The expanding distribution of theAedes albopictus mosquito (also known as the Asian tiger mosquito) throughout Europe is a health and safety threat due to its potential to spread tropical and sub-tropical arboviruses to temperate climate regions. Therefore, it is becoming increasingly important to broaden the use of all tools within integrated pest management systems to control this threat. Since this mosquito species in Europe breeds mainly in manhole drains, managing them is therefore essential. The UNFO-Pest Lock System (UNFO-PLS) mechanical control device fits in siphoned manholes and prevents mosquitoes from entering the manhole and breeding in the water below. We tested and quantified the efficacy of these UNFO-PLS devices against standard control manholes (no mechanical devices attached) in Balerna, Switzerland (Canton Ticino). Weekly counts of five developmental stages (larval stages L1-L4 and the pupal stage) were conducted at 12 experimental and 12 standard control manholes during the mosquito breeding and activity periods of 2020 and 2021. For each developmental stage, we compared the counts of mosquitoes in manholes fitted with and without the UNFO-PLS devices using generalised mixed-effect models. Results show that these devices reduced the presence of mosquitoes at all developmental stages between 92.6-97.2%. The use of the UNFO-PLS mechanical control device, or similar devices, should be considered for use in integrated mosquito pest management plans, especially in places that might present challenges for maintaining adequate larvicide applications, such as around schools and secondary homes.

The objective of the present study was to evaluate the differences in the selection behaviour by horses and by their owners given a choice of three hay types differing in chemical and nutritional composition. Horse groups included 12 French mountain (FM) stallions, 12 Icelandic (IS) and 12 warmblood horses (WB), with the latter groups consisting of mares and geldings. Hays were offered in a free choice experiment over a period of six consecutive days (baseline choice on day 1, adaption period from day 2-5 and a final preference test on day 6). Additionally, the owners of the horses selected among the three hay types, the one which they assumed would best meet their horse's assumed nutritional requirements. Hay types (A, B and C) showed distinct characteristics based on colour, texture morphology and odour and nutrient contents. The latter were analysed with near-infrared spectroscopy. Most horses (69%) preferred hay type B and 31% preferred hay type C, while horses completely avoided hay type A. The group of FM horses exclusively preferred hay B on both test days, the groups of IS and WB horses also showed a preference for hay B, but less pronounced than FM horses choosing both between hay B and C, respectively. Horses did not choose randomly among the hays offered (P < 0.05). In addition, the horses hay preference was influenced by their group assignment (P < 0.05) which, however, cannot be generalized to breed or gender (mares, stallions or geldings). The content of crude protein (r = 0.68), and leaves (r = 0.48) of the hay was positively correlated with the preference of the horses for that particular hay type, although correlations were moderate. Among the owners, 42% chose hay type A, which was completely avoided by the horses, while approximately 30% of the owners chose either hay type B or C, which were richer in energy and protein as the best hay quality for their horses assumed nutritional requirements. Therefore, no significant correlation could be observed between the choice preference of the horses and the selection by the owners. The distinct preference for energy- and protein-rich hay of horses underlines the importance for owners to carefully adjust the hay quality to the horse's activity level and requirements. For horses with lower energy requirements, omission of concentrate feeds, while providing roughage only diets by mixing different hays qualities, appears suitable.

Neurological disorders usually present very heterogeneous recovery patterns. Nonetheless, accurate prediction of future clinical end-points and robust definition of homogeneous cohorts are necessary for scientific investigation and targeted care. For this, unbiased recursive partitioning with conditional inference trees (URP-CTREE) have received increasing attention in medical research, especially, but not limited to traumatic spinal cord injuries (SCIs). URP-CTREE was introduced to SCI as a clinical guidance tool to explore and define homogeneous outcome groups by clinical means, while providing high accuracy in predicting future clinical outcomes. The validity and predictive value of URP-CTREE to provide improvements compared with other more common approaches applied by clinicians has recently come under critical scrutiny. Therefore, a comprehensive simulation study based on traumatic, cervical complete spinal cord injuries provides a framework to investigate and quantify the issues raised. First, we assessed the replicability and robustness of URP-CTREE to identify homogeneous subgroups. Second, we implemented a prediction performance comparison of URP-CTREE with traditional statistical techniques, such as linear or logistic regression, and a novel machine learning method. URP-CTREE's ability to identify homogeneous subgroups proved to be replicable and robust. In terms of prediction, URP-CTREE yielded a high prognostic performance comparable to a machine learning algorithm. The simulation study provides strong evidence for the robustness of URP-CTREE, which is achieved without compromising prediction accuracy. The slightly lower prediction performance is offset by URP-CTREE's straightforward interpretation and application in clinical settings based on simple, data-driven decision rules.

