Avian area vasculosa and CAM as rapid in vivo pro-angiogenic and antiangiogenic models.

Angiogenesis, the development of new blood vessels from preexisting ones, is driven by coordinated signaling pathways governed by specific molecules, hemodynamic forces, and endothelial and periendothelial cells. The processes involve adhesion, migration, and survival machinery within the target endothelial and periendothelial cells. Factors that interfere with any of these processes may therefore influence angiogenesis either positively (pro-angiogenesis) or negatively (antiangiogenesis). The avian area vasculosa (AV) and the avian chorioallantoic membrane (CAM) are two useful tools for studying both angiogenesis and antiangiogenesis since they are amenable to both intravascular and topical administration of target, agents, are relatively rapid assays, and can be adapted very easily to study angiogenesis-dependent processes, such as tumor growth. Both models provide a physiological setting that permits investigation of pro-angiogenic and antiangiogenic agent interactions in vivo.

Triple-peptide receptor targeting in vitro allows detection of all tested gut and bronchial NETs.

UNLABELLED A high proportion of gut and bronchial neuroendocrine tumors (NETs) overexpresses somatostatin receptors, especially the sst2 subtype. It has also recently been observed that incretin receptors, namely glucagonlike peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) receptors, can be overexpressed in gut and bronchial NETs. However, because not all tumors can express these receptors in sufficient amounts, in vivo imaging with a single radioligand may not always be successful. We therefore evaluated with in vitro methods whether a cocktail of radioligands targeting these 3 receptors would improve tumor labeling. METHODS In vitro receptor autoradiography was performed on 55 NETs, comparing in each successive section of tumor the binding with a single radioligand, either (125)I-Tyr(3)-octreotide, (125)I-GLP-1(7-36)amide, or (125)I-GIP(1-30), with the binding using a cocktail of all 3 radioligands, given concomitantly under identical experimental conditions. RESULTS Using the cocktail of radioligands, all tumors without exception showed moderate to very high binding, with a receptor density corresponding to 1,000-10,000 dpm/mg of tissue; conversely, single-ligand binding, although identifying most tumors as receptor-positive, failed to detect receptors or measured only a low density of receptors below 1,000 dpm/mg in a significant number of tumors. In addition, the cocktail of radioligands always provided a homogeneous labeling of the whole tumor, whereas single radioligands occasionally showed heterogeneous labeling. CONCLUSION The study suggests that the use of a cocktail of 3 radioligands binding to somatostatin receptors, GLP-1 receptors, and GIP receptors would allow detecting virtually all NETs and labeling them homogeneously in vivo, representing a significant improvement for imaging and therapy in NETs.

Chapter 4 The Challenges in Developing Realistic NEG Simulation Models for East Asia

This chapter attempts to identify some important issues in developing realistic simulation models based on new economic geography, and it suggests a direction for solving the difficulties. Specifically, adopting the IDE Geographical Simulation Model (IDE-GSM) as an example, we discuss some problems in developing a realistic simulation model for East Asia. The first and largest problem in this region is the lack of reliable economic datasets at the sub-national level, and this issue needs to be resolved in the long term. However, to deal with the existing situation in the short term, we utilize some techniques to produce more realistic and reliable simulation models. One key compromise is to use a 'topology' representation of geography, rather than a 'mesh' or 'grid' representation or simple 'straight lines' connecting each city which are used in many other models. In addition to this, a modal choice model that takes into consideration both money and time costs seems to work well.

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Up-regulation of specific tyrosinase mRNAs in mouse melanomas with the c2j gene substituted for the wild-type tyrosinase allele: Utilization in design of syngeneic immunotherapy models

