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ABSTRACT: Dairy cow fertility is a complex trait that depends on the cow's physiological status, the farm's environmental and management conditions, and their interactions. Already the slightest improvement in fertility can positively impact a farm's profitability and sustainability. In research, milk progesterone (P4) has often been used as an accurate and feasible way to identify a dairy cow's reproduction status. Moreover, in Europe and Canada, it has been used to improve fertility management on commercial farms as it allows to accurately identify reproduction issues, pregnancy, and the optimal insemination window. An on-farm P4 device (OPD) automatically samples, measures, and monitors the milk P4 concentration of individual cows. To this end, the P4 data are smoothed to be robust for measurement errors and outliers, and fixed thresholds are used to estimate the time of luteolysis preceding ovulation, thereby generating a luteolysis alert (LA). By smoothing the P4 data, the OPD introduces a time lag on the LA. Variation in this time lag is not considered in the estimation of the optimal insemination window that is advised to the farmer. Ignoring this variation might decrease the accuracy of the optimal insemination window and, therefore, decreases the likelihood of conception. We hypothesize that considering the length of the time lag and adapting the advice accordingly improves the conception rate. This observational retrospective study uses an extensive dataset from 17 commercial dairy farms that are equipped with an OPD. We estimated the time lag on the alerts and evaluated their relationship with the interval from LA to insemination for successful (n = 3,721) and unsuccessful inseminations (n = 3,896) separately. Results showed that the probability of conception increases when a longer LA time lag is compensated with a shorter interval from LA to insemination and vice versa. In addition, for successful inseminations, we found a clear negative relation between the time lag and the interval from LA to insemination and the interval was significantly shorter when the time lag of the LA was longer. This negative relation between time lag and interval from LA to insemination was less pronounced for unsuccessful inseminations. Additionally, we evaluated the conception rates for inseminations that are performed too early, in time, or too late with respect to the optimal insemination window advised by the OPD, in function of their associated time lags. We found that, for inseminations that were preceded by a short time lag (

Abstract An established hallmark of cancer cells is metabolic reprogramming, largely consisting in the exacerbated glucose uptake. Adipocytes in the tumor microenvironment contribute toward breast cancer (BC) progression and are highly responsive to metabolic fluctuations. Metabolic conditions characterizing obesity and/or diabetes associate with increased BC incidence and mortality. To explore BC-adipocytes interaction and define the impact of glucose in such dialogue, Mammary Adipose-derived Mesenchymal Stem Cells (MAd-MSCs) were differentiated into adipocytes and co-cultured with ER+ BC cells while exposed to glucose concentration resembling hyperglycemia or normoglycemia in humans (25mM or 5.5mM). The transcriptome of both cell types in co-culture as in mono-culture was profiled by RNA-Seq to define the impact of adipocytes on BC cells and viceversa (i), the action of glucose on BC cells, adipocytes (ii) and their crosstalk (iii). Noteworthy, we provided evidence that co-culture with adipocytes in a glucose-rich environment determined a re-program of BC cell transcriptome driving lipid accumulation, a hallmark of BC aggressiveness, promoting stem-like properties and reducing Tamoxifen responsiveness. Moreover, our data point out to a transcriptional effect through which BC cells induce adipocytes de-lipidation, paralleled by pluripotency gain, as source of lipids when glucose lowering occurs. Thus, modulating plasticity of peri-tumoral adipocytes may represent a key point for halting BC progression in metabolically unbalanced patients.

ABSTRACT: Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow one to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post–peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large dataset, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance.

Abstract Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters’ association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

ABSTRACT: Lameness in dairy cattle is a costly and highly prevalent problem that affects all aspects of sustainable dairy production, including animal welfare. Automation of gait assessment would allow monitoring of locomotion in which the cows' walking patterns can be evaluated frequently and with limited labor. With the right interpretation algorithms, this could result in more timely detection of locomotion problems. This in turn would facilitate timely intervention and early treatment, which is crucial to reduce the effect of abnormal behavior and pain on animal welfare. Gait features of dairy cows can potentially be derived from key points that locate crucial anatomical points on a cow's body. The aim of this study is 2-fold: (1) to demonstrate automation of the detection of dairy cows' key points in a practical indoor setting with natural occlusions from gates and races, and (2) to propose the necessary steps to postprocess these key points to make them suitable for subsequent gait feature calculations. Both the automated detection of key points as well as the postprocessing of them are crucial prerequisites for camera-based automated locomotion monitoring in a real farm environment. Side-view video footage of 34 Holstein-Friesian dairy cows, captured when exiting the milking parlor, were used for model development. From these videos, 758 samples of 2 successive frames were extracted. A previously developed deep learning model called T-LEAP was trained to detect 17 key points on cows in our indoor farm environment with natural occlusions. To this end, the dataset of 758 samples was randomly split into a train (n = 22 cows; no. of samples = 388), validation (n = 7 cows; no. of samples = 108), and test dataset (n = 15 cows; no. of samples = 262). The performance of T-LEAP to automatically assign key points in our indoor situation was assessed using the average percentage of correctly detected key points using a threshold of 0.2 of the head length (PCKh0.2). The model's performance on the test set achieved a good result with PCKh0.2: 89% on all 17 key points together. Detecting key points on the back (n = 3 key points) of the cow had the poorest performance PCKh0.2: 59%. In addition to the indoor performance of the model, a more detailed study of the detection performance was conducted to formulate postprocessing steps necessary to use these key points for gait feature calculations and subsequent automated locomotion monitoring. This detailed study included the evaluation of the detection performance in multiple directions. This study revealed that the performance of the key points on a cows' back were the poorest in the horizontal direction. Based on this more in-depth study, we recommend the implementation of the outlined postprocessing techniques to address the following issues: (1) correcting camera distortion, (2) rectifying erroneous key point detection, and (3) establishing the necessary procedures for translating hoof key points into gait features.

