Exploring models for the length of stay distribution.

Diagnosis Related Groups (DRG) are frequently used to standardize the comparison of consumption variables, such as length of stay (LOS). In order to be reliable, this comparison must control for the presence of outliers, i.e. values far removed from the pattern set by the majority of the data. Indeed, outliers can distort the usual statistical summaries, such as means and variances. A common practice is to trim LOS values according to various empirical rules, but there is little theoretical support for choosing between alternative procedures. This pilot study explores the possibility of describing LOS distributions with parametric models which provide the necessary framework for the use of robust methods.

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Characterization of tumor antigen specific CD8+ T cells and semi-invariant Vα24/Vβ11 NKT cells in tumor invaded liver

Summary: Detailed knowledge on tumor antigen expression and specific immune cells is required for a rational design of immunotherapy for patients with tumor invaded liver. In this study, we confirmed that Cancer/Testis (CT) tumor-associated antigens are frequently expressed in hepatocellular carcinoma (HCC) and searched for the presence of CD8+ T cells specific for these antigens. In 2/10 HLA-A2+ patients with HCC, we found that MAGE-A10 and/or SSX-2 specific CD8+ T cells naturally responded to the disease, since they were enriched in tumor lesions but not in non-tumoral liver. Isolated T cells specifically and strongly killed tumor cells in vitro, suggesting that these CTL were selected in vivo for high avidity antigen recognition, providing the rational for specific immunotherapy of HCC, based on immunization with CT antigens such as MAGE-Al 0 and SSX-2. Type 1 NKT cells express an invariant TCR α chain (Vα24.1α18, paired with Vβ11 in human) and share a specific reactivity to αGalactosylceramide (αGC) presented by CD1d. These cells can display paradoxical immuno-regulatory properties including strong anti-tumor effects upon αGC administration in murine models. To understand why NKT cells were not sufficiently protective against tumor development in patients with tumor invaded liver, we characterized the diversity of Vα24/Vβ11 NKT cells in healthy donors (HD) and cancer patients: NKT cells from HD and patients were generally diverse in terms of TCR β chain (Vβ11) variability and NKT cells from HD showed a variable recognition of αGC loaded CD 1 d multimers. Vα24/ Vβ11 NKT cells can be divided in 3 populations, the CD4, DN (CD4-/CD8-) and CD8 NKT cell subsets that show distinct ability of cytokine production. In addition, our functional analysis revealed that DN and CD8 subsets displayed a higher cytolytic potential and a weaker IFNγ release than the CD4 NKT cell subset. NKT cell subsets were variably represented in the blood of HD and cancer patients. However, HD with high NKT cell frequencies displayed an enrichment of the DN and CD8 subsets, and few of them were suggestive of an oligoclonal expansion in vivo. Comparable NKT cell frequencies were found between blood, non-tumoral liver and tumor of patients. In contrast, we identified a gradual enrichment of CD4 NKT cells from blood to the liver and to the tumor, together with a decrease of DN and CD8 NKT cell subsets. Most patient derived NKT cells were unresponsive upon αGalactosylceramide stimulation ex vivo; NKT cells from few patients displayed a weak responsiveness with different cytokine polarization. The NKT cell repertoire was thus different in tumor tissue, suggesting that CD4 NKT cells infiltrating tumors may be detrimental for protection against tumors and instead may favour the tumor growth/recurrence as recently reported in mice. Résumé en français scientifique : Afin de développer le traitement des patients porteurs d'une tumeur dans le foie par immunothérapie, de nouvelles connaissances sont requises concernant l'expression d'antigènes par les tumeurs et les cellules immunitaires spécifiques de ces antigènes. Nous avons vérifié que des antigènes associés aux tumeurs, tels que les antigènes « Cancer-Testis » (CT), sont fréquemment exprimés par le carcinome hepatocéllulaire (CHC). La recherche de lymphocytes T CD8+ spécifiques (CTL) de ces antigènes a révélé que des CTL spécifiques de MAGE-A10 et/ou SSX-2 ont répondu naturellement à la tumeur chez 2/10 patients étudiés. Ces cellules étaient présentes dans les lésions tumorales mais pas dans le foie adjacent. De plus, ces CTL ont démontré une activité cytolytique forte et spécifique contre les cellules tumorales in vitro, ce qui suggère que ces CTL ont été sélectionnés pour une haute avidité de reconnaissance de l'antigène in vivo. Ces données fournissent une base pour l'immunothérapie spécifique du CHC, en proposant de cibler les antigènes CT tels que MAGE-A10 ou SSX-2. Les cellules NKT de type 1 ont une chaîne α de TCR qui est invariante (chez l'homme, Vα24Jα18, apparié avec Vβ11) et reconnaissent spécifiquement l'αGalactosylceramide (αGC) présenté par CD1d. Ces cellules ont des propriétés immuno¬régulatrices qui peuvent être parfois contradictoires et leur activation par l'αGC induit une forte protection anti-tumorale chez la souris: Afin de comprendre pourquoi ces cellules ne sont pas assez protectrices contre le développement des tumeurs dans le foie chez l'homme, nous avons étudié la diversité des cellules NKT Vα24/Vβ11 d'individus sains (IS) et de patients cancéreux. Les cellules NKT peuvent être sous-divisées