Gamma Knife radiosurgery in benign skull base tumors after planned subtotal resection: preliminary results in combined approaches

Aim: The management of large lesions of the skull base, such as vestibular schwanommas (VS), meningiomas (MEN) or pituitary adenomas (PA), is challenging, with microsurgery remaining the main treatment option. Planned subtotal resection is now being increasingly considered to reduce the risk of neurological deficits following complete resection. The residual part of the tumor can then be treated with Gamma Knife Radiosurgery (GKR) to achieve long-term growth control. Methods: This case series documents early results with planned subtotal resection followed by GKR in Lausanne University Hospital, between July 2010 and March 2012. There were 24 patients who underwent surgery, with 22 having already undergone GKR and 2 waiting for GKR. We analyzed clinical symptoms for all patients, as well as audiograms, ophthalmological and endocrinological tests, when indicated. Results: Nine patients had VS surgery (mean diameter 35 mm; range 30-44.5) through a retrosigmoid approach. There were no post-operative facial nerve deficits. Of the 3 patients whom had useful hearing pre-operatively, this improved in 2 and remained stable in 1. Four patients with clinoid MEN (mean diameter 26.5 mm; range 17-42) underwent subtotal resection of the tumor, and the component in the cavernous sinus was later treated with GKR. The visual status remained stable in 3 patients and one had complete visual recovery. 4 patients underwent subtotal resection of petro-clival MEN (mean diameter 36 mm; range 32-42): 3 had House-Brackmann (HB) grade 2 facial function that recovered completely; one continues to have HB grade 4 facial deficit following surgery. Of the 7 patients with PA (mean diameter 34.5 mm; range 20-54.5), 2 had acromegaly, the others were non functional PA. Six patients underwent trans-sphenoidal surgery, while one patient had a transcavernous sinus resection of the tumor (with prior staged trans-sphenoidal surgery). Visual status improved in 3 patients while the others remained stable. Two patients had transient diabetes insipidus following surgery. Up to now, no additional deficit or worsening has been reported after GKR. Conclusions: Our data suggest that planned subtotal resection has an excellent clinical outcome with respect to preservation of cranial nerves, and other neurological functions, and a good possibility of recovery of many of the pre-operative cranial nerve dysfunctions. The results in terms of tumor control following GKR need further long-term evaluation.

Sweet sixteen for ANLS.

Since its introduction 16 years ago, the astrocyte-neuron lactate shuttle (ANLS) model has profoundly modified our understanding of neuroenergetics by bringing a cellular and molecular resolution. Praised or disputed, the concept has never ceased to attract attention, leading to critical advances and unexpected insights. Here, we summarize recent experimental evidence further supporting the main tenets of the model. Thus, evidence for distinct metabolic phenotypes between neurons (mainly oxidative) and astrocytes (mainly glycolytic) have been provided by genomics and classical metabolic approaches. Moreover, it has become clear that astrocytes act as a syncytium to distribute energy substrates such as lactate to active neurones. Glycogen, the main energy reserve located in astrocytes, is used as a lactate source to sustain glutamatergic neurotransmission and synaptic plasticity. Lactate is also emerging as a neuroprotective agent as well as a key signal to regulate blood flow. Characterization of monocarboxylate transporter regulation indicates a possible involvement in synaptic plasticity and memory. Finally, several modeling studies captured the implications of such findings for many brain functions. The ANLS model now represents a useful, experimentally based framework to better understand the coupling between neuronal activity and energetics as it relates to neuronal plasticity, neurodegeneration, and functional brain imaging.

On Consumer Decision Strategies: New Approaches for Studying and Aiding Preferential Choices