Abstract Background Tissue-specific insulin resistance (IR) predominantly in muscle (muscle IR) or liver (liver IR) has previously been linked to distinct fasting metabolite profiles, but postprandial metabolite profiles have not been investigated in tissue-specific IR yet. Given the importance of postprandial metabolic impairments in the pathophysiology of cardiometabolic diseases, we compared postprandial plasma metabolite profiles in response to a high-fat mixed meal between individuals with predominant muscle IR or liver IR. Methods This cross-sectional study included data from 214 women and men with BMI 25–40 kg/m2, aged 40–75 years, and with predominant muscle IR or liver IR. Tissue-specific IR was assessed using the muscle insulin sensitivity index (MISI) and hepatic insulin resistance index (HIRI), which were calculated from the glucose and insulin responses during a 7-point oral glucose tolerance test. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, 240 min) consumption of a high-fat mixed meal and 247 metabolite measures, including lipoproteins, cholesterol, triacylglycerol (TAG), ketone bodies, and amino acids, were quantified using nuclear magnetic resonance spectroscopy. Differences in postprandial plasma metabolite iAUCs between muscle and liver IR were tested using ANCOVA with adjustment for age, sex, center, BMI, and waist-to-hip ratio. P-values were adjusted for a false discovery rate (FDR) of 0.05 using the Benjamini–Hochberg method. Results Sixty-eight postprandial metabolite iAUCs were significantly different between liver and muscle IR. Liver IR was characterized by greater plasma iAUCs of large VLDL (p = 0.004), very large VLDL (p = 0.002), and medium-sized LDL particles (p = 0.026), and by greater iAUCs of TAG in small VLDL (p = 0.025), large VLDL (p = 0.003), very large VLDL (p = 0.002), all LDL subclasses (all p

Abstract Invasive alien species are one of the major threats to global biodiversity, ecosystem integrity, nature's contributions to people and human health. While scenarios about potential future developments have been available for other global change drivers for quite some time, we largely lack an understanding of how biological invasions might unfold in the future across spatial scales. Based on previous work on global invasion scenarios, we developed a workflow to downscale global scenarios to a regional and policy‐relevant context. We applied this workflow at the European scale to create four European scenarios of biological invasions until 2050 that consider different environmental, socio‐economic and socio‐cultural trajectories, namely the European Alien Species Narratives (Eur‐ASNs). We compared the Eur‐ASNs with their previously published global counterparts (Global‐ASNs), assessing changes in 26 scenario variables. This assessment showed a high consistency between global and European scenarios in the logic and assumptions of the scenario variables. However, several discrepancies in scenario variable trends were detected that could be attributed to scale differences. This suggests that the workflow is able to capture scale‐dependent differences across scenarios. We also compared the Global‐ and Eur‐ASNs with the widely used Global and European Shared Socioeconomic Pathways (SSPs), a set of scenarios developed in the context of climate change to capture different future socio‐economic trends. Our comparison showed considerable divergences in the scenario space occupied by the different scenarios, with overall larger differences between the ASNs and SSPs than across scales (global vs. European) within the scenario initiatives. Given the differences between the ASNs and SSPs, it seems that the SSPs do not adequately capture the scenario space relevant to understanding the complex future of biological invasions. This underlines the importance of developing independent but complementary scenarios focussed on biological invasions. The downscaling workflow we implemented and presented here provides a tool to develop such scenarios across different regions and contexts. This is a major step towards an improved understanding of all major drivers of global change, including biological invasions. Read the free Plain Language Summary for this article on the Journal blog.

ABSTRACT: The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Harassment and abuse represent a pervasive and critical problem in sport with far-reaching consequences. Survivors’ testimonials underscore the profound and enduring impact of these experiences at individual, interpersonal, organizational and community level. Many of their stories reveal painful inaction from responsible adults in the sport organization, aggravating the harm. Other contributing factors to the harm inflicted include a culture of silence, lack of knowledge and understanding of what constitutes abuse, unawareness of reporting and supporting mechanisms, and fear of potential consequences. While effective bystander interventions have been developed outside the sport context, particularly targeting students in higher education, such initiatives have yet to be extensively adapted and assessed within the sport context. To address this gap, the Safe Sport Allies Erasmus+ collaborative partnership relied on the intervention mapping approach as a guiding framework to systematically develop a bystander training program (i.e., Safe Sport Allies) to train youth sport participants and youth sport coaches to act as effective bystanders. The current paper describes the comprehensive development process and provides an overview of implementation and evaluation possibilities. Throughout the paper, it is explained how each step of the Intervention Mapping approach shaped the Safe Sport Allies bystander training program. The program development, and the developed plans for implementation and evaluation are presented, shedding light on challenges encountered. The bystander training program developed in this paper and the implementation and evaluation plans can serve as an outline to build future interventions within this critical domain of safeguarding in sport.

