Economic analysis of high-oleic soybeans in dairy rations.

We evaluate the potential economic impact of using high-oleic soybeans (HOS) in dairy rations based on a synthesis of results from 5 prior feeding trials. Milk income less feed costs (MILFC) per cow per day is calculated based on assumed increases in milkfat production and increased cost of rations including HOS. The effects of changes in MILFC are evaluated for herds with different numbers of milking cows, and the total volume of HOS required to support different proportions of US dairy cows is calculated. A dynamic supply-chain model assesses the potential market impacts of increases in butterfat supply. The increase in milkfat from the substitution of 5% of ration dry matter with whole HOS (1.4 kg/cow per day) has the potential to increase MILFC by up to $0.27/cow per day or increase the average value of milk by $0.29/45.4 kg for a cow producing 41 kg/d. Changes in MILFC are highly correlated with the price of butter but were positive for butter prices observed from January 2014 to September 2020. The effects of HOS on MILFC suggest the potential for increases in farm profitability of $33,000/yr for a dairy feeding 500 milking cows. Scaled-up use of HOS by US dairy farmers would increase butterfat supplies and lower the butterfat price to a small extent, but these aggregated effects do not offset the positive effects of MILFC at the farm level.

Analysing the potential impacts of three interventions on fruit and vegetable consumption in urban Kenya using participatory systems modelling.

OBJECTIVE: This study uses participatory modelling with stakeholders to assess the potential impacts of three interventions intended to increase fruit and vegetable (F&V) consumption in urban Kenya. DESIGN: A participatory process using Group Model Building (GMB) developed a conceptual model of the determinants of vegetable consumption. A subsequent quantitative System Dynamics model using data from primary and secondary sources simulated vegetable consumption from 2020 to 2024 under three proposed interventions suggested by stakeholders: increasing consumer awareness, reducing post-harvest losses and increasing farm yields. Model analyses assumed mean parameter values and assessed uncertainty using 200 simulations with randomised parameter values. SETTING: The research was implemented in Nairobi, Kenya with simulation analyses of mean per capita consumption in this location. PARTICIPANTS: Workshops convened diverse F&V value chain stakeholders (farmers, government officials, NGO staff and technical experts) to develop the conceptual model, data inputs and intervention scenarios. RESULTS: Increasing consumer awareness was simulated to increase vegetable consumption by relatively modest amounts by 2024 (5 g/person/d from a base of 131 g/person/d) under mean assumed value of value chain response parameters. Reducing perishability was simulated to reduce consumption due to the higher costs required to reduce losses. Increasing farm yields was simulated to have the largest impact on consumption at assumed parameter values (about 40 g/person/d) but would have a negative impact on farm profits, which could undermine efforts to implement this intervention. CONCLUSIONS: The combination of GMB and simulation modelling informed intervention priorities for an important public health nutrition issue.

Sheet and void porous media models for brain interstitial space.

The interstitial space (ISS) component of brain extracellular space resembles an unconsolidated porous medium. Previous analysis of the diffusion of small molecules in this domain shows that the typical porosity is 0.2 and typical tortuosity 1.6. An ensemble of cubic cells separated by uniform sheets of ISS cannot generate the measured tortuosity, even if some of the tortuosity value is attributed to interstitial viscosity, so more complex models are needed. Here two models are analysed: the corner cubic void (CCV) and the edge tunnel void (ETV). Both models incorporate dead spaces formed from local expansions of the ISS to increase geometrical tortuosity. Using Monte Carlo simulation of diffusion it is found that in the range of normal porosities, the square of the tortuosity is a linear function of the ratio of void to sheet volumes for the CCV model and this model can generate the experimentally observed tortuosities. For abnormally high porosities, however, the linear relation fails. The ETV model shows a quartic functional relation and can only generate the observed tortuosity if interstitial viscosity is present. The CCV model is used to analyse the recently described changes in porosity between asleep and awake brain states.

