The Estimation of Technical Efficiency Effects Models with an Example Applied to the Thai Manufacturing Sector

This paper does two things. First, it presents alternative approaches to the standard methods of estimating productive efficiency using a production function. It favours a parametric approach (viz. the stochastic production frontier approach) over a nonparametric approach (e.g. data envelopment analysis); and, further, one that provides a statistical explanation of efficiency, as well as an estimate of its magnitude. Second, it illustrates the favoured approach (i.e. the ‘single stage procedure’) with estimates of two models of explained inefficiency, using data from the Thai manufacturing sector, after the crisis of 1997. Technical efficiency is modelled as being dependent on capital investment in three major areas (viz. land, machinery and office appliances) where land is intended to proxy the effects of unproductive, speculative capital investment; and both machinery and office appliances are intended to proxy the effects of productive, non-speculative capital investment. The estimates from these models cast new light on the five-year long, post-1997 crisis period in Thailand, suggesting a structural shift from relatively labour intensive to relatively capital intensive production in manufactures from 1998 to 2002.

Two different 3D biogeochemical models for the NW mediterranean sea: validation with Meris data and intercomparison

Report for the scientific sojourn carried out at the University of New South Wales from February to June the 2007. Two different biogeochemical models are coupled to a three dimensional configuration of the Princeton Ocean Model (POM) for the Northwestern Mediterranean Sea (Ahumada and Cruzado, 2007). The first biogeochemical model (BLANES) is the three-dimensional version of the model described by Bahamon and Cruzado (2003) and computes the nitrogen fluxes through six compartments using semi-empirical descriptions of biological processes. The second biogeochemical model (BIOMEC) is the biomechanical NPZD model described in Baird et al. (2004), which uses a combination of physiological and physical descriptions to quantify the rates of planktonic interactions. Physical descriptions include, for example, the diffusion of nutrients to phytoplankton cells and the encounter rate of predators and prey. The link between physical and biogeochemical processes in both models is expressed by the advection-diffusion of the non-conservative tracers. The similarities in the mathematical formulation of the biogeochemical processes in the two models are exploited to determine the parameter set for the biomechanical model that best fits the parameter set used in the first model. Three years of integration have been carried out for each model to reach the so called perpetual year run for biogeochemical conditions. Outputs from both models are averaged monthly and then compared to remote sensing images obtained from sensor MERIS for chlorophyll.

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.

Sound Texture Modelling

In the PhD thesis “Sound Texture Modeling” we deal with statistical modelling or textural sounds like water, wind, rain, etc. For synthesis and classification. Our initial model is based on a wavelet tree signal decomposition and the modeling of the resulting sequence by means of a parametric probabilistic model, that can be situated within the family of models trainable via expectation maximization (hidden Markov tree model ). Our model is able to capture key characteristics of the source textures (water, rain, fire, applause, crowd chatter ), and faithfully reproduces some of the sound classes. In terms of a more general taxonomy of natural events proposed by Graver, we worked on models for natural event classification and segmentation. While the event labels comprise physical interactions between materials that do not have textural propierties in their enterity, those segmentation models can help in identifying textural portions of an audio recording useful for analysis and resynthesis. Following our work on concatenative synthesis of musical instruments, we have developed a pattern-based synthesis system, that allows to sonically explore a database of units by means of their representation in a perceptual feature space. Concatenative syntyhesis with “molecules” built from sparse atomic representations also allows capture low-level correlations in perceptual audio features, while facilitating the manipulation of textural sounds based on their physical and perceptual properties. We have approached the problem of sound texture modelling for synthesis from different directions, namely a low-level signal-theoretic point of view through a wavelet transform, and a more high-level point of view driven by perceptual audio features in the concatenative synthesis setting. The developed framework provides unified approach to the high-quality resynthesis of natural texture sounds. Our research is embedded within the Metaverse 1 European project (2008-2011), where our models are contributting as low level building blocks within a semi-automated soundscape generation system.

