The use of airborne laser scanning to develop a pixel-based stratification for a verified carbon offset project

Background The voluntary carbon market is a new and growing market that is increasingly important to consider in managing forestland. Monitoring, reporting, and verifying carbon stocks and fluxes at a project level is the single largest direct cost of a forest carbon offset project. There are now many methods for estimating forest stocks with high accuracy that use both Airborne Laser Scanning (ALS) and high-resolution optical remote sensing data. However, many of these methods are not appropriate for use under existing carbon offset standards and most have not been field tested. Results This paper presents a pixel-based forest stratification method that uses both ALS and optical remote sensing data to optimally partition the variability across an ~10,000 ha forest ownership in Mendocino County, CA, USA. This new stratification approach improved the accuracy of the forest inventory, reduced the cost of field-based inventory, and provides a powerful tool for future management planning. This approach also details a method of determining the optimum pixel size to best partition a forest. Conclusions The use of ALS and optical remote sensing data can help reduce the cost of field inventory and can help to locate areas that need the most intensive inventory effort. This pixel-based stratification method may provide a cost-effective approach to reducing inventory costs over larger areas when the remote sensing data acquisition costs can be kept low on a per acre basis

Prevalence, Distribution, and Impact of Mild Cognitive Impairment in Latin America, China, and India: A 10/66 Population-Based Study

A set of cross-sectional surveys carried out in Cuba, Dominican Republic, Peru, Mexico, Venezuela, Puerto Rico, China, and India reveal the prevalence and between-country variation in mild cognitive impairment at a population level

New fossils from Jebel Irhoud, Morocco and the pan-African origin of Homo sapiens

Fossil evidence points to an African origin of Homo sapiens from a group called either H. heidelbergensis or H. rhodesiensis. However, the exact place and time of emergence of H. sapiens remain obscure because the fossil record is scarce and the chronological age of many key specimens remains uncertain. In particular, it is unclear whether the present day ‘modern’ morphology rapidly emerged approximately 200 thousand years ago (ka) among earlier representatives of H. sapiens1 or evolved gradually over the last 400 thousand years2. Here we report newly discovered human fossils from Jebel Irhoud, Morocco, and interpret the affinities of the hominins from this site with other archaic and recent human groups. We identified a mosaic of features including facial, mandibular and dental morphology that aligns the Jebel Irhoud material with early or recent anatomically modern humans and more primitive neurocranial and endocranial morphology. In combination with an age of 315?±?34 thousand years (as determined by thermoluminescence dating)3, this evidence makes Jebel Irhoud the oldest and richest African Middle Stone Age hominin site that documents early stages of the H. sapiens clade in which key features of modern morphology were established. Furthermore, it shows that the evolutionary processes behind the emergence of H. sapiens involved the whole African continent

Assessing discriminative ability of risk models in clustered data.

BACKGROUND: The discriminative ability of a risk model is often measured by Harrell's concordance-index (c-index). The c-index estimates for two randomly chosen subjects the probability that the model predicts a higher risk for the subject with poorer outcome (concordance probability). When data are clustered, as in multicenter data, two types of concordance are distinguished: concordance in subjects from the same cluster (within-cluster concordance probability) and concordance in subjects from different clusters (between-cluster concordance probability). We argue that the within-cluster concordance probability is most relevant when a risk model supports decisions within clusters (e.g. who should be treated in a particular center). We aimed to explore different approaches to estimate the within-cluster concordance probability in clustered data. METHODS: We used data of the CRASH trial (2,081 patients clustered in 35 centers) to develop a risk model for mortality after traumatic brain injury. To assess the discriminative ability of the risk model within centers we first calculated cluster-specific c-indexes. We then pooled the cluster-specific c-indexes into a summary estimate with different meta-analytical techniques. We considered fixed effect meta-analysis with different weights (equal; inverse variance; number of subjects, events or pairs) and random effects meta-analysis. We reflected on pooling the estimates on the log-odds scale rather than the probability scale. RESULTS: The cluster-specific c-index varied substantially across centers (IQR = 0.70-0.81; I2 = 0.76 with 95% confidence interval 0.66 to 0.82). Summary estimates resulting from fixed effect meta-analysis ranged from 0.75 (equal weights) to 0.84 (inverse variance weights). With random effects meta-analysis - accounting for the observed heterogeneity in c-indexes across clusters - we estimated a mean of 0.77, a between-cluster variance of 0.0072 and a 95% prediction interval of 0.60 to 0.95. The normality assumptions for derivation of a prediction interval were better met on the probability than on the log-odds scale. CONCLUSION: When assessing the discriminative ability of risk models used to support decisions at cluster level we recommend meta-analysis of cluster-specific c-indexes. Particularly, random effects meta-analysis should be considered

