The roles and values of wild foods in agricultural systems

Almost every ecosystem has been amended so that plants and animals can be used as food, fibre, fodder, medicines, traps and weapons. Historically, wild plants and animals were sole dietary components for hunter–gatherer and forager cultures. Today, they remain key to many agricultural communities. The mean use of wild foods by agricultural and forager communities in 22 countries of Asia and Africa (36 studies) is 90–100 species per location. Aggregate country estimates can reach 300–800 species (e.g. India, Ethiopia, Kenya). The mean use of wild species is 120 per community for indigenous communities in both industrialized and developing countries. Many of these wild foods are actively managed, suggesting there is a false dichotomy around ideas of the agricultural and the wild: hunter–gatherers and foragers farm and manage their environments, and cultivators use many wild plants and animals. Yet, provision of and access to these sources of food may be declining as natural habitats come under increasing pressure from development, conservation-exclusions and agricultural expansion. Despite their value, wild foods are excluded from official statistics on economic values of natural resources. It is clear that wild plants and animals continue to form a significant proportion of the global food basket, and while a variety of social and ecological drivers are acting to reduce wild food use, their importance may be set to grow as pressures on agricultural productivity increase.</jats:p

Identifying perinatal depression with case-finding instruments : a mixed-methods study (BaBY PaNDA – Born and Bred in Yorkshire PeriNatal Depression Diagnostic Accuracy)

Background: Perinatal depression is well recognised as a mental health condition but < 50% of cases are identified in routine practice. A case-finding strategy using the Whooley questions is currently recommended by the National Institute for Health and Care Excellence. Objectives: To determine the diagnostic accuracy, acceptability and cost-effectiveness of the Whooley questions and the Edinburgh Postnatal Depression Scale (EPDS) to identify perinatal depression. Design: A prospective diagnostic accuracy cohort study, with concurrent qualitative and economic evaluations. Setting: Maternity services in England. Participants: A total of 391 pregnant women. Main outcome measures: Women completed the Whooley questions, EPDS and a diagnostic reference standard (Clinical Interview Schedule – Revised) during pregnancy (20 weeks) and postnatally (3–4 months). Qualitative interviews were conducted with health professionals (HPs) and a subsample of women. Results: Diagnostic accuracy results: depression prevalence rates were 10.3% during pregnancy and 10.5% postnatally. The Whooley questions and EPDS (cut-off point of ≥ 10) performed reasonably well, with comparable sensitivity [pregnancy: Whooley questions 85.0%, 95% confidence interval (CI) 70.2% to 94.3%; EPDS 82.5%, 95% CI 67.2% to 92.7%; postnatally: Whooley questions 85.7%, 95% CI 69.7% to 95.2%; EPDS 82.9%, 95% CI 66.4% to 93.4%] and specificity (pregnancy: Whooley questions 83.7%, 95% CI 79.4% to 87.4%; EPDS 86.6%, 95% CI 82.5% to 90.0%; postnatally: Whooley questions 80.6%, 95% CI 75.7% to 84.9%; EPDS 87.6%, 95% CI 83.3% to 91.1%). Diagnostic accuracy of the EPDS (cut-off point of ≥ 13) was poor at both time points (pregnancy: sensitivity 45%, 95% CI 29.3% to 61.5%, and specificity 95.7%, 95% CI 93.0% to 97.6%; postnatally: sensitivity 62.9%, 95% CI 44.9% to 78.5%, and specificity 95.7%, 95% CI 92.7% to 97.7%). Qualitative evaluation: women and HPs were supportive of screening/case-finding for perinatal depression. The EPDS was preferred to the Whooley questions by women and HPs, mainly because of its ‘softer’ wording. Whooley question 1 was thought to be less acceptable, largely because of the terms ‘depressed’ and ‘hopeless’, leading to women not revealing their depressive symptoms. HPs identified a ‘patient-centred’ environment that focused on the mother and baby to promote discussion about mental health. Cost-effectiveness results: screening/case-finding using the Whooley questions or the EPDS alone was not the most cost-effective strategy. A two-stage strategy, ‘Whooley questions followed by the Patient Health Questionnaire’ (a measure assessing depression symptomatology), was the most cost-effective strategy in the range between £20,000 and £30,000 per quality-adjusted life-year in both the prenatal and postnatal decision models. Limitations: Perinatal depression diagnosis was not cross-referenced with women’s medical records so the proportion of new cases identified is unknown. The clinical effectiveness and cost-effectiveness of screening/case-finding strategies was not assessed as part of a randomised controlled trial. Conclusions: The Whooley questions and EPDS had acceptable sensitivity and specificity, but their use in practice might be limited by low predictive value and variation in their acceptability. A two-stage strategy was more cost-effective than single-stage strategies. Neither case-finding instrument met National Screening Committee criteria. Future work: The yield of screening/case-finding should be established with reference to health-care records. The clinical effectiveness and cost-effectiveness of screening/case-finding for perinatal depression needs to be tested in a randomised controlled trial. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Automating the Maintenance of Non-functional System Properties using Demonstration-based Model Transformation

International audienceDomain-Specific Modeling Languages (DSMLs) are playing an increasingly significant role in software development. By raising the level of abstraction using notations that are representative of a specific domain, DSMLs allow the core essence of a problem to be separated from irrelevant accidental complexities that are typically found at the implementation level in source code. In addition to modeling the functional aspects of a system, a number of non-functional properties (e.g., quality of service constraints, timing requirements) also need to be integrated into models in order to reach a complete specification of a system. This is particularly true for domains that have distributed real-time and embedded needs. Given a base model with functional components, maintaining the non-functional properties that crosscut the base model has become an essential modeling task when using DSMLs. The task of maintaining non-functional properties in DSMLs is traditionally supported by manual model editing or using model transformation languages. However, these approaches are challenging to use for those unfamiliar with the specific details of a modeling transformation language and the underlying metamodel of the domain, which presents a steep learning curve for many users. This paper presents a demonstration-based approach to automate the maintenance of non-functional properties in DSMLs. Instead of writing model transformation rules explicitly, users demonstrate how to apply the non-functional properties by directly editing the concrete model instances and simulating a single case of the maintenance process. By recording a user's operations, an inference engine analyzes the user's intention and generates generic model transformation patterns automatically, which can be refined by users and then reused to automate the same evolution and maintenance task in other models. Using this approach, users are able to automate the maintenance tasks without learning a complex model transformation language. In addition, because the demonstration is performed on model instances, users are isolated from the underlying abstract metamodel definitions. Our demonstration-based approach has been applied to several scenarios, such as auto-scaling and model layout. The specific contribution in this paper is the application of the demonstration-based approach to capture crosscutting concerns representative of aspects at the modeling level. Several examples are presented across multiple modeling languages to demonstrate the benefits of our approach