Amazon Rainforest Exchange of Carbon and Subcanopy Air Flow: Manaus LBA Site-A Complex Terrain Condition

On the moderately complex terrain covered by dense tropical Amazon Rainforest (Reserva Biologica do Cuieiras-ZF2-02 degrees 36'17.1 '' S, 60 degrees 12'24.4 '' W), subcanopy horizontal and vertical gradients of the air temperature, CO2 concentration and wind field were measured for the dry and wet periods in 2006. We tested the hypothesis that horizontal drainage flow over this study area is significant and can affect the interpretation of the high carbon uptake rates reported by previous works at this site. A similar experimental design as the one by Tota et al. (2008) was used with a network of wind, air temperature, and CO2 sensors above and below the forest canopy. A persistent and systematic subcanopy nighttime upslope (positive buoyancy) and daytime downslope (negative buoyancy) flow pattern on a moderately inclined slope (12%) was observed. The microcirculations observed above the canopy (38 m) over the sloping area during nighttime presents a downward motion indicating vertical convergence and correspondent horizontal divergence toward the valley area. During the daytime an inverse pattern was observed. The microcirculations above the canopy were driven mainly by buoyancy balancing the pressure gradient forces. In the subcanopy space the microcirculations were also driven by the same physical mechanisms but probably with the stress forcing contribution. The results also indicated that the horizontal and vertical scalar gradients (e. g., CO2) were modulated by these micro-circulations above and below the canopy, suggesting that estimates of advection using previous experimental approaches are not appropriate due to the tridimensional nature of the vertical and horizontal transport locally. This work also indicates that carbon budget from tower-based measurement is not enough to close the system, and one needs to include horizontal and vertical advection transport of CO2 into those estimates.

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.

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Collaborative fuzzy clustering algorithms: some refinements and design guidelines

There are some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data distributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustering. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set of systematically structured guidelines such as a selection of the specific algorithm depending on the nature of the data environment and the assumptions being made about the number of clusters. A thorough complexity analysis, including space, time, and communication aspects, is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study.

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.

Globally solvable systems of complex vector fields

We consider a class of involutive systems of n smooth vector fields on the n + 1 dimensional torus. We obtain a complete characterization for the global solvability of this class in terms of Liouville forms and of the connectedness of all sublevel and superlevel sets of the primitive of a certain 1-form in the minimal covering space.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Divide to conquer: a complex pattern of biodiversity depicted by vertebrate components in the Brazilian Atlantic Forest

The identification of northern and southern components in different vertebrate species led researchers to accept a two-component hypothesis for the Brazilian Atlantic forest (BAF). Nevertheless, neither a formal proposal nor a meta-analysis to confirm this coincidence was ever made. Our main objective here was therefore to systematically test in how many vertebrate components the BAF could be divided by analysing existing empirical data. We used two approaches: (1) mapping and comparing the proposed areas of vertebrate endemism in the BAF and (2) analysing studies mentioning spatial subdivisions in distinct forest-dependent vertebrates within the biome, by the use of panbiogeography. The four large-scale endemism area components together with the six small-scale panbiogeographical ones allowed the definition of three BAF greater regions, subdivided into nine vertebrate components, latitudinally and longitudinally organized. Empirical time estimates of the diversification events within the BAF were also reviewed. Diversification of these vertebrates occurred not only in the Pleistocene but also throughout the Miocene. Our results confirm the BAF's complex history, both in space and time. We propose that future research should be small-scale and focused in the vertebrate components identified herein. Given the BAF's heterogeneity, studying via sections will be much more useful in identifying the BAF's historical biogeography. (c) 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 107, 39-55.

Symmetry insights for design of supercomputer network topologies: roots and weights lattices

We present a family of networks whose local interconnection topologies are generated by the root vectors of a semi-simple complex Lie algebra. Cartan classification theorem of those algebras ensures those families of interconnection topologies to be exhaustive. The global arrangement of the network is defined in terms of integer or half-integer weight lattices. The mesh or torus topologies that network millions of processing cores, such as those in the IBM BlueGene series, are the simplest member of that category. The symmetries of the root systems of an algebra, manifested by their Weyl group, lends great convenience for the design and analysis of hardware architecture, algorithms and programs.

What is Missing for Traditional Design-Centric Engineering Education to Better Prepare Newly Graduated Engineers for the Global Era?

This paper presents an analysis of the capacity of design centric methodologies to prepare engineering students to succeed in the market. Gaps are brainstormed and analyzed with reference to their importance. Reasons that may lead the newly graduated engineers not to succeed right from the beginning of their professional lives have also been evaluated. A comparison among the two subjects above was prepared, reviewed and analyzed. The influence of multidisciplinary, multicultural and complex environmental influences created in the current global business era is taken into account. The industry requirements in terms of what they expect to 'receive' from their engineers are evaluated and compared to the remaining of the study above. An innovative approach to current engineering education that utilizes traditional design-centric methodologies is then proposed, aggregating new disciplines to supplement the traditional engineering education. The solution encompasses the inclusion of disciplines from Human Sciences and Emotional Intelligence fields willing to better prepare the engineer of tomorrow to work in a multidisciplinary, globalized, complex and team working environment. A pilot implementation of such an approach is reviewed and conclusions are drawn from this educational project.

