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Cotton fiber length is one of the most critical parameters of fiber quality for ring-spun and air-jet-spun yarns. Currently, two of the most common instruments for testing cotton fiber length are the USTER High Volume Instrument (HVI) and the USTER Advanced Fiber Information System (AFIS). The HVI bases its length measurement on the fibrogram concept. It is fast but reports only two length measurements—the upper half mean length (UHML) and the uniformity index (UI), where the UI is the percentage ratio of mean length (ML) to UHML. The AFIS is slower and more costly per test because it individualizes fibers to provide a complete fiber length distribution per sample. This paper presents a method that can reconstruct a complete fiber length distribution from an HVI fibrogram based on established fibrogram theory. Results show that the algorithm can accurately recover different types of distributions based on synthetically generated data. Results also show that reconstructed distributions of three different types of samples—upland, pima, and viscose—differ in ways that make sense based on known characteristics of their length distributions. Finally, a variety of statistics computed from the reconstructed distributions are compared to the HVI-reported length parameters. Results show a good correlation with HVI output with R2 ranging from 0.730 to 0.965 across nine different methods of calculating the ML, UHML, and UI. Interestingly, statistics calculated from an approximation of the length distribution by weight are the most closely related to the HVI length parameters.

Rapid molecular testing has revolutionized the management of suspected viral meningitis and encephalitis by providing an etiological diagnosis in90 min with potential to improve outcomes and shorten inpatient stays. However, use of molecular assays can vary widely.To evaluate current practice for molecular testing of pediatric cerebrospinal fluid (CSF) samples across the United Kingdom using a structured questionnaire.A structured telephone questionnaire survey was conducted between July and August 2020. Data was collected on the availability of viral CSF nucleic acid amplification testing (NAAT), criteria used for testing and turnaround times including the impact of the coronavirus disease 2019 pandemic.Of 196/212 (92%) microbiology laboratories responded; 63/196 (32%) were excluded from final analysis as they had no on-site microbiology laboratory and outsourced their samples. Of 133 Laboratories included in the study, 47/133 (35%) had onsite facilities for viral CSF NAAT. Hospitals currently undertaking onsite NAAT (Onsite/near-patient rapid NAAT (including polymerase chain reaction) is recommended wherever possible to optimize patient management in the acute setting.

Background Despite improvements in food access and nutrition security over the last few decades, malnutrition remains a major public health problem. One of the significant contributors to these problems is affordability of nutritious food. This study aimed to examine the association between perceived food affordability and pre-school children’s diet diversity in Addis Ababa, Ethiopia. Methods Cross-sectional data were used for the analysis. A 24 h dietary recall assessment was done to assess children’s dietary diversity (DD). We used a modified operational definition of affordability indicator called perceived affordability of dietary diversity (afford-DD) to evaluate the impact of the food environment in terms of affordability at the household level. A sample (n 4,898) of children aged 6–59 months representative of Addis Ababa was randomly selected using a multistage sampling procedure including all districts in the city. Mixed-effects linear regression models were used to assess the association between children’s DD and afford-DD. Results The survey revealed that the mean of children’s DD was 3.9 [± 1.4] while the mean of afford-DD was 4.6 [± 2.1]. Overall, 59.8% of children met the minimum dietary diversity (≥ 4 food groups). White roots and tubers were the most commonly consumed food groups regardless of their affordability. Considerable variations were observed between households that reported the food item affordable and not affordable in consumption of Vitamin A rich vegetables and fruits, meat and fish, egg, and dairy. The children’s DD was positively associated with afford-DD after adjusting for maternal education, household wealth status and other relevant confounding. Higher maternal education modified the association between affordability and children’s diet diversity. Conclusions This study suggests higher perceived food affordability was associated with better diet diversity in children. A higher level of maternal education had the potential to mitigate affordability challenges in meeting the children’s dietary diversity needs.

