Healthcare organisations serve clients from diverse Indigenous and other ethnic and racial groups on a daily basis, and require appropriate client-centred systems and services for provision of optimal healthcare. Despite advocacy for systems-level approaches to cultural competence, the primary focus in the literature remains on competency strategies aimed at health promotion initiatives, workforce development and student education. This paper aims to bridge the gap in available evidence about systems approaches to cultural competence by systematically mapping key concepts, types of evidence, and gaps in research.A literature search was completed as part of a larger systematic search of evaluations and measures of cultural competence interventions in health care in Canada, the United States, Australia and New Zealand. Seventeen peer-reviewed databases, 13 websites and clearinghouses, and 11 literature reviews were searched from 2002 to 2015. Overall, 109 studies were found, with 15 evaluating systems-level interventions or describing measurements. Thematic analysis was used to identify key implementation principles, intervention strategies and outcomes reported.Twelve intervention and three measurement studies met our inclusion criteria. Key principles for implementing systems approaches were: user engagement, organisational readiness, and delivery across multiple sites. Two key types of intervention strategies to embed cultural competence within health systems were: audit and quality improvement approaches and service-level policies or strategies. Outcomes were found for organisational systems, the client/practitioner encounter, health, and at national policy level.We could not determine the overall effectiveness of systems-level interventions to reform health systems because interventions were context-specific, there were too few comparative studies and studies did not use the same outcome measures. However, examined together, the intervention and measurement principles, strategies and outcomes provide a preliminary framework for implementation and evaluation of systems-level interventions to improve cultural competence. Identified gaps in the literature included a need for cost and effectiveness studies of systems approaches and explication of the effects of cultural competence on client experience. Further research is needed to explore the extent to which cultural competence improves health outcomes and reduces ethnic and racially-based healthcare disparities. Healthcare organisations serve clients from diverse Indigenous and other ethnic and racial groups on a daily basis, and require appropriate client-centred systems and services in order to provide optimal healthcare. Yet there is extensive research evidence demonstrating that racial and ethnic minorities do not receive equal treatment when accessing healthcare services . DispariThe concept of cultural competence was first identified in the late 1980s to address the effects of cultural and linguistic barriers in the interpersonal encounter between healthcare practitioners and clients on health service access and delivery . It was Cultural competence was defined in the late 1980s as a “set of congruent behaviours, attitudes and policies that come together in a healthcare system, agency or among professionals that enable that system, agency or professions to work effectively in cross- cultural situations” . The earSystems approaches are increasingly being applied in the delivery and management of various aspects of healthcare . A systeIn the decades since the first definition of cultural competence, racial and ethnic diversity has increased in Canada, Australia, New Zealand (NZ) and the US (the CANZUS nations), with Census projections predicting continuing diversification . In thesMulticulturalism Act is aimed at providing all citizens with equal access and opportunities to ensure that needs associated with culture are considered in decision-making processes [Multi-levelled and multi-strategic systemic responses have been enacted in the four countries to improve cultural competence. At national levels, the governments of NZ and the US have enacted legislation  in health and health care which have been legislatively mandated by at least six states) to improve culturally competent care, language access services and organisational supports for cultural competence , 17–19. rocesses . NationaAt regional and local levels, healthcare organisations, including hospitals and primary healthcare services are increasingly recognising cultural competence as an organisational strategy to address the needs of diverse client populations . HealthcDespite such efforts to enact systems-level approaches to cultural competence, few studies have described or assessed the extent of systems approaches to cultural competence . This paIdentify systems-level interventions that have been evaluated in the literature;Report the effects of these interventions in improving cultural competence;Report on how cultural competence at a systems-level has been measured;Summarise the quality of available evidence.The objectives of the paper are to:The paper is based on the results of a broader systematic scoping review of the literature to identify intervention strategies and indicators which have been applied to increase cultural competency in health care, along with the outcomes of these interventions . The revHowever, important contextual differences in broad national healthcare systems and cultural competence approaches affect its implementation.The search strategy employed for the review comprised an initial search in 2012, for the period 1 January 2002–31 July 2012. The start date was determined by the seminal US Institute of Medicine report on Unequal Treatment: Confronting Racial and Ethnic disparities in healthcare [For each search, a qualified librarian systematically searched 17 electronic databases and relevant websites . Included studies were designed to addresscultural awareness of health staff; Indigenous or ethnic minority peoples’ access to health services, procedures, and/or culturally specific programs; the provision of culturally respectful services; Indigenous or ethnic minority workforce development; and culturally tailored interventions. We did not include studies with a primary focus on racial or ethnic disparities in health, the recruitment and retention of staff members who reflect the cultural diversity of the community served, nor the identification of Indigenous peoples or ethnic minorities in health service records.Related to cultural competence in any health care service , and for both health care outcomes and population health outcomes;Were intervention evaluations or studies of indicators/measures of cultural competency. Following Sanson-Fisher, Campbell , interveStudies were included in the broader review  if they:As shown in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram  in Fig. In this paper, we report on the evidence for healthcare systems approaches to cultural competence. A total of 141 publications relevant to this review were included in the broader review. For the purposes of this review, studies focussed on healthcare organisational systems approaches were mined and extracted from the broader review.Data that related to the author, year and type of publication; country where developed and population; health care setting; type of intervention/measurement; healthcare outcomes assessed; outcome indicator and/or measure; study design and study quality were extracted Table . The quaThematic analysis methods  were useWe found 15/109 (13.8%) papers that met the inclusion criteria as evaluating or providing measures for systems approaches to cultural competence. Of these, 12 were intervention studies and three were measurement studies. There was a significant variation in focus, content, mode of delivery and duration of interventions. There was also heterogeneity in the outcomes reported across the studies. A summary of the extracted characteristics of the included studies is provided in Table The quantity of publications was somewhat evenly spread over the time period (2002–2016). Four publications were included from the first 5 years (2002–2006), six from the second (2007–2011) and five from the third 4-year period (2012–15).n = 5 publications), primary healthcare services (n = 4) and specialist mental health (n = 4), antenatal (n = 1) and disability (n = 1) services.Seven publications were from the US ; four were from Australia; and four from NZ, and one was NZ/Australian. No studies were from Canada. All but one of the US publications focussed on ethnic and racial minority clients other than Indigenous peoples, or diversity in general; one on American Indian and Alaskan native clients. The Australian and NZ publications all focussed on Indigenous clients. The healthcare settings that were the focus on the cultural competence intervention/measure were hospitals , the aim of all 15 papers was to increase cultural competency through systems level-approaches. A detailed overview of intervention components is provided in Table n = 8), organisational readiness or commitment (n = 8) and delivery across multiple sites (n = 12). Other principles mentioned in publications included: being grounded in a social view of health, employing minority group staff, creating a welcoming service, supporting access, integrating cultural protocols, self-rating of services’ processes of change against the end goal of cultural security, using multi-level strategies and careful coordination.The three most commonly reported principles for implementing systems-level interventions and measures to improve culture competence were: user engagement  audit and quality improvement approaches conducted across or within health services; and 2) evaluations of organisation-level systemic policies or strategies for cultural competence.We found five intervention publications that reported on the trialling and/or implementation of audit and quality improvement approaches through targeted strategies , 38–41. In these diverse healthcare settings, each study documented the development or tailoring of audit tools for the setting. In some studies, audit processes were used simply to identify the need for quality improvement. For example, persistent and significantly poorer Aboriginal perinatal outcomes motivated Reibel and Walker  to auditOne study documenting the full audit and quality improvement cycle from NZ described the development and use of culturally and clinically reliable bicultural audit tools, the 25-item Consumer Notes Clinical Indicators (CNCI), to measure the achievement of culturally competent mental health nursing practice standards against standards of expected health care , 46.Client ethnicity data was collected and linked to these quality measures. Another pre-post mixed methods study by Liaw  also docWe found six evaluations within or across service-level policies or strategies for cultural competence , 43–45. Two studies evaluated the effect of cultural competence on clients’ experiences of care. Weech- Maldonado et al.  exploredThree studies evaluated the extent to which (or how well) organisational or national cultural competence policies/strategies had been implemented. Whitman and Davis  considerFour types of outcomes of systems-level cultural competence were identified. These were: 1) organisational systems outcomes including improved resources/tools for providing cultural competence and identification of needs for improvement; 2) outcomes related to the client/practitioner encounter including identification of cultural respect/communication, client/family satisfaction, and practitioner outcomes/satisfaction; 3) healthcare and health outcomes; and 4) broader outcomes such as informing national standards for cultural competence.Seven of the 12 intervention papers described outcomes of improved resources/tools for providing cultural competence, and all 12 papers identified needs for systems improvements in promoting cultural competence. The publications that reported audit and quality improvement approaches –41 consiAll 12 publications identified areas of further need for improved implementation of systems approaches to cultural competence. For example, Reibel and Walker  identifiSimilarly, studies based on surveys of healthcare system administrators also identified needs for systems improvements in promoting cultural competence. For example, Noe et al.  determinStudy outcomes also included the increased involvement of clients and their families in their own healthcare, improved relationships in the client/practitioner