Avaluació de la toxicitat de metalls pesants i arsènic en diferents models biològics

En aquest estudi, la toxicitat de diversos metalls pesants i l'arsènic va ser analitzada utilitzant diferents models biològics. En la primera part d'aquest treball, el bioassaig de toxicitat Microtox, el qual està basat en la variació de l'emissió lumínica del bacteri luminiscent Vibrio fischeri, va ser utilitzat per establir les corbes dosi-resposta de diferents elements tòxics com el Zn(II), Pb(II), Cu(II), Hg(II), Ag(I), Co(II), Cd(II), Cr(VI), As(V) i As(III) en solucions aquoses. Els experiments es varen portar a terme a pH 6.0 i 7.0 per tal de mostrar que el pH pot influir en la toxicitat final mesurada d'alguns metalls degut als canvis relacionats amb la seva especiació química. Es varen trobar diferents tipus de corbes dosi-resposta depenent del metall analitzat i el pH del medi. En el cas de l'arsènic, l'efecte del pH en la toxicitat de l'arsenat i l'arsenit es va investigar utilitzant l'assaig Microtox en un rang de pHs comprès entre pH 5.0 i 9.0. Els valors d'EC50 determinats per l'As(V) disminueixen, reflectint un augment de la toxicitat, a mesura que el pH de la solució augmenta mentre que, en el cas de l'As(III), els valors d'EC50 quasi bé no varien entre pH 6.0 i 8.0 i només disminueixen a pH 9.0. HAsO42- i H2AsO3- es varen definir com les espècies més tòxiques. Així mateix, una anàlisi estadística va revelar un efecte antagònic entre les espècies químiques d'arsenat que es troben conjuntament a pH 6.0 i 7.0. D'altra banda, els resultats de dos mètodes estadístics per predir la toxicitat i les possibles interaccions entre el Co(II), Cd(II), Cu(II), Zn(II) i Pb(II) en mescles binàries equitòxiques es varen comparar amb la toxicitat observada sobre el bacteri Vibrio fischeri. L'efecte combinat d'aquests metalls va resultar ser antagònic per les mescles de Co(II)-Cd(II), Cd(II)-Zn(II), Cd(II)-Pb(II) i Cu(II)-Pb(II), sinèrgic per Co(II)-Cu(II) i Zn(II)-Pb(II) i additiu en els altres casos, revelant un patró complex de possibles interaccions. L'efecte sinèrgic de la combinació Co(II)-Cu(II) i la forta disminució de la toxicitat del Pb(II) quan es troba en presència de Cd(II) hauria de merèixer més atenció quan s'estableixen les normatives de seguretat ambiental. La sensibilitat de l'assaig Microtox també va ser determinada. Els valors d'EC20, els quals representen la toxicitat llindar mesurable, varen ser determinats per cada element individualment i es va veure que augmenten de la següent manera: Pb(II) < Ag(I) < Hg(II)  Cu(II) < Zn(II) < As(V) < Cd(II)  Co(II) < As(III) < Cr(VI). Aquests valors es varen comparar amb les concentracions permeses en aigues residuals industrials establertes per la normativa oficial de Catalunya (Espanya). L'assaig Microtox va resultar ser suficientment sensible per detectar els elements assajats respecte a les normes oficials referents al control de la contaminació, excepte en el cas del cadmi, mercuri, arsenat, arsenit i cromat. En la segona part d'aquest treball, com a resultats complementaris dels resultats previs obtinguts utilitzant l'assaig de toxicitat aguda Microtox, els efectes crònics del Cd(II), Cr(VI) i As(V) es varen analitzar sobre la taxa de creixement i la viabilitat en el mateix model biològic. Sorprenentment, aquests productes químics nocius varen resultar ser poc tòxics per aquest bacteri quan es mesura el seu efecte després de temps d'exposició llargs. Tot i això, en el cas del Cr(VI), l'assaig d'inhibició de la viabilitat va resultar ser més sensible que l'assaig de toxicitat aguda Microtox. Així mateix, també va ser possible observar un clar fenomen d'hormesis, especialment en el cas del Cd(II), quan s'utilitza l'assaig d'inhibició de la viabilitat. A més a més, diversos experiments es varen portar a terme per intentar explicar la manca de toxicitat de Cr(VI) mostrada pel bacteri Vibrio fischeri. La resistència mostrada per aquest bacteri podria ser atribuïda a la capacitat d'aquest bacteri de convertir el Cr(VI) a la forma menys tòxica de Cr(III). Es va trobar que aquesta capacitat de reducció depèn de la composició del medi de cultiu, de la concentració inicial de Cr(VI), del temps d'incubació i de la presència d'una font de carboni. En la tercera part d'aquest treball, la línia cel·lular humana HT29 i cultius primaris de cèl·lules sanguínies de Sparus sarba es varen utilitzar in vitro per detectar la toxicitat llindar de metalls mesurant la sobreexpressió de proteines d'estrès. Extractes de fangs precedents de diverses plantes de tractament d'aigues residuals i diferents metalls, individualment o en combinació, es varen analitzar sobre cultius cel·lulars humans per avaluar el seu efecte sobre la taxa de creixement i la capacitat d'induir la síntesi de les proteïnes Hsp72 relacionades amb l'estrès cel·lular. No es varen trobar efectes adversos significatius quan els components s'analitzen individualment. Nogensmenys, quan es troben conjuntament, es produeix un afecte advers sobre tan la taxa de creixement com en l'expressió de proteins d'estrès. D'altra banda, cèl·lules sanguínies procedents de Sparus sarba es varen exposar in vitro a diferents concentracions de cadmi, plom i crom. La proteïna d'estrès HSP70 es va sobreexpressar significativament després de l'exposició a concentracions tan febles com 0.1 M. Sota les nostres condicions de treball, no es va evidenciar una sobreexpressió de metal·lotioneïnes. Nogensmenys, les cèl·lules sanguínies de peix varen resultar ser un model biològic interessant per a ser utilitzat en anàlisis de toxicitat. Ambdós models biològics varen resultar ser molt adequats per a detectar acuradament la toxicitat produïda per metalls. En general, l'avaluació de la toxicitat basada en l'anàlisi de la sobreexpressió de proteïnes d'estrès és més sensible que l'avaluació de la toxicitat realitzada a nivell d'organisme. A partir dels resultats obtinguts, podem concloure que una bateria de bioassaigs és realment necessària per avaluar acuradament la toxicitat de metalls ja que existeixen grans variacions entre els valors de toxicitat obtinguts emprant diferents organismes i molts factors ambientals poden influir i modificar els resultats obtinguts.