Multiple-year farm-level assessments of trade-offs between GHG emissions and income: case examples of two farming systems in Northern Nigeria.

This study quantifies the trade-offs between welfare (measured by income) and greenhouse gas (GHG) emissions in two farming systems of Northern Nigeria using data for 5 years from 2015 to 2019. The analyses employ a farm-level optimization model that maximizes the value of production less purchased input costs for agricultural activities including the production of trees, sorghum, groundnut or soybeans, and multiple livestock species. We compare income and GHG emissions without constraints to scenarios requiring reductions in emissions of either 10% or the maximum reduction feasible while maintaining minimum household consumption levels. For both locations and all years, we find that reductions in GHG emissions would lower household incomes and require substantive modifications to production patterns and input use. However, the extents to which reductions are possible and the patterns of income-GHG trade-offs vary, indicating that such effects are site-specific and time-variable. The variable nature of these trade-offs suggests challenges for the design of any program that would seek to compensate farmers for reductions in their GHG emissions.

The glymphatic system: Current understanding and modeling.

We review theoretical and numerical models of the glymphatic system, which circulates cerebrospinal fluid and interstitial fluid around the brain, facilitating solute transport. Models enable hypothesis development and predictions of transport, with clinical applications including drug delivery, stroke, cardiac arrest, and neurodegenerative disorders like Alzheimer's disease. We sort existing models into broad categories by anatomical function: Perivascular flow, transport in brain parenchyma, interfaces to perivascular spaces, efflux routes, and links to neuronal activity. Needs and opportunities for future work are highlighted wherever possible; new models, expanded models, and novel experiments to inform models could all have tremendous value for advancing the field.

A machine learning and clustering-based approach for county-level COVID-19 analysis.

COVID-19 is a global pandemic threatening the lives and livelihood of millions of people across the world. Due to its novelty and quick spread, scientists have had difficulty in creating accurate forecasts for this disease. In part, this is due to variation in human behavior and environmental factors that impact disease propagation. This is especially true for regionally specific predictive models due to either limited case histories or other unique factors characterizing the region. This paper employs both supervised and unsupervised methods to identify the critical county-level demographic, mobility, weather, medical capacity, and health related county-level factors for studying COVID-19 propagation prior to the widespread availability of a vaccine. We use this feature subspace to aggregate counties into meaningful clusters to support more refined disease analysis efforts.

High-yield dairy cattle breeds improve farmer incomes, curtail greenhouse gas emissions and reduce dairy import dependency in Tanzania.

Tanzania's dairy sector is poorly developed, creating reliance on imports for processed, value-added dairy products and threatening food security, particularly when supply chains are disrupted due to market volatility or armed conflicts. The Tanzanian Dairy Development Roadmap is a domestic development initiative that aims to achieve dairy self-sufficiency by 2030. Here, we model different outcomes of the roadmap, finding that adoption of high-yield cattle breeds is essential for reducing dairy import dependency. Avoided land use change resulting from fewer, higher yielding dairy cattle would lead to lower greenhouse gas emissions. Dairy producers' average incomes could increase despite capital expenditure and land allocation required for the adoption of high-yield breeds. Our findings demonstrate the importance of bottom-up development policies for sustainable food system transformations, which also support food sovereignty, increase incomes for smallholder farmers and contribute towards Tanzania's commitments to reduce greenhouse gas emissions.

Projected losses of ecosystem services in the US disproportionately affect non-white and lower-income populations.

Addressing how ecosystem services (ES) are distributed among groups of people is critical for making conservation and environmental policy-making more equitable. Here, we evaluate the distribution and equity of changes in ES benefits across demographic and socioeconomic groups in the United States (US) between 2020 and 2100. Specifically, we use land cover and population projections to model potential shifts in the supply, demand, and benefits of the following ES: provision of clean air, protection against a vector-borne disease (West Nile virus), and crop pollination. Across the US, changes in ES benefits are unevenly distributed among socioeconomic and demographic groups and among rural and urban communities, but are relatively uniform across geographic regions. In general, non-white, lower-income, and urban populations disproportionately bear the burden of declines in ES benefits. This is largely driven by the conversion of forests and wetlands to cropland and urban land cover in counties where these populations are expected to grow. In these locations, targeted land use policy interventions are required to avoid exacerbating inequalities already present in the US.