encounter, and consequently increased health service access and frequency of visits. For example, Weech-Maldonado et al.  found thHealth outcomes were also reported. Lieu et al.  found thFinally, there were national policy outcomes from cultural competence interventions. For example, elements of the quality improvement toolkit developed for hospitals by Chong et al.  were incMany of the intervention studies incorporated measurement instruments, but we also found three studies that specifically reported the development of quality improvement and other indicators to measure cultural competence at systems levels. Of these, two were from the US and focussed on diverse ethnic and racial groups, and one from NZ which described the development of clinical and cultural competence indicators for mental health service improvements for Maori clients. The rigorous processes of indicator development and testing described demonstrated the considerable resources which have been invested into developing and piloting instruments to audit service performance across sectors within health , 46.A US study by Siegal et al.  describeAlso targeting mental health services, O’Brien et al.  describeWeech-Maldonado et al.  describeOnly one of the 12 intervention studies was rated of strong quality . Four stThere were no randomised controlled studies. One was a prospective cohort study, which used multiple data sources ; one a pThe publications reviewed were identified using a rigorous search strategy which incorporated electronic databases, websites/clearinghouses and reference lists of reviews designed to discover peer and non-peer reviewed publications. Therefore, it is highly likely that the studies in this review are representative of published cultural competence research from the US, Canada, NZ and Australia. However, being a non-exhaustive search strategy, it is possible some relevant publications were not found. Additionally, due to the breadth and complexity of systems-level cultural competency, this review only included studies which explicitly aimed to improve, or included an indicator of cultural competency, possibly excluding studies which implicitly aimed to increase cultural competence. Given the complexity of systems-level approaches, studies may have used terms  which were not included in our search. To further develop the evidence base on systems-based interventions to improve cultural competency and their impacts on relevant outcomes, it is important that studies use consistent terminology and explicitly address this in their aims.Systems approaches focus attention on how things work together to achieve an intended outcome and on understanding of the ‘whole’ system . By undeAll of the 12 intervention studies explicitly iterated some core principles for implementing cultural competence across healthcare systems. There was variation across studies in the explication of important implementation principles. The three highlighted in this review were user engagement in the development and/or implementation of strategies, organisational readiness, and delivery across multiple sites. Other reviews of the cultural competence literature have also reported the value of user engagement to ensure congruence of strategies with the cultural beliefs, values and practices of the affected population groups , 27, 28.The issue of organisational readiness/commitment has also been identified in other reviews. It makes sense that organisational commitment is required for systems approaches, given the complexity required to coordinate organisational sub-systems to work together in a coordinated way to achieve culturally competent healthcare provision . OrganisAn interesting review finding was that all intervention studies of systems approaches were implemented across multiple sites. This may be a result of efforts to scale up interventions and for maximum reach and outcomes, or simply due to a quest for stronger research outcomes. Further research is needed within singular health organisation and across multiple organisations.The two key types of intervention strategies to embed cultural competence within health systems identified were: audit and quality improvement approaches; and service-level policies or strategies for cultural competence. Audit and quality improvement approaches were implemented across diverse healthcare settings and relevant to improving healthcare practice against national benchmark standards. They resulted in improved relationships with local communities, increased health service access and frequency of visits, and the increased involvement of clients and their families in their own healthcare and ultimately improved recovery following mental illness , 40, 41.We could not determine the overall effectiveness of systems-level interventions to reform health systems because interventions were context specific to both the country, setting and population, and to the type of health care services concerned. As well, there were either too few comparative studies, or studies did not examine the same outcome measures. The preponderance of the literature about systems-level cultural competence interventions focussed on qualitative process evaluations, which explore the concepts and issues and described the interventions and formative or intermediate outcomes. It is likely that this is because the field is still in the relatively early stages of development, therefore there has not been enough elapsed time for follow-up studies and thus we do not know the full impact of systems-level cultural competence interventions on healthcare services or their clients. Further, almost every included study utilised a different measure, suggesting that measures of cultural competence at systems level require further elucidation. The domains of the mental health performance measures for administrative and service entities  and moreIdentified gaps in the literature included a need for cost-effectiveness studies of systems approaches to improve cultural competence, further explication of the effects of cultural competence on client experience, and studies to further explore the ultimate effect of cultural competence on improving health outcomes and reducing ethnic and racially-based healthcare disparities. Doing so will require a concerted commitment to adequately funding the implementation and monitoring of such initiatives , 51.Few studies have previously examined the impact of systems-level approaches to cultural competence , 25, 49.


Moreover, decreased CBF when hungry in the left insula was associated with greater hunger ratings on the fasted day for RAN. This represents the first study to show that women remitted from AN have aberrant resting neurovascular function in homeostatic neural circuitry in response to hunger. Regions involved in homeostatic regulation showed group differences in the Hungry − Fed contrast, suggesting altered cellular energy metabolism in this circuitry that may reduce motivation to eat.The etiology of pathological eating in anorexia nervosa (AN) remains poorly understood. Cerebral blood flow (CBF) is an indirect marker of neuronal function. In healthy adults, fasting increases CBF, reflecting increased delivery of oxygen and glucose to support brain metabolism. This study investigated whether women remitted from restricting-type AN (RAN) have altered CBF in response to hunger that may indicate homeostatic dysregulation contributing to their ability to restrict food. We compared resting CBF measured with pulsed arterial spin labeling in 21 RAN and 16 healthy comparison women (CW) when hungry (after a 16-h fast) and after a meal. Only remitted subjects were examined to avoid the confounding effects of malnutrition on brain function. Compared to CW, RAN demonstrated a reduced difference in the Hungry − Fed CBF contrast in the right ventral striatum, right subgenual anterior cingulate cortex ( The motivation to eat in humans is a complex process influenced by intrinsic mechanisms relating to hunger and satiety, and extrinsic mechanisms based on the appetitive incentive value of food . Eating Appetite regulation involves the integration of multiple physiological signals regulating energy balance with cognitive processes supporting motivation and reward. Homeostatic and hedonic brain regions, including the hypothalamus, amygdala, striatum, orbitofrontal cortex, insula, and anterior cingulate cortex (ACC), are tightly interconnected and form a network that controls feeding behavior , 11, 12.15O-labeled water, single-photon emission tomography (SPECT), and more recently, arterial spin labeling (ASL) functional magnetic resonance imaging (fMRI). Hunger and food intake are associated with localized changes in CBF. In healthy adults, PET 15O-labeled water studies reveal hunger increases CBF in homeostatic and gustatory brain regions including the hypothalamus, insula, striatum, ACC, amygdala, limbic/paralimbic regions , thalamus, precuneus, and cerebellum  is tightly coupled with brain metabolism underlying cognition by increasing local delivery of oxygen and glucose to support neural function and remove metabolic by-products . This herebellum . Food inrebellum , 15, 23 rebellum , 15, 16,Recent evidence suggests homeostatic influences on reward processing may be altered in AN, which may contribute to disordered eating. Hunger and satiety have powerful effects on reward and inhibitory processes. Hunger makes rewards more enticing, and satiety increases self-control in healthy adults . ImagingThe majority of studies examining cerebral perfusion in ill AN compared to controls report hypoperfusion at rest in regions of the gustatory/homeostatic circuit –28. FindThe purpose of this study was to determine whether women with a prior history of AN have a diminished CBF response when hungry, suggesting that altered homeostatic regulation might contribute to reduced motivation to eat in AN. This study used pulsed ASL fMRI and a region of interest analysis to investigate differential CBF response at rest in healthy comparison women (CW) and RAN when hungry (after a 16-h fast) and after a meal. We examined remitted subjects to avoid the confounding effects of malnutrition on neural function. We hypothesized that RAN would show less difference in CBF between hungry and fed states than CW in homeostatic and hedonic brain regions supporting feeding behavior. A better understanding of physiological changes in AN in response to hunger and satiety could provide a brain-specific marker relevant to treatment and outcome.DSM-IV Axis I disorders were assessed using either the Structured Clinical Interview for DSM-IV Axis I disorders , raising the possibility that this difference between RAN and healthy controls was driven by the hungry RAN women, which might reflect a disconnect between the hunger sensation and brain response in AN. None of the secondary analyses between CBF and other clinical variables reached significance after controlling for multiple comparisons .Huber robust regression analysis revealed that greater pre-scan hunger ratings were associated with decreased CBF in the left insula , VST, and subgenual ACC, and associations between diminished CBF in the posterior insula when hungry and increased hunger ratings further support the potential role of the insula in integrating homeostatic information  and reward value with feeding behavior . The dorBetween group CBF differences in the striatum, with reduced Hungry − Fed CBF for RAN, support the notion that alterations in metabolic function when hungry may contribute to food avoidance and/or impaired reward learning in AN . Many invia this contact, they can influence CBF and control the transport of nutrients, such as glucose, in and out of the brain to ensure proper brain homeostasis and support neurons metabolically  (Decreased CBF at rest in RAN when hungry compared to when satiated may contribute to the decreased fMRI blood oxygenation level dependent (BOLD) response to food and money rewards associated with hunger in AN , 5. Geneeir work . The posnd CMRO2 . Thus, ded fMRI) , the imp15O-labeled water PET is the gold standard for CBF measurement, ASL fMRI has been shown to be as reliable a technique  may account for some of the discrepancies across studies. To limit multiple comparisons, we restricted our primary analyses findings , 16, we findings  have fedfindings , 16. Howfindings  induced Obesity has long been known to result from dysfunction of key biochemical, neural, and behavioral components of the homeostatic control system , emphasiThe capacity to adjust food intake in response to changing energy requirements is essential