The expression of cell-specialization genes is likely to be changing in tumor cells as their differentiation declines. Functional changes in these genes might yield unusual peptide epitopes with anti-tumor potential and could occur without modification in the DNA sequence of the gene. Melanomas undergo a characteristic decline in melanization that may reflect altered contributions of key melanocytic genes such as tyrosinase. Quantitative reverse transcriptase–PCR of the wild-type (C) tyrosinase gene in transgenic (C57BL/6 strain) mouse melanomas has revealed a shift toward alternative splicing of the pre-mRNA that generated increased levels of the Δ1b and Δ1d mRNA splice variants. The spontaneous c2j albino mutation of tyrosinase (in the C57BL/6 strain) changes the pre-mRNA splicing pattern. In c2j/c2j melanomas, alternative splicing was again increased. However, while some mRNAs (notably Δ1b) present in C/C were obligatorily absent, others (Δ3 and Δ1d) were elevated. In c2j/c2j melanomas, the percentage of total tyrosinase transcripts attributable to Δ3 reached approximately 2-fold the incidence in c2j/c2j or C/C skin melanocytes. The percentage attributable to Δ1d rose to approximately 2-fold the incidence in c2j/c2j skin, and to 10-fold that in C/C skin. These differences provide a basis for unique mouse models in which the melanoma arises in skin grafted from a C/C or c2j/c2j transgenic donor to a transgenic host of the same or opposite tyrosinase genotype. Immunotherapy designs then could be based on augmenting those antigenic peptides that are novel or overrepresented in a tumor relative to the syngeneic host.

Checkpoints in the progression of autoimmune disease: lessons from diabetes models.

In the last few years, data from experiments employing transgenic models of autoimmune disease have strengthened a particular concept of autoimmunity: disease results not so much from cracks in tolerance induction systems, leading to the generation of anti-self repertoire, as from the breakdown of secondary systems that keep these cells in check. T cells with anti-self specificities are readily found in disease-free individuals but ignore target tissues. This is also the case in some transgenic models, in spite of overwhelming numbers of autoreactive cells. In other instances, local infiltration and inflammation result, but they are well tolerated for long periods of time and do not terminally destroy target tissue. We review the possible molecular and cellular mechanisms that underlie these situations, with a particular emphasis on the destruction of pancreatic beta cells in transgenic models of insulin-dependent disease.

On dendrites in Down syndrome and DS murine models: a spiny way to learn

Since the discovery in the 1970s that dendritic abnormalities in cortical pyramidal neurons are the most consistent pathologic correlate of mental retardation, research has focused on how dendritic alterations are related to reduced intellectual ability. Due in part to obvious ethical problems and in part to the lack of fruitful methods to study neuronal circuitry in the human cortex, there is little data about the microanatomical contribution to mental retardation. The recent identification of the genetic bases of some mental retardation associated alterations, coupled with the technology to create transgenic animal models and the introduction of powerful sophisticated tools in the field of microanatomy, has led to a growth in the studies of the alterations of pyramidal cell morphology in these disorders. Studies of individuals with Down syndrome, the most frequent genetic disorder leading to mental retardation, allow the analysis of the relationships between cognition, genotype and brain microanatomy. In Down syndrome the crucial question is to define the mechanisms by which an excess of normal gene products, in interaction with the environment, directs and constrains neural maturation, and how this abnormal development translates into cognition and behaviour. In the present article we discuss mainly Down syndrome-associated dendritic abnormalities and plasticity and the role of animal models in these studies. We believe that through the further development of such approaches, the study of the microanatomical substrates of mental retardation will contribute significantly to our understanding of the mechanisms underlying human brain disorders associated with mental retardation. (C) 2004 Elsevier Ltd. All rights reserved.

Improving learning in undergraduate control engineering courses using context-based learning models

Control Engineering is an essential part of university electrical engineering education. Normally, a control course requires considerable mathematical as well as engineering knowledge and is consequently regarded as a difficult course by many undergraduate students. From the academic point of view, how to help the students to improve their learning of the control engineering knowledge is therefore an important task which requires careful planning and innovative teaching methods. Traditionally, the didactic teaching approach has been used to teach the students the concepts needed to solve control problems. This approach is commonly adopted in many mathematics intensive courses; however it generally lacks reflection from the students to improve their learning. This paper addresses the practice of action learning and context-based learning models in teaching university control courses. This context-based approach has been practised in teaching several control engineering courses in a university with promising results, particularly in view of student learning performances.

