Quantitative assessment of interplay between urbanization dynamics and land surface temperature variations using generalized additive model coupled PDP for sustainable urban planning and management.

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51 °C in 1990 to 46.73 °C in 2020 and the highest temperature category (50-60 °C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

Associations Between Anthropogenic Factors, Meteorological Factors, and Cause-Specific Emergency Department Admissions.

Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause-specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non-linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause-specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01-1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03-1.15) during increased rainfall (13.21-16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03-1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31-1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05-1.27 for PM10) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69-4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 µg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered.

Long-term changes in spatiotemporal distribution of Noctiluca scintillans in the southern North Sea.

To assess the spatiotemporal evolution of the heterotrophic dinoflagellate Noctiluca scintillans in the North Sea, the Helgoland Roads time series and Continuous Plankton Recorder survey were analysed using generalized additive models. Over the last decades, blooms of N. scintillans have occurred more frequently and intensively in many regions. This harmful algal bloom forming species can alter food webs, reduce ecosystem productivity, and lead to economic losses while causing lower aquacultural yields. After the 1990s, N. scintillans abundances have significantly increased by 1.65-fold and a significant prolongation of the bloom window was found (from 27.5 to 98 days in recent decades) off the island of Helgoland, Germany. Significant correlations were found between bloom initiation and nutrients, as well as light availability since these factors lead to increased prey availability. Highest abundances of N. scintillans were associated with water temperatures around 17 °C and wind speed below 6 ms-1 causing dense surface accumulations. Solar radiation of more than 200 Wm-2 was identified as a main driver for post-bloom conditions as it can deteriorate the cells and lead to the decline of N. scintillans abundances. In the southern North Sea, N. scintillans occurrences have intensified and spread since the 1980s with hotspots identified as the coastal waters adjacent to the estuaries of the Elbe and Rhine rivers.

Multilevel Semiparametric Latent Variable Modeling in R with "galamm".

We present the R package galamm, whose goal is to provide common ground between structural equation modeling and mixed effect models. It supports estimation of models with an arbitrary number of crossed or nested random effects, smoothing splines, mixed response types, factor structures, heteroscedastic residuals, and data missing at random. Implementation using sparse matrix methods and automatic differentiation ensures computational efficiency. We here briefly present the implemented methodology, give an overview of the package and an example demonstrating its use.

Non-linear relationships in clinical research.

True linear relationships are rare in clinical data. Despite this, linearity is often assumed during analyses, leading to potentially biased estimates and inaccurate conclusions. In this introductory paper, we aim to first describe - in a non-mathematical manner - how to identify non-linear relationships. Various methods are then discussed that can be applied to deal with non-linearity, including transformations, polynomials, splines, and Generalized Additive Models (GAMs), along with their strengths and weaknesses. Finally, we illustrate the use of these methods with a practical example from nephrology, providing guidance on how to report the results from non-linear relationships.

Site characteristics determine the prevalence of extreme weather events affecting freshwater macroinvertebrate communities.

Understanding the impacts of extreme weather events on freshwater ecosystems is imperative during a time when a multitude of challenges compromises these environments' health. Exploring how such events affect macroinvertebrate communities in rivers sheds light on the resilience of freshwater ecosystems, which is essential for human well-being and biodiversity conservation. In this study, long-term time series of benthic macroinvertebrate communities from four sites along three freshwater streams within the Rhine-Main-Observatory Long-Term Ecological Research site in Germany were analyzed. Each of them was sampled annually over a span of ~20 years to assess the impacts of extreme weather events (floods, droughts, and extreme heat) on macroinvertebrate communities. The findings reveal that the effects of extreme events are site-specific, suggesting that the impacts of an extreme event can vary based on several potential factors, including the life history traits of the organisms within the community and, among others, the hydrography of the site. Moreover, the analysis highlights that the cumulative impact of these events over time is more significant than the impact of a single event's magnitude, while following distinct temporal dynamics. This underscores the importance of considering both the temporal dynamics and the biological characteristics of communities when evaluating the consequences of extreme weather events on biodiversity, illustrating that the resilience of freshwater ecosystems and their biodiversity under such conditions depends on a complex interplay of factors rather than the severity of individual events.

Environmental drivers behind the exceptional increase in cyanobacterial blooms in Okavango Delta, Botswana.

