Lateralised courtship behaviour and its impact on mating success in Ostrinia furnacalis (Lepidoptera: Crambidae).

Lateralisation is a well-established phenomenon observed in an increasing number of insect species. This study aims to obtain basic details on lateralisation in courtship and mating behaviour in Ostrinia furnacalis, the Asian corn borer. We conducted laboratory investigations to observe lateralisation in courtship and mating behaviours in adult O. furnacalis. Our goal was also to detect lateralised mating behaviour variations during sexual interactions and to elucidate how these variances might influence the mating success of males. Our findings reveal two distinct lateralised traits: male approaches from the right or left side of the female and the direction of male turning displays. Specifically, males approaching females from their right side predominantly exhibited left-biased 180° turning displays, while males approaching females from the left-side primarily displayed right-biased 180° turning displays. Notably, left-biased males, executing a 180° turn for end-to-end genital contact, initiated copulation with fewer attempts and began copulation earlier than their right-biased approaches with left-biased 180° turning displays. Furthermore, mating success was higher when males subsequently approached the right side of females during sexual encounters. Left-biased 180° turning males exhibited a higher number of successful mating interactions. These observations provide the first report on lateralisation in the reproductive behaviour of O. furnacalis under controlled laboratory conditions and hold promise for establishing reliable benchmarks for assessing and monitoring the quality of mass-produced individuals in pest control efforts.

Mitigating dengue incidence through advanced Aedes larval surveillance and control: A successful experience from Pakistan.

Dengue fever is a viral disease caused by one of four dengue stereotypes (Flavivirus: Flaviviridae) that are primarily transmitted by Aedes albopictus (Skuse) and Aedes aegypti (L.). To safeguard public health, it is crucial to conduct surveys that examine the factors favouring the presence of these species. Our study surveyed 42 councils across four towns within the Bhakkar district of Punjab Province, by inspecting man-made or natural habitats containing standing water. First, door-to-door surveillance teams from the district health department were assigned to each council to surveillance Aedes species and dengue cases. Second, data collection through surveillance efforts, and validation procedures were implemented, and the verified data was uploaded onto the Dengue Tracking System by Third Party Validation teams. Third, data were analysed to identify factors influencing dengue fever cases. The findings demonstrated the following: (1) Predominantly, instances were discerned among individuals who had a documented history of having travelled beyond the confines of the province. (2) Containers associated with evaporative air coolers and tyre shops were responsible for approximately 30% of the Aedes developmental sites. (4) Variability in temperature was responsible for approximately 45% of the observed differences in the quantity of recorded Aedes mosquito developmental sites. (5) Implementation of dengue prevention initiatives precipitated a 50% reduction in Aedes-positive containers, alongside a notable 70% decline in reported cases of dengue fever during the period spanning 2019 to 2020, while the majority of reported cases were of external origin. Aedes control measures substantially curtailed mosquito populations and lowered vector-virus interactions. Notably, local dengue transmission was eliminated through advanced and effective Aedes control efforts, emphasising the need for persistent surveillance and eradication of larval habitats in affected regions.

How aggressive interactions with biomimetic agents optimize reproductive performances in mass-reared males of the Mediterranean fruit fly.

Mass-rearing procedures of insect species, often used in biological control and Sterile Insect Technique, can reduce the insects competitiveness in foraging, dispersal, and mating. The evocation of certain behaviours responsible to induce specific neuroendocrine products may restore or improve the competitiveness of mass-reared individuals. Herein, we used a mass-reared strain of Ceratitis capitata as model organism. C. capitata is a polyphagous pest exhibiting territorial displays that are closely related to its reproductive performance. We tested if the behaviour of C. capitata males could be altered by hybrid aggressive interactions with a conspecific-mimicking robotic fly, leading to more competitive individuals in subsequent mating events. Aggressive interactions with the robotic fly had a notable effect on subsequent courtship and mating sequences of males that performed longer courtship displays compared to naïve individuals. Furthermore, previous interactions with the robotic fly produced a higher mating success of males. Reproductive performances of C. capitata males may be improved by specific octopaminergic neurones activated during previous aggressive interactions with the robotic fly. This study adds fundamental knowledge on the potential role of specific neuro-behavioural processes in the ecology of tephritid species and paves the way to innovative biotechnological control methods based on robotics and bionics.

