Variable characters of the pelican tarsometatarsus and their distribution across <i>Pelecanus</i> species.

<p>See text and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone-0111210-t001" target="_blank">Table 1</a> for the full list of specimens examined. The states in <i>P. crispus</i>, <i>P</i>. <i>onocrotalus</i>, and <i>P</i>. <i>rufescens</i> are scored from illustrations in ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone.0111210-Harrison2" target="_blank">[23]</a>.</p><p>Characters:</p><p>1. Medial hypotarsal crest: parallel to the tarsometatarsus shaft (0); ‘slanting’ with the proximal end located more dorsal relative to the distal end (1).</p><p>2. Attachment of the extensor retinaculum just medial to the dorsal pneumatic opening on the proximal end of the tarsometatarsus: short, either not reaching or just reaching distally to the proximodistal midpoint of the pneumatic opening (0); longer, extending distal to the proximodistal midpoint of the opening (1).</p><p>3. Trochlea II and IV: extend about equally far distally (0); trochlea II extends more distal than trochlea IV (1).</p><p>4. Concave area on the medial face of trochlea II plantar to the collateral ligamental pit: absent (0); present (1).</p><p>5. Proximal face of the medial hypotarsal crest: concave with a ridge bounding its edge (0); not concave and ridge absent (1).</p><p>Variable characters of the pelican tarsometatarsus and their distribution across <i>Pelecanus</i> species.</p

Comparison of the proximal end of the tarsometatarsus of various pelican species.

<p>A. <i>Pelecanus sivalensis</i>? (KP/KK/BS/100); B. <i>Pelecanus occidentalis californicus</i> (MVZ 66500); C. <i>Pelecanus erythrorhynchos</i> (MVZ 182793); D. <i>Pelecanus conspicillatus</i> (MVZ 143248); E. <i>Pelecanus philippensis</i> (IVPP 1031); F. <i>Pelecanus crispus</i>; G. <i>Pelecanus rufescens</i>; H. <i>Pelecanus aethiopicus</i>; I. <i>Pelecanus onocrotalus</i>. Images in F–I are redrawn from Harrison and Walker <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone.0111210-Harrison2" target="_blank">[23]</a>. The arrow in B and C indicates the plantar medial extension of the proximal end discussed in the text. The individual photographs and drawings are set to be roughly equal in width to enhance morphological differences (rather than those of size). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone-0111210-g002" target="_blank">Figure 2</a> for explanation of the abbreviations.</p

Geology, stratigraphy and magnetostratigraphy of the Khetpurali section, India, showing the fossil site and its approximate age.

<p>The geological map is modified from Kumar and Tandon <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone.0111210-Kumar1" target="_blank">[25]</a>. B. The Khetpurali section, its magnetostratigraphy, and its correlation with the GPTS (data from Tandon et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone.0111210-Tandon1" target="_blank">[16]</a> and Gradstein et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone.0111210-Gradstein1" target="_blank">[20]</a>).</p

Tarsometatarsus measurements (in mm) of some extant and extinct pelican species.

<p>Tarsometatarsus measurements (in mm) of some extant and extinct pelican species.</p

Tarsometatarsus KP/KK/BS/100 tentatively referred to <i>Pelecanus sivalensis</i>.

<p>A. dorsal; B. medial; C. plantar; D. lateral; E. distal; and F. proximal views. The arrow in E indicates the concave notch in the medial side of trochlea II that is a Steganopodes (sensu Smith <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111210#pone.0111210-Smith1" target="_blank">[22]</a>) synapomorphy. The scale bar is 1 cm (with one scale bar for parts A–D and one for E–F). Abbreviations: df—distal foramen; f—small pneumatic foramen; ie—intercondylar eminence; mc—medial crest of the hypotarsus; mf—fossa for metatarsal I; mr—ridge medial to the dorsal pneumatic foramen that is part of the extensor retinaculum attachment; pf—dorsal pneumatic foramen where the proximal foramina would be in other taxa; pl—lateral plantar opening of the proximal foramen; pm—medial plantar opening of the proximal foramen; r—ridge on the medial hypotarsal crest that bounds a concave area to its medial side; tc—tendinal canal opening; tg—tendinal groove.</p

