Cohort profile : the Baependi Heart Study-a family-based, highly admixed cohort study in a rural Brazilian town

PURPOSE: Cardiovascular disease (CVD) is a major challenge to global health. The same epidemiological transition scenario is replayed as countries develop, but with variations based on environment, culture and ethnic mixture. The Baependi Heart Study was set up in 2005 to develop a longitudinal family-based cohort study that reflects on some of the genetic and lifestyle-related peculiarities of the Brazilian populations, in order to evaluate genetic and environmental influences on CVD risk factor traits. PARTICIPANTS: Probands were recruited in Baependi, a small rural town in the state of Minas Gerais, Brazil, following by first-degree and then increasingly more distant relatives. The first follow-up wave took place in 2010, and the second in 2016. At baseline, the study evaluated 1691 individuals across 95 families. Cross-sectional data have been collected for 2239 participants. FINDINGS TO DATE: Environmental and lifestyle factors and measures relevant to cardiovascular health have been reported. Having expanded beyond cardiovascular health outcomes, the phenotype datasets now include genetics, biochemistry, anthropometry, mental health, sleep and circadian rhythms. Many of these have yielded heritability estimates, and a shared genetic background of anxiety and depression has recently been published. In spite of universal access to electricity, the population has been found to be strongly shifted towards morningness compared with metropolitan areas. FUTURE PLANS: A new follow-up, marking 10 years of the study, is ongoing in 2016, in which data are collected as in 2010 (with the exception of the neuropsychiatric protocol). In addition to this, a novel questionnaire package collecting information about intelligence, personality and spirituality is being planned. The data set on circadian rhythms and sleep will be amended through additional questionnaires, actimetry, home sleep EEG recording and dim light melatonin onset (DLMO) analysis. Finally, the anthropometric measures will be expanded by adding three-dimensional facial photography, voice recording and anatomical brain MRI

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

INSECTOS PLAGAS Y PARASITOIDES ASOCIADOS AL CULTIVO DE GUANDÚ (Cajanus cajan (L.) MILLSP. FABACEAE) EN CHIRIQUÍ, REPÚBLICA DE PANAMÁ

This work was carried out in a plantation located in Macano, Boqueron, Chiriqui, Republic of Panama, with the purpose of identifying the insects associated to pigeon pea crop and the organs they affect. From March to December 2010, 20 samples from the vegetative growth phase to the production phase were taken. By means of an entomological net, insects associated or that may caused some crop damage in pigeon pea plants were collected, 50 in each sampling. 197 individual associated with the pigeon pea belonging to 24 genera (plus seven undetermined), 21 families and six orders of the Class Insecta were collected. Nodonota, Diphaulaca (Coleoptera: Chrysomelidae) and Trigona (Hymenoptera: Apidae) were the most abundantin the foliage, Trigona was the outstanding during the flowering stage and Ectatomma and Crematogaster (Hymenoptera: Formicidae) were found in the stem, probably associated with some membracids. Melanagromyza obtusa (Diptera: Agromyzidae) and Etiella zinckenella (Lepidoptera: Pyralidae) were found in the pigeon pea pods and Bracon (Hymenoptera: Braconidae) raised pods infested with larvae and pupae of M. obtusa.    Este trabajo se realizó en una plantación de guandú ubicada en Macano, Boquerón, Chiriquí, República de Panamá, con el objetivo de identificar los insectos asociados al cultivo de guandú y a qué órganos de la planta afectan. Se realizaron 20 muestreos desde la fase de crecimiento vegetativo hasta la fase de producción, de marzo a diciembre de 2010. Con una red entomológica se recolectaron los insectos asociados o que ocasionaron algún daño al cultivo, en 50 plantas de guandú en cada muestreo. Se recolectaron 197 individuos asociados al cultivo de guandú, pertenecientes a 24 géneros (más siete no determinados), 21 familias y seis órdenes de la Clase Insecta. Nodonota, Diphaulaca (Coleoptera: Chrysomelidae) y Trigona (Hymenoptera: Apidae) fueron los géneros más abundantes en el follaje, Trigona fue el más sobresaliente durante la etapa de floración y Ectatomma y Crematogaster (Hymenoptera: Formicidae) se encontraron en el tallo, probablemente asociados con algunos membrácidos. Melanagromyza obtusa (Diptera: Agromyzidae) y Etiella zinckenella (Lepidoptera: Pyralidae) se encontraron en las vainas del guandú, y Bracon (Hymenoptera: Braconidae) se crió de vainas infestadas con larvas y pupas de M. obtusa.&nbsp

