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The gut microbiota of dung beetles comprises bacteria, archaea, and fungi. Most studies have only considered the bacterial element and focused on differences between species reared in the lab. This study considered microorganisms from the gut of Trypocopris pyrenaeus and concentrated on differences among wild individuals of an alpine Italian population. We revealed remarkable interindividual variation in the taxonomic composition of microbiota. Core taxa were few, while individuals harboured many exclusive taxa. Therefore, considering only a few individuals to describe the microbiota of a species is not sufficient. The study also revealed that the nMDS ordination of individuals based on prokaryotes was different from that based on fungi, meaning that both groups should be considered in microbiota analyses and that one microbic group cannot be considered a surrogate of the other. We identified many functions potentially provided by the microbiota and found the taxonomic richness of prokaryotes to be positively correlated with their functional richness. The analysis of the functions potentially provided by these microorganisms confirmed that the gut microbiota, in addition to being essential for the health of their host, may also contribute to the effective functioning of the ecosystems of which dung beetles are part.

The contribution aims to present the results obtained from the archaeological analysis of the architecture of the Cathedral of San Massimo in Forcona (AQ), which took place in 2021, focusing on the digital systems used for the documentation and valorization of the monument. The work is part of a broader research project developed by the University of L'Aquila and focused on the archaeological study of medieval architecture as a function of the creation of a database on a chronological-typological basis of the masonry construction techniques of the city and its territory. The first structure to be analyzed was the Cathedral of San Massimo in Forcona, which, although it is located at a significant distance from the city center, represented a power pole of fundamental importance for the political-economic administration of the territory, at least until the construction of the city of L'Aquila. The documentation carried out through integrated TLS/SfM digital surveys and archaeological analysis allowed first to establish a chronological sequence of the construction phases of the church, but also to hypothesize through 3D reconstructions the historical evolution of the entire architectural complex. These reconstructions then became the digital support for the creation of a series of immersive VR systems aimed at providing experiences of interactive storytelling and dissemination of research outcomes.

Generative adversarial networks (GANs) create images by pitting a generator (G) against a discriminator (D) network, aiming to find a balance between the networks. However, achieving this balance is difficult because G is trained based on just one value representing D’s prediction, and only D can access image features. We introduce a novel approach for training GANs using explainable artificial intelligence (XAI) to enhance the quality and diversity of generated images in histopathological datasets. We leverage XAI to extract feature information from D and incorporate it into G via the loss function, a unique strategy not previously explored in this context. We demonstrate that this approach enriches the training with relevant information and promotes improved quality and more variability in the artificial images, decreasing the FID by up to 32.7% compared to traditional methods. In the data augmentation task, these images improve the classification accuracy of Transformer models by up to 3.81% compared to models without data augmentation and up to 3.01% compared to traditional GAN data augmentation. The Saliency method provides G with the most informative feature information. Overall, our work highlights the potential of XAI for enhancing GAN training and suggests avenues for further exploration in this field.

ABSTRACT Objective: To identify the needs and preferences of individuals with type 2 diabetes regarding the functionalities and characteristics for a mobile application to support foot self-care. Method: Qualitative research with 16 individuals diagnosed with type 2 diabetes recruited during clinical care at a university hospital in Porto, Portugal. Data were collected through semi-structured interviews between March and June 2022 and analyzed using inductive content analysis. Results: Three categories and nine subcategories were identified. Categories included informational needs, essential functionalities for foot health self-care, and user-relevant experience. The preference for objective, limited data input, flexible, and customizable applications was an important factor influencing technology engagement. Conclusion: The research highlighted a preference for customizable and flexible applications, aiding nurses in creating solutions that transform care delivery and enhance the quality of life for individuals living with diabetes.

This paper presents the results of a long-term research project focused on the documentation and digitization of the Florence Air Warfare School, a 1938 important Italian Rationalism architectural complex designed by Raffaello Fagnoni. The study highlights the methodological protocols employed in the project and presents the several activities carried out for documenting the complex and developing specific digital elaborations. The research project involved first the study of archival documents related to the original executive project, and historical-multimedia materials, such as newspaper, magazines, site photos and audiovisual newsreels.In order to obtain a reliable description from the metric-morphological point of view of its current state, integrated reality-based surveying techniques, such as TLS and SfM photogrammetry, were used to acquire and process data. The data results from these surveys, a global point cloud and high-poly 3D models, were thus integrated and optimized, thus making them a morphologically and metrically reliable support for the development of a graphical atlas of 2D digital drawings descriptive of the elevations of each building and the general plan layout of the architectural complex. On the basis of the digital survey outputs, a specific coded and semantic classification of the elements in the complex was finally studied through a functional subdivision of it. These analyses allowed the development of a multiscalar digital repository that, thanks to an appropriate naming-convention, could become a semantic support for HBIM-based and VR systems on which enhancing the management of Florence Air Warfare School Heritage and guide its re-discovery.DOI: https://doi.org/10.20365/disegnarecon.30.2023.11

