Shaping the interaction landscape of bioactive molecules.

MOTIVATION: Most bioactive molecules perform their action by interacting with proteins or other macromolecules. However, for a significant fraction of them, the primary target remains unknown. In addition, the majority of bioactive molecules have more than one target, many of which are poorly characterized. Computational predictions of bioactive molecule targets based on similarity with known ligands are powerful to narrow down the number of potential targets and to rationalize side effects of known molecules. RESULTS: Using a reference set of 224 412 molecules active on 1700 human proteins, we show that accurate target prediction can be achieved by combining different measures of chemical similarity based on both chemical structure and molecular shape. Our results indicate that the combined approach is especially efficient when no ligand with the same scaffold or from the same chemical series has yet been discovered. We also observe that different combinations of similarity measures are optimal for different molecular properties, such as the number of heavy atoms. This further highlights the importance of considering different classes of similarity measures between new molecules and known ligands to accurately predict their targets. CONTACT: olivier.michielin@unil.ch or vincent.zoete@unil.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stratigraphical discontinuities, tropical landscape evolution and soil distribution relationships in a case study in SE-Brazil

On a regional summit surface in the county of Piracicaba (SP) within the Peripheric Depression of São Paulo, formed of discontinued flattened tops, there is an abrupt transition between a Typic Hapludox and a Kandiudalfic Eutrudox, together with two stoneline layers. Using stratigraphical, mineralogical, and cartographic studies, this transition and the soil distribution of this surface were studied, correlating them with the different parent materials and the morphoclimatic model of landscape evolution in Southeastern Brazil. The Typic Hapludox was formed on a sandy Cenozoic deposit (Q) that overlies a pellitic deposit of the Iratí formation (Pi), representing a regional erosive discordance. Westwards to the Piracicaba River, this sequence is interrupted by a diabase sill overlain by a red clayey material which gave origin to the Kandiudalfic Eutrudox. Two post-Permian depositional events were identified by the two stonelines and stratigraphical discontinuities. The first event generated the deposition of a sandy sediment in the form of levelled alveoluses on regional barriers, most of these formed by dikes and diabase sills, probably during a drier phase. The second depositional event, leading to the deposition of the red clay was probably the dissection of the previously formed pediplane during a humid climate, followed by another pedimentation process during a later, drier period.

Comunicar la ciudad desde el paisaje: hacia una teoría del landscape branding

La necesidad de descodificar los significados inherentes al paisaje, la interactuación sociedadpaisaje (comunicación intra e interpersonal) y, más recientemente, los usos de paisaje a modo de aparador territorial mediático en el ámbito, por ejemplo, de la comunicación publicitaria, del citymarketing o del place branding (comunicación masiva), sirven para plantear el estudio de lo que, de algún modo, representa la persuasión del paisaje, la cual incluye claros tintes emocionales y simbólicos y, por tanto, también comunicacionales. El paisaje en su condición de imagen y/o rostro del territorio acumula la esencia del mensaje implícito en el espacio, posicionándose, de este modo, como la gran metáfora comunicativa de la ciudad. En este sentido, el trabajo de comunicación específico con el intangible paisajístico, unido a la reciente explosión de las denominadas geografías emocionales, plantea una teoría del mensaje territorial basada en la unión de las variables geografía, paisaje, emoción y comunicación. Históricamente, de los estudios de paisaje se han ocupado los geógrafos, arquitectos, historiadores, sociólogos o ambientólogos, entre muchos otros, sin embargo, el paisaje se ha mantenido poco explorado desde la perspectiva de la comunicación. En este sentido, es notoria la proliferación de análisis que ponen el acento en el papel que desarrolla el territorio como mediador de procesos de comunicación o en el estudio de procesos de retroalimentación entre la sociedad y sus espacios (cognición y/o percepción). El actual mercadeo identitario con los lugares se concreta en la creciente producción de marcas territoriales, las cuales acumulan, en los últimos tiempos, un importante protagonismo.