Distributional semantics and machine learning for statistical machine translation

[EU]Lan honetan semantika distribuzionalaren eta ikasketa automatikoaren erabilera aztertzen dugu itzulpen automatiko estatistikoa hobetzeko. Bide horretan, erregresio logistikoan oinarritutako ikasketa automatikoko eredu bat proposatzen dugu hitz-segiden itzulpen- probabilitatea modu dinamikoan modelatzeko. Proposatutako eredua itzulpen automatiko estatistikoko ohiko itzulpen-probabilitateen orokortze bat dela frogatzen dugu, eta testuinguruko nahiz semantika distribuzionaleko informazioa barneratzeko baliatu ezaugarri lexiko, hitz-cluster eta hitzen errepresentazio bektorialen bidez. Horretaz gain, semantika distribuzionaleko ezagutza itzulpen automatiko estatistikoan txertatzeko beste hurbilpen bat lantzen dugu: hitzen errepresentazio bektorial elebidunak erabiltzea hitz-segiden itzulpenen antzekotasuna modelatzeko. Gure esperimentuek proposatutako ereduen baliagarritasuna erakusten dute, emaitza itxaropentsuak eskuratuz oinarrizko sistema sendo baten gainean. Era berean, gure lanak ekarpen garrantzitsuak egiten ditu errepresentazio bektorialen mapaketa elebidunei eta hitzen errepresentazio bektorialetan oinarritutako hitz-segiden antzekotasun neurriei dagokienean, itzulpen automatikoaz haratago balio propio bat dutenak semantika distribuzionalaren arloan.