This dissertation focuses on the strategies consumers use when making purchase decisions. It is organized in two main parts, one centering on descriptive and the other on applied decision making research. In the first part, a new process tracing tool called InterActive Process Tracing (IAPT) is pre- sented, which I developed to investigate the nature of consumers' decision strategies. This tool is a combination of several process tracing techniques, namely Active Information Search, Mouselab, and retrospective verbal protocol. To validate IAPT, two experiments on mobile phone purchase de- cisions were conducted where participants first repeatedly chose a mobile phone and then were asked to formalize their decision strategy so that it could be used to make choices for them. The choices made by the identified strategies correctly predicted the observed choices in 73% (Experiment 1) and 67% (Experiment 2) of the cases. Moreover, in Experiment 2, Mouselab and eye tracking were directly compared with respect to their impact on information search and strategy description. Only minor differences were found between these two methods. I conclude that IAPT is a useful research tool to identify choice strategies, and that using eye tracking technology did not increase its validity beyond that gained with Mouselab. In the second part, a prototype of a decision aid is introduced that was developed building in particular on the knowledge about consumers' decision strategies gained in Part I. This decision aid, which is called the InterActive Choice Aid (IACA), systematically assists consumers in their purchase decisions. To evaluate the prototype regarding its perceived utility, an experiment was conducted where IACA was compared to two other prototypes that were based on real-world consumer decision aids. All three prototypes differed in the number and type of tools they provided to facilitate the process of choosing, ranging from low (Amazon) to medium (Sunrise/dpreview) to high functionality (IACA). Overall, participants slightly preferred the prototype of medium functionality and this prototype was also rated best on the dimensions of understandability and ease of use. IACA was rated best regarding the two dimensions of ease of elimination and ease of comparison of alternatives. Moreover, participants choices were more in line with the normatively oriented weighted additive strategy when they used IACA than when they used the medium functionality prototype. The low functionality prototype was the least preferred overall. It is concluded that consumers can and will benefit from highly functional decision aids like IACA, but only when these systems are easy to understand and to use.

Stoneley wave modeling in heterogeneous porous media with viscous pore fluids

We implemented Biot-type porous wave equations in a pseudo-spectral numerical modeling algorithm for the simulation of Stoneley waves in porous media. Fourier and Chebyshev methods are used to compute the spatial derivatives along the horizontal and vertical directions, respectively. To prevent from overly short time steps due to the small grid spacing at the top and bottom of the model as a consequence of the Chebyshev operator, the mesh is stretched in the vertical direction. As a large benefit, the Chebyshev operator allows for an explicit treatment of interfaces. Boundary conditions can be implemented with a characteristics approach. The characteristic variables are evaluated at zero viscosity. We use this approach to model seismic wave propagation at the interface between a fluid and a porous medium. Each medium is represented by a different mesh and the two meshes are connected through the above described characteristics domain-decomposition method. We show an experiment for sealed pore boundary conditions, where we first compare the numerical solution to an analytical solution. We then show the influence of heterogeneity and viscosity of the pore fluid on the propagation of the Stoneley wave and surface waves in general.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Modeling of the TCR-MHC-peptide complex.

The methodology for generating a homology model of the T1 TCR-PbCS-K(d) class I major histocompatibility complex (MHC) class I complex is presented. The resulting model provides a qualitative explanation of the effect of over 50 different mutations in the region of the complementarity determining region (CDR) loops of the T cell receptor (TCR), the peptide and the MHC's alpha(1)/alpha(2) helices. The peptide is modified by an azido benzoic acid photoreactive group, which is part of the epitope recognized by the TCR. The construction of the model makes use of closely related homologs (the A6 TCR-Tax-HLA A2 complex, the 2C TCR, the 14.3.d TCR Vbeta chain, the 1934.4 TCR Valpha chain, and the H-2 K(b)-ovalbumine peptide), ab initio sampling of CDR loops conformations and experimental data to select from the set of possibilities. The model shows a complex arrangement of the CDR3alpha, CDR1beta, CDR2beta and CDR3beta loops that leads to the highly specific recognition of the photoreactive group. The protocol can be applied systematically to a series of related sequences, permitting the analysis at the structural level of the large TCR repertoire specific for a given peptide-MHC complex.

Making sense of AMPA receptor trafficking by modeling molecular mechanisms of synaptic plasticity.

Synaptic plasticity involves a complex molecular machinery with various protein interactions but it is not yet clear how its components give rise to the different aspects of synaptic plasticity. Here we ask whether it is possible to mathematically model synaptic plasticity by making use of known substances only. We present a model of a multistable biochemical reaction system and use it to simulate the plasticity of synaptic transmission in long-term potentiation (LTP) or long-term depression (LTD) after repeated excitation of the synapse. According to our model, we can distinguish between two phases: first, a "viscosity" phase after the first excitation, the effects of which like the activation of NMDA receptors and CaMKII fade out in the absence of further excitations. Second, a "plasticity" phase actuated by an identical subsequent excitation that follows after a short time interval and causes the temporarily altered concentrations of AMPA subunits in the postsynaptic membrane to be stabilized. We show that positive feedback is the crucial element in the core chemical reaction, i.e. the activation of the short-tail AMPA subunit by NEM-sensitive factor, which allows generating multiple stable equilibria. Three stable equilibria are related to LTP, LTD and a third unfixed state called ACTIVE. Our mathematical approach shows that modeling synaptic multistability is possible by making use of known substances like NMDA and AMPA receptors, NEM-sensitive factor, glutamate, CaMKII and brain-derived neurotrophic factor. Furthermore, we could show that the heteromeric combination of short- and long-tail AMPA receptor subunits fulfills the function of a memory tag.

