Lokalisierung der Fortsatz-induzierenden Domäne von Transmembran-Agrin

Aggregation oder Überexpression der Transmembran-Isoform des extrazellulären Matrix-Proteoglycans Agrin in Neuronen führt zur Bildung zahlreicher filopodienartiger Fortsätze auf Axonen und Dendriten. Ähnliche Fortsätze können auch durch Überexpression von Transmembran-Agrin in verschiedenen nicht-neuronalen Zelllinien induziert werden. Untersuchungen zu dieser Fortsatz-induzierenden Aktivität in Neuronen und nicht-neuronalen Zellen zeigen, dass der extrazelluläre Teil von Transmembran-Agrin für die Fortsatzbildung notwendig ist. In dieser Arbeit wurde mittels verschiedener Deletions- und Mutationskonstrukte der Bereich zwischen den Cysteinen C535 und C567 der siebten Follistatin-ähnlichen Domäne von Transmembran-Agrin als essentiell für die Bildung der filopodienartigen Fortsätze identifiziert. Die siebte Follistatin-ähnliche Domäne konnte durch die erste oder sechste, jedoch nicht durch die achte Follistatin-ähnliche Domäne funktionell ersetzt werden, was für eine funktionelle Redundanz bei einigen Follistatin-ähnlichen Domänen Agrins spricht. Zudem scheint eine kritische Distanz der siebten Follistatin-ähnlichen Domäne zur Plasmamembran für die Fortsatzbildung wichtig zu sein. Diese Ergebnisse zeigen, dass unterschiedliche Regionen innerhalb Agrins für die Bildung der Synapse an der neuromuskulären Endplatte und der Fortsätze im Zentralnervensystem verantwortlich sind, und deuten auf eine Funktion der Follistatin-ähnlichen Domänen Agrins bei der Entwicklung des Zentralnervensystems hin.

Listeria monocytogenes spreads within the brain by actin-based intra-axonal migration

Listeria monocytogenes rhombencephalitis is a severe progressive disease despite a swift intrathecal immune response. Based on previous observations, we hypothesized that the disease progresses by intra-axonal spread within the central nervous system. To test this hypothesis, neuroanatomical mapping of lesions, immunofluorescence analysis, and electron microscopy were performed on brains of ruminants with naturally occurring rhombencephalitis. In addition, infection assays were performed in bovine brain cell cultures. Mapping of lesions revealed a consistent pattern with a preferential affection of certain nuclear areas and white matter tracts, indicating that Listeria monocytogenes spreads intra-axonally within the brain along interneuronal connections. These results were supported by immunofluorescence and ultrastructural data localizing Listeria monocytogenes inside axons and dendrites associated with networks of fibrillary structures consistent with actin tails. In vitro infection assays confirmed that bacteria were moving within axon-like processes by employing their actin tail machinery. Remarkably, in vivo, neutrophils invaded the axonal space and the axon itself, apparently by moving between split myelin lamellae of intact myelin sheaths. This intra-axonal invasion of neutrophils was associated with various stages of axonal degeneration and bacterial phagocytosis. Paradoxically, the ensuing adaxonal microabscesses appeared to provide new bacterial replication sites, thus supporting further bacterial spread. In conclusion, intra-axonal bacterial migration and possibly also the innate immune response play an important role in the intracerebral spread of the agent and hence the progression of listeric rhombencephalitis.

Regional surface morphology of comet 67P/Churyumov-Gerasimenko from Rosetta/OSIRIS images

