The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research

Exploring Unsupervised Learning Techniques for the Internet of Things

Nowadays, machine learning (ML) techniques can provide new perspectives to identify hidden patterns and classes inside data. Applying ML to the Internet of Things (IoT) and its produced data represents a great challenge in every application domain, since analyzing IoT data increasingly requires the use of advanced mathematical algorithms, novel computational techniques, and services. In this article, we present and discuss the application of unsupervised learning techniques on IoT data collected in a cultural heritage framework. Behavioral data have been gathered in a noninvasive way in order to achieve an ML classification that can be exploited by cultural stakeholders in terms of the medium-to long-term strategy and also in terms of strictly operational decisions. The application of ML and other learning techniques will acquire a key role to complement the more traditional services with new intelligent ones able to satisfy the needs of companies, stakeholders, and consumers

Decision Making in IoT Environment through Unsupervised Learning

Nowadays Unsupervised Learning can provide new perspectives to identify hidden patterns and classes inside the huge amount of data coming from the Internet of Things world. Analyzing IoT data through machine learning techniques requires the use of mathematical algorithms, computational techniques and an accurate tuning of the input parameters. In this paper we present a study of unsupervised learning techniques applied on IoT data to support decision making processes inside intelligent environments. To assess the proposed approach we discuss two case-of-study in which behavioural IoT data have been collected, also in a non-invasive way, in order to achieve an unsupervised classification that can be adopted during a decision making process. The use of Unsupervised Learning techniques is acquiring a key role to complement the more traditional services with new decision making ones supporting the needs of companies, stakeholders and consumer

A Deep Learning approach for Path Prediction in a Location-based IoT system

Knowing in real-time the position of objects and people, both in indoor and outdoor spaces, allows companies and organizations to improve their processes and offer new kind of services. Nowadays Location-based Services (LBS) generate a significant amount of data thank to the widespread of the Internet of Things; since they have been quickly perceived as a potential source of profit, several companies have started to design and develop a wide range of such services. One of the most challenging research tasks is undoubtedly represented by the analysis of LBS data through Machine Learning algorithms and methodologies in order to infer new knowledge and build-up even more customized services. Cultural Heritage is a domain that can benefit from such studies since it is characterized by a strong interaction between people, cultural items and spaces. Data gathered in a museum on visitor movements and behaviours can constitute the knowledge base to realize an advanced monitoring system able to offer museum stakeholders a complete and real-time snapshot of the museum locations occupancy. Furthermore, exploiting such data through Deep Learning methodologies can lead to the development of a predictive monitoring system able to suggest stakeholders the museum locations occupancy not only in real-time but also in the next future, opening new scenarios in the management of a museum. In this paper, we present and discuss a Deep Learning methodology applied to data coming from a non-invasive Bluetooth IoT monitoring system deployed inside a cultural space. Through the analysis of visitors’ paths, the main goal is to predict the occupancy of the available rooms. Experimental results on real data demonstrate the feasibility of the proposed approach; it can represent a useful instrument, in the hands of the museum management, to enhance the quality-of-service within this kind of spaces

Path prediction in IoT systems through Markov Chain algorithm

In the Data Technology Era, inferring knowledge from data is an ubiquitous and pervasive research topic. Digital Ecosystems based on the Internet of Things (IoT) are generally designed for generating and collecting complex, real-time and (un)structured data. As one of the main component of the Smart City framework, the huge amount of IoT data has to be opportunely processed, also through Machine Learning algorithms in order to discover new knowledge and to improve the quality-of-life of the citizens. In our research work we propose some learning methodologies to analyse and forecast visitors’ paths within a cultural and complex space. Starting from data collected in a museum equipped with a non-invasive monitoring IoT system, we show how it is possible to discover and predict useful information on visitors’ movements and, finally, we present and discuss some useful insights on their behaviours within a real case-of-study

PEGylated cationic nanoassemblies based on triblock copolymers to combine siRNA therapeutics with anticancer drugs

