In vivo targeting of an anti-tumor antibody coupled to antigenic MHC class I complexes induces specific growth inhibition and regression of established syngeneic tumor grafts.

The concept of antibody-mediated targeting of antigenic MHC/peptide complexes on tumor cells in order to sensitize them to T-lymphocyte cytotoxicity represents an attractive new immunotherapy strategy. In vitro experiments have shown that an antibody chemically conjugated or fused to monomeric MHC/peptide can be oligomerized on the surface of tumor cells, rendering them susceptible to efficient lysis by MHC-peptide restricted specific T-cell clones. However, this strategy has not yet been tested entirely in vivo in immunocompetent animals. To this aim, we took advantage of OT-1 mice which have a transgenic T-cell receptor specific for the ovalbumin (ova) immunodominant peptide (257-264) expressed in the context of the MHC class I H-2K(b). We prepared and characterized conjugates between the Fab' fragment from a high-affinity monoclonal antibody to carcinoembryonic antigen (CEA) and the H-2K(b) /ova peptide complex. First, we showed in OT-1 mice that the grafting and growth of a syngeneic colon carcinoma line transfected with CEA could be specifically inhibited by systemic injections of the conjugate. Next, using CEA transgenic C57BL/6 mice adoptively transferred with OT-1 spleen cells and immunized with ovalbumin, we demonstrated that systemic injections of the anti-CEA-H-2K(b) /ova conjugate could induce specific growth inhibition and regression of well-established, palpable subcutaneous grafts from the syngeneic CEA-transfected colon carcinoma line. These results, obtained in a well-characterized syngeneic carcinoma model, demonstrate that the antibody-MHC/peptide strategy can function in vivo. Further preclinical experimental studies, using an anti-viral T-cell response, will be performed before this new form of immunotherapy can be considered for clinical use.

Improving generalized regression analysis for the spatial prediction of forest communities

Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.

Hippocampal volume predicts fluid intelligence in musically trained people.

Recently, age-related hippocampal (HP) volume loss could be associated with a decrease in general fluid intelligence (gF). In the present study we investigated whether and how extensive musical training modulates human HP volume and gF performance. Previously, some studies demonstrated positive effects of musical training on higher cognitive functions such as learning and memory, associated with neural adaptations beyond the auditory domain. In order to detect possible associations between musical training and gF, we bilaterally segmented the HP formation and assessed the individual gF performance of people with different levels of musical expertise. Multiple regression analyses revealed that HP volume predicts gF in musicians but not in nonmusicians; in particular, bilaterally enhanced HP volume is associated with increased gF exclusively in musically trained people (amateurs and experts). This result suggests that musical training facilitates the recruitment of cognitive resources, which are essential for gF and linked to HP functioning. Musical training, even at a moderate level of intensity, can thus be considered as a potential strategy to decelerate age-related effects of cognitive decline. © 2013 Wiley Periodicals, Inc.

Random measurement error and regression dilution bias.

Summary points: - The bias introduced by random measurement error will be different depending on whether the error is in an exposure variable (risk factor) or outcome variable (disease) - Random measurement error in an exposure variable will bias the estimates of regression slope coefficients towards the null - Random measurement error in an outcome variable will instead increase the standard error of the estimates and widen the corresponding confidence intervals, making results less likely to be statistically significant - Increasing sample size will help minimise the impact of measurement error in an outcome variable but will only make estimates more precisely wrong when the error is in an exposure variable

Prediction of 18-month survival in patients with primary myelodysplastic syndrome. A regression model and scoring system based on the combination of chromosome findings and the Bournemouth score.

The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of myelodysplasia and which describe two distinct evolutional pathways.

Assessment of changes in body water by bioimpedance in acutely ill surgical patients.