Bedinvetmab (Librela®) represents a new class of canine osteoarthritis pain therapy. The aim of this study was to understand patient selection, usage behaviours, and satisfaction amongst veterinarians using bedinvetmab. Overall, 1932 patient record forms (PRF) were collected from 375 veterinarians across five countries in Europe. Veterinarians were asked to provide 5–7 PRF representing an average patient prescribed bedinvetmab. Veterinarian satisfaction with bedinvetmab usage averaged 8.0 out of 10.0 across all countries. Dissatisfaction as a reason for discontinuation was less than 1% for veterinarians. Veterinarians prescribed bedinvetmab broadly, across patient severity stages, weights, and ages. Adherence to monthly dosing per the product label was over 99%, and compliance with bedinvetmab treatment regimens was 85%. Following initiation of bedinvetmab, the proportion of patients requiring multiple pharmacological therapies for osteoarthritis pain fell from 47% to 31% (p < 0.05). After initiation of bedinvetmab, the mean total number of pharmacological therapies per patient across the population was 1.3, a reduction from 1.9 pre-treatment (p < 0.05). This investigation provides evidence on the benefit of bedinvetmab use post-launch in a broad population of dogs across the five most populous countries in western Europe. Compliance and satisfaction appear high and the use of other analgesic therapies to treat osteoarthritis pain is reduced in most cases following administration of bedinvetmab.

Abstract Background Diagnosis of infected individuals with Mycoplasma genitalium (MG) is often performed by real-time PCR or transcription-mediated amplification (TMA). A limitation of the MG-TMA assay is the relatively short time span of 24 h in which the collected urine is required to be transferred into a Urine Specimen Transport Tube, according to the manufacturer’s guidelines. If not transferred within 24 h, the manufacturer’s claimed sensitivity cannot be guaranteed anymore, and samples may instead be tested with an in-house validated real-time PCR, despite its recognized lower sensitivity. This study aimed to validate an exception to the sample transport and storage conditions of the MG-TMA assay as set by the manufacturer, being the prolongation of the acceptable testing time limit of 24 h. Methods From June to December 2022, first-void urines were collected from clients attending the clinic for sexual health in Amsterdam, the Netherlands. Urine samples that tested positive for MG by TMA assay at the day of collection were concomitantly stored at room (18–24 °C) and refrigerator temperature (4–8 °C) for 15 days. The stored urine samples were tested with both an in-house validated real-time PCR and MG-TMA assay after transfer of the original urine samples to the respective test tubes at 3, 7, 12 and 15 days post collection. Results In total, 47 MG-positive urine samples were collected, stored and tested for MG by real-time PCR and TMA assays. After storage at room temperature, the MG-detection rate by TMA was significantly higher compared to real-time PCR, at days 0 (p ≤ 0.001), 7 (p ≤ 0.001) and 12 (p

IntroductionSpecies distribution models (SDMs) are often used to produce risk maps to guide conservation management and decision-making with regard to invasive alien species (IAS). However, gathering and harmonizing the required species occurrence and other spatial data, as well as identifying and coding a robust modeling framework for reproducible SDMs, requires expertise in both ecological data science and statistics.MethodsWe developed WiSDM, a semi-automated workflow to democratize the creation of open, reproducible, transparent, invasive alien species risk maps. To facilitate the production of IAS risk maps using WiSDM, we harmonized and openly published climate and land cover data to a 1 km2 resolution with coverage for Europe. Our workflow mitigates spatial sampling bias, identifies highly correlated predictors, creates ensemble models to predict risk, and quantifies spatial autocorrelation. In addition, we present a novel application for assessing the transferability of the model by quantifying and visualizing the confidence of its predictions. All modeling steps, parameters, evaluation statistics, and other outputs are also automatically generated and are saved in a R markdown notebook file.ResultsOur workflow requires minimal input from the user to generate reproducible maps at 1 km2 resolution for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission representative concentration pathway (RCP) scenarios. The confidence associated with the predicted risk for each 1km2 pixel is also mapped, enabling the intuitive visualization and understanding of how the confidence of the model varies across space and RCP scenarios.DiscussionOur workflow can readily be applied by end users with a basic knowledge of R, does not require expertise in species distribution modeling, and only requires an understanding of the ecological theory underlying species distributions. The risk maps generated by our repeatable workflow can be used to support IAS risk assessment and surveillance.

Summary: Invasive alien species (IAS) adversely impact biodiversity, ecosystem functions, and socio-economics. Citizen science can be an effective tool for IAS surveillance, management, and research, providing large datasets over wide spatial extents and long time periods, with public participants generating knowledge that supports action. We demonstrate how citizen science has contributed knowledge across the biological invasion process, especially for early detection and distribution mapping. However, we recommend that citizen science could be used more for assessing impacts and evaluating the success of IAS management. Citizen science does have limitations, and we explore solutions to two key challenges: ensuring data accuracy and dealing with uneven spatial coverage of potential recorders (which limits the dataset’s “fit for purpose”). Greater co-development of citizen science with public stakeholders will help us better realize its potential across the biological invasion process and across ecosystems globally while meeting the needs of participants, local communities, scientists, and decision-makers.

