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.

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.

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.

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.

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.

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.

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.

Automated image-based analysis unveils acute effects due to sub-lethal pesticide doses exposure.

Pesticides are still abused in modern agriculture. The effects of their exposure to even sub-lethal doses can be detrimental to ecosystem stability and human health. This work aims to validate the use of machine learning techniques for recognizing motor abnormalities and to assess any effect post-exposure to a minimal dosage of these substances on a model organism, gaining insights into potential risks for human health. The test subject was the Mediterranean fruit fly, Ceratitis capitata (Wiedemann) (Diptera: Tephritidae), exposed to food contaminated with the LC30 of Carlina acaulis essential oil. A deep learning approach enabled the pose estimation within an arena. Statistical analysis highlighted the most significant features between treated and untreated groups. Based on this analysis, two learning-based algorithms, Random Forest (RF) and XGBoost were employed. The results were compared through different metrics. RF algorithm generated a model capable of distinguishing treated subjects with an area under the receiver operating characteristic curve of 0.75 and an accuracy of 0.71. Through an image-based analysis, this study revealed acute effects due to minimal pesticide doses. So, even small amounts of these biocides drifted far from distribution areas may negatively affect the environment and humans.

Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant.

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health.

Daphnia's phototaxis as an indicator in ecotoxicological studies: A review.

Animal-based sensors have been increasingly applied to many water monitoring systems and ecological studies. One of the staple organisms used as living sensors for such systems is Daphnia. This organism has been extensively studied and, with time, used in many toxicological and pharmaceutical bioassays, often used for exploring the ecology of freshwater communities. One of its behaviours used for evaluating the state of the aquatic environment is phototaxis. A disruption in the predicted behaviour is interpreted as a sign of stress and forms the basis for further investigation. However, phototaxis is a result of complex processes counteracting and interacting with each other. Predator presence, food quality, body pigmentation and other factors can greatly affect the predicted phototactic response, hampering its reliability as a bioindicator. Therefore, a holistic approach and meticulous documentation of the methods are needed for the correct interpretation of this behavioural indicator. In this review, we present the current methods used for studying phototaxis, the factors affecting it and proposed ways to optimise the reliability of the results.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring.

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as 'biohybrids', designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra musselDreissena polymorphaand water fleaDaphniaspp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.

Larval competition analysis and its effect on growth of Ostrinia furnacalis (Lepidoptera: Crambidae) at natural conditions in Northeast China.

The Asian corn borer (Ostrinia furnacalis, Lepidoptera, Crambidae) and Oriental armyworm (Mythimna separata, Lepidoptera, Noctuidae) are 2 major lepidopteran pests of the maize plant, especially the whorls and tassels. The aim of this study was to investigate competition between 2 lepidopteran pests of maize. Intraspecific and interspecific competition occurs when O. furnacalis and M. separata larvae interact with various stages of the maize plant. Therefore, determining whether this competition can decrease larval damage by causing adverse effects on larval growth is crucial. During the maize growing season of 2022, the interaction of these species was assessed in the experimental field of Jilin Agricultural University, China. Interspecific and intraspecific competition of larvae in different maize tissues and the influence of competition on larval development was determined in the fields. The results showed that first, probing behavior was significantly frequent in O. furnacalis larvae; intraspecific and interspecific attack was significant at 4th instar (with leaf, silk, and kernel). Interspecific defense behavior was significant at 3rd instar (without food). O. furnacalis larvae showed attack behavior toward M. separata larvae frequently. Second, competition increased the mortality rate of O. furnacalis larvae (intraspecific, 67%; interspecific, 33%) and decreased pupation emergence rate. Thus, intraspecific and interspecific competition might affect the competitive displacement of pest species sharing the same ecological niche, as well as the prevalence and population dynamics of pests, and help to develop integrated pest management 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.

Turning earthworms into moonworms: Earthworms colonization of lunar regolith as a bioengineering approach supporting future crop growth in space.

The earthworms beneficial effects on soils may be promising to improve lunar soil fertility, enabling the use of local substrates for space farming. Herein, we investigated the effects of the lunar regolith simulant (LHS-1) at different concentrations in cow manure mixtures on the survival and fitness of Eisenia fetida. During 14 and 60-day experiments, although E. fetida showed an increased mortality with LHS-1 alone, most of the population survived. More numerous tunnels were observed when exposed to the higher concentrations of LHS-1 (poor in nutrients for earthworms). This may be related to an increased mobility for food search. The cocoons production was not affected by different substrate treatments, except for the highest concentration of LHS-1. No effects of different LHS-1 concentrations on the amount of ingested substrate were recorded. This study shows that E. fetida can potentially colonize lunar regolith representing a future valuable biological tool for supporting crops growth on the Moon.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

The beehive of the future is a robot socially interacting with honeybees.

A robotic beehive may unveil insights into honeybee collective behavior and sustain the colony in harsh weather.

A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance.

Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (Ceratitis capitata and Bactrocera oleae) free to move and change their posture. Results showed a successful automatic detection (i.e., precision rate about 93%) in real-time of C. capitata and B. oleae adults using a camera sensor at a fixed height. In addition, the similar shape and movement patterns of the two insects did not interfere with the network precision. The proposed method can be extended to other pest species, needing minimal data pre-processing and similar architecture.

Going Beyond the "Synthetic Method": New Paradigms Cross-Fertilizing Robotics and Cognitive Neuroscience.

In so-called ethorobotics and robot-supported social cognitive neurosciences, robots are used as scientific tools to study animal behavior and cognition. Building on previous epistemological analyses of biorobotics, in this article it is argued that these two research fields, widely differing from one another in the kinds of robots involved and in the research questions addressed, share a common methodology, which significantly differs from the "synthetic method" that, until recently, dominated biorobotics. The methodological novelty of this strategy, the research opportunities that it opens, and the theoretical and technological challenges that it gives rise to, will be discussed with reference to the peculiarities of the two research fields. Some broad methodological issues related to the generalization of results concerning robot-animal interaction to theoretical conclusions on animal-animal interaction will be identified and discussed.

Any colour you like: fish interacting with bioinspired robots unravel mechanisms promoting mixed phenotype aggregations.

Collective behaviours in homogeneous shoals provide several benefits to conspecifics, although mixed-species aggregations have been reported to often occur. Mixed aggregations may confer several beneficial effects such as antipredator and foraging advantages. However, the mechanisms promoting phenotypically heterogeneous fish aggregations have been poorly explored so far. Herein, the neon tetraParacheirodon innesiwas selected as the ideal model organism to test the role of visible phenotypic traits in promoting fish shoaling. Robotic fish replicas of different colours, but with a morphology inspired byP. innesi, were developed to test the affiliation behaviour of neon tetra individuals towards fish replicas with different phenotypic traits.P. innesiindividuals showed a decreasing preference in shoaling with the biomimetic, the blue, the red, and the grey replicas. This could be due to the greater visibility of the blue colour even in dark conditions. Furthermore, an increased reddening of the livery is often caused by physiological processes related to a nonoptimal behavioural status. The time spent in shoaling with each fish replica was strongly influenced by different ecological contexts. The longest shoaling duration was observed when a biomimetic predator was present, while the shortest shoaling duration was recorded in the presence of food. This confirms the hypothesis that heterogeneous shoals are promoted by the antipredator benefits, and reduced by competition. This study allowed us to understand basic features of the behavioural ecology favouring heterogeneous aggregations in shoaling fish, and provided a novel paradigm for biohybrid robotics.