STUDY DESIGN:Retrospective analysis of prospectively collected longitudinal data. Variables of interest are timed and untimed walking assessments (10MWT, 6MWT, TUG, WISCI, SCIM3a, SCIM3b) and lower extremities motor scores (LEMS) from both sides' lower limb motor segments, measured five times within the first year after acute spinal cord injury (SCI).OBJECTIVES:Assessing concurrent validity of single and groups of walking assessments in comparison with LEMS in SCI patients.SETTING:European Multicenter study about Spinal Cord Injury, a collaboration of 22 centers.METHODS:Canonical correlation analysis (CCA) was applied to single and groups of assessments at each time point, separately for patients able to perform timed walking assessments (less impaired; patient subgroup I) and for all patients (no selection; patient subgroup II).RESULTS:In patient subgroup I, SCIM3b, WISCI, 10MWT and 6MWT all had high and similar concurrent validity one year after injury. Among all groups of three walking assessments, SCIM3a, WISCI and 10MWT had highest concurrent validity, similar to all six walking assessments together. Timed walking assessments generally had higher concurrent validity than untimed ones. In patient subgroup II, WISCI distinctly had highest concurrent validity one year after injury, similar to all three untimed walking assessments together.CONCLUSIONS:CCA can assess concurrent validity of single and groups of assessments. Minimal sets of walking assessments with comparable concurrent validity as all assessments together were proposed. As these sets differ by patient group, walking assessments should be specified according to expected walking ability to allow for targeted, cost-effective application of assessments.

Background A number of potential therapeutic approaches for neurological disorders have failed to provide convincing evidence of efficacy, prompting pharmaceutical and health companies to discontinue their involvement in drug development. Limitations in the statistical analysis of complex endpoints have very likely had a negative impact on the translational process. Methods We propose a transitional ordinal model with an autoregressive component to overcome previous limitations in the analysis of Upper Extremity Motor Scores, a relevant endpoint in the field of Spinal Cord Injury. Statistical power and clinical interpretation of estimated treatment effects of the proposed model were compared to routinely employed approaches in a large simulation study of two-arm randomized clinical trials. A revisitation of a key historical trial provides further comparison between the different analysis approaches. Results The proposed model outperformed all other approaches in virtually all simulation settings, achieving on average 14 % higher statistical power than the respective second-best performing approach (range: -1 %, +34 %). Only the transitional model allows treatment effect estimates to be interpreted as conditional odds ratios, providing clear interpretation and visualization. Conclusion The proposed model takes into account the complex ordinal nature of the endpoint under investigation and explicitly accounts for relevant prognostic factors such as lesion level and baseline information. Superior statistical power, combined with clear clinical interpretation of estimated treatment effects and widespread availability in commercial software, are strong arguments for clinicians and trial scientists to adopt, and further extend, the proposed approach.

Background. Several novel drug- and cell-based potential therapies for spinal cord injury (SCI) have either been applied or will be considered for future clinical trials. Limitations on the number of eligible patients require trials be undertaken in a highly efficient and effective manner. However, this is particularly challenging when people living with incomplete SCI (iSCI) represent a very heterogeneous population in terms of recovery patterns and can improve spontaneously over the first year after injury. Objective. The current study addresses 2 requirements for designing SCI trials: first, enrollment of as many eligible participants as possible; second, refined stratification of participants into homogeneous cohorts from a heterogeneous iSCI population. Methods. This is a retrospective, longitudinal analysis of prospectively collected SCI data from the European Multicenter study about Spinal Cord Injury (EMSCI). We applied conditional inference trees to provide a prediction-based stratification algorithm that could be used to generate decision rules for the appropriate inclusion of iSCI participants to a trial. Results. Based on baseline clinical assessments and a defined subsequent clinical endpoint, conditional inference trees partitioned iSCI participants into more homogeneous groups with regard to the illustrative endpoint, upper extremity motor score. Assuming a continuous endpoint, the conditional inference tree was validated both internally as well as externally, providing stable and generalizable results. Conclusion. The application of conditional inference trees is feasible for iSCI participants and provides easily implementable, prediction-based decision rules for inclusion and stratification. This algorithm could be utilized to model various trial endpoints and outcome thresholds.

