Spatio-temporal changes in chimpanzee density and abundance in the Greater Mahale Ecosystem, Tanzania.

Species conservation and management require reliable information about animal distribution and population size. Better management actions within a species' range can be achieved by identifying the location and timing of population changes. In the Greater Mahale Ecosystem (GME), western Tanzania, deforestation due to the expansion of human settlements and agriculture, annual burning, and logging are known threats to wildlife. For one of the most charismatic species, the endangered eastern chimpanzee (Pan troglodytes schweinfurthii), approximately 75% of the individuals are distributed outside national park boundaries, requiring monitoring and protection efforts over a vast landscape of various protection statuses. These efforts are especially challenging when we lack data on trends in density and population size. To predict spatio-temporal chimpanzee density and abundance across the GME, we used density surface modeling, fitting a generalized additive model to a 10-year time-series data set of nest counts based on line-transect surveys. The chimpanzee population declined at an annual rate of 2.41%, including declines of 1.72% in riparian forests (from this point forward, forests), 2.05% in miombo woodlands (from this point forward, woodlands) and 3.45% in nonforests. These population declines were accompanied by ecosystem-wide declines in vegetation types of 1.36% and 0.32% per year for forests and woodlands, respectively; we estimated an annual increase of 1.35% for nonforests. Our model predicted the highest chimpanzee density in forests (0.86 chimpanzees/km2 , 95% confidence intervals (CIs) 0.60-1.23; as of 2020), followed by woodlands (0.19, 95% CI 0.12-0.30) and nonforests (0.18, 95% CI 0.10-1.33). Although forests represent only 6% of the landscape, they support nearly one-quarter of the chimpanzee population (769 chimpanzees, 95% CI 536-1103). Woodlands dominate the landscape (71%) and therefore support more than a half of the chimpanzee population (2294; 95% CI 1420-3707). The remaining quarter of the landscape is represented by nonforests and supports another quarter of the chimpanzee population (750; 95% CI 408-1381). Given the pressures on the remaining suitable habitat in Tanzania, and the need of chimpanzees to access both forest and woodland vegetation to survive, we urge future management actions to increase resources and expand the efforts to protect critical forest and woodland habitat and promote strategies and policies that more effectively prevent irreversible losses. We suggest that regular monitoring programs implement a systematic random design to effectively inform and allocate conservation actions and facilitate interannual comparisons for trend monitoring, measuring conservation success, and guiding adaptive management.

Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields.

Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and from their base station. Onboard sensors such as machine vision cameras offer a flexible guidance alternative to more expensive solutions for structured environments such as scanning lidar or RTK-GNSS. The main challenges for visual crop row guidance are the dramatic differences in appearance of crops between farms and throughout the season and the variations in crop spacing and contours of the crop rows. Here we present a visual guidance pipeline for an agri-robot operating in strawberry fields in Norway that is based on semantic segmentation with a convolution neural network (CNN) to segment input RGB images into crop and not-crop (i.e., drivable terrain) regions. To handle the uneven contours of crop rows in Norway's hilly agricultural regions, we develop a new adaptive multi-ROI method for fitting trajectories to the drivable regions. We test our approach in open-loop trials with a real agri-robot operating in the field and show that our approach compares favourably to other traditional guidance approaches.

Selective attention in the honeybee optic lobes precedes behavioral choices.

Attention allows animals to respond selectively to competing stimuli, enabling some stimuli to evoke a behavioral response while others are ignored. How the brain does this remains mysterious, although it is increasingly evident that even animals with the smallest brains display this capacity. For example, insects respond selectively to salient visual stimuli, but it is unknown where such selectivity occurs in the insect brain, or whether neural correlates of attention might predict the visual choices made by an insect. Here, we investigate neural correlates of visual attention in behaving honeybees (Apis mellifera). Using a closed-loop paradigm that allows tethered, walking bees to actively control visual objects in a virtual reality arena, we show that behavioral fixation increases neuronal responses to flickering, frequency-tagged stimuli. Attention-like effects were reduced in the optic lobes during replay of the same visual sequences, when bees were not able to control the visual displays. When bees were presented with competing frequency-tagged visual stimuli, selectivity in the medulla (an optic ganglion) preceded behavioral selection of a stimulus, suggesting that modulation of early visual processing centers precedes eventual behavioral choices made by these insects.