Unveiling social distancing mechanisms via a fish-robot hybrid interaction.

Pathogen transmission is a major limit of social species. Social distancing, a behavioural-based response to diseases, has been regularly reported in nature. However, the identification of distinctive stimuli associated with an infectious disease represents a challenging task for host species, whose cognitive mechanisms are still poorly understood. Herein, the social fish Paracheirodon innesi, was selected as model organism to investigate animal abilities in exploiting visual information to identify and promote social distancing towards potentially infected conspecifics. To address this, a robotic fish replica mimicking a healthy P. innesi subject, and another mimicking P. innesi with morphological and/or locomotion anomalies were developed. P. innesi individuals were attracted by the healthy fish replica, while they avoided the fish replica with morphological abnormalities, as well as the fish replica with an intact appearance, but performing locomotion anomalies (both symptoms associated with a microsporidian parasite infesting P. innesi and other fish). Furthermore, the fish replica presenting both morphology and locomotion anomalies in conjunction, triggered a significantly stronger social distancing response. This confirms the hypothesis that group living animals overgeneralize cues that can be related with a disease to minimize transmission, and highlights the important role of visual cues in infection risk contexts. This study prompts more attention on the role of behavioural-based strategies to avoid pathogen/parasite diffusion, and can be used to optimize computational approaches to model disease dynamics.

Freshwater organisms potentially useful as biosensors and power-generation mediators in biohybrid robotics.

Facing the threat of rapidly worsening water quality, there is an urgent need to develop novel approaches of monitoring its global supplies and early detection of environmental fluctuations. Global warming, urban growth and other factors have threatened not only the freshwater supply but also the well-being of many species inhabiting it. Traditionally, laboratory-based studies can be both time and money consuming and so, the development of a real-time, continuous monitoring method has proven necessary. The use of autonomous, self-actualizing entities became an efficient way of monitoring the environment. The Microbial Fuel Cells (MFC) will be investigated as an alternative energy source to allow for these entities to self-actualize. This concept has been improved with the use of various lifeforms in the role of biosensors in a structure called "biohybrid" which we aim to develop further within the framework of project Robocoenosis relying on animal-robot interaction. We introduce a novel concept of a fully autonomous biohybrid agent with various lifeforms in the role of biosensors. Herein, we identify most promising organisms in the context of underwater robotics, among others Dreissena polymorpha, Anodonta cygnaea, Daphnia sp. and various algae. Special focus is placed on the "ecosystem hacking" based on their interaction with the electronic parts. This project uses Austrian lakes of various trophic levels (Millstättersee, Hallstättersee and Neusiedlersee) as case studies and as a "proof of concept".

Beetle-robot hybrid interaction: sex, lateralization and mating experience modulate behavioural responses to robotic cues in the larger grain borer Prostephanus truncatus (Horn).

Ethorobotics, a new fascinating field of biorobotics, proposes the use of robotic replicas as an advanced method for investigating animal behaviour. This novel research approach can also encourage the development of advanced bioinspired robots. In the present study, we investigated the pushing behaviour, a particular display occurring in several beetle species, such as the larger grain borer, Prostephanus truncatus, during both male-female and male-male contexts. We developed a robotic apparatus actuating female and male-mimicking dummies to study if sex, mating experience and asymmetries of robotic cues can modulate the escalation of pushing behaviour. Results showed that the time needed by P. truncatus to react to female-smelling biomimetic dummies was chiefly affected by their mating experience and the dummy odour. This was likely due to reduce waste of costly sperm in mated males during the subsequent sexual interactions. The pushing behaviour was performed longer and with a higher number of acts when virgin females were approached from their right side. More and longer pushing acts were noted when virgin males were approached from their left side. Dedicated neural circuits would likely act in opposite direction in females and males producing population-level lateralized sensory-motor displays, which may be evolved to promote male approaches from the left side of females, thus improving short-distance sex recognition. Overall, this study provides new insights on the behavioural ecology of stored-product beetles, as well as on self-organization and decentralized decision making that can be exploited to develop bioinspired algorithms for task optimization, involving real-world scenarios.

Encoding lateralization of jump kinematics and eye use in a locust via bio-robotic artifacts.