High value manufacturing systems still require ergonomically intensive manual activities. Examples include the aerospace industry where the fitting of pipes and wiring into confined spaces in aircraft wings is still a manual operation. In these environments, workers are subjected to ergonomically awkward forces and postures for long periods of time. This leads to musculoskeletal injuries that severely limit the output of a shopfloor leading to loss of productivity. The use of tools such as wearable sensors could provide a way to track the ergonomics of workers in real time. However, an information processing architecture is required in order to ensure that data is processed in real time and in a manner that meaningful action points are retrieved for use by workers.\ud \ud \ud \ud In this work, based on the Adaptive Control of Thought—Rational (ACT-R) cognitive framework, we propose a Cognitive Architecture for Wearable Sensors (CAWES); a wearable sensor system and cognitive architecture that is capable of taking data streams from multiple wearable sensors on a worker’s body and fusing them to enable digitisation, tracking and analysis of human ergonomics in real time on a shopfloor. Furthermore, through tactile feedback, the architecture is able to inform workers in real time when ergonomics rules are broken. The architecture is validated through the use of an aerospace case study undertaken in laboratory conditions. The results from the validation are encouraging and in the future, further tests will be performed in an actual working environment.

Over the last 2 decades, many African countries have undergone dietary and nutrition transitions fueled by globalization, rapid urbanization, and development. These changes have altered African food environments and, subsequently, dietary behaviors, including food acquisition and consumption. Dietary patterns associated with the nutrition transition have contributed to Africa's complex burden of malnutrition - obesity and other diet-related noncommunicable diseases (DR-NCDs) - along with persistent food insecurity and undernutrition. Available evidence links unhealthy or obesogenic food environments (including those that market and offer energy-dense, nutrient-poor foods and beverages) with suboptimal diets and associated adverse health outcomes. Elsewhere, governments have responded with policies to improve food environments. However, in Africa, the necessary research and policy action have received insufficient attention. Contextual evidence to motivate, enable, and create supportive food environments in Africa for better population health is urgently needed. In November 2020, the Measurement, Evaluation, Accountability, and Leadership Support for Noncommunicable Diseases Prevention Project (MEALS4NCDs) convened the first Africa Food Environment Research Network Meeting (FERN2020). This 3-d virtual meeting brought researchers from around the world to deliberate on future directions and research priorities related to improving food environments and nutrition across the African continent. The stakeholders shared experiences, best practices, challenges, and opportunities for improving the healthfulness of food environments and related policies in low- and middle-income countries. In this article, we summarize the proceedings and research priorities identified in the meeting to advance the food environment research agenda in Africa, and thus contribute to the promotion of healthier food environments to prevent DR-NCDs, and other forms of malnutrition.

This paper explores the use of discrete event simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of discrete event simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as the lack of example cases where multi-objective optimisation methods have been applied to simulation models and the need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show that the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~ 0.066 on the training data and ~ 0.081 on test data.

Food environment research is increasingly gaining prominence in low- and middle-income countries (LMICs). However, in the absence of a systematic review of the literature, little is known about the emerging body of evidence from these settings. This systematic scoping review aims to address this gap. A systematic search of 6 databases was conducted in December 2017 and retrieved 920 records. In total, 70 peer-reviewed articles met the eligibility criteria and were included. Collectively, articles spanned 22 LMICs, including upper-middle-income countries (n = 49, 70%) and lower-middle-income countries (n = 18, 26%). No articles included low-income countries. Articles featured quantitative (n = 45, 64%), qualitative (n = 17, 24%), and mixed-method designs (n = 11, 8%). Studies analyzed the food environment at national, community, school, and household scales. Twenty-three articles (55%) assessed associations between food environment exposures and outcomes of interest, including diets (n = 14), nutrition status (n = 13), and health (n = 1). Food availability was associated with dietary outcomes at the community and school scales across multiple LMICs, although associations varied by vendor type. Evidence regarding associations between the food environment and nutrition and health outcomes was inconclusive. The paucity of evidence from high-quality studies is a severe limitation, highlighting the critical need for improved study designs and standardized methods and metrics. Future food environment research must address low-income and lower-middle-income countries, and include the full spectrum of dietary, nutrition, and health outcomes. Improving the quality of food environment research will be critical to the design of feasible, appropriate, and effective interventions to improve public health nutrition in LMICs.

