A Dynamic Latent-Space Model for Asset Clustering

Periods of financial turmoil are not only characterized by higher correlation across assets but also by modifications in their overall clustering structure. In this work, we develop a dynamic Latent-Space mixture model for capturing changes in the clustering structure of financial assets at a fine scale. Through this model, we are able to project stocks onto a lower dimensional manifold and detect the presence of clusters. The infinite-mixture assumption ensures tractability in inference and accommodates cases in which the number of clusters is large. The Bayesian framework we rely on accounts for uncertainty in the parameters’ space and allows for the inclusion of prior knowledge. After having tested our model’s effectiveness and inference on a suitable synthetic dataset, we apply the model to the cross-correlation series of two reference stock indices. Our model correctly captures the presence of time-varying asset clustering. Moreover, we notice how assets’ latent coordinates may be related to relevant financial factors such as market capitalization and volatility. Finally, we find further evidence that the number of clusters seems to soar in periods of financial distress

Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model

News outlets are now more than ever incentivized to provide their audience with slanted news, while the intrinsic homophilic nature of online social media may exacerbate polarized opinions. Here, we propose a new dynamic latent space model for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. Our model contributes to the literature in several directions: 1) we provide a model-embedded data-driven interpretation for the latent leaning of news outlets in terms of media bias; 2) we endow our model with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; 3) we contribute to the literature on the statistical properties of latent space network models. The proposed model is applied to a set of data on the online activity of national and local news outlets from four European countries in the years 2015 and 2016. We find evidence of a strong positive correlation between our media slant measure and a well-grounded external source of media bias. In addition, we provide insight into the polarization regimes across the four countries considered

Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model

News outlets are now more than ever incentivized to provide their audience with slanted news, while the intrinsic homophilic nature of online social media may exacerbate polarized opinions. Here, we propose a new dynamic latent space model for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. Our model contributes to the literature in several directions: 1) we provide a model-embedded data-driven interpretation for the latent leaning of news outlets in terms of media bias; 2) we endow our model with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; 3) we contribute to the literature on the statistical properties of latent space network models. The proposed model is applied to a set of data on the online activity of national and local news outlets from four European countries in the years 2015 and 2016. We find evidence of a strong positive correlation between our media slant measure and a well-grounded external source of media bias. In addition, we provide insight into the polarization regimes across the four countries considered

Β-blockers treatment of cardiac surgery patients enhances isolation and improves phenotype of cardiosphere-derived cells

Β-blockers (BB) are a primary treatment for chronic heart disease (CHD), resulting in prognostic and symptomatic benefits. Cardiac cell therapy represents a promising regenerative treatment and, for autologous cell therapy, the patients clinical history may correlate with the biology of resident progenitors and the quality of the final cell product. This study aimed at uncovering correlations between clinical records of biopsy-donor CHD patients undergoing cardiac surgery and the corresponding yield and phenotype of cardiospheres (CSs) and CS-derived cells (CDCs), which are a clinically relevant population for cell therapy, containing progenitors. We describe a statistically significant association between BB therapy and improved CSs yield and CDCs phenotype. We show that BB-CDCs have a reduced fibrotic-like CD90 + subpopulation, with reduced expression of collagen-I and increased expression of cardiac genes, compared to CDCs from non-BB donors. Moreover BB-CDCs had a distinctive microRNA expression profile, consistent with reduced fibrotic features (miR-21, miR-29a/b/c downregulation), and enhanced regenerative potential (miR-1, miR-133, miR-101 upregulation) compared to non-BB. In vitro adrenergic pharmacological treatments confirmed cytoprotective and anti-fibrotic effects of β1-blocker on CDCs. This study shows anti-fibrotic and pro-commitment effects of BB treatment on endogenous cardiac reparative cells, and suggests adjuvant roles of β-blockers in cell therapy applications

Nomenclature and typification of plant names related to Centaurea aplolepa and C. leucophaea (Asteraceae) from Italy and France

Centaurea aplolepa Moretti and C. leucophaea Jord. (Asteraceae) are endemic to the central-western Mediterranean and include, respectively, ten and six subspecies, mostly occurring in north-western Italy and south-eastern France. As part of an ongoing systematic study on Centaurea L. sect. Centaurea from the central Mediterranean, 17 nomenclatural types (13 lectotypes, three neotypes and one epitype) are designated to fix the application of all names of the taxa described for France and Italy and related to C. aplolepa and C. leucophaea. In addition, previous typifications are critically revised and discussed. Centaurea aplolepa subsp. maremmana (Fiori) Dostál and C. litigiosa (Fiori) Arrigoni, two currently accepted taxa endemic to Tuscany (central Italy), are respectively considered here as heterotypic synonyms of C. aplolepa subsp. carueliana (Micheletti) Dostál and C. aplolepa subsp. cosana (Fiori) Dostál. Finally, C. aplolepa subsp. gallinariae (Briq. & Cavill.) Dostál, a currently accepted subspecies narrowly endemic to the Gallinara island (Liguria, northern Italy), is considered here as a heterotypic synonym of C. leucophaea subsp. brunnescens (Briq.) Dostál

Unveiling the hidden agenda: Biases in news reporting and consumption

Recognizing the presence and impact of news outlets’ biases on public discourse is a crucial challenge. Biased news significantly shapes how individuals perceive events, potentially jeopardizing public and individual well-being. In assessing news outlet reliability, the focus has predominantly centered on narrative bias, sidelining other biases such as selecting events favoring specific perspectives (selection bias). Leveraging machine learning techniques, we have compiled a six-year dataset of articles related to vaccines, categorizing them based on narrative and event types. Employing a Bayesian latent space model, we quantify both selection and narrative biases in news outlets. Results show third-party assessments align with narrative bias but struggle to identify selection bias accurately. Moreover, extreme and negative perspectives attract more attention, and consumption analysis unveils shared audiences among ideologically similar outlets, suggesting an echo chamber structure. Quantifying news outlets’ selection bias is crucial for ensuring a comprehensive representation of global events in online debates

Bridging aortic valve surgery to 21st century. what can a surgeon do

Aortic valve stenosis is the most clinically relevant valvular heart disease in the elderlies. Surgical aortic valve replacement (SAVR) represented, for decades, the standard of care for the treatment of severe aortic stenosis. Although SAVR still represents a valid option in this clinical scenario, transcatheter aortic valve implantation proved to be superior to medical therapy and comparable to SAVR in several randomized trials in patients at high or intermediate operative risk. At the same time, the growing aging population carrying on greater morbidities and high risk profiles has led to the development of minimally invasive technologies, as rapid deployment aortic valve replacement or Sutureless, to minimize surgical impact on patients. The Heart Team is nowadays tasked to determine the best option tailored for each patient considering patient-related factors and mastering all the surgical options in terms of both different techniques and types of available valves. Nevertheless, some open issues need to be already answered as: which has the longest durability, which the lower complication rate and the lower overall mortality. The aim of this review is to briefly resume the main features of these different options and explore what kind of open questions these newer-generation prosthetic valves and delivery devices carry

How News May Affect Markets’ Complex Structure: The Case of Cambridge Analytica

The claim of Cambridge Analytica, a political consulting firm, that it was possible to influence voting behavior by using data mined from the social platform Facebook created a sudden fear in its users of being manipulated; consequently, even the market price of the social platform was shocked.We propose a case study analyzing the effect of this data scandal not only on Facebook stock price, but also on the whole stock market. To such a scope, we consider 15-minutes prices and returns of the set of the NASDAQ-100 components before and after the Cambridge Analytica case. We analyze correlations and Mutual Information among components finding that assets become more correlated and their Mutual Information grows higher. We also observe that correlation and Mutual Information are mutually increasing and seem to follow a master curve. Hence, the market appears more fragile after the Cambridge Analytica event. In fact, as it is well-known in finance, an increase in the average value of correlations augments the systemic risk (i.e., all the market can collapse as a whole) and decreases the possibility of allocating a safe investment portfolio

A novel closed-chest porcine model of chronic ischemic heart failure suitable for experimental research in cardiovascular disease

Cardiac pathologies are among the leading causes of mortality and morbidity in industrialized countries, with myocardial infarction (MI) representing one of the major conditions leading to heart failure (HF). Hitherto, the development of consistent, stable, and reproducible models of closed-chest MI in large animals, meeting the clinical realism of a patient with HF subsequent to chronic ischemic necrosis, has not been successful. We hereby report the design and ensuing application of a novel porcine experimental model of closed-chest chronic ischemia suitable for biomedical research, mimicking post-MI HF. We also emphasize the key procedural steps involved in replicating this unprecedented model, from femoral artery and vein catheterization to MI induction by permanent occlusion of the left anterior descending coronary artery through superselective deployment of platinum-nylon coils, as well as endomyocardial biopsy sampling for histologic analysis and cell harvesting. Our model could indeed represent a valuable contribution and tool for translational research, providing precious insights to understand and overcome the many hurdles concerning, and currently quenching, the preclinical steps mandatory for the clinical translation of new cardiovascular technologies for personalized HF treatments