for survival. Eating behavior is modulated by metabolic and ingestive factors in the service of homeostasis, and current results suggest possible metabolic dysfunction associated with homeostatic regulation may explain disordered eating in AN.The study was conducted in compliance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and according to the IRB regulations of the University of California, San Diego, and written informed consent was obtained.The authors made substantial contributions to the conception or design of the work  or the acquisition (GR), analysis (CW), or interpretation of data  for the work; drafting the work (CW) or revising it critically for important intellectual content ; provided final approval of the version to be published ; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Environmental mercury (Hg) contamination is an urgent global health threat. The complexity of Hg in the environment can hinder accurate determination of ecological and human health risks, particularly within the context of the rapid global changes that are altering many ecological processes, socioeconomic patterns, and other factors like infectious disease incidence, which can affect Hg exposures and health outcomes. However, the success of global Hg-reduction efforts depends on accurate assessments of their effectiveness in reducing health risks. In this paper, we examine the role that key extrinsic and intrinsic drivers play on several aspects of Hg risk to humans and organisms in the environment. We do so within three key domains of ecological and human health risk. First, we examine how extrinsic global change drivers influence pathways of Hg bioaccumulation and biomagnification through food webs. Next, we describe how extrinsic socioeconomic drivers at a global scale, and intrinsic individual-level drivers, influence human Hg exposure. Finally, we address how the adverse health effects of Hg in humans and wildlife are modulated by a range of extrinsic and intrinsic drivers within the context of rapid global change. Incorporating components of these three domains into research and monitoring will facilitate a more holistic understanding of how ecological and societal drivers interact to influence Hg health risks. The health effects of mercury (Hg) exposure represent a significant threat to ecosystems and human welfare worldwide  and intrinsic biological factors  alter the availability of Hg for biological uptake, and modify Hg toxicity to humans and other animals. Some of these drivers—e.g., climate change, land use changes—are occurring at unprecedented global rates and challenge the development of effective Hg control actions, alter recovery trajectories, and confound interpretations of monitoring outcomes. The growing extent and intensity of some drivers will continue to influence key processes associated with Hg exposure and bioaccumulation in humans and other animals; thus, there is a critical need to understand how these drivers alter mechanisms of Hg bioavailability in order to better predict changes in Hg exposure trajectories and mitigate adverse health effects.0) and ionic Hg (HgII)) exposures (particularly in humans) largely occur through inhalation, dermal, or oral routes  modulate mechanisms of Hg bioaccumulation in organisms and biomagnification through food webs, Hg exposure in humans, and Hg toxicity and health outcomes. Our goal is to integrate across disciplines that commonly are not linked, but nevertheless can exert cumulative influence on Hg health risks, in order to encourage scientific development of a more unified strategy for quantifying Hg risk during a time of rapid global change. As such, our objectives are to 1) examine the relationship between global change drivers and Hg bioaccumulation and biomagnification through ecosystems, (2) discuss key factors that influence human exposure to MeHg and inorganic Hg, and (3) summarize interactive factors that can modulate toxic responses to Hg in wildlife and humans. This approach explicitly incorporates ecological, socioeconomic, and physiological factors to assist with quantifying the effectiveness of Hg-reduction efforts on health outcomes and ecological risk. We address these objectives within a conceptual framework based upon pathways of Hg cycling through the ecosphere  primary productivity, 2) habitat use, (3) bioenergetics, and (4) food web structure. Although there are some inherent overlaps among these mechanisms , their unique influences on MeHg bioaccumulation, biomagnification, and exposure warrant separate discussions. We then examine how each of these mechanisms can modulate  MeHg bioaccumulation and biomagnification when influenced by widespread global change drivers  eventually return to near pre-impoundment levels, although it can take several decades . For example, direct toxicity, changes in nutrient flow, and habitat loss can each result in losses of sensitive species and biodiversity, which affects food web structure  introductions across the western U.S. may have modified the hydrology of reservoirs, streams, and floodplains due to evapotranspiration  forms dense monocultures in both freshwater and brackish systems, excluding other plant and animal species, reducing light penetration, and modifying decomposition rates and nutrient cycling . LikewiDorosoma petenense) introduced to a lake in California, USA outcompeted resident planktivorous species for zooplankton, causing resident fishes to shift from pelagic to benthic prey. The dietary shift resulted in a 50% increase in Hg concentrations of resident planktivorous fish  in Canadian lakes feed at slightly higher trophic positions than native forage fish and could thus theoretically expose predatory fish to higher concentrations of dietary Hg. However, support for this hypothesis is mixed . It directly influences each mechanism by changing the physical and chemical properties of the environment, which triggers ecological responses. It also indirectly influences each mechanism of MeHg bioaccumulation because climate change motivates adaptation in land use Dale  and wateBecause diet is the primary MeHg exposure route, MeHg follows energetic pathways through food webs and is tied to an organism’s energy expenditure, acquisition, and storage. The link between MeHg bioaccumulation and bioenergetics is most commonly exhibited through growth dilution, whereby Hg concentrations are lower in faster growing individuals with higher growth efficiency than those that grow more slowly and have lower growth efficiency. This process has been demonstrated in both fish  will continue to change patterns of MeHg bioaccumulation and biomagnification in complex ways. The contexts in which these drivers act is important to define in order to begin incorporating their effects into risk determination because their influence on MeHg bioaccumulation patterns will ultimately affect MeHg concentrations in fish, which is the primary source of MeHg to most humans.Domain 1 that influence Hg concentrations in food items that humans consume, but also by global economic and societal drivers.Most human populations are exposed to Hg through the consumption of MeHg-contaminated fish, shellfish, and marine mammals . This is an important aspect of understanding human exposure to Hg because standard risk assessments often assume a constant and linear relationship between Hg exposure and different biomarker levels, despite evidence of tremendous variability in those relationships  by approximately 10–15 million miners, including approximately 4–5 million women and children UNEP . Within  Hg UNEP . It has  Hg UNEP . Biomoni Hg UNEP . This isAlthough fish consumption and ASGM are the dominant drivers of Hg exposure to most humans, other sources such as medical and personal care products can be important contributors for specific subsets of individuals. Historically, Hg was used for therapeutic medical purposes such as treatment for syphilis and as a component in teething powders (Clarkson and Magos 0 and HgII) in certain human populations. Application of Hg-containing skin-lightening creams to achieve desired physical appearances is a common practice in some populations, with prevalence rates as high as 50% in some countries  gene. Cross-sectional studies from across the world are also showing that polymorphisms in certain environmentally responsive genes  are associated with the main effect  and gene–environment interactions . Similar observations linking genotype and realized Hg exposure have been made in studies of various populations, including dentists  are important tools for monitoring and estimating Hg exposure in human populations. However, discrepancies exist in the relationships between actual exposure as measured through dietary intake or inhalation estimates and realized exposure as measured through biomarkers  ingested MeHg is bioavailable, substantial evidence exists suggesting that this may not be valid. In particular, a review by Bradley et al.  identifiMercury exposure can affect multiple organ systems, and manifests as diverse adverse health outcomes in humans, fish, and wildlife. The devastating neurologic impacts of acute, high-level MeHg and elemental Hg exposure are well documented, but chronic, low-level exposure also has important health impacts that are less well understood. A number of recent reviews have summarized the range of outcomes, known mechanisms, and toxicity benchmarks associated with different levels of Hg exposure or in situ Hg concentrations in humans  modify associations between Hg exposure and disease, as covered in the “infectious disease; (3) affect the risk of adverse health effects, as outlined in the “diet and co-contaminants; or (4) independently increase the risks of Hg-associated health outcomes, as discussed in the “genetics.We examine the interactions of Hg exposure with various drivers that modulate organism health, such as the microbiome, infectious disease, coexposures to other toxicants, diet and nutrition, and genetics Fig. . These iThe microorganisms living commensally in and on humans and other animals exceed the number of somatic cells by a factor of 10 or more , low folate, high homocysteine) exhibited a positive relationship between blood Hg concentrations and antibody titers for the two viruses  and docosahexaenoic acid (DHA), are of particular interest because of their known health benefits and co-occurrence at high concentrations with Hg in many marine fish species  and higher blood Hg levels had increased risk for low birth weight  will help partition out their influence on Hg concentrations in ecosystems. To do so requires refining the quantitative understanding of how each of the four underlying mechanisms MeHg bioaccumulation act, and in what contexts they are altered by global extrinsic drivers. Development of data application tools that facilitate incorporating these metrics into monitoring programs with a standardized approach is an important need, and would enable more robust comparisons of analogous temporal trends of Hg bioaccumulation among geographically diverse locations. The emergence of high-throughput genetic tools has opened new avenues for understanding (1) how intrinsic genetic factors influence assimilation of and sensitivity to Hg; (2) epigenetic responses to Hg exposure that can reveal specific mechanisms of action; and (3) interactions between the gut microbiome and Hg. These emerging areas represent potentially important contributions to the field for refining risk estimates based on genetic variation among populations, and for developing more effective biomarkers of Hg exposure. Substantial research in these areas is still needed for them to be applicable to public health and ecological conservation. Similarly, not enough is known about the interactions between Hg exposure, immunotoxicity, and infectious disease. Clearly the immune system is highly sensitive to both inorganic Hg and MeHg exposure, but extrapolating that sensitivity to the toxicologic implications at the whole organism scale is an important next step, particularly as it relates to infectious disease interactions. Finally, despite nearly five decades of research, and evidence from hundreds of scientific articles supporting Se as strong antagonist to Hg toxicity, there is still no consensus regarding the efficacy of Se in ameliorating different manifestations of Hg toxicity. Consequently, widely accepted tools for incorporating Se into health risk assessment for Hg exposure are still lacking. New criteria are emerging that take into account some of the mechanisms of the Hg–Se antagonism (Zhang et al.