The effect of previous exposure to lateral sensory stimuli in shaping the response to subsequent symmetric stimuli represents an important overlooked issue in neuroethology, with special reference to arthropods. In this research, we investigated the hypothesis to 'programme' jumping escape direction as well as surveillance orientation in young and adult individuals of Locusta migratoria as an adaptive consequence of prior exposure to directional-biased predator approaches generated by a robotic leopard gecko representing Eublepharis macularius The manipulation of the jumping escape direction was successfully achieved in young locusts, although young L. migratoria did not exhibit innately lateralized jumping escapes. Jumping escape direction was also successfully manipulated in adult locusts, which exhibited innate lateralized jumping escape at the individual level. The innate lateralization of each instar of L. migratoria in using a preferential eye during surveillance was not affected by prior lateralized exposure to the robotic gecko. Our results indicate a high plasticity of the escape motor outputs that are occurring almost in real time with the perceived stimuli, making them greatly adaptable and compliant to environmental changes in order to be effective and reliable. In addition, surveillance lateralization innately occurs at population level in each instar of L. migratoria Therefore, its low forgeability by environmental factors would avoid disorganization at swarm level and improve swarm coordination during group tasks. These findings are consistent with the fact that, as in vertebrates, in insects the right hemisphere is specialized in controlling fear and escape functions.

A review on animal-robot interaction: from bio-hybrid organisms to mixed societies.

Living organisms are far superior to state-of-the-art robots as they have evolved a wide number of capabilities that far encompass our most advanced technologies. The merging of biological and artificial world, both physically and cognitively, represents a new trend in robotics that provides promising prospects to revolutionize the paradigms of conventional bio-inspired design as well as biological research. In this review, a comprehensive definition of animal-robot interactive technologies is given. They can be at animal level, by augmenting physical or mental capabilities through an integrated technology, or at group level, in which real animals interact with robotic conspecifics. Furthermore, an overview of the current state of the art and the recent trends in this novel context is provided. Bio-hybrid organisms represent a promising research area allowing us to understand how a biological apparatus (e.g. muscular and/or neural) works, thanks to the interaction with the integrated technologies. Furthermore, by using artificial agents, it is possible to shed light on social behaviours characterizing mixed societies. The robots can be used to manipulate groups of living organisms to understand self-organization and the evolution of cooperative behaviour and communication.

Lateralized courtship in a parasitic wasp.

Lateralization (i.e. left-right asymmetries in the brain and behaviour) of courtship displays has been examined in a growing number vertebrate species, while evidence for invertebrates is limited. In this study, we investigated lateralization of courtship and mating displays in the parasitic wasp Leptomastidea abnormis. Results showed a population-level lateralization of male courtship displays. Male antennal tapping on the female's head was right-biased. However, right-biased male courtship acts were not characterized by higher male antennal tapping frequencies, nor success in mating although antennal tapping frequency was higher in males with mating success with respect to unsuccessful males. Overall, our results add basic knowledge to the behavioural ecology of insect parasitoids. To the best of our knowledge, this is the first report of behavioural lateralization in parasitic Hymenoptera.

Lateralisation of aggressive displays in a tephritid fly.

Lateralisation (i.e. different functional and/or structural specialisations of the left and right sides of the brain) of aggression has been examined in several vertebrate species, while evidence for invertebrates is scarce. In this study, we investigated lateralisation of aggressive displays (boxing with forelegs and wing strikes) in the Mediterranean fruit fly, Ceratitis capitata. We attempted to answer the following questions: (1) do medflies show lateralisation of aggressive displays at the population-level; (2) are there sex differences in lateralisation of aggressive displays; and (3) does lateralisation of aggression enhance fighting success? Results showed left-biased population-level lateralisation of aggressive displays, with no consistent differences among sexes. In both male-male and female-female conflicts, aggressive behaviours performed with left body parts led to greater fighting success than those performed with right body parts. As we found left-biased preferential use of body parts for both wing strikes and boxing, we predicted that the left foreleg/wing is quicker in exploring/striking than the right one. We characterised wing strike and boxing using high-speed videos, calculating mean velocity of aggressive displays. For both sexes, aggressive displays that led to success were faster than unsuccessful ones. However, left wing/legs were not faster than right ones while performing aggressive acts. Further research is needed on proximate causes allowing enhanced fighting success of lateralised aggressive behaviour. This is the first report supporting the adaptive role of lateralisation of aggressive displays in insects.

