Soil carbon balance in a tropical grassland: Estimation of soil respiration and its partitioning using a semi-empirical model

In savannah and tropical grasslands, which account for 60% of grasslands worldwide, a large share of ecosystem carbon is located below ground due to high root:shoot ratios. Temporal variations in soil CO2 efflux (R-S) were investigated in a grassland of coastal Congo over two years. The objectives were (1) to identify the main factors controlling seasonal variations in R-S and (2) to develop a semi-empirical model describing R-S and including a heterotrophic component (R-H) and an autotrophic component (R-A). Plant above-ground activity was found to exert strong control over soil respiration since 71% of seasonal R-S variability was explained by the quantity of photosynthetically active radiation absorbed (APAR) by the grass canopy. We tested an additive model including a parameter enabling R-S partitioning into R-A and R-H. Assumptions underlying this model were that R-A mainly depended on the amount of photosynthates allocated below ground and that microbial and root activity was mostly controlled by soil temperature and soil moisture. The model provided a reasonably good prediction of seasonal variations in R-S (R-2 = 0.85) which varied between 5.4 mu mol m(-2) s(-1) in the wet season and 0.9 mu mol m(-2) s(-1) at the end of the dry season. The model was subsequently used to obtain annual estimates of R-S, R-A and R-H. In accordance with results reported for other tropical grasslands, we estimated that R-H accounted for 44% of R-S, which represented a flux similar to the amount of carbon brought annually to the soil from below-ground litter production. Overall, this study opens up prospects for simulating the carbon budget of tropical grasslands on a large scale using remotely sensed data. (C) 2012 Elsevier B.V. All rights reserved.

Photodynamic inactivation of microorganisms present on complete dentures. A clinical investigation

The aim of this study was to evaluate the effectiveness of photodynamic therapy (PDT) for the disinfection of complete dentures. Biofilm samples were collected from dentures of 60 denture users who were randomly divided into four experimental groups (n = 15 each): subjects whose maxillary dentures were sprayed with 50 and 100 mg/l of PhotogemA (R) suspension (groups P50S and P100S) and patients whose maxillary dentures were treated with 50 and 100 mg/l of PhotogemA (R) gel (groups P50G and P100G). Dentures with photosensitizers were left in the dark for 30 min (pre-irradiation time) and then irradiated with blue LED light at 37.5 J/cm(2) (26 min). Denture samples were taken with sterile cotton swab before (left side surfaces) and after (right side surfaces) PDT. All microbial material was diluted and plated on selective media for Candida spp., Staphylococcus mutans spp., streptococci and a non-selective media. After incubation (48 h/37A degrees C), the number of colony-forming units (cfu/ml) was counted. Microorganisms grown on selective media were identified using biochemical methods before and after PDT. The data were submitted to McNemar and Kruskal-Wallis tests (alpha = 0.05). No growth after PDT was observed in 60, 53, 47, and 40% of dentures from P100G, P50G, P100S, and P50S groups, respectively. When evidence of microorganisms' growth was observed, PDT regimens eliminated over 90% of microorganisms on dentures. This clinical study showed that PDT was effective for disinfecting dentures.

Detailed H-1 and C-13 NMR structural assignment and relative stereochemistry determination for three new and one known semi-synthetic sesquiterpene lactones

In this work is described a complete H-1 and C-13 NMR analysis for a group of four sesquiterpene lactones, three previously unknown. The unequivocal assignments were achieved by H-1 NMR, C-13{H-1} NMR, gCOSY. gHMQC, gHMBC and NOESY experiments and no ambiguities were left behind. All hydrogen coupling constants were measured, clarifying all hydrogen signals multiplicities. (C) 2011 Elsevier B.V. All rights reserved.