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.

Business Models in OER, a Contingency Approach

We will present an analysis of data from a literature review and semi-structured interviews with experts on OER, to identify different aspects of OER business models and to establish how the success of the OER initiatives is measured. The results collected thus far show that two different business models for OER initiatives exist, but no data on their success or failure is published. We propose a framework for measuring success of OER initiatives.

Biological activity of neosergeolide and isobrucein B (and two semi-synthetic derivatives) isolated from the Amazonian medicinal plant Picrolemma sprucei (Simaroubaceae)

In the present study, in vitro techniques were used to investigate a range of biological activities of known natural quassinoids isobrucein B (1) and neosergeolide (2), known semi-synthetic derivative 1,12-diacetylisobrucein B (3), and a new semi-synthetic derivative, 12-acetylneosergeolide (4). These compounds were evaluated for general toxicity toward the brine shrimp species Artemia franciscana, cytotoxicity toward human tumour cells, larvicidal activity toward the dengue fever mosquito vector Aedes aegypti, haemolytic activity in mouse erythrocytes and antimalarial activity against the human malaria parasite Plasmodium falciparum. Compounds 1 and 2 exhibited the greatest cytotoxicity against all the tumor cells tested (IC50 = 5-27 µg/L) and against multidrug-resistant P. falciparum K1 strain (IC50 = 1.0-4.0 g/L) and 3 was only cytotoxic toward the leukaemia HL-60 strain (IC50 = 11.8 µg/L). Quassinoids 1 and 2 (LC50 = 3.2-4.4 mg/L) displayed greater lethality than derivative 4 (LC50 = 75.0 mg/L) toward A. aegypti larvae, while derivative 3 was inactive. These results suggest a novel application for these natural quassinoids as larvicides. The toxicity toward A. franciscana could be correlated with the activity in several biological models, a finding that is in agreement with the literature. Importantly, none of the studied compounds exhibited in vitro haemolytic activity, suggesting specificity of the observed cytotoxic effects. This study reveals the biological potential of quassinoids 1 and 2 and to a lesser extent their semi-synthetic derivatives for their in vitro antimalarial and cytotoxic activities.

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.

Monetary policy misspecification in VAR models

We examine the effects of extracting monetary policy disturbances with semi-structural and structural VARs, using data generated bya limited participation model under partial accommodative and feedback rules. We find that, in general, misspecification is substantial: short run coefficients often have wrong signs; impulse responses and variance decompositions give misleadingrepresentations of the dynamics. Explanations for the results and suggestions for macroeconomic practice are provided.

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.

Cost-Based Models of Economic Growth

In this paper we highlight the importance of the operational costs in explaining economic growth and analyze how the industrial structure affects the growth rate of the economy. If there is monopolistic competition only in an intermediate goods sector, then production growth coincides with consumption growth. Moreover, the pattern of growth depends on the particular form of the operational cost. If the monopolistically competitive sector is the final goods sector, then per capita production is constant but per capita effective consumption or welfare grows. Finally, we modify again the industrial structure of the economy and show an economy with two different growth speeds, one for production and another for effective consumption. Thus, both the operational cost and the particular structure of the sector that produces the final goods determines ultimately the pattern of growth.

Cost-Based Models of Economic Growth

In this paper we highlight the importance of the operational costs in explaining economic growth and analyze how the industrial structure affects the growth rate of the economy. If there is monopolistic competition only in an intermediate goods sector, then production growth coincides with consumption growth. Moreover, the pattern of growth depends on the particular form of the operational cost. If the monopolistically competitive sector is the final goods sector, then per capita production is constant but per capita effective consumption or welfare grows. Finally, we modify again the industrial structure of the economy and show an economy with two different growth speeds, one for production and another for effective consumption. Thus, both the operational cost and the particular structure of the sector that produces the final goods determines ultimately the pattern of growth.