First report of behavioural lateralisation in mosquitoes: right-biased kicking behaviour against males in females of the Asian tiger mosquito, Aedes albopictus.

Lateralisation (i.e. functional and/or structural specialisations of left and right sides of the brain) of aggressive traits has been studied in a number of vertebrates, while evidence for invertebrates is scarce. Mosquito females display aggressive responses against undesired males, performing rejection kicks with the hind legs. In this research, we examined lateralisation of kicking behaviour in females of the Asian tiger mosquito, Aedes albopictus. We found a right-biased population-level lateralisation of kicking behaviour. Four repeated testing phases on mosquito females confirmed the preferential use of right legs. However, when left legs were used, the mean number of kicks per rejection event was not different to that performed with right legs. Both left and right kicking behaviour lead to successful displacement of undesired partners. This is the first report about behavioural lateralisation in mosquitoes.

Animal-robot interaction-an emerging field at the intersection of biology and robotics.

The field of animal-robot and organism-robot interaction systems (ARIS, ORIS) is a currently rapidly emerging field in biorobotics. In this special issue we aim for providing a comprehensive overview of the cutting-edge advancements and pioneering breakthroughs within this scientific and engineering discipline. Therefore, we collected scientific articles that delineate and expound upon the complexity of these remarkable biohybrid systems. These configurations stand as engineered conduits, facilitating the accurate investigation and profound exploration of the multifaceted interactions between robotic devices and biological entities, including various fish species, honeybees and plants. Also the human factor plays a role in this collection, as we also include a philosophical perspective on such systems as well as an augmented reality setup that brings humans into the loop with living fish. Within our editorial purview, we categorize the scientific contributions based on their focal points, differentiating between examinations of singular agent-to-agent interactions, extensions to the social stratum, and further expansions to the intricate levels of swarm dynamics, colonies, populations, and ecosystems. Considering potential applications, we delve into the multifaceted domains wherein these biohybrid systems might be applied. This discourse culminates in a tentative glimpse into the future trajectories these technologies might traverse, elucidating their promising prospects for both scientific advancement and societal enrichment. In sum, this special issue aims at facilitating the convergence of diverse insights, at encapsulating the richness of the ARIS and ORIS domain, and at charting a course toward the untapped prospects lying at the nexus of biology and robotics.

Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach.

Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.

Tapping for love: courtship, mating, and behavioral asymmetry in two aphid parasitoids, Aphidius ervi and Aphidius matricariae (Hymenoptera: Braconidae: Aphidinae).

Understanding the biology and ecology of parasitoids can have direct implications for their evaluation as biological control agents, as well as for the development and implementation of mass-rearing techniques. Nonetheless, our current knowledge of the possible influence of lateralized displays (i.e., the asymmetric expression of cognitive functions) on their reproductive behavior is scarce. Herein, we characterized the behavioral elements involved in courtship, and quantified the durations of 2 important aphid parasitoids, Aphidius ervi Haliday and Aphidius matricariae Haliday (Hymenoptera: Braconidae: Aphidiinae). We quantified the main indicators of copulation and examined the occurrence of lateralized traits at population level. Results indicated that A. matricariae exhibited longer durations of wing fanning, antennal tapping, pre-copula and copula phases compared to A. ervi. Postcopulatory behavior was observed only in A. matricariae. Unlike other parasitoid species, the duration of wing fanning, chasing, and antennal tapping did not affect the success of the mating of male A. ervi and A. matricariae. Both species exhibited a right-biased female kicking behavior at the population level during the pre-copula. Our study provides insights into the fundamental biology of aphidiine parasitoids and reports the presence of population-level lateralized mating displays, which can serve as useful benchmarks to evaluate the quality of mass-rearing systems.

Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy.

Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors.

Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification.

Background: Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC. Results: The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state. Discussion: These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.

Unraveling the microbiome-metabolome nexus: a comprehensive study protocol for personalized management of Behçet's disease using explainable artificial intelligence.

The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet's disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement's impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome's role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multi-modal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes.

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.