en 3 populations : Les CD4, DN (CD4- /CD8-) ou CDS, qui ont la capacité de produire des cytokines différentes. Nos analyses fonctionnelles ont aussi révélé que les sous-populations DN et CD8 ont un potentiel cytolytique plus élevé et une production d'IFNγ plus faible que la sous-population CD4. Ces sous-populations sont représentées de manière variable dans le sang des IS ou des patients. Cependant, les IS avec un taux élevé de cellules NKT ont un enrichissement des sous- populations DN ou CDS, et certains suggèrent qu'il s'agit d'une expansion oligo-clonale in vivo. Les patients avaient des fréquences comparables de cellules NKT entre le sang, le foie et la tumeur. Par contre, la sous-population CD4 était progressivement enrichie du sang vers le foie et la tumeur, tandis que les sous-populations DN ou CD8 était perdues. La plupart des cellules NKT des patients ne réagissaient pas lors de stimulation avec l'αGC ex vivo et les cellules NKT de quelques patients répondaient faiblement et avec des polarisations de cytokines différentes. Ces données suggèrent que les cellules NKT CD4, prédominantes dans les tumeurs, sont inefficaces pour la lutte anti-tumorale et pourraient même favoriser la croissance ou la récurrence tumorale. Donc, une mobilisation spécifique des cellules NKT CD4 négatives par immunothérapie pourrait favoriser l'immunité contre des tumeurs chez l'homme. Résumé en français pour un large public Au sein des globules blancs, les lymphocytes T expriment un récepteur (le TCR), qui est propre à chacun d'entre eux et leur permet d'accrocher de manière très spécifique une molécule appelée antigène. Ce TCR est employé par les lymphocytes pour inspecter les antigènes associés avec des molécules présentatrices à la surface des autres cellules. Les lymphocytes T CD8 reconnaissent un fragment de protéine (ou peptide), qui est présenté par une des molécules du Complexe Majeur d'Histocompatibilité de classe I et tuent la cellule qui présente ce peptide. Ils sont ainsi bien adaptés pour éliminer les cellules qui présentent un peptide issu d'un virus quand la cellule est infectée. D'autres cellules T CD8 reconnaissent des peptides comme les antigènes CT, qui sont produits anormalement par les cellules cancéreuses. Nous avons confirmé que les antigènes CT sont fréquemment exprimés par le cancer du foie. Nous avons également identifié des cellules T CD8 spécifiques d'antigènes CT dans la tumeur, mais pas dans le foie normal de 2 patients sur 10. Cela signifie que ces lymphocytes peuvent être naturellement activés contre la tumeur et sont capables de la trouver. De plus les lymphocytes issus d'un patient ont démontré une forte sensibilité pour reconnaître l'antigène et tuent spécifiquement les cellules tumorales. Les antigènes CT représentent donc des cibles intéressantes qui pourront être intégrés dans des vaccins thérapeutiques du cancer du foie. De cette manière, les cellules T CD8 du patient lui-même pourront être induites à détruire de manière spécifique les cellules cancéreuses. Un nouveau type de lymphocytes T a été récemment découvert: les lymphocytes NKT. Quand ils reconnaissent un glycolipide présenté par la molécule CD1d, ils sont capables, de manière encore incomprise, d'initier, d'augmenter, ou à l'inverse d'inhiber la défense immunitaire. Ces cellules NKT ont démontré qu'elles jouent un rôle important dans la défense contre les tumeurs et particulièrement dans le foie des souris. Nous avons étudié les cellules NKT de patients atteints d'une tumeur dans le foie, afin de comprendre pourquoi elles ne sont pas assez protectrice chez l'homme. Les lymphocytes NKT peuvent être sous-divisés en 3 populations: Les CD4, les DN (CD4-/CD8-) et les CD8. Ces 3 classes de NKT peuvent produire différents signaux chimiques appelés cytokines. Contrairement aux cellules NKT DN ou CDS, seules les cellules NKT CD4 sont capables de produire des cytokines qui sont défavorables pour la défense anti-tumorale. Par ailleurs nous avons trouvé que les cellules NKT CD4 tuent moins bien les cellules cancéreuses que les cellules NKT DN ou CD8. L'analyse des cellules NKT, fraîchement extraites du sang, du foie et de la tumeur de patients a révélé que les cellules NKT CD4 sont progressivement enrichies du sang vers le foie et la tumeur. La large prédominance des NKT CD4 à l'intérieur des tumeurs suggère que, chez l'homme, ces cellules sont inappropriées pour la lutte anti-tumorale. Par ailleurs, la plupart des cellules NKT de patients n'étaient pas capables de produire des cytokines après stimulation avec un antigène. Cela explique également pourquoi ces cellules ne protègent pas contre les tumeurs dans le foie.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

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. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

An evaluation of new parsimony-based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae

Aim  Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersal-extinction-cladogenesis (DEC), against a parsimony-based method, dispersal-vicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location  World-wide.Methods  Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results  Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events - which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions  By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

Downregulation of cannabinoid receptor 1 from neuropeptide Y interneurons in the basal ganglia of patients with Huntington's disease and mouse models.