Aims. The OSIRIS camera onboard the Rosetta spacecraft has been acquiring images of the comet 67P/Churyumov-Gerasimenko (67P)'s nucleus at spatial resolutions down to similar to 0.17 m/px ever since Aug. 2014. These images have yielded unprecedented insight into the morphological diversity of the comet's surface. This paper presents an overview of the regional morphology of comet 67P. Methods. We used the images that were acquired at orbits similar to 20-30 km from the center of the comet to distinguish different regions on the surface and introduce the basic regional nomenclature adopted by all papers in this Rosetta special feature that address the comet's morphology and surface processes. We used anaglyphs to detect subtle regional and topographical boundaries and images from close orbit (similar to 10 km from the comet's center) to investigate the fine texture of the surface. Results. Nineteen regions have currently been defined on the nucleus based on morphological and/or structural boundaries, and they can be grouped into distinctive region types. Consolidated, fractured regions are the most common region type. Some of these regions enclose smooth units that appear to settle in gravitational sinks or topographically low areas. Both comet lobes have a significant portion of their surface covered by a dusty coating that appears to be recently placed and shows signs of mobilization by aeolian-like processes. The dusty coatings cover most of the regions on the surface but are notably absent from a couple of irregular large depressions that show sharp contacts with their surroundings and talus-like deposits in their interiors, which suggests that short-term explosive activity may play a significant role in shaping the comet's surface in addition to long-term sublimation loss. Finally, the presence of layered brittle units showing signs of mechanical failure predominantly in one of the comet's lobes can indicate a compositional heterogeneity between the two lobes.

A formalism and method for representing and reasoning with process models authored by subject matter experts

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic in order to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82% were well-formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25% and 30% with respect to the base case, respectively.

Type 1 nitrergic (ND1) cells of the rabbit retina: Comparison with other axon-bearing amacrine cells

NADPH diaphorase (NADPHd) histochemistry labels two types of nitrergic amacrine cells in the rabbit retina. Both the large ND1 cells and the small ND2 cells stratify in the middle of the inner plexiform layer, and their overlapping processes produce a dense plexus, which makes it difficult to trace the morphology of single cells. The complete morphology of the ND1 amacrine cells has been revealed by injecting Neurobiotin into large round somata in the inner nuclear layer, which resulted in the labelling of amacrine cells whose proximal morphology and stratification matched those of the ND1 cells stained by NADPHd histochemistry. The Neurobiotin-injected ND1 cells showed strong homologous tracer coupling to surrounding ND1 cells, and double-labelling experiments confirmed that these coupled cells showed NADPHd reactivity. The ND1 amacrine cells branch in stratum 3 of the inner plexiform layer, where they produce a sparsely branched dendritic tree of 400-600 mum diameter in ventral peripheral retina. In addition, each cell gives rise to several fine beaded processes, which arise either from a side branch of the dendritic tree or from the tapering of a distal dendrite. These axon-like processes branch successively within the vicinity of the dendritic field before extending, with little or no further branching, for 3-5 mm from the soma in ventral peripheral retina. Consequently, these cells may span one-third of the visual field of each eye, and their spatial extent appears to be greater than that of most other types of axon-bearing amacrine cells injected with Neurobiotin in this study. The morphology and tracer-coupling pattern of the ND1 cells are compared with those of confirmed type 1 catecholaminergic cells, a presumptive type 2 catecholaminergic cell, the type 1 polyaxonal. cells, the long-range amacrine cells, a novel type of axon-bearing cell that also branches in stratum 3, and a type of displaced amacrine cell that may correspond to the type 2 polyaxonal cell. (C) 2004 Wiley-Liss, Inc.

The type 1 polyaxonal amacrine cells of the rabbit retina: A tracer-coupling study

