Numerical cognition in bees and other insects

The ability to perceive the number of objects has been known to exist in vertebrates for a few decades, but recent behavioral investigations have demonstrated that several invertebrate species can also be placed on the continuum of numerical abilities shared with birds, mammals, and reptiles. In this review article, we present the main experimental studies that have examined the ability of insects to use numerical information. These studies have made use of a wide range of methodologies, and for this reason it is striking that a common finding is the inability of the tested animals to discriminate numerical quantities greater than four. Furthermore, the finding that bees can not only transfer learnt numerical discrimination to novel objects, but also to novel numerosities, is strongly suggestive of a true, albeit limited, ability to count. Later in the review, we evaluate the available evidence to narrow down the possible mechanisms that the animals might be using to solve the number-based experimental tasks presented to them. We conclude by suggesting avenues of further research that take into account variables such as the animals' age and experience, as well as complementary cognitive systems such as attention and the time sense.This publication was funded by the German Research Foundation (DFG) and the University of Wuerzburg in the funding program Open Access Publishing. Shaowu Zhang was supported by the ARC-CoE in Vision Science

Large Scale Homing in Honeybees

Honeybee foragers frequently fly several kilometres to and from vital resources, and communicate those locations to their nest mates by a symbolic dance language. Research has shown that they achieve this feat by memorizing landmarks and the skyline panorama, using the sun and polarized skylight as compasses and by integrating their outbound flight paths. In order to investigate the capacity of the honeybees' homing abilities, we artificially displaced foragers to novel release spots at various distances up to 13 km in the four cardinal directions. Returning bees were individually registered by a radio frequency identification (RFID) system at the hive entrance. We found that homing rate, homing speed and the maximum homing distance depend on the release direction. Bees released in the east were more likely to find their way back home, and returned faster than bees released in any other direction, due to the familiarity of global landmarks seen from the hive. Our findings suggest that such large scale homing is facilitated by global landmarks acting as beacons, and possibly the entire skyline panorama.This study was supported by the ARC COE in Vision Sciences (CE0561903), ARC DP-0450535 to SWZ, MP, and HZ (http://www.vision.edu.au/). MP was supported by a grant of the German Excellence Initiative to the Graduate School of Life Sciences, Würzburg University (http://www.graduateschools.uni-wuerzburg.de/life_sciences). This publication was funded by the German Research Foundation (DFG) and the University of Wuerzburg in the funding programme Open Access Publishing. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Visually guided decision making in foraging honeybees

Honeybees can easily be trained to perform different types of discrimination tasks under controlled laboratory conditions. This review describes a range of experiments carried out with free-flying forager honeybees under such conditions. The research done over the past 30 or so years suggests that cognitive abilities (learning and perception) in insects are more intricate and flexible than was originally imagined. It has become apparent that honeybees are capable of a variety of visually guided tasks, involving decision making under challenging situations: this includes simultaneously making use of different sensory modalities, such as vision and olfaction, and learning to use abstract concepts such as "sameness" and "difference." Many studies have shown that decision making in foraging honeybees is highly flexible. The trained animals learn how to solve a task, and do so with a high accuracy, but when they are presented with a new variation of the task, they apply the learnt rules from the earlier setup to the new situation, and solve the new task as well. Honeybees therefore not only feature a rich behavioral repertoire to choose from, but also make decisions most apt to the current situation. The experiments in this review give an insight into the environmental cues and cognitive resources that are probably highly significant for a forager bee that must continually make decisions regarding patches of resources to be exploited

East Learns from West: Asiatic Honeybees Can Understand Dance Language of European Honeybees

The honeybee waggle dance, through which foragers advertise the existence and location of a food source to their hive mates, is acknowledged as the only known form of symbolic communication in an invertebrate. However, the suggestion, that different species of honeybee might possess distinct ‘dialects’ of the waggle dance, remains controversial. Furthermore, it remains unclear whether different species of honeybee can learn from and communicate with each other. This study reports experiments using a mixed-species colony that is composed of the Asiatic bee Apis cerana cerana (Acc), and the European bee Apis mellifera ligustica (Aml). Using video recordings made at an observation hive, we first confirm that Acc and Aml have significantly different dance dialects, even when made to forage in identical environments. When reared in the same colony, these two species are able to communicate with each other: Acc foragers could decode the dances of Aml to successfully locate an indicated food source. We believe that this is the first report of successful symbolic communication between two honeybee species; our study hints at the possibility of social learning between the two honeybee species, and at the existence of a learning component in the honeybee dance language

