Confined crystallization of nanolayered poly(ethylene terephthalate) using X-ray diffraction methods

The development of crystalline lamellae in ultra-thin layers of poly(ethylene terephthalate) PET confined between polycarbonate (PC) layers in an alternating assembly is investigated as a function of layer thickness by means of X-ray diffraction methods. Isothermal crystallization from the glassy state is in-situ followed by means of small-angle X-ray diffraction. It is found that the reduced size of the PET layers influences the lamellar nanostructure and induces a preferential lamellar orientation. Two lamellar populations, flat-on and edge-on, are found to coexist in a wide range of crystallization temperatures (Tc = 117–150 °C) and within layer thicknesses down to 35 nm. Flat-on lamellae appear at a reduced crystallization rate with respect to bulk PET giving rise to crystals of similar dimensions separated by larger amorphous regions. In addition, a narrower distribution of lamellar orientations develops when the layer thickness is reduced or the crystallization temperature is raised. In case of edge-on lamellae, crystallization conditions also influence the development of lamellar orientation; however, the latter is little affected by the reduced size of the layers. Results suggest that flat-on lamellae arise as a consequence of spatial confinement and edge-on lamellae could be generated due to the interactions with the PC interface. En este trabajo se investiga mediante difracción de rayos X a ángulos bajos (SAXS) y a ángulos altos (WAXS), la cristalización de láminas delgadas de Polietilén tereftalato (PET) confinadas entre láminas de Policarbonato (PC), tomando como referencia PET sin confinar. El espesor de las capas de PET varía entre 35nm y 115 nm. Se realizaron medidas de difracción a tres temperaturas de cristalización (117ºC, 132ºC y 150ºC) encontrándose que el reducido espesor de las capas de PET influye en la estructura lamelar que se desarrolla, induciendo una orientación preferente de las láminas. Se integró la intensidad difractada alrededor del máximo en SAXS para obtener una representación de la intensidad en función del ángulo acimutal. Mediante análisis de mínimos cuadrados se separó la curva experimental obtenida en tres contribuciones diferentes: una función Gausiana que describe la distribución de las orientaciones de las lamelas, una función lorenziana asociada a los máximos meridionales (asociados a las interfases PET-PC) y un background constante. Por otra parte la cantidad de material cristalizado se estimó asumiendo que la intensidad del background en el barrido acimutal, una vez restado el background del primer difractograma (sin máximos en SAXS) se asocia con la contribución del material isotrópico que resta en la muestra cristalizada. Se observa la coexistencia de dos poblaciones de lamelas: flat-on y edge-on. A medida que el espesor de las láminas de PET disminuye la población de las lamelas flat-on experimenta los siguientes cambios: 1) la distribución de orientación se estrecha, 2) la fracción de material cristalizado orientado aumenta, 3) la cinética de cristalización se ralentiza y 4) el largo espaciado aumenta es decir las regiones amorfas entre lamelas aumentan su tamaño. Parece demostrarse que es en las primeras etapas del crecimiento lamelar cuando la restricción espacial fuerza a las lamelas a esta orientación tipo flat-on frente a la orientación edge-on.

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.

Development of an in vitro mRNA decay system for Escherichia coli: Poly(A) polymerase I is necessary to trigger degradation

Using a novel Escherichia coli in vitro decay system in which polysomes are the source of both enzymes and mRNA, we demonstrate a requirement for poly(A) polymerase I (PAP I) in mRNA turnover. The in vitro decay of two different mRNAs (trxA and lpp) is triggered by the addition of ATP only when polysomes are prepared from a strain carrying the wild-type gene for PAP I (pcnB+). The relative decay rates of these two messages are similar in vitro and in vivo. Poly(A) tails are formed on both mRNAs, but no poly(A) tails are detected on the 3′ end of mature 23S rRNA. The size distribution of poly(A) tails generated in vitro, averaging 50 nt in length, is comparable to that previously reported in vivo. PAP I activity is associated exclusively with the polysomes. Exogenously added PAP I does not restore mRNA decay to PAP I− polysomes, suggesting that, in vivo, PAP I may be part of a multiprotein complex. The potential of this in vitro system for analyzing mRNA decay in E. coli is discussed.

Electrostatic interaction between helical macromolecules in dense aggregates: An impetus for DNA poly- and meso-morphism

DNA exhibits a surprising multiplicity of structures when it is packed into dense aggregates. It undergoes various polymorphous transitions (e.g., from the B to A form) and mesomorphous transformations (from hexagonal to orthorhombic or monoclinic packing, changes in the mutual alignment of nearest neighbors, etc). In this report we show that such phenomena may have their origin in the specific helical symmetry of the charge distribution on DNA surface. Electrostatic interaction between neighboring DNA molecules exhibits strong dependence on the patterns of molecular surface groups and adsorbed counter-ions. As a result, it is affected by such structural parameters as the helical pitch, groove width, the number of base pairs per helical turn, etc. We derive expressions which relate the energy of electrostatic interaction with these parameters and with the packing variables characterizing the axial and azimuthal alignment between neighboring macromolecules. We show, in particular, that the structural changes upon the B-to-A transition reduce the electrostatic energy by ≈kcal/mol per base pair, at a random adsorption of counter ions. Ion binding into the narrow groove weakens or inverts this effect, stabilizing B-DNA, as it is presumably the case in Li+-DNA assemblies. The packing symmetry and molecular alignment in DNA aggregates are shown to be affected by the patterns of ion binding.

Occurrence of poly(alpha2,8-deaminoneuraminic acid) in mammalian tissues: widespread and developmentally regulated but highly selective expression on glycoproteins.

In tissues of higher organisms homopolymers of alpha2,8-linked N-acetylneuraminic acid can be found as a posttranslational modification on selected proteins. We report here the discovery of homopolymers of alpha2,8-linked deaminoneuraminic acid [poly(alpha2,8-KDN)] in various tissues derived from all three germ layers in vertebrates including mammals. The monoclonal antibody kdn8kdn in conjunction with a bacterial KDNase permitted the detection of poly(alpha2,8-KDN) by immunohistochemistry and immunoblotting. Further evidence for the existence of poly(alpha2,8-KDN) was obtained by gas/liquid chromatography. The poly(alpha2,8-KDN) glycan was detectable in all tissues studied with the exception of mucus-producing cells present in various organs, the extracellular matrix, and basement membranes. However, in certain organs such as muscle, kidney, lung, and brain its expression was developmentally regulated. Despite its widespread tissue distribution, the poly(alpha2,8-KDN) glycan was detected on a single 150-kDa glycoprotein except for a single >350-kDa glycoprotein in kidney, which makes it most distinctive among polysialic acids. The ubiquitous yet selective expression may be indicative of a general function of the poly(alpha2,8-KDN)-bearing glycoproteins.

Influence of thymol and silver nanoparticles on the degradation of poly(lactic acid) based nanocomposites: thermal and morphological properties

Biopolymers, such as poly(lactic acid) (PLA), have been proposed as environmentally-friendly alternatives in applications such as food packaging. In this work, silver nanoparticles and thymol were used as active additives in PLA matrices, combining the antibacterial activity of silver with the antioxidant performance of thymol. The combined action of both additives influenced PLA thermal degradation in ternary systems. DSC results showed that the addition of thymol resulted in a clear decrease of the glass transition temperature (Tg) of PLA, suggesting its plasticizing effect in PLA matrices. Slight modifications in mechanical properties of dog-bone bars were also observed after the addition of the active components, especially in the elastic modulus. FESEM analyses showed the good distribution of active additives through the PLA matrix, obtaining homogenous surfaces and highlighting the presence of silver nanoparticles successfully embedded into the bulk matrix. Degradation of these PLA-based nanocomposites with thymol and silver nanoparticles in composting conditions indicated that the inherent biodegradable character of this biopolymer was improved after this modification. The obtained nanocomposites showed suitable properties to be used as biodegradable active-food packaging systems with antioxidant and antimicrobial effects.