Comparison of targeted and shotgun animal gut metagenomics

The understanding of the composition and functions of the intestinal environment is particularly useful to evaluate and improve productivity and health of farmed animals, such as chickens. By sequencing the metagenome, that represents the genetic material recovered directly from enviromental samples such as gut sections, scientists attempt to retrieve the abundances of microorganisms that inhabit the gut of animals, in order to access information about the interaction with the host. Our purpose is to compare, with a statistical approach, the reliability of two sequencing techniques, called metataxonomics and metagenomics, that can both provide a solid approach to investigate the populations of bacteria in gut microbiome. Although metagenomics, based on shotgun sequencing of the full metagenome, is usually known as the best suited option to recover abundance profiles of bacteria, recent studies have highlighted remarkable results using amplicon sequencing, that targets and recognizes particular regions of 16S rRNA gene. In our study, we take advantage of a well-structured dataset of 78 samples collected from caeca and crops of 40 chickens, at different days of life(1,14,35) and fed (or not) with a probiotic supplemented to drinking water. The study of abundance profiles retrieved by metagenomics and metataxonomics separately, highlights several differences between the two techniques, in terms of detection of rare genera and connection to biological markers. Shotgun sequencing detects around five times more genera than those commonly detected by both techniques, even if several shotgun sets have low coverage. Furthermore, using silhouette scores to evaluate the space segmentation of abundance profiles in a 2-dimensional PCoA space according to biological metadata, we observe that low-abundance bacteria detected only by shotgun contain important biologic information, hidden to 16S sequencing

Caratterizzazione di transistor organici a effetto di campo come detector di raggi X

I semiconduttori organici sono alla base dell'elettronica organica, un campo di ricerca che negli ultimi anni ha coinvolto diversi gruppi di lavoro, sia a livello accademico che industriale. Diversi studi hanno portato all'idea di impiegare materiali di questo tipo come detector di raggi X, sfruttando la loro flessibilità meccanica, la facile fabbricazione di dispositivi su larga area e tramite tecniche a basso costo (es. stampa a getto di inchiostro) e le basse tensioni operative. In questa tesi in particolare si utilizzeranno degli OFET (Organic Field-Effect Transistor) a questo scopo, dimostrando la possibilità amplificare la sensibilità alla radiazione X e di pilotare le prestazioni del detector mediante l'applicazione della tensione all'elettrodo di gate. Presenteremo quindi uno studio sperimentale atto a caratterizzare elettricamente dei transistor realizzati con differenti semiconduttori organici, prima, durante e dopo l'esposizione a raggi X, in maniera da stimarne la sensibilità, le proprietà di conduzione intrinseche e la resistenza all'invecchiamento

Dynamics of social media behavior before and after SARS-CoV-2 infection

Introduction: Online social media have been both a field of research and a source of data for research since the beginning of the COVID-19 pandemic. In this study, we aimed to determine how and whether the content of tweets by Twitter users reporting SARS-CoV-2 infections changed over time. Methods: We built a regular expression to detect users reporting being infected, and we applied several Natural Language Processing methods to assess the emotions, topics, and self-reports of symptoms present in the timelines of the users. Results: Twelve thousand one hundred and twenty-one twitter users matched the regular expression and were considered in the study. We found that the proportions of health-related, symptom-containing, and emotionally non-neutral tweets increased after users had reported their SARS-CoV-2 infection on Twitter. Our results also show that the number of weeks accounting for the increased proportion of symptoms was consistent with the duration of the symptoms in clinically confirmed COVID-19 cases. Furthermore, we observed a high temporal correlation between self-reports of SARS-CoV-2 infection and officially reported cases of the disease in the largest English-speaking countries. Discussion: This study confirms that automated methods can be used to find digital users publicly sharing information about their health status on social media, and that the associated data analysis may supplement clinical assessments made in the early phases of the spread of emerging diseases. Such automated methods may prove particularly useful for newly emerging health conditions that are not rapidly captured in the traditional health systems, such as the long term sequalae of SARS-CoV-2 infections

Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota

In this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance

Anesthesiological Management of a Patient with Williams Syndrome Undergoing Spine Surgery

Williams Syndrome (WS) is a complex neurodevelopmental disorder associated with a mutation on chromosome 7. Patients with WS usually display dysmorphic facial and musculoskeletal features, congenital heart diseases, metabolic disturbances and cognitive impairment. Structural cardiovascular abnormalities are present in the majority of the children and may provide a substrate for perioperative Sudden Cardiac Death, as presented by several reports, something that creates a great challenge to the anesthetic conduct. We present the case of a 12-year old girl who required anesthetic care for surgical correction of an acquired kyphoscoliosis. Potential anesthesiological implications of WS are subsequently reviewed

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale

Are We Ready for the Arrival of the New COVID-19 Vaccinations? Great Promises and Unknown Challenges Still to Come

Abstract: While the SARS-CoV-2 pandemic continues to strike and collect its death toll throughout the globe, as of 31 January 2021, the vaccine candidates worldwide were 292, of which 70 were in clinical testing. Several vaccines have been approved worldwide, and in particular, three have been so far authorized for use in the EU. Vaccination can be, in fact, an efficient way to mitigate the devastating effect of the pandemic and offer protection to some vulnerable strata of the population (i.e., the elderly) and reduce the social and economic burden of the current crisis. Regardless, a question is still open: after vaccination availability for the public, will vaccination campaigns be effective in reaching all the strata and a sufficient number of people in order to guarantee herd immunity? In other words: after we have it, will we be able to use it? Following the trends in vaccine hesitancy in recent years, there is a growing distrust of COVID-19 vaccinations. In addition, the online context and competition between pro- and anti-vaxxers show a trend in which anti-vaccination movements tend to capture the attention of those who are hesitant. Describing this context and analyzing its possible causes, what interventions or strategies could be effective to reduce COVID-19 vaccine hesitancy? Will social media trend analysis be helpful in trying to solve this complex issue? Are there perspectives for an efficient implementation of COVID-19 vaccination coverage as well as for all the other vaccinations

Mis-tweeting communication: a Vaccine Hesitancy analysis among twitter users in Italy

Background and aim: A previously unseen body of scientific knowledge of varying quality has been produced during the ongoing COVID-19 pandemic. It has proven extremely difficult to navigate for experts and laymen alike, originating a phenomenon described as "Infodemic", a breeding ground for misinformation. This has a potential impact on vaccine hesitancy that must be considered in a situation where efficient vaccination campaigns are of the greatest importance. We aimed at describing the polarization and volumes of Italian language tweets in the months before and after the start of the vaccination campaign in Italy. Methods: Tweets were sampled in the October 2020-January 2021 period. The characteristics of the dataset were analyzed after manual annotation as Anti-Vax, Pro-Vax and Neutral, which allowed for the definition of a polarity score for each tweet. Results: Based on the annotated tweets, we could identify 29.6% of the 2,538 unique users as anti-Vax and 12.1% as pro-Vax, with a strong disagreement in annotation in 7.1% of the tweets. We observed a change in the proportion of retweets to anti-Vax and pro-Vax messages after the start of the vaccination campaign in Italy. Although the most shared tweets are those of opposite orientation, the most retweeted users are moderately polarized.  Conclusions: The disagreement on the manual classification of tweets highlights a potential risk for misinterpretation of tweets among the general population. Our study reinforces the need to focus Public Health's attention on the new social media with the aim of increasing vaccine confidence, especially in the context of the current pandemic

Author Correction: Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study

No abstract availabl