Plasma Proteins Associated with COVID-19 Severity in Puerto Rico.

Viral strains, age, and host factors are associated with variable immune responses against SARS-CoV-2 and disease severity. Puerto Ricans have a genetic mixture of races: European, African, and Native American. We hypothesized that unique host proteins/pathways are associated with COVID-19 disease severity in Puerto Rico. Following IRB approval, a total of 95 unvaccinated men and women aged 21-71 years old were recruited in Puerto Rico from 2020-2021. Plasma samples were collected from COVID-19-positive subjects (n = 39) and COVID-19-negative individuals (n = 56) during acute disease. COVID-19-positive individuals were stratified based on symptomatology as follows: mild (n = 18), moderate (n = 13), and severe (n = 8). Quantitative proteomics was performed in plasma samples using tandem mass tag (TMT) labeling. Labeled peptides were subjected to LC/MS/MS and analyzed by Proteome Discoverer (version 2.5), Limma software (version 3.41.15), and Ingenuity Pathways Analysis (IPA, version 22.0.2). Cytokines were quantified using a human cytokine array. Proteomics analyses of severely affected COVID-19-positive individuals revealed 58 differentially expressed proteins. Cadherin-13, which participates in synaptogenesis, was downregulated in severe patients and validated by ELISA. Cytokine immunoassay showed that TNF-α levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Ricans.

Understanding of Contemporary Regional Sea-Level Change and the Implications for the Future.

Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea-level observing system, the knowledge of regional sea-level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea-level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea-level change. Here we review the individual processes which lead to sea-level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea-level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea-level observation network-particularly as related to satellite observations-in the improved scientific understanding of the contributors to regional sea-level change.

Predicting regional coastal sea level changes with machine learning.

All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales). In view of this complexity, it deems essential to move towards more sophisticated data-driven techniques as well as diagnostic and prognostic prediction models to interpret observations of ocean warming and sea level variations at local or regional sea basins. In this context, we present a machine learning approach that exploits key ocean temperature estimates (as proxies for the regional thermosteric sea level component) to model coastal sea level variability and associated uncertainty across a range of timescales (from months to several years). Our findings also demonstrate the utility of machine learning to estimate the possible tendency of near-future regional sea levels. When compared to actual sea-level records, our models perform particularly well in the coastal areas most influenced by internal climate variability. Yet, the models are widely applicable to evaluate the patterns of rising and falling sea levels across many places around the globe. Thus, our approach is a promising tool to model and anticipate sea level changes in the coming (1-3) years, which is crucial for near-term decision making and strategic planning about coastal protection measures.

Maximum entropy distributions of scale-invariant processes.

Organizations of many variables in nature such as soil moisture and topography exhibit patterns with no dominant scales. The maximum entropy (ME) principle is proposed to show how these variables can be statistically described using their scale-invariant properties and geometric mean. The ME principle predicts with great simplicity the probability distribution of a scale-invariant process in terms of macroscopic observables. The ME principle offers a universal and unified framework for characterizing such multiscaling processes.

Assessment of the effectiveness of BG-Sentinel traps baited with CO2 and BG-Lure for the surveillance of vector mosquitoes in Miami-Dade County, Florida.

Vector-borne diseases are an increasing issue to public health, endangering billions of people worldwide. Controlling vector mosquitoes is widely accepted as the most effective way to prevent vector-borne disease outbreaks. Mosquito surveillance is critical for the development of control strategies under the integrated vector management framework. We hypothesize that the effectiveness and reliability of using BG-Sentinel traps for the surveillance strongly depend on the bait used to attract mosquitoes. The objective of this study was to compare the effectiveness of BG-Sentinel traps baited with CO2 and BG-Lure. A total of 72 traps were deployed for 48 hours once a week for four weeks. For the initial 24-hour period, the traps were baited with CO2, and then for an additional 24 hours using the BG-Lure. Collected mosquitoes were analyzed using the Generalized Estimating Equation for repeated measures analysis. Biodiversity was assessed by the Shannon and Simpson indices and by individual rarefaction curves and SHE profiles. A total of 5,154 mosquitoes were collected, from which 3,514 by traps baited with CO2 and 1,640 mosquitoes by traps baited with BG-Lure. Aedes aegypti and Culex quinquefasciatus were the most abundant and dominant species. Results from the Generalized Estimating Equation models indicated that more than twice as many mosquitoes were attracted CO2 than to the BG-Lure. The comparison of attractiveness of CO2 and BG-Lure to Ae. aegypti and Cx. quinquefasciatus was non-significant, suggesting that both species were equally attracted by the baits. The individual rarefaction curves for Ae. aegypti and Cx. quinquefasciatus imply that traps baited with BG-Lure underestimated mosquito species richness compared to those baited with CO2. BG-Lure were less effective in attracting mosquitoes with low abundances and failed to collect Cx. coronator and Cx. nigripalpus, which were consistently collected by traps baited with CO2. According to our results, CO2 significantly (P<0.05) attracted more mosquitoes (2.67 adjusted odds ratios) than the BG-Lure when adjusted for time and species, being more effective in assessing the relative abundance of vector mosquitoes and yielding more trustworthy results. Traps baited with CO2 collected not only more specimens, but also more species in a more consistent pattern.

The global warming hiatus: Slowdown or redistribution?

Global mean surface temperatures (GMST) exhibited a smaller rate of warming during 1998-2013, compared to the warming in the latter half of the 20th Century. Although, not a "true" hiatus in the strict definition of the word, this has been termed the "global warming hiatus" by IPCC (2013). There have been other periods that have also been defined as the "hiatus" depending on the analysis. There are a number of uncertainties and knowledge gaps regarding the "hiatus." This report reviews these issues and also posits insights from a collective set of diverse information that helps us understand what we do and do not know. One salient insight is that the GMST phenomenon is a surface characteristic that does not represent a slowdown in warming of the climate system but rather is an energy redistribution within the oceans. Improved understanding of the ocean distribution and redistribution of heat will help better monitor Earth's energy budget and its consequences. A review of recent scientific publications on the "hiatus" shows the difficulty and complexities in pinpointing the oceanic sink of the "missing heat" from the atmosphere and the upper layer of the oceans, which defines the "hiatus." Advances in "hiatus" research and outlooks (recommendations) are given in this report.

GLOBAL WARMING. Recent hiatus caused by decadal shift in Indo-Pacific heating.

Recent modeling studies have proposed different scenarios to explain the slowdown in surface temperature warming in the most recent decade. Some of these studies seem to support the idea of internal variability and/or rearrangement of heat between the surface and the ocean interior. Others suggest that radiative forcing might also play a role. Our examination of observational data over the past two decades shows some significant differences when compared to model results from reanalyses and provides the most definitive explanation of how the heat was redistributed. We find that cooling in the top 100-meter layer of the Pacific Ocean was mainly compensated for by warming in the 100- to 300-meter layer of the Indian and Pacific Oceans in the past decade since 2003.