Equivalence of the Peleg, Pilosof and Singh-Kulshrestha models for water absorption in food

The similarity between the Peleg, Pilosof –Boquet–Batholomai and Singh–Kulshrestha models was investigated using the hydration behaviours of whey protein concentrate, wheat starch and whey protein isolate at 30 °C in 100% relative humidity. The three models were shown to be mathematically the same within experimental variations, and they yielded parameters that are related. The models, in their linear and original forms, were suitable (r2 > 0.98) in describing the sorption behaviours of the samples, and are sensitive to the length of the sorption segment used in the computation. The whey proteins absorbed more moisture than the wheat starch, and the isolate exhibited a higher sorptive ability than the concentrate.

Living with uncertainty in the age of runtime models

Uncertainty can be defined as the difference between information that is represented in an executing system and the information that is both measurable and available about the system at a certain point in its life-time. A software system can be exposed to multiple sources of uncertainty produced by, for example, ambiguous requirements and unpredictable execution environments. A runtime model is a dynamic knowledge base that abstracts useful information about the system, its operational context and the extent to which the system meets its stakeholders' needs. A software system can successfully operate in multiple dynamic contexts by using runtime models that augment information available at design-time with information monitored at runtime. This chapter explores the role of runtime models as a means to cope with uncertainty. To this end, we introduce a well-suited terminology about models, runtime models and uncertainty and present a state-of-the-art summary on model-based techniques for addressing uncertainty both at development- and runtime. Using a case study about robot systems we discuss how current techniques and the MAPE-K loop can be used together to tackle uncertainty. Furthermore, we propose possible extensions of the MAPE-K loop architecture with runtime models to further handle uncertainty at runtime. The chapter concludes by identifying key challenges, and enabling technologies for using runtime models to address uncertainty, and also identifies closely related research communities that can foster ideas for resolving the challenges raised. © 2014 Springer International Publishing.

Role of fluctuations in membrane models:thermal versus nonthermal

We study the comparative importance of thermal to nonthermal fluctuations for membrane-based models in the linear regime. Our results, both in 1+1 and 2+1 dimensions, suggest that nonthermal fluctuations dominate thermal ones only when the relaxation time τ is large. For moderate to small values of τ, the dynamics is defined by a competition between these two forces. The results are expected to act as a quantitative benchmark for biological modeling in systems involving cytoskeletal and other nonthermal fluctuations. © 2011 American Physical Society.

Self-explanation in adaptive systems based on runtime goal-based models

The behaviour of self adaptive systems can be emergent, which means that the system’s behaviour may be seen as unexpected by its customers and its developers. Therefore, a self-adaptive system needs to garner confidence in its customers and it also needs to resolve any surprise on the part of the developer during testing and maintenance. We believe that these two functions can only be achieved if a self-adaptive system is also capable of self-explanation. We argue a self-adaptive system’s behaviour needs to be explained in terms of satisfaction of its requirements. Since self-adaptive system requirements may themselves be emergent, we propose the use of goal-based requirements models at runtime to offer self-explanation of how a system is meeting its requirements. We demonstrate the analysis of run-time requirements models to yield a self-explanation codified in a domain specific language, and discuss possible future work.

Integrating OpenMI and Uncertweb:managing uncertainty in OpenMI models

OpenMI is a widely used standard allowing exchange of data between integrated models, which has mostly been applied to dynamic, deterministic models. Within the FP7 UncertWeb project we are developing mechanisms and tools to support the management of uncertainty in environmental models. In this paper we explore the integration of the UncertWeb framework with OpenMI, to assess the issues that arise when propagating uncertainty in OpenMI model compositions, and the degree of integration possible with UncertWeb tools. In particular we develop an uncertainty-enabled model for a simple Lotka-Volterra system with an interface conforming to the OpenMI standard, exploring uncertainty in the initial predator and prey levels, and the parameters of the model equations. We use the Elicitator tool developed within UncertWeb to identify the initial condition uncertainties, and show how these can be integrated, using UncertML, with simple Monte Carlo propagation mechanisms. The mediators we develop for OpenMI models are generic and produce standard Web services that expose the OpenMI models to a Web based framework. We discuss what further work is needed to allow a more complete system to be developed and show how this might be used practically.

Online learning in discrete hidden Markov models

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented. © 2006 American Institute of Physics.