Les biomarqueurs prédictifs dans le cancer colorectal [Predictive biomarkers in colorectal cancer]

While for many years the diagnosis and therapy of colon cancer did not change drastically, recently new drugs (irinotecan and oxaliplatin, used in adjuvant or neo-adjuvant approaches) and even more recently the introduction of therapies targeting the epidermal growth factor receptor (EGFR) through the monoclonal antibodies cetuximab and panitumumab, are revolutionizing the field. The finding that only patients with a tumor with a wild type (non mutated) KRAS gene respond to anti-EGFR therapy has also affected the way pathologists address colorectal cancer. Molecular analysis of the KRAS gene has become almost a routine in a very short period of time. Pathologists will have to be prepared for a new era: from standard morphology based diagnostic procedures to the prediction of response to therapy using molecular tools.

SAT Modulo Linear Arithmetic for Solving Polynomial

Polynomial constraint solving plays a prominent role in several areas of hardware and software analysis and verification, e.g., termination proving, program invariant generation and hybrid system verification, to name a few. In this paper we propose a new method for solving non-linear constraints based on encoding the problem into an SMT problem considering only linear arithmetic. Unlike other existing methods, our method focuses on proving satisfiability of the constraints rather than on proving unsatisfiability, which is more relevant in several applications as we illustrate with several examples. Nevertheless, we also present new techniques based on the analysis of unsatisfiable cores that allow one to efficiently prove unsatisfiability too for a broad class of problems. The power of our approach is demonstrated by means of extensive experiments comparing our prototype with state-of-the-art tools on benchmarks taken both from the academic and the industrial world.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

A Canonical Genetic Algorithm for Blind Inversion of Linear Channels

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA is justified in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Another way of thinking about ADHD: the predictive role of early attachment deprivation in adolescents' level of symptoms.

PURPOSE: Attention deficit and hyperactivity disorder (ADHD) is one of the most frequent disorders in childhood and adolescence. Both neurocognitive and environmental factors have been related to ADHD. The current study contributes to the documentation of the predictive relation between early attachment deprivation and ADHD. METHOD: Data were collected from 641 adopted adolescents (53.2 % girls) aged 11-16 years in five countries, using the DSM oriented scale for ADHD of the Child Behavior Checklist (CBCL) (Achenbach and Rescorla, Manual for the ASEBA school-age forms and profiles. University of Vermont, Research Center for Children, Youth and Families, Burlington, 2001). The influence of attachment deprivation on ADHD symptoms was initially tested taking into consideration several key variables that have been reported as influencing ADHD at the adoptee level (age, gender, length of time in the adoptive family, parents' educational level and marital status), and at the level of the country of origin and country of adoption (poverty, quality of health services and values). The analyses were computed using the multilevel modeling technique. RESULTS: The results showed that an increase in the level of ADHD symptoms was predicted by the duration of exposure to early attachment deprivation, estimated from the age of adoption, after controlling for the influence of adoptee and country variables. The effect of the age of adoption was also demonstrated to be specific to the level of ADHD symptoms in comparison to both the externalizing and internalizing behavior scales of the CBCL. CONCLUSION: Deprivation of stable and sensitive care in infancy may have long-lasting consequences for children's development.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

A new spin on a compositionalist predictive modelling framework for conservation planning: a tropical case study in Ecuador

Knowledge about spatial biodiversity patterns is a basic criterion for reserve network design. Although herbarium collections hold large quantities of information, the data are often scattered and cannot supply complete spatial coverage. Alternatively, herbarium data can be used to fit species distribution models and their predictions can be used to provide complete spatial coverage and derive species richness maps. Here, we build on previous effort to propose an improved compositionalist framework for using species distribution models to better inform conservation management. We illustrate the approach with models fitted with six different methods and combined using an ensemble approach for 408 plant species in a tropical and megadiverse country (Ecuador). As a complementary view to the traditional richness hotspots methodology, consisting of a simple stacking of species distribution maps, the compositionalist modelling approach used here combines separate predictions for different pools of species to identify areas of alternative suitability for conservation. Our results show that the compositionalist approach better captures the established protected areas than the traditional richness hotspots strategies and allows the identification of areas in Ecuador that would optimally complement the current protection network. Further studies should aim at refining the approach with more groups and additional species information.