Linking Intensive Care Unit functional scales to the International Classification of Functioning: proposal of a new assessment approach

Abstract Background There are several tools to assess functional and physical status in critical ill patients. These tools can guide rehabilitation strategies in Intensive care units (ICU). However, they are not standardized, and this can compromise their applicability. The aim of the study is to identify common contents between International Classification of Functioning, Disability and Health (ICF) and Medical Research Council sum score (MRC-ss), Functional Status Score for the ICU (FSS-ICU), and Physical Function in ICU Test-scored (PFIT-s). As well as to propose a new assessment approach based on the ICF to ICU patients. Methods Pilot cross-sectional study. ICU in-patients, both genders, aged between 50 and 75 years were assessed with MRC-ss, FSS-ICU, PFIT-s and the linking rules used were proposed by Cieza et al. The inter-rater agreement for the linking process was performed using the Kappa coefficient. Results The ICF categories identified in the tools covered a total of 14 items. Common contents were identified in 13 of the 14 and two were related to body functions, six to body structures and five to activities and participation. The inter-rater agreement was considered substantial for the linking of MRC-ss (k = 0.665) and PFIT-s (k = 0.749) to the ICF, and almost perfect for the FSS-ICU (k = 0.832). Conclusions This study synthesizes and categorizes commonly used tools and presents a new proposal based on the ICF to guide future studies. The proposed model combines the ICF with the contents of the most relevant instruments used in critical care

Correlation of cephalometric and anthropometric measures with obstructive sleep apnea severity

INTRODUCTION: Patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) often have associated changes in craniofacial morphology and distribution of body fat, either alone or in combination. AIM: To correlate cephalometric and anthropometric measures with OSAHS severity by using the apnea-hypopnea index (AHI). METHOD: A retrospective cephalometry study of 93 patients with OSAHS was conducted from July 2010 to July 2012. The following measurements were evaluated: body mass index (BMI), neck circumference (NC), waist circumference (WC), hip circumference (HC), the angles formed by the cranial base and the maxilla (SNA) and the mandible (SNB), the difference between SNA and SNB (ANB), the distance from the mandibular plane to the hyoid bone (MP-H), the space between the base of the tongue and the posterior pharyngeal wall (PAS), and the distance between the posterior nasal spine and the tip of the uvula (PNS-P). Means, standard deviations, and Pearson's correlation coefficients were calculated and analyzed. RESULTS: AHI correlated significantly with BMI (r = 0.207, p = 0.047), NC (r = 0.365, p = 0.000), WC (r = 0.337, p = 0.001), PNS-P (r = 0.282, p = 0.006), and MP-H (r = 0.235, p = 0.023). CONCLUSION: Anthropometric measurements (BMI, NC, and WC) and cephalometric measurements (MP-H and PNS-P) can be used as predictors of OSAHS severity

Differential exoproteome analysis of two [i]Corynebacterium pseudotuberculosis[/i] biovar [i]ovis[/i] strains isolated from goat (1002) and sheep (C231)

[i]Corynebacterium pseudotuberculosis[/i] is the etiologic agent of caseous lymphadenitis a chronic infectious disease affecting small ruminants. The 2D-DIGE technique was used to compare the exoproteomes of two [i]C. pseudotuberculosis[/i] biovar [i]ovis[/i] strains isolated from goat (strain 1002) and sheep (strain C231). Seventeen proteins differentially produced were identified here. Nine proteins appeared over-produced in the exoproteome of 1002 goat strain and 8 in that of C231 sheep strain. These proteins were related to various biological functions, such as the cell envelope, respiratory metabolism and proteolysis. This proteomic analysis revealed strain-specific exoproteins although each of the corresponding genes was found in both strain genomes. Such differential expression pattern may reflect inter-strain differences in adaptation to a specific host, in pathogenicity and or in antigenicity of this pathogenic bacterium