Mapping child growth failure across low- and middle-income countries

Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0–59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards3–5. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization’s median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications

Mapping disparities in education across low- and middle-income countries

Educational attainment is an important social determinant of maternal, newborn, and child health1–3. As a tool for promoting gender equity, it has gained increasing traction in popular media, international aid strategies, and global agenda-setting4–6. The global health agenda is increasingly focused on evidence of precision public health, which illustrates the subnational distribution of disease and illness7,8; however, an agenda focused on future equity must integrate comparable evidence on the distribution of social determinants of health9–11. Here we expand on the available precision SDG evidence by estimating the subnational distribution of educational attainment, including the proportions of individuals who have completed key levels of schooling, across all low- and middle-income countries from 2000 to 2017. Previous analyses have focused on geographical disparities in average attainment across Africa or for specific countries, but—to our knowledge—no analysis has examined the subnational proportions of individuals who completed specific levels of education across all low- and middle-income countries12–14. By geolocating subnational data for more than 184 million person-years across 528 data sources, we precisely identify inequalities across geography as well as within populations

Additional file 3 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 3: Supplemental figures.Figure S1. Prevalence of male circumcision. Figure S2. Prevalence of signs and symptoms of sexually transmitted infections. Figure S3. Prevalence of marriage or living as married. Figure S4. Prevalence of partner living elsewhere among females. Figure S5. Prevalence of condom use during most recent sexual encounter. Figure S6. Prevalence of sexual activity among young females. Figure S7. Prevalence of multiple partners among males in the past year. Figure S8. Prevalence of multiple partners among females in the past year. Figure S9. HIV prevalence predictions from the boosted regression tree model. Figure S10. HIV prevalence predictions from the generalized additive model. Figure S11. HIV prevalence predictions from the lasso regression model. Figure S12. Modeling regions. Figure S13. Age- and sex-specific vs. adult prevalence modeling. Figure S14. Data sensitivity. Figure S15. Model specification validation. Figure S16. Modeled and re-aggregated adult prevalence comparison. Figure S17. HIV prevalence raking factors for males. Figure S18. HIV prevalence raking factors for females. Figure S19. Age-specific HIV prevalence in males, 2000. Figure S20. Age-specific HIV prevalence in females, 2000. Figure S21. Age-specific HIV prevalence in males, 2005. Figure S22. Age-specific HIV prevalence in females, 2005. Figure S23. Age-specific HIV prevalence in males, 2010. Figure S24. Age-specific HIV prevalence in females, 2010. Figure S25. Age-specific HIV prevalence in males, 2018. Figure S26. Age-specific HIV prevalence in females, 2018. Figure S27. Age-specific uncertainty interval range estimates in males, 2000. Figure S28. Age-specific uncertainty interval range estimates in females, 2000. Figure S29. Age-specific uncertainty interval range estimates in males, 2005. Figure S30. Age-specific uncertainty interval range estimates in females, 2005. Figure S31. Age-specific uncertainty interval range estimates in males, 2010. Figure S32. Age-specific uncertainty interval range estimates in females, 2010. Figure S33. Age-specific uncertainty interval range estimates in males, 2018. Figure S34. Age-specific uncertainty interval range estimates in females, 2018. Figure S35. Change in HIV prevalence in males, 2000-2005. Figure S36. Change in HIV prevalence in females, 2000-2005. Figure S37. Change in HIV prevalence in males, 2005-2010. Figure S38. Change in HIV prevalence in females, 2005-2010. Figure S39. Change in HIV prevalence in males, 2010-2018. Figure S40. Change in HIV prevalence in females, 2010-2018. Figure S41. Space mesh for geostatistical models

Additional file 4 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 4: Supplemental results.1. README. 2. Prevalence range across districts. 3. Prevalence range between sexes. 4. Prevalence range between ages. 5. Age-specific district ranges

Additional file 1 of Mapping age- and sex-specific HIV prevalence in adults in sub-Saharan Africa, 2000–2018

Additional file 1: Supplemental information.1. Compliance with the Guidlines for Accurate and Transparent Health Estimates Reporting (GATHER). 2. HIV data sources and data processing. 3. Covariate and auxiliary data. 4. Statistical model. 5. References