The Okavango Delta region in Botswana experienced exceptionally intense landscape-wide cyanobacterial harmful algal blooms (CyanoHABs) in 2020. In this study, the drivers behind CyanoHABs were determined from thirteen independent environmental variables, including vegetation indices, climate and meteorological parameters, and landscape variables. Annual Land Use Land Cover (LULC) maps were created from 2017 to 2020, with ∼89% accuracy to compute landscape variables such as LULC change. Generalized Additive Models (GAM) and Structural Equation Models (SEM) were used to determine the most important drivers behind the CyanoHABs. Normalized Difference Chlorophyll Index (NDCI) and Green Line Height (GLH) algorithms served as proxies for chlorophyll-a (green algae) and phycocyanin (cyanobacteria) concentrations. GAM models showed that seven out of the thirteen variables explained 89.9% of the variance for GLH. The models showcased that climate variables, including monthly precipitation (8.8%) and Palmer Severity Drought Index- PDSI (3.2%), along with landscape variables such as changes in Wetlands area (7.5%), and Normalized Difference Vegetation Index (NDVI) (5.4%) were the determining drivers behind the increased cyanobacterial activity within the Delta. Both PDSI and NDVI showed negative correlations with GLH, indicating that increased drought conditions could have led to large increases in toxic CyanoHAB activity within the region. This study provides new information about environmental drivers which can help monitor and predict regions at risk of future severe CyanoHABs outbreaks in the Okavango Delta, Botswana, and other similar data-scarce and ecologically sensitive areas in Africa. Plain Language Summary: The waters of the Okavango Delta in Northern Botswana experienced an exceptional increase in toxic cyanobacterial activity in recent years. Cyanobacterial blooms have been shown to affect local communities and wildlife in the past. To determine the drivers behind this increased bloom activity, we analyzed the effects of thirteen independent environmental variables using two different statistical models. Within this research, we focused on vegetation indices, meteorological, and landscape variables, as previous studies have shown their effect on cyanobacterial activity in other parts of the world. While driver determination for cyanobacteria has been done before, the environmental conditions most important for cyanobacterial growth can be specific to the geographic setting of a study site. The statistical analysis indicated that the increases in cyanobacterial bloom activity within the region were mainly driven by persistent drier conditions. To our knowledge, this is the first study to determine the driving factors behind cyanobacterial activity in this region of the world. Our findings will help to predict and monitor areas at risk of future severe cyanobacterial blooms in the Okavango Delta and other similar African ecosystems.

Trace metals pollution and trophic position of three sea urchin species in the Gulf of California.

Sea urchin species are ecologically important in the Gulf of California and are becoming popular as a local fishery due to their commercial value. The most abundant species are Echinometra vanbrunti, Eucidaris thouarsii, and Tripneustes depressus. The objective of this study was to evaluate cadmium, lead, copper, zinc, and iron concentrations, as well as stable isotope values in these sea urchin species in the Santa Rosalia mining area (STR), in three sites: Punta Gorda, Punta el Aterrizaje, and Punta Salina. The highest Fe concentration (100.2 mg kg-1) was found in E. vanbrunti, while the highest concentrations of Pb (15.1 mg kg-1), Cu (14.5 mg kg-1), and Zn (347.7 mg kg-1) were recorded in E. thouarsii, and the highest Cd concentration (10.8 mg kg-1) was found in T. depressus. The main health risk of trace metal pollution in STR may be caused by Cd and Pb. δ15N and δ13C values were higher in E. thouarsii and T. depressus, respectively; E. thouarsii has the highest trophic position. Specimen size was not related to metal concentrations, but a positive relationship was observed between specimen size and isotopic values in T. depressus. The three species showed different bioaccumulation patterns for the metals analyzed. Additionally, collection sites and seasons play an important role in the variability of metal concentration.

The global burden of breast cancer in women from 1990 to 2030: assessment and projection based on the global burden of disease study 2019.

Background and aim: This study aims to analyze the worldwide prevalence, mortality rates, and disability-adjusted life years (DALYs) attributed to breast cancer in women between 1990 and 2019. Additionally, it seeks to forecast the future trends of these indicators related to the burden of breast cancer in women from 2020 to 2030. Methods: Data from the Global Burden of Disease Study (GBD) 2019 was analyzed to determine the age-standardized incidence rate (ASIR) and age-standardized death rate (ASDR) of DALYs due to breast cancer in women across 204 countries and territories from 1990 to 2019. Socio-economic development levels of countries and regions were assessed using Socio-demographic Indexes, and trends in the burden of breast cancer in women worldwide from 2020 to 2030 were projected using generalized additive models (GAMs). Results: The estimated annual percentage change (EAPC) in the ASIR breast cancer in women globally was 0.36 from 1990 to 2019 and is expected to increase to 0.44 from 2020 to 2030. In 2019, the ASIR of breast cancer in women worldwide was 45.86 and is projected to reach 48.09 by 2030. The burden of breast cancer in women generally rises with age, with the highest burden expected in the 45-49 age group from 2020 to 2030. The fastest increase in burden is anticipated in Central sub-Saharan Africa (EAPC in the age-standardized death rate: 1.62, EAPC in the age-standardized DALY rate: 1.52), with the Solomon Islands (EAPC in the ASIR: 7.25) and China (EAPC in the ASIR: 2.83) projected to experience significant increases. Furthermore, a strong positive correlation was found between the ASIR breast cancer in women globally in 1990 and the projected rates for 2030 (r = 0.62). Conclusion: The anticipated increase in the ASIR of breast cancer in women globally by 2030 highlights the importance of focusing on women aged 45-49 in Central sub-Saharan Africa, Oceania, the Solomon Islands, and China. Initiatives such as breast cancer information registries, raising awareness of risk factors and incidence, and implementing universal screening programs and diagnostic tests are essential in reducing the burden of breast cancer and its associated morbidity and mortality.