Vitamin A deficiency is common among preschoolers in low-income settings and a serious public health concern due to its association to increased morbidity and mortality. The limited consumption of vitamin A-rich food is contributing to the problem. Many factors may influence children’s diet, including residential food environment, household wealth, and maternal education. However, very few studies in low-income settings have examined the relationship of these factors to children’s diet together. This study aimed to assess the importance of residential food availability of three plant-based groups of vitamin A-rich foods, household wealth, and maternal education for preschoolers’ consumption of plant-based vitamin A-rich foods in Addis Ababa. A multistage sampling procedure was used to enroll 5467 households with under-five children and 233 residential food environments with 2568 vendors. Data were analyzed using a multilevel binary logistic regression model. Overall, 36% (95% CI: 34.26, 36.95) of the study children reportedly consumed at least one plant-based vitamin A-rich food group in the 24-h dietary recall period. The odds of consuming any plant-based vitamin A-rich food were significantly higher among children whose mothers had a higher education level (AOR: 2.55; 95% CI: 2.01, 3.25), those living in the highest wealth quintile households (AOR: 2.37; 95% CI: 1.92, 2.93), and in residentials where vitamin A-rich fruits were available (AOR: 1.20; 95% CI: 1.02, 1.41). Further research in residential food environment is necessary to understand the purchasing habits, affordability, and desirability of plant-based vitamin A-rich foods to widen strategic options to improve its consumption among preschoolers in low-income and low-education communities.

The impact of incivility in terms of individual and team performance in clinical environments is increasingly acknowledged and supported by a growing evidence base. However, clinical environments are not just areas where patient care is delivered, they are also rich, key learning arenas for healthcare professionals. To date, the potential impact of incivility in clinical environments on healthcare professional learning and development has not been comprehensively explored. This article provides an overview of the physiological mechanisms that inhibit learning and memory recall in individuals experiencing or observing incivility and social stress. It establishes a clear need for focus on the impact of incivility on clinical learners and educators and further evidence for the need for clinical environments in which civility is firmly rooted into the pervading culture.

The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered, not just for the reduction of its own direct impact but also for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research acts as a primer for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose.

Purpose The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques. Design/methodology/approach This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined. Findings This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable. Originality/value Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.

This study aimed to determine if the Turner et al. (2018) framework allows researchers to identify and study relationships between food environments and individual food choice in diverse contexts and if updates to the framework are warranted. We mapped evidence from 15 drivers of food choice projects to the framework, using thematic analysis to identify common drivers within the food environment across countries and emergent characteristics not listed in the framework. The framework contained breadth and depth of content necessary to identify common drivers – prices and affordability, availability, and vendor and product properties. Insights regarding common drivers and emergent characteristics may inform iterative development of conceptual and empirical research and reinforce current strategies seeking to improve nutrition and health outcomes in LMICs through targeted policies and interventions.

Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.

A new resource—DataJoint Elements—provides modular designs for assembling complete workflow solutions to organize data and computations for common neurophysiology experiments. The designs are derived from working solutions developed in leading research groups using the open-source DataJoint framework to integrate data collection and analysis in collaborative workflows.

This paper explores the notion of the modular building construction site as an applied instance of redistributed manufacturing, in so doing, this research seeks to reduce the environmental footprint of building sites, treating them as small digitally connected subunits. In seeking to provide a whole lifecycle appreciation of a construction project, it is noted that the presence of a framework to provide guidance on the consideration of Internet of Things (IoT) data streams and connected construction objects is currently lacking. This paper proposes use of embedded IoT enabled sensing technology within all stages of a modular building lifecycle. An expanded four-phase model of intelligent assets use in construction is proposed along with an outline of the required data flows between the stages of a given building’s entire lifecycle that need to be facilitated for a BIM (Buildings Information Modelling) representation to begin to describe a building project as a sustainable asset within the circular economy. This paper also describes the use of concrete as a modular sensing structure, proposing that health monitoring of the material in situ along with the recoding of environmental factors over time could help to extend the longevity of such structures.