Simulating SOC changes in 11 land use change chronosequences from the Brazilian Amazon with RothC and Century models

Land use and land cover changes in the Brazilian Amazon have major implications for regional and global carbon (C) cycling. Cattle pasture represents the largest single use (about 70%) of this once-forested land in most of the region. The main objective of this study was to evaluate the accuracy of the RothC and Century models at estimating soil organic C (SOC) changes under forest-to-pasture conditions in the Brazilian Amazon. We used data from 11 site-specific 'forest to pasture' chronosequences with the Century Ecosystem Model (Century 4.0) and the Rothamsted C Model (RothC 26.3). The models predicted that forest clearance and conversion to well managed pasture would cause an initial decline in soil C stocks (0-20 cm depth), followed in the majority of cases by a slow rise to levels exceeding those under native forest. One exception to this pattern was a chronosequence in Suia-Missu, which is under degraded pasture. In three other chronosequences the recovery of soil C under pasture appeared to be only to about the same level as under the previous forest. Statistical tests were applied to determine levels of agreement between simulated SOC stocks and observed stocks for all the sites within the 11 chronosequences. The models also provided reasonable estimates (coefficient of correlation = 0.8) of the microbial biomass C in the 0-10 cm soil layer for three chronosequences, when compared with available measured data. The Century model adequately predicted the magnitude and the overall trend in delta C-13 for the six chronosequences where measured 813 C data were available. This study gave independent tests of model performance, as no adjustments were made to the models to generate outputs. Our results suggest that modelling techniques can be successfully used for monitoring soil C stocks and changes, allowing both the identification of current patterns in the soil and the projection of future conditions. Results were used and discussed not only to evaluate soil C dynamics but also to indicate soil C sequestration opportunities for the Brazilian Amazon region. Moreover, modelling studies in these 'forest to pasture' systems have important applications, for example, the calculation of CO, emissions from land use change in national greenhouse gas inventories. (0 2007 Elsevier B.V. All rights reserved.

Mediating between hearer's and speaker's views in the generation of adaptive explanations

In this article, we examine the case of a system that cooperates with a “direct” user to plan an activity that some “indirect” user, not interacting with the system, should perform. The specific application we consider is the prescription of drugs. In this case, the direct user is the prescriber and the indirect user is the person who is responsible for performing the therapy. Relevant characteristics of the two users are represented in two user models. Explanation strategies are represented in planning operators whose preconditions encode the cognitive state of the indirect user; this allows tailoring the message to the indirect user's characteristics. Expansion of optional subgoals and selection among candidate operators is made by applying decision criteria represented as metarules, that negotiate between direct and indirect users' views also taking into account the context where explanation is provided. After the message has been generated, the direct user may ask to add or remove some items, or change the message style. The system defends the indirect user's needs as far as possible by mentioning the rationale behind the generated message. If needed, the plan is repaired and the direct user model is revised accordingly, so that the system learns progressively to generate messages suited to the preferences of people with whom it interacts.

Diagnosis of variability and trends in a global precipitation dataset using a physically motivated statistical model

A physically motivated statistical model is used to diagnose variability and trends in wintertime ( October - March) Global Precipitation Climatology Project (GPCP) pentad (5-day mean) precipitation. Quasi-geostrophic theory suggests that extratropical precipitation amounts should depend multiplicatively on the pressure gradient, saturation specific humidity, and the meridional temperature gradient. This physical insight has been used to guide the development of a suitable statistical model for precipitation using a mixture of generalized linear models: a logistic model for the binary occurrence of precipitation and a Gamma distribution model for the wet day precipitation amount. The statistical model allows for the investigation of the role of each factor in determining variations and long-term trends. Saturation specific humidity q(s) has a generally negative effect on global precipitation occurrence and with the tropical wet pentad precipitation amount, but has a positive relationship with the pentad precipitation amount at mid- and high latitudes. The North Atlantic Oscillation, a proxy for the meridional temperature gradient, is also found to have a statistically significant positive effect on precipitation over much of the Atlantic region. Residual time trends in wet pentad precipitation are extremely sensitive to the choice of the wet pentad threshold because of increasing trends in low-amplitude precipitation pentads; too low a choice of threshold can lead to a spurious decreasing trend in wet pentad precipitation amounts. However, for not too small thresholds, it is found that the meridional temperature gradient is an important factor for explaining part of the long-term trend in Atlantic precipitation.