Insects modify their behaviour depending on the feedback sensor used when walking on a trackball in virtual reality.

When using virtual-reality paradigms to study animal behaviour, careful attention must be paid to how the animal's actions are detected. This is particularly relevant in closed-loop experiments where the animal interacts with a stimulus. Many different sensor types have been used to measure aspects of behaviour, and although some sensors may be more accurate than others, few studies have examined whether, and how, such differences affect an animal's behaviour in a closed-loop experiment. To investigate this issue, we conducted experiments with tethered honeybees walking on an air-supported trackball and fixating a visual object in closed-loop. Bees walked faster and along straighter paths when the motion of the trackball was measured in the classical fashion - using optical motion sensors repurposed from computer mice - than when measured more accurately using a computer vision algorithm called 'FicTrac'. When computer mouse sensors were used to measure bees' behaviour, the bees modified their behaviour and achieved improved control of the stimulus. This behavioural change appears to be a response to a systematic error in the computer mouse sensor that reduces the sensitivity of this sensor system under certain conditions. Although the large perceived inertia and mass of the trackball relative to the honeybee is a limitation of tethered walking paradigms, observing differences depending on the sensor system used to measure bee behaviour was not expected. This study suggests that bees are capable of fine-tuning their motor control to improve the outcome of the task they are performing. Further, our findings show that caution is required when designing virtual-reality experiments, as animals can potentially respond to the artificial scenario in unexpected and unintended ways.

Review of 1.75 million referrals over 34 months identifies the disruptive impact of the SARS-CoV-2 pandemic on oral surgery care in England: a service evaluation.

Aims This study aimed to use electronic referral management system (eRMS) oral surgery data across multiple sites in England to evaluate the service over a 34-month period in relation to: 1) pre- and post-pandemic referral rates in oral surgery; 2) examining the data for signs of inequality in obtaining a referral for oral surgery; and 3) considering the impact on service provision for oral surgery in England.Methods Oral surgery referral data were available from an eRMS for areas of England covered by this service for the 34-month period of March 2019 to December 2021 (inclusive), which included 12 months of pre-pandemic data and the first 22 months of the pandemic. The data were from the following regions in England: Central Midlands; Cheshire and Merseyside; East Anglia and Essex; Greater Manchester; Lancashire; Thames Valley; and Yorkshire and the Humber.Results The total number of referrals received was 1,766,895 during this 34-month period, with pre-pandemic referral levels averaging at 25,498 per month, with a reduction to 698 per month in April 2020. Referrals have risen to a peak of 217,646 for the month of November 2021. An average of 1.5% of referrals were rejected pre-pandemic, compared with 2.7% per month post-pandemic.Discussion Pre-pandemic referral numbers were predictably stable within a narrow range which have then increased dramatically post-pandemic. The variations in oral surgery referral patterns place significant strain on oral surgery services across England. This not only has consequences on the patient experience, but also on workforce and workforce development, to ensure that there is not a long-term destabilising impact.Conclusion Analysis of 1.75 million referrals to oral surgery services in England has highlighted the ongoing impact of the pandemic and the need to actively minimise adverse impacts on patients, NHS services and the workforce.

An evaluation of referrer factors for 98,671 referrals made to the West Yorkshire oral surgery managed clinical network over a three-year period.

