Analyzing and Visualizing American Congress Polarization and Balance with Signed Networks

Signed networks and balance theory provide a natural setting for real-world scenarios that show polarization dynamics, positive/negative relationships, and political partisanships. For example, they have been proven effective for studying the increasing polarization of the votes in the two chambers of the American Congress from World War II on. To provide further insights into this particular case study, we propose the application of a framework to analyze and visualize a signed graph's configuration based on the exploitation of the corresponding Laplacian matrix' spectral properties. The overall methodology is comparable with others based on the frustration index, but it has at least two main advantages: first, it requires a much lower computational cost; second, it allows for a quantitative and visual assessment of how arbitrarily small subgraphs (even single nodes) contribute to the overall balance (or unbalance) of the network. The proposed pipeline allows to explore the polarization dynamics shown by the American Congress from 1945 to 2020 at different resolution scales. In fact, we are able to spot and to point out the influence of some (groups of) congressmen in the overall balance, as well as to observe and explore polarization's evolution of both chambers across the years

Bridging Representation and Visualization in Prosopographic Research: A Case Study

In the last decade, the research on ancient civilizations has started to rely more and more on data science to extract knowledge on ancient societies from the written sources delivered from the past. In this paper, we combine two well-established frameworks: Linked Data to obtain a rich data structure, and Network Science to explore different research questions regarding the structure and the evolution of ancient societies. We propose a multi-disciplinary pipeline where, starting from a semantically annotated prosopographic archive, a research question is translated into a query on the archive and the obtained dataset is the input to the network model. We applied this pipeline to different archives, a Hittite and a Kassite collection of cuneiform tablets. Finally, network visualization is presented as a powerful tool to highlight both the data structure and the social network analysis results

“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media

The paper describes the Web platform built within the project “Contro l’Odio”, for monitoring and contrasting discrimination and hate speech against immigrants in Italy. It applies a combination of computational linguistics techniques for hate speech detection and data visualization tools on data drawn from Twitter.It allows users to access a huge amount of information through interactive maps, also tuning their view, e.g. visualizing the most viral tweets and interactively reducing the inherent complexity of data. Educational courses for high school students have been developed which are centered on the platform and focused on the deconstruction of negative stereotypes against immigrants, Rom and religious minorities, and on the creation of positive narratives. The data collected and analyzed by the platform are also currently used for benchmarking activities within an evaluation campaign, and for paving the way to new projects against hate

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Hyperpolarization via dissolution dynamic nuclear polarization: new technological and methodological advances

Dissolution-DNP is a method to boost liquid-state NMR sensitivity by several orders of magnitude. The technique consists in hyperpolarizing samples by solid-state dynamic nuclear polarization at low temperature and moderate magnetic field, followed by an instantaneous melting and dilution of the sample happening inside the polarizer. Although the technique is well established and the outstanding signal enhancement paved the way towards many applications precluded to conventional NMR, the race to develop new methods allowing higher throughput, faster and higher polarization, and longer exploitation of the signal is still vivid. In this work, we review the most recent advances on dissolution-DNP methods trying to overcome the original technique's shortcomings. The review describes some of the new approaches in the field, first, in terms of sample formulation and properties, and second, in terms of instrumentation

Hyperpolarized water through dissolution dynamic nuclear polarization with UV-generated radicals

The upload here contains all the liquid and solid-state NMR raw data (RawData.zip) as well as the ESR raw data (ESR.zip) and the magnetic field measurements. Magnetic field measurement are used for figure 1. NMR data are used for figures 3,4,5 and 6, while the ESR data are used for figures 2,3 and 4 of the publication. Raw NMR data are files that can be read with MestreNova. ESR data and magnetic field measurements are simple excel sheets

A Data Viz Platform as a Support to Study, Analyze and Understand the Hate Speech Phenomenon

In this paper we present a data visualization platform designed to support the Natural Language Processing (NLP) scholar to study and analyze di erent corpora collected with the purpose to understand the hate speech phenomenon in social media. The project started with the creation of a corpus which collects tweets addressed to speci c groups of ethnic minorities considered very controversial in the Italian public debate. Each tweet has been manually tagged with a series of attributes in order to capture the di erent features used to characterize the hate speech phenomenon. This corpus is mainly built to be used for training an automatic classi er and helping us in its testing and validation, before being it adopted to detect tweets targeted as hate speech on larger scale datasets. As opposed as many other traditional machine learning tasks, to build a good classi er achieving high scores in terms of accuracy is very challenging in such scenario, because of the intrin- sic ambiguity of the language, the lack of a proper and explicable context in social media, and the attitude of on line users of being sarcastic and ironical. Therefore, in order to properly validate an e ective feature selection process, correlations between selected attributes must be studied and analyzed. This motivated us to build an interactive platform to explore data in our corpora across the dimensions that have been used to characterize collected tweets. In our paper, after a brief introduction of the hate speech dataset, we will show how the dashboard can t into the NLP pipeline, and how its architecture can be structured. Finally, we will present some of the challenges we have faced to visualize data with spatial, temporal and numerical attributes

UV-Irradiated 2-Keto-(1-C-13)Isocaproic Acid for High-Performance C-13 Hyperpolarized MR

Enhancing the sensitivity of magnetic resonance spectroscopy/imaging (MRS/MRI) by dissolution dynamic nuclear polarization (dDNP) has expanded the scope of MRS applications to new fields of research. Most importantly, it has paved the way toward noninvasive studies of the fate of a metabolite in real time. As its name implies, in a typical dDNP experiment, the hyperpolarized (HP) sample is extracted from the polarizer in the liquid state. This procedure limits the HP signal exploitation time window to approximately 1 min, but it is also the only way to preserve the high spin order created in the solid state at low temperatures of 1-1.5 K and moderate magnetic fields of 3.35-7 T by means of microwave irradiation. Indeed, although necessary for the DNP process to happen, the presence of free radicals in the sample would prevent its extraction as a solid for relatively longer-term storage and transport to remote locations. Moreover, for biological or clinical applications, the radical should be removed from the hyperpolarized (HP) solution. This limitation can be overcome using thermally labile ultraviolet (UV)-generated radicals that have been shown to be efficient polarizing agents, to provide a radical-free HP solution, and most importantly, to pave the way for the transport of HP solid samples to remote sites. Herein, we demonstrate that 2-keto[1-C-13]isocaproate (KIC), an important metabolic biomarker in the brain, can be highly polarized via dDNP using the nonpersistent ketyl radical generated by UV irradiation of the substrate itself. We investigated the precursor molecule and radical properties via UV-vis measurements and ESR measurements at both X-band and high field. After optimizing sample preparation and microwave irradiation conditions, we obtained 56% C-13 liquid-state polarization in 1 h by performing dDNP at 6.7 T and 1.1 +/- 0.1 K