Does seawater acidification affect survival, growth and shell integrity in bivalve juveniles?

Anthropogenic emissions of carbon dioxide are leading to decreases in pH and changes in the carbonate chemistry of seawater. Ocean acidification may negatively affect the ability of marine organisms to produce calcareous structures while also influencing their physiological responses and growth. The aim of this study was to evaluate the effects of reduced pH on the survival, growth and shell integrity of juveniles of two marine bivalves from the Northern Adriatic sea: the Mediterranean mussel Mytilus galloprovincialis and the striped venus clam Chamelea gallina. An outdoor flow-through plant was set up and two pH levels (natural seawater pH as a control, pH 7.4 as the treatment) were tested in long-term experiments. Mortality was low throughout the first experiment for both mussels and clams, but a significant increase, which was sensibly higher in clams, was observed at the end of the experiment (6 months). Significant decreases in the live weight (-26%) and, surprisingly, in the shell length (-5%) were observed in treated clams, but not in mussels. In the controls of both species, no shell damage was ever recorded; in the treated mussels and clams, damage proceeded via different modes and to different extents. The severity of shell injuries was maximal in the mussels after just 3 months of exposure to a reduced pH, whereas it progressively increased in clams until the end of the experiment. In shells of both species, the damaged area increased throughout the experiment, peaking at 35% in mussels and 11% in clams. The shell thickness of the treated and control animals significantly decreased after 3 months in clams and after 6 months in mussels. In the second experiment (3 months), only juvenile mussels were exposed to a reduced pH. After 3 months, the mussels at a natural pH level or pH 7.4 did not differ in their survival, shell length or live weight. Conversely, shell damage was clearly visible in the treated mussels from the 1st month onward. Monitoring the chemistry of seawater carbonates always showed aragonite undersaturation at 7.4 pH, whereas calcite undersaturation occurred in only 37% of the measurements. The present study highlighted the contrasting effects of acidification in two bivalve species living in the same region, although not exactly in the same habitat.

Effects of pre-hatching pCO2 on development and survival of zoea stages of Arctic spider crab Hyas araneus

Sensitivity of marine crustaceans to anthropogenic CO2 emissions and the associated acidification of the oceans may be less than that of other, especially lower, invertebrates. However, effects on critical transition phases or carry-over effects between life stages have not comprehensively been explored. Here we report the impact of elevated seawater PCO2 values (3100 µatm) on Hyas araneus during the last 2 weeks of their embryonic development (pre-hatching phase) and during development while in the consecutive zoea I and zoea II larval stages (post-hatching phase). We measured oxygen consumption, dry weight, developmental time and mortality in zoea I to assess changes in performance. Feeding rates and survival under starvation were investigated at different temperatures to detect differences in thermal sensitivities of zoea I and zoea II larvae depending on pre-hatch history. When embryos were pre-exposed to elevated PCO2 during maternal care, mortality increased about 60% under continued CO2 exposure during the zoea I phase. The larvae that moulted into zoea II, displayed a developmental delay by about 20 days compared to larvae exposed to control PCO2 during embryonic and zoeal phases. Elevated PCO2 caused a reduction in zoea I dry weight and feeding rates, while survival of the starved larvae was not affected by the seawater CO2 concentration. In conclusion, CO2 effects on egg masses under maternal care carried over to the first larval stages of crustaceans and reduced their survival and development to levels below those previously reported in studies exclusively focussing on acute PCO2 effects on the larval stages.

Modelling seed germination in forest tree species through survival analysis. The Pinus pinea L. case study

The direct application of existing models for seed germination may often be inadequate in the context of ecology and forestry germination experiments. This is because basic model assumptions are violated and variables available to forest managers are rarely used. In this paper, we present a method which addresses the aforementioned shortcomings. The approach is illustrated through a case study of Pinus pinea L. Our findings will also shed light on the role of germination in the general failure of natural regeneration in managed forests of this species. The presented technique consists of a mixed regression model based on survival analysis. Climate and stand covariates were tested. Data for fitting the model were gathered from a 5-year germination experiment in a mature, managed P. pinea stand in the Northern Plateau of Spain in which two different stand densities can be found. The model predictions proved to be unbiased and highly accurate when compared with the training data. Germination in P. pinea was controlled through thermal variables at stand level. At microsite level, low densities negatively affected the probability of germination. A time-lag in the response was also detected. Overall, the proposed technique provides a reliable alternative to germination modelling in ecology/forestry studies by using accessible/ suitable variables. The P. pinea case study highlights the importance of producing unbiased predictions. In this species, the occurrence and timing of germination suggest a very different regeneration strategy from that understood by forest managers until now, which may explain the high failure rate of natural regeneration in managed stands. In addition, these findings provide valuable information for the management of P. pinea under climate-change conditions.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Analysis of tape tether survival in LEO against orbital debris

