Transport policy and climate change: how to decide when experts disagree

Transport is the sector with the fastest growth of greenhouse gases emissions in many countries. Accumulation of these emissions may cause uncertain and irreversible adverse climate change impacts. In this context, we use the analytic hierarchy process (AHP) to face the question on how to select the best transport policy if the experts have different opinions and beliefs on the occurrence of these impacts. Thus, both the treatment of uncertainty and dissent are examined for the ranking of transport policies. The opinions of experts have been investigated by a means of a survey questionnaire. A sensitivity analysis of the experts’ weights and the criteria’ weights confirms the robustness of the results

Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques

With the increase in available data from computer systems and their security threats, interest in anomaly detection has increased as well in recent years. The need to diagnose faults and cyberattacks has also focused scientific research on the automated classification of outliers in big data, as manual labeling is difficult in practice due to their huge volumes. The results obtained from data analysis can be used to generate alarms that anticipate anomalies and thus prevent system failures and attacks. Therefore, anomaly detection has the purpose of reducing maintenance costs as well as making decisions based on reports. During the last decade, the approaches proposed in the literature to classify unknown anomalies in log analysis, process analysis, and time series have been mainly based on machine learning and deep learning techniques. In this study, we provide an overview of current state-of-the-art methodologies, highlighting their advantages and disadvantages and the new challenges. In particular, we will see that there is no absolute best method, i.e., for any given dataset a different method may achieve the best result. Finally, we describe how the use of metaheuristics within machine learning algorithms makes it possible to have more robust and efficient tools

Sulfatase modifying factor 1–mediated fibroblast growth factor signaling primes hematopoietic multilineage development

Self-renewal and differentiation of hematopoietic stem cells (HSCs) are balanced by the concerted activities of the fibroblast growth factor (FGF), Wnt, and Notch pathways, which are tuned by enzyme-mediated remodeling of heparan sulfate proteoglycans (HSPGs). Sulfatase modifying factor 1 (SUMF1) activates the Sulf1 and Sulf2 sulfatases that remodel the HSPGs, and is mutated in patients with multiple sulfatase deficiency. Here, we show that the FGF signaling pathway is constitutively activated in Sumf1−/− HSCs and hematopoietic stem progenitor cells (HSPCs). These cells show increased p-extracellular signal-regulated kinase levels, which in turn promote β-catenin accumulation. Constitutive activation of FGF signaling results in a block in erythroid differentiation at the chromatophilic erythroblast stage, and of B lymphocyte differentiation at the pro–B cell stage. A reduction in mature myeloid cells and an aberrant development of T lymphocytes are also seen. These defects are rescued in vivo by blocking the FGF pathway in Sumf1−/− mice. Transplantation of Sumf1−/− HSPCs into wild-type mice reconstituted the phenotype of the donors, suggesting a cell autonomous defect. These data indicate that Sumf1 controls HSPC differentiation and hematopoietic lineage development through FGF and Wnt signaling

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19

Background COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain. Subjects/methods In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry. Results Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 x 10(-6)), obesity status (P = 4.81 x 10(-5)), higher serum fasting insulin (P = 5.32 x 10(-4)), BMI (P = 3.94 x 10(-4)), and lower serum HDL levels (P = 1.92 x 10(-7)). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 x 10(-4)) and higher proportion of macrophages (P = 2.74 x 10(-5)). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression. Conclusions Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.Peer reviewe

Diagnosis, treatment and prevention of pediatric obesity: consensus position statement of the Italian Society for Pediatric Endocrinology and Diabetology and the Italian Society of Pediatrics

The Italian Consensus Position Statement on Diagnosis, Treatment and Prevention of Obesity in Children and Adolescents integrates and updates the previous guidelines to deliver an evidence based approach to the disease. The following areas were reviewed: (1) obesity definition and causes of secondary obesity; (2) physical and psychosocial comorbidities; (3) treatment and care settings; (4) prevention.The main novelties deriving from the Italian experience lie in the definition, screening of the cardiometabolic and hepatic risk factors and the endorsement of a staged approach to treatment. The evidence based efficacy of behavioral intervention versus pharmacological or surgical treatments is reported. Lastly, the prevention by promoting healthful diet, physical activity, sleep pattern, and environment is strongly recommended since the intrauterine phase

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

Chronic constipation diagnosis and treatment evaluation: The "CHRO.CO.DI.T.E." study

Background: According to Rome criteria, chronic constipation (CC) includes functional constipation (FC) and irritable bowel syndrome with constipation (IBS-C). Some patients do not meet these criteria (No Rome Constipation, NRC). The aim of the study was is to evaluate the various clinical presentation and management of FC, IBS-C and NRC in Italy. Methods: During a 2-month period, 52 Italian gastroenterologists recorded clinical data of FC, IBS-C and NRC patients, using Bristol scale, PAC-SYM and PAC-QoL questionnaires. In addition, gastroenterologists were also asked to record whether the patients were clinically assessed for CC for the first time or were in follow up. Diagnostic tests and prescribed therapies were also recorded. Results: Eight hundred seventy-eight consecutive CC patients (706 F) were enrolled (FC 62.5%, IBS-C 31.3%, NRC 6.2%). PAC-SYM and PAC-QoL scores were higher in IBS-C than in FC and NRC. 49.5% were at their first gastroenterological evaluation for CC. In 48.5% CC duration was longer than 10 years. A specialist consultation was requested in 31.6%, more frequently in IBS-C than in NRC. Digital rectal examination was performed in only 56.4%. Diagnostic tests were prescribed to 80.0%. Faecal calprotectin, thyroid tests, celiac serology, breath tests were more frequently suggested in IBS-C and anorectal manometry in FC. More than 90% had at least one treatment suggested on chronic constipation, most frequently dietary changes, macrogol and fibers. Antispasmodics and psychotherapy were more frequently prescribed in IBS-C, prucalopride and pelvic floor rehabilitation in FC. Conclusions: Patients with IBS-C reported more severe symptoms and worse quality of life than FC and NRC. Digital rectal examination was often not performed but at least one diagnostic test was prescribed to most patients. Colonoscopy and blood tests were the "first line" diagnostic tools. Macrogol was the most prescribed laxative, and prucalopride and pelvic floor rehabilitation represented a "second line" approach. Diagnostic tests and prescribed therapies increased by increasing CC severity