The type 1 polyaxonal (PA1) cell is a distinct type of axon-bearing amacrine cell whose soma commonly occupies an interstitial position in the inner plexiform layer; the proximal branches of the sparse dendritic tree produce 1-4 axon-like processes, which form an extensive axonal arbor that is concentric with the smaller dendritic tree (Dacey, 1989; Famiglietti, 1992a,b). In this study, intracellular injections of Neurobiotin have revealed the complete dendritic and axonal morphology of the PA1 cells in the rabbit retina, as well as labeling the local array of PA1 cells through homologous tracer coupling. The dendritic-field area of the PA1 cells increased from a minimum of 0.15 mm(2) (0.44-mm equivalent diameter) on the visual streak to a maximum of 0.67 mm(2) (0.92-mm diameter) in the far periphery; the axonal-field area also showed a 3-fold variation across the retina, ranging from 3.1 mm(2) (2.0-mm diameter) to 10.2 mm(2) (3.6-mm diameter). The increase in dendritic- and axonal-field size was accompanied by a reduction in cell density, from 60 cells/mm(2) in the visual streak to 20 cells/mm(2) in the far periphery, so that the PA1 cells showed a 12 times overlap of their dendritic fields across the retina and a 200-300 times overlap of their axonal fields. Consequently, the axonal plexus was much denser than the dendritic plexus, with each square millimeter of retina containing similar to100 mm of dendrites and similar to1000 mm of axonal processes. The strong homologous tracer coupling revealed that similar to45% of the PA1 somata were located in the inner nuclear layer, similar to50% in the inner plexiform layer, and similar to5% in the ganglion cell layer. In addition, the Neurobiotin-injected PA1 cells sometimes showed clear heterologous tracer coupling to a regular array of small ganglion cells, which were present at half the density of the PA1 cells. The PA1 cells were also shown to contain elevated levels of gamma-aminobutyric acid (GABA), like other axon-bearing amacrine cells.

BRACHYURY confers cancer stem cell characteristics on colorectal cancer cells

Cancer stem cells (CSCs) are initiating cells in colorectal cancer (CRC). Colorectal tumours undergo epithelial to mesenchymal transition (EMT)-like processes at the invasive front, enabling invasion and metastasis, and recent studies have linked this process to the acquisition of stem cell-like properties. It is of fundamental importance to understand the molecular events leading to the establishment of cancer initiating cells and how these mechanisms relate to cellular transitions during tumourigenesis. We use an in vitro system to recapitulate changes in CRC cells at the invasive front (mesenchymal-like cells) and central mass (epithelial-like cells) of tumours. We show that the mesoderm inducer BRACHYURY is expressed in a subpopulation of CRC cells that resemble invasive front mesenchymal-like cells, where it acts to impose characteristics of CSCs in a fully reversible manner, suggesting reversible formation and modulation of such cells. BRACHYURY, itself regulated by the oncogene β-catenin, influences NANOG and other 'stemness' markers including a panel of markers defining CRC-CSC whose presence has been linked to poor patient prognosis. Similar regulation of NANOG through BRACHYURY was observed in other cells lines, suggesting this might be a pathway common to cancer cells undergoing mesenchymal transition. We suggest that BRACHYURY may regulate NANOG in mesenchymal-like CRC cells to impose a 'plastic-state', allowing competence of cells to respond to signals prompting invasion or metastasis. Copyright © 2011 UICC.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Knowledge-Intensive Business Processes: Proceedings of the 1st International Workshop on Knowledge-intensive Business Processes [CEUR Workshop Proceedings, Volume 861]