GIS analysis of change in an agricultural landscape in Central Finland

Selostus: Keskisuomalaisen maatalousmaiseman muutosten GIS-analyysi

Sheeder Prairie Preserve A Natural Prairie Landscape in Guthrie County, Iowa

A report to the Iowa State Conservation Commission and to the Iowa State Preserves Advisory Board

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area (Silos and Smokestacks), in Waterloo, Iowa for the years ended December 31, 2009 and 2008

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area (Silos and Smokestacks), in Waterloo, Iowa for the years ended December 31, 2009 and 2008

Plant species richness and environmental heterogeneity in a mountain landscape: effects of variability and spatial configuration

The loss of biodiversity has become a matter of urgent concern and a better understanding of local drivers is crucial for conservation. Although environmental heterogeneity is recognized as an important determinant of biodiversity, this has rarely been tested using field data at management scale. We propose and provide evidence for the simple hypothesis that local species diversity is related to spatial environmental heterogeneity. Species partition the environment into habitats. Biodiversity is therefore expected to be influenced by two aspects of spatial heterogeneity: 1) the variability of environmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal or competition. Earlier, simulation experiments predicted that both aspects of heterogeneity will influence plant species richness at a particular site. For the first time, these predictions were tested for plant communities using field data, which we collected in a wooded pasture in the Swiss Jura mountains using a four-level hierarchical sampling design. Richness generally increased with increasing environmental variability and "roughness" (i.e. decreasing spatial aggregation). Effects occurred at all scales, but the nature of the effect changed with scale, suggesting a change in the underlying mechanisms, which will need to be taken into account if scaling up to larger landscapes. Although we found significant effects of environmental heterogeneity, other factors such as history could also be important determinants. If a relationship between environmental heterogeneity and species richness can be shown to be general, recently available high-resolution environmental data can be used to complement the assessment of patterns of local richness and improve the prediction of the effects of land use change based on mean site conditions or land use history.

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area, in Waterloo, Iowa for the years ended December 31, 2010 and 2009

Audit report on America’s Agricultural Industrial Heritage Landscape, Inc., d/b/a Silos and Smokestacks National Heritage Area, in Waterloo, Iowa for the years ended December 31, 2010 and 2009

Validation of network communicability metrics for the analysis of brain structural networks.

Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

Highlands of the upper Jequitinhonha valley, Brazil: II - mineralogy, micromorphology, and landscape evolution¹

Palm swanp formations, the so-called veredas, typically occur in the Brazilian biome known as "Cerrado" (savanna-like vegetation), especially on flattened areas or tablelands (chapadas). The aim of this study was to characterize the mineralogy and micromorphology of soil materials from a representative toposequence of the watershed of the vereda Lagoa do Leandro, located in Minas Novas, state of Minas Gerais, Brazil, on plains in the region of the upper Jequitinhonha valley, emphasizing essential aspects of their genesis and landscape evolution. The toposequence is underlain by rocks of the Macaúbas group and covered with detrital and metamorphic rocks (schists of Proterozoic diamictites). The soil profiles were first pedologically described; samples of the disturbed and undisturbed soils were collected from all horizons for further micromorphological and mineralogical analyses. The mineralogical analysis was mainly based on powder X ray diffractometry (XRD) and micromorphological descriptions of thin sections under a petrographic microscope. The soils from the bottom to the top of this toposequence were classified as: Typic Albaquult (GXbd), Xanthic Haplustox, gray color, here called "Gray Haplustox" ("LAC"), Xanthic Haplustox (LA) and Typic Haplustox (LVA). The clay mineralogy of all soils was found to be dominated by kaolinite. In soil of LA and LVA, the occurrence of goethite, gibbsite, and anatase was evidenced; "LAC" also contained anatase and the GXbd, illite, anatase, and traces of vermiculite. The micromorphological analyses of the LVA, LA and "LAC" soils showed the prevalence of a microaggregate-like or granular microstructure, and aggregate porosity has a stacked/packed structure, which is typical of Oxisols. A massive structure was observed in GXbd material, with the presence of illuviation cutans of clay minerals and iron compounds. Paleogleissolos, which are strongly weathered, due to the action of the excavating fauna , and resulted in the present "LAC". The GXbd at the base of the vereda preserved the physical, mineralogical and micromorphological properties that are typical of a pedogenesis with a strong influence of long dry periods.

Landscape structure affects dispersal in the greater white-toothed shrew: inference between genetic and simulated ecological distances

Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled. We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path

Newton-Hooke algebras, nonrelativistic branes, and generalized pp-wave metrics

The Newton-Hooke algebras in d dimensions are constructed as contractions of dS(AdS) algebras. Nonrelativistic brane actions are WZ terms of these Newton-Hooke algebras. The NH algebras appear also as subalgebras of multitemporal relativistic conformal algebras, SO(d+1,p+2). We construct generalizations of pp-wave metrics from these algebras.

Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.