Nowadays, the clinical administration of siRNA therapeutics is still challenging due to the need of safe and efficient delivery carriers. In this context, biodegradable and amphiphilic triblock copolymers (ABC) containing amine-based cationic segments could be a powerful tool for siRNA delivery. Herein, we propose a range of poly(ethylene glycol) (PEG)-poly(2-dimethyl(aminoethyl) methacrylate) (pDMAEMA)-polycaprolactone (PCL) copolymers with different lengths of the blocks and hydrophilic/lipophilic balance to deliver siRNA alone or in association with a conventional anticancer drug. mPEG-pDMAEMA-PCL copolymers were synthesized by a combination of techniques and characterized by NMR analysis, Fourier transform infrared (FTIR) spectroscopy, gel permeation chromatography (GPC) and differential scanning calorimetry (DSC). Copolymers were then employed to prepare NPs through nanoprecipitation. NPs based on copolymers with long PCL chains (SSL-NPs and LLL-NPs) showed the best colloidal properties and a highly stable core-shell structure with a better orientation of the PEG fringe on the surface. Concerning siRNA delivery, SSL-NPs based on copolymers with short PEG and pDMAEMA chains showed optimized ability to complex and then deliver siRNA at the cell level. The strong interaction between the nucleic acid and the cationic pDMAEMA blocks of NPs was then confirmed by release studies that showed a sustained release of siRNA within 48 h. The transfection efficiency of NPs was assessed in human melanoma cells. NPs were complexed with a therapeutic siRNA against TUBB3 (TUB-siRNA). We observed the best results with SSL-NPs, probably due to the higher preserved buffer capacity of the pDMAEMA blocks. Finally, in order to give a proof of concept of a possible application in the combined chemo/gene-therapy of cancer, SSL-NPs complexed with TUB-siRNA were loaded with docetaxel (DTX) and then cytotoxicity was evaluated in the same cell line. The co-delivery of TUB-siRNA into NPs appeared to strongly potentiate the anti-proliferative activity of DTX, thus highlighting the combinatory activity of the NPs

Post COVID-19 vaccination headache: A clinical and epidemiological evaluation

INTRODUCTION: This study aimed to assess the prevalence and clinical characteristics of headaches, in particular secondary headaches. MATERIALS AND METHODS: This observational study was performed at the ASST Spedali Civili of Brescia, Italy. Visits to the Emergency Department (ED) and subsequent hospitalizations regarding a new or worsening headache in the 16 days following the administration of the COVID-19 vaccine between January 2021 and January 2022 were recorded and compared with those of January 2019-January 2020. RESULTS: The ratio between ED admissions due to headaches and total ED admissions was significantly higher in 2021 compared with 2019 (4.84% vs. 4.27%; p < 0.0001). Two-hundred and eighty-nine ED headache admissions (10.8% of all ED headache admissions) were time-correlated to the COVID-19 vaccination, of which 40 were hospitalized in order to exclude a symptomatic etiology. At discharge, 32 patients had a diagnosis of benign headache not attributed to any cranial/extracranial disorder and eight patients of secondary headache, whose diagnoses were the following: Headache attributed to cranial and/or cervical vascular disorder (n = 4); headache attributed to nonvascular intracranial disorder (n = 2); headache or facial pain attributed to disorder of the cranium, neck, eyes, ears, nose, sinuses, teeth, mouth, or other facial or cervical structure (n = 1); and painful lesions of the cranial nerves (n = 1). The headache most frequently reported by patients had migraine-like characteristics: the localization was predominantly frontal or temporal, the pain was described as throbbing and severe in intensity and it was frequently accompanied by nausea/vomit, and photo-phonophobia. Over half-regardless of the final diagnosis-of hospitalized patients had a history of primary headaches. CONCLUSIONS: Following the spread of COVID-19 vaccination, the number of ED admissions due to headaches significantly increased. However, less than 14% of all the ED visits due to a headache time-correlated to the COVID-19 vaccination were actually hospitalized, with most patients documenting a benign headache, possibly related to the generic side effects of the vaccination. Only 8/40 hospitalized patients were diagnosed with a secondary headache. These benign headaches would actually fulfill diagnostic criteria for 8.1 Headaches attributed to the use of or exposure to a substance (ICHD-3), although, at the time being, it does not include vaccines as possible substances.The headache migraine-like characteristics' reported by most patients could suggest activation of the trigeminovascular pathway by all the cytokines and other pro-inflammatory molecules released following the vaccination

