In Vitro Macroscopic and Endoscopic Analysis of Three-Rooted Maxillary Premolars and Two-Rooted Mandibular Premolars in the Same Individual: a Case Report

The recent addition of endoscopy in dental practice has enabled clinicians to have an excellent view of the operative field, yielding highly successful visualization of anatomical structures that are difficult to access, both in oral surgery and endodontics. The purpose of this report is to provide an in vitro macroscopic, radiographic, and endoscopic description of the anatomic variation of the roots of maxillary and mandibular first premolars in the same patient. A 22-year-old patient was referred by an orthodontist for the extraction of all the first premolars. Once extracted, the premolars were examined macroscopically and then analyzed radiographically after trepanation and filled root canal systems. Subsequently, a diaphanization process was carried out and the samples were sectioned at the middle and apical third for observation by endoscope. It was found that both the maxillary first premolars had three roots, and mandibular first premolars had two roots, all with complete root formation. Apical deltas or accessory canals were not identified in the radiographic images; however, through endoscope at the middle third, it was possible to observe an accessory canal to the first maxillary and mandibular right premolars. Thus, it can be concluded that the view through the endoscope allows better identification of accessory canals than X-rays.

The Islamic Crossed-Arch Domes in Cordoba: Geometry and Structural Analysis of the “Capilla de Villaviciosa”

Crossed-arch domes are a singular type of ribbed vaults. Their characteristic feature is that the ribs that form the vault are intertwined, forming polygons or stars, leaving an empty space in the centre. The earliest known vaults of this type are found in the Great Mosque of Córdoba, built ca. 960 a.C. The type spread through Spain, and the north of Africa in the 10th to the 16th Centuries, and was used by Guarini and Vittone in the 17th and 18th Centuries in Italy. However, it was used only in a few buildings. Though the literature about the structural behaviour of ribbed Gothic vaults is extensive, so far no structural analysis of crossed arch domes has been made. The purpose of this work is, first to show the way to attack such an analysis within the frame of Modern Limit Analysis of Masonry Structures (Heyman 1995), and then to apply the approach to study the stability of the dome of the Capilla de Villaviciosa. The work may give some clues to art and architectural historians to understand better the origin and development of Islamic dome architecture.

Macrophage migration inhibitory factor (MIF) family in arthropods : cloning and expression analysis of two MIF and one D-dopachrome tautomerase (DDT) homologues in Mud crabs, Scylla paramamosain

5. Acknowledgements This research was supported by grants from the National Natural Science Foundation of China (Nos. 31172438 and U1205123), the Natural Science Foundation of Fujian Province (No. 2012J06008 and 201311180002) and the projects-sponsored by SRF. TW received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.

Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Effect Of Different Hydrological Model Structures On The Assimilation Of Distributed Uncertain Observations

The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of different structure of a semi-distributed hydrological model in the assimilation of distributed uncertain discharge observations. The study was applied to the Bacchiglione catchment, located in Italy. The first methodological step was to divide the basin in different sub-basins according to topographic characteristics. Secondly, two different structures of the semi-distributed hydrological model were implemented in order to estimate the outflow hydrograph. Then, synthetic observations of uncertain value of discharge were generated, as a function of the observed and simulated value of flow at the basin outlet, and assimilated in the semi-distributed models using a Kalman Filter. Finally, different spatial patterns of sensors location were assumed to update the model state as response of the uncertain discharge observations. The results of this work pointed out that, overall, the assimilation of uncertain observations can improve the hydrologic model performance. In particular, it was found that the model structure is an important factor, of difficult characterization, since can induce different forecasts in terms of outflow discharge. This study is partly supported by the FP7 EU Project WeSenseIt.

Newly Automated System For Integrated Assessment Of The Conditions Of Underwater Gas Pipelines

An underwater gas pipeline is the portion of the pipeline that crosses a river beneath its bottom. Underwater gas pipelines are subject to increasing dangers as time goes by. An accident at an underwater gas pipeline can lead to technological and environmental disaster on the scale of an entire region. Therefore, timely troubleshooting of all underwater gas pipelines in order to prevent any potential accidents will remain a pressing task for the industry. The most important aspect of resolving this challenge is the quality of the automated system in question. Now the industry doesn't have any automated system that fully meets the needs of the experts working in the field maintaining underwater gas pipelines. Principle Aim of this Research: This work aims to develop a new system of automated monitoring which would simplify the process of evaluating the technical condition and decision making on planning and preventive maintenance and repair work on the underwater gas pipeline. Objectives: Creation a shared model for a new, automated system via IDEF3; Development of a new database system which would store all information about underwater gas pipelines; Development a new application that works with database servers, and provides an explanation of the results obtained from the server; Calculation of the values MTBF for specified pipelines based on quantitative data obtained from tests of this system. Conclusion: The new, automated system PodvodGazExpert has been developed for timely and qualitative determination of the physical conditions of underwater gas pipeline; The basis of the mathematical analysis of this new, automated system uses principal component analysis method; The process of determining the physical condition of an underwater gas pipeline with this new, automated system increases the MTBF by a factor of 8.18 above the existing system used today in the industry.

Reflections on practical approaches to involving children and young people in the data analysis process

This article reflects on key methodological issues emerging from children and young people's involvement in data analysis processes. We outline a pragmatic framework illustrating different approaches to engaging children, using two case studies of children's experiences of participating in data analysis. The article highlights methods of engagement and important issues such as the balance of power between adults and children, training, support, ethical considerations, time and resources. We argue that involving children in data analysis processes can have several benefits, including enabling a greater understanding of children's perspectives and helping to prioritise children's agendas in policy and practice. (C) 2007 The Author(s). Journal compilation (C) 2007 National Children's Bureau.