Abstract Brittle stars, unlike most other echinoderms, do not use their small tube feet for locomotion but instead use their flexible arms to produce a rowing or reverse rowing movement. They are among the fastest-moving echinoderms with the ability of complex locomotory behaviors. Considering the high species diversity and variability in morphotypes, a proper understanding of intra- and interspecies variation in arm flexibility and movement is lacking. This study focuses on the exploration of the methods to investigate the variability in brittle star locomotion and individual arm use. We performed a two-dimensional (2D) image processing on horizontal movement only. The result indicated that sinuosity, disc displacement and arm angle are important parameters to interpret ophiuroid locomotion. A dedicated Python script to calculate the studied movement parameters and visualize the results applicable to all 5-armed brittle stars was developed. These results can serve as the basis for further research in robotics inspired by brittle star locomotion.

Sensor technologies are increasingly used to monitor laboratory animal behaviour. The aim of this study was to investigate the added value of using accelerometers and video to monitor the activity and drinking behaviour of three rams from 5 days before to 22 days after inoculation with Toxoplasma gondii. We computed the activity from accelerometer data as the vectorial dynamic body acceleration (VDBA). In addition, we assessed individual drinking behaviour from video, using frame differencing above the drinker to identify drinking bouts, and Aruco markers for individual identification. Four days after inoculation, rams developed fever and activity decreased. The daytime VDBA from days 4 to 10 was 60–80% of that before inoculation. Animal caretakers scored rams as lethargic on days 5 and 6 and, for one ram, also on the morning of day 7. Video analysis showed that each ram decreased its number of visits to the drinker, as well as its time spent at the drinker, by up to 50%. The fever and corresponding sickness behaviours lasted until day 10. Overall, while we recognize the limited conclusiveness due to the small number of animals, the sensor technologies provided continuous, individual, detailed, and objective data and offered additional insights as compared to routine observations. We recommend the wider implementation of such technologies in animal disease trials to refine experiments and guarantee the quality of experimental results.

Monitoring of milk composition can support several dimensions of dairy management such as identification of the health status of individual dairy cows and the safeguarding of dairy quality. The quantification of milk composition has been traditionally executed employing destructive chemical or laboratory Fourier-transform infrared (FTIR) spectroscopy analyses which can incur high costs and prolonged waiting times for continuous monitoring. Therefore, modern technology for milk composition quantification relies on non-destructive near-infrared (NIR) spectroscopy which is not invasive and can be performed on-farm, in real-time.The current dataset contains NIR spectral measurements in transmittance mode in the wavelength range from 960 nm to 1690 nm of 1224 individual raw milk samples, collected on-farm over an eight-week span in 2017, at the experimental dairy farm of the province of Antwerp, ‘Hooibeekhoeve’ (Geel, Belgium). For these spectral measurements, laboratory reference values corresponding to the three main components of raw milk (fat, protein and lactose), urea and somatic cell count (SCC) are included.This data has been used to build multivariate calibration models to predict the three milk compounds, as well as develop strategies to monitor the prediction performance of the calibration models.

Abstract Allele-specific expression (ASE) analysis detects the relative abundance of alleles at heterozygous loci as a proxy for cis-regulatory variation, which affects the personal transcriptome and proteome. This study describes the development and application of an ASE analysis pipeline on a unique cohort of 87 well phenotyped and RNA sequenced patients from the Maastricht Cardiomyopathy Registry with dilated cardiomyopathy (DCM), a complex genetic disorder with a remaining gap in explained heritability. Regulatory processes for which ASE is a proxy might explain this gap. We found an overrepresentation of known DCM-associated genes among the significant results across the cohort. In addition, we were able to find genes of interest that have not been associated with DCM through conventional methods such as genome-wide association or differential gene expression studies. The pipeline offers RNA sequencing data processing, individual and population level ASE analyses as well as group comparisons and several intuitive visualizations such as Manhattan plots and protein–protein interaction networks. With this pipeline, we found evidence supporting the case that cis-regulatory variation contributes to the phenotypic heterogeneity of DCM. Additionally, our results highlight that ASE analysis offers an additional layer to conventional genomic and transcriptomic analyses for candidate gene identification and biological insight.