CSF &#946;-amyloid predicts early cerebellar atrophy and is associated with a poor prognosis in multiple sclerosis

BACKGROUND: Neurodegeneration is present from the earliest stages of multiple sclerosis (MS) and is critically involved in MS related clinical disability. Aim of the present study was to assess the connection between amyloid burden and early cerebellar grey matter (GM) atrophy compared to early brain GM atrophy in MS patients. METHODS: Forty newly diagnosed relapsing-remitting (RR-) MS patients were recruited. \u3b2-amyloid1-42 (A\u3b2) levels were determined in cerebrospinal fluid (CSF) samples from all subjects. All participants underwent neurological examination and brain magnetic resonance imaging (MRI) at baseline. Twenty-nine out of 40 patients repeated a brain MRI at 1-year follow-up. T1-weighted scans were segmented using the Voxel-Based Morphometry (VBM) protocol and the Spatially Unbiased Infratentorial Toolbox (SUIT) from Statistical Parametric Mapping (SPM12). RESULTS: Between-group comparison of cerebellar parenchymal fraction (GM+WM/total cerebellar volume%) showed significant differences between A\u3b2high and A\u3b2low at baseline (p &lt; 0.0001) and follow-up (p\u202f=\u202f0.02). Similarly, a between-group comparison of cerebellar GM fraction (GMF) showed significant differences between A\u3b2high and A\u3b2low at baseline (p\u202f=\u202f0.002) and follow-up (p\u202f=\u202f0.04). The multiple regression analysis showed CSF A\u3b2 concentration as the best predictor of GMF both at baseline and over time (\u3b2 = 0.505, \u3b2=0.377; p &lt; 0.05). No significant results were found regarding global brain atrophy and CSF A\u3b2 concentration. CONCLUSIONS: Early cerebellar atrophy seems to be crucial in predicting a poor prognosis in MS, more than early global brain atrophy

Intersection of exogenous, endogenous and anthropogenic factors in the Holocene landscape: A study of the Naples coastline during the last 6000 years

New data on the ancient landscape of Naples (southern Italy) during the middle and late Holocene from geo-archaeological excavations were used to reconstruct the hill and coastal environment to the west of the ancient Graeco-Roman polis. Between the 5th and 4th millennia BP, a rocky profile with a wave-cut platform cutting across pyroclastites emplaced from the surrounding volcanoes was predominant in the coastal landscape. During the 3rd millennium BP, this rocky coast was progressively replaced by a sandy littoral environment primarily due to marine deposition, with a coastline located some hundred meters inland with respect to the modern one. The sedimentary record of the Greek and Roman periods indicates short-term fluctuations of the coastline, leading to the establishment of a backshore environment towards the end of the 6th century AD, when prograding river mouths and lobes of debris flows contributed to the advancing trend of the shoreline. The frequent archaeological remains from these periods indicate a stable settled area since Roman times. The shoreline was still subject to short-lived fluctuations between the 12th and 16th centuries, and attained its present position during the modern era with man-made reshaping of its profile. The construction of Relative Sea Level curves for two coastal sites reveals that the persistence of the foreshore environment in the Naples coastal strip during the 5th and 4th millennia BP was controlled by the counterbalancing effect of either the concurrent eustatic sea level rise or subsidence. On the other hand, the morpho-stratigraphic record for the last two millennia shows a significant correlation between sedimentation rate and settlement history, accounting for the dominant role of the anthropogenic forcing-factor in late Holocene landscape history. In particular, land mismanagement during Late Antiquity seems to have triggered a slope disequilibrium phase, exacerbating soil erosion and increasing the sediment accumulation rate in both foothill and coastal areas. Nonetheless, the environmental changes of the Chiaia coast during the last 2000 years clearly show volcanotectonic perturbations influencing coastline development up to the modern era