acid) (PAA) and poly(methyl acrylate) (PMA), PAA(52)-b-PMA(128), and subsequent covalent crosslinking by amidation upon reaction with 2,2'-(ethylenedioxy)bis(ethylamine) throughout the shell layer. The properties of these materials, including non-toxicity towards mammalian cells, non-immunogenicity within mice, and capability for polyvalent targeting, make them ideal candidates for utilization within biological systems. The synthesis of SCKs derivatized with Gd-III and designed for potential use as a unique nanometer-scale contrast agent for MRI applications is described herein. Utilization of an amino-functionalized diethylenetriaminepentaacetic acid-Gd analogue allowed for direct covalent conjugation throughout the hydrophilic shell layer of the SCKs and served to increase the rotational correlation lifetime of the Gd. In addition, the highly hydrated nature of the shell layer in which the Gd was located allowed for rapid water exchange; thus, the resulting material demonstrated large ionic relaxivities (39 s(-1) mM(-1)) in an applied magnetic field of 0.47 T at 40 degrees C and, as a result of the large loading capacity of the material, also demonstrated high molecular relaxivities (20 000 s(-1) mM(-1)).

Modelling biological processes under anaerobic conditions through integrating titrimetric and off-gas measurements - applied to EBPR systems

An innovative method for modelling biological processes under anaerobic conditions is presented and discussed. The method is based on titrimetric and off-gas measurements. Titrimetric data is recorded as the addition rate of hydroxyl ions or protons that is required to maintain pH in a bioreactor at a constant level. An off-gas analysis arrangement measures, among other things, the transfer rate of carbon dioxide. The integration of these signals results in a continuous signal which is solely related to the biological reactions. When coupled with a mathematical model of the biological reactions, the signal allows a detailed characterisation of these reactions, which would otherwise be difficult to achieve. Two applications of the method to the enhanced biological phosphorus removal processes are presented and discussed to demonstrate the principle and effectiveness of the method.