Invasive alien species (IAS) are a key driver of global biodiversity loss. Reducing their spread and impact is a target of the Sustainable Development Goals (SDG target 15.8) and of the EU IAS Regulation 1143/2014. The use of citizen science offers various benefits to alien species’ decision-making and to society, since public participation in research and management boosts awareness, engagement and scientific literacy and can reduce conflict in IAS management. We report the results of a survey on alien species citizen science initiatives within the framework of the European Cooperation in Science and Technology (COST) Action Alien-CSI. We gathered metadata on 103 initiatives across 41 countries, excluding general biodiversity reporting portals, spanning from 2005 to 2020, offering the most comprehensive account of alien species citizen science initiatives on the continent to date. We retrieved information on project scope, policy relevance, engagement methods, data capture, data quality and data management, methods and technologies applied and performance indicators such as the number of records coming from projects, the numbers of participants and publications. The 103 initiatives were unevenly distributed geographically, with countries with a tradition of citizen science showing more active projects. The majority of projects were contributory and were run at a national scale, targeting the general public, alien plants and insects, and terrestrial ecosystems. These factors of project scope were consistent between geographic regions. Most projects focused on collecting species presence or abundance data, aiming to map presence and spread. As 75% of the initiatives specifically collected data on IAS of Union Concern, citizen science in Europe is of policy relevance. Despite this, only half of the projects indicated sustainable funding. Nearly all projects had validation in place to verify species identifications. Strikingly, only about one third of the projects shared their data with open data repositories such as the Global Biodiversity Information Facility or the European Alien Species Information Network. Moreover, many did not adhere to the principles of FAIR data management. Finally, certain factors of engagement, feedback and support, had significant impacts on project performance, with the provision of a map with sightings being especially beneficial. Based on this dataset, we offer suggestions to strengthen the network of IAS citizen science projects and to foster knowledge exchange among citizens, scientists, managers, policy-makers, local authorities, and other stakeholders.

Pay-for-outcome financing mechanisms have been used to address agricultural runoffs to overcome the inefficiencies associated with push-based solutions, which are dependent on subsidies or philanthropic funding. As a market-based approach, pay-for-outcome platforms seek to incentivize sustainable practices, compensated by beneficiaries of the positive outcomes. Execution of pay-for-outcome financing mechanisms in an agriculture context is a complex transaction, involving investors, farmers, third party verifiers of outcomes, government and corporate beneficiaries, and thus requires a costly governance structure. Effective governance mechanisms are needed to meet the transaction costs identified in performance measurements. This study investigates the efficacy of blockchain technology to address transaction costs in pay-for-outcome financing for sustainable agriculture. Through a proof-of-concept, this study quantifies and explores the potential cost-saving benefits of utilizing blockchain. The proof-of-concept is an application of blockchain within a pay-for-outcome incentive model, namely the Soil and Water Outcomes Fund, for sustainable agriculture. Utilizing the Ethereum blockchain, transactions are facilitated through crypto wallets and a hybrid smart contract, while precipitation is used as a proxy for agricultural runoff measurements. Drawing from Transaction Cost Economics theory, a discussion is presented on how blockchains can reduce transaction costs, enhancing the governance and efficiency of pay-for-outcome mechanisms. Furthermore, the article presents blockchain transaction fees in the context of the scale of operations, considering the total number of participants in the Soil and Water Outcomes Fund. Our findings indicate that blockchain technology has the capacity to simplify intricate transactions, boost measurement accuracy, cut administrative expenses, and foster trust and transparency among stakeholders, thereby reducing the overall transaction costs associated with pay-for-outcome incentives. While blockchain has its limitations and is not a universally applicable solution for every type of transaction cost, we believe that blockchains are well-suited to facilitate pay-for-outcome financing such as the Soil and Water Outcomes Fund.

The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

Global sustainability issues such as climate change, biodiversity loss and food security require food systems to become more resource efficient and better embedded in the local environment. This needs a transition towards more diverse, circular and low-input dairy farming systems with animals best suited to the specific environmental conditions. When varying environmental challenges are posed to animals, cows need to become resilient to disturbances they face. This resilience of dairy cows for disturbances can be quantified using sensor features and resilience indicators derived from daily milk yield records. The aim of this study was to explore milk yield based sensor features and resilience indicators for different cattle groups according to their breeds and herds. To this end, we calculated 40 different features to describe the dynamics and variability in milk production of first parity dairy cows. After correction for milk production level, we found that various aspects of the milk yield dynamics, milk yield variability and perturbation characteristics indeed differed across herds and breeds. On farms with a lower breed proportion of Holstein Friesian across cows, there was more variability in the milk yield, but perturbations were less severe upon critical disturbances. Non-Holstein Friesian breeds had a more stable milk production with less (severe) perturbations. These differences can be attributed to differences in genetics, environments, or both. This study demonstrates the potential to use milk yield sensor features and resilience indicators as a tool to quantify how cows cope with more dynamic production conditions and select animals for features that best suit a farms’ breeding goal and specific environment.

BackgroundJoint discomfort is a widespread and growing problem in active adults. The rising interest in preventative nutrition has increased the demand for supplements reducing joint discomfort. Protocols assessing the effect of a nutritional intervention on health commonly involve a series of face-to-face meetings between participants and study staff that can weigh on resources, participant availabilities, and even increase dropout rates. Digital tools are increasingly added to protocols to facilitate study conduct, but fully digitally run studies are still scarce. With the increasing interest in real-world studies, the development of health apps for mobile devices to monitor study outcomes is of great importance. ObjectiveThe purpose of this real-world study was to develop a specific mobile app, Ingredients for Life, to conduct a 100% digital study testing the effectiveness of a hydrolyzed cartilage matrix (HCM) supplement on joint discomfort in a heterogeneous group of healthy, active consumers. MethodsThe Ingredients for Life mobile app using a visual analog scale was specifically developed to monitor the variation in joint pain after exercise by the study participants. A total of 201 healthy and physically active women and men (18-72 years old) with joint pain completed the study over a period of 16 weeks. Participants were randomly allocated to the study groups and did not receive any dietary or lifestyle advice. Each participant indicated one area of joint pain and logged the type and duration of their weekly activities. They received blinded study supplements and took a daily regimen of 1 g of HCM (HCM group) or 1 g of maltodextrin (placebo group) for 12 weeks while weekly logging joint pain scores in the app. This was followed by a 4-week washout period during which participants continued reporting their joint pain scores (until the end of week 16). ResultsJoint pain was reduced within 3 weeks of taking a low dosage of HCM (1 g/day), regardless of gender, age group, and activity intensity when compared with the placebo group. After stopping supplementation, joint pain scores gradually increased but still remained significantly lower than those of the placebo group after 4 weeks of washout. The low dropout rate (