Typification of farming systems for constructing representative farm models: two illustrations of the application of multi-variate analyses in Chile and Pakistan

If the fundamental precepts of Farming Systems Research were to be taken literally then it would imply that for each farm 'unique' solutions should be sought. This is an unrealistic expectation, but it has led to the idea of a recommendation domain, implying creating a taxonomy of farms, in order to increase the general applicability of recommendations. Mathematical programming models are an established means of generating recommended solutions, but for such models to be effective they have to be constructed for 'truly' typical or representative situations. The multi-variate statistical techniques provide a means of creating the required typologies, particularly when an exhaustive database is available. This paper illustrates the application of this methodology in two different studies that shared the common purpose of identifying types of farming systems in their respective study areas. The issues related with the use of factor and cluster analyses for farm typification prior to building representative mathematical programming models for Chile and Pakistan are highlighted. (C) 2003 Elsevier Science Ltd. All rights reserved.

Statistical methods for ordered categorical data based on a constrained odds model

The proportional odds model provides a powerful tool for analysing ordered categorical data and setting sample size, although for many clinical trials its validity is questionable. The purpose of this paper is to present a new class of constrained odds models which includes the proportional odds model. The efficient score and Fisher's information are derived from the profile likelihood for the constrained odds model. These results are new even for the special case of proportional odds where the resulting statistics define the Mann-Whitney test. A strategy is described involving selecting one of these models in advance, requiring assumptions as strong as those underlying proportional odds, but allowing a choice of such models. The accuracy of the new procedure and its power are evaluated.

Mixture models for capture-recapture count data

The contribution investigates the problem of estimating the size of a population, also known as the missing cases problem. Suppose a registration system is targeting to identify all cases having a certain characteristic such as a specific disease (cancer, heart disease, ...), disease related condition (HIV, heroin use, ...) or a specific behavior (driving a car without license). Every case in such a registration system has a certain notification history in that it might have been identified several times (at least once) which can be understood as a particular capture-recapture situation. Typically, cases are left out which have never been listed at any occasion, and it is this frequency one wants to estimate. In this paper modelling is concentrating on the counting distribution, e.g. the distribution of the variable that counts how often a given case has been identified by the registration system. Besides very simple models like the binomial or Poisson distribution, finite (nonparametric) mixtures of these are considered providing rather flexible modelling tools. Estimation is done using maximum likelihood by means of the EM algorithm. A case study on heroin users in Bangkok in the year 2001 is completing the contribution.

Statistical modeling suggests that antiandrogens in effluents from wastewater treatment works contribute to widespread sexual disruption in fish living in English rivers.