Background: Honeybees provide economically and ecologically vital pollination services to crops and wild plants. During the last decade elevated colony losses have been documented in Europe and North America. Despite growing consensus on the involvement of multiple causal factors, the underlying interactions impacting on honeybee health and colony failure are not fully resolved. Parasites and pathogens are among the main candidates, but sublethal exposure to widespread agricultural pesticides may also affect bees.Methodology/Principal Findings: To investigate effects of sublethal dietary neonicotinoid exposure on honeybee colony performance, a fully crossed experimental design was implemented using 24 colonies, including sister-queens from two different strains, and experimental in-hive pollen feeding with or without environmentally relevant concentrations of thiamethoxam and clothianidin. Honeybee colonies chronically exposed to both neonicotinoids over two brood cycles exhibited decreased performance in the short-term resulting in declining numbers of adult bees (-28%) and brood (-13%), as well as a reduction in honey production (-29%) and pollen collections (-19%), but colonies recovered in the medium-term and overwintered successfully. However, significantly decelerated growth of neonicotinoid-exposed colonies during the following spring was associated with queen failure, revealing previously undocumented long-term impacts of neonicotinoids: queen supersedure was observed for 60% of the neonicotinoid-exposed colonies within a one year period, but not for control colonies. Linked to this, neonicotinoid exposure was significantly associated with a reduced propensity to swarm during the next spring. Both short-term and long-term effects of neonicotinoids on colony performance were significantly influenced by the honeybees' genetic background.Conclusions/Significance: Sublethal neonicotinoid exposure did not provoke increased winter losses. Yet, significant detrimental short and long-term impacts on colony performance and queen fate suggest that neonicotinoids may contribute to colony weakening in a complex manner. Further, we highlight the importance of the genetic basis of neonicotinoid susceptibility in honeybees which can vary substantially.

Study design: This is a review article.Objectives: This study discusses the following: (1) concepts and constraints for the determination of minimal clinically important difference (MCID), (2) the contrasts between MCID and minimal detectable difference (MDD), (3) MCID within the different domains of International Classification of Functioning, disability and health, (4) the roles of clinical investigators and clinical participants in defining MCID and (5) the implementation of MCID in acute versus chronic spinal cord injury (SCI) studies.Methods: The methods include narrative reviews of SCI outcomes, a 2-day meeting of the authors and statistical methods of analysis representing MDD.Results: The data from SCI study outcomes are dependent on many elements, including the following: the level and severity of SCI, the heterogeneity within each study cohort, the therapeutic target, the nature of the therapy, any confounding influences or comorbidities, the assessment times relative to the date of injury, the outcome measurement instrument and the clinical end-point threshold used to determine a treatment effect. Even if statistically significant differences can be established, this finding does not guarantee that the experimental therapeutic provides a person living with SCI an improved capacity for functional independence and/or an increased quality of life. The MDD statistical concept describes the smallest real change in the specified outcome, beyond measurement error, and it should not be confused with the minimum threshold for demonstrating a clinical benefit or MCID. Unfortunately, MCID and MDD are not uncomplicated estimations; nevertheless, any MCID should exceed the expected MDD plus any probable spontaneous recovery.Conclusion: Estimation of an MCID for SCI remains elusive. In the interim, if the target of a therapeutic is the injured spinal cord, it is most desirable that any improvement in neurological status be correlated with a functional (meaningful) benefit.

Pollinating insects provide crucial and economically important ecosystem services to crops and wild plants, but pollinators, particularly bees, are globally declining as a result of various driving factors, including the prevalent use of pesticides for crop protection. Sublethal pesticide exposure negatively impacts numerous pollinator life‐history traits, but its influence on reproductive success remains largely unknown. Such information is pivotal, however, to our understanding of the long‐term effects on population dynamics. We investigated the influence of field‐realistic trace residues of the routinely used neonicotinoid insecticides thiamethoxam and clothianidin in nectar substitutes on the entire life‐time fitness performance of the r ed m ason bee Osmia bicornis . We show that chronic, dietary neonicotinoid exposure has severe detrimental effects on solitary bee reproductive output. Neonicotinoids did not affect adult bee mortality; however, monitoring of fully controlled experimental populations revealed that sublethal exposure resulted in almost 50% reduced total offspring production and a significantly male‐biased offspring sex ratio. Our data add to the accumulating evidence indicating that sublethal neonicotinoid effects on non‐ Apis pollinators are expressed most strongly in a rather complex, fitness‐related context. Consequently, to fully mitigate long‐term impacts on pollinator population dynamics, present pesticide risk assessments need to be expanded to include whole life‐cycle fitness estimates, as demonstrated in the present study using O. bicornis as a model.

