Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Background Hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. Methods We used data from 1990 to 2019 on people aged 30–79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. Findings The number of people aged 30–79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306–359) million women and 317 (292–344) million men in 1990 to 626 (584–668) million women and 652 (604–698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55–62) of women and 49% (46–52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43–51) of women and 38% (35–41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20–27) for women and 18% (16–21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. Interpretation Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings

Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight NCD Risk Factor Collaboration (NCD-RisC)

From 1985 to 2016, the prevalence of underweight decreased, and that of obesity and severe obesity increased, in most regions, with significant variation in the magnitude of these changes across regions. We investigated how much change in mean body mass index (BMI) explains changes in the prevalence of underweight, obesity, and severe obesity in different regions using data from 2896 population-based studies with 187 million participants. Changes in the prevalence of underweight and total obesity, and to a lesser extent severe obesity, are largely driven by shifts in the distribution of BMI, with smaller contributions from changes in the shape of the distribution. In East and Southeast Asia and sub-Saharan Africa, the underweight tail of the BMI distribution was left behind as the distribution shifted. There is a need for policies that address all forms of malnutrition by making healthy foods accessible and affordable, while restricting unhealthy foods through fiscal and regulatory restrictions

Insights from an Integrated View of the Biology of Apple Snails (Caenogastropoda: Ampullariidae)

Submitted by sandra infurna ([email protected]) on 2016-02-16T12:59:35Z No. of bitstreams: 1 silvana_thiengo_etal_IOC_2015.pdf: 1030588 bytes, checksum: 1feaf6021ccd94c9bf314dbc7b49ccc8 (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-02-16T13:49:31Z (GMT) No. of bitstreams: 1 silvana_thiengo_etal_IOC_2015.pdf: 1030588 bytes, checksum: 1feaf6021ccd94c9bf314dbc7b49ccc8 (MD5)Made available in DSpace on 2016-02-16T13:49:31Z (GMT). No. of bitstreams: 1 silvana_thiengo_etal_IOC_2015.pdf: 1030588 bytes, checksum: 1feaf6021ccd94c9bf314dbc7b49ccc8 (MD5) Previous issue date: 2015Howard University. Department of Biology. Washington, DC, USA / University of Hawaii. Pacific Biosciences Research Center. Honolulu, Hawaii, USA /Smithsonian Institution. National Museum of Natural History. Washington, DC, USA.Southwestern University. Department of Biology. Georgetown, Texas, USA.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina.University of West Florida. Department of Biology. Pensacola, Florida, USA.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Universidad Nacional del Sur-CONICET. Laboratorio de Ecología, INBIOSUR. Bahia Blanca, Argentina.Hong Kong Baptist University. Department of Biology. Kowloon, Hong Kong.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Malacologia. Rio de Janeiro, RJ, Brasil.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina / Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Área de Biologia. Mendoza, Argentina.NARO Kyushu Okinawa Agricultural Research Center. Kumamoto, Japan.Nara Women’s University. Faculty of Science. Kitauoya-nishi, Nara, Japan.Universidad Nacional del Sur-CONICET. Laboratorio de Ecología, INBIOSUR. Bahia Blanca, Argentina.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina / Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Área de Biologia. Mendoza, Argentina.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina / Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Área de Biologia. Mendoza, Argentina.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina / Comisión de Investigaciones Científicas (CIC). La Plata, Argentina.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina / Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Área de Biologia. Mendoza, Argentina.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina / Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Área de Biologia. Mendoza, Argentina.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina / Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Área de Biologia. Mendoza, Argentina.University of Hawaii. Pacific Biosciences Research Center. Honolulu, Hawaii, USA / NARO Kyushu Okinawa Agricultural Research Center. Koshi, Kumamoto, Japan.Universidad Nacional de La Plata. Facultad de Ciencias Médicas. Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP, CONICET). La Plata, Argentina.Instituto de Fisiología (FCM-UNCuyo). Laboratorio de Fisiología (IHEM-CONICET). Mendoza, Argentina.Universidad Nacional del Sur-CONICET. Laboratorio de Ecología, INBIOSUR. Bahia Blanca, Argentina.Universidad Nacional del Sur-CONICET. Laboratorio de Ecología, INBIOSUR. Bahia Blanca, Argentina.Smithsonian Institution. National Museum of Natural History. Washington, DC, USA..Hong Kong Baptist University. Department of Biology. Kowloon, Hong Kong.Universidad Nacional del Sur-CONICET. Laboratorio de Ecología, INBIOSUR. Bahia Blanca, Argentina.Universidad Nacional del Sur-CONICET. Laboratorio de Ecología, INBIOSUR. Bahia Blanca, Argentina.Florida Institute of Technology. Biological Sciences Department. Melbourne, Florida, USA.The Pomacea Project, Inc., Pensacola, Florida, USA.University of Hawaii. Pacific Biosciences Research Center. Honolulu, Hawaii, USA.Apple snails (Ampullariidae) are among the largest and most ecologically important freshwater snails. The introduction of multiple species has reinvigorated the field and spurred a burgeoning body of research since the early 1990s, particularly regarding two species introduced to Asian wetlands and elsewhere, where they have become serious agricultural pests. This review places these recent advances in the context of previous work, across diverse fields ranging from phylogenetics and biogeography through ecology and developmental biology, and the more applied areas of environmental health and human disease. The review does not deal with the role of ampullariids as pests, nor their control and management, as this has been substantially reviewed elsewhere. Despite this large and diverse body of research, significant gaps in knowledge of these important snails remain, particularly in a comparative framework. The great majority of the work to date concerns a single species, Pomacea canaliculata, which we see as having the potential to become a model organism in a wide range of fields. However, additional comparative data are essential for understanding this diverse and potentially informative group. With the rapid advances in genomic technologies, many questions, seemingly intractable two decades ago, can be addressed, and ampullariids will provide valuable insights to our understanding across diverse fields in integrative biology