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Abstract Camera‐traps are valuable tools for estimating wildlife population density, and recently developed models enable density estimation without the need for individual recognition. Still, processing and analysis of camera‐trap data are extremely time‐consuming. While algorithms for automated species classification are becoming more common, they have only served as supporting tools, limiting their true potential in being implemented in ecological analyses without human supervision. Here, we assessed the capability of two camera‐trap based models to provide robust density estimates when image classification is carried out by machine learning algorithms. We simulated density estimation with Camera‐Trap Distance Sampling (CT‐DS) and Random Encounter Model (REM) under different scenarios of automated image classification. We then applied the two models to obtain density estimates of three focal species (roe deer Capreolus capreolus, red fox Vulpes vulpes and Eurasian badger Meles meles) in a reserve in central Italy. Species detection and classification was carried out both by the user and machine learning algorithms (respectively, MegaDetector and Wildlife Insights), and all outputs were used to estimate density and ultimately compared. Simulation results suggested that the CT‐DS model could provide robust density estimates even at poor algorithm performances (down to 50% of correctly classified images), while the REM model is more unpredictable and depends on multiple factors. Density estimates obtained from the MegaDetector output were highly consistent for both models with the manually labelled images. While Wildlife Insights' performance differed greatly between species (recall: badger = 0.15; roe deer = 0.56; fox = 0.75), CT‐DS estimates did not vary significantly; on the contrary, REM systematically overestimated density, with little overlap in standard errors. We conclude that CT‐DS and REM models can be robust to the loss of images when machine learning algorithms are used to identify animals, with the CT‐DS being an ideal candidate for applications in a fully unsupervised framework. We propose guidelines to evaluate when and how to integrate machine learning in the analysis of camera‐trap data for density estimation, further strengthening the applicability of camera‐traps as a cost‐effective method for density estimation in (spatially and temporally) extensive multi‐species monitoring programmes.

Abstract Introduction This prospective multicentre study evaluates the impact of Palliative Care Unit (PCU) intervention (Experimental Group, EG), during autologous hematopoietic stem cell transplantation (AHSCT) on quality of life (QoL), symptom control and healthcare resource use compared to standard practice (Control Group, CG). We used validated scales on Days 0 (stem cell infusion), + 7 (bone marrow aplasia, acute symptoms) and + 21 (aplasia recovery). Results In 40 patients (20 EG/ 20 CG: 45%/25% female, median age 57.5/59), QoL differed significantly at Day + 7 (EG: median 0.50; CG: -63.00; p

Anthropogenic disturbance elicits adaptive responses in wildlife, generally aimed at risk-avoidance, ultimately imposing constraints on their spatial and temporal niches. Previous studies have largely focused on long-term adaptive responses to stable human pressure, but rapid adjustments in wildlife’s diel and habitat use patterns in response to fine-scale variations in human presence have so far been overlooked. In this study we estimate short-term spatio-temporal deviations in local habitat use and diel activity of medium and large mammal species in response to rapid variations in human disturbance. We employed a year-long camera-trapping within a small private reserve, and recorded spatio-temporal information on all sources of anthropogenic disturbance in the area. By controlling for the average habitat use and diel activity, we explored fine-scale spatiotemporal adjustments in seven mammal species. We found evidence of spatial and/or temporal avoidance across all species, except wild boar, with variations in magnitude and direction coherent with species traits and expected levels of human-tolerance. Most species exhibited temporal avoidance of human activities, with porcupine and roe deer eliciting particularly strong responses. Notably, foxes concurrently displayed temporal avoidance and spatial attraction, likely driven by the presence of anthropogenic trophic resources. Our study underscores the role of behavioral plasticity in enabling wildlife to adjust daily habitat use and activity patterns to varying levels of human pressure across space and time. Understanding these nuanced behavioral strategies can help to promote wildlife-human coexistence and mitigating the adverse impacts of human presence on wildlife fitness.

Safety-related concerns may emerge during the operation of unmanned aerial vehicles (UAVs), reported by users and developers in the form of issue reports and pull requests. To help UAV developers identify safety-related concerns, we propose SALIENT, a machine learning (ML)-enabled tool that analyzes individual sentences composing the issue reports and automatically recognizes those describing a safety-related concern. The assessment of the classification performance of the tool on the issues of popular open-source UAV-related projects demonstrates that SALIENT represents a viable solution to assist developers in timely identifying and triaging safety–critical UAV issues, outperforming baselines based on ChatGPT and Google’s Bard.

