ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process

Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filter helps to smooth out any irregularities in the images, while the image scaling method adjusts the size and resolution of the images for better analysis. Once the preprocessing is complete, the preprocessed image is given to the segmentation phase. The segmentation phase is crucial in medical image analysis as it helps to identify and separate different structures within the image, such as organs or tumors. This process involves dividing the preprocessed image into meaningful regions or segments based on intensity, color, texture, or other features. The segmentation process is done using Adaptive Thresholding with Region Growing Fusion Model (AT-RGFM)". This model combines the advantages of both thresholding and region-growing techniques to accurately identify and delineate specific structures within the image. By utilizing AT-RGFM, the segmentation phase can effectively differentiate between different parts of the image, allowing for more precise analysis and diagnosis. It plays a vital role in the medical image analysis process, providing crucial insights for healthcare professionals. Here, the Modified Garter Snake Optimization Algorithm (MGSOA) is used to optimize the parameters. It helps to optimize parameters for accurately identifying and delineating specific structures within medical images and also helps healthcare professionals in providing more precise analysis and diagnosis, ultimately playing a vital role in the medical image analysis process. MGSOA enhances the segmentation phase by effectively differentiating between different parts of the image, leading to more accurate results. Then, the segmented image is fed into the detection phase. The tumor detection is performed by the Vision Transformer-based Multiscale Adaptive EfficientNetB7 (ViT-MAENB7) model. This model utilizes a combination of advanced algorithms and deep learning techniques to accurately identify and locate tumors within the segmented medical image. By incorporating a multiscale adaptive approach, the ViT-MAENB7 model can analyze the image at various levels of detail, improving the overall accuracy of tumor detection. This crucial step in the medical image analysis process allows healthcare professionals to make more informed decisions regarding patient treatment and care. Here, the created MGSOA algorithm is used to optimize the parameters for enhancing the performance of the model. The suggested breast cancer diagnosis performance is compared to conventional cancer diagnosis models and it showed high accuracy. The accuracy of the developed MGSOA-ViT-MAENB7 is 96.6 %, and others model like RNN, LSTM, EffNet, and ViT-MAENet given the accuracy to be 90.31 %, 92.79 %, 94.46 % and 94.75 %. The developed model's ability to analyze images at multiple scales, combined with the optimization provided by the MGSOA algorithm, results in a highly accurate and efficient system for detecting tumors in medical images. This cutting-edge technology not only improves the accuracy of diagnosis but also helps healthcare professionals tailor treatment plans to individual patients, ultimately leading to better outcomes. By outperforming traditional cancer diagnosis models, the proposed model is revolutionizing the field of medical imaging and setting a new standard for precision and effectiveness in healthcare.

Environmental drivers of vigilance behaviour in painted stork (Mycteria leucocephala) nesting colonies

Vigilance in animals, crucial for predator detection, impacts survival and reproduction by diverting time from activities like foraging and mating. Painted Stork (Mycteria leucocephala), a colonially nesting bird, experiences disturbances from avian predators and human presence while nesting in colonies across varied habitats in India. We measured environmental vigilance behaviour at two diverse types of nesting sites in North India, the National Zoological Park and Keoladeo National Park. We studied the study role of several variables viz. sex, nestling age, number of neighbours, number of visitors, and other variables on vigilance, by camera techniques. Our findings revealed significant sex-based differences, with males being more vigilant than females, likely due to their roles in territoriality, mate guarding, and nest protection. Parental vigilance increased as nestlings aged, underscoring its importance as parental investment. Generalized Linear Mixed Models (GLMMs) indicated that number of visitors, number of neighbours, and other habitat variables significantly affected vigilance behaviour. Painted Storks at the National Zoological Park displayed higher vigilance than Storks in Keoladeo National Park, attributed to site specific variables.

Anthropogenic disturbance affects calling and collective behaviour in corvid roosts

Acoustic communication plays an important role in coordinating group dynamics and collective movements across a range of taxa. However, anthropogenic disturbance can inhibit the production or reception of acoustic signals. Here, we investigate the effects of noise and light pollution on the calling and collective behaviour of wild jackdaws (Corvus monedula), a highly social corvid species that uses vocalizations to coordinate collective movements at winter roosting sites. Using audio and video monitoring of roosts in areas with differing degrees of urbanization, we evaluate the influence of anthropogenic disturbance on vocalizations and collective movements. We found that when levels of background noise were higher, jackdaws took longer to settle following arrival at the roost in the evening and also called more during the night, suggesting that human disturbance may cause sleep disruption. High levels of overnight calling were, in turn, linked to disruption of vocal consensus decision-making and less cohesive group departures in the morning. These results raise the possibility that, by affecting cognitive and perceptual processes, human activities may interfere with animals' ability to coordinate collective behaviour. Understanding links between anthropogenic disturbance, communication, cognition and collective behaviour must be an important research priority in our increasingly urbanized world. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Body mass mediates spatio-temporal responses of mammals to human frequentation across Italian protected areas