Identification of HMX1 target genes: a predictive promoter model approach.

PURPOSE: A homozygous mutation in the H6 family homeobox 1 (HMX1) gene is responsible for a new oculoauricular defect leading to eye and auricular developmental abnormalities as well as early retinal degeneration (MIM 612109). However, the HMX1 pathway remains poorly understood, and in the first approach to better understand the pathway's function, we sought to identify the target genes. METHODS: We developed a predictive promoter model (PPM) approach using a comparative transcriptomic analysis in the retina at P15 of a mouse model lacking functional Hmx1 (dmbo mouse) and its respective wild-type. This PPM was based on the hypothesis that HMX1 binding site (HMX1-BS) clusters should be more represented in promoters of HMX1 target genes. The most differentially expressed genes in the microarray experiment that contained HMX1-BS clusters were used to generate the PPM, which was then statistically validated. Finally, we developed two genome-wide target prediction methods: one that focused on conserving PPM features in human and mouse and one that was based on the co-occurrence of HMX1-BS pairs fitting the PPM, in human or in mouse, independently. RESULTS: The PPM construction revealed that sarcoglycan, gamma (35kDa dystrophin-associated glycoprotein) (Sgcg), teashirt zinc finger homeobox 2 (Tshz2), and solute carrier family 6 (neurotransmitter transporter, glycine) (Slc6a9) genes represented Hmx1 targets in the mouse retina at P15. Moreover, the genome-wide target prediction revealed that mouse genes belonging to the retinal axon guidance pathway were targeted by Hmx1. Expression of these three genes was experimentally validated using a quantitative reverse transcription PCR approach. The inhibitory activity of Hmx1 on Sgcg, as well as protein tyrosine phosphatase, receptor type, O (Ptpro) and Sema3f, two targets identified by the PPM, were validated with luciferase assay. CONCLUSIONS: Gene expression analysis between wild-type and dmbo mice allowed us to develop a PPM that identified the first target genes of Hmx1.

Improvement of walking speed prediction by accelerometry and altimetry, validated by satellite positioning.

Activity monitors based on accelerometry are used to predict the speed and energy cost of walking at 0% slope, but not at other inclinations. Parallel measurements of body accelerations and altitude variation were studied to determine whether walking speed prediction could be improved. Fourteen subjects walked twice along a 1.3 km circuit with substantial slope variations (-17% to +17%). The parameters recorded were body acceleration using a uni-axial accelerometer, altitude variation using differential barometry, and walking speed using satellite positioning (DGPS). Linear regressions were calculated between acceleration and walking speed, and between acceleration/altitude and walking speed. These predictive models, calculated using the data from the first circuit run, were used to predict speed during the second circuit. Finally the predicted velocity was compared with the measured one. The result was that acceleration alone failed to predict speed (mean r = 0.4). Adding altitude variation improved the prediction (mean r = 0.7). With regard to the altitude/acceleration-speed relationship, substantial inter-individual variation was found. It is concluded that accelerometry, combined with altitude measurement, can assess position variations of humans provided inter-individual variation is taken into account. It is also confirmed that DGPS can be used for outdoor walking speed measurements, opening up new perspectives in the field of biomechanics.

The impact of insight in a first-episode mania with psychosis population on outcome at 18 months.