Este artigo apresenta reflexões sobre a concepção de “casa” de crianças e adolescentes que moram na Ocupação Justo, em São Leopoldo, considerando as artes visuais como instrumento de ressignificação da identidade social em meio ao contexto habitado. A pesquisa de campo foi realizada entre os meses de outubro a novembro de 2023, com um grupo de, aproximadamente, 40 crianças e adolescentes, entre sete e 17 anos, participantes da Tenda do Encontro, espaço da Ocupação para atividades com a comunidade. A partir dos trabalhos realizados nos encontros, buscou-se analisar sobre o impacto das oficinas de arte/educação, no sentido de capturar e transmitir as narrativas subjacentes a esses espaços ocupados, para a tomada de consciência do contexto em que vivem ressignificando o sentimento de pertencimento social dos moradores daquela região. As práticas envolveram a apresentação de pessoas e projetos que abordam as casas e os sentimentos promovidos por elas, expressando suas identidades e o sentimento de pertencimento, refletindo histórias e realidades da ocupação, são eles: Marcelino Melo (Projeto Quebradinha), o Projeto Morrinho (RJ), a artista visual Mônica Nador (Projeto JAMAC), o festival africano Bogo Ja e a aldeia-casa yanomami. O referencial teórico que embasa a reflexão contou com Walter Benjamin, Ana Mae Barbosa, Georges Henri Luquet e Gaston Bachelard. A reflexão acerca da investigação realizada aponta que os jovens utilizam o ensino da arte como refúgio para se fazer o que gostam, além de ter a Ocupação Justo como uma extensão de suas próprias casas. Além disso, o grupo participante da pesquisa demonstrou uma ressignificação quanto a percepção de suas casas, da Ocupação e do contexto em que vivem, revelando que a arte tem um papel essencial na construção de identidades e sua presença é essencial nos espaços de educação não escolares.

BackgroundAcromegaly is caused by excessive growth hormone (GH) and insulin-like growth factor 1 (IGF1). Medical therapy plays a role as a treatment option for persistent disease after non-curative surgery or as a first-line therapy when surgery is not feasible. Pasireotide-LAR (Pas-LAR) is recommended for patients with acromegaly as second-line treatment.AimTo evaluate the patients characteristics predictive of an adequate response to Pas-LAR and the long-term efficacy and safety of the Pas-LAR treatment.MethodsData from 19 patients with active acromegaly, who were and resistant or intolerant to first-line medical therapy and were switched to pas-LAR have been retrospectively collected. We compared the baseline clinical and biochemical characteristics of patients who were found to respond to Pas-LAR therapy (responders, n=14) with those of patients who did not respond (non-responders, n=5). We then evaluated the Pas-LAR efficacy and safety during long-term follow-up in responders.ResultsIGF1 normalization occurred in 71.4% of responders after one injection. IGF1 levels, [median(interquartile range) of the upper limit of the normal range (ULN) fold increase] were higher in non-responders compared to responders within the initial month of therapy [1.40(1.30-2.34) vs 0.70(0.55-1.25), respectively, p=0.009] and after three [1.77(1.74-2.29) vs 0.94(0.82-1.13), respectively, p=0.029] and six months [1.68(1.33-1.72) vs 1.00(0.65 -1.28), respectively, p=0.002]. Out of 6 patients with symptomatic headache (all in responder group), 5 and 1 reported the resolution and improvement of headache, respectively, already after the first injection. Median HbA1c levels tended to increase from baseline to 6 months both in responder (36 mMol/Mol to 42 mMol/Mol) and non-responder patients (45 mMol/Mol to 48 mMol/Mol). During long term follow up, in the responder group 2 new patients developed diabetes. Tumor shrinkage was observed in 6 out of 7 evaluated responders, with no cases of size increase during the long-term follow-up.ConclusionPas-LAR is effective and safe and the early identification of responders is possible just after the first administration.

# Background Many medical devices in pediatric and newborn intensive care units can potentially expose healthcare workers (HCWs) and others to transmission of respiratory and other viruses and bacteria. Such fomites include ventilators, nebulizers, and monitoring equipment. # Approach We report the general, novel approach we have taken to identify and mitigate these risks and to protect HCWs, visitors and patients from exposure while maintaining the optimal performance of such respiratory equipment. # Findings The approach combined a high level of personal protective equipment (PPE), strict hand hygiene, air filtration and air conditioning and other relevant viral risk mitigation guidelines. This report describes the experiences from the SARS-CoV-2 pandemic to provide a reference framework that can be applied generally. The steps we took consisted of auditing our equipment and processes to identify risk through sources of potentially contaminated gas that may contain aerosolized virus, seeking advice and liaising with suppliers/manufacturers, devising mitigation strategies using indirect and direct approaches (largely filtering), performing tests on equipment to verify proper function and the absence of negative impacts and the development and implementation of relevant procedures and practices. We had a multidisciplinary team to guide the process. We monitored daily for hospital-acquired infections among staff caring for SARS-CoV-2 patients. # Conclusion Our approach was successful as we have continued to offer optimal intensive care to our patients, and we did not find any healthcare worker who was infected through the course of caring for patients at the bedside. The lessons learnt will be of benefit to future local outbreaks or pandemics.

The aim of this research is to develop a multiparametric downscaling analysis for the detection of abandoned waste in the environment. This methodology, using a multi-technological approach, involves the adoption VHR satellite images, Unmanned Aircraft System (UAS) and Unmanned Ground Vehicles (UGV). The identified Warning Areas (WA) will be investigated through an in-situ analysis with air quality measurement devices based on advanced sensors mounted on drones. The creation of a Cadastre Accumulation of Abandoned Materials (CAMA) and the related APP will allow the administrations to monitor the phenomenon. Finally, the waste product analysis, retrieved by means of UAS dataset computation, allows to retrieve some interesting prospects regarding Waste to Energy framework. Here, preliminary results obtained by the on-going INTESA Project are presented.

Aerosols, as well as suspended particulate matter, impact atmospheric pollution, the climate, and human health, directly or indirectly. Particle size, chemical composition, and other aerosol characteristics are determinant factors for atmospheric pollution dynamics and more. In the last decade, low-cost devices have been widely used in instrumentation to measure aerosols. However, they present some issues, such as the problem of discriminating whether the aerosol is composed of liquid particles or solid. This issue could lead to errors in the estimation of mass concentration in monitoring environments where there is fog. In this study, we investigate the use of an optical particle counter (OPC) coupled to a quartz crystal microbalance with an integrated microheater (H-QCM) to enhance measurement performances. The H-QCM was used not only to measure the collected mass on its surface but also, by using the integrated microheater, it was able to heat the collected mass by performing heating cycles. In particular, we tested the developed system with aerosolized saline solutions of sodium chloride (NaCl), with three decreasing concentrations of salt and three electronic cigarette solutions (e-liquid), with different concentrations of propylene glycol and glycerin mixtures. The results showed that the OPC coherently counted the salt dilution effects, and the H-QCM output confirmed the presence of liquid and solid particles in the aerosols. In the case of e-liquid aerosols, the OPC counted the particles, and the HQCM output highlighted that in the aerosol, there were no solid particles but a liquid phase only. These findings contribute to the refinement of aerosol measurement methodologies by low-cost sensors, fostering a more comprehensive understanding.

Osilodrostat is a novel, orally administered cortisol synthesis inhibitor, approved in 2020 by the European Medicines Agency (EMA) for the treatment of Cushing’s syndrome in adults. A significant amount of the studies currently available in the literature focus on treatment in patients with Cushing’s disease. However, data collected from patients treated with osilodrostat in real-life settings still represents a small entity. For this reason, in this article, we will discuss two real-life cases of patients with Cushing’s disease treated with this drug. The first report is about a 35-year-old woman with an adrenocorticotrophic hormone (ACTH)-secreting adenoma. After non-curative trans-nasal-sphenoidal (TNS) surgery, due to a small remnant of the adenoma, medical therapy with osilodrostat achieved fast and effective biochemical and clinical response. During treatment, progressive increase of ACTH levels and an enlargement of the pituitary remnant were documented, with planned radiosurgical treatment. The second case reports a 32-year-old man diagnosed with Cushing’s disease in 2020, who, after surgery refusal, started osilodrostat at progressively up-titrated doses, according to 24 h urinary free cortisol levels, up to 5 mg twice a day. With osilodrostat, the patient reached biochemical and clinical control of disease until TNS surgery in October 2021, with complete remission. The first post-surgical biochemical assessment was equivocal in spite of a transient clinical hypoadrenalism, reverted after 2 months with the restoration of physiological hypothalamic-pituitary-adrenal axis (HPA) function.

With the evolution of CAD/CAM technology, custom titanium and/or zirconia abutments are increasingly being used, leading to several comparisons in the literature, both mechanical and aesthetic, to evaluate performance differences between these two types of abutments. Therefore, the aim of this comprehensive review is to present the most recent data on the latest comparisons between CAD/CAM and stock abutment applications. The PICO model was used to perform this review, through a literature search of the PubMed (MEDLINE) and Scopus electronic databases. CAD/CAM abutments allow individualization of abutment parameters with respect to soft tissue, allow increased fracture toughness, predict the failure mode, show no change in the fracture toughness over time, reduce the prosthetic steps, and reduce the functional implant prosthesis score and pain perceived by patients in the early stages. The advantages associated with the use of stock abutments mainly concern the risk of corrosion, time spent, cost, and fit, evaluated in vitro, in the implant–abutment connection. Equal conditions are present regarding the mechanical characteristics during dynamic cycles, screw loss, radiographic fit, and degree of micromotion. Further randomized controlled clinical trials should be conducted to evaluate the advantages reported to date, following in vitro studies about titanium and/or zirconia stock abutments.

Motivation: Population density is a fundamental parameter in ecology and conservation, and taxonomic and geographic patterns of population density have been an important focus of macroecological research. However, population density data are time‐consuming and costly to collect, so their availability is limited. Leveraging decades of research, TetraDENSITY 1.0 was developed as a global repository containing over 18,000 population density estimates for > 2100 terrestrial vertebrate species, aiding researchers in investigating patterns of population density, its intrinsic and extrinsic drivers, and for developing predictive models. Here we present a substantially expanded version of the database, which now includes marine tetrapods and encompasses over 54,300 estimates for 3717 species associated with error estimates and geographical coordinates when available, hence enabling meta‐analytical approaches and better spatial matching of estimates with environmental conditions. Main Types of Variables Contained: Population density estimates and associated errors, time and location of data collection, taxonomic information, estimation method. Spatial Location: Global. Time Period and Grain: 1925–2024. Major Taxa and Level of Measurement: Amphibia, Reptilia, Aves and Mammalia. Estimates reported at the population level. Software Format:.csv. [ABSTRACT FROM AUTHOR]

Camera‐traps are valuable tools for estimating wildlife population density, and recently developed models enable density estimation without the need for individual recognition. Still, processing and analysis of camera‐trap data are extremely time‐consuming. While algorithms for automated species classification are becoming more common, they have only served as supporting tools, limiting their true potential in being implemented in ecological analyses without human supervision. Here, we assessed the capability of two camera‐trap based models to provide robust density estimates when image classification is carried out by machine learning algorithms.We simulated density estimation with Camera‐Trap Distance Sampling (CT‐DS) and Random Encounter Model (REM) under different scenarios of automated image classification. We then applied the two models to obtain density estimates of three focal species (roe deer Capreolus capreolus, red fox Vulpes vulpes and Eurasian badger Meles meles) in a reserve in central Italy. Species detection and classification was carried out both by the user and machine learning algorithms (respectively, MegaDetector and Wildlife Insights), and all outputs were used to estimate density and ultimately compared.Simulation results suggested that the CT‐DS model could provide robust density estimates even at poor algorithm performances (down to 50% of correctly classified images), while the REM model is more unpredictable and depends on multiple factors. Density estimates obtained from the MegaDetector output were highly consistent for both models with the manually labelled images. While Wildlife Insights' performance differed greatly between species (recall: badger = 0.15; roe deer = 0.56; fox = 0.75), CT‐DS estimates did not vary significantly; on the contrary, REM systematically overestimated density, with little overlap in standard errors.We conclude that CT‐DS and REM models can be robust to the loss of images when machine learning algorithms are used to identify animals, with the CT‐DS being an ideal candidate for applications in a fully unsupervised framework. We propose guidelines to evaluate when and how to integrate machine learning in the analysis of camera‐trap data for density estimation, further strengthening the applicability of camera‐traps as a cost‐effective method for density estimation in (spatially and temporally) extensive multi‐species monitoring programmes. [ABSTRACT FROM AUTHOR]

Aims: To assess whether impaired vestibular perception of self‐motion is a risk factor for unsteadiness and falls in elderly patients with type 2 diabetes (T2D). Materials and methods: 113 participants (65–75 years old) with T2D underwent tests of roll and pitch discrimination, postural stability (Berg Balance Scale, Modified Romberg Test, and quantitative posturography), clinical examination and blood chemistry analyses. Falls 1‐year after enrolment were self‐reported. We performed cluster analysis based on the values of the vestibular motion thresholds, and logistic stepwise regression to compare the clinical‐biochemical parameters between clusters. Results: We identified two clusters (VC1 n = 65 and VC2 n = 48 participants). VC2 had significantly (p < 0.001) higher (poorer) thresholds than VC1: mean pitch threshold 1.62°/s (95% CI 1.48–1.78) in VC2 and 0.91°/s (95% CI 0.84–0.98) in VC1, mean roll threshold 1.34°/s (95% CI 1.21–1.48) in VC2 and 0.69°/s (95% CI 0.64–0.74) in VC1. Diabetes duration was significantly (p = 0.024) longer in VC2 (11.96 years, 95% CI 9.23–14.68) than in VC1 (8.37 years, 95% CI 6.85–9.88). Glycaemic control was significantly (p = 0.014) poorer in VC2 (mean HbA1c 6.74%, 95% CI 6.47–7.06) than in VC1 (mean HbA1c 6.34%, 95% CI 6.16–6.53). VC2 had a significantly higher incidence of postural instability than VC1, with a higher risk of failing the Modified Romberg Test C4 (RR = 1.57, χ2 = 5.33, p = 0.021), reporting falls during follow‐up (RR = 11.48, χ2 = 9.40, p = 0.002), and greater postural sway in the medio‐lateral direction (p < 0.025). Conclusions: Assessing vestibular motion thresholds identifies individuals with T2D at risk of postural instability due to altered motion perception and guides vestibular rehabilitation. [ABSTRACT FROM AUTHOR]

This scoping review aims to investigate the latest literature concerning the use of smartphone applications (apps) in the prevention, management, and monitoring of oral diseases. Smartphone applications are software programs that are designed to run on smartphones. Nowadays, smartphones are regularly used by people of all ages, and mobile health apps (MHAs) represent an important means of spreading information related to oral health, which is the state of the mouth and teeth, including the gums and other tissues. Several apps have been designed to promote prevention, diagnosis, and therapeutic adherence monitoring. This scoping review considered randomized clinical trials, cross-sectional studies, before–after (pre–post) studies with no control group, and observational studies. Once the inclusion and exclusion criteria had been defined, a preliminary confined search was performed on PubMed and Scopus; key terms from the collected articles were selected to design a search strategy, and then a search of all the included articles’ reference lists was run for further research. Studies were excluded if they did not fulfill the inclusion criteria. The preferred reporting items for scoping reviews (PRISMA-ScR) consensus was followed. The risk of bias was evaluated by providing a qualitative analysis of the clinical studies via the National Heart, Lung, and Blood Institute (NHLBI) Quality Assessment of Controlled Intervention Studies, Observational Cohort Studies, and Cross-Sectional Studies (NHLBI, NIH). A total of 21 studies were included in this review. As it is clear from the studies selected, the literature indicates that MHAs are effective in improving oral hygiene in adolescents and children and reducing the dental plaque index, including in patients undergoing orthodontic treatment. MHAs are also able to reduce the symptoms of patients affected by obstructive sleep apnea–hypopnea syndrome (OSAHS) and improve the swallowing-related quality of life of elderly patients. MHAs are furthermore recommended to decrease dental anxiety among patients, both during dental procedures and the post-operative period. MHAs are useful to spread knowledge about traumatic dental injuries among non-oral health professionals and to monitor dental erosion and awake bruxism. MHAs’ clinical outcomes might have been influenced by the demographic features of the subjects involved. Further studies considering a longer follow-up period and larger samples are needed. In conclusion, MHAs can be considered a useful tool to monitor oral disease and increase patients’ quality of life related to oral health.