Nowadays, Workflow Management Systems (WfMSs) and, more generally, Process Management Systems (PMPs) are process-aware Information Systems (PAISs), are widely used to support many human organizational activities, ranging from well-understood, relatively stable and structures processes (supply chain management, postal delivery tracking, etc.) to processes that are more complicated, less structured and may exhibit a high degree of variation (health-care, emergency management, etc.). Every aspect of a business process involves a certain amount of knowledge which may be complex depending on the domain of interest. The adequate representation of this knowledge is determined by the modeling language used. Some processes behave in a way that is well understood, predictable and repeatable: the tasks are clearly delineated and the control flow is straightforward. Recent discussions, however, illustrate the increasing demand for solutions for knowledge-intensive processes, where these characteristics are less applicable. The actors involved in the conduct of a knowledge-intensive process have to deal with a high degree of uncertainty. Tasks may be hard to perform and the order in which they need to be performed may be highly variable. Modeling knowledge-intensive processes can be complex as it may be hard to capture at design-time what knowledge is available at run-time. In realistic environments, for example, actors lack important knowledge at execution time or this knowledge can become obsolete as the process progresses. Even if each actor (at some point) has perfect knowledge of the world, it may not be certain of its beliefs at later points in time, since tasks by other actors may change the world without those changes being perceived. Typically, a knowledge-intensive process cannot be adequately modeled by classical, state of the art process/workflow modeling approaches. In some respect there is a lack of maturity when it comes to capturing the semantic aspects involved, both in terms of reasoning about them. The main focus of the 1st International Workshop on Knowledge-intensive Business processes (KiBP 2012) was investigating how techniques from different fields, such as Artificial Intelligence (AI), Knowledge Representation (KR), Business Process Management (BPM), Service Oriented Computing (SOC), etc., can be combined with the aim of improving the modeling and the enactment phases of a knowledge-intensive process. The 1st International Workshop on Knowledge-intensive Business process (KiBP 2012) was held as part of the program of the 2012 Knowledge Representation & Reasoning International Conference (KR 2012) in Rome, Italy, in June 2012. The workshop was hosted by the Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio Ruberti of Sapienza Universita di Roma, with financial support of the University, through grant 2010-C26A107CN9 TESTMED, and the EU Commission through the projects FP7-25888 Greener Buildings and FP7-257899 Smart Vortex. This volume contains the 5 papers accepted and presented at the workshop. Each paper was reviewed by three members of the internationally renowned Program Committee. In addition, a further paper was invted for inclusion in the workshop proceedings and for presentation at the workshop. There were two keynote talks, one by Marlon Dumas (Institute of Computer Science, University of Tartu, Estonia) on "Integrated Data and Process Management: Finally?" and the other by Yves Lesperance (Department of Computer Science and Engineering, York University, Canada) on "A Logic-Based Approach to Business Processes Customization" completed the scientific program. We would like to thank all the Program Committee members for the valuable work in selecting the papers, Andrea Marrella for his valuable work as publication and publicity chair of the workshop, and Carola Aiello and the consulting agency Consulta Umbria for the organization of this successful event.

Determination of coastal ground and surface water processes and character by use of hydrochemistry and stable isotopes, Fraser Coast, Queensland

This study was part of an integrated project developed in response to concerns regarding current and future land practices affecting water quality within coastal catchments and adjacent marine environments. Two forested coastal catchments on the Fraser Coast, Australia, were chosen as examples of low-modification areas with similar geomorphological and land-use characteristics to many other coastal zones in southeast Queensland. For this component of the overall project, organic , physico-chemical (Eh, pH and DO), ionic (Fe2+, Fe3+), and isotopic (ä13CDIC, ä15NDIN ä34SSO4) data were used to characterise waters and identify sources and processes contributing to concentrations and form of dissolved Fe, C, N and S within the ground and surface waters of these coastal catchments. Three sites with elevated Fe concentrations are discussed in detail. These included a shallow pool with intermittent interaction with the surface water drainage system, a monitoring well within a semi-confined alluvial aquifer, and a monitoring well within the fresh/saline water mixing zone adjacent to an estuary. Conceptual models of processes occurring in these environments are presented. The primary factors influencing Fe transport were; microbial reduction of Fe3+ oxyhydroxides in groundwaters and in the hyporheic zone of surface drainage systems, organic input available for microbial reduction and Fe3+ complexation, bacterial activity for reduction and oxidation, iron curtain effects where saline/fresh water mixing occurs, and variation in redox conditions with depth in ground and surface water columns. Data indicated that groundwater seepage appears a more likely source of Fe to coastal waters (during periods of low rainfall) via tidal flux. The drainage system is ephemeral and contributes little discharge to marine waters. However, data collected during a high rainfall event indicated considerable Fe loads can be transported to the estuary mouth from the catchment.

Kallikrein 4 (hK4) and prostate-specific antigen (PSA) are associated with the loss of E-cadherin and an epithelial-mesenchymal transition (EMT)-like effect in prostate cancer cells

