ALAD and APOE polymorphisms are associated with lead and mercury levels in Italian pregnant women and their newborns with adequate nutritional status of zinc and selenium

The impacts of single-nucleotide polymorphisms (SNPs) in ALAD and VDR genes on Pb health effects and/or kinetics are inconclusive at low exposure levels, while studies including APOE SNPs are rare. In this study, we examined the associations of ALAD, VDR and APOE SNPs with exposure biomarkers of Pb and other trace elements (TEs) in Italian pregnant women (N = 873, aged 18–44 years) and their newborns (N = 619) with low-level mixed-element exposure through diet, the environment or endogenously. DNA from maternal peripheral venous blood (mB), sampled during the second and third trimesters, was genotyped for ALAD (rs1800435, rs1805313, rs1139488, rs818708), VDR (rs2228570, rs1544410, rs7975232, rs731236) and APOE (rs429358, rs7421) using TaqMan SNP assays. Personal and lifestyle data and TE levels (mB, maternal plasma, hair and mixed umbilical cord blood [CB]) from the PHIME project were used. Multiple linear regression models, controlling for confounding variables, were performed to test the associations between SNPs and TEs. The geometric means of mB-Pb, mB-Hg, mB-As and mB-Cd (11.0 ng/g, 2.16 ng/g, 1.38 ng/g and 0.31 ng/g, respectively) indicated low exposure levels, whereas maternal plasma Zn and Se (0.72 μg/mL and 78.6 ng/g, respectively) indicated adequate micronutritional status. Variant alleles of ALAD rs1800435 and rs1805313 were negatively associated with mB-Pb levels, whereas a positive association was observed for rs1139488. None of the VDR SNPs or their haplotypes had any association with Pb levels. Regarding APOE, the ϵ4 allele was associated with lower mB-Hg and CB-Hg, while a positive association was found with the ϵ2 allele and CB-Pb when the model included only newborn girls. The observed associations indicate possible modification effects of ALAD and APOE SNPs on Pb or Hg kinetics in women and their newborns with low exposure to non-essential TEs, as well as an adequate nutritional status of Zn and Se

When do apples stop growing, and why does it matter?

Apples in the commercial food chain are harvested up to two weeks before maturity. We explore apple fruit development through the growing season to establish the point at which physical features differentiating those cultivars become evident. This is relevant both for the understanding of the growing process and to ensure that any identification and classification tools can be used both on ripened-on-tree and stored fruit. Current literature presents some contradictory findings on apple growth, we studied 12 apple cultivars in the Brogdale National Fruit Collection, UK over two seasons to establish patterns of growth. Fruit were sampled at regular time points throughout the growing season and four morphometrics (maximum length, maximum diameter, weight, and centroid size) were collected. These were regressed against growing degree days in order to appropriately describe the growth pattern observed. All four morphometrics were adequately described using log-log linear regressions, with adjusted R2 estimates ranging from 78.3% (maximum length) to 86.7% (weight). For all four morphometrics, a 10% increase in growing degree days was associated with a 1% increase in the morphometric. Our findings refine previous work presenting rapid early growth followed by a plateau in later stages of development and contrast with published expo-linear models. We established that apples harvested for commercial storage purposes, two weeks prior to maturity, showed only a modest decrease in size compared with ripened-on-tree fruit, demonstrating that size morphometric approaches are appropriate for classification of apple fruit at point of harvest

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Harmonized human biomonitoring in European children, teenagers and adults: EU-wide exposure data of 11 chemical substance groups from the HBM4EU Aligned Studies (2014-2021)

As one of the core elements of the European Human Biomonitoring Initiative (HBM4EU) a human biomonitoring (HBM) survey was conducted in 23 countries to generate EU-wide comparable HBM data. This survey has built on existing HBM capacity in Europe by aligning national or regional HBM studies, referred to as the HBM4EU Aligned Studies. The HBM4EU Aligned Studies included a total of 10,795 participants of three age groups: (i) 3,576 children aged 6-12 years, (ii) 3,117 teenagers aged 12-18 years and (iii) 4,102 young adults aged 20-39 years. The participants were recruited between 2014 and 2021 in 11-12 countries per age group, geographically distributed across Europe. Depending on the age group, internal exposure to phthalates and the substitute DINCH, halogenated and organophosphorus flame retardants, per- and polyfluoroalkyl substances (PFASs), cadmium, bisphenols, polycyclic aromatic hydrocarbons (PAHs), arsenic species, acrylamide, mycotoxins (deoxynivalenol (total DON)), benzophenones and selected pesticides was assessed by measuring substance specific biomarkers subjected to stringent quality control programs for chemical analysis. For substance groups analyzed in different age groups higher average exposure levels were observed in the youngest age group, i.e., phthalates/DINCH in children versus teenagers, acrylamide and pesticides in children versus adults, benzophenones in teenagers versus adults. Many biomarkers in teenagers and adults varied significantly according to educational attainment, with higher exposure levels of bisphenols, phthalates, benzophenones, PAHs and acrylamide in participants (from households) with lower educational attainment, while teenagers from households with higher educational attainment have higher exposure levels for PFASs and arsenic. In children, a social gradient was only observed for the non-specific pyrethroid metabolite 3-PBA and di-isodecyl phthalate (DiDP), with higher levels in children from households with higher educational attainment. Geographical variations were seen for all exposure biomarkers. For 15 biomarkers, the available health-based HBM guidance values were exceeded with highest exceedance rates for toxicologically relevant arsenic in teenagers (40%), 3-PBA in children (36%), and between 11 and 14% for total DON, Sigma (PFOA + PFNA + PFHxS + PFOS), bisphenol S and cadmium. The infrastructure and harmonized approach succeeded in obtaining comparable European wide internal exposure data for a prioritized set of 11 chemical groups. These data serve as a reference for comparison at the global level, provide a baseline to compare the efficacy of the European Commission's chemical strategy for sustainability and will give leverage to national policy makers for the implementation of targeted measures