Protected area (PA) networks are a pivotal tool to fight biodiversity loss, yet they often need to balance the mission of nature conservation with the socio-economic need of giving opportunity for outdoor recreation. Recreation in natural areas is important for human health in an urbanized society, but can prompt behavioural modifications in wild animals. Rarely, however, have these responses being studied across multiple PAs and using standardized methods. We deployed a systematic camera trapping protocol at over 200 sites to sample medium and large mammals in four PAs within the European Natura 2000 network to assess their spatio-temporal responses to human frequentation, proximity to towns, amount of open habitat and topographical variables. By applying multi-species and single-species models for the number of diurnal, crepuscular and nocturnal detections and a multi-species model for nocturnality index, we estimated both species-specific- and meta-community-level effects, finding that increased nocturnality appeared the main strategy that the mammal meta-community used to cope with human disturbance. However, responses in the diurnal, crepuscular and nocturnal site use were mediated by species' body mass, with larger species exhibiting avoidance of humans and smaller species more opportunistic behaviours. Our results show the effectiveness of standardized sampling and provide insights for planning the expansion of PA networks as foreseen by the Kunming-Montreal biodiversity agreement.

When death comes: linking predator-prey activity patterns to timing of mortality to understand predation risk

The assumption that activity and foraging are risky for prey underlies many predator-prey theories and has led to the use of predator-prey activity overlap as a proxy of predation risk. However, the simultaneous measures of prey and predator activity along with timing of predation required to test this assumption have not been available. Here, we used accelerometry data on snowshoe hares (Lepus americanus) and Canada lynx (Lynx canadensis) to determine activity patterns of prey and predators and match these to precise timing of predation. Surprisingly we found that lynx kills of hares were as likely to occur during the day when hares were inactive as at night when hares were active. We also found that activity rates of hares were not related to the chance of predation at daily and weekly scales, whereas lynx activity rates positively affected the diel pattern of lynx predation on hares and their weekly kill rates of hares. Our findings suggest that predator-prey diel activity overlap may not always be a good proxy of predation risk, and highlight a need for examining the link between predation and spatio-temporal behaviour of predator and prey to improve our understanding of how predator-prey behavioural interactions drive predation risk.

Does a roosting flock of migratory birds also echelon in high winds?

The organized aerial manoeuvres of birds in “V” and “J” flock echelons have always captivated onlookers and several of these aspects are still a matter of ongoing research. However, we could not find any published evidence or report on echeloning in a roosting flock of birds in high wind conditions. Here, we provide first evidence of an echelon in a roosting flock of the Eurasian oystercatcher (Haematopus ostralegus ostralegus) at the onset of Storm Malik in Scotland on the morning of the 29th of January 2022, under ~ 11 ms−1 winds. This observation opens-up several new research questions on if, how, and why birds position themselves in a flock while roosting in high winds.

Behavioral Attributes of Social Groups Determine the Strength and Direction of Selection on Neural Investment

The evolution of social systems can place novel selective forces on investment in expensive neural tissue by changing cognitive demands. Previous hypotheses about the impact of sociality on neural investment have received equivocal support when tested across diverse taxonomic groups and social structures. We suggest previous models for social behavior-brain relationships have overlooked important variation in social groups. Social groups vary significantly in structure and function, and the specific attributes of a social group may be more relevant to setting cognitive demands than sociality in general. We have identified intragroup competition, relationship differentiation, information sharing, dominance hierarchies, and task specialization and redundancy as attributes of social behavior which may impact selection for neural investment, and outline how variation in these attributes can result in increased or decreased neural investment with transitions to sociality in different taxa. Finally, we test some of the predictions generated using this framework in a phylogenetic comparison of neural tissue investment in Anelosimus social spiders. Social Anelosimus spiders engage in cooperative prey capture and brood care, which allows for individual redundancy in the completion of these tasks. We hypothesized that in social spider species, the presence of redundancy would reduce selection for individual neural investment relative to subsocial species. We found that social species had significantly decreased investment in the arcuate body, the cognitive center of the spider brain, supporting our predictions. Future comparative tests of brain evolution in social species should account for the special behavioral characteristics that accompany social groups in the subject taxa.

Urban noise restricts, fragments, and lightens sleep in Australian magpies

Urban areas are inherently noisy, and this noise can disrupt biological processes as diverse as communication, migration, and reproduction. We investigated how exposure to urban noise affects sleep, a process critical to optimal biological functioning, in Australian magpies (Cracticus tibicen). Eight magpies experimentally exposed to noise in captivity for 24-h spent more time awake, and less time in non-rapid eye movement (non-REM) and REM sleep at night than under quiet conditions. Sleep was also fragmented, with more frequent interruptions by wakefulness, shorter sleep episode durations, and less intense non-REM sleep. REM sleep was particularly sensitive to urban noise. Following exposure to noise, magpies recovered lost sleep by engaging in more, and more intense, non-REM sleep. In contrast, REM sleep showed no rebound. This might indicate a long-term cost to REM sleep loss mediated by noise, or contest hypotheses regarding the functional value of this state. Overall, urban noise has extensive, disruptive impacts on sleep composition, architecture, and intensity in magpies. Future work should consider whether noise-induced sleep restriction and fragmentation have long-term consequences.