Fig 2 - by Masaru Sakai (1669627)

Does the Mexican sugar-sweetened beverage tax have a signaling effect? ENSANUT 2016 by Cristina Álvarez-Sánchez (5675087)

“…</p><p>Results</p><p>Compared with adults not aware, adults who were aware of the SSB tax were more likely (<i>OR</i> = 1.30) to report a decrease in SSB consumption (<i>p</i> = .012). …”

Association among Dietary Flavonoids, Flavonoid Subclasses and Ovarian Cancer Risk: A Meta-Analysis by Xiaoli Hua (2558284)

“…Nevertheless, further investigations on a larger population covering more flavonoid subclasses are warranted.…”

Computation of marginal distributions for all variables (arcs and nodes) of a hypothetical toy graph. by Andrey Rzhetsky (42928)

“…</p> <p>A higher probability is depicted with a lighter color and larger ball radius (see key to the node color and size); when the <i>P</i>(<i>Vi</i> = 1) drops to 0, the node disappears from the figure (the ball radius drops to zero).…”

Coast-wide larval networks. by Aaron Kornbluth (11001875)

Effects of Membrane Degradation due to Chlorine Exposure on the Removal of Neutral Organic Compounds by Reverse Osmosis for Potable Reuse by Carson Odell Lee (17810305)

“…Chlorine exposure periods up to 8370 ppm-h were examined. Rejection decreased as chlorine exposure increased, and the decrease was not linear. …”

Effect of the distance from the wind turbine on activity of the bat <i>Pipistrellus pipistrellus</i> windward (upwind; wind incidence angle 0°), leeward (downwind; 180°) and latera... by Camille Leroux (18470820)

Age-Related Macular Degeneration and Incident Stroke: A Systematic Review and Meta-Analysis by Antonio B. Fernandez (573228)

“…Meta-regression using a mixed linear model was used to understand potential heterogeneity amongst studies.…”

Image 2_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 8_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 7_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 3_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 10_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Trend surface models of 2005 district-level JE incidence as a function of temperature and precipitation. by Daniel E. Impoinvil (213005)

“…</p>†<p>All regression β coefficients represent a non-linear increase (or decrease when the coefficient value is negative) in JE incidence when there is a 1-unit increase in each respective predictor variable.…”

Image 4_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 6_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 5_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 1_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Image 9_A comparison of design algorithms for choosing the training population in genomic models.jpeg by Alexandra Stadler (20718002)

Why people hunt. by Mona Estrella Bachmann (13531933)

“…The categories “ecological function” and “social function” displayed a larger hunting motivation with an increasing HDI. …”