ORCID Profile
0000-0002-2502-1567
Current Organisations
James Cook University
,
Nanjing Institute of Geology and Palaeontology Chinese Academy of Sciences
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Publisher: Figshare
Date: 2016
Publisher: Cambridge University Press (CUP)
Date: 30-08-2016
DOI: 10.1017/S136898001600183X
Abstract: To investigate the association of seasonality with dietary ersity, household food security and nutritional status of pregnant women in a rural district of northern Bangladesh. A cross-sectional study was conducted from February 2013 to February 2015. Data were collected on demographics, household food security (using the Household Food Insecurity Access Scale), dietary ersity (using the women’s dietary ersity questionnaire) and mid-upper arm circumference. Descriptive statistics were used to explore demographics, dietary ersity, household food security and nutritional status, and inferential statistics were applied to explore the role of seasonality on ersity, household food security and nutritional status. Twelve villages of Pirganj sub-district, Rangpur District, northern Bangladesh. Pregnant women ( n 288). Seasonality was found to be associated with dietary ersity ( P =0·026) and household food security ( P =0·039). Dietary ersity was significantly lower in summer ( P =0·029) and spring ( P =0·038). Food security deteriorated significantly in spring ( P =0·006) and late autumn ( P =0·009). Seasons play a role in women’s household food security status and dietary ersity, with food security deteriorating during the lean seasons and dietary ersity deteriorating during the second ‘lesser’ lean season and the season immediately after. Interventions that aim to improve the diet of pregnant women from low-income, subsistence-farming communities need to recognise the role of seasonality on diet and food security and to incorporate initiatives to prevent seasonal declines.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 17-01-2020
Abstract: We have pressing, human-generated reasons to explore the influence of environmental change on bio ersity. Looking into the past can not only inform our understanding of this relationship but also help us to understand current change. Paleontological records depend on fossil availability and predictive modeling, however, and thus tend to give us a picture with large temporal jumps, millions of years wide. Such a scale makes it difficult to truly understand the action of environmental forces on ecological processes. Enabled by a supercomputer, Fan et al. used machine learning to analyze a large marine Paleozoic dataset, creating a record with time intervals of only ∼26,000 years (see the Perspective by Wagner). This fine-scale resolution revealed new events and important details of previously described patterns. Science , this issue p. 272 see also p. 249
Publisher: Springer Science and Business Media LLC
Date: 04-07-2018
Publisher: Informa UK Limited
Date: 10-11-2016
Location: China
No related grants have been discovered for Briony Stevens.