Overweight and obesity are the most common findings in adolescents with elevated ALT levels. Even modest alcohol consumption may significantly increase the likelihood of obese adolescents developing obesity-related liver disease.

Toxic nectar is an ecological paradox [1, 2]. Plants divert substantial resources to produce nectar that attracts pollinators [3], but toxins in this reward could disrupt the mutualism and reduce plant fitness [4]. Alternatively, such compounds could protect nectar from robbers [2], provided that they do not significantly alter pollinator visitation to the detriment of plant fitness [1, 5-8]. Indeed, very few studies have investigated the role of plant toxins in nectar for defense against nectar robbers [4, 9, 10]. Here, we compared two Aconitum species (A. napellus and A. lycoctonum) that have flowers specialized for long-tongued bumblebee pollinators (Bombus hortorum) but are occasionally robbed by short-tongued bumblebees (B. terrestris) [6, 11-13]. Pollinator visits to flowers were much more frequent than by robbers, but visits correlated negatively with nectar alkaloid concentration and declined sharply between 200 and 380 ppm. However, alkaloid concentrations of >20 ppm were deterrent to B. terrestris, suggesting that robbers were less tolerant of nectar alkaloids. Nectar of both plant species contained similar concentrations of carbohydrates and toxic alkaloids, but A. lycoctonum was more likely to secrete nectar in each flower and was also visited more frequently by pollinators and robbers. We conclude that alkaloids in Aconitum spp. nectar affect rates of both pollinator visitation and robbery but may have co-evolved with nectar availability to maintain the fitness benefits of specialized plant-pollinator relationships. Chemical defense of nectar is, however, ultimately constrained by pollinator gustatory sensitivity.

Ecosystems are at increasing risk from the global pollination crisis. Gaining better knowledge about pollinators and their interactions with plants is an urgent need. However, conventional methods of manually recording pollinator activity in the field can be time- and cost-consuming in terms of labour. Field-deployable video recording systems have become more common in ecological studies as they enable the capture of plant-insect interactions in fine detail. Standard video recording can be effective, although there are issues with hardware reliability under field-conditions (e.g. weatherproofing), and reviewing raw video manually is a time-consuming task. Automated video monitoring systems based on motion detection partly overcome these issues by only recording when activity occurs hence reducing the time needed to review footage during post-processing. Another advantage of these systems is that the hardware has relatively low power requirements. A few systems have been tested in the field which permit the collection of large datasets. Compared with other systems, automated monitoring allows vast increases in sampling at broad spatiotemporal scales. Some tools such as post-recording computer vision software and data-import scripts exist, further reducing users’ time spent processing and analysing the data. Integrated computer vision and automated species recognition using machine learning models have great potential to further the study of pollinators in the field. Together, it is predicted that future advances in technology-based field monitoring methods will contribute significantly to understanding the causes underpinning pollinator declines and, hence, developing effective solutions for dealing with this global challenge.

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Highly unacceptable species to D. reticulatum are unlikely to be selectively grazed by slugs during the seedling recruitment phase, and were predominantly target restoration species. Seedlings of highly acceptable species may be less likely to survive slug herbivory and contribute to seedling recruitment at restoration sites. Selective slug herbivory, influenced by acceptability, may influence community-level processes if seedling recruitment and establishment of key functional species, such as T. pratense is reduced.

Detecting the arbitrary movements of fast-moving insects under field conditions is notoriously difficult because existing technologies are limited by issues of size, weight, range and cost. Here, we establish proof-of-concept for a prototype long-range, passive radio frequency identification (RFID) tagging system for detecting bumblebees and similar sized insects. The prototype tags, weighing 81 mg (49% of mean bee body weight), were flown by bumblebees in a glasshouse and detected at a distance of 1.5 m from a 2 W UHF reader with two aerials. This detection distance is two orders of magnitude greater than existing RFID tags that can be flown by medium-sized bees and, thus, is a significant breakthrough for insect tracking that could be applied to plant conservation and restoration efforts in fragmented landscapes.Proof-of-concept has been successfully established and, with further development, we are likely to optimize the system by reducing tag size and weight to limit effects on bee behaviour, and by increasing the detection distance. We envisage the production system being used to detect and track bee movement pathways within a designed network of field-deployed low-cost readers and aerials. The production system could be used in a wide variety of scientific and commercial applications.Electronic supplementary materialThe online version of this article (10.1186/s13036-019-0143-x) contains supplementary material, which is available to authorized users.

Summary Anemone pulsatilla, the pasque flower, is described and illustrated. Details of its distribution, relationships and studies on conservation requirements in England are presented. Cultivation and propagation are discussed.

This paper explores a multi-UAV trajectory optimization methodology for confined environments. One potential application of this technology is performing warehouse inventory audits; this application is used to evaluate the methodologie's impact on minimizing total mission times. This paper investigates existing algorithms and improves upon them to better address the constraints of warehouse-like environments. An existing inventory scanning algorithm generates suboptimal, collision free paths for multi-UAV operations, which has two sequential processes: solving a vehicle routing problem, and determining optimal deployment time without any collision. To improve the sub-optimal results, this paper introduces three possible improvements on the multi-UAV inventory tracking scenario. First, a new algorithm logic which seeks to minimize the total mission time once collision avoidance has been ensured rather than having separate processes. Next, an objective function that seeks to minimize the maximum UAV mission time rather than minimizing the total of all UAV mission times. Last, an operational setup consisting of multiple deployment locations instead of only one. These algorithms are evaluated individually and in combination with one another to assess their impact on the overall mission time using a representative inventory environment. The best combination will be further analyzed through a design of experiments by varying several inputs and examining the resulting fleet size, computation time, and overall mission time.