Rapid volume pulsation of the extracellular space coincides with epileptiform activity in mice and depends on the NBCe1 transporter.

KEY POINTS: Extracellular space (ECS) rapid volume pulsation (RVP) accompanying epileptiform activity is described for the first time. Such RVP occurs robustly in several in vitro and in vivo mouse models of epileptiform activity. In the in vitro 4-aminopyridine model of epileptiform activity, RVP depends on the activity of the electrogenic Na+ /HCO3- cotransporter (NBCe1). NBCe1 pharmacological inhibition suppresses RVP and epileptiform activity. Inhibition of changes in ECS volume may be a useful target in epilepsy patients who are resistant to current treatments. ​ ABSTRACT: The extracellular space (ECS) of the brain shrinks persistently by approximately 35% during epileptic seizures. Here we report the discovery of rapid volume pulsation (RVP), further transient drops in ECS volume which accompany events of epileptiform activity. These transient ECS contractions were observed in multiple mouse models of epileptiform activity both in vivo (bicuculline methiodide model) and in vitro (hyaluronan synthase 3 knock-out, picrotoxin, bicuculline and 4-aminopyridine models). By using the probe transients quantification (PTQ) method we show that individual pulses of RVP shrank the ECS by almost 15% in vivo. In the 4-aminopyridine in vitro model, the individual pulses of RVP shrank the ECS by more than 4%, and these transient changes were superimposed on a persistent ECS shrinkage of 36% measured with the real-time iontophoretic method. In this in vitro model, we investigated several channels and transporters that may be required for the generation of RVP and epileptiform activity. Pharmacological blockages of Na+ /K+ /2Cl- cotransporter type 1 (NKCC1), K+ /Cl- cotransporter (KCC2), the water channel aquaporin-4 (AQP4) and inwardly rectifying potassium channel 4.1 (Kir4.1) were ineffective in halting the RVP and the epileptiform activity. In contrast, pharmacological blockade of the electrogenic Na+ /HCO3- cotransporter (NBCe1) by 4,4'-diisothiocyano-2,2'-stilbenedisulfonic acid (DIDS) eliminated both the RVP and the persistent ECS shrinkage. Importantly, this blocker also stopped the epileptiform activity. These results demonstrate that RVP is closely associated with epileptiform activity across several models of epileptiform activity and therefore the underlying mechanism could potentially represent a novel target for epilepsy management and treatment.

Corrections to "Visual Analytics for Decision-Making During Pandemics".

[This corrects the article DOI: 10.1109/MCSE.2020.3023288.].

COVID-19 and the agri-food system in the United States and Canada.

Agri-food supply chains in North America have become remarkably efficient, supplying an unprecedented variety of items at the lowest possible cost. However, the initial stages of the COVID-19 pandemic and the near-total temporary loss of the foodservice distribution channel, exposed a vulnerability that many found surprising. Instead of continued shortages, however, the agri-food sector has since moved back to near normal conditions with prices and production levels similar to those typically observed in years prior to the pandemic. Ironically, the specialization in most food supply chains designed for "just-in-time" delivery to specific customers with no reserve capacity, which led to the initial disruptions, may have also been responsible for its rapid rebound. A common theme in assessing the impacts across the six commodities examined is the growing importance of understanding the whole supply chain. Over the longer term, a continuation of the pandemic could push the supply chain toward greater consolidation of firms and diversification of products given the increasing option value of maintaining flexibility. Other structural changes will be felt through input markets, most notably labour, as the trend toward greater automation will continue to accelerate as a response to meeting concerns about a consistent supply of healthy and productive workers. The economic fall out from the pandemic may lead to greater concentration in the sector as some firms are not able to survive the downturn and changes in consumer food buying behaviour, including movement toward online shopping and enhanced demand for attributes associated with resiliency, such as local. On the other hand, online shopping may provide opportunities for small producers and processors to shorten supply chains and reach customers directly. In the long term, COVID-19 impacts on global commerce and developing country production are more uncertain and could influence poverty reduction. While COVID-19's impacts on North American agriculture should have minimal effect on the Sustainable Development Goals (SDGs) through food prices, the ongoing global trends in trade and agribusiness accelerated by the pandemic are relevant for achievement of the SDGs.