Conceptual knowledge discovery and data analysis

In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA in a wide range of domains. In this paper, we use such an application in database marketing to demonstrate how methods and procedures of CKDD can be applied in Data Analysis. In particular, we show the interplay and integration of data mining and data analysis techniques based on Formal Concept Analysis. The main concern of this paper is to explain how the transition from data to knowledge can be supported by a TOSCANA system. To clarify the transition steps we discuss their correspondence to the five levels of knowledge representation established by R. Brachman and to the steps of empirically grounded theory building proposed by A. Strauss and J. Corbin.

In vitro fermentation of grape seed flavan-3-ol fractions by human faecal microbiota: changes in microbial groups and phenolic metabolites

With the aim of investigating the potential of flavan-3-ols to influence the growth of intestinal bacterial groups, we have carried out the in vitro fermentation, with human faecal microbiota, of two purified fractions from grape seed extract (GSE): GSE-M (70% monomers and 28% procyanidins) and GSE-O (21% monomers and 78 % procyanidins). Samples were collected at 0, 5, 10, 24, 30 and 48 h of fermentation for bacterial enumeration by fluorescent in situ hybridization and for analysis of phenolic metabolites. Both GSE-M and GSE-O fractions promoted growth of Lactobacillus/Enterococcus and decrease in the Clostridium histolyticum group during fermentation, although the effects were only statistically significant with GSE-M for Lactobacillus/Enterococcus (at 5 and 10 h of fermentation) and GSE-O for C. histolyticum (at 10 h of fermentation). Main changes in polyphenol catabolism also occurred during the first 10 h of fermentation, however no significant correlation coefficients (P>0.05) were found between changes in microbial populations and precursor flavan-3-ols or microbial metabolites. Together these data suggest that the flavan-3-ol profile of a particular food source could affect the microbiota composition and its catabolic activity, inducing changes that could in turn affect the bioavailability and potential bioactivity of these compounds.

Novel FGFR1 Mutations in Kallmann Syndrome and Normosmic Idiopathic Hypogonadotropic Hypogonadism: Evidence for the Involvement of an Alternatively Spliced Isoform

OBJECTIVE: To determine the prevalence of fibroblast growth factor receptor 1 (FGFR1) mutations and their predicted functional consequences in patients with idiopathic hypogonadotropic hypogonadism (IHH). DESIGN: Cross-sectional study. SETTING: Multicentric. PATIENT(S): Fifty unrelated patients with IHH (21 with Kallmann syndrome and 29 with normosmic IHH). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Patients were screened for mutations in FGFR1. The functional consequences of mutations were predicted by in silico structural and conservation analysis. RESULT(S): Heterozygous FGFR1 mutations were identified in six (12%) kindreds. These consisted of frameshift mutations (p.Pro33-Alafs*17 and p.Tyr654*) and missense mutations in the signal peptide (p.Trp4Cys), in the D1 extracellular domain (p.Ser96Cys) and in the cytoplasmic tyrosine kinase domain (p.Met719Val). A missense mutation was identified in the alternatively spliced exon 8A (p.Ala353Thr) that exclusively affects the D3 extracellular domain of FGFR1 isoform IIIb. Structure-based and sequence-based prediction methods and the absence of these variants in 200 normal controls were all consistent with a critical role for the mutations in the activity of the receptor. Oligogenic inheritance (FGFR1/CHD7/PROKR2) was found in one patient. CONCLUSION(S): Two FGFR1 isoforms, IIIb and IIIc, result from alternative splicing of exons 8A and 8B, respectively. Loss-of-function of isoform IIIc is a cause of IHH, whereas isoform IIIb is thought to be redundant. Ours is the first report of normosmic IHH associated with a mutation in the alternatively spliced exon 8A and suggests that this disorder can be caused by defects in either of the two alternatively spliced FGFR1 isoforms.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Nitric oxide plasma/serum levels in patients with schizophrenia: a systematic review and meta-analysis

For the last 40 years, schizophrenia has been considered to be the result primarily of a dysfunction in brain dopaminergic pathways. In this review, it is described and discussed findings concerning nitric oxide-mediated neurotransmission in schizophrenia. Studies were searched in PubMed, SciELO, and LILACS using the terms schizophrenia and nitric oxide plasma levels or nitric oxide serum levels, with no time limit. The reference lists of selected articles were also hand-searched for additional articles. From 15 potential reports, 10 were eligible to be included in the review and meta-analysis. These studies included a total of 505 patients with schizophrenia and 339 healthy volunteers. No significant difference was found between patients and healthy controls regarding total nitrite plasma/serum levels (effect size g = 0.285, 95%CI = -0.205 to 0.774, p = 0.254). However, when studies with patients under antipsychotic treatment were examined separately, there was a significant difference between patients and healthy volunteers (effect size g = 0.663, 95%CI = 0.365 to 0.961, p < 0.001), showing that patients under treatment have higher levels of plasma/serum nitric oxide than controls. These results suggest that antipsychotics increase nitric oxide plasma/serum levels and that the nitrergic pathway would be a fertile target for the development of new treatments for patients with schizophrenia.

Modelling the results of health promotion activities in Switzerland: development of the Swiss Model for Outcome Classification in Health Promotion and Prevention

This paper describes the Model for Outcome Classification in Health Promotion and Prevention adopted by Health Promotion Switzerland (SMOC, Swiss Model for Outcome Classification) and the process of its development. The context and method of model development, and the aim and objectives of the model are outlined. Preliminary experience with application of the model in evaluation planning and situation analysis is reported. On the basis of an extensive literature search, the model is situated within the wider international context of similar efforts to meet the challenge of developing tools to assess systematically the activities of health promotion and prevention.