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.

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.

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.

Lateralization of Courtship Traits Impacts Pentatomid Male Mating Success-Evidence from Field Observations.

Lateralization has been documented in many insect species, but limited information on courtship and mating lateralization in wild conditions is available. We conducted field investigation on the courtship and mating behavior of the neem bug, Halys dentatus, a polyphagous insect mainly infesting Azadirachta indica, with particular attention to lateralization of mating displays. We investigated the presence of population-level behavioral asymmetries during H. dentatus sexual interactions and their influence on male mating success. Two lateralized traits were found: left or right-biased male approaches to the female and left or right-biased male turning displays. Males approaching females from their left side were mainly right-biased in the 180° turning display, and males that approached females from their right side were mainly left-biased. Right-biased males by turning 180° to carry out end-to-end genital contact, performed a lower number of copulation attempts, thus starting copula earlier than left-biased males. Mating success was higher when males approached the left side of females during sexual interactions. A higher number of successful mating interactions was observed in right-biased males when turning 180°. Our results add useful knowledge on the reproductive behavior of H. dentatus in the field, with potential applications for identifying useful benchmarks to monitor the quality of individuals mass-reared for pest control purposes over time.

Bio-robotic cues show how the Trinidadian guppy male recognises the morphological features of receptive females.

Sensory fusion is used by the males of several animal species to discriminate the mating status of females by evaluating their phenotypic traits. The predominant trait used is olfactory cues, and the role of visual cues is not yet fully understood. The ability of Poecilia reticulata males to evaluate females' receptivity based on visual cues was investigated. Guppy males adopt two different mating strategies, courtship displays and forced copulation, towards receptive and pregnant females, respectively. Robotic counterparts mimicking receptive and pregnant females were developed to test whether males relied only on visual information to determine a females' mating status. Exposure to the robotic receptive females evoked courtship behaviours, while forced copulation attempts were more frequent towards the robotic pregnant females. When the robotic fish were simultaneously exposed, regardless of the presence or absence of receptive-female olfactory cues, males expressed their preference for the receptive female. Visual processing showed that fish social interactions played a strategic role in their collection of information, especially when other stimuli were not available. The proposed ethorobotic approach allowed for assessing the role of visual cues in the mating choice of P. reticulata males and highlighting the information processing methods and cognition in aquatic animals.

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.