Food value chain interventions and nutritional outcomes: a review of evidence and recommendations for future assessments.

Food value chains (FVC) have become an important framework for the assessment of interventions to improve nutritional outcomes during the past decade, and recent literature indicates considerable agreement about FVC importance and potential impact pathways. Despite the usefulness of the FVC framework, the majority of studies reviewed provide only conceptual models or descriptive analyses of linkages with nutrition, limiting their usefulness for quantitative assessment of intervention impacts. Fewer than five studies of 113 reviewed measure the impacts of FVC interventions on nutritional outcomes or provide study protocols for that purpose. In addition to randomized controlled trials, comparative analysis and systems modeling methods will provide relevant evidence about the effectiveness of FVCs for improvement of nutrition.

Feature Extraction for Heroin-Use Classification Using Imbalanced Random Forest Methods.

BACKGROUND AND AIMS: The National Survey on Drug Use and Health (NSDUH) contains a large number of responses and many features. This study aims to identify features from within NSDUH that are important in classifying heroin use. Proper implementation of random forest (RF) techniques copes with the highly imbalanced nature of heroin usage among respondents to identify features that are prominent in classification models involving nonlinear combinations of predictive variables. To date, methods for the proper application of RF to imbalanced medical datasets have not been defined. Methods: Three different RF classification techniques are applied to the 2016 NSDUH. The techniques are compared using scoring criteria, including area under the precision recall curve (AUPRC), to identify the best model. Variable importance scores (VIS) are checked for stability across the three models and the VIS from the best model are used to highlight features and categories of features that most influence the classification of heroin users. Findings: The best performing method was RF with random oversampling (AUPRC = 0.5437). The category of features regarding other drug use was most important (average z-scored VIS = 1.66) followed by age-of-first-use features (0.32). The most important individual feature was cocaine usage (z-scored VIS = 11.05), followed by crack usage (6.51). The most important individual feature other than specific drug use flags was the use of marijuana under the age of 18 (3.11). This study demonstrates a method for the use of RF in feature extraction from imbalanced medical datasets with many predictors.

Determinants of farmers' loyalty to dairy processors in Minas Gerais, Brazil / Determinantes da fidelidade dos produtores de leite aos laticínios em Minas Gerais, Brasil

ABSTRACT: The current study explores variables associated with the loyalty of dairy farmers to dairy processors in the Brazilian context. A multivariate discrete choice (Logit) model and alternative formulations assess the associations between loyalty metrics and farm and processor characteristics for a sample of 32 dairy farmers in 16 municipalities at the Zona da Mata in Minas Gerais. Twenty-two dairy processors were identified as milk buyers in the area studied, but each farmer indicated that they could sell to an average of five alternative buyers of milk. Farmers' attributes such as production scale or the technological level are not statistically significantly associated with loyalty in this sample. The current milk price paid to farmers in our sample is not associated with increased loyalty (sales to a single processor for 6 or more years) in all estimated models; although, further research on this impact is merited to inform buyer-pricing policy. Variables associated with increased loyalty include payment of premiums for quality, farmer years of experience and cooperation among farmers in the purchase of inputs. Delayed payment is associated with reduced loyalty. We could not determine the effect of participation in technical assistance programs offered by processors on loyalty, because in our sample all farmers received free university-provided technical assistance. The payment of a premium based on milk volume was also unassociated with loyalty determination. The small size of our sample limits the ability to generalize our results but provides exploratory results that facilitate future investigation.