Implant Stability Measurements of Two Immediate Loading Protocols for the Edentulous Mandible: Rigid and Semi-rigid Splinting of the Implants

Aim: Primary and secondary stabilities of immediately loaded mandibular implants restored with fixed prostheses (FP) using rigid or semirigid splinting systems were clinically and radiographically evaluated. Methods: Fifteen edentulous patients were rehabilitated using hybrid FP; each had 5 implants placed between the mental foramens. Two groups were randomly divided: group 1-FP with the conventional rigid bar splinting the implants and group 2-semi-rigid cantilever extension system with titanium bars placed in the 2 distal abutment cylinders. Primary stability was evaluated using resonance frequency analysis after installation of the implant abutments. The measurements were made at 3 times: T0, at baseline; T1, 4 months after implant placement; and T2, 8 months after implant placement. Presence of mobility and inflammation in the implant surrounding regions were checked. Stability data were submitted to statistical analysis for comparison between groups (P, 0.05). Results: Implant survival rate for the implants was of 100% in both groups. No significant differences in the mean implant stability quotient values were found for both groups from baseline and after the 8-month follow-up. Conclusion: The immediate loading of the implants was satisfactory, and both splinting conditions (rigid and semi-rigid) can be successfully used for the restoration of edentulous mandibles. (Implant Dent 2012;21:486-490)

COMPLETE H-1 AND C-13 NMR STRUCTURAL ASSIGNMENTS FOR A GROUP OF FOUR GOYAZENSOLIDE-TYPE FURANOHELIANGOLIDES

Four goyazensolide-type sesquiterpene lactones - lychnofolide, centratherin, goyazensolide and goyazensolide acetate - were thoroughly studied by NMR experimental techniques. H-1 NMR, C-13 NMR {H-1}, COSY, HMQC, HMBC, J-res. and NOE experiments were performed to provide the needed structural information. Complete and unequivocal assignment, including the determination of all multiplicities, was obtained for each structure and the data collections are presented in tables.

Computational framework to support integration of biomolecular and clinical data within a translational approach

Background The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information. Results We have implemented an extension of Chado – the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications. Conclusions Open-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different “omics” technologies with patient’s clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be accessed in http://dcm.ffclrp.usp.br/caib/pg=iptrans webcite.

Complete ¹H and 13C NMR structural assignments for a group of four goyazensolide-type furanoheliangolides

Four goyazensolide-type sesquiterpene lactones - lychnofolide, centratherin, goyazensolide and goyazensolide acetate - were thoroughly studied by NMR experimental techniques. ¹H NMR, 13C NMR {¹H}, COSY, HMQC, HMBC, J-res. and NOE experiments were performed to provide the needed structural information. Complete and unequivocal assignment, including the determination of all multiplicities, was obtained for each structure and the data collections are presented in tables.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provade a very Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that interferences can be performed in time linear in the number of nodes if there is a single observed node. Because our proof is construtive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynominal-time algorithm for SQPn. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.

Issues on the mean stress effect in fretting fatigue of a 7050-T7451 Al alloy posed by new experimental data

The aims of this work are: (i) to produce new experimental data for fretting fatigue considering the presence of a mean bulk stress and (ii) to assess two design methodologies against failure by fretting fatigue. Tests on a cylinder–flat contact configuration were conducted using a fretting apparatus mounted on a servo-hydraulic machine. The material used for both the pads and fatigue specimen was an aeronautical 7050-T7451 Al alloy. The experimental program was designed with all relevant parameters, apart from the mean bulk load (always applied before the contact loads), kept constant. The mean bulk stress varied from compressive to tensile values while maintaining a high peak pressure in order to encourage crack initiation. Two methodologies against fretting fatigue are proposed and confronted against the experimental data. The non-local stress-based methodology considers the evaluation of a critical plane fatigue criterion at the center of a process zone located beneath the contacting surfaces. The results showed that it correctly predicts crack initiation, but was not capable to provide successful prediction of the integrity of the specimens. Alternatively, we considered a crack arrest criterion which has the potential to provide a more complete description about the integrity of the specimens.

Monte Carlo Semi-Empirical Model for Si(Li) X-Ray Detector: Differences between Nominal and Fitted Parameters.

A detailed characterization of a X-ray Si(Li) detector was performed to obtain the energy dependence of efficiency in the photon energy range of 6.4 - 59.5 keV. which was measured and reproduced by Monte Carlo (MC) simulations. Significant discrepancies between MC and experimental values were found when lhe manufacturer parameters of lhe detector were used in lhe simulation. A complete Computerized Tomagraphy (CT) detector scan allowed to find the correct crystal dimensions and position inside the capsule. The computed efficiencies with the resulting detector model differed with the measured values no more than 10% in most of the energy range.