A Role for the Lipid Droplet Protein HIG2 in Promoting Lipid Deposition in Liver and Adipose Tissue: A Dissertation

Chronic exposure of humans or rodents to high calorie diets leads to hypertriglyceridemia and ectopic lipid deposition throughout the body, resulting in metabolic disease. Cellular lipids are stored in organelles termed lipid droplets (LDs) that are regulated by tissue-specific LD proteins. These proteins are critical for lipid homeostasis, as humans with LD protein mutations manifest metabolic dysfunction. Identification of novel components of the LD machinery could shed light on human disease mechanisms and suggest potential therapeutics for Type 2 Diabetes. Microarray analyses pinpointed the largely unstudied Hypoxia-Inducible Gene 2 (Hig2) as a gene that was highly expressed in obese human adipocytes. Imaging studies demonstrated that Hig2 localized to LDs in mouse hepatocytes and the human SGBS adipocyte cell line. Thus, this work examined the role of Hig2 as a LD protein in liver and adipose tissue. Hig2 deficiency reduced triglyceride deposition in hepatocytes; conversely, ectopic Hig2 expression promoted lipid deposition. Furthermore, liver-specific Hig2-deficient mice displayed improved glucose tolerance and reduced liver triglyceride content. Hig2 deficiency increased lipolysis and -oxidation, accounting for the reduced triglyceride accumulation. Similarly, adipocyte-specific Hig2-deficient mice displayed improved glucose tolerance, reduced adipose tissue weight and brown adipose tissue that was largely cleared of lipids. These improvements were abrogated when the animals were placed in thermoneutral housing and brown adipocyte-specific Hig2-deficient mice also displayed improved glucose tolerance, suggesting that active brown fat largely mediates the metabolic phenotype of Hig2 deletion. Thus, this work demonstrates that Hig2 localizes to LDs in liver and adipose tissue and promotes glucose intolerance

Feasible alternatives to green growth

Climate change and increasing income inequality have emerged as twin threats to contemporary standards of living, peace and democracy. These two problems are usually tackled separately in the policy agenda. A new breed of radical proposals have been advanced to manage a fair low-carbon transition. In this spirit, we develop a dynamic macrosimulation model to investigate the long-term effects of three scenarios: green growth, policies for social equity, and degrowth. The green growth scenario, based on technological progress and environmental policies, achieves a significant reduction in greenhouse gas emissions at the cost of increasing income inequality and unemployment. The policies for social equity scenario adds direct labour market interventions that result in an environmental performance similar to green growth while improving social conditions at the cost of increasing public deficit. The degrowth scenario further adds a reduction in consumption and exports, and achieves a greater reduction in emissions and inequality with higher public deficit, despite the introduction of a wealth tax. We argue that new radical social policies can combine social prosperity and low-carbon emissions and are economically and politically feasible

Cluster of legionnaires’ disease in an Italian prison

Background: Legionella pneumophila (Lp) is the most common etiologic agent causing Legionnaires’ Disease (LD). Water systems offer the best growth conditions for Lp and support its spread by producing aerosols. From 2015 to 2017, the Regional Reference Laboratory of Clinical and Environmental Surveillance of Legionellosis of Palermo monitored the presence of Lp in nine prisons in Western Sicily. During this investigation, we compared Lp isolates from environmental samples in a prison located in Palermo with isolates from two prisoners in the same prison. Methods: We collected 93 water samples from nine Sicilian prisons and the bronchoalveolar lavages (BALs) of two prisoners considered cases of LD. These samples were processed following the procedures described in the Italian Guidelines for the Prevention and Control of Legionellosis of 2015. Then, genotyping was performed on 19 Lp colonies (17 from water samples and 2 from clinical samples) using the Sequence-Based Typing (SBT) method, according to European Study Group for Legionella Infections (ESGLI) protocols. Results: Lp serogroup (sg) 6 was the most prevalent serogroup isolated from the prisons analyzed (40%), followed by Lp sg 1 (16%). Most of all, in four penitentiary institutions, we detected a high concentration of Lp >104 Colony Forming Unit/Liter (CFU/L). The environmental molecular investigation found the following Sequence Types (STs) in Lp sg 6: ST 93, ST 292, ST 461, ST 728, ST 1317 and ST 1362, while most of the isolates in sg 1 belonged to ST 1. We also found a new ST that has since been assigned the number 2451 in the ESGLI-SBT database. From the several Lp sg 1 colonies isolated from the two BALs, we identified ST 2451. Conclusions: In this article, we described the results obtained from environmental and epidemiological investigations of Lp isolated from prisons in Western Sicily. Furthermore, we reported the first cluster of Legionnaires’ in an Italian prison and the molecular typing of Lp sg 1 from one prison’s water system and two BALs, identified the source of the contamination, and discovered a new ST

Analysis of a thunderstorm downburst

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Meteorology and Physical Oceanography, 1983.Microfiche copy available in Archives and Science.Bibliography: leaf 120.by John Thomas DiStefano.M.S

FAUSTA: Scaling Dynamic Analysis with Traffic Generation at WhatsApp

We introduce Fausta, an algorithmic traffic gener-ation platform that enables analysis and testing at scale. Fausta has been deployed at Meta to analyze and test the WhatsApp plat-form infrastructure since September 2020, enabling WhatsApp developers to deploy reliable code changes to a code base of millions of lines of code, supporting over 2 billion users who rely on WhatsApp for their daily communications. Fausta covers expected and unexpected program behaviors in a privacy-safe controlled environment to support multiple use cases such as reliability testing, privacy analysis and performance regression detection. It currently supports three different algorithmic input generation strategies, each of which construct realistic backend server traffic that closely simulates production data, without replaying any real user data. Fausta has been deployed and closely integrated into the WhatsApp continuous integration process, catching bugs in development before they hit production. We report on the development and deployment of Fausta's reliability use case between September 2020 and August 2021. During this period it has found 1,876 unique reliability issues, with a fix rate of 74%, indicating a high degree of true positive fault revelation. We also report on the distribution of fault types revealed by Fausta, and the correlation between coverage and faults found. Overall, we do find evidence that higher coverage is correlated with fault revelation

Preschool Verbal and Nonverbal Ability Mediate the Association Between Socioeconomic Status and School Performance

We compared the extent to which the long-term influence of family socioeconomic status (SES) on children's school performance from age 7 through 16 years was mediated by their preschool verbal and nonverbal ability. In 661 British children, who completed 17 researcher-administered ability tests at age 4.5 years, SES correlated more strongly with verbal than nonverbal ability (.39 vs.26). Verbal ability mediated about half of the association between SES and school performance at age 7, while nonverbal ability accounted for a third of the link. Only SES, but not verbal or nonverbal ability, was associated with changes in school performance from age 7 to 16. We found that SES-related differences in school performance are only partly transmitted through children's preschool verbal abilities

Dataspaces: Concepts, Architectures and Initiatives

Despite not being a new concept, dataspaces have become a prominent topic due to the increasing availability of data and the need for efficient management and utilization of diverse data sources. In simple terms, a dataspace refers to an environment where data from various sources, formats, and domains can be integrated, shared, and analyzed. It aims to provide a unified view of heterogeneous data by bridging the gap between different data silos, enabling interoperability. The concept of dataspaces promotes the idea that data should be treated as a cohesive entity, rather than being fragmented across different systems and applications. Dataspaces often involve the integration of structured and unstructured data, including databases, documents, sensor data, social media feeds, and more. The goal is to enable organizations to harness the full potential of their data assets by facilitating data discovery, access, and analysis. By bringing together diverse data sources, dataspaces can offer new insights, support decision-making processes, and drive innovation. In the context of European Commission-funded research projects, dataspaces are often explored as part of initiatives focused on data management, data sharing, and the development of data-driven technologies. These projects aim to address challenges related to data integration, data privacy, data governance, and scalability. The goal is to advance the state of the art in data management and enable organizations to leverage data more effectively for societal, economic, and scientific advancements. It is important to notice that while dataspaces offer potential benefits, they also come with challenges. These challenges include data quality assurance, data privacy and security, semantic interoperability, scalability, and the need for appropriate data governance frameworks. Overall, dataspaces represent an approach to managing and utilizing data that emphasizes integration, interoperability, and accessibility. The concept is being explored and researched to develop innovative solutions that can unlock the value of data in various domains and sectors

Rab7b modulates autophagic flux by interacting with Atg4B

Autophagy (macroautophagy) is a highly conserved eukaryotic degradation pathway in which cytosolic components and organelles are sequestered by specialized autophagic membranes and degraded through the lysosomal system. The autophagic pathway maintains basal cellular homeostasis and helps cells adapt during stress; thus, defects in autophagy can cause detrimental effects. It is therefore crucial that autophagy is properly regulated. In this study, we show that the cysteine protease Atg4B, a key enzyme in autophagy that cleaves LC3, is an interactor of the small GTPase Rab7b. Indeed, Atg4B interacts and co‐localizes with Rab7b on vesicles. Depletion of Rab7b increases autophagic flux as indicated by the increased size of autophagic structures as well as the magnitude of macroautophagic sequestration and degradation. Importantly, we demonstrate that Rab7b regulates LC3 processing by modulating Atg4B activity. Taken together, our findings reveal Rab7b as a novel negative regulator of autophagy through its interaction with Atg4B

Pathwise Sensitivity Analysis in Transient Regimes

The instantaneous relative entropy (IRE) and the corresponding instanta- neous Fisher information matrix (IFIM) for transient stochastic processes are pre- sented in this paper. These novel tools for sensitivity analysis of stochastic models serve as an extension of the well known relative entropy rate (RER) and the corre- sponding Fisher information matrix (FIM) that apply to stationary processes. Three cases are studied here, discrete-time Markov chains, continuous-time Markov chains and stochastic differential equations. A biological reaction network is presented as a demonstration numerical example