Establishment of animal models to analyze the kinetics and distribution of human tumor antigen-specific CD8⁺ T cells.

Many patients develop tumor antigen-specific T cell responses detectable in peripheral blood mononuclear cells (PBMCs) following cancer vaccine. However, measurable tumor regression is observed in a limited number of patients receiving cancer vaccines. There is a need to re-evaluate systemically the immune responses induced by cancer vaccines. Here, we established animal models targeting two human cancer/testis antigens, NY-ESO-1 and MAGE-A4. Cytotoxic T lymphocyte (CTL) epitopes of these antigens were investigated by immunizing BALB/c mice with plasmids encoding the entire sequences of NY-ESO-1 or MAGE-A4. CD8(+) T cells specific for NY-ESO-1 or MAGE-A4 were able to be detected by ELISPOT assays using antigen presenting cells pulsed with overlapping peptides covering the whole protein, indicating the high immunogenicity of these antigens in mice. Truncation of these peptides revealed that NY-ESO-1-specific CD8(+) T cells recognized D(d)-restricted 8mer peptides, NY-ESO-181-88. MAGE-A4-specific CD8(+) T cells recognized D(d)-restricted 9mer peptides, MAGE-A4265-273. MHC/peptide tetramers allowed us to analyze the kinetics and distribution of the antigen-specific immune responses, and we found that stronger antigen-specific CD8(+) T cell responses were required for more effective anti-tumor activity. Taken together, these animal models are valuable for evaluation of immune responses and optimization of the efficacy of cancer vaccines.

Orthotropic active strain models for the numerical simulation of cardiac biomechanics

An active strain formulation for orthotropic constitutive laws arising in cardiac mechanics modeling is introduced and studied. The passive mechanical properties of the tissue are described by the Holzapfel-Ogden relation. In the active strain formulation, the Euler-Lagrange equations for minimizing the total energy are written in terms of active and passive deformation factors, where the active part is assumed to depend, at the cell level, on the electrodynamics and on the specific orientation of the cardiac cells. The well-posedness of the linear system derived from a generic Newton iteration of the original problem is analyzed and different mechanical activation functions are considered. In addition, the active strain formulation is compared with the classical active stress formulation from both numerical and modeling perspectives. Taylor-Hood and MINI finite elements are employed to discretize the mechanical problem. The results of several numerical experiments show that the proposed formulation is mathematically consistent and is able to represent the main key features of the phenomenon, while allowing savings in computational costs.

Plastinated Ethmoidal Region : II. The Preparation and Use of Radio-Opaque Artery Casts in Clinical Teaching.

In endoscopic sinus surgery, knowledge of the course of the internal ethmoida and orbital arteries is crucial.The maxillary and the internal carotid arteries of cadavers were injected with radio-opaque , red colorede silicone. The ethmoidal regions were perpared and plastinated using the standard S10 technique. On some specimens, the ophtalmic and ethmoidal arteries were dissected prior to plastination. The plastinated specimens of the ethmoidal blocks were successfullyintroduced into clinical teaching of sinus anatomy and surgery as an aid to study vaascularization an dits relationship to surgical procedures. Among the advantages of this method are the long-lasting preservation of dissected tissue, visualization of arteries during endoscopic and radiological examinations, and invaluable teachjing and training resources for endoscopic sinus surgery.

Repression and Protest Structural Models and Strategic Interactions

The recent wave of upheavals and revolts in Northern Africa and the Middle East goes back to an old question often raised by theories of collective action: does repression act as a negative or positive incentive for further mobilization? Through a review of the vast literature devoted to this question, this article aims to go beyond theoretical and methodological dead-ends. The article moves on to non-Western settings in order to better understand, via a macro-sociological and dynamic approach, the causal effects between mobilizations and repression. It pleads for a meso- and micro-level approach to this issue: an approach that puts analytical emphasis both on protest organizations and on individual activists' careers.

Concise reviews: in vitro-produced pancreas organogenesis models in three dimensions: self-organization from few stem cells or progenitors.

Three-dimensional models of organ biogenesis have recently flourished. They promote a balance between stem/progenitor cell expansion and differentiation without the constraints of flat tissue culture vessels, allowing for autonomous self-organization of cells. Such models allow the formation of miniature organs in a dish and are emerging for the pancreas, starting from embryonic progenitors and adult cells. This review focuses on the currently available systems and how these allow new types of questions to be addressed. We discuss the expected advancements including their potential to study human pancreas development and function as well as to develop diabetes models and therapeutic cells.

New validated prognostic models and prognostic calculators in patients with low-grade gliomas diagnosed by central pathology review: a pooled analysis of EORTC/RTOG/NCCTG phase III clinical trials.

Background In a previous study, the European Organisation for Research and Treatment of Cancer (EORTC) reported a scoring system to predict survival of patients with low-grade gliomas (LGGs). A major issue in the diagnosis of brain tumors is the lack of agreement among pathologists. New models in patients with LGGs diagnosed by central pathology review are needed. Methods Data from 339 EORTC patients with LGGs diagnosed by central pathology review were used to develop new prognostic models for progression-free survival (PFS) and overall survival (OS). Data from 450 patients with centrally diagnosed LGGs recruited into 2 large studies conducted by North American cooperative groups were used to validate the models. Results Both PFS and OS were negatively influenced by the presence of baseline neurological deficits, a shorter time since first symptoms (<30 wk), an astrocytic tumor type, and tumors larger than 5 cm in diameter. Early irradiation improved PFS but not OS. Three risk groups have been identified (low, intermediate, and high) and validated. Conclusions We have developed new prognostic models in a more homogeneous LGG population diagnosed by central pathology review. This population better fits with modern practice, where patients are enrolled in clinical trials based on central or panel pathology review. We could validate the models in a large, external, and independent dataset. The models can divide LGG patients into 3 risk groups and provide reliable individual survival predictions. Inclusion of other clinical and molecular factors might still improve models' predictions.

Rates of cultural change and patterns of cultural accumulation in stochastic models of social transmission.

Cultural variation in a population is affected by the rate of occurrence of cultural innovations, whether such innovations are preferred or eschewed, how they are transmitted between individuals in the population, and the size of the population. An innovation, such as a modification in an attribute of a handaxe, may be lost or may become a property of all handaxes, which we call "fixation of the innovation." Alternatively, several innovations may attain appreciable frequencies, in which case properties of the frequency distribution-for example, of handaxe measurements-is important. Here we apply the Moran model from the stochastic theory of population genetics to study the evolution of cultural innovations. We obtain the probability that an initially rare innovation becomes fixed, and the expected time this takes. When variation in cultural traits is due to recurrent innovation, copy error, and sampling from generation to generation, we describe properties of this variation, such as the level of heterogeneity expected in the population. For all of these, we determine the effect of the mode of social transmission: conformist, where there is a tendency for each naïve newborn to copy the most popular variant; pro-novelty bias, where the newborn prefers a specific variant if it exists among those it samples; one-to-many transmission, where the variant one individual carries is copied by all newborns while that individual remains alive. We compare our findings with those predicted by prevailing theories for rates of cultural change and the distribution of cultural variation.

Teaching motivational interviewing to medical students : a pre-post test pilot study

Objective: To test the efficacy of teaching motivational interviewing (MI) to medical students. Methods: Thirteen 4th year medical students volunteered to participate. Seven days before and 7 days after an 8-hour interactive training MI workshop, each student performed a videorecorded interview with two standardized patients: a 60 year old alcohol dependent woman and a 50 year old cigarette smoking man. Students' counseling skills were coded by two blinded clinicians using the Motivational Interviewing Treatment Integrity 3.0 (MITI). Inter-rater reliability was calculated for all interviews and a test-retest was completed in a sub-sample of 10 consecutive interviews three days apart. Difference between MITI scores before and after training were calculated and tested using non-parametric tests. Effect size was approximated by calculating the probability that posttest scores are greater than pretest scores (P*=P(Pre<Post)+1/2P(Pre=Post)), P*>1/2 indicating greater scores in posttest, P*=1/2 no effect, and P*<1/2 smaller scores in posttest. Results: Median differences between MITI scores before and after MI training indicated a general progression in MI skills: MI spirit global score (median difference=1.5, Inter quartile range=1.5, p<0.001, P*=0.90); Empathy global score (med diff=1, IQR=0.5, p<0.001, P*=0.85); Percentage of MI adherent skills (med diff=36.6, IQR=50.5, p<0.001, P*=0.85); Percentage of open questions (med diff=18.6, IQR=21.6, p<0.001, P*=0.96); reflections/ questions ratio (med diff=0.2, IQR=0.4, p<0.001, P*=0.81). Only Direction global score and the percentage of complex reflections were not significantly improved (med diff=0, IQR=1, p=0.53, P*=0.44, and med diff=4.3, IQR=24.8, p=0.48, P*=0.62, respectively). Inter-rater reliability indicated weighted kappa ranged between 0.14 for Direction to 0.51 for Collaboration and ICC ranged between 0.28 for Simple reflection to 0.95 for Closed question. Test-retests indicated weighted kappa ranged between 0.27 for Direction to 0.80 for Empathy and ICC ranged between 0.87 for Complex reflection to 0.98 for Closed question. Conclusion: This pilot study indicated that an 8-hour training in MI for voluntary 4th year medical students resulted in significant improvement of MI skills. Larger sample of unselected medical students should be studied to generalize the benefit of MI training to medical students. Interrater reliability and test-retests suggested that coders' training should be intensified.

Swain: Stata module to correct the SEM chi-square overidentification test in small sample sizes or complex models. Statistical Software Components S457617, Boston College Department of Economics.

Swain corrects the chi-square overidentification test (i.e., likelihood ratio test of fit) for structural equation models whethr with or without latent variables. The chi-square statistic is asymptotically correct; however, it does not behave as expected in small samples and/or when the model is complex (cf. Herzog, Boomsma, & Reinecke, 2007). Thus, particularly in situations where the ratio of sample size (n) to the number of parameters estimated (p) is relatively small (i.e., the p to n ratio is large), the chi-square test will tend to overreject correctly specified models. To obtain a closer approximation to the distribution of the chi-square statistic, Swain (1975) developed a correction; this scaling factor, which converges to 1 asymptotically, is multiplied with the chi-square statistic. The correction better approximates the chi-square distribution resulting in more appropriate Type 1 reject error rates (see Herzog & Boomsma, 2009; Herzog, et al., 2007).

Survey of aspergillosis in non-neutropenic patients in Swiss teaching hospitals.

Clin Microbiol Infect 2011; 17: 1366-1371 ABSTRACT: Invasive aspergillosis (IA) is a live-threatening opportunistic infection that is best described in haematological patients with prolonged neutropenia or graft-versus-host disease. Data on IA in non-neutropenic patients are limited. The aim of this study was to establish the incidence, disease manifestations and outcome of IA in non-neutropenic patients diagnosed in five Swiss university hospitals during a 2-year period. Case identification was based on a comprehensive screening of hospital records. All cases of proven and probable IA were retrospectively analysed. Sixty-seven patients were analysed (median age 60 years; 76% male). Sixty-three per cent of cases were invasive pulmonary aspergillosis (IPA), and 17% of these were disseminated aspergillosis. The incidence of IPA was 1.2/10 000 admissions. Six of ten cases of extrapulmonary IA affected the brain. There were six cases of invasive rhinosinusitis, six cases of chronic pulmonary aspergillosis, and cases three of subacute pulmonary aspergillosis. The most frequent underlying condition of IA was corticosteroid treatment (57%), followed by chronic lung disease (48%), and intensive-care unit stays (43%). In 38% of patients with IPA, the diagnosis was established at autopsy. Old age was the only risk factor for post-mortem diagnosis, whereas previous solid organ transplantation and chronic lung disease were associated with lower odds of post-mortem diagnosis. The mortality rate was 57%.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

PACIC Instrument: disentangling dimensions using published validation models.

OBJECTIVE: To better understand the structure of the Patient Assessment of Chronic Illness Care (PACIC) instrument. More specifically to test all published validation models, using one single data set and appropriate statistical tools. DESIGN: Validation study using data from cross-sectional survey. PARTICIPANTS: A population-based sample of non-institutionalized adults with diabetes residing in Switzerland (canton of Vaud). MAIN OUTCOME MEASURE: French version of the 20-items PACIC instrument (5-point response scale). We conducted validation analyses using confirmatory factor analysis (CFA). The original five-dimension model and other published models were tested with three types of CFA: based on (i) a Pearson estimator of variance-covariance matrix, (ii) a polychoric correlation matrix and (iii) a likelihood estimation with a multinomial distribution for the manifest variables. All models were assessed using loadings and goodness-of-fit measures. RESULTS: The analytical sample included 406 patients. Mean age was 64.4 years and 59% were men. Median of item responses varied between 1 and 4 (range 1-5), and range of missing values was between 5.7 and 12.3%. Strong floor and ceiling effects were present. Even though loadings of the tested models were relatively high, the only model showing acceptable fit was the 11-item single-dimension model. PACIC was associated with the expected variables of the field. CONCLUSIONS: Our results showed that the model considering 11 items in a single dimension exhibited the best fit for our data. A single score, in complement to the consideration of single-item results, might be used instead of the five dimensions usually described.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Explicit ruin formulas for models with dependence among risks

We show that a simple mixing idea allows one to establish a number of explicit formulas for ruin probabilities and related quantities in collective risk models with dependence among claim sizes and among claim inter-occurrence times. Examples include compound Poisson risk models with completely monotone marginal claim size distributions that are dependent according to Archimedean survival copulas as well as renewal risk models with dependent inter-occurrence times.

SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realised properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate assemblage properties. Here, we propose to unify the two approaches in a single 'spatially-explicit species assemblage modelling' (SESAM) framework. This framework uses relevant species source pool designations, macroecological factors, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.