Background. The reliable stratification of homogeneous subgroups and the prediction of future clinical outcomes within heterogeneous neurological disorders is a particularly challenging task. Nonetheless, it is essential for the implementation of targeted care and effective therapeutic interventions. Objective. This study was designed to assess the value of a recently developed regression tool from the family of unbiased recursive partitioning methods in comparison to established statistical approaches (eg, linear and logistic regression) for predicting clinical endpoints and for prospective patients’ stratification for clinical trials. Methods. A retrospective, longitudinal analysis of prospectively collected neurological data from the European Multicenter study about Spinal Cord Injury (EMSCI) network was undertaken on C4-C6 cervical sensorimotor complete subjects. Predictors were based on a broad set of early (<2 weeks) clinical assessments. Endpoints were based on later clinical examinations of upper extremity motor scores and recovery of motor levels, at 6 and 12 months, respectively. Prediction accuracy for each statistical analysis was quantified by resampling techniques. Results. For all settings, overlapping confidence intervals indicated similar prediction accuracy of unbiased recursive partitioning to established statistical approaches. In addition, unbiased recursive partitioning provided a direct way of identification of more homogeneous subgroups. The partitioning is carried out in a data-driven manner, independently from a priori decisions or predefined thresholds. Conclusion. Unbiased recursive partitioning techniques may improve prediction of future clinical endpoints and the planning of future SCI clinical trials by providing easily implementable, data-driven rationales for early patient stratification based on simple decision rules and clinical read-outs.

Background. There is inherent heterogeneity within individuals suffering from cervical spinal cord injury (SCI), and early prediction of upper limb function and self-care is challenging. As a result, considerable uncertainty exists regarding the prediction of functional outcome following cervical SCI within 1 year of injury. Objective. To evaluate the value of Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP) in predicting upper limb function and self-care outcomes in individuals with cervical SCI. Method. A prospective longitudinal multicenter study was performed. Data from the GRASSP, the Spinal Cord Independence Measure (SCIM III), and the American Spinal Injury Association (ASIA) Impairment Scale were recorded at 1, 6, and 12 months after cervical SCI. For prediction of functional outcome at 6 and 12 months, a logistic regression model, receiver operating characteristics (ROC), and unbiased recursive partitioning conditional inference tree (URP-CTREE) were used with 8 different predictor variables. Results. Logistic regression analysis, ROC analysis, and URP-CTREE all revealed that the strength subtest within GRASSP is the strongest predictor for upper limb function and self-care outcomes. URP-CTREE provides useful information on the distribution of different outcomes in acute cervical SCI and can be used to predict cohorts with homogeneous outcomes. Conclusion. The GRASSP at 1 month can accurately predict upper limb function and self-care outcomes even in a heterogeneous group of individuals across a wide spectrum of neurological recovery. The application of URP-CTREE can reveal the distribution of outcome categories and, based on this, inform trial protocols with respect to outcomes analysis and patient stratification.

Monitoring is an integral part of species conservation. Monitoring programs must take imperfect detection of species into account in order to be reliable. Theory suggests that detection probability may be determined by population size but this relationship has not yet been assessed empirically. Population size is particularly important because it may induce heterogeneity in detection probability and thereby cause bias in estimates of biodiversity. We used a site occupancy model to analyse data from a volunteer-based amphibian monitoring program to assess how well different variables explain variation in detection probability. An index to population size best explained detection probabilities for four out of six species (to avoid circular reasoning, we used the count of individuals at a previous site visit as an index to current population size). The relationship between the population index and detection probability was positive. Commonly used weather variables best explained detection probabilities for two out of six species. Estimates of site occupancy probabilities differed depending on whether the population index was or was not used to model detection probability. The relationship between the population index and detectability has implications for the design of monitoring and species conservation. Most importantly, because many small populations are likely to be overlooked, monitoring programs should be designed in such a way that small populations are not overlooked. The results also imply that methods cannot be standardized in such a way that detection probabilities are constant. As we have shown here, one can easily account for variation in population size in the analysis of data from long-term monitoring programs by using counts of individuals from surveys at the same site in previous years. Accounting for variation in population size is important because it can affect the results of long-term monitoring programs and ultimately the conservation of imperiled species.