Alternate wetting and drying (AWD) system, in which water has been reduced by approximately 35% with an increased occurrence of beneficial arbuscular mycorrhizal (AM) symbiosis and no negative impact on rice yield, was proposed to utilize water and nutrients more sustainable. In this study, we selected six rice cultivars (Centauro, Loto, Selenio, Vialone nano, JSendra and Puntal) grown under AWD conditions, and investigated their responsiveness to AM colonization and how they select diverse AM taxa. In order to investigate root-associated AM fungus communities, molecular cloning-Sanger sequencing on small subunit rDNA data were obtained from five out of the six rice cultivars and compared with Next Generation Sequencing (NGS) data, which were previously obtained in Vialone nano. The results showed that all the cultivars were responsive to AM colonization with the development of AM symbiotic structures, even if with differences in the colonization and arbuscule abundance in the root systems. We identified 16 virtual taxa (VT) in the soil compartment and 7 VT in the root apparatus. We emphasized that the NGS analysis gives additional value to the results thanks to a more in-depth reading of the less represented AM fungus taxa.

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure. Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.

Introdução: A obesidade é hoje uma epidemia a nível mundial e muitos doentes obesos sofrem de sintomas e doenças respiratória com impactos na função pulmonar de origem multifatorial, nomeadamente devido a aspectos mecânicos e inflamatórios (Dixon e Peters, 2018). O EDIC é uma intervenção que visa promover a saúde cardiovascular e a capacidade respiratória por meio de exercícios físicos específicos (Postiaux, 2014). Existe ainda evidencia que demonstra que a prática de exercício em crianças impacta positivamente a função cardiorrespiratória com a diminuição da percentagem de gordura corporal para além de outros efeitos a nível físico e emocional. A fisioterapia cardiorrespiratória tem apresentado progressos, focando-se na prevenção, visando a promoção de maior capacidade funcional e consequente melhoria da qualidade de vida de crianças e adultos (Močnik, M.; Varda, 2021). Objetivo: verificar o efeito da aplicação de uma técnica de Fisioterapia Respiratória – EDIC na função cardiorrespiratória em três grupos específicos de crianças. Material e Métodos: Foram selecionadas 30 crianças com as idades compreendidas entre os 8 a 12 anos sem patologias. As crianças foram divididas em três grupos (n=10): saudável sem prática de atividade física (GS), saudável com prática de atividade física (GA) e sobrepeso/obeso (GO). Executamos a avaliação dos parâmetros de pressão arterial, frequência cardíaca e teste de espirometria. Efetuou-se o EDIC combinado com a aplicação de ortótese de incentivo e foram novamente avaliados os parâmetros iniciais. Resultados: Aumento estatisticamente significativo da pressão arterial e da frequência cardíaca antes e após a técnica. A CVF é significativamente maior no GA, a CVFI e o TEF são significativamente maiores no GS. Após o EDIC observamos diferenças estatisticamente significativas no TEF entre os três grupos, aumento significativamente estatístico no PFE do GO e no PFI do GS, com a exceção da CVF e CVFI no GA existindo uma redução significativamente estatística. Conclusões: O EDIC parece exercer algum efeito sobre a função cardiorrespiratória nas três condições específicas.

Enquadramento: As aplicações móveis podem ser úteis para a autogestão da diabetes. Conhecer as necessidades e preferências dos utilizadores é fundamental para que estas ferramentas sejam um suporte efetivo neste processo. Objetivos: Conhecer as experiências, identificar as necessidades e explorar as preferências de pessoas com diabetes mellitus tipo 2 relativamente a aplicações móveis de apoio à autogestão da doença e regime terapêutico. Metodologia: Estudo descritivo, de natureza qualitativa, com 12 participantes recrutados por técnica amostral de bola de neve. Dados recolhidos por entrevistas semiestruturadas e analisados pela técnica de análise de conteúdo. Resultados: Emergiram seis categorias (funções e características técnicas, conteúdos de informação, autodeterminação, gestão de emoções, profissional de saúde e fatores facilitadores). Os dados sugerem baixas competências digitais, necessidade de acesso à informação e a preferência por aplicações multifuncionais e interativas. Conclusão: Apesar da utilização parca de aplicações relacionadas com a saúde, as pessoas assumem interesse e percecionam o benefício de utilizar estas ferramentas para gerir a sua diabetes.

Abstract Aim To evaluate students' self‐perceived pedagogical outcomes when using a digital scenario‐based tool compared to traditional scenarios printed on paper. Design This study used a within‐subjects experiment. Method A digital platform for scenario development was developed, focusing on patients' regaining independence and returning home after an acute event. Students participated in two simulation activities, differing only in the type of scenario used and completed a questionnaire to evaluate their learning experience. Results Students considered that the new scenario template provided a clearer understanding of the situation under analysis, allowing them to recognize the focuses of attention to be prioritized when formulating the intervention plan. No Patient or Public Contribution: A digital platform for a standardized process of scenario writing to help realistic simulation in nursing education is a novelty in this study and will likely contribute to substantial learning gains.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.