The low earth orbit (LEO) environment contains a large number of artificial debris, of which a significant portion is due to dead satellites and fragments of satellites resulted from explosions and in-orbit collisions. Deorbiting defunct satellites at the end of their life can be achieved by a successful operation of an Electrodynamic Tether (EDT) system. The effectiveness of an EDT greatly depends on the survivability of the tether, which can become debris itself if cut by debris particles; a tether can be completely cut by debris having some minimal diameter. The objective of this paper is to develop an accurate model using power laws for debris-size ranges, in both ORDEM2000 and MASTER2009 debris flux models, to calculate tape tether survivability. The analytical model, which depends on tape dimensions (width, thickness) and orbital parameters (inclinations, altitudes) is then verified with fully numerical results to compare for different orbit inclinations, altitudes and tape width for both ORDEM2000 and MASTER2009 flux data.

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

Survival of reproductive behaviors in estrogen receptor β gene-deficient (βERKO) male and female mice

Previously, it was shown that the lack of a functional estrogen receptor (ER) α gene (ERα) greatly affects reproduction-related behaviors in both female and male mice. However, widespread expression of a novel second ER gene, ERβ, demanded that we examine the possible participation of ERβ in regulation of these behaviors. In dramatic contrast to our results with ERα knockout (αERKO) males, βERKO males performed at least as well as wild-type controls in sexual behavior tests. Moreover, not only did βERKO males exhibit normal male-typical aggressive behavior, including offensive attacks, but they also showed higher levels of aggression than wild-type mice under certain conditions of social experience. These data revealed a significant interaction between genotype and social experience with respect to aggressive behavior. Finally, females lacking a functional β isoform of the ER gene showed normal lordosis and courtship behaviors, extending in some cases beyond the day of behavioral estrus. These results highlight the importance of ERα for the normal expression of natural reproductive behaviors in both sexes and also provide a background for future studies evaluating ERβ gene contributions to other, nonreproductive behaviors.

Endothelin 3 selectively promotes survival and proliferation of neural crest-derived glial and melanocytic precursors in vitro

Genetic data in the mouse have shown that endothelin 3 (ET3) and its receptor B (ETRB) are essential for the development of two neural crest (NC) derivatives, the melanocytes and the enteric nervous system. We report here the effects of ET3 in vitro on the differentiation of quail trunk NC cells (NCC) in mass and clonal cultures. Treatment with ET3 is highly mitogenic to the undifferentiated NCC population, which leads to expansion of the population of cells in the melanocytic, and to a lesser extent, the glial lineages. The effect of ET3 on these two NC derivatives was confirmed by the quantitative analysis of clones derived from individual NCC subjected to ET3: we found a large increase in the survival and proliferation of unipotent and bipotent precursors for glial cells and melanocytes, with no significant effect on multipotent cells generating neurons. ET3 first stimulates expression of both ETRB and ETRB2 by cultured NCC. Then, under prolonged exposure to ET3, ETRB expression decreases and switches toward an ETRB2-positive melanogenic cell population. We therefore propose that the present in vitro experiments (long-lasting exposure to a high concentration of ET3) mimic the environment encountered by NCC in vivo when they migrate to the skin under the ectoderm that expresses ET3.

A subset of skin tumor modifier loci determines survival time of tumor-bearing mice

Studies of mouse models of human cancer have established the existence of multiple tumor modifiers that influence parameters of cancer susceptibility such as tumor multiplicity, tumor size, or the probability of malignant progression. We have carried out an analysis of skin tumor susceptibility in interspecific Mus musculus/Mus spretus hybrid mice and have identified another seven loci showing either significant (six loci) or suggestive (one locus) linkage to tumor susceptibility or resistance. A specific search was carried out for skin tumor modifier loci associated with time of survival after development of a malignant tumor. A combination of resistance alleles at three markers [D6Mit15 (Skts12), D7Mit12 (Skts2), and D17Mit7 (Skts10)], all of which are close to or the same as loci associated with carcinoma incidence and/or papilloma multiplicity, is significantly associated with increased survival of mice with carcinomas, whereas the reverse combination of susceptibility alleles is significantly linked to early mortality caused by rapid carcinoma growth (χ2 = 25.22; P = 5.1 × 10−8). These data indicate that host genetic factors may be used to predict carcinoma growth rate and/or survival of individual backcross mice exposed to the same carcinogenic stimulus and suggest that mouse models may provide an approach to the identification of genetic modifiers of cancer survival in humans.

Empirical laws of survival and evolution: Their universality and implications

Presented analysis of human and fly life tables proves that with the specified accuracy their entire survival and mortality curves are uniquely determined by a single point (e.g., by the birth mortality q0), according to the law, which is universal for species as remote as humans and flies. Mortality at any age decreases with the birth mortality q0. According to life tables, in the narrow vicinity of a certain q0 value (which is the same for all animals of a given species, independent of their living conditions), the curves change very rapidly and nearly simultaneously for an entire population of different ages. The change is the largest in old age. Because probability to survive to the mean reproductive age quantifies biological fitness and evolution, its universal rapid change with q0 (which changes with living conditions) manifests a new kind of an evolutionary spurt of an entire population. Agreement between theoretical and life table data is explicitly seen in the figures. Analysis of the data on basic metabolism reduces it to the maximal mean lifespan (for animals from invertebrates to mammals), or to the maximal mean fission time (for bacteria), and universally scales them with the total number of body atoms only. Phenomenological origin of this unification and universality of metabolism, survival, and evolution is suggested. Their implications and challenges are discussed.

Survival function of ERK1/2 as IL-3-activated, staurosporine-resistant Bcl2 kinases

Bcl2 phosphorylation at Ser-70 may be required for the full and potent suppression of apoptosis in IL-3-dependent myeloid cells and can result from agonist activation of mitochondrial protein kinase C (PKC). Paradoxically, expression of exogenous Bcl2 can protect parental cells from apoptosis induced by the potent PKC inhibitor, staurosporine (stauro). High concentrations of stauro of up to 1 μM only partially inhibit IL-3-stimulated Bcl2 phosphorylation but completely block PKC-mediated Bcl2 phosphorylation in vitro. These data indicate a role for a stauro-resistant Bcl2 kinase (SRK). We show that aurintricarboxylic acid (ATA), a nonpeptide activator of cellular MEK/mitogen-activated protein kinase (MAPK) kinase, can induce Ser-70 phosphorylation of Bcl2 and support survival of cells expressing wild-type but not the phosphorylation-incompetent S70A mutant Bcl2. A role for a MEK/MAPK as a responsible SRK was implicated because the highly specific MEK/MAPK inhibitor, PD98059, also can only partially inhibit IL-3-induced Bcl2 phosphorylation, whereas the combination of PD98059 and stauro completely blocks phosphorylation and synergistically enhances apoptosis. p44MAPK/extracellular signal-regulated kinase 1 (ERK1) and p42 MAPK/ERK2 are activated by IL-3, colocalize with mitochondrial Bcl2, and can directly phosphorylate Bcl2 on Ser-70 in a stauro-resistant manner both in vitro and in vivo. These findings suggest a role for the ERK1/2 kinases as SRKs. Thus, the SRKs can serve to functionally link the IL-3-stimulated proliferative and survival signaling pathways and, in a novel capacity, may explain how Bcl2 can suppress stauro-induced apoptosis. In addition, although the mechanism of regulation of Bcl2 by phosphorylation is not yet clear, our results indicate that phosphorylation may functionally stabilize the Bcl2-Bax heterodimerization.

Impaired survival of T helper cells in the absence of CD4

Signal transduction in response to ligand recognition by T cell receptors regulates T cell fate within and beyond the thymus. Herein we examine the involvement of the CD4 molecule in the regulation of T helper cell survival. T helper cells that lack CD4 expression are prone to apoptosis and show diminished survival after adoptive transfer to irradiated recipients. The helper lineage in CD4−/− animals shows a higher than normal apparent rate of cell division and is also enriched for cells exhibiting a memory cell phenotype. Thus the data point to a necessary role for CD4 in the regulation of T helper cell survival and homeostasis.

Low IGF-I suppresses VEGF-survival signaling in retinal endothelial cells: Direct correlation with clinical retinopathy of prematurity

Retinopathy of prematurity is a blinding disease, initiated by lack of retinal vascular growth after premature birth. We show that lack of insulin-like growth factor I (IGF-I) in knockout mice prevents normal retinal vascular growth, despite the presence of vascular endothelial growth factor, important to vessel development. In vitro, low levels of IGF-I prevent vascular endothelial growth factor-induced activation of protein kinase B (Akt), a kinase critical for endothelial cell survival. Our results from studies in premature infants suggest that if the IGF-I level is sufficient after birth, normal vessel development occurs and retinopathy of prematurity does not develop. When IGF-I is persistently low, vessels cease to grow, maturing avascular retina becomes hypoxic and vascular endothelial growth factor accumulates in the vitreous. As IGF-I increases to a critical level, retinal neovascularization is triggered. These data indicate that serum IGF-I levels in premature infants can predict which infants will develop retinopathy of prematurity and further suggests that early restoration of IGF-I in premature infants to normal levels could prevent this disease.