Introduction Patients referred from primary dental care to hospital-based specialists in high volumes can contribute to significant NHS service pressures. Surprisingly, little is understood about what contributes to referral factors.Aims To gain new insight into the referral factors from primary dental care by interrogating the tri-speciality West Yorkshire managed clinical network (MCN) referral pathway data for a 36-month period (2016-2019).Methods Anonymised referrals from the electronic referral management system were collated for analyses.Results There were 98,671 referrals within the 36-month period, 12.3% of which were rejected. Of those accepted for triage, 76% were directed at oral surgery, with >60% accounted for by exodontia. In total, 10% of referrers accounted for 60% of all referrals. Peak referral occurred five years after General Dental Council registration.Discussion This is the first report of referral data from a tri-speciality MCN with exodontia referrals predominating. The data set demonstrates variation in referrer behaviours despite referral guidance. Referrals should be based upon patient need but patterns observed in this study suggested possible associations with high and low referral patterns which warrant further research.Conclusions Interrogation of the referral database suggests that there are interesting patterns of referral which may be associated with characteristics of the referrer as well as their patients' needs. Further investigation could inform improved processes and service design, as well as education delivery and workforce development.

Simplifying differential diagnoses of orofacial conditions - a guide to surgical sieves and red flags.

Formulation of a differential diagnosis with appropriate diagnostic tests is critical in day-to-day clinical practice. Many specialists or hospital-based practitioners in specialties such as oral medicine and oral surgery will be familiar with the use of surgical sieves and the term 'red flags' in their practice. However, it is the authors' experience that general dental practitioners may not be as familiar with such tools. Primary care practitioners are often the first port of call for patients with uncommon symptoms; therefore, it is essential that appropriate and timely referrals are made. The use of a surgical sieve and heightened awareness of red flags will assist practitioners significantly.

The Northern California Chronic Care Network for Dementia.

The Northern California Chronic Care Network for Dementia brings together Northern California's major providers of managed care, community-based care, consumer education, and advocacy in new partnerships to improve the care of persons with dementia enrolled in managed care plans and their family caregivers. These partnerships are part of a national initiative entitled the Chronic Care Network for Alzheimer's Disease (CCN/AD) sponsored by the National Chronic Care Consortium and the Alzheimer's Association. This initiative selected eight promising provider-consumer partnerships across the country to implement and evaluate a new model of coordinated care for people with dementia and their families. This paper describes the Northern California network's partnerships and its intervention and challenges. The intervention is grounded in the key components of the CCN/AD model: "identification of patients with possible dementia, diagnostic assessment, care management and family caregiver information and support." These components, in turn, are translated into protocols and pathways designed to create timely, comprehensive, appropriate, and effective systems of care services that address the unique needs of dementia patients and their caregivers over the course of the disease.

FicTrac: a visual method for tracking spherical motion and generating fictive animal paths.

Studying how animals interface with a virtual reality can further our understanding of how attention, learning and memory, sensory processing, and navigation are handled by the brain, at both the neurophysiological and behavioural levels. To this end, we have developed a novel vision-based tracking system, FicTrac (Fictive path Tracking software), for estimating the path an animal makes whilst rotating an air-supported sphere using only input from a standard camera and computer vision techniques. We have found that the accuracy and robustness of FicTrac outperforms a low-cost implementation of a standard optical mouse-based approach for generating fictive paths. FicTrac is simple to implement for a wide variety of experimental configurations and, importantly, is fast to execute, enabling real-time sensory feedback for behaving animals. We have used FicTrac to record the behaviour of tethered honeybees, Apis mellifera, whilst presenting visual stimuli in both open-loop and closed-loop experimental paradigms. We found that FicTrac could accurately register the fictive paths of bees as they walked towards bright green vertical bars presented on an LED arena. Using FicTrac, we have demonstrated closed-loop visual fixation in both the honeybee and the fruit fly, Drosophila melanogaster, establishing the flexibility of this system. FicTrac provides the experimenter with a simple yet adaptable system that can be combined with electrophysiological recording techniques to study the neural mechanisms of behaviour in a variety of organisms, including walking vertebrates.