Unsupervised Burned Area Estimation through Satellite Tiles: A multimodal approach by means of image segmentation over remote sensing imagery

Climate change is increasing the number and the magnitude of wildfires, which become every year more severe. An accurate delineation of burned areas, which is often done through time consuming and inaccurate manual approaches, is of paramount importance to estimate the economic impact of such events. In this paper we introduce Burned Area Estimation through satellite tiles (BAE), an unsupervised algorithm that couples image processing techniques and an unsupervised neural network to automatically delineate the burned areas of wildfires from satellite imagery. We show its capabilities by performing an evaluation over past wildfires across European and non-European countries

Supervised Burned Areas delineation by means of Sentinel-2 imagery and Convolutional Neural Networks

Wildfire events are increasingly threatening our lands, cities, and lives. To contrast this phenomenon and to limit its damages, governments around the globe are trying to find proper counter-measures, identifying prevention and monitoring as two key factors to reduce wildfires impact worldwide. In this work, we propose two deep convolutional neural networks to automatically detect and delineate burned areas from satellite acquisitions, assessing their performances at scale using validated maps of burned areas of historical wildfires. We demonstrate that the proposed networks substantially improve the burned area delineation accuracy over conventional methods

The challenges and prospects of the intersection of humanities and data science: A white paper from The Alan Turing Institute

This paper was produced as part of the activities of the Humanities and Data Science Special Interest Group based at The Alan Turing Institute. The group has created the opportunity for fruitful conversations in this area and has brought together voices from a range of different disciplinary backgrounds. This document shows an example of how conversations of this type can benefit and advance computational methods and understandings in and between the humanities and data science, bringing together a diverse community. We believe the Turing can act as a nexus of discussion on humanities and data science research at the national (and international) level, in areas such as education strategy, research best practices, and funding policy, and can promote and encourage research activities in this interdisciplinary area

Comprehensive dissection of prevalence rates, sex differences, and blood level-dependencies of clozapine-associated adverse drug reactions

Clozapine is often underused due to concerns about adverse drug reactions (ADRs) but studies into their prevalences are inconclusive. We therefore comprehensively examined prevalences of clozapineassociated ADRs in individuals with schizophrenia and demographic and clinical factors associated with their occurrence. Data from a multi-center study (n=698 participants) were collected. The mean number of ADRs during clozapine treatment was 4.8, with 2.4% of participants reporting no ADRs. The most common ADRs were hypersalivation (74.6%), weight gain (69.3%), and increased sleep necessity (65.9%), all of which were more common in younger participants. Participants with lower BMI prior to treatment were more likely to experience significant weight gain (>10%). Constipation occurred more frequently with higher clozapine blood levels and doses. There were no differences in ADR prevalence rates between participants receiving clozapine monotherapy and polytherapy. These findings emphasize the high prevalence of clozapine-associated ADRs and highlight several demographic and clinical factors contributing to their occurrence. By understanding these factors, clinicians can better anticipate and manage clozapine-associated ADRs, leading to improved treatment outcomes and patient well-being

Prescription appropriateness of anti-diabetes drugs in elderly patients hospitalized in a clinical setting: evidence from the REPOSI Register

Diabetes is an increasing global health burden with the highest prevalence (24.0%) observed in elderly people. Older diabetic adults have a greater risk of hospitalization and several geriatric syndromes than older nondiabetic adults. For these conditions, special care is required in prescribing therapies including anti- diabetes drugs. Aim of this study was to evaluate the appropriateness and the adherence to safety recommendations in the prescriptions of glucose-lowering drugs in hospitalized elderly patients with diabetes. Data for this cross-sectional study were obtained from the REgistro POliterapie-Società Italiana Medicina Interna (REPOSI) that collected clinical information on patients aged ≥ 65 years acutely admitted to Italian internal medicine and geriatric non-intensive care units (ICU) from 2010 up to 2019. Prescription appropriateness was assessed according to the 2019 AGS Beers Criteria and anti-diabetes drug data sheets.Among 5349 patients, 1624 (30.3%) had diagnosis of type 2 diabetes. At admission, 37.7% of diabetic patients received treatment with metformin, 37.3% insulin therapy, 16.4% sulfonylureas, and 11.4% glinides. Surprisingly, only 3.1% of diabetic patients were treated with new classes of anti- diabetes drugs. According to prescription criteria, at admission 15.4% of patients treated with metformin and 2.6% with sulfonylureas received inappropriately these treatments. At discharge, the inappropriateness of metformin therapy decreased (10.2%, P < 0.0001). According to Beers criteria, the inappropriate prescriptions of sulfonylureas raised to 29% both at admission and at discharge. This study shows a poor adherence to current guidelines on diabetes management in hospitalized elderly people with a high prevalence of inappropriate use of sulfonylureas according to the Beers criteria

Follow-up After Treatment for Renal Cell Carcinoma: The Evidence Beyond the Guidelines

CONTEXT: Postoperative follow-up is considered the standard of care for nonmetastatic renal cell carcinoma (RCC). However, level 1 evidence regarding a proper follow-up protocol for RCC is still lacking, making clinical practice extremely heterogeneous. OBJECTIVE: To evaluate systematically and summarise the evidence supporting the current clinical guidelines on follow-up after RCC treatment. EVIDENCE ACQUISITION: A search of Medline, PubMed and Scopus was performed to identify articles published in the last 5 yr addressing the role of follow-up in the RCC setting. Relevant studies were then screened, and the data were extracted, analysed, and summarised. The Preferred Reporting Items for Systematic Reviews and Meta-analysis criteria were applied. EVIDENCE SYNTHESIS: Although several series regarding oncologic outcomes and protocols of surveillance after nephrectomy for localised RCC have been published in the literature, the individual preferences of the treating urologist make the daily clinical scenario extremely heterogeneous regarding follow-up indications and modality. Clinical guidelines support a stage-specific stratification of patient prognosis based on pathologic staging or prognostic models. In the context of a prospectively durable follow-up protocol exposing patients to several imaging tests, concerns about radiation exposure must be taken into account. A better understanding of tumour biology, which would lead to a correct individualisation of patient prognosis through the use of validated prognostic tools, would allow for a more tailored follow-up treatment. CONCLUSIONS: A consensus regarding the pattern and modalities of surveillance after treatment for RCC is still lacking. A standardised evidence-based surveillance protocol that would allow for the early detection of recurrences and limit unnecessary radiation exposure and unwarranted costs is mandatory. PATIENT SUMMARY: A surveillance protocol after treatment for a renal tumour is essential for the early detection and treatment of eventual metastases. A general consensus regarding timing and modalities for follow-up protocol still does not exist, but published evidence commonly sustains some general principles