CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

Forest wildfires represent one of the catastrophic events that, over the last decades, have caused huge environmental and humanitarian damage. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools

Prognostic Factors for Overall Survival In Chronic Myeloid Leukemia Patients: A Multicentric Cohort Study by the Italian CML GIMEMA Network

An observational prospective study was conducted by the CML Italian network to analyze the role of baseline patient characteristics and first line treatments on overall survival and CML-related mortality in 1206 newly diagnosed CML patients, 608 treated with imatinib (IMA) and 598 with 2nd generation tyrosine kinase inhibitors (2GTKI). IMA-treated patients were much older (median age 69 years, IQR 58-77) than the 2GTKI group (52, IQR 41-63) and had more comorbidities. Estimated 4-year overall survival of the entire cohort was 89% (95%CI 85.9-91.4). Overall, 73 patients (6.1%) died: 17 (2.8%) in the 2GTKI vs 56 (9.2%) in the IMA cohort (adjusted HR=0.50; 95% CI=0.26-0.94), but no differences were detected for CML-related mortality (10 (1.7%) vs 11 (1.8%) in the 2GTKIs vs IMA cohort (sHR=1.61; 0.52-4.96). The ELTS score was associated to CML mortality (high risk vs low, HR=9.67; 95%CI 2.94-31.74; p<0.001), while age (per year, HR=1.03; 95%CI 1.00-1.06; p=0.064), CCI (4-5 vs 2, HR=5.22; 95%CI 2.56-10.65; p<0.001), ELTS score (high risk vs low, HR=3.11; 95%CI 1.52-6.35, p=0.002) and 2GTKI vs IMA (HR=0.26; 95%CI 0.10-0.65, p=0.004) were associated to an increased risk of non-related CML mortality. The ELTS score showed a better discriminant ability than the Sokal score in all comparisons

Managing chronic myeloid leukemia for treatment-free remission: a proposal from the GIMEMA CML WP

Several papers authored by international experts have proposed recommendations on the management of BCR-ABL1+ chronic myeloid leukemia (CML). Following these recommendations, survival of CML patients has become very close to normal. The next, ambitious, step is to bring as many patients as possible into a condition of treatment-free remission (TFR). The Gruppo Italiano Malattie EMatologiche dell'Adulto (GIMEMA; Italian Group for Hematologic Diseases of the Adult) CML Working Party (WP) has developed a project aimed at selecting the treatment policies that may increase the probability of TFR, taking into account 4 variables: the need for TFR, the tyrosine kinase inhibitors (TKIs), the characteristics of leukemia, and the patient. A Delphi-like method was used to reach a consensus among the representatives of 50 centers of the CML WP. A consensus was reached on the assessment of disease risk (EUTOS Long Term Survival [ELTS] score), on the definition of the most appropriate age boundaries for the choice of first-line treatment, on the choice of the TKI for first-line treatment, and on the definition of the responses that do not require a change of the TKI (BCR-ABL1 6410% at 3 months, 641% at 6 months, 640.1% at 12 months, 640.01% at 24 months), and of the responses that require a change of the TKI, when the goal is TFR (BCR-ABL1 >10% at 3 and 6 months, >1% at 12 months, and >0.1% at 24 months). These suggestions may help optimize the treatment strategy for TFR

The hOCT1 and ABCB1 polymorphisms do not influence the pharmacodynamics of nilotinib in chronic myeloid leukemia

First-line nilotinib in chronic myeloid leukemia is more effective than imatinib to achieve early and deep molecular responses, despite poor tolerability or failure observed in one-third of patients. The toxicity and efficacy of tyrosine kinase inhibitors might depend on the activity of transmembrane transporters. However, the impact of transporters genes polymorphisms in nilotinib setting is still debated. We investigated the possible correlation between single nucleotide polymorphisms of hOCT1 (rs683369 [c.480C > G]) and ABCB1 (rs1128503 [c.1236C > T], rs2032582 [c.2677G > T/A], rs1045642 [c.3435C > T]) and nilotinib efficacy and toxicity in a cohort of 78 patients affected by chronic myeloid leukemia in the context of current clinical practice. The early molecular response was achieved by 81% of patients while 64% of them attained deep molecular response (median time, 26 months). The 36-month event-free survival was 86%, whereas 58% of patients experienced toxicities. Interestingly, hOCT1 and ABCB1 polymorphisms alone or in combination did not influence event-free survival or the adverse events rate. Therefore, in contrast to data obtained in patients treated with imatinib, hOCT1 and ABCB1 polymorphisms do not impact on nilotinib efficacy or toxicity. This could be relevant in the choice of the first-line therapy: patients with polymorphisms that negatively condition imatinib efficacy might thus receive nilotinib as first-line therapy

Equity and justice in global warming policy

Many countries are implementing or at least considering policies to counter increasingly certain negative impacts from climate change. An increasing amount of research has been devoted to the analysis of the costs of climate change and its mitigation, as well as to the design of policies, such as the international Kyoto Protocol, post-Kyoto negotiations, regional initiatives, and unilateral actions. Although most studies on climate change policies in economics have considered efficiency aspects, there is a growing literature on equity and justice. Climate change policy has important dimensions of distributive justice, both within and across generations, but in this paper we survey only studies on the intragenerational aspect, i.e.., within a generation. We cover several domains including the international, regional, national, sectoral and inter-personal, and examine aspects such as the distribution of burdens from climate change, climate change policy negotiations in general, implementation of climate agreements using tradable emission permits, and the uncertainty of alternatives to emission reductions

A Model-based Curriculum Learning Strategy for Training SegFormer

The use of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision opened up new tracks in this area. However, a significant drawback of these models is the large amount of data required to obtain competitive results. This critical issue limits their application in domains where large labeled data collections are unavailable. Some strategies have been proposed to use relatively limited labeled data sets to train CNN-based models. Curriculum learning is one of the currently available strategies to train deep learning models faster and with less data. However, to our knowledge, curriculum learning techniques have never been used at the model level to support ViT training for semantic segmentation. We propose a new curriculum learning technique tailored to ViT models to fill this gap. The results show the effectiveness of the proposed strategy in training ViT models from scratch to solve the semantic segmentation task

Vision Transformers for Burned Area Delineation

The automatic identification of burned areas is an important task that was mainly managed manually or semi-automatically in the past. In the last years, thanks to the availability of novel deep neural network architectures, automatic segmentation solutions have been proposed also in the emergency management domain. The most recent works in burned area delineation leverage on Convolutional Neural Networks (CNNs) to automatically identify regions that were previously affected by forest wildfires. A largely adopted segmentation model, U-Net, demonstrated good performances for the task under analysis, but in some cases a high overestimation of burned areas is given, leading to low precision scores. Given the recent advances in the field of NLP and the first successes also in the vision domain, in this paper we investigate the adoption of vision transformers for semantic segmentation to address the burned area identification task. In particular, we explore the SegFormer architecture with two of its variants: the smallest MiT-B0 and the intermediate one MiT-B3. The experimental results show that SegFormer provides better predictions, with higher precision and F1 score, but also better performance in terms of the number of parameters with respect to CNNs

Business Process Analysis and Simulation: An Industrial Application

Analysis and automation of business processes are a relevant topic in Industry 4.0. This document describes a framework called BP-M* for the analysis, restructuring and implementation of business processes, starting with the creation of a process model and ending with the implementation of the process itself on a workflow management system. The BP-M* framework has been applied to a real case study, the production of fabrics for the collection that will be distributed worldwide by an Italian woolen mill. This process was analyzed and automated, providing the company with useful information to simplify processes and support human operators

Estimating surface-wave dispersion curves from 3D seismicacquisition schemes: Part 1 — 1D models

Surface-wave analysis is based on the estimation of surface-wave dispersion curves, which are then inverted to provide 1D S-wave velocity profiles. Surface-wave dispersion curves can be extracted from P-wave records obtained in seismic exploration and used to characterize the ground structure at a shallow depth. Dispersion curve estimation using 2D wavefield transforms is well-established for 2D acquisition schemes (in-line source and receiver spread). It is possible to extract surface-wave dispersion curves using 2D wavefield transforms from 3D seismic data acquired with any acquisition scheme. In particular, we focus on areal geometry and orthogonal geometry, and we provide a method based on the analysis in the offset domain and the f -k multiple signal classification (MUSIC) transform.We assess the performance of the method on synthetic and field data concerning 1D sites