Contract design in agriculture supply chains with random yield

In an agricultural setting it is natural to consider yield risk in the context of a three level supply chain: with a small number of suppliers, large numbers of growers, and a small number of buyers. In the cereal growing case that is our focus, there is a supplier of fertiliser, a potentially large number of growers of cereal crops and a buyer, who purchases grain from the growers. The yield depends both on the input level of fertiliser and also on random weather-related factors. We study the impact of a new type of contract structure in which the grower purchases inputs at a discount, but agrees to a reduced price for the crop. The buyer makes a payment to the supplier to compensate for the discount offered. We show how this can coordinate the supply chain and demonstrate the potential advantages of this contract form when producers are risk averse. We look in detail at the implications of the use of these contracts by Australian wheat growers using data generated by APSIM, a growth simulation tool, to understand the connection between yields, fertiliser use and the weather. By using APSIM we can estimate the distribution of yields implied by the grower’s decision on fertiliser application and hence estimate optimal fertiliser use for risk averse growers

Mother-reported pain experience between ages 7 and 10: A prospective study in a population-based birth cohort

Background Trajectory studies suggest considerable stability of persistent or recurrent pain in adolescence. This points to the first decade of life as an important aetiologic window for shaping future pain, where the potential for prevention may be optimised. Objectives We aimed to quantify changes in mother-reported pain experience in children between ages 7 and 10 and describe clusters of different pain experiences defined by complementary pain features. Methods We conducted a prospective study using data from 4036 Generation XXI birth cohort participants recruited in 2005-06. Pain history was reported by mothers at ages 7 and 10 using the Luebeck pain screening questionnaire. We tracked changes in six pain features over time using relative risks (RRs) and their 95% confidence intervals (95% CIs). Clusters were obtained using the k-medoids algorithm. Results The risk of severe pain at age 10 increased with increasing severity at age 7, with RRs ranging from 2.18 (95% CI 1.90, 2.50) for multisite to 4.43 (95% CI 3.19, 6.15) for high frequency pain at age 7. A majority of children (59.4%) had transient or no pain but two clusters included children with stable recurrent pain (n = 404, 10.2% of the sample). One of those (n = 177) was characterised by higher probabilities of multisite pain (74.6% and 66.7% at ages 7 and 10, respectively), with psychosocial triggers/contexts (59.3% and 61.0%) and daily-living restrictions (72.2% and 84.6%). Most children in that cluster (58.3%) also self-reported recent pain at age 10 and had more frequent family history of chronic pain (60.5%). Conclusions All pain features assessed tracked with a positive gradient between ages 7 and 10, arguing for the significance of the first decade of life in the escalation of the pain experience. Multisite pain and psychosocial attributions appeared to be early markers of more adverse pain experiences.This study was funded by the European Regional Development Fund (ERDF), through COMPETE 2020 Operational Programme ‘Competitiveness and Internationalisation’ together with national funding from the Foundation for Science and Technology (FCT)—Portuguese Ministry of Science, Technology and Higher Education—through the projects “STEPACHE—The paediatric roots of amplified pain: from contextual influences to risk stratification” (POCI-01-0145-FEDER-029087, info:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC/SAU-EPI/29087/2017/PT), and “HIneC: When do health inequalities start? Understanding the impact of childhood social adversity on health trajectories from birth to early adolescence” (POCI-01-0145-FEDER-029567, info:eu-repo/grantAgreement/FCT/9471 - RIDTI/info:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC/SAU-PUB/29567/2017/PT017/PT). This work was also supported by the Epidemiology Research Unit—Instituto de Saúde Pública, Universidade do Porto (EPIUnit) (POCI-01-0145-FEDER-006862; UID/DTP/04750/2019), by Administração Regional de Saúde Norte (Regional Department of the Portuguese Ministry of Health) and Calouste Gulbenkian Foundation

Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy. The number of immunogold particles in the cell walls allows the assessment of the differences in their compositions providing a tool to analise the quality of different plants. For its quantization one requires a laborious manual labeling (or annotation) of images containing hundreds of particles. The system that is proposed in this paper can leverage significantly the burden of this manual task. For particle detection we use a LoG filter coupled with a SDA. In order to improve the recognition, we also study the applicability of TL settings for immunogold recognition. TL reuses the learning model of a source problem on other datasets (target problems) containing particles of different sizes. The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells. This novel dataset as well as the code for reproducing our experiments is made publicly available. We determined that the LoG detector alone attained more than 84\% of accuracy with the F-measure. Developing immunogold recognition with TL also provided superior performance when compared with the baseline models augmenting the accuracy rates by 10\%

The population impact of rheumatic and musculoskeletal diseases in relation to other non-communicable disorders: comparing two estimation approaches

The aim of this study was to quantify the population impact of rheumatic and musculoskeletal diseases (RMDs) with other non-communicable diseases (NCDs), using two complementary strategies: standard multivariate models based on global burden of disease (GBD)-defined groups vs. empirical mutually exclusive patterns of NCDs. We used cross-sectional data from the Portuguese Fourth National Health Survey (n = 23,752). Six GBD-defined groups were included: RMDs, chronic obstructive pulmonary disease or asthma, cancer, depression, diabetes or renal failure, and stroke or myocardial infarction. The empirical approach comprised the patterns “low disease probability”, “cardiometabolic conditions”, “respiratory conditions” and “RMDs and depression”. As recommended by the outcome measures in rheumatology (OMERACT) initiative, health outcomes included life impact, pathophysiological manifestations, and resource use indicators. Population attributable fractions (PAF) were computed for each outcome and bootstrap confidence intervals (95% CI) were estimated. Among GBD-defined groups, RMDs had the highest impact across all the adverse health outcomes, from frequent healthcare utilization (PAF 7.8%, 95% CI 6.2–9.3) to negative self-rated health (PAF 18.1%, 95% CI 15.4–20.6). In the empirical approach, patterns “cardiometabolic conditions” and “RMDs and depression” had similar PAF estimates across all adverse health outcomes, but “RMDs and depression” showed significantly higher impact on chronic pain (PAF 8.9%, 95% CI 7.6–10.3) than the remaining multimorbidity patterns. RMDs revealed the greatest population impact across all adverse health outcomes tested, using both approaches. Empirical patterns are particularly interesting to evaluate the impact of RMDs in the context of their co-occurrence with other NCDs.This study received no specific funding. The funding for EPI Unit is obtained from the National Foundation for Science and Technology (FCT UID/DTP/04750/2013/002). FAA is supported by Grant FCT SFRH/BD/85398/2012, TM by Grant FCT SFRH/BD/92370/2013 and RL by Grant FCT SFRH/BPD/88729/2012