First-principles studies of complex magnetism in Mn nanostructures on the Fe(001) surface

The magnetic properties of Mn nanostructures on the Fe(001) surface have been studied using the noncollinear first-principles real space-linear muffin-tin orbital-atomic sphere approximation method within density-functional theory. We have considered a variety of nanostructures such as adsorbed wires, pyramids, and flat and intermixed clusters of sizes varying from two to nine atoms. Our calculations of interatomic exchange interactions reveal the long-range nature of exchange interactions between Mn-Mn and Mn-Fe atoms. We have found that the strong dependence of these interactions on the local environment, the magnetic frustration, and the effect of spin-orbit coupling lead to the possibility of realizing complex noncollinear magnetic structures such as helical spin spiral and half-skyrmion.

A new Pennsylvanian Oriocrassatellinae from Brazil and the distribution of the genus Oriocrassatella in space and time

Oriocrassatella Etheridge Jr., 1907 is a long range crassatellid bivalve genus well recognized in shallow waters of epeiric seas throughout the upper part of Paleozoic. The first occurrences of this genus are recorded in the sedimentary successions of the Gondwana, both in Australia and South America. However, the geographic and age distribution of Oriocrassatella in Late Mississippian deposits of Australia and Argentina may indicate an earliest Visean or even a pre-Visean origin for the genus. Following its origin in Early Carboniferous a complex paleobiogeographic history from Southern to Northern Hemisphere took place in the Permian. During its initial dispersal phase from Late Carboniferous to the Early Permian the genus thrived in cold water environments associated to the Late Paleozoic Gondwana glaciation. Shallow-water bottoms of the warm waters of the central Gondwana fringe and Laurussia were colonized by Oriocrassatella only during Early Permian times when the genus became cosmopolitan. A new species of this genus is described herein, Oriocrassatella piauiensis n. sp., recorded from the Piaui Formation, Pennsylvanian of the Parnaiba Basin. This new species may represent an early adaptation to warm waters. However, based on available data, species of this genus seem to have adapted definitely to warm water environments probably related the Late Pennsylvanian interglacial phases. In these phases, climatic barrier were interrupted allowing the faunal interchange and larval dispersion following a South to North migration route through the eastern margins of Gondwana and the eastern Paleotethys.

Complex, 3D strain patterns in a synkinematic tonalite batholith from the Aracuai Neoproterozoic orogen (Eastern Brazil): Evidence from combined magnetic and isotopic chronology studies

This work combines structural and geochronological data to improve our understanding of the mechanical behaviour of continental crust involving large amount of magma or partially melted material in an abnormally hot collisional belt. We performed a magnetic and geochronological (U/Pb) study on a huge tonalitic batholith from the Neoproterozoic Aracual belt of East Brazil to determine the strain distribution through space and time. Anisotropy of magnetic susceptibility, combined with rock magnetism investigations, supports that the magnetic fabric is a good proxy of the structural fabric. Field measurements together with the magnetic fabrics highlight the presence in the batholith of four domains characterized by contrasted magmatic flow patterns. The western part is characterized by a gently dipping, orogen-parallel (similar to NS) magmatic foliation that bears down-dip lineations, in agreement with westward thrusting onto the Sao Francisco craton. Eastward, the magmatic foliation progressively turns sub-vertical with a lineation that flips from sub-horizontal to sub-vertical over short distances. This latter domain involves an elongated corridor in which the magmatic foliation is sub-horizontal and bears an orogen-parallel lineation. Finally the fourth, narrow domain displays sub-horizontal lineations on a sub-vertical magmatic foliation oblique (similar to N150 degrees E) to the trend of the belt. U/Pb dating of zircons from the various domains revealed homogeneity in age for all samples. This, together with the lack of solid-state deformation suggests that: 1) the whole batholith emplaced during a magmatic event at similar to 580 Ma, 2) the deformation occurred before complete solidification. and 3) the various fabrics are roughly contemporaneous. The complex structural pattern mapped in the studied tonalitic batholith suggests a 3D deformation of a slowly cooling, large magmatic body and its country rock. We suggest that the development of the observed 3D flow field was promoted by the low viscosity of the middle crust that turned gravitational force as an active tectonic force combining with the East-West convergence between the Sao Francisco and Congo cratons. (C) 2012 Elsevier Ltd. All rights reserved.

Structure- and ligand-based drug design approaches for neglected tropical diseases

Drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. Structure( SBDD) and ligand-based drug design (LBDD) approaches bring together the most powerful concepts in modern chemistry and biology, linking medicinal chemistry with structural biology. The definition and assessment of both chemical and biological space have revitalized the importance of exploring the intrinsic complementary nature of experimental and computational methods in drug design. Major challenges in this field include the identification of promising hits and the development of high-quality leads for further development into clinical candidates. It becomes particularly important in the case of neglected tropical diseases (NTDs) that affect disproportionately poor people living in rural and remote regions worldwide, and for which there is an insufficient number of new chemical entities being evaluated owing to the lack of innovation and R&D investment by the pharmaceutical industry. This perspective paper outlines the utility and applications of SBDD and LBDD approaches for the identification and design of new small-molecule agents for NTDs.

Design and synthesis of a new coumarin-based 'turn-on' fluorescent probe selective for Cu+2

The novel coumarin-based 'turn-on' fluorescent probe (E)-3-(2,5-dimethoxybenzylideneamino)-7-hydroxy-2H-chromen-2-one (MGM) was designed, synthesized, and characterized. This compound shows high selectivity for Cu+2, combined with a large fluorescence enhancement upon binding to Cu2+. Benesi-Hildebrand and Job plots demonstrate that the stoichiometry of the Cu+2 complex formed is 2:1. Preliminary studies employing epifluorescence microscopy demonstrated that Cu+2 could be imaged in human neuroblastoma SH-SY5Y cells treated with MGM. (c) 2012 Elsevier Ltd. All rights reserved.