BACKGROUND: To explore whether poor initial insight during a first episode of mania with psychotic features was predictive of poor psychosocial and clinical outcomes at 18 months. METHODS: Secondary analysis was performed on data collected during an 8-week RCT comparing the efficacy of olanzapine versus chlorpromazine as an adjunct to lithium, and at 18-month follow-up. 74 participants were divided into three groups (no insight, partial insight, and full insight) according to the insight item from the Young Mania Rating Scale (YMRS). Differences between these three groups were examined at baseline and at 18 months on measures of symptoms (YMRS, HAMD-21, and CGI-S), and social and occupational functioning (SOFAS). Baseline differences between the three groups were determined using general linear models and chi-squared analyses. Group differences from baseline to 18-month follow-up were determined using repeated measures general linear models. RESULTS: At baseline there were significant differences between the three insight groups in terms of mania and functioning, but at 18 months all groups had improved significantly in terms of psychopathology, mania, depression and social and occupational functioning. There were no significant differences between the three groups at study completion with respect to these domains. LIMITATIONS: The study was limited by the lack of availability of a more detailed rating scale for insight, and it did not account for the duration of untreated psychosis (DUI). CONCLUSIONS: Poor initial insight during a first episode of mania with psychotic features does not predict poor clinical and psychosocial outcome at 18 months.

Short-term stability of testosterone and epitestosterone conjugates in urine samples: quantification by liquid chromatography-linear ion trap mass spectrometry.

A simple method using liquid chromatography-linear ion trap mass spectrometry for simultaneous determination of testosterone glucuronide (TG), testosterone sulfate (TS), epitestosterone glucuronide (EG) and epitestosterone sulfate (ES) in urine samples was developed. For validation purposes, a urine containing no detectable amount of TG, TS and EG was selected and fortified with steroid conjugate standards. Quantification was performed using deuterated testosterone conjugates to correct for ion suppression/enhancement during ESI. Assay validation was performed in terms of lower limit of detection (1-3ng/mL), recovery (89-101%), intraday precision (2.0-6.8%), interday precision (3.4-9.6%) and accuracy (101-103%). Application of the method to short-term stability testing of urine samples at temperature ranging from 4 to 37 degrees C during a time-storage of a week lead to the conclusion that addition of sodium azide (10mg/mL) is required for preservation of the analytes.

Prognostic and predictive value of centrally reviewed Ki-67 labeling index in postmenopausal women with endocrine-responsive breast cancer: results from Breast International Group Trial 1-98 comparing adjuvant tamoxifen with letrozole.

PURPOSE: To evaluate the prognostic and predictive value of Ki-67 labeling index (LI) in a trial comparing letrozole (Let) with tamoxifen (Tam) as adjuvant therapy in postmenopausal women with early breast cancer. PATIENTS AND METHODS: Breast International Group (BIG) trial 1-98 randomly assigned 8,010 patients to four treatment arms comparing Let and Tam with sequences of each agent. Of 4,922 patients randomly assigned to receive 5 years of monotherapy with either agent, 2,685 had primary tumor material available for central pathology assessment of Ki-67 LI by immunohistochemistry and had tumors confirmed to express estrogen receptors after central review. The prognostic and predictive value of centrally measured Ki-67 LI on disease-free survival (DFS) were assessed among these patients using proportional hazards modeling, with Ki-67 LI values dichotomized at the median value of 11%. RESULTS: Higher values of Ki-67 LI were associated with adverse prognostic factors and with worse DFS (hazard ratio [HR; high:low] = 1.8; 95% CI, 1.4 to 2.3). The magnitude of the treatment benefit for Let versus Tam was greater among patients with high tumor Ki-67 LI (HR [Let:Tam] = 0.53; 95% CI, 0.39 to 0.72) than among patients with low tumor Ki-67 LI (HR [Let:Tam] = 0.81; 95% CI, 0.57 to 1.15; interaction P = .09). CONCLUSION: Ki-67 LI is confirmed as a prognostic factor in this study. High Ki-67 LI levels may identify a patient group that particularly benefits from initial Let adjuvant therapy.