Cuspal movement related to different polymerization protocols

Objective: The aim of this study to investigate the effects of different polymerization protocols on the cuspal movement in class II composite restorations. Materials and methods: Human premolar teeth were prepared with class II cavities and then restored with composite and three-step and two-step etch-and-rinse adhesive systems under different curing techniques (n = 10). It was used a lightemittingdiode curing unit and the mode of polymerization were: standard (exposure for 40 seconds at 700 mW/cm2), pulse-delay (initial exposure for 6 seconds at 350 mW/cm2 followed by a resting period of 3 minutes and a final exposure of 37 seconds at 700 mW/cm2) and soft-start curing (exposure 10 seconds at 350 mW/cm2 and 35 seconds at 700 mW/cm2). The cuspal distance (µm) was measured before and after the restorative procedure and the difference was recorded as cuspal movement. The data were submitted to two-way ANOVA and Bonferroni test (p < 0.05). Results: The type of adhesive system did not influenced the cuspal movement for all the curing methods. Standard protocol showed the highest values of cuspal movement and was statistically different from the pulse-delay and soft-start curing modes. Conclusion: Although the cuspal displacement was not completely avoided, alternative methods of photocuring should be considered to minimize the clinical consequences of composites contraction stress.

Estudo epidemiologico do complexo teníase-cisticercose: nova abordagem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Problem of the statistical model in deep inelastic scattering phenomenology

Recent deep inelastic data leads to an up-down quark asymmetry of the nucleon sea. Explanations of the flavour asymmetry and the di-lepton production in proton-nucleus collisions call for a temperature T ≈ 100 MeV in a statistical model. This T may be conjectured as being due to the Fulling-Davies-Unruh effect. But it is not possible to fit the structure function itself.

A geometria euclidiana na licenciatura em matemática do ponto de vista de professores formadores

Pós-graduação em Educação Matemática - IGCE

Simulação numérica do escoamento em difusores radiais com discos frontais fixos e móveis

Pós-graduação em Engenharia Mecânica - FEIS

Statistical detection of spurious variations in daily raingauge data caused by changes in observation practices, as applied to records from various parts of the world

In the instrumental records of daily precipitation, we often encounter one or more periods in which values below some threshold were not registered. Such periods, besides lacking small values, also have a large number of dry days. Their cumulative distribution function is shifted to the right in relation to that for other portions of the record having more reliable observations. Such problems are examined in this work, based mostly on the two-sample Kolmogorov–Smirnov (KS) test, where the portion of the series with more number of dry days is compared with the portion with less number of dry days. Another relatively common problem in daily rainfall data is the prevalence of integers either throughout the period of record or in some part of it, likely resulting from truncation during data compilation prior to archiving or by coarse rounding of daily readings by observers. This problem is identified by simple calculation of the proportion of integers in the series, taking the expected proportion as 10%. The above two procedures were applied to the daily rainfall data sets from the European Climate Assessment (ECA), Southeast Asian Climate Assessment (SACA), and Brazilian Water Resources Agency (BRA). Taking the statistic D of the KS test >0.15 and the corresponding p-value <0.001 as the condition to classify a given series as suspicious, the proportions of the ECA, SACA, and BRA series falling into this category are, respectively, 34.5%, 54.3%, and 62.5%. With relation to coarse rounding problem, the proportions of series exceeding twice the 10% reference level are 3%, 60%, and 43% for the ECA, SACA, and BRA data sets, respectively. A simple way to visualize the two problems addressed here is by plotting the time series of daily rainfall for a limited range, for instance, 0–10 mm day−1.

Saving the Vaquita: Immediate Action, Not More Data

The recent likely extinction of the baiji (Chinese river dolphin [Lipotes vexillifer]) (Turvey et al. 2007) makes the vaquita (Gulf of California porpoise [Phocoena sinus]) the most endangered cetacean. The vaquita has the smallest range of any porpoise, dolphin, or whale and, like the baiji, has long been threatened primarily by accidental deaths in fishing gear (bycatch) (Rojas-Bracho et al. 2006). Despite repeated recommendations from scientific bodies and conservation organizations, no effective actions have been taken to remove nets from the vaquita’s environment. Here, we address three questions that are important to vaquita conservation: (1) How many vaquitas remain? (2) How much time is left to find a solution to the bycatch problem? and (3) Are further abundance surveys or bycatch estimates needed to justify the immediate removal of all entangling nets from the range of the vaquita? Our answers are, in short: (1) there are about 150 vaquitas left, (2) there are at most 2 years within which to find a solution, and (3) further abundance surveys or bycatch estimates are not needed. The answers to the first two questions make clear that action is needed now, whereas the answer to the last question removes the excuse of uncertainty as a delay tactic. Herein we explain our reasoning.