Tsuga canadensis) canopy health affected by an invasive insect, the hemlock woolly adelgid (Adelges tsugae). Efforts such as those deployed at HF are demonstrating the power of TLS as a tool for monitoring ecological dynamics, identifying emerging forest health issues, measuring forest biomass and capturing ecological data relevant to other disciplines. This paper highlights various aspects of the ForestGEO plot that are important to current TLS work, the potential for exchange between forest ecology and TLS, and emphasizes the strength of combining TLS data with long-term ecological field data to create emerging opportunities for scientific study.Contemporary terrestrial laser scanning (TLS) is being used widely in forest ecology applications to examine ecosystem properties at increasing spatial and temporal scales. Harvard Forest (HF) in Petersham, MA, USA, is a long-term ecological research (LTER) site, a National Ecological Observatory Network (NEON) location and contains a 35 ha plot which is part of Smithsonian Institution's Forest Global Earth Observatory (ForestGEO). The combination of long-term field plots, eddy flux towers and the detailed past historical records has made HF very appealing for a variety of remote sensing studies. Terrestrial laser scanners, including three pioneering research instruments: the Echidna Validation Instrument, the Dual-Wavelength Echidna Lidar and the Compact Biomass Lidar, have already been used both independently and in conjunction with airborne laser scanning data and forest census data to characterize forest dynamics. TLS approaches include three-dimensional reconstructions of a plot over time, establishing the impact of ice storm damage on forest canopy structure, and characterizing eastern hemlock ( In 19881.1.As the research programme on forest ecology and change has continued to develop, extensive field infrastructure has been installed to make long-term measurements. On Prospect Hill, three eddy flux towers have been constructed: one located within a mature hemlock forest (operating since 2000), one within a mixed-hardwood forest (operating since 1989—the longest running eddy flux tower in the world) and the newly constructed National Ecological Observatory Network (NEON) tower . A portiIn 2010, HF established a 35 ha Forest Global Earth Observatory (ForestGEO) plot in the area surrounding the flux towers . ResearcAdelges tsugae). Since its introduction in 1950, HWA has been killing off eastern hemlock trees (Tsuga canadensis) across forests in eastern North America [The ForestGEO plot also provides a baseline for documenting a major environmental change in forest structure caused by an invasive insect, the hemlock woolly adelgid  satellite-derived and flux-tower-derived land surface albedo  and an estrument –19. Furtstrument , an aeristrument .The emerging technology of using terrestrial laser scanning (TLS) has been applied to the well-established sites in the HF to test and validate its capabilities to improve forest structure measurements and to discover and answer new questions on forest ecology. Terrestrial laser scanners are ground-based active remote sensing instruments that emit pulses of light at a specific set of wavelengths and record the returning energy that is reflected off objects in the space surrounding the instrument. TLS analyses this reflected light to generate a series of discrete points that outline the objects intercepted by the emitted pulses. TLS data are commonly referred to as a point cloud since the data generate a series of points with coordinates in three-dimensional (3D) space. Thus, the result of each terrestrial laser scan is a sample of the structural and reflective properties of the 3D space and the objects that surround the instrument.TLS technology is relatively new, and, while there are a variety of commercial TLS instruments available for purchase, many researchers choose to design and build their instruments in house. One such instrument used extensively at HF is the Compact Biomass Lidar (CBL). The CBL instrument design is based on an initial concept by the Katholieke Universiteit Leuven , realize1.3.As advances in lidar technology and data availability have improved , additio2.HF is located in Petersham, in north-central Massachusetts, USA. The Prospect Hill Tract is located on the northern, highest portion of the major ridge in Petersham, and contains the 35 ha ForestGEO plot . The ploThe ForestGEO location has served as a hub of research activity for decades (summarized in ). In add3.This paper examines the exchange between TLS and forest ecology in three sections. The first section discusses ongoing work with TLS at HF, with a few case studies illustrating how current TLS technologies have benefited from the data resources at HF and vice versa. A second section builds on the first to discuss emerging opportunities for TLS study at HF given the challenges to current studies, including the potential to incorporate TLS studies into the LTER infrastructure already present at HF. Due to rapid advances in TLS technology and increased use of TLS instruments, there is a need to refine TLS sampling strategies for ecological applications. We provide a conceptual framework for improving sampling scheme design in the third section. We conclude this paper by highlighting the exchange of knowledge occurring between TLS and ecological data at HF.3.1.In the process of reviewing the variety of TLS work in the ForestGEO plot at HF, three general categories of TLS studies have emerged: studies that have employed TLS to develop and measure forest parameters, studies that aim to improve TLS observations by combining with other remote sensing technologies for a multi-angle view of the forest, and, finally, studies that have used TLS to characterize forest type and condition. These three categories are outlined with case studies of published work from HF.3.1.1.Over the past decade, ground-based Echidna lidar scans have been used to estimate mean DBH values of trees, stem density, basal area and woody biomass , and effA recent advance in TLS technology has enabled new ways of quantifying photosynthetic activity in forests by integrating multiple lasers at different wavelengths to collect multispectral scans. One such research instrument, the Dual-Wavelength Echidna Lidar (DWEL), has been deployed at several sites at the HF, including hardwood-dominated and hemlock-dominated plots in the ForestGEO region. The DWEL uses two lasers pulsing simultaneously at two different wavelengths of 1064 and 1548 nm. At the 1548 nm wavelength, laser power returned from leaves is much lower than from woody materials, such as trunks and branches, due to absorption by liquid water in leaves. In contrast, returned power from both leaves and woody materials is similar at the other wavelength of 1064 nm –42. SuchIn general, ongoing work with TLS on forest structural metrics has contributed to the TLS field, establishing the technology as a means of developing new metrics and providing a means for direct quantification of parameters that previously had been obtained through extensive fieldwork, or predicted from other remote sensing data at coarse scales . A recent US National Science Foundation/Research Coordination Network-funded  field caFor instance, plot data measurements of canopy structure have been combined with high spatial resolution airborne lidar from the Goddard LiDAR, Hyperspectral, and Thermal sensor package (G-LiHT)  to valid3.1.2.A major challenge for TLS studies in dense forests is occlusion from understorey and mid-storey branches that prevent scanners from collecting reliable information from the top of canopies. One solution to this issue has been the combination of airborne laser scanning (ALS) and TLS data together, combining information about the canopy from above and below . TerrestAdditional opportunities to merge airborne with terrestrial lidar are possible in the future with a plan for annual acquisition by the NEON Airborne Observation Platform  which inWhile airborne lidar observations can help reduce issues of occlusion, they are only collected infrequently (often only one data collection per year), and the point density for discrete airborne observations is much lower than for TLS. Rapid-scanning TLS instruments, however, can capture high-resolution canopy structure when raised into the canopy. A higher resolution dataset can be obtained by deploying TLS instruments at various height