Isolated angiitis of the central nervous system with tumor-like lesion, mimicking brain malignant glioma: a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Isolated angiitis of the central nervous system (IACNS) is a rare but severe vascular disease, which could present like an isolated inflammatory lesion on magnetic resonance imaging (MRI). To date, only a few such cases with tumor-like IACNS have been reported.</p> <p>Case Presentation</p> <p>A 35-year-old woman presented with headache and left-sided weakness. MRI scans initially mislead us to a diagnosis of glioblastoma (GBM). Surgery was performed. The mass was sub-totally resected. Pathological examination confirmed a cerebral vasculitis. Radiological features, such as disproportionate mass effect, striped hemorrhage and abnormal enhancement of adjacent vessels, could be helpful to distinguish a tumor-like IACNS from a GBM. Single therapy with high doses of steroid did not improve the patient's condition. Combined therapy with prednisolone and cyclophosphamide showed great benefit to the patient. No relapse occurred during the period of 18 months follow-up.</p> <p>Conclusions</p> <p>Although a tumor-like IACNS has no established imaging features, a diagnosis of tumor-like IACNS should be suspected when MRI shows inappropriate presentations of a tumor. Greater awareness of this potential manifestation of IACNS may facilitate more prompt diagnosis and treatment.</p

Number-Based Visual Generalisation in the Honeybee

Although the numerical abilities of many vertebrate species have been investigated in the scientific literature, there are few convincing accounts of invertebrate numerical competence. Honeybees, Apis mellifera, by virtue of their other impressive cognitive feats, are a prime candidate for investigations of this nature. We therefore used the well-established delayed match-to-sample paradigm, to test the limits of honeybees' ability to match two visual patterns solely on the basis of the shared number of elements in the two patterns. Using a y-maze, we found that bees can not only differentiate between patterns containing two and three elements, but can also use this prior knowledge to differentiate three from four, without any additional training. However, bees trained on the two versus three task could not distinguish between higher numbers, such as four versus five, four versus six, or five versus six. Control experiments confirmed that the bees were not using cues such as the colour of the exact configuration of the visual elements, the combined area or edge length of the elements, or illusory contours formed by the elements. To our knowledge, this is the first report of number-based visual generalisation by an invertebrate

Honeybee navigation: properties of the visually driven 'odometer'

Recent work has revealed that honeybees determine distance flown by gauging the extent to which the image of the environment moves in the eye as they fly toward their destination. Here we examine the properties of this visually driven 'odometer', by training bees to fly to a feeder in a tunnel lined with a range of, different visual patterns, and analysing their dances when they return to the hive. We find that the odometric signal is relatively unaffected by variations,in the contrast and spatial frequency content of the patterns. Furthermore, a strong signal is generated even when the walls or the floor of the tunnel provide only weak optic-flow cues. Thus, distance flown is measured by a visually driven odometer that is surprisingly robust to variations in the texture or sparseness of the visual environment through which the bee flies

A Novel Human-Based Meta-Heuristic Algorithm: Dragon Boat Optimization

(Aim) Dragon Boat Racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. (Method) It models the unique behaviors of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in different situations. During each iteration, DBO implements different state updating strategies. By modelling the crew's behavior and adjusting the state updating strategies, DBO is able to maintain high-performance efficiency. (Results) We have tested the DBO algorithm with 29 mathematical optimization problems and 2 structural design problems. (Conclusion) The experimental results demonstrate that DBO is competitive with state-of-the-art meta-heuristic algorithms as well as conventional methods

Differential expression of miRNAs related to caste differentiation in the honey bee, Apis mellifera

International audienceAbstractHoney bees are very important eusocial insects and are involved in the pollination of many plants. Queen bees and worker bees can develop from the same fertilized eggs and are thus genetically identical despite their substantial behavioral and physiological differences. The mechanism governing developmental differences between worker and queen bees has always attracted much interest. While there are several reports on messenger RNA (mRNA) expression related to caste differentiation or microRNA (miRNA) expression in one time point of caste differentiation, no systematic investigation of the dynamic expression of small RNAs along with these two caste development has, thus far, been carried out. In this study, we present the dynamic expression profiles of queen and worker bee small RNAs and show caste-specific miRNA expression patterns between them, indicating that miRNAs may be related to the differential development of worker and queen bee larvae. Results presented here will make a valuable contribution to understanding of the caste switch between worker and queen bees

EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection

In the face of rapid advances in medical imaging, cross-domain adaptive medical image detection is challenging due to the differences in lesion representations across various medical imaging technologies. To address this issue, we draw inspiration from large language models to propose EAFP-Med, an efficient adaptive feature processing module based on prompts for medical image detection. EAFP-Med can efficiently extract lesion features of different scales from a diverse range of medical images based on prompts while being flexible and not limited by specific imaging techniques. Furthermore, it serves as a feature preprocessing module that can be connected to any model front-end to enhance the lesion features in input images. Moreover, we propose a novel adaptive disease detection model named EAFP-Med ST, which utilizes the Swin Transformer V2 - Tiny (SwinV2-T) as its backbone and connects it to EAFP-Med. We have compared our method to nine state-of-the-art methods. Experimental results demonstrate that EAFP-Med ST achieves the best performance on all three datasets (chest X-ray images, cranial magnetic resonance imaging images, and skin images). EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance. This holds significant potential for improving medical image analysis and diagnosis