A New Cryptographic Scheme for Securing Dynamic Conferences in Data Networks

Dynamic conferencing refers to a scenario wherein any subset of users in a universe of users form a conference for sharing confidential information among themselves. The key distribution (KD) problem in dynamic conferencing is to compute a shared secret key for such a dynamically formed conference. In literature, the KD schemes for dynamic conferencing either are computationally unscalable or require communication among users, which is undesirable. The extended symmetric polynomial based dynamic conferencing scheme (ESPDCS) is one such KD scheme which has a high computational complexity that is universe size dependent. In this paper we present an enhancement to the ESPDCS scheme to develop a KD scheme called universe-independent SPDCS (UI-SPDCS) such that its complexity is independent of the universe size. However, the UI-SPDCS scheme does not scale with the conference size. We propose a relatively scalable KD scheme termed as DH-SPDCS that uses the UI-SPDCS scheme and the tree-based group Diffie- Hellman (TGDH) key exchange protocol. The proposed DH-SPDCS scheme provides a configurable trade-off between computation and communication complexity of the scheme.

Daily Problem-Solving Warm-Ups: Harboring Mathematical Thinking In The Middle School Classroom

In this action research study of my classroom of 8th grade mathematics, I investigated the use of daily warm-ups written in problem-solving format. Data was collected to determine if use of such warm-ups would have an effect on students’ abilities to problem solve, their overall attitudes regarding problem solving and whether such an activity could also enhance their readiness each day to learn new mathematics concepts. It was also my hope that this practice would have some positive impact on maximizing the amount of time I have with my students for math instruction. I discovered that daily exposure to problem-solving practices did impact the students’ overall abilities and achievement (though sometimes not positively) and similarly the students’ attitudes showed slight changes as well. It certainly seemed to improve their readiness for the day’s lesson as class started in a more timely manner and students were more actively involved in learning mathematics (or perhaps working on mathematics) than other classes not involved in the research. As a result of this study, I plan to continue using daily warm-ups and problem-solving (perhaps on a less formal or regimented level) and continue gathering data to further determine if this methodology can be useful in improving students’ overall mathematical skills, abilities and achievement.

An animal movement model incorporating home range and habitat selection

Wildlife biologists are often interested in how an animal uses space and the habitat resources within that space. We propose a single model that estimates an animal’s home range and habitat selection parameters within that range while accounting for the inherent autocorrelation in frequently sampled telemetry data. The model is applied to brown bear telemetry data in southeast Alaska.

A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly auto-correlated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Role of CCR2 in orthodontic tooth movement

Introduction: Cytokines and chemokines regulate bone remodeling during orthodontic tooth movement. CC chemokine ligand 2 (CCL2) is involved in osteoclast recruitment and activity, and its expression is increased in periodontal tissues under mechanical loading. In this study, we investigated whether the CC chemokine receptor 2 (CCR2)-CCL2 axis influences orthodontic tooth movement. Methods: A coil spring was placed in CCR2-deficient (CCR2(-/-)), wild-type, vehicle-treated, and P8A-treated (CCL2 analog) mice. In a histopathologic analysis, the amounts of orthodontic tooth movement and numbers of osteoclasts were determined. The expression of mediators involved in bone remodeling was evaluated by real-time polymerase chain reaction. Results: Orthodontic tooth movement and the number of TRAP-positive cells were significantly decreased in CCR2(-/-) and P8A-treated mice in relation to wild-type and vehicle-treated mice, respectively. The expressions of RANKL, RANK, and osteoblasts markers (COL-1 and OCN) were lower in CCR2(-/-) than in wild-type mice. No significant difference was found in osteoprotegerin levels between the groups. Conclusions: These data suggested a reduction of osteoclast and osteoblast activities in the absence of CCR2. The CCR2-CCL2 axis is positively associated with osteoclast recruitment, bone resorption, and orthodontic tooth movement. Therefore, blockage of the CCR2-CCL2 axis might be used in the future for modulating the extent of orthodontic tooth movement. (Am J Orthod Dentofacial Orthop 2012;141:153-60)

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.