Evidence supports Allport's (1954) contention that social contact reduces mental illness stigma and promotes symptom recognition. However, an important limitation of existing research is that it typically relies on relatively simplistic measures of contact (e.g., any contact, number of contacts). Here, we build on prior work by examining how contact with persons with mental illness within social networks shapes labeling processes and beliefs about the causes of mental illness. Using egocentric network methods and vignette data from the 2018 General Social Survey (N = 1173), findings reveal that connections to valued ties (e.g., friends, family) that disconfirm commonly held stereotypes about people with mental illness contribute to improved recognition of mental illness and reduce the likelihood of endorsing stigmatizing beliefs about causes of mental illness. By using network theory and methods, this research extends current understanding of the role of contact by revealing not only whether contact matters, but how it matters and under what circumstances it may reduce prejudice and discrimination attached to mental illness in contemporary society.

Studies using transcranial direct current stimulation (tDCS) typically incorporate a fade‐in, short‐stimulation, fade‐out sham (placebo) protocol, which is assumed to be indistinct from a 10–30 min active protocol on the scalp. However, many studies report that participants can dissociate active stimulation from sham, even during low‐intensity 1 mA currents. We recently identified differences in the perception of an active (10 min of 1 mA) and a sham (20 s of 1 mA) protocol that lasted for 5 min after the cessation of sham. In the present study we assessed whether delivery of a higher‐intensity 2 mA current would exacerbate these differences. Two protocols were delivered to 32 adults in a double‐blinded, within‐subjects design (active: 10 min of 2 mA, and sham: 20 s of 2 mA), with the anode over the left primary motor cortex and the cathode on the right forehead. Participants were asked “Is the stimulation on?” and “How sure are you?” at 30 s intervals during and after stimulation. The differences between active and sham were more consistent and sustained during 2 mA than during 1 mA. We then quantified how well participants were able to track the presence and absence of stimulation (i.e. their sensitivity) during the experiment using cross‐correlations. Current strength was a good classifier of sensitivity during active tDCS, but exhibited only moderate specificity during sham. The accuracy of the end‐of‐study guess was no better than chance at predicting sensitivity. Our results indicate that the traditional end‐of‐study guess poorly reflects the sensitivity of participants to stimulation, and may not be a valid method of assessing sham blinding., We quantified how well participants were able to track the presence and absence of a tDCS current (i.e. their sensitivity), using cross‐correlations. Current strength was a good classifier of sensitivity (higher for 2mA than 1mA), but the accuracy of their end‐of‐study guess was no better than chance at predicting sensitivity. The traditional end‐of‐study guess method poorly reflects the sensitivity of participants to stimulation, and may not be a valid method of assessing sham blinding.

Letter: Reply to Mendes LL, Pessoa MC, Duarte CM. Comments on the article: food environment research in low- and middle-income countries: a systematic scoping review. Adv Nutr 2020;11(4):1046–7.

Current OEM (Original Equipment Manufacturer) facilities tend to be highly integrated and are often situated on one site. While providing scale of production such centralisation may create barriers to the achievement of fully flexible, adaptable, and reconfigurable factories. The advent of Industry 4.0 opens up opportunities to address these barriers by decentralising information and decision-making in manufacturing systems through CPS (Cyber Physical Systems) use. This research presents a qualitative study that investigates the possibility of distributing information and decision-making logic into ‘smart workpieces’ which can actively participate in assembly operations. To validate the concept, a use-case demonstrator, corresponding to the assembly of a ‘flat-pack’ table, was explored. Assembly parts in the demonstrator, were equipped with computation, networking, and interaction capabilities. Ten participants were invited to evaluate the smart assembly method and compare its results to the traditional assembly method. The results showed that in its current configuration the smart assembly was slower. However, it made the assembly process more flexible, adaptable and reconfigurable.