A Case Study in Macao

Funding Information: This research was funded by Fundação para a Ciência e Tecnologia, I.P., Portugal, grant number UID/AMB/04085/2020, and the APC was funded by CENSE. Funding Information: The work developed was supported by The Macao Meteorological and Geophysical Bureau (SMG). Publisher Copyright: © 2022 by the authors.Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.publishersversionpublishe

Gains and losses in ecosystem services and disservices after converting native forest to agricultural land on an oceanic island

Habitat conversion to agricultural land is one of the main threats to terrestrial biodiversity and can affect ecosystem processes and cause changes in ecosystem services (ESs) and disservices (EDs). Yet, studies often rely only on the abundance and diversity of the service providers; the effects on ecological processes of habitat conversion are rarely directly monitored. In this study, we used the sentinel approach to evaluate how habitat conversion from native forest to agricultural land affected ESs and EDs on an oceanic island. We quantified herbivory on lettuce plants, invertebrate and vertebrate predation rates on artificial caterpillars, pollination on strawberry plants, seed predation on wheat and mustard seeds, and leaf decomposition rates in native forests, maize fields and pastures on Terceira Island, Azores (Portugal). Herbivory, invertebrate predation rates, and pollination service were not significantly different between habitats. Vertebrate predation rates in native forests (mean 6.1% d⁻¹) were significantly higher than that in pastures (0.3% d⁻¹), or high-elevation maize fields (0.5% d⁻¹), and marginally higher than in low-elevation maize fields (2.2% d⁻¹). Overall seed predation after 48 h was significantly higher on wheat (mean 16.8%) than mustard seeds (5.6%). High-elevation maize fields also had higher seed predation (27.8%) than low-elevation ones (0.6%) or pastures (3.6%), but did not differ from the native forest (12.9%). Decomposition after 90 days was highest in pastures (78.4% and 45.9%, for tea and rooibos, respectively); although no significant differences between habitats were detected, except for low-elevation maize fields (64.4% and 33.6%). Conversion from native forest to cultivated land did not cause a clear decrease in the intensity of the studied ESs/EDs except for vertebrate predation. Using direct monitoring tools to simultaneously and consistently quantify multiple ecological processes is not only possible but needed, as ecological processes can respond differently to landscape changes.This work was financed by FEDER (European Regional Development Fund) in 85% and by Azorean public funds by 15% through Operational Program Azores 2020, under the project AGRO-ECOSERVICES (ACORES01-0145-FEDER-000073).info:eu-repo/semantics/publishedVersio

Quantitative modelling of hip fracture trends in 14 European countries: testing variations of a shared reversal over time

Qualitative similarities between hip fracture trends in different countries suggests variations of the same epidemic. We tested a single statistical shape to describe time trends in Europe, while allowing for country-level variability. Using data from 14 countries, we modelled incidence rates over time using linear mixed-effects models, including the fixed effects of calendar year and age. Random effects were tested to quantify country-level variability in background rates, timing of trend reversal and tempo of reversal. Mixture models were applied to identify clusters of countries defined by common behavioural features. A quadratic function of time, with random effects for background rates and timing of trend reversal, adjusted well to the observed data. Predicted trend reversal occurred on average in 1999 in women (peak incidence about 600 per 100 000) and 2000 in men (about 300 per 100 000). Mixture modelling of country-level effects suggested three clusters for women and two for men. In both sexes, Scandinavia showed higher rates but earlier trend reversals, whereas later trend reversals but lower peak incidences were found in Southern Europe and most of Central Europe. Our finding of a similar overall reversal pattern suggests that different countries show variations of a shared hip fracture epidemic

The greatest air quality experiment ever: Policy suggestions from the COVID-19 lockdown in twelve European cities

COVID-19 (Coronavirus disease 2019) hit Europe in January 2020. By March, Europe was the active centre of the pandemic. As a result, widespread "lockdown" measures were enforced across the various European countries, even if to a different extent. Such actions caused a dramatic reduction, especially in road traffic. This event can be considered the most significant experiment ever conducted in Europe to assess the impact of a massive switch-off of atmospheric pollutant sources. In this study, we focus on in situ concentration data of the main atmospheric pollutants measured in twelve European cities, characterized by different climatology, emission sources, and strengths. We propose a methodology for the fair comparison of the impact of lockdown measures considering the non-stationarity of meteorological conditions and emissions, which are progressively declining due to the adoption of stricter air quality measures. The analysis of these unmatched circumstances allowed us to estimate the impact of a nearly zero-emission urban transport scenario on air quality in 12 European cities. The clearest result, common to all the cities, is that a dramatic traffic reduction effectively reduces NO2 concentrations. In contrast, each city’s PM and ozone concentrations can respond differently to the same type of emission reduction measure. From the policy point of view, these findings suggest that measures targeting urban traffic alone may not be the only effective option for improving air quality in cities