People make an important contribution to the study and management of biological invasions, as many monitoring and control projects rely heavily on volunteer assistance. Understanding the reasons why people participate in such projects is critical for successful recruitment and retention of volunteers. We used a meta-synthesis approach to extract, analyze and synthesize the available information from 28 selected studies investigating motivations of volunteers to engage in monitoring and control of invasive alien species (IAS). Our findings show how motivations fit three broad themes, reflecting environmental concerns, social motivations, and personal reasons. An important outcome of this study is the description of motivations that are unique to the IAS context: supporting IAS management, protecting native species and habitats, and livelihood/food/income protection or opportunities. In addition, our study reflects on important methodological choices for investigating volunteer motivations as well as ethical issues that may arise in practice. We conclude with a set of recommendations for project design and future research on volunteer motivations in IAS contexts, emphasizing the importance of collaboration with social scientists.

Preservation of widely available crop residues as silage could reduce feed shortages in Ethiopia. Four mixtures of banana pseudostem (BPS) and fresh maize stover (FMS) were prepared for fermentation considering the local conditions and available resources: 100% FMS, 80% FMS + 20% BPS, 60% FMS + 40% BPS and 95% BPS + 5% molasses. Each of the four mixtures was fermented in plastic bags as well as in plastic drums. Apart from the effect of the mixture and fermentation package, two fermentation locations were also considered. The fermentation was replicated three times for each combination of mixture, fermentation package and fermentation condition. The pH, microbial counts (total viable count, lactic acid bacteria count, Enterobacteriaceae count, yeast and mold count) and nutritional values of the fresh material and mixed silage were measured. Fermentation was successful for all mixed silages, reaching a pH below four, while the total viable count, Enterobacteriaceae count, yeast and mold count dropped (all p ≤ 0.05) and digestibility and metabolizable energy increased compared to the fresh mixtures. Enterobacteriaceae counts reached values below the detection limit in all mixed silages fermented in drums unlike the bag silages. The plastic bags used as fermentation package were found to be sensitive to damage, resulting in a a higher pH and visible signs of yeast and mold. Although fermentation of BPS with molasses resulted in a significant increase in dry matter digestibility (41.14 to 46.17–49.92%) and organic matter digestibility (50.52 to 55.22–58.75%), they were lower compared to most mixed silages with FMS. Fermentation of 80 and 60% FMS mixtures increased the crude protein content from 44.30 to 71.27–82.20 g/kg DM, and from 43.63 to 63.10–65.83 g/kg DM, respectively. The highest increase (1.77 MJ/kg DM) in metabolizable energy was recorded for 80% FMS fermented in drums. The location of fermentation had no effect on pH, microbial counts and nutritional values. This study demonstrates that crop by-products can be successfully fermented under conditions prevailing in Ethiopia, with drums being preferred over bags. Mixing BPS with FMS is advised to absorb BPS juice losses and obtain silage with more crude protein, neutral and acid detergent fibers and metabolizable energy, as well as a higher digestibility.

The metabolic axis linking the gut microbiome and heart is increasingly being researched in the context of cardiovascular health. The gut microbiota-derived trimethylamine/trimethylamine N-oxide (TMA/TMAO) pathway is responsible along this axis for the bioconversion of dietary precursors into TMA/TMAO and has been implicated in the progression of heart failure and dysbiosis through a positive-feedback interaction. Systems biology approaches in the context of researching this interaction offer an additional dimension for deepening the understanding of metabolism along the gut-heart axis. For instance, genome-scale metabolic models allow to study the functional role of pathways of interest in the context of an entire cellular or even whole-body metabolic network. In this mini review, we provide an overview of the latest findings on the TMA/TMAO super pathway and summarize the current state of knowledge in a curated pathway map on the community platform WikiPathways. The pathway map can serve both as a starting point for continual curation by the community as well as a resource for systems biology modeling studies. This has many applications, including addressing remaining gaps in our understanding of the gut-heart axis. We discuss how the curated pathway can inform a further curation and implementation of the pathway in existing whole-body metabolic models, which will allow researchers to computationally simulate this pathway to further understand its role in cardiovascular metabolism.

The Pallas’s squirrel (Callosciurus erythraeus) is an invasive alien species in several countries in Europe, but also in Argentina and Japan. The species is a threat to indigenous squirrel species and difficult to eradicate. In 2008 a population of this species was discovered in the south of the Netherlands, close to the Belgian border. The range and number of Pallas’s squirrels in the area was determined using sightings of local citizens, foresters and an extensive inventory with hair tubes and camera traps. The population was successfully eradicated during an eradication campaign with several stages from 2011 till 2015. The population appeared to be larger than expected. In total, 249 individuals were trapped in an area as large as 50 km2. Trapped squirrels were not killed, but sterilized and rehomed in animal parks across Europe. The eradication campaign was successful due to the cooperation and support of the general public, who were informed by flyers, lectures and several appeals in local media and on social media platforms. Both helped in getting support of the general public, but sterilization and rehoming must be properly supervised. The total costs of eradication summed up to at least €330,000. No Pallas’s squirrels were reported after October 2015, however sightings of ‘dark colored’ squirrels from the area are still reported by the general public and verified by an expert of the Dutch Mammal Society until today.

Abstract Invasive non‐native species (INNS) are recognized as a major threat to island biodiversity, ecosystems, and economies globally. Preventing high‐risk INNS from being introduced is the most cost‐effective way to avoid their adverse impacts. We applied a horizon scanning approach to identify potentially INNS in the United Kingdom Overseas Territories (OTs), ranging from Antarctica to the Caribbean, and from the Pacific to the Atlantic. High‐risk species were identified according to their potential for arrival, establishment, and likely impacts on biodiversity and ecosystem function, economies, and human health. Across OTs, 231 taxa were included on high‐risk lists. The highest ranking species were the Asian green mussel (Perna viridis), little fire ant (Wasmannia auropunctata), brown rat (Rattus norvegicus), and mesquite tree (Prosopis juliflora). Shipping containers were identified as the introduction pathway associated with the most species. The shared high‐risk species and pathways identified provide a guide for other remote islands and archipelagos to focus ongoing biosecurity and surveillance aimed at preventing future incursions.

The transition between two lactations remains one of the most critical periods during the productive life of dairy cows. In this study, we aimed to develop a model that predicts the milk yield of dairy cows from test day milk yield data collected in the previous lactation. In the past, data routinely collected in the context of herd improvement programmes on dairy farms have been used to provide insights in the health status of animals or for genetic evaluations. Typically, only data from the current lactation is used, comparing expected (i.e., unperturbed) with realised milk yields. This approach cannot be used to monitor the transition period due to the lack of unperturbed milk yields at the start of a lactation. For multiparous cows, an opportunity lies in the use of data from the previous lactation to predict the expected production of the next one. We developed a methodology to predict the first test day milk yield after calving using information from the previous lactation. To this end, three random forest models (nextMILKFULL, nextMILKPH, and nextMILKP) were trained with three different feature sets to forecast the milk yield on the first test day of the next lactation. To evaluate the added value of using a machine-learning approach against simple models based on contemporary animals or production in the previous lactation, we compared the nextMILK models with four benchmark models. The nextMILK models had an RMSE ranging from 6.08 to 6.24 kg of milk. In conclusion, the nextMILK models had a better prediction performance compared to the benchmark models. Application-wise, the proposed methodology could be part of a monitoring tool tailored towards the transition period. Future research should focus on validation of the developed methodology within such tool.

Abstract Background Due to the Corona Virus Disease 2019 (COVID-19), the Belgian government set out a range of measures to prevent the spread of the virus. One measure included closing all non-food shops, including vape shops. Methods A retrospective online questionnaire was used to investigate the impact of closing the vape shops on the vaping and/or smoking behavior of current vapers. Results The sample (n = 202) reached consisted of 70% exclusive vapers, 29% dual users and 1% no-product users. Over half (55%, 112/202) of participants needed to buy e-liquid during the lockdown, with a small majority being able to buy e-liquids – mostly with their usual nicotine concentrations, flavor or brand –, but as much as 39% (44/112) of them ran out of e-liquid. Those buying e-liquid mainly did so by making purchases via foreign online webshops. A similar pattern was observed with respect to purchasing hardware, with about half (47%, 95/202) of participants reporting hardware availability and with a small majority (53%, 107/202) reporting hardware unavailability. Of those indicating that hardware was not available, 38% (41/107) ran out of a properly functioning e-cigarette. A non-trivial minority had to consume e-liquids with another nicotine concentration, flavor or brand than usual. One seventh of exclusive vapers before lockdown relapsed partly or completely to smoking during the lockdown. The main reasons for changing vaping and/or smoking behavior included the unavailability of e-liquid with nicotine, the unavailability of hardware, and stress/worries about COVID-19. Conclusions The majority of vapers succeeded in maintaining their vaping behavior as usual, highly likely due to (illegally) buying consumables online. Nevertheless, for a minority the lockdown period resulted in unintended consequences and these vapers relapsed (completely) to smoking. Even during periods of lockdown, smokers and vapers should be able to purchase low(er)-risk alternatives to smoking, for example e-cigarettes.