OBJECTIVE: To evaluate the relationship between changes in body bioelectrical impedance (BI) at 0.5, 50 and kHz and the changes in body weight, as an index of total body water changes, in acutely ill surgical patients during the rapid infusion of isotonic saline solution. DESIGN: Prospective clinical study. SETTING: Multidisciplinary surgical ICU in a university hospital. PATIENTS: Twelve male patients treated for acute surgical illness (multiple trauma n = 5, major surgery n = 7). Selection criteria: stable cardiovascular parameters, normal cardiac function, signs of hypovolemia (CVP < or = 5 mmHg, urine output < 1 ml/kg x h). INTERVENTIONS: After baseline measurements, a 60 min fluid challenge test was performed with normal saline solution, 0.25 ml/kg/min [corrected]. MEASUREMENTS AND RESULTS: Body weight (platform digital scale), total body impedance (four-surface electrode technique; measurements at 0.5, 50 and 100 kHz) and urine output. Fluid retention induced a progressive decrease in BI at 0.5, 50 and 100 kHz, but the changes were significant for BI 0.5 and BI 100 only, from 40 min after the beginning of the fluid therapy onwards. There was a significant negative correlation between changes in water retention and BI 0.5, with individual correlation coefficients ranging from -0.72 to 0.95 (p < 0.01-0.0001). The slopes of the regression lines indicated that for each kg of water change, there was a mean decrease in BI of 18 ohm, but a substantial inter-individual variability was noted. CONCLUSION: BI measured at low frequency can represent a valuable index of acute changes in body water in a group of surgical patients but not in a given individual.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

Complete and long-lasting regression of disseminated multiple skin melanoma metastases under treatment with cyclooxygenase-2 inhibitor.

Experimental and clinical evidence indicates that non-steroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors may have anti-cancer activities. Here we report on a patient with a metastatic melanoma of the leg who experienced a complete and sustained regression of skin metastases upon continuous single treatment with the cyclooxygenase-2 inhibitor rofecoxib. Our observations indicate that the inhibition of cyclooxygenase-2 can lead to the regression of disseminated skin melanoma metastases, even after failure of chemotherapy.

What is the influence of vaccination's routes on the regression of tumors located at mucosal sites?

Tumor-regressions following tumor-associated-antigen vaccination in animal models contrast with the limited clinical outcomes in cancer patients. Most animal studies however used subcutaneous-tumor-models and questions arise as whether these are relevant for tumors growing in mucosae; whether specific mucosal-homing instructions are required; and how this may be influenced by the tumor.

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

Heat capacity measurements on the equiatomic compounds in Ni-X (X = Al, In, Si, Ge and Bi) and M-Sb (with M = Ni, Co and Fe) systems

Molar heat capacities of the binary compounds NiAl, NiIn, NiSi, NiGe, NiBi, NiSb, CoSb and FeSb were determined every 10 K by differential scanning calorimetry in the temperature range 310-1080 K. The experimental results have been fitted versus temperature according to C-p = a + b . T + c . T-2 + d . T-2. Results are given, discussed and compared to estimations found in the literature. Two compounds, NiBi and FeSb, are subject to transformations between 460 and 500 K. (C) 1999 Elsevier Science Ltd. All rights reserved.

Retrospective feasibility study of simultaneous integrated boost in cervical cancer using Tomotherapy: the impact of organ motion and tumor regression.

BACKGROUND: Whole pelvis intensity modulated radiotherapy (IMRT) is increasingly being used to treat cervical cancer aiming to reduce side effects. Encouraged by this, some groups have proposed the use of simultaneous integrated boost (SIB) to target the tumor, either to get a higher tumoricidal effect or to replace brachytherapy. Nevertheless, physiological organ movement and rapid tumor regression throughout treatment might substantially reduce any benefit of this approach. PURPOSE: To evaluate the clinical target volume - simultaneous integrated boost (CTV-SIB) regression and motion during chemo-radiotherapy (CRT) for cervical cancer, and to monitor treatment progress dosimetrically and volumetrically to ensure treatment goals are met. METHODS AND MATERIALS: Ten patients treated with standard doses of CRT and brachytherapy were retrospectively re-planned using a helical Tomotherapy - SIB technique for the hypothetical scenario of this feasibility study. Target and organs at risk (OAR) were contoured on deformable fused planning-computed tomography and megavoltage computed tomography images. The CTV-SIB volume regression was determined. The center of mass (CM) was used to evaluate the degree of motion. The Dice's similarity coefficient (DSC) was used to assess the spatial overlap of CTV-SIBs between scans. A cumulative dose-volume histogram modeled estimated delivered doses. RESULTS: The CTV-SIB relative reduction was between 31 and 70%. The mean maximum CM change was 12.5, 9, and 3 mm in the superior-inferior, antero-posterior, and right-left dimensions, respectively. The CTV-SIB-DSC approached 1 in the first week of treatment, indicating almost perfect overlap. CTV-SIB-DSC regressed linearly during therapy, and by the end of treatment was 0.5, indicating 50% discordance. Two patients received less than 95% of the prescribed dose. Much higher doses to the OAR were observed. A multiple regression analysis showed a significant interaction between CTV-SIB reduction and OAR dose increase. CONCLUSIONS: The CTV-SIB had important regression and motion during CRT, receiving lower therapeutic doses than expected. The OAR had unpredictable shifts and received higher doses. The use of SIB without frequent adaptation of the treatment plan exposes cervical cancer patients to an unpredictable risk of under-dosing the target and/or overdosing adjacent critical structures. In that scenario, brachytherapy continues to be the gold standard approach.

Bi-planar 2D-to-3D registration in Fourier domain for stereoscopic X-ray motion tracking

In this paper we present a new method to track bonemovements in stereoscopic X-ray image series of the kneejoint. The method is based on two different X-ray imagesets: a rotational series of acquisitions of the stillsubject knee that will allow the tomographicreconstruction of the three-dimensional volume (model),and a stereoscopic image series of orthogonal projectionsas the subject performs movements. Tracking the movementsof bones throughout the stereoscopic image series meansto determine, for each frame, the best pose of everymoving element (bone) previously identified in the 3Dreconstructed model. The quality of a pose is reflectedin the similarity between its simulated projections andthe actual radiographs. We use direct Fourierreconstruction to approximate the three-dimensionalvolume of the knee joint. Then, to avoid the expensivecomputation of digitally rendered radiographs (DRR) forpose recovery, we reformulate the tracking problem in theFourier domain. Under the hypothesis of parallel X-raybeams, we use the central-slice-projection theorem toreplace the heavy 2D-to-3D registration of projections inthe signal domain by efficient slice-to-volumeregistration in the Fourier domain. Focusing onrotational movements, the translation-relevant phaseinformation can be discarded and we only consider scalarFourier amplitudes. The core of our motion trackingalgorithm can be implemented as a classical frame-wiseslice-to-volume registration task. Preliminary results onboth synthetic and real images confirm the validity ofour approach.

Regression analysis of spatial data.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Lateglacial evolution of the Brenno glacier (Southern Swiss Alps) : preliminary results

The Lateglacial evolution of the Ticino glacier and tributaries is poorly known because of the lack of research by Quaternary geomorphologists during the last decades. In spite of the interest for the cryosphere reactions during the Lateglacial climate warming, only few scientific studies were carried out about the history of the northern valleys of the Ticino Alps during the deglaciation (e.g. Seiffert 1953, Renner 1982, Hantke 1983). Within the framework of geomorphological investigations on the Lateglacial and Holocene glacier/permafrost evolution in the Ticino Alps, the history of the Brenno glacier (Blenio Valley, Eastern Ticino Alps) during the end of the Pleistocene has been studied. The deglaciation sequence of the Blenio Valley is still not complete (Scapozza et al. 2009). Only the first glacial stadial of the Brenno glacier and the last Lateglacial stadials of the Greina region (northern Blenio valley, see Fontana et al. 2008) and of the upper Malvaglia Valley (eastern Blenio Valley, see Scapozza et al. 2008) have been unequivocally defined. For every stadial, the surface of the palaeoglacier and the depression of the Equilibrium Line Altitude (ELA) have been reconstructed on the base of geomorphological mapping. The first individual glacial stadial of the Brenno glacier corresponds to the Biasca stadial of the Ticino glacier defined by Hantke (1983). The ELA depression of 1100-1200 meters and its morphological and glaciological characteristics allow us to correlate this stadial with the Weissbad stadial defined by Keller (1988). In the Greina region, three stadials corresponding to the end of the Lateglacial have been identified, with an ELA depression of 110, 210 and 310-350 meters (Fontana et al. 2008). In the upper Malvaglia Valley, three stadials corresponding to the end of the Oldest Dryas and the Younger Dryas have been identified for the Orino glacier, with an ELA depression of 290, 400-420 and 470-560 meters (Scapozza et al. 2008). If we consider the other (fragmentary) glacial deposits of the Blenio Valley, it is possible to define a regression sequence of the Brenno glacier with 8 stadials, from the Biasca stadial to the end of the Younger Dryas. An attempt of correlation with the model "Gothard" developed by Renner (1982) and Hantke (1983) and with the model "Eastern Swiss Alps" developed by Maisch (1982) is proposed in Table 1. The following chronological conclusions are, therefore, proposed: (1) the Biasca stadial is probably the first stadial after the transition Pleniglacial - Lateglacial; (2) the stadials BRE 7 to BRE 3 are positioned between the beginning of the Lateglacial and the Bølling-Allerød interstadial; (3) the stadials BRE 2 and BRE 1 are assumed to be related to the Younger Dryas event.