Approaches and Strategies to Improve Writing Skills

I believe and, I hope others believe that writing well is not only a personal accomplishment but also a valuable form of social participation and leadership. After reviewing the literature, this work tries to identify the problem and to identify the key issues to EFL writing so as to suggest a remedial procedure that may help the learners to express themselves better in English. It aims at finding areas of difficulty in the writing skill among the students and to work out remedial procedures to help them overcome their weakness. A survey carried out on 12th grade students, regarding their comprehension, shows that organizing idea on paper is one of the factors which influences their ability to write. It also shows that students feel the need to be instructed on strategies of writing success and consequently improving their writing. There are a lot systematic approaches and methods for discerning which skills and type of writing teacher should focus on and meaningful classroom activities and strategies that teacher can use to help students improving their writing.

Modelos estadísticos y evaluación: tres estudios en educación

The educational system in Spain is undergoing a reorganization. At present, high-school graduates who want to enroll at a public university must take a set of examinations Pruebas de Aptitud para el Acceso a la Universidad (PAAU). A "new formula" (components, weights, type of exam,...) for university admission is been discussed. The present paper summarizes part of the research done by the author in her PhD. The context for this thesis is the evaluation of large-scale and complex systems of assessment. The main objectives were: to achieve a deep knowledge of the entire university admissions process in Spain, to discover the main sources of uncertainty and topromote empirical research in a continual improvement of the entire process. Focusing in the suitable statistical models and strategies which allow to high-light the imperfections of the system and reduce them, the paper develops, among other approaches, some applications of multilevel modeling.

Modeling the forensic two-trace problem with Bayesian networks

The forensic two-trace problem is a perplexing inference problem introduced by Evett (J Forensic Sci Soc 27:375-381, 1987). Different possible ways of wording the competing pair of propositions (i.e., one proposition advanced by the prosecution and one proposition advanced by the defence) led to different quantifications of the value of the evidence (Meester and Sjerps in Biometrics 59:727-732, 2003). Here, we re-examine this scenario with the aim of clarifying the interrelationships that exist between the different solutions, and in this way, produce a global vision of the problem. We propose to investigate the different expressions for evaluating the value of the evidence by using a graphical approach, i.e. Bayesian networks, to model the rationale behind each of the proposed solutions and the assumptions made on the unknown parameters in this problem.

Current approaches and challenges for the metabolite profiling of complex natural extracts.

Metabolite profiling is critical in many aspects of the life sciences, particularly natural product research. Obtaining precise information on the chemical composition of complex natural extracts (metabolomes) that are primarily obtained from plants or microorganisms is a challenging task that requires sophisticated, advanced analytical methods. In this respect, significant advances in hyphenated chromatographic techniques (LC-MS, GC-MS and LC-NMR in particular), as well as data mining and processing methods, have occurred over the last decade. Together, these tools, in combination with bioassay profiling methods, serve an important role in metabolomics for the purposes of both peak annotation and dereplication in natural product research. In this review, a survey of the techniques that are used for generic and comprehensive profiling of secondary metabolites in natural extracts is provided. The various approaches (chromatographic methods: LC-MS, GC-MS, and LC-NMR and direct spectroscopic methods: NMR and DIMS) are discussed with respect to their resolution and sensitivity for extract profiling. In addition the structural information that can be generated through these techniques or in combination, is compared in relation to the identification of metabolites in complex mixtures. Analytical strategies with applications to natural extracts and novel methods that have strong potential, regardless of how often they are used, are discussed with respect to their potential applications and future trends.

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling

The interpretation of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all crossloadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.

Groundwater Availability Modeling: Lower Dakota Aquifer, 2008

This monthly report from the Iowa Department of Natural Resources is about the water quality management of Iowa's rivers, streams and lakes.