Associations between sarcopenia with asthmatic prevalence, lung function and comorbidity

Background Sarcopenia was listed as a treatment trait in behavioral/risk factors of severe asthma, but studies between asthma and sarcopenia were scant. This study plans to determine the associations between sarcopenia with asthmatic prevalence, symptoms, lung function and comorbidity. Methods 15404 individuals from the China Health and Retirement Longitudinal Study(CHARLS) and 10263 individuals from Study on global AGEing and adult health(SAGE) in China were included in this study. Four components of this study were respectively used to assess bidirectional association in the prevalence between sarcopenia with asthma, and estimate the relationships between sarcopenia with asthmatic symptoms, lung function and comorbidity via generalized additive models. The 10-item Center for Epidemiological Studies–Depression Scale≥12 scores was classified as depression in CHARLS. Results In CHARLS and SAGE, the prevalence of sarcopenia in asthmatics was higher than those without asthma. Asthmatics with sarcopenia had a significantly increased prevalence of severe shortness of breath(sarcopenia yes vs no, adjusted OR=3.71, 95%CI: 1.43-9.60) and airway obstruction in SAGE(sarcopenia yes vs no, adjusted OR=6.82, 95%CI: 2.54-18.34) and an obvious reduction of PEF in CHARLS and SAGE(sarcopenia yes vs no, adjusted RR=0.86, 95%CI: 0.82-0.91) compared to asthmatics without sacropenia. The presence of sarcopenia was positively associated with the prevalence of chronic obstructive pulmonary disease(sarcopenia yes vs no, adjusted OR=5.76, 95%CI:2.01-16.5) and depression(sarcopenia yes vs no, adjusted OR=1.87, 95%CI:1.11-3.14) in asthmatics. Conclusions Our findings indicated that sarcopenia partakes in the development of asthma by affecting lung function and comorbidity and maybe considered a treatable trait of asthma management.

Increases in Picocyanobacteria Abundance in Agriculturally Eutrophic Pampean Lakes Inferred from Historical Records of Secchi Depth and Chlorophyll-a

Phytoplankton size structure has profound consequences on food-web organization and energy transfer. Presently, picocyanobacteria (size < 2 µm) represent a major fraction of the autotrophic plankton of Pampean lakes. Glyphosate is known to stimulate the development of picocyanobacteria capable of degrading the herbicide. Due to the worldwide adoption of glyphosate-resistant crops, herbicide usage has increased sharply since the mid-1990s. Unfortunately, there are very few studies (none for the Pampa region) reporting picocyanobacteria abundance before 2000. The proliferation of µm sized particles should decrease Secchi disc depth (ZSD). Therefore ZSD, conditional to chlorophyll-a, may serve as an indicator of picocyanobacteria abundance. We use generalized additive models (GAMs) to analyze a “validation” dataset consisting of 82 records of ZSD, chlorophyll-a, and picocyanobacteria abundance from two Pampean lakes surveys (2009 and 2015). In support of the hypothesis, ZSD was negatively related to picocyanobacteria after accounting for the effect of chlorophyll-a. We then fitted a “historical” dataset using hierarchical GAMs to compare ZSD conditional to chlorophyll-a, before and after 2000. We estimated that ZSD levels during 2000–2021 were, on average, only about half as deep as those during 1980–1999. We conclude that the adoption of glyphosate-resistant crops has stimulated outbreaks of picocyanobacteria populations, resulting in lower water transparency.

Evidence that Pacific tuna mercury levels are driven by marine methylmercury production and anthropogenic inputs

Pacific Ocean tuna is among the most-consumed seafood products but contains relatively high levels of the neurotoxin methylmercury. Limited observations suggest tuna mercury levels vary in space and time, yet the drivers are not well understood. Here, we map mercury concentrations in skipjack tuna across the Pacific Ocean and build generalized additive models to quantify the anthropogenic, ecological, and biogeochemical drivers. Skipjack mercury levels display a fivefold spatial gradient, with maximum concentrations in the northwest near Asia, intermediate values in the east, and the lowest levels in the west, southwest, and central Pacific. Large spatial differences can be explained by the depth of the seawater methylmercury peak near low-oxygen zones, leading to enhanced tuna mercury concentrations in regions where oxygen depletion is shallow. Despite this natural biogeochemical control, the mercury hotspot in tuna caught near Asia is explained by elevated atmospheric mercury concentrations and/or mercury river inputs to the coastal shelf. While we cannot ignore the legacy mercury contribution from other regions to the Pacific Ocean (e.g., North America and Europe), our results suggest that recent anthropogenic mercury release, which is currently largest in Asia, contributes directly to present-day human mercury exposure.

