The Future of Science: A Study on Interests Across the World

<p>This study was conducted to identify correlations between Altmetric data and its set of scientific topics. The scholarly articles published online were organized by distribution source, totaling the popularity of each article and its topic. In this case, topic refers to any scientific field of study and the data sets used were retrieved from Altmetric and Twitter sources. The purpose of this study is to help gain insight into where the future of science lies in subject matter as well as professional and public practice. as well as distinguish between what research the public favours to focus on in relation to privatized interests.</p><p></p><p>Publishing dates, Scopus subjects, and Altmetric scores were extracted from the Altmetric data. This data was compared with Twitter posters, helping differentiate the professional and public demographics that were interested in this field. Through the results, it was identified that the data showed that different cohorts – types of demographics – have different interests. Therefore, the future of science cannot be limited to single fields, rather an amalgamation of those popular among scholarly and public interests.</p

Additional file 2: Figure S2. of In search of druggable targets for GBM amino acid metabolism

Heat map of expression of 34 genes in 4 types of pediatric brain tumors and non-diseased (nd) brain tissue (from left to right: glioblastomas, nd, ependymomas, medulloblastomas and pilocytic astrocytomas). Names of genes are abbreviated as in Table 1. (DOCX 111 kb

Spatial Distribution of Tree Species Governs the Spatio-Temporal Interaction of Leaf Area Index and Soil Moisture across a Forested Landscape

<div><p>Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (<i>L</i>) and volumetric soil water content (<i>θ</i>) on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of <i>L</i> and <i>θ</i>. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of <i>L</i>, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in <i>L</i> and decline in <i>θ</i>. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests.</p> </div

Temporal dynamics of surface (10 cm) volumetric soil water content (<i>θ</i>: m³ m⁻³) across Susquehanna Shale Hills CZO during 2010.

<p>Kriging was performed using hierarchical Bayesian model and maps of posterior mean are displayed.</p

Summary of semivariogram parameters estimated from Bayesian model for leaf area index (<i>L</i>: m² m⁻²).

<p><b>Notes:</b> NSR is noise to signal ratio and R² is linear model fit of leave-one-out cross validation between modeled and observed <i>L</i>. Parameter estimates represent mean of posterior distribution and values inside the parenthesis are quantile based 5% and 95% credible intervals.</p

Relationship between (a) leaf area index (<i>L</i>: m² m⁻²) observed from space (MODIS-<i>L</i>, 1 pixel for entire watershed) and ground (LI2200-<i>L</i>, average of ∼90 points across the watershed); and (b) <i>L</i> and volumetric water content (<i>θ</i>: m³ m⁻³) of surface soil.

<p>Filled circles represent the data at the same date and filled square represent 11 days lagged data.</p

Temporal dynamics of surface (10 cm) volumetric soil water content (<i>θ</i>: m³ m⁻³) across Susquehanna Shale Hills CZO during 2010.

<p>Kriging was performed using hierarchical Bayesian model and maps of posterior variance are displayed.</p

Distribution of deciduous (<i>Acer saccharum</i>-ACSA, <i>A. rubrum</i>-ACRU, <i>Carya cordiformis</i>-CACO, <i>C. glabra</i>-CAGL, <i>C. ovata</i>-CAOV, <i>C. tomentosa</i>-CATO, <i>Quercus alba</i>-QUAL, <i>Q. prinus</i>-QUPR, <i>Q. rubra</i>-QURU) and evergreen (<i>Tsuga canadensis</i>-TSCA, <i>Pinus strobus</i>-PIST, <i>P. virginiana</i>-PIVI) species across a gradient of (a–c) elevation, and (d–f) slope.

<p>Vertical lines represent the center (mode) of the distribution. Density curves were calculated from the values sampled at each tree location (total trees = 1832).</p

Summary of semivariogram parameters estimated from Bayesian model for surface (10 cm) soil water content (<i>θ</i>: m³ m⁻³).

<p><b>Notes:</b> NSR is noise to signal ratio and R² is linear model fit of leave-one-out cross validation between modeled and observed <i>θ</i>. Parameter estimates represent mean of posterior distribution and values inside the parenthesis are quantile based 5% and 95% credible intervals.</p

Temporal dynamics of spatial model parameters of leaf area index (<i>L</i>: m² m⁻²) and volumetric soil (10 cm) water content (<i>θ</i>: m³ m⁻³) from April–November, 2010.

<p><i>β</i> is trend parameter, <i>φ</i> is range parameter (range = 3<i>φ</i>), <i>σ</i>² is partial sill, and <i>τ</i>² is nugget. Each point represents the posterior mean of an estimated parameter for one date and solid line represents the fitted curve. Shaded regions mark budbreak and senescence periods when spatial structure [nugget (<i>τ</i>²) and sill (<i>σ</i>²)] of <i>L</i> and <i>θ</i> was uncoupled.</p