BACKGROUND: The widespread occurrence of feminized male fish downstream of some wastewater treatment works has led to substantial interest from ecologists and public health professionals. This concern stems from the view that the effects observed have a parallel in humans, and that both phenomena are caused by exposure to mixtures of contaminants that interfere with reproductive development. The evidence for a "wildlife-human connection" is, however, weak: Testicular dysgenesis syndrome, seen in human males, is most easily reproduced in rodent models by exposure to mixtures of antiandrogenic chemicals. In contrast, the accepted explanation for feminization of wild male fish is that it results mainly from exposure to steroidal estrogens originating primarily from human excretion. OBJECTIVES: We sought to further explore the hypothesis that endocrine disruption in fish is multi-causal, resulting from exposure to mixtures of chemicals with both estrogenic and antiandrogenic properties. METHODS: We used hierarchical generalized linear and generalized additive statistical modeling to explore the associations between modeled concentrations and activities of estrogenic and antiandrogenic chemicals in 30 U.K. rivers and feminized responses seen in wild fish living in these rivers. RESULTS: In addition to the estrogenic substances, antiandrogenic activity was prevalent in almost all treated sewage effluents tested. Further, the results of the modeling demonstrated that feminizing effects in wild fish could be best modeled as a function of their predicted exposure to both anti-androgens and estrogens or to antiandrogens alone. CONCLUSION: The results provide a strong argument for a multicausal etiology of widespread feminization of wild fish in U.K. rivers involving contributions from both steroidal estrogens and xeno-estrogens and from other (as yet unknown) contaminants with antiandrogenic properties. These results may add farther credence to the hypothesis that endocrine-disrupting effects seen in wild fish and in humans are caused by similar combinations of endocrine-disrupting chemical cocktails.

Statistical inferences in phylogeography

In conventional phylogeographic studies, historical demographic processes are elucidated from the geographical distribution of individuals represented on an inferred gene tree. However, the interpretation of gene trees in this context can be difficult as the same demographic/geographical process can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods.

A comparison of three different models for estimating relative risk in meta-analysis of clinical trials under unobserved heterogeneity

We focus on the comparison of three statistical models used to estimate the treatment effect in metaanalysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model. Copyright (C) 2006 John Wiley & Sons, Ltd.

Diagnosis of variability and trends in a global precipitation dataset using a physically motivated statistical model

A physically motivated statistical model is used to diagnose variability and trends in wintertime ( October - March) Global Precipitation Climatology Project (GPCP) pentad (5-day mean) precipitation. Quasi-geostrophic theory suggests that extratropical precipitation amounts should depend multiplicatively on the pressure gradient, saturation specific humidity, and the meridional temperature gradient. This physical insight has been used to guide the development of a suitable statistical model for precipitation using a mixture of generalized linear models: a logistic model for the binary occurrence of precipitation and a Gamma distribution model for the wet day precipitation amount. The statistical model allows for the investigation of the role of each factor in determining variations and long-term trends. Saturation specific humidity q(s) has a generally negative effect on global precipitation occurrence and with the tropical wet pentad precipitation amount, but has a positive relationship with the pentad precipitation amount at mid- and high latitudes. The North Atlantic Oscillation, a proxy for the meridional temperature gradient, is also found to have a statistically significant positive effect on precipitation over much of the Atlantic region. Residual time trends in wet pentad precipitation are extremely sensitive to the choice of the wet pentad threshold because of increasing trends in low-amplitude precipitation pentads; too low a choice of threshold can lead to a spurious decreasing trend in wet pentad precipitation amounts. However, for not too small thresholds, it is found that the meridional temperature gradient is an important factor for explaining part of the long-term trend in Atlantic precipitation.

Parametric accelerated failure time models with random effects and an application to kidney transplant survival

Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney transplantation. Different combinations of the distribution of the random effects and baseline hazard function are considered and the fit of such models to the transplant data is critically assessed. A mixture model that combines short- and long-term components of a hazard function is then developed, which provides a more flexible model for the hazard function. The model can incorporate different explanatory variables and random effects in each component. The model is straightforward to fit using standard statistical software, and is shown to be a good fit to the transplant data. Copyright (C) 2004 John Wiley Sons, Ltd.

Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo

This article presents a statistical method for detecting recombination in DNA sequence alignments, which is based on combining two probabilistic graphical models: (1) a taxon graph (phylogenetic tree) representing the relationship between the taxa, and (2) a site graph (hidden Markov model) representing interactions between different sites in the DNA sequence alignments. We adopt a Bayesian approach and sample the parameters of the model from the posterior distribution with Markov chain Monte Carlo, using a Metropolis-Hastings and Gibbs-within-Gibbs scheme. The proposed method is tested on various synthetic and real-world DNA sequence alignments, and we compare its performance with the established detection methods RECPARS, PLATO, and TOPAL, as well as with two alternative parameter estimation schemes.