levels, whether by raising the instrument up on a tripod , climbin3.1.3.TLS observations in HF have been used to demonstrate the ability of lidar instruments to be sensitive to the ecosystem type and condition of the scanned space. CBL scans from HF have shown promise in separating distinctly different forest structures by using the method of metaproperties analysis to distil aggregate statistics from the geometry and pulse properties of the data . FurtherThe insect infestation at HF has also provided an ideal opportunity to test the ability of lidar to capture progressive ecosystem change in scan data. An initial study has proved successful in applying metaproperties analysis to classify infested areas of forest from healthy areas  were assessed for hemlock mortality. Since 2010, 435 additional hemlock stems have died within this region, for a total of 723 stems, which now represents 20% of hemlock stems in this region of the plot , gaps open up in the forest canopy allowing understorey to grow (figure 2c). During the early years of infestation, these changes are often subtle and may not be quantifiable by a human observer. With consistent monitoring of infested and control plots at the same scan angles and positions, TLS can potentially detect the onset of infestation before it becomes obvious to human observers, quantify needle and branch loss, and characterize understorey response as new tree species establish themselves in the gaps in the canopy left by defoliated and dead hemlocks.Long-term monitoring with TLS also has the potential to quantify progressive structural change following disturbances. Long-term study plots are currently being set up at HF to monitor the impacts of the HWA infestation . As HWA figure 2Studies of progressive forest change are possible at sites where ecological data are bountiful and freely available. Long-term monitoring of the HWA infestation in the HF ForestGEO plot, for example, is only made possible by the efforts of ecologists and field-hands, first working to tag and record all stems over 1 cm in a 35 ha plot from 2010 to 2013, and further reassessing hemlock status  in a section of the ForestGEO plot in 2016 . These f3.3.The rapid advance of TLS technology has resulted in improvements in the retrieval of many ecologically important variables from forest ecosystems. However, while the specifications of TLS instruments have improved iteratively, deployment strategies have remained largely untouched, typically involving the placement of TLS according to a rigid grid of a predetermined size and resolution. To fully leverage the enhanced capabilities of TLS, as well as the diversification of the technology to include more lightweight scanners, mobile scanners and flexible deployment platforms, sampling strategies are now a prominent target for refinement. Many TLS practitioners are seeking to investigate the appropriate parametrization of rigid sampling strategies for different ecological variables, instrument specifications and ecosystem structural scenarios. Additionally, the increasing power of mobile computing technology may eventually enable the quality of TLS observations to be assessed in the field, with sampling strategies altered or designed on-the-fly to improve data completeness and quality.To enable improvements in sampling scheme design and parametrization, the attributes of TLS sampling schemes must be linked to the error observed downstream, in the retrieval of ecological variables of interest . This pra priori to increase confidence in the results. The accessibility of the site enables the repeated acquisitions necessary to iteratively refine sampling strategies, as well as test their effectiveness across changing seasons.The HF ForestGEO plot is an ideal study site for TLS practitioners to carry out investigations aimed at improving sampling schemes. There are a variety of sub-regions in relatively close proximity, representing a range of dominant tree morphologies, structural scenarios and even progressive stages of parasitic infestation. The complete documentation of the trees, including stem locations, stem density, DBH and samples of height and crown size, can directly provide the required validation, when these are the ecological variables of interest. In addition, the availability of stem density, size distributions and species composition provides the opportunity to investigate sampling schemes across a range of forest structure scenarios, including the ability to form stratified random sampling schemes 4.(1)TLS has produced measurements of forest structure characteristics with improved efficiency and speed, allowing for wider scale and more frequent ecological observations.(2)TLS has generated new forest parameters to quantitatively describe obscured features, such as canopy structures, by coordinating with airborne observations, space-borne remote sensing and forestry field data.(3)TLS has characterized the condition of forest plots and has the ability to identify forest type. These instruments are valuable tools for monitoring forest health with high temporal resolution, long-term observations.HF has been used at all systematic levels of TLS research, from instrument design, validation, algorithm development and testing to emerging ecological applications. HF TLS studies have contributed to ecological research in several broad ways:(1)ForestGEO plot data provide identifiable plots, locations and forest census data that allow for validation of TLS-derived forest metrics and for a quantification of error in TLS sampling .(2)Tree mortality assessment data following the HWA infestation provide baseline data for plot-scale measurements of forest health, allowing for TLS measurements to be finely attuned to structural changes due to this progressive condition figures  and 3.(3)HF provides an arena for calibration and validation of instrument measurements. The presence of the 35 ha ForestGEO plot with locations and characteristics of every woody stem ≥1 cm DBH, and a continuous 10×10 m grid, which is recommended for a variety of TLS sampling protocols , provideIn turn, HF field studies coordinated with TLS studies have contributed and can contribute in the future to the TLS field. The useful array of infrastructure already present at HF, including eddy flux towers, terrestrial and aerial lidar acquisitions, and long-term field plots, has provided growing opportunities for ecological TLS applications:2 measured at the towers. In addition, there are growing opportunities to integrate TLS technology with airborne and space-borne observation data to better characterize forest structure and composition. These studies linking ecological function to structure are a new frontier for collaborative research.This exchange of knowledge has contributed widely to both fields, yet there are a variety of emerging opportunities to synthesize ecological and TLS data that were not fully covered in this paper. With the eddy flux tower infrastructure in place at HF, future work to better characterize the forest structure surrounding towers could refine the factors influencing the fluxes of water and COFinally, with the growing use of various TLS technologies of different capacities, there is a need for instrument inter-comparisons and field calibrations at well-documented test locations. These calibration activities will be essential in developing consistent measurements, evaluating TLS error and developing forest parameters. A recent example of this kind of collaborative work involved a two-week calibration activity that occurred during the summer 2017 TLS field campaign at HF.The use of TLS and development of applications at HF has increased over the last several years and appears to be a promising tool to characterize various forest structural parameters, including estimates of tree height, biomass and crown geometry, as well as documenting dynamics associated with insect and pathogen outbreaks, natural disturbances and land-use change. The HF ForestGEO plot provides a consistent flow of ecological data that future TLS research can harness, with stem surveys occurring every 5 years, mortality assessments, constant recording of flux data, intermittent ecological and geological studies, and periodic airborne remote sensing campaigns. This wealth of data makes the ForestGEO plot unique among study sites across the globe, serving as an ideal place for TLS researchers to explore new ecological questions with emerging technology.


Reports of neglected and underutilized crops' (NUS) potential remain mostly anecdotal with limited and often incoherent research available to support them. This has been attributed to lack of clear research goals, limited funding directed at NUS and journal apathy toward publishing work on NUS. The latter points also explain the lack of interest from emerging and established researchers. Additionally, the NUS community's inability to articulate a roadmap for NUS' promotion may have unintentionally contributed to this. The current study is a sequel to an initial study that assessed the status of NUS in South Africa. The objective of this follow-up study was then to (i) identify priority NUS, and (ii) articulate a strategy and actionable recommendations for promoting NUS in South Africa. The study identified 13 priority NUS, categorized into cereals, legumes, root, and tuber crops and leafy vegetables based on drought and heat stress tolerance and nutritional value. It is recommended that the available limited resources should be targeted on improving these priority NUS as they offer the best prospects for success. Focus should be on developing value chains for the priority NUS. This should be underpinned by science to provide evidence-based outcomes. This would assist to attract more funding for NUS research, development and innovation in South Africa. It is envisaged that through this roadmap, NUS could be transformed from the peripheries into mainstream agriculture. This study provides a template for developing a roadmap for promoting NUS that could be transposed and replicated among the 14 other southern African states. Securing future food and nutrition security requires a paradigm shift from the current lock-in situation. According to Pahl-Wostl , a lock-Several NUS are drought and heat stress tolerant, resistant to pests and diseases, and adapted to semi-arid and arid environments , food and nutrition security and building resilience  literature search, (ii) resource identification, and (iii) prioritization.Phase 1: Literature searchA mixed method approach, which included combining quantitative and qualitative research or outcomes with process studies, was used. Emphasis was placed exclusively on identifying literature on NUS from South Africa. Briefly, the study initially identified the terms or key words commonly used to refer to underutilized crops in South African literature. The five (5) commonly used terms that were identified were; (i) underutilized crops, (ii) indigenous crops, (iii) traditional crops, (iv) neglected crops, and (v) orphan crops. This was based on common terminology identified in the initial study , amaranth (Amaranthus sp.), bottle gourd (Lagenaria siceraria), maize landraces (Zea mays), cowpea (Vigna unguiculata (L.) Walp), sweet potato (Ipomoea batatas), taro (Colocasia esculenta), sword bean , black jack (Bidens pilosa), marama bean (Tylosema esculentum) jews mallow (Corchorus olitorius), spider plant (Cleome gynandra), pearl millet (Eleusine coracana), nightshade (Solanum nigrum), chinese cabbage (Brassica chinensis), cocoyam (Xanthosoma spp.), sunberry (Solanum nigrum), wild water melon (Citrullus lanatus), wild mustard (Sinapis arvensis), sorghum (Sorghum bicolour), sesame (Sesamum indicum L.), and tef (Eragrostis tef). It was noted that from the list of identified underutilized crops, not all of them have CWR in South Africa. However, despite most crops not being indigenous to South Africa, they have become traditional crops in that their cultivation has formed part of traditional cropping systems for more than 100 years, from which local landraces have derived  common underutilized crops, and (ii) research themes or focus areas. The resource identification was also developed as an initial step toward developing a tool for archiving information on and developing a database for underutilized crops research in South Africa.Phase 3: Identifying priority underutilized cropsviz. the initial list of NUS identified in Phase 2. The objective was to identify an initial priority list of NUS of interest in South Africa based on drought tolerance and nutrient density of different food groups . Crops that were reported in the literature as being drought and heat stress tolerant and that were nutrient dense were identified. Based on this, a list of priority NUS was developed based on food groups of cereals, legumes, leafy vegetables and root, and tuber crops.The objective of this phase was to develop a list of priority drought tolerant and nutrient dense NUS for South Africa. Initially, the list of priority NUS for research in Africa proposed by Williams and Haq  was usedStage 2: Developing a roadmapThe development of the strategy involved a three-step approach, namely, (i) a diagnosis of challenges, obstacles and opportunities, (ii) priority setting and developing a framework for addressing challenges, and (iii) developing a guiding framework outlining coherent actions and activities for future funding of RDI on NUS for South Africa. This followed a conceptual and theoretical framework approach . Among the cereal crops, maize landraces and sorghum were the highest with four and three publications, respectively. With respect to root and tuber crops, taro received the most research attention (12 publications) followed by sweet potato (three publications)  Table  could bes) Table .A major reason for the low investments in NUS research is the perception that they offer less returns on investments compared to the major crops  for NUS that would guide future funding for NUS at a national level. Secondly, there was a need to ensure that the proposed strategy was aligned to existing national development priorities while also advocating for paradigm shifts in policy. The proposed roadmap also complements the existing approach to funding research on NUS that was developed by Water Research Commission of South Africa which emphases on (i) drought and heat stress tolerance, (ii) water use and nutritional value, and (iii) nutritional water productivity  development of appropriate technologies, (ii) dissemination to end users, and (iii) diagnosis of impacts, and (iv) redefining and/or customizing technologies are key to promoting NUS.Adaptive research aims at devising site specific dynamic technology packages for increasing agricultural production  and this has contributed to their dominance. Contrary to this, many NUS have neither the coherent body of work, nor comprehensive research concerning how such a body of knowledge could or should be funded, assembled and put to work in raising the status of NUS  1, 2, 3, 8, and 15. It also speaks to the Southern African Development Community's (SADC) Regional Indicative Strategic Development Plan (RISDP) RISDP, , which pHowever, while there are demonstrable linkages and alignment to existing policies, the challenge has always been on raising awareness of the potential benefits of NUS, increasing research funding allocated to NUS, and demonstrating practically how NUS can contribute to sustainably increasing food production, addressing food and nutrition security, and creating new opportunities for growth and employment in rural areas. The roadmap outlined in this study will address these challenges through (i) targeting a few priority NUS with potential for success, (ii) systematically building the body of available empirical information on these selected crops across the entire value chain, and (iii) focussing on nexus approaches that demonstrate capabilities to address cross-cutting issues.While NUS's promotion shows alignment to existing policies, the opposite is not true. Existing policies do not explicitly identify NUS as a viable option for achieving policy objectives. The inclusion of NUS within mainstream agriculture requires that the existing institutional and policy environment recognize the opportunities that NUS offer for rural economic development. The outputs of targeted research on identified priority NUS and demonstrated successes will feed into developing a policy framework that (i) encourages a stable and supportive macro- and micro-economic and regulatory environment for NUS, (ii) promotes capacity development needed for NUS RDI, (iii) provides sufficient financial and material support for NUS RDI, and (iv) promotes innovation of technologies linked to agro-processing for NUS.Increased availability of empirical information on NUS could be used to advocate for updating and/or strengthening existing policies so that they can be explicit on the role of South Africa's agro-biodiversity—NUS. For example, the strategic grain reserves system, which currently only prioritizes maize and wheat, could be revised so that there is inclusion of a broader set of crops along the mentioned categories of cereals and legumes crops. Such inclusion would also address dietary diversity through the formal recognition of the broader set of crops. Another example would be to ensure that cross cutting issues such as the water-energy-food nexus, poverty-unemployment-inequality nexus, water-food-nutrition-health nexus and agriculture-environment-health are mainstreamed into new policies that are currently being developed. Through such mainstreaming, NUS could be more widely recognized for the different roles that they can also play.www.bioversityinternational.org) and Crops For the Future (www.cffresearch.org), these have limited resources. There is a need to establish and support a national or regional center of excellence dedicated to driving research on NUS. While this may sound ambitious, this option may offer advantages in terms of (i) nurturing capacity needed for underutilized crops RDI, (ii) coordinating the implementation of the strategy through, (iii) establishing national, regional and international partnerships, and (iv) attracting research funding to drive implementation of the strategy. Importantly, the establishment of such a center of excellence would create continuity, which often is the primary challenge in the case of ‘champions.’ This would be consistent with the regional strategy on developing centers of excellence for key strategic areas.It is important to note that while the support of policy and a well-articulated strategy is critical for the success of RDI for NUS, much more is required to change the existing paradigms for NUS. There is a need to identify and support researchers and/or institutions that have shown a demonstrated record of accomplishment and commitment to advancing the status of NUS in South Africa. In this regard, the agenda places emphasis on the role of “champions” in supporting the strategy and involvement in developing future research priorities. While there are international centers focussing on NUS development such as Bioversity International  priority NUS were identified across different crop categories, based on their drought and heat stress tolerance and nutritional value; this is the first such list for South Africa. Research funding should then be directed toward priority research areas that have the greatest capacity to translate into notable gains. This includes crop improvement, linking food production to nutrition  and climate change adaptation. This should be underpinned by human capacity development and knowledge management (integrating indigenous knowledge) adaptive priority setting to ensure sustainability. The new knowledge generated, and the success stories should be used to influence existing and new policies to advocate for increased allocation of research funding for NUS. In the long-term, the promotion of NUS should adopt a socio-ecological approach that integrates linking food production to nutrition and sustainable livelihood outcomes. The roadmap for promoting NUS could also apply to the 14 other countries in southern Africa that share a similar context, agro-ecologies, culture and heritage with South Africa.TM and AM had the original idea for the article and carried out the conceptualization of the article. TC did the initial data collection. TM and VC then led the write-up of the manuscript and all co-authors read and revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We aimed to apply collaborative business intelligence (BI) system to hospital supply, processing and distribution (SPD) logistics management model.We searched Engineering Village database, China National Knowledge Infrastructure (CNKI) and Google for articles (Published from 2011 to 2016), books, Web pages, etc., to understand SPD and BI related theories and recent research status. For the application of collaborative BI technology in the hospital SPD logistics management model, we realized this by leveraging data mining techniques to discover knowledge from complex data and collaborative techniques to improve the theories of business process.For the application of BI system, we: (i) proposed a layered structure of collaborative BI system for intelligent management in hospital logistics; (ii) built data warehouse for the collaborative BI system; (iii) improved data mining techniques such as supporting vector machines (SVM) and swarm intelligence firefly algorithm to solve key problems in hospital logistics collaborative BI system; (iv) researched the collaborative techniques oriented to data and business process optimization to improve the business processes of hospital logistics management.Proper combination of SPD model and BI system will improve the management of logistics in the hospitals. The successful implementation of the study requires: (i) to innovate and improve the traditional SPD model and make appropriate implement plans and schedules for the application of BI system according to the actual situations of hospitals; (ii) the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial; (iii) timely response of external suppliers. Hospital logistics is one of the application fields for logistics. It mainly covers the procurement, storage, distribution, usage and the control of medical equipment, drugs and supplies in hospital. Since 1950s, scholars have paid attention to hospital material management, especially in the inventory management of medical materials. With the development of supply chain and logistics related theories, the hospital logistics attracted extensive attention, and many scholars started to systematically explore the hospital logistics .Their primarily focus was on the development of management models in the field of procurement, inventory and distribution.In recent years, several models have been proposed by scholars in terms of procurement. Lapierre et al.  designedIn some developed countries, several hospitals have adopted a new type of logistics management model, which is called SPD  model . The SPDBusiness intelligence (BI) transfers various data from enterprises into information or knowledge displayed in a cooperative manner that enterprise managers interested in. They were able to provide scientific evidence for the enterprise managers’ decision-making and finally strengthen the competitive advantage of enterprises. In the early stage, scholars primarily focused on the data warehouse and data marts. Ariyachandra et al.  put forwWith the development of hospitals in terms of construction scale, structure complication and management informationization, the need for scientific, standardized and lean management of hospitals is more obvious than ever. As one of the important aspects in hospital management, logistics management is now facing the realistic problems of low efficiency and high cost.This study aimed to explore the key technologies of hospital logistics collaborative BI system including architecture, data warehouse, data mining and collaboration, so as to improve the efficiency of hospital SPD logistics management model.We performed literature review to understand SPD and BI related theories and recent research status. Firstly, we searched Engineering Village database for English articles and China National Knowledge Infrastructure (CNKI) for Chinese articles published from 2011 to 2016. In addition, we searched Google for related books, conference papers, Webpages, etc. The keywords for literature review included “SPD”, “supply, processing and distribution”, “BI”, “ business intelligence”, “hospital”, “logistics”, and “supply chain”. At last, we incorporated the ideas from the literature review into a practical study protocol of the application of collaborative BI technology in the hospital SPD logistics management model.In this study, several technologies and methods were employed. Specifically, we adopted the methods of rough set theory and firefly algorithm-based data preprocessing to construct the data warehouse, we utilized improved data mining technologies including support vector machine (SVM) supplier selection, supply chain collaboration-based inventory optimization and swarm intelligence firefly algorithm-based multi-objective decision-making to solve key problems in hospital logistics collaborative BI system, we used instant communication technology and SPD workflow optimization method to realize the collaboration of BI system, and we selected Windows Vista operating system as the development environment of experimental platform. The overall research technology roadmap of study is presented in Level 1: Business data source level refers to structured, semi-structured and unstructured source data provided by different main bodies of hospital logistics supply chains and internal departments of hospital. They are original source data and problems on the hospital logistics supply chain.Level 2: Data collection level provides connect and access to a variety of hospital logistics operation management related data source. It maps various data sources to the handler to realize the data extraction, transformation and update.Level 3: Information integration level is the level that integrates many of the underlying data sources. It uses the source data from data warehouse to describe data extracted from data collection level and reshape the data syntax and semantics of the next level, to realize the construction of shared data warehouse and complete the multi-source data transformation and integration of hospital logistics supply chain.Level 4: Knowledge model level describes the business processes, knowledge and information models of hospital logistics management, and the semantics regarding their interdependence as well as correlation. Besides, it will modeling based on the relationships between shaped knowledge models extracted from the perspective of business processes and users. This way, it would realize information and business collaborative process logic modeling between different main bodies of hospital logistics supply chain.Level 5: Query analysis level provides tools and applications, which are considered the cores of the collaborative BI function. These include query, report, correlation analysis, trend and predictive analysis. These functions can be called by virtual collaboration level to set up the views and reports, to learn and perform model analysis, as well as to provide visualized exploration of real-time data to facilitate comparison and prediction of final information.Level 6: Virtual collaboration level provides a rich user interface and virtual spaces based on logistics business specific parameter setting, which are employed to display and solve problems in hospital logistics including medical consumables supplier selection, inventory optimization, product distribution, information coordination, and business coordination, etc. These facilitate users to check, share, analyze, and coordinate different forms of data, and finally to realize the users’ assessment and decision-making .Collaborative BI combines the traditional BI and collaborative technology to satisfy the specific needs of business enterprises’ collaborative decision-making and business process. Hospital logistics collaborative BI system is divided into 6 levels including business data source, data collection, information integration, knowledge model, query analysis and virtual collaboration:The architecture mainly consists of 7 function modules, these include multi-source heterogeneous data preprocessing module, shared data warehouse construction module, SVM-based medical consumable materials supplier classification service module, swarm intelligence firefly algorithm-based inventory optimization module, swarm intelligence firefly algorithm-based medical consumables distribution module, information collaboration service module and business collaboration service module. System management is composed of operation maintenance management and security management.The construction of data warehouse used centralized storage, retrieval, and other corresponding multi-source data process in the environment of collaborative BI. At the same time, rough set data mining algorithm and swarm intelligence firefly optimization algorithm were also employed to deal with the data in the data warehouse. These provided reliable data for data analysis services of collaborative BI system, and to improve accuracy and efficiency of data analysis services.Shared data warehouse can be divided into two types including centralized and decentralized models. The centralized shared data warehouse requires creating physical data warehouse at first. The storage and management of data in centralized shared data warehouse is operational data that has been processed and arranged. When decisions are made, every related enterprises and internal departments transmit data to the data warehouse via specified data marts. This made centralized data warehouse to have some extent of advantages in keeping the consistency of data. By contrast, decentralized data warehouse is set up based on virtual overall data warehouse. Our study aimed to establish a centralized shared data warehouse via the integration of data marts in hospital logistics management, in order to realize the multi-agent data support in the collaborative BI system and satisfy the collaborative BI system users’ demands of data analyses and data-mining of specified business information.There were many unstructured or semi-structured data in different parts of the hospital logistics supply chain including the evaluation information of suppliers, the qualifications of medical manufacturers, as well as document information for hospital internal communications. In order to better deal with these types of data we first created corresponding document template according to specific application needs and subsequently tried to write transform programs to read the contents of these unstructured/semi-structured files and adopted different rules to transform them to standard XML documents. Finally, we analyzed the XML documents and relational database and figured out some rules for the transformation from XML documents to relational database tables. This was supported by the relational model-based data warehouse. The construction of data warehouse in hospital logistics BI system was the standardized integration of information flows from upstream to downstream of logistics supply chain. Considering the fact that the flow of information upstream was heterogeneous and the structure of raw data varied across different parts of supply chain, information systems, as well as modules, we took several measures to define the metadata uniformly. At first, according to the Berlin core data set, we provided metadata information for every member in the supply chain by reference to uniformed metadata specifications that were defined by the logistics alliance. Secondly, data exchange and integration among various systems were achieved through metadata mapping. Finally, according to the idea of oriented to application integration (OAI), we designed a metadata framework and information exchange model from the perspective of logistics information exchange to realize the data integration of different parts in the hospital logistics supply chain.Data storage refers to the storage mechanism of shared data warehouse in hospital logistics collaborative BI system. In order to solve the existing contradiction between model stability and demand variability, we stratified the data storage of shared data warehouse into three levels: (i) temporary buffer area, (ii) integrated data area and (iii) summary data area. The buffer function loads multi-source system data in hospital logistics supply chain into a temporary buffer area, the area works in the course of transform and load from data source system to integrated data area. Integrated data area, the core of the whole data storage, analyses every event from different dimensions via constructing multi-dimensional model. The integrated data area can be divided into three sub-areas: (i) operational data area, (ii) data analysis area and (iii) data archiving area. The operational data area stores data that were classified according to the application themes and provides uniformed hospital logistics data views based on real-time operation data. The data analysis area mainly stores historical data of hospital logistics operation decision analysis. The data archiving area is used to store the historical archived data derived from operational data area and data analysis area. The summary data area is a virtual zone without actual stored data, it is arranged to store the extra-preprocessed data for the use of the font-end. According to the relevant definition in the database, the data are named with clear business meaning, i.e., integrate the tables, fields and their complicated relationships into business terms or indicator names that could directly be displayed in the font-end.