Prostate-specific antigen (PSA) and the related kallikrein family of serine proteases are current or emerging biomarkers for prostate cancer detection and progression. Kallikrein 4 (KLK4/hK4) is of particular interest, as KLK4 mRNA has been shown to be elevated in prostate cancer. In this study, we now show that the comparative expression of hK4 protein in prostate cancer tissues, compared with benign glands, is greater than that of PSA and kallikrein 2 (KLK2/hK2), suggesting that hK4 may play an important functional role in prostate cancer progression in addition to its biomarker potential. To examine the roles that hK4, as well as PSA and hK2, play in processes associated with progression, these kallikreins were separately transfected into the PC-3 prostate cancer cell line, and the consequence of their stable transfection was investigated. PC-3 cells expressing hK4 had a decreased growth rate, but no changes in cell proliferation were observed in the cells expressing PSA or hK2. hK4 and PSA, but not hK2, induced a 2.4-fold and 1.7-fold respective increase, in cellular migration, but not invasion, through Matrigel, a synthetic extracellular matrix. We hypothesised that this increase in motility displayed by the hK4 and PSA-expressing PC-3 cells may be related to the observed change in structure in these cells from a typical rounded epithelial-like cell to a spindle-shaped, more mesenchymal-like cell, with compromised adhesion to the culture surface. Thus, the expression of E-cadherin and vimentin, both associated with an epithelial-mesenchymal transition (EMT), was investigated. E-cadherin protein was lost and mRNA levels were significantly decreased in PC-3 cells expressing hK4 and PSA (10-fold and 7-fold respectively), suggesting transcriptional repression of E-cadherin, while the expression of vimentin was increased in these cells. The loss of E-cadherin and associated increase in vimentin are indicative of EMT and provides compelling evidence that hK4, in particular, and PSA have a functional role in the progression of prostate cancer through their promotion of tumour cell migration.

Synthesis, Structure, Spirocyclization Mechanism, and Glutathione Peroxidase-like Antioxidant Activity of Stable Spirodiazaselenurane and Spirodiazatellurane

The first examples of stable spirodiazaselenurane and spirodiazatellurane were synthesized by oxidative spirocyclization of the corresponding diaryl selenide and telluride and were structurally characterized. X-ray crystal structures of the spirodiazaselenurane and spirodiazatellurane suggest that the structures are distorted trigonal bipyramidal (TBP) with the electronegative nitrogen atoms occupying the apical positions and two carbon atoms and the lone pair of Se/Te occupying the equatorial positions. Interestingly, the spirodiazatellurane underwent spontaneous chiral resolution during crystallization, and the absolute configurations of its enantiomers were confirmed by single-crystal X-ray analyses. A detailed mechanistic study indicates that the cyclization to spirodiazaselenurane and spirodiazatellurane occurs via selenoxide and telluroxide intermediates. The chalcogenoxides cyclize to the corresponding spiro compounds in a stepwise manner via the involvement of hydroxyl chalcogenurane intermediates, and the activation energy for them spirocyclization reaction decreases in the order S > Se > Te. In addition to the synthesis, characterization, and mechanism of cyclization, the glutathione peroxidase (GPx) mimetic activity of the newly synthesized compounds was evaluated. These studies suggest that the tellurium compounds are more effective as GPx mimics than their selenium counterparts due to the fast oxidation of the tellurium center in the presence of peroxide and the involvement of an efficient redox cycle between the telluride and telluroxide intermediate.

Simple chemical aqueous synthesis of dahlia nanoflower consisting of finger-like ZnO nanorods and observation of stable ultraviolet photoluminescence emission

In this work, we have reported the synthesis of dahlia flower-like ZnO nanostructures consisting of human finger-like nanorods by the hydrothermal method at 120 degrees C and without using any capping agent. Optical properties of the samples, including UV-vis absorption and photoluminescence (PL) emission characteristics are determined by dispersing the samples in water as well as in ethanol media. The quenching of PL emission intensity along-with the red shifting of the PL emission peak are observed when the samples are dispersed in water in comparison to those obtained after dispersing the samples in ethanol. It has been found that PL emission characteristic, particularly the spectral nature of PL emission, of the samples remains almost unaltered (except some improvement in UV PL emission) even after thermally annealing it for 2 h at the temperature of 300 degrees C. Also the synthesized powder samples, kept in a plastic container, showed a very stable PL emission even after 15 months of synthesis. Therefore, the synthesized samples might be useful for their applications in future optoelectronics devices. (C) 2014 Elsevier Ltd. All rights reserved.