Mortalidade infantil no Estado de São Paulo, 1999: uma análise das causas múltiplas de morte a partir de componentes principais Infant mortality in the State of São Paulo, 1999: principal components analysis of multiple causes of death

OBJETIVOS: Descrever o padrão da mortalidade infantil no Estado de São Paulo em 1999, segundo causas múltiplas de morte, bem como comparar os dados de causas básicas e múltiplas de óbito. MATERIAL E MÉTODOS: Utilizou-se dados de 12.793 óbitos infantis para 1999, obtidos da Fundação Sistema Estadual de Análise de Dados (Seade). As causas de óbito haviam sido codificadas de acordo com a Décima Classificação Internacional de Doenças e foram categorizadas em 28 grupos de causas. Para análise das causas múltiplas de morte, fez-se uma tabulação simples das mesmas e utilizou-se a análise de componentes principais, a fim de se obter os principais grupos de enfermidades que conduziram ao óbito. RESULTADOS: As principais causas múltiplas de óbito foram os transtornos respiratórios e cardiovasculares específicos do período perinatal (24,2% do total de causas múltiplas), os transtornos relacionados com a duração da gestação e com o crescimento fetal (20,2%), as malformações congênitas (8,6%) e as infecções perinatais (7,6%). A análise de componentes principais revelou três componentes interpretáveis, relativos aos óbitos devidos a causas de origem "pós-neonatais, infecciosas, redutíveis", às "complicações de procedimentos e causas externas" e aos "transtornos perinatais não associados ao baixo peso e/ou à imaturidade". CONCLUSÃO: A sistematização das causas múltiplas de morte em conjuntos de enfermidades permitiu analisá-las e entender como se associavam, desdobrando-se em manifestações de doenças que conduziram à morte, o que não é possível através da análise segundo causas básicas. Foi possível, então, observar com maior clareza os conjuntos de enfermidades que levaram ao óbito, o que é mais elucidativo para fins de Saúde Pública, visando a prevenção das doenças em suas diversas fases de causação.<br>OBJECTIVE: To describe infant mortality in the State of São Paulo, in 1999, based on multiple causes of death and to compare data from underlying and multiple causes of death. METHODS: Data came from 12,793 infant death records in 1999, of Seade Foundation (State Data Analysis System Foundation). Causes of death were coded according to the Tenth Revision of the International Statistical Classification of Diseases and Related Problems and were classified into 28 meaningful groups for the purpose of this article. In order to analyze multiple causes of death, simple frequencies were used in addition to principal components analysis to obtain the main groups of causes that contributed to death. RESULTS: The most frequent multiple causes of death were respiratory and cardiovascular diseases of the perinatal period (24.2% of all multiple causes), diseases related to growth and maturity of the fetus and the newborn (20.2%), congenital malformations (8.6%) and perinatal infections (7.6%). Principal components analysis revealed three major interpretable components: "post-neonatal, infectious and avoidable deaths", "complications of procedures and external causes" and "perinatal disorders, not related to low birth weight and/or immaturity". CONCLUSION: By using principal components analysis it was easier to understand how the multiple causes were associated. This is more interesting for Public Health purposes, because it may help clarify the steps in disease causation

Viewpoint: medical infertility care in low income countries: the case for concern in policy and practice

Based on published, 'grey' and anecdotal information, this paper explores some aspects of infertility, its medical treatment and their burden in poor countries. Many cases of infertility result from sexually transmitted infections (STI) and unsafe abortion and there is no doubt that their prevention and adequate treatment are of utmost importance, especially as effective infertility treatment, if any, comes at a high price for the consumer, materially as well as physically. Medical infertility interventions are apt to fail a free market of provision because of major information asymmetry. This renders patients in low- resource countries prone to exploitation, potentially damaging practices and waste of their savings. The authors argue that in countries struggling with limited funds and a range of pressing public health problems, public investment in infertility treatment should not have priority. But governments should take an active role in quality control and regulation of treatment practice, as well as invest in counseling skills for lower-level reproductive health staff to achieve rational referral of patients