Cannabinoid receptor 1 (CB(1) receptor) controls several neuronal functions, including neurotransmitter release, synaptic plasticity, gene expression and neuronal viability. Downregulation of CB(1) expression in the basal ganglia of patients with Huntington's disease (HD) and animal models represents one of the earliest molecular events induced by mutant huntingtin (mHtt). This early disruption of neuronal CB(1) signaling is thought to contribute to HD symptoms and neurodegeneration. Here we determined whether CB(1) downregulation measured in patients with HD and mouse models was ubiquitous or restricted to specific striatal neuronal subpopulations. Using unbiased semi-quantitative immunohistochemistry, we confirmed previous studies showing that CB(1) expression is downregulated in medium spiny neurons of the indirect pathway, and found that CB(1) is also downregulated in neuropeptide Y (NPY)/neuronal nitric oxide synthase (nNOS)-expressing interneurons while remaining unchanged in parvalbumin- and calretinin-expressing interneurons. CB(1) downregulation in striatal NPY/nNOS-expressing interneurons occurs in R6/2 mice, Hdh(Q150/Q150) mice and the caudate nucleus of patients with HD. In R6/2 mice, CB(1) downregulation in NPY/nNOS-expressing interneurons correlates with diffuse expression of mHtt in the soma. This downregulation also occludes the ability of cannabinoid agonists to activate the pro-survival signaling molecule cAMP response element-binding protein in NPY/nNOS-expressing interneurons. Loss of CB(1) signaling in NPY/nNOS-expressing interneurons could contribute to the impairment of basal ganglia functions linked to HD.

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.

Pediatric reference intervals for plasma free and total metanephrines established with a parametric approach: Relevance to the diagnosis of neuroblastoma.

BACKGROUND: Urine catecholamines, vanillylmandelic, and homovanillic acid are recognized biomarkers for the diagnosis and follow-up of neuroblastoma. Plasma free (f) and total (t) normetanephrine (NMN), metanephrine (MN) and methoxytyramine (MT) could represent a convenient alternative to those urine markers. The primary objective of this study was to establish pediatric centile charts for plasma metanephrines. Secondarily, we explored their diagnostic performance in 10 patients with neuroblastoma. PROCEDURE: We recruited 191 children (69 females) free of neuroendocrine disease to establish reference intervals for plasma metanephrines, reported as centile curves for a given age and sex based on a parametric method using fractional polynomials models. Urine markers and plasma metanephrines were measured in 10 children with neuroblastoma at diagnosis. Plasma total metanephrines were measured by HPLC with coulometric detection and plasma free metanephrines by tandem LC-MS. RESULTS: We observed a significant age-dependence for tNMN, fNMN, and fMN, and a gender and age-dependence for tMN, fNMN, and fMN. Free MT was below the lower limit of quantification in 94% of the children. All patients with neuroblastoma at diagnosis were above the 97.5th percentile for tMT, tNMN, fNMN, and fMT, whereas their fMN and tMN were mostly within the normal range. As expected, urine assays were inconstantly predictive of the disease. CONCLUSIONS: A continuous model incorporating all data for a given analyte represents an appealing alternative to arbitrary partitioning of reference intervals across age categories. Plasma metanephrines are promising biomarkers for neuroblastoma, and their performances need to be confirmed in a prospective study on a large cohort of patients. Pediatr Blood Cancer 2015;62:587-593. © 2015 Wiley Periodicals, Inc.

Impact of the Femoral Head Position on Moment Arms in Total Hip Arthroplasty: A Parametric Finite Element Study.

BACKGROUND: Although the importance of accurate femoral reconstruction to achieve a good functional outcome is well documented, quantitative data on the effects of a displacement of the femoral center of rotation on moment arms are scarce. The purpose of this study was to calculate moment arms after nonanatomical femoral reconstruction. METHODS: Finite element models of 15 patients including the pelvis, the femur, and the gluteal muscles were developed. Moment arms were calculated within the native anatomy and compared to distinct displacement of the femoral center of rotation (leg lengthening of 10 mm, loss of femoral offset of 20%, anteversion ±10°, and fixed anteversion at 15°). Calculations were performed within the range of motion observed during a normal gait cycle. RESULTS: Although with all evaluated displacements of the femoral center of rotation, the abductor moment arm remained positive, some fibers initially contributing to extension became antagonists (flexors) and vice versa. A loss of 20% of femoral offset led to an average decrease of 15% of abductor moment. Femoral lengthening and changes in femoral anteversion (±10°, fixed at 15°) led to minimal changes in abductor moment arms (maximum change of 5%). Native femoral anteversion correlated with the changes in moment arms induced by the 5 variations of reconstruction. CONCLUSION: Accurate reconstruction of offset is important to maintaining abductor moment arms, while changes of femoral rotation had minimal effects. Patients with larger native femoral anteversion appear to be more susceptible to femoral head displacements.