Spatio-Temporal Modeling of Small-Scale Ultrafine Particle Variability Using Generalized Additive Models

High-resolution measurements of ultrafine particle concentrations in ambient air are needed for the study of health human effects of long-term exposure. This work, carried out in the framework of the VIEPI project (Integrated Evaluation of Indoor Particulate Exposure), aims to extend current knowledge on small-scale spatio-temporal variability of Particle Number Concentration (PNC, considered a proxy of the ultrafine particles) at a local scale domain (1 km × 1 km). PNC measurements were made in the university district of San Lorenzo in Rome using portable condensation particle counters for 7 consecutive days at 21 sites in November 2017 and June 2018. Generalized Additive Models (GAMs) were performed in the area for winter, summer and the overall period. The log-transformed two-hour PNC averages constitute the response variable, and covariates were grouped by urban morphology, land use, traffic and meteorology. Winter PNC values were about twice the summer ones. PNC recorded in the university area were significantly lower than those observed in the external routes. GAMs showed a rather satisfactory result in order to capture the spatial variability, in accordance with those of other previous studies: variances were equal to 71.1, 79.7 and 84%, respectively, for winter, summer and the overall period.

Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using Generalized Additive Models

Previous studies have confirmed the inextricable connection between meteorological factors and air pollutants. This study presents the complex nonlinear relationship between meteorological variables and four major air pollutants under high-concentration air pollution in Beijing. The generalized additive model combined with marginal effects is used for quantitative analysis. After controlling the confounding factors such as long-term trends, seasonality and spatio-temporal deviation, the final fitting results exhibit that temperature, relative humidity and visibility are the most significant meteorological variables associating with PM2.5 concentration, and the marginal effect reaches 80%, −23% and 270%, respectively. Temperature and relative humidity are the most significant variables for SO2, and the marginal effect reaches 15% and 7%. The most significant variables for O3 are temperature and solar radiation, with marginal effect of up to 70% and 8%. Atmospheric pressure and temperature results in a positive effect on CO, and the marginal effect can reach 18% and 80%. All these indicate that local meteorological variables are a significant driving factor for air quality in Beijing. Other variables, such as wind speed, visibility, and precipitation, display some influence on air pollutants, but have less explanatory power in the model. Overall, our study provides a better understanding of the relationship between local meteorological variables and air quality, as well as an insight into how climate change affects air quality.

Environmental and Household-Based Spatial Risks for Tungiasis in an Endemic Area of Coastal Kenya

Tungiasis is a cutaneous parasitosis caused by an embedded female sand flea. The distribution of cases can be spatially heterogeneous even in areas with similar risk profiles. This study assesses household and remotely sensed environmental factors that contribute to the geographic distribution of tungiasis cases in a rural area along the Southern Kenyan Coast. Data on household tungiasis case status, demographic and socioeconomic information, and geographic locations were recorded during regular survey activities of the Health and Demographic Surveillance System, mainly during 2011. Data were joined with other spatial data sources using latitude/longitude coordinates. Generalized additive models were used to predict and visualize spatial risks for tungiasis. The household-level prevalence of tungiasis was 3.4% (272/7925). There was a 1.1% (461/41,135) prevalence of infection among all participants. A significant spatial variability was observed in the unadjusted model (p-value < 0.001). The number of children per household, earthen floor, organic roof, elevation, aluminum content in the soil, and distance to the nearest animal reserve attenuated the odds ratios and partially explained the spatial variation of tungiasis. Spatial heterogeneity in tungiasis risk remained even after a factor adjustment. This suggests that there are possible unmeasured factors associated with the complex ecology of sand fleas that may contribute to the disease’s uneven distribution.

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020–March 2021) Using a Statistical Learning Strategy

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

Distribution and Catch Rate Characteristics of Narrow-Barred Spanish Mackerel (Scomberomorus commerson) in Relation to Oceanographic Factors in the Waters Around Taiwan

This study investigated the relationship of the catch rates (CRs) of Spanish mackerel (Scomberomorus commerson) with oceanographic factors in the waters around Taiwan by using high-resolution fishery and environmental data for the period 2011–2016. The investigation results revealed that trammel nets accounted for 69.79% of the total catch of S. commerson and were operated mostly in the Taiwan Strait (TS). We noted seasonal variations in the distribution of high CRs. These CRs were observed in the southwestern TS, including the waters along the southwestern coast of Taiwan and around the Penghu Islands, and extended to the Taiwan Bank during autumn; they increased in winter. To predict the spatial and temporal patterns of Spanish mackerel density and their relationship with oceanographic and spatiotemporal variables, generalized additive models were used. These models explained 48.4% of the total deviance, which was consistent with the assumed Gaussian distribution. Moreover, all variables examined were significant CR predictors (p &lt; 0.05). Latitude and longitude were the key factors influencing the spatiotemporal distribution of S. commerson, and sea surface chlorophyll a concentration was a key oceanographic factor. Observing projected changes in El Niño/Southern Oscillation events for S. commerson revealed that CRs were higher and distributed further southward during La Niña events than during other events. We inferred that the S. commerson distribution gradually moved toward the southwest with the northeast monsoon, which was enhanced during La Niña in winter.