Effect of biofunctionalized implant surface on osseointegration: a histomorphometric study in dogs

Among the different properties that influence bone apposition around implants, the chemical or biochemical composition of implant surface may interfere on its acceptance by the surrounding bone. The aim of this study was to investigate if a biofunctionalization of implant surface influences the bone apposition in a dog model and to compare it with other surfaces, such as a microstructured created by the grit-blasting/acid-etching process. Eight young adult male mongrel dogs had the bilateral mandibular premolars extracted and each one received 6 implants after 12 weeks, totaling 48 implants in the experiment. Four groups of implants were formed with the same microrough topography with or without some kind of biofunctionalization treatment. After histomorphometric analysis, it was observed that the modified microstructured surface with a "low concentration of the bioactive peptide" provided a higher adjacent bone density (54.6%) when compared to the other groups (microstructured + HA coating = 46.0%, microstructured only = 45.3% and microstructured + "high concentration of the bioactive peptide" = 40.7%), but this difference was not statistically significant. In conclusion, biofunctionalization of the implant surface might interfere in the bone apposition around implants, especially in terms of bone density. Different concentrations of bioactive peptide lead to different results.Entre as diferentes propriedades de uma superfície capazes de influenciar a deposição óssea ao redor de implantes, a composição química e bioquímica pode atuar no reconhecimento do tecido ósseo circundante. O presente trabalho investigou a influência da biofuncionalização de superfícies de implante na deposição óssea ao redor dos mesmos em um modelo animal, comparando-as com outras superfícies, como a microtexturizada obtida pelo processo de jateamento e ataque ácido. Metodologicamente, os pré-molares mandibulares bilaterais de 8 cães foram extraídos e após 12 semanas foram instalados 6 implantes em cada cão, constituindo uma amostra de 48 implantes. Dos 4 grupos experimentais de diferentes superfícies, todos continham a mesma microtopografia rugosa, porém possuindo ou não alguma biofuncionalização. A análise histomorfométrica revelou que a superfície microtexturizada modificada pela adição de baixa concentração peptídica obteve uma maior densidade óssea adjacente (54,6%) quando comparada aos outros grupos (microtexturizada + HA = 46%, somente microtexturizada = 45,3% e microtexturizada com adição de alta concentração peptídica = 40,7%), no entanto estas diferenças numéricas não foram estatisticamente significantes. Dentro deste contexto, conclui-se que a biofuncionalização da superfície de implantes pode interferir na aposição óssea, em particular na densidade óssea, e que diferentes concentrações peptídicas podem conduzir a diferentes resultados.FAPES

GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications

Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.</p

First measurement of the BSB_S meson mass

If simplified, every information retrieval problem can be solved when the information need implied by its expression has been identified. We are interested in the criteria used in realising a good information retrieval problem expression. We have listed these criteria through some principles and maxims which first characterized the communication between two persons are applied. We choose to use the gricean maxims because they are the most favoured for this type of situation. Secondly, we have tried to identify some others principles that can be used to realise a good information retrieval problem expression. The principles by Grice can not resolve all forms of error associated with this particular form of communication. In our work, we defined three other principles namely: adhesion principle, reformulation principle, memorization principle. We give some examples of situations where the principles we have formulated are not applicable and the consequences. We present the possible applications of our new model: MIRABEL, which can help in the description of information retrieval problem from. It also compels its user to use essential good expression principle implicitly

Can a simple stochastic model generate rich patterns of rainfall events?

Several of the existing rainfall models involve diverse assumptions, a variety of uncertain parameters, complicated mechanistic structures, use of different model schemes for different time scales, and possibly classifications of rainfall patterns into different types. However, the parsimony of a model is recognized as an important desideratum as it improves its comprehensiveness, its applicability and possibly its predictive capacity. To investigate the question if a single and simple stochastic model can generate a plethora of temporal rainfall patterns, as well as to detect the major characteristics of such a model (if it exists), a data set with very fine timescale rainfall is used. This is the well-known data set of the University of Iowa comprising measurements of seven storm events at a temporal resolution of 5-10 s. Even though only seven such events have been observed, their diversity can help investigate these issues. An evident characteristic resulting from the stochastic analysis of the events is the scaling behaviors both in state and in time. Utilizing these behaviors, a stochastic model is constructed which can represent all rainfall events and all rich patterns, thus suggesting a positive reply to the above question. In addition, it seems that the most important characteristics of such a model are a power-type distribution tail and an asymptotic power-type autocorrelation function. Both power-type distribution tails and autocorrelation functions can be viewed as properties enhancing randomness and uncertainty, or entropy

Can a simple stochastic model generate rich patterns of rainfall events?

none3Several of the existing rainfall models involve diverse assumptions, a variety of uncertain parameters, complicated mechanistic structures, use of different model schemes for different time scales, and possibly classifications of rainfall patterns into different types. However, the parsimony of a model is recognized as an important desideratum as it improves its comprehensiveness, its applicability and possibly its predictive capacity. To investigate the question if a single and simple stochastic model can generate a plethora of temporal rainfall patterns, as well as to detect the major characteristics of such a model (if it exists), a data set with very fine timescale rainfall is used. This is the well-known data set of the University of Iowa comprising measurements of seven storm events at a temporal resolution of 5-10 s. Even though only seven such events have been observed, their diversity can help investigate these issues. An evident characteristic resulting from the stochastic analysis of the events is the scaling behaviors both in state and in time. Utilizing these behaviors, a stochastic model is constructed which can represent all rainfall events and all rich patterns, thus suggesting a positive reply to the above question. In addition, it seems that the most important characteristics of such a model are a power-type distribution tail and an asymptotic power-type autocorrelation function. Both power-type distribution tails and autocorrelation functions can be viewed as properties enhancing randomness and uncertainty, or entropy.openPapalexiou S.-M.; Koutsoyiannis D.; Montanari APapalexiou S.-M.; Koutsoyiannis D.; Montanari