Fibre steering is involved in the development of non-conventional variable stiffness laminates (VSL) with curvilinear paths as well as in the lay-up of conventional laminates with complex shapes. Manufacturability is generally overlooked in design and, as a result, industrial applications do not take advantage of the potential of composite materials. This work develops a design for manufacturing (DFM) tool for the introduction in design of the manufacturing requirements and limitations derived from the fibre placement technology. This tool enables the automatic generation of continuous fibre paths for manufacturing. Results from its application to a plate with a central hole and an aircraft structure – a windshield front fairing – are presented, showing good correlation of resulting manufacturable paths to initial fibre trajectories. The effect of manufacturing constraints is assessed to elucidate the extent to which the structurally optimal design can be reached while conforming to existing manufacturing specifications.

Discrete-Event Simulation (DES) is commonly used for the simulation of manufacturing systems. In many practical cases, DES practitioners have to make simplifications or to use the software in an unconventional or convoluted fashion to meet their needs. Petri nets enable the development of transparent models which conciseness are a synonym of increased flexibility and control for designers. Furthermore, Petri nets take advantage of a solid mathematical ground and constitute a simple language. However, Petri nets lack the software capabilities to realise their full potential. This study investigates the suitability and relevance of Discrete-Event Simulation (DES) software for Petri net modelling in the context of manufacturing systems. A framework is developed for the modelling of different classes of Petri nets on DES. Analytical models of asynchronous flow lines are developed. Initial results show that the analytical models are without closed-form solution and the explosion of the state space is observed, justifying the use of computational methods and simulation for the analysis of manufacturing systems. This study shows that the gain in flexibility provided by Petri nets provides a new insight into the effects of stochasticity on setup and failure times in manufacturing systems.

International audience; Multiple recent global nutrition initiatives have recommended actions to transform food systems to improve food environments and food choice. This study aimed to identify actions recommended by these initiatives and understand their similarities and differences. Twelve global nutrition initiatives were reviewed, collectively spanning 13 action themes and accompanying strategies. Action themes were analyzed according to primary focus on either food environments and their food system drivers or food choice. Representation of the 13 actions varied across initiatives. Some actions overlapped; others were infrequently represented. Strategies targeting food environments and their food system drivers were more frequently recommended than strategies targeting food choice for 11 of the 13 action themes. Although these global initiatives share a mission to improve nutrition through food systems and food environments, less attention has been allocated to individual food choice and sustainability.

This study investigates consumer experiences of food environments and food acquisition practices during the Covid-19 pandemic. Our rapid assessment online survey featured a convenience sample of 2015 individuals from 119 countries, spanning Western Europe, North America, Latin America, Asia-Pacific, and Africa. Data collection took place in April 2020 during the second month of the pandemic. Participants were recruited via existing networks of the United Nations System Standing Committee on Nutrition, through social media, and by snowballing. The majority of participants were female (71.9%), from low- and middle-income countries (51.0%), and working in nutrition or healthcare (39.3%). Qualitative thematic analysis and descriptive statistics reveal a series of common global experiences related to food availability and accessibility, food prices and affordability, food acquisition practices, and food preparation and consumption. The importance of community food participation, food sharing, and resource allocation are highlighted, along with increasing awareness of healthy diets and food waste. We identify ten synergistic policy entry points to: 1) build resilient and equitable food environments resistant to stresses and shocks; 2) harness positive dietary-related behaviors manifested during the pandemic; and, 3) mitigate the projected nutrition crisis and promote sustainable healthy diets for all.

Summary Background Bronchiolitis is the most common reason for hospitalization in young children. In addition to respiratory syncytial virus (RSV), other viruses have been increasingly implicated. Guidance on testing has also changed. Aims To compare clinicopathological outcomes in young children admitted with bronchiolitis due to RSV in comparison with rhinovirus (RV), and identify associated risk/epidemiological factors. Methods Children aged less than two years admitted to hospital with a clinical diagnosis of bronchiolitis with positive results for either RSV or RV were included in this study. Polymerase-chain-reaction-negative cases using an extended respiratory virus panel served as a control group. Retrospective data were collected on sex, risk factors, respiratory support, intravenous fluids and antibiotics. Outcomes such as length of stay (LOS) and need for transfer to the high-dependency unit/paediatric intensive care unit were included. Findings Two hundred and twenty-seven out of 437 nasopharyngeal aspirate samples were positive for either RSV (N = 162) or RV (N = 65). The median age of cases was three months and 75% had at least one risk factor. Risk factors were higher in the RV group (P = 0.004). RV accounted for the majority of cases outside the RSV season (P

The capacity for induced pluripotent stem (iPS) cells to be differentiated into a wide range of neural cell types makes them an attractive donor source for autologous neural transplantation therapies aimed at brain repair. Translation to the in vivo setting has been difficult, however, with mixed results in a wide variety of preclinical models of brain injury and limited information on the basic in vivo properties of neural grafts generated from human iPS cells. Here we have generated a human iPS cell line constitutively expressing green fluorescent protein as a basis to identify and characterize grafts resulting from transplantation of neural progenitors into the adult rat brain. The results show that the grafts contain a mix of neural cell types, at various stages of differentiation, including neurons that establish extensive patterns of axonal growth and progressively develop functional properties over the course of 1 year after implantation. These findings form an important basis for the design and interpretation of preclinical studies using human stem cells for functional circuit re-construction in animal models of brain injury.

The development agenda over the next 15 years will be framed by the Sustainable Development Goals (SDGs), of which more than half relate either directly or indirectly to the agricultural sector, highlighting its importance in economic growth and development of low- and middle-income countries (LMICs) (FAO 2015). It is therefore imperative that agricultural systems are better understood, so that they may be strengthened and optimised to deliver outcomes in line with the SDGs. The interface between agriculture, nutrition and health is particularly multifaceted and complex, and the development of successful strategies will require an integrated and multi-sectoral approach (Dorward and Dangour 2012; Jones and Ejeta 2016).\ud \ud ‘Agri-health’ is an evolving paradigm seeking to unify research approaches and methodologies between agriculture and health. Research within the field encapsulates a broad range of disciplines, locations and actors, and aligns these into a common research agenda. In doing so, agri-health aims to transcend barriers imposed by the longstanding institutional and disciplinary silos. Much progress has been made in recent years in this regard (Harris et al. 2013; Kanter et al. 2014; Picchioni et al. 2015). However, more coordinated efforts are required to generate consensus and target strategic priorities amongst the many existing information gaps (Webb and Kennedy 2014). Understanding the linkages between complex issues such as globalisation, climate change, food systems, and evolving burdens of malnutrition is central to agri-health research.\ud \ud The Leverhulme Centre for Integrative Research on Agriculture and Health (LCIRAH) was created in 2010 with a key focus on agri-health interdisciplinary research. LCIRAH includes experts from across the member colleges of the University of London: the London School of Hygiene and Tropical Medicine (LSHTM), the School of Oriental and African Studies (SOAS), and the Royal Veterinary College (RVC).\ud \ud The Agriculture, Nutrition and Health Academy (ANH Academy), was established in 2015 as a platform to facilitate learning, knowledge sharing, capacity building and collaborative partnerships among the growing global community of researchers, practitioners and policy makers working within agri-health. It was developed with support from the Innovative Metrics and Methods for Agriculture and Nutrition Actions (IMMANA) research programme,Footnote1 led by LCIRAH and Tufts University, in partnership with the CGIAR Programme on Agriculture for Nutrition and Health (A4NH). Within this scope, the ANH Academy hosts an annual ANH Academy Week, building on the legacy of five agri-health research conferences organised by LCIRAH; as well as events and activities coordinated under the CGIAR A4NH.\ud \ud The inaugural ANH Academy Week took place in Addis Ababa, Ethiopia, in June 2016 and included two days of interactive ‘learning labs’ (training sessions on skills and methods across a broad range of disciplines in agri-health), followed by a three-day research conference. The conference included a mix of abstract-driven sessions, round table discussions, and keynote speeches from across the spectrum of agriculture, nutrition and health disciplines, and a wide range of countries.\ud \ud Hawkes et al. (2012) developed a conceptual framework (Fig. 2, hereafter referred to as the Framework), outlining the key pathways through which agriculture may affect nutritional status in Low and Middle Income Countries (LMICs), as well as broader drivers of these pathways. Drawing on this Framework, this paper aims to provide an analytical synthesis of the ANH Academy week by mapping the research and debates presented during the conference.