Brittle stars are one of the most diverse classes of echinoderms distributed worldwide in marine habitats. In this study, brittle stars were sampled by hand from the intertidal zone of the Persian Gulf's western part to the Oman Sea, from locations accessible without the need of a boat or diving equipment. Sampling time was set in the first days of the lunar month or at least the time of the lowest tide in each day. The specimens were first immobilized in freshwater, then fixed in neutralized buffered formalin, and finally preserved in 70% ethanol after one week of fixation. Of 22 sampling points, seven localities yielded intertidal brittle stars (Macrophiothrix hirsuta cheneyi, Macrophiothrix elongata, and Ophiocoma scolopendrina) during the sampling period (December 2017–March 2018). Ophiocoma scolopendrina is reported for the first time from Dayyer and Nayband in the Persian Gulf. We also re-evaluated recently reported data on ophiuroids from the studied area.

In precision livestock farming, technology-based solutions are used to monitor and manage livestock and support decisions based on on-farm available data. In this study, we developed a methodology to monitor the lying behaviour of dairy cows using noisy spatial positioning data, thereby combining time-series segmentation based on statistical changepoints and a machine learning classification algorithm using bagged decision trees. Position data (x, y, z -coordinates) collected with an ultra-wide band positioning system from 30 dairy cows housed in a freestall barn were used. After the data pre-processing and selection, statistical changepoints were detected per cow-day (no. included = 331) in normalized 'distance from the centre of the barn' and (z) time series. Accelerometer-based lying bout data were used as a practical ground truth. For the segmentation, changepoint detection was compared with getting-up or lying-down events as indicated by the accelerometers. For the classification of segments into lying or non-lying behaviour, two data splitting techniques resulting in 2 different training and test sets were implemented to train and evaluate performance: one based on the data collection day and one based on cow identity. In 85.5% of the lying-down or getting-up events a changepoint was detected in a window of 5 minutes. Of the events where no detection had taken place, 86.2% could be associated with either missing data (large gaps) or a very short lying or non-lying bout. Overall classification and lying behaviour prediction performance was above 91% in both independent test sets, with a very high consistency across cow-days. Per cow-day, the average error in the estimation of the lying durations were 7.1% and 7.8% for the cow-identity and time-based data splits respectively. This resulted in sufficient accuracy for automated quantification of lying behaviour in dairy cows, for example for health or welfare monitoring purposes.

Climate change, with its increasing frequency of environmental disturbances puts pressures on the livestock sector. To deal with these pressures, more complex traits such as resilience must be considered in our management strategies and in our breeding programs. Resilient animals respond well to environmental challenges, and have a decreased probability of needing assistance to overcome them. This paper discusses the need for operational measures of resilience that can be deployed at large scale across different farm types and livestock species. Such measures are needed to provide more precise phenotypes of resilience for use in farm management, but also for use in animal breeding. Any measure of response and recovery reflects both the animals resilience and the perceived size of the environmental disturbance, which can vary over time, depending on multiple animal and farm-related contexts. Therefore, and because universal definitions of resilience are too broad to be operational, we argue that resilience should be seen as a latent construct that cannot be directly measured and selected for. This leads to the following two points: (1) any postulated operational measure of resilience to a disturbance should be constructed from a sufficient number of indicators that each individually capture different facets of the resilience, such that when combined they better reflect the full resilience response; and (2) any postulated operational measure of resilience will have to be validated against reference measures that are the accumulated consequences of good resilience (e.g. productive lifespan or ability to re-calve). In a dairy cow case study, a practical resilience definition for dairy cattle was proposed and tested based on a scoring system containing several categories. In general terms and within a given parity, a cow receives plus points for each calving, and for a shorter calving interval, fewer inseminations and a higher milk production compared to her herd peers. She will receive minus points in case the number of inseminations increases, for each curative treatment day, and if her milk production is lower compared to her herd peers. By using readily available farm data, we were able to assess a practical lifetime resilience score, based on which cows can then be ranked within the herd. Cows that reach a next parity were shown to have a higher rank than cows that are culled before the next parity. To examine the usefulness of such a score, this resilience ranking was linked to two precision livestock technology-derived measures, related to milk yield deviations and accelerometer-derived deviations. Higher resilience ranking cows had fewer drops in milk yield and a more stable activity pattern during the lactation. This case study, taking the operational approach to quantifying and defining resilience, shows the promise of a data-driven approach for identifying resilience measures when applied within a biologically logical framework.

Domestic and captive animals and cultivated plants should be recognised as integral components in contemporary ecosystems. They interact with wild organisms through such mechanisms as hybridization, predation, herbivory, competition and disease transmission and, in many cases, define ecosystem properties. Nevertheless, it is widespread practice for data on domestic, captive and cultivated organisms to be excluded from biodiversity repositories, such as natural history collections. Furthermore, there is a lack of integration of data collected about biodiversity in disciplines, such as agriculture, veterinary science, epidemiology and invasion science. Discipline-specific data are often intentionally excluded from integrative databases in order to maintain the “purity” of data on natural processes. Rather than being beneficial, we argue that this practise of data exclusivity greatly limits the utility of discipline-specific data for applications ranging from agricultural pest management to invasion biology, infectious disease prevention and community ecology. This problem can be resolved by data providers using standards to indicate whether the observed organism is of wild or domestic origin and by integrating their data with other biodiversity data (e.g. in the Global Biodiversity Information Facility). Doing so will enable efforts to integrate the full panorama of biodiversity knowledge across related disciplines to tackle pressing societal questions.