Harmonized human biomonitoring in European children, teenagers and adults: EU-wide exposure data of 11 chemical substance groups from the HBM4EU Aligned Studies (2014–2021)

As one of the core elements of the European Human Biomonitoring Initiative (HBM4EU) a human biomonitoring (HBM) survey was conducted in 23 countries to generate EU-wide comparable HBM data. This survey has built on existing HBM capacity in Europe by aligning national or regional HBM studies, referred to as the HBM4EU Aligned Studies. The HBM4EU Aligned Studies included a total of 10,795 participants of three age groups: (i) 3,576 children aged 6–12 years, (ii) 3,117 teenagers aged 12–18 years and (iii) 4,102 young adults aged 20–39 years. The participants were recruited between 2014 and 2021 in 11–12 countries per age group, geographically distributed across Europe. Depending on the age group, internal exposure to phthalates and the substitute DINCH, halogenated and organophosphorus flame retardants, per- and polyfluoroalkyl substances (PFASs), cadmium, bisphenols, polycyclic aromatic hydrocarbons (PAHs), arsenic species, acrylamide, mycotoxins (deoxynivalenol (total DON)), benzophenones and selected pesticides was assessed by measuring substance specific biomarkers subjected to stringent quality control programs for chemical analysis. For substance groups analyzed in different age groups higher average exposure levels were observed in the youngest age group, i.e., phthalates/DINCH in children versus teenagers, acrylamide and pesticides in children versus adults, benzophenones in teenagers versus adults. Many biomarkers in teenagers and adults varied significantly according to educational attainment, with higher exposure levels of bisphenols, phthalates, benzophenones, PAHs and acrylamide in participants (from households) with lower educational attainment, while teenagers from households with higher educational attainment have higher exposure levels for PFASs and arsenic. In children, a social gradient was only observed for the non-specific pyrethroid metabolite 3-PBA and di-isodecyl phthalate (DiDP), with higher levels in children from households with higher educational attainment. Geographical variations were seen for all exposure biomarkers. For 15 biomarkers, the available health-based HBM guidance values were exceeded with highest exceedance rates for toxicologically relevant arsenic in teenagers (40%), 3-PBA in children (36%), and between 11 and 14% for total DON, Σ (PFOA + PFNA + PFHxS + PFOS), bisphenol S and cadmium. The infrastructure and harmonized approach succeeded in obtaining comparable European wide internal exposure data for a prioritized set of 11 chemical groups. These data serve as a reference for comparison at the global level, provide a baseline to compare the efficacy of the European Commission's chemical strategy for sustainability and will give leverage to national policy makers for the implementation of targeted measures

Automated Crop Yield Estimation for Apple Orchards

Abstract Crop yield estimation is an important task in apple orchard management. The current manual sampling-based yield estimation is time-consuming, labor-intensive and inaccurate. To deal with this challenge, we developed a computer vision-based system for automated, rapid and accurate yield estimation. The system uses a two-camera stereo rig for image acquisition. It works at nighttime with controlled artificial lighting to reduce the variance of natural illumination. An autonomous orchard vehicle is used as the support platform for automated data collection. The system scans both sides of each tree row in orchards. A computer vision algorithm detects and registers apples from acquired sequential images, and then generates apple counts as crop yield estimation. We deployed the yield estimation system in Washington state in September, 2011. The results show that the system works well with both red and green apples in the tall-spindle planting system. The crop yield estimation errors are-3.2 % for a red apple block with about 480 trees, and 1.2 % for a green apple block with about 670 trees.

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool

Biomarkers of exposure in environment-wide association studies – Opportunities to decode the exposome using human biomonitoring data

Background: The European Union's 7th Framework Programme (EU's FP7) project HEALS – Health and Environment-wide Associations based on Large Population Surveys – aims a refinement of the methodology to elucidate the human exposome. Human biomonitoring (HBM) provides a valuable tool for understanding the magnitude of human exposure from all pathways and sources. However, availability of specific biomarkers of exposure (BoE) is limited. Objectives: The objective was to summarize the availability of BoEs for a broad range of environmental stressors and exposure determinants and corresponding reference and exposure limit values and biomonitoring equivalents useful for unraveling the exposome using the framework of environment-wide association studies (EWAS). Methods: In a face-to-face group discussion, scope, content, and structure of the HEALS deliverable “Guidelines for appropriate BoE selection for EWAS studies” were determined. An expert-driven, distributed, narrative review process involving around 30 individuals of the HEALS consortium made it possible to include extensive information targeted towards the specific characteristics of various environmental stressors and exposure determinants. From the resulting 265 page report, targeted information about BoE, corresponding reference values (e.g., 95th percentile or measures of central tendency), exposure limit values (e.g., the German HBM I and II values) and biomonitoring equivalents (BEs) were summarized and updated. Results: 64 individual biological, chemical, physical, psychological and social environmental stressors or exposure determinants were included to fulfil the requirements of EWAS. The list of available BoEs is extensive with a number of 135; however, 12 of the stressors and exposure determinants considered do not leave any measurable specific substance in accessible body specimens. Opportunities to estimate the internal exposure stressors not (yet) detectable in human specimens were discussed. Conclusions: Data about internal exposures are useful to decode the exposome. The paper provides extensive information for EWAS. Information included serves as a guideline – snapshot in time without any claim to comprehensiveness – to interpret HBM data and offers opportunities to collect information about the internal exposure of stressors if no specific BoE is available