RESUMO: O objetivo deste estudo é explorar as variáveis importantes associadas à fidelidade dos produtores de leite aos laticínios no contexto brasileiro. Um modelo de escolha discreta multivariada (Logit) e formulações alternativas foram usadas para avaliar as associações entre métricas de fidelidade e características de fazendas e processadores em uma amostra de 32 produtores de leite de 16 municípios da Zona da Mata, em Minas Gerais. Vinte e dois laticínios foram identificados como compradores de leite na região estudada, entretanto, cada produtor indicou que tem, em média, a possibilidade de vender o leite para cinco empresas diferentes. Os atributos dos agricultores, como escala de produção ou nível tecnológico, não foram estatisticamente associados significativamente à fidelidade. O preço atual do leite pago aos agricultores nessa amostra não está associado a probabilidade de aumento da fidelidade (vendas para um único laticínio por seis anos ou mais) em todos os modelos estimados. No entanto, pesquisas adicionais sobre esse impacto são necessárias para subsidiar políticas de preços aos produtores. As variáveis associadas ao aumento da fidelidade incluem pagamentos de prêmios pela qualidade, anos de experiência dos agricultores e a cooperação entre os agricultores na compra de insumos. O atraso no pagamento está associado à redução de fidelidade. Não foi possível determinar o efeito da participação em programas de assistência técnica oferecidos pelos processadores na fidelidade, provavelmente porque todos os produtores estudados recebem assistência técnica de graça de uma Universidade. O pagamento de prêmios com base no volume de leite também não foi associado à determinação da fidelidade. O pequeno tamanho de nossa amostra limita a capacidade de generalizar nossos resultados, mas fornece resultados exploratórios que facilitam investigações futuras.

Visual Analytics for Decision-Making During Pandemics.

We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.

Conceptual frameworks linking agriculture and food security.

Many conceptual frameworks have been developed to facilitate understanding and analysis of the linkages between agriculture and food security. Despite having usefully guided analysis and investment, these frameworks exhibit wide diversity in perspectives, assumptions and application. This Review Article examines this diversity, providing an approach to assess frameworks and suggesting improvements in the way they are specified and applied. Using criteria-based systems modelling conventions, we evaluate 36 frameworks. We find that many frameworks are developed for the purpose of illustration rather than analysis and do not clearly indicate causal relationships, tending to ignore the dynamic (stability) dimensions of agriculture and food security and lacking clear intervention points for improving food security through agriculture. By applying system modelling conventions to a widely used framework, we illustrate how such conventions can enhance the usefulness of a framework for overall illustration purposes, delineate hypotheses on agriculture-food security links and examine potential impacts of interventions.

Reduction of Dimensionality in Monte Carlo Simulation of Diffusion in Extracellular Space Surrounding Cubic Cells.

The real-time iontophoretic method has measured volume fraction and tortuosity of the interstitial component of extracellular space in many regions and under different conditions. To interpret these data computer models of the interstitial space (ISS) of the brain are constructed by representing cells as Basic Cellular Structures (BCS) surrounded by a layer of ISS and replicating this combination to make a 3D ensemble that approximates brain tissue with a specified volume fraction. Tortuosity in such models is measured by releasing molecules of zero size into the ISS and allowing them to execute random walks in the ISS of the ensemble using a Monte Carlo algorithm. The required computational resources for such simulations may be high and here we show that in many situations the 3D problem may be reduced to a quasi-1D problem with consequent reduction in resources. We take the simplest BCS in the form of cubes and use MCell software to perform the Monte Carlo simulations but the analysis described here may be extended in principle to more complex BCS and an ISS that has a defined viscosity and an extracellular matrix that interacts with diffusing molecules. In the course of this study we found that the original analytical description of the relation between volume fraction and tortuosity for an ensemble of cubes may require a small correction.

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Modeling complex problems with system dynamics: applications in animal agriculture1.

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction.