Prediction of chemical plants operating performances: a machine learning approach

Modern environmental regulations require rigorous optimization of operations in process engineering to reduce waste, pollution, and risks while maximizing efficiency. However, the nature of chemical plants, which include components with non-linear behavior, challenges the use of consolidated tuning and control techniques. Instead, ad-hoc, self-adapting, and time-variant controls, with a balanced tuning of parameters at both the subsystem and system level, may be necessary. Needed computing processes may require significant resources and high performance systems, if managed by means of traditional approaches and with exact solution methods. In this regard, domain experts suggest instead the use of integrated techniques based on Artificial Intelligence (AI), which include Explainable AI (XAI) and Trustworthy AI (TAI), which are unique in this industry and still in the early stages of development. To pave the way for a real-time, cost-effective solution for this problem, this paper proposes an AI-based approach to model the performance of a real chemical plant, i.e. a marine scrubber installed on a Ro-Ro ship. The study aims to investigate Machine Learning (ML) techniques which can be used to model such processes. Notably, this analysis is the first of its kind, at the best of the authors’ knowledge. Overall, the study highlights the potential of using ML-based techniques, to optimize environmental compliance in the shipping industr

Virus-induced tumorigenesis and IFN system

Oncogenic viruses favor the development of tumors in mammals by persistent infection and specific cellular pathways modifications by deregulating cell proliferation and inhibiting apop-tosis. They counteract the cellular antiviral defense through viral proteins as well as specific cellular effectors involved in virus-induced tumorigenesis. Type I interferons (IFNs) are a family of cyto-kines critical not only for viral interference but also for their broad range of properties that go be-yond the antiviral action. In fact, they can inhibit cell proliferation and modulate differentiation, apoptosis, and migration. However, their principal role is to regulate the development and activity of most effector cells of the innate and adaptive immune responses. Various are the mechanisms by which IFNs exert their effects on immune cells. They can act directly, through IFN receptor trigger-ing, or indirectly by the induction of chemokines, the secretion of further cytokines, or by the stimulation of cells useful for the activation of particular immune cells. All the properties of IFNs are crucial in the host defense against viruses and bacteria, as well as in the immune surveillance against tumors. IFNs may be affected by and, in turn, affect signaling pathways to mediate anti-proliferative and antiviral responses in virus-induced tumorigenic context. New data on cellular and viral mi-croRNAs (miRNAs) machinery, as well as cellular communication and microenvironment modifi-cation via classical secretion mechanisms and extracellular vesicles-mediated delivery are reported. Recent research is reviewed on the tumorigenesis induced by specific viruses with RNA or DNA genome, belonging to different families (i.e., HPV, HTLV-1, MCPyV, JCPyV, Herpesviruses, HBV, HCV) and the IFN system involvement

Bioethical and medico-legal implications of withdrawing artificial nutrition and hydration from adults in critical care

The withdrawal of artificial nutrition and hydration or other life-sustaining treatments is a clinical decision, made in ICUs or in other settings, involving patients suffering from serious and irreversible diseases or impaired consciousness. Such clinical decisions must be made in the best interests of the patient, and must respect the wishes previously expressed by patients, laid down in their wills, in advance directives or in information passed on by relatives or legally appointed health-care agents, and in observance of common bioethical and legal rules in individual nations. Intensivists who are expert in the management of lifesustaining treatments are also involved in deciding when to withdraw futile therapies and instigate end-of-life care procedures for dying patients, with the sole aim of providing comfort and ensuring that suffering is not prolonged unnecessaril

A posteriori error estimates for the virtual element method

An a posteriori error analysis for the virtual element method (VEM) applied to general elliptic problems is presented. The resulting error estimator is of residual-type and applies on very general polygonal/polyhedral meshes. The estimator is fully computable as it relies only on quantities available from the VEM solution, namely its degrees of freedom and element-wise polynomial projection. Upper and lower bounds of the error estimator with respect to the VEM approximation error are proven. The error estimator is used to drive adaptive mesh refinement in a number of test problems. Mesh adaptation is particularly simple to implement since elements with consecutive co-planar edges/faces are allowed and, therefore, locally adapted meshes do not require any local mesh post-processing

Status, sources and contamination levels of organochlorine pesticide residues in urban and agricultural areas: a preliminary review in central–southern Italian soils

Organochlorine pesticides (OCPs) are synthetic chemicals commonly used in agricultural activities to kill pests and are persistent organic pollutants (POPs). They can be detected in different environmental media, but soil is considered an important reservoir due to its retention capacity. Many different types of OCPs exist, which can have different origins and pathways in the environment. It is therefore important to study their distribution and behaviour in the environment, starting to build a picture of the potential human health risk in different contexts. This study aimed at investigating the regional distribution, possible sources and contamination levels of 24 OCP compounds in urban and rural soils from central and southern Italy. One hundred and forty-eight topsoil samples (0–20 cm top layer) from 78 urban and 70 rural areas in 11 administrative regions were collected and analysed by gas chromatography–electron capture detector (GC–ECD). Total OCP residues in soils ranged from nd (no detected) to 1043 ng/g with a mean of 29.91 ng/g and from nd to 1914 ng/g with a mean of 60.16 ng/g in urban and rural area, respectively. Endosulfan was the prevailing OCP in urban areas, followed by DDTs, Drins, Methoxychlor, HCHs, Chlordane-related compounds and HCB. In rural areas, the order of concentrations was Drins > DDTs > Methoxychlor > Endosulfans > HCHs > Chlordanes > HCB. Diagnostic ratios and robust multivariate analyses revealed that DDT in soils could be related to historical application, whilst (illegal) use of technical DDT or dicofol may still occur in some urban areas. HCH residues could be related to both historical use and recent application, whilst there was evidence that modest (yet significant) application of commercial technical HCH may still be happening in urban areas. Drins and Chlordane compounds appeared to be mostly related to historical application, whilst Endosulfan presented a complex mix of results, indicating mainly historical origin in rural areas as well as potential recent applications on urban areas. Contamination levels were quantified by Soil Quality Index (SoQI), identifying high levels in rural areas of Campania and Apulia, possibly due to the intensive nature of some agricultural practices in those regions (e.g., vineyards and olive plantations). The results from this study (which is in progress in the remaining regions of Italy) will provide an invaluable baseline for OCP distribution in Italy and a powerful argument for follow-up studies in contaminated areas. It is also hoped that similar studies will eventually constitute enough evidence to push towards an institutional response for more adequate regulation as well as a full ratification of the Stockholm Convention

Plant Molecular Farming as a Strategy Against COVID-19 – The Italian Perspective

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed more than 37,000 people in Italy and has caused widespread socioeconomic disruption. Urgent measures are needed to contain and control the virus, particularly diagnostic kits for detection and surveillance, therapeutics to reduce mortality among the severely affected, and vaccines to protect the remaining population. Here we discuss the potential role of plant molecular farming in the rapid and scalable supply of protein antigens as reagents and vaccine candidates, antibodies for virus detection and passive immunotherapy, other therapeutic proteins, and virus-like particles as novel vaccine platforms. We calculate the amount of infrastructure and production capacity needed to deal with predictable subsequent waves of COVID-19 in Italy by pooling expertise in plant molecular farming, epidemiology and the Italian health system. We calculate the investment required in molecular farming infrastructure that would enable us to capitalize on this technology, and provide a roadmap for the development of diagnostic reagents and biopharmaceuticals using molecular farming in plants to complement production methods based on the cultivation of microbes and mammalian cells

Is Metabolic Syndrome Useful for Identifying Youths with Obesity at Risk for NAFLD?

The definition of metabolic syndrome (MetS) in childhood is controversial. Recently, a modified version of the International Diabetes Federation (IDF) definition was proposed using reference data from an international population for high waist circumference (WC) and blood pressure (BP), while the fixed cutoffs for lipids and glucose were not changed. We analyzed MetS prevalence using this modified definition (MetS-IDFm) and its association with non-alcoholic fatty liver disease (NAFLD) in 1057 youths (age 6–17 years) with overweight/obesity (OW/OB). A comparison with another modified definition of MetS according to the Adult Treatment Panel III (MetS-ATPIIIm) was performed. The prevalence of MetS-IDFm was 27.8% and 28.9% by MetS-ATPIIIm. The Odds (95% Confidence Intervals) of NAFLD was 2.70 (1.30–5.60) (p = 0.008) for high WC, 1.68 (1.25–2.26)(p = 0.001) for MetS, 1.54 (1.12–2.11)(p = 0.007) for low HDL-Cholesterol, 1.49 (1.04–2.13)(p = 0.032) for high triglycerides and 1.37 (1.03–1.82)(p = 0.033) for high BP. No substantial difference was found in the prevalence of MetS-IDFm and frequency of NAFLD compared to Mets-ATPIIIm definition. Our data demonstrate that one third of youths with OW/OB have MetS, whichever was the criterion. Neither definition was superior to some of their components in identifying youths with OW/OB at risk for NAFLD

Severe Obesity Defined by Percentiles of WHO and Cardiometabolic Risk in Youth with Obesity

Background/Objectives: The pediatric definition of severe obesity (OB) depends on the body mass index (BMI) references. We evaluated different BMI-derived metrics of the World Health Organization (WHO) system to define which cut-off is associated with the highest cardiometabolic risk (CMR); Methods: In this multicentric study, data were retrieved for 3727 youths (1937 boys; 2225 children, 1502 adolescents). OB was defined as BMI > 97th percentile (BMI97th), severe OB was defined as BMI > 99th percentile (BMI99th), BMI ≥ 120% of the 97th percentile (120% BMI97th), or BMI Z-score > 3 (WHO tables), or BMI ≥ the International Obesity Task Force (IOTF) value crossing a BMI of 35 kg/m2 at the age of 18 (IOTF35). The continuous CMR Z-score (sum of residual standardized for age and sex of waist-to-height ratio, systolic and diastolic blood pressure, triglycerides, and HDL-cholesterol x −1) and the cluster of at least two CMR factors (hypertension, high triglycerides, low HDL-cholesterol, and high waist-to-height ratio) were calculated. Results: Continuous CMR Z-score was significantly higher both in children or adolescents with severe OB defined by 120% BMI97th compared to BMI99th (p 3 (p 3 and IOTF35 had higher specificity, but lower sensitivity in identifying children and adolescents with clustered CMR factors. Conclusions: The definition of severe OB based on 120% BMI97th is superior to BMI99th but it is inferior to BMI Z score > 3 as far as the association between severe OB and CMR factors is concerned. Pediatricians should take into consideration the implication of the use of different BMI metrics in those countries that recommend the WHO system. WHO BMI Z-score > 3 and IOTF35 can be used interchangeably to predict cardiometabolic risk

BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women. Advances in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by pathologists is cumbersome, time-consuming and is subject to significant inter-and intra-observer variability. Recently, the advent of whole-slide scanning systems has empowered the rapid digitization of pathology slides and enabled the development of Artificial Intelligence (AI)-Assisted digital workflows. However, AI techniques, especially Deep Learning, require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as data-Acquisition level constraints, time-consuming and expensive annotations and anonymization of patient information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin and Eosin (H&E)-stained images to advance AI development in the automatic characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs) and 4539 Regions Of Interest (ROIs) extracted from the WSIs. Each WSI and respective ROIs are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI and ROI levels. Furthermore, by including the understudied atypical lesions, BRACS offers a unique opportunity for leveraging AI to better understand their characteristics. We encourage AI practitioners to develop and evaluate novel algorithms on the BRACS dataset to further breast cancer diagnosis and patient care. Database URL: https://www.bracs.icar.cnr.it