γx(D) refers to the quality of data sets; R is the subtraction of C (condition attribute). If the decision tables are continuous, In order to improve the efficiency and accuracy of swarm intelligence algorithm-based data mining, we used a rough set theory to perform data preprocessing of data warehouse. The data mining technology combined the rough set theory and firefly algorithm together, which not only took the quality of sub-data sets into consideration, but also ensured the subtraction of raw data to the maximum extent. The objective function of the firefly algorithm is: γs(D) = c(D)γ by iteration. These steps helped us not only get the subsets with the smallest data scale, but also keep the original characters of data sets at the maximum extent.The basic steps are as follows: at first, we identified a proper method of subtraction (R), depending on which firefly populations were randomly generated by coding. Then each individual was evaluated by fitness function that has already been designed. At last, we figured out subsets that met the function The data of collaborative BI system in the hospital SPD model is complex, multidimensional, and nonlinear. Using data mining, we were able to solve several problems including medical consumables supplier selection, inventory optimization, as well as medical consumables distribution. These were very important in the hospital logistics management. In other words, by adopting improved data mining algorithm that met the demand of collaborative BI system in the hospital SPD model, we achieved the following goals: (i) we realized more accurate and effective classification and selection of medical consumables suppliers; (ii) we completed the procurement control, inventory optimization and inventory shortage warning in the first-level warehouse of the SPD model; (iii) we solved the optimization problems associated with medical consumables distribution in the secondary consumption points of the SPD model, especially the problems associated with the dynamic adjustment of distribution point, period and amount.The SVM method is widely used in the system evaluation. By mapping the points in low-dimensional space to high-dimensional space, we used the principle of linear partition to determine the classification boundaries. We attempted to design medical consumables supplier evaluation and classification methods based on SVM, to deal with specific problems of supplier selection in the hospital SPD model. We, first, extracted the objective function and constraint condition of medical consumables supplier selection in the hospital SPD model. Then we extracted feature data sets as the training sample source according to supplier multidimensional attribute to complete the design of classifier. Finally, we attempted to realize the actual classification of medical consumables suppliers by classifying the medical consumables supplier sample into corresponding categories via the already designed classifier.The core of the supply chain inventory optimization in hospital SPD logistics model is the effective control of first-stage warehouse inventory level. Given the close association existed among inventory level setting and procurement strategy and safe inventory, the amount of procurement and procurement period directly affects the level of inventory. Therefore, hospital logistics inventory optimization is considered a type of data-driven analysis and design task, which requires analysis on large amounts of data followed by establishing a relational model between inventory costs and service levels. According to the two-stage inventory structure design, we divided the inventory optimization method into two steps: (i) we figured out the key parameters’ approximate fluctuation scopes of first-stage warehouse inventory in the SPD model using the time series method; (ii) we designed discrete control parameters and construct corresponding solution spaces, and then identified the most feasible inventory optimization solutions by using swarm intelligence firefly algorithm.The effect of medical consumables distribution in SPD model mainly depends on the satisfaction of multiple targets including distribution amount, distribution point, distribution lead time, as well as dynamic adjustment of the distribution amount. Considering the medical consumables distribution multi-objective decision-making in the model of SPD may face several conditions such as large data scale, complicate structure and dynamic objective, we employed the swarm intelligence firefly algorithm method to deal with the multi-objective decision-making problems. We, first, established a mathematical model of multi-objective decision-making via extracting multiple decision-making objectives such as timeliness, accuracy and economy. The construction of constraint condition set was then analyzed. Secondly, we identified the solution spaces of multi-objective decision-making using swarm intelligence firefly algorithm method. Finally, we periodically used swarm intelligence firefly algorithm to deal with dynamic data in the data warehouse, and to provide dynamic adjustment strategy for distribution amount, point, and lead time, as well as distribution amount adjustment.Collaborative technology refers to all types of information and communication technologies that can be collaboratively supported in different levels including information sharing support and task coordination support. Therefore, the business cooperation in the hospital SPD logistics model contains not only the information sharing in traditional information system, but also workflow optimization of information intelligent delivery and business-oriented collaboration.Instant communication combines audio and video communication and files transfer, and provides necessary platform for information sharing and delivering of logistics collaborative BI system. Therefore, the full use of the instant communication technology requires necessary integration of heterogeneous information in the hospital logistics collaborative BI system. In this study, we provided uniformed structural descriptive data for describing information resources in the hospital logistics collaborative BI system with the help of metadata. We accomplished this by establishing metadata level of hospital logistics data warehouse using the Dublin core data sets, and built logistics information integration model. We also attempted to build dynamic matching models of the supply chain and logistics related information  in BI system via analyzing the attribute and operation information of the supply chain in the SPD model.Workflow is a part of Computer Supported Cooperative Work (CSCW), which plays an important role in researching how to make the groups work cooperatively within the framework of informationization. The SPD workflow is chiefly used to describe the activities of tasks with fixed procedures in the hospital logistics businesses. The core of the SPD workflow optimization is to figure out the “control points” that requires cooperating by mining “up and down streams” and “time rules” in the original workflow. This is to achieve several goals including improved work efficiency, better control process, and effective management of logistics business processes. Therefore, we constructed the up and down streams and time constraint relations of workflow by mining the time data from SPD workflow in the data warehouse, and then built the workflow time constraint relations-based Activity On Edge (AOE) Network. Based on which we figured out the critical paths in SPD workflow net structure through the utilizing of swarm intelligence firefly algorithm and Critical Path Method (CPM).This study intended to construct hospital logistics SPD model-oriented collaborative BI system, by which we realized the data and information sharing between the hospitals, the suppliers and the manufacturers in the hospital logistics supply chain. By using SVM and swarm intelligence firefly algorithm methods, we analyzed the recent years’ basic information database of hospital information system (HIS) and data provided by companies in the logistics supply chain. We also established a hospital logistics SPD model-oriented collaborative BI system that realized such roles as collaboration, data mining, data analysis, as well as application.The timeliness and collaboration of hospital logistics collaborative BI system in SPD model were improved by the following procedures: (i) through constructing the module of sharing data warehouse, we realized the integration of data marts in hospital logistics management, so as to ensure the data safety and provide services such as basic data process and analysis; (ii) for the extraction of inherent characteristics of data sets, we constructed the module of data preprocessing to reduce the data dimension of collaborative BI system; (iii) by setting up supplier selection optimization module and using the method of SVM, we improved the business processes to meet the demands of supplier evaluation, classification and selection in the hospital logistics SPD model-oriented collaborative BI system; (iv) via establishing inventory optimization module, we were able to solve the inventory optimization problems in hospital logistics warehouse; (v) by creating medical consumables constant distribution module, we could deal with the decision-making processes such as distribution amount, distribution point, distribution lead time, as well as dynamic adjustment of distribution amount; (vi) in order to realize the collaboration of logistics information and information sharing among every parts of the supply chain, we built information collaborative service module; (vii) by building business collaborative service module, we provided the decision-making basis for logistics process optimization in the hospital SPD model. In this study, we chose Windows Vista operating system as the development environment. Other specific requirements are as follows: software structure: J2EE; compile environment of Java language: version JDK 1.5.07; server: open source server developed by Apache Group Jakarta group, version TOMCAT 5.0.28; JSP front-end development platform: Dreamweaver MX 2014; back-end development environment: Eclipse 5.1; database system: Microsoft SQL Server 2008; ETL tools in BI system: PowerCenter; shared data warehouse: Microsoft SQL Server; OLAP server: SAP Essbase; front display tools: Business Object.With the rapid development of medicine and health services and in the face of ever increasing demand, the use of medical consumables in hospitals is increasing. A hospital, usually, requires multiple varieties of medical consumables with complicated specifications from several suppliers, which results in considerable management difficulties, low management efficiency, as well as unsafe medical activities. We advocate the use of the SPD logistics management model, which have several advantages such as: (i) the procurement information is automatically generated by the system after analyzing the historical data on consumption, inventory level and response time of suppliers. This may largely improve the standardization and automation of procurement; (ii) the inventory of every stage  is monitored by the logistics management departments, the inventory related variables can be scientifically and coordinately arranged including the ordering point, safe inventory, maximum inventory, and lead time of ordering. This way, we can significantly reduce the cost as well as the inventory risk. (iii) The distribution of consumables is based on inventory optimization, by analyzing the historical consumption habit of clinical departments, the distribution related variables including distribution amount, distribution point, distribution lead time, as well as dynamic adjustment of distribution amount can be properly assigned. By adopting this model, we may reduce the waste of consumables and significantly reduce the workload associated with material management and improve the efficiency of clinical activity.In China, the informationization of hospital management mainly focuses on the information support of the medical activity management, especially the clinical treatment. The information support for hospital logistics management is relatively weak, which makes it difficult to support the construction and implement of the SPD model. Specific problems were mainly manifested in two aspects from the perspective of management informationization. At first, the data utilization rate was relatively low and the existing information construction hardly met the data mining demands in the complex logistics data. Additionally, the collaboration between information systems was quite lacking. Traditional construction of informationization mainly concentrated on the information electronization in the hospital, rather than the outside links of the supply chain, which ignored the collaborative demands among the hospitals, suppliers and manufacturers. With the development of recent decades, BI system has been widely applied in many fields including manufacturing , bank 2, logistiWe may encounter many difficulties in this study, which requires innovating and improving the traditional SPD model and making appropriate implement plans and schedules for the application of BI system according to the actual situations in hospitals. Additionally, given the fact that the study is based on information integration, the successful implementation of this study requires the collaborative participation of internal departments in hospital including the department of information, logistics, nursing, medical and financial. The timely response of external suppliers is the other significant requirement of this study.BI transfers various data from enterprises into information or knowledge displayed in a cooperative manner that enterprise managers interested in. Introducing and applying the collaborative BI system to hospital SPD logistics management model may help largely in realizing the business collaboration of hospital logistics management and achieving a few goals such as improving logistics management level, reducing hospital logistics costs, and increasing health service quality as well.Ethical issues  have been completely observed by the authors.