Feasibility of aligning creatine kinase MB activity and mass data in multicentre trials using generalized additive modelling.

OBJECTIVES: Elevated serum creatine kinase isoenzyme MB (CK-MB) levels indicate myocardial ischaemia and periprocedural myocardial injury during treatment of heart diseases. We established a method to predict CK-MB mass from activity data based on a prospective pilot study in order to simplify multicentre trials. METHODS: 38 elective cardiac surgery patients without acute myocardial ischaemia and terminal renal failure were recruited. CK-MB mass and activity were determined in venous blood samples drawn preoperatively, postoperatively, 6 h post-op, and 12 h post-op. Linear regression and generalized additive models (GAMs) were applied to describe the relationship of mass and activity. Influences of demographic and perioperative factors on the fit of GAMs was evaluated. The agreement of predicted and measured CK-MB masses was assessed by Bland-Altman analyses. RESULTS: Linear regression provided an acceptable overall fit (r2 = 0.834) but showed deviances at low CK-MB levels. GAMs did not benefit from the inclusion of age, body mass index and surgical times. The minimal adequate model predicted CK-MB masses from activities, sex and sampling time with an r2 of 0.981. Bland-Altman analyses confirmed narrow limits of agreement (spread: 8.87 µg/l) and the absence of fixed (P = 0.41) and proportional (P = 0.21) biases. CONCLUSIONS: GAM-based modelling of CK-MB data in a representative patient cohort allowed to predict CK-MB masses from activities, sex and sampling time. This approach simplifies the integration of study centres with incompatible CK-MB data into multicentre trials in order to facilitate inclusion of CK-MB levels in statistical models.

Human immune and metabolic biomarker levels, and stress-biomarker associations, differ by season: Implications for biomedical health research.

Although seasonal changes in physiology are well documented, little is known about how human immune and metabolic markers vary across seasons, and no studies have examined how stress → health biomarker associations differ across the year. To investigate these issues, we analyzed data from 2118 participants of the Midlife in the United States (MIDUS) study to determine whether there were differences in (a) levels of 19 immune and metabolic markers, and (b) the association between perceived stress and each biomarker across the year. Results of component-wide boosted generalized additive models revealed seasonal patterning for most biomarkers, with immune proteins generally peaking when days were shorter. Moreover, whereas levels of hemoglobin A1C rose from late fall to spring, triglycerides were elevated in the summer and fall, and high-density lipoprotein decreased steadily from January to December. Urinary cortisol and cortisone exhibited opposite patterns, peaking at the beginning and end of the year, respectively. Most critically, we found that the effects of perceived stress on 18 of the 19 health biomarkers assessed varied by month of measurement. In some cases, these differences involved the magnitude of the stress → biomarker association but, in other cases, it was the direction of the effect that changed. Studies that do not account for month of biomarker assessment may thus yield misleading or unreproducible results.

Advances in the Early Warning of Shellfish Toxification by Dinophysis acuminata.

In Western Europe, the incidence of DST is likely the highest globally, posing a significant threat with prolonged bans on shellfish harvesting, mainly caused by species of the dinoflagellate genus Dinophysis. Using a time series from 2014 to 2020, our study aimed (i) to determine the concentration of D. acuminata in water at which shellfish toxin levels could surpass the regulatory limit (160 µg OA equiv kg-1) and (ii) to assess the predictability of toxic events for timely mitigation actions, especially concerning potential harvesting bans. The analysis considered factors such as (i) overdispersion in the data, (ii) distinct periods of presence and absence, (iii) the persistence of cells, and (iv) the temporal lag between cells in the water and toxins in shellfish. Four generalized additive models were tested, with the Tweedie (TW-GAM) model showing superior performance (>85%) and lower complexity. The results suggest existing thresholds currently employed (200 and 500 cells L-1) are well-suited for the Portuguese coast, supported by empirical evidence (54-79% accuracy). The developed algorithm allows for thresholds to be tailored on a case-by-case basis, offering flexibility for regional variations.

Multiple prenatal exposures and acute-care clinical encounters for asthma among children born to mothers living near a Superfund site.

Prenatal exposures are associated with childhood asthma, and risk may increase with simultaneous exposures. Pregnant women living in lower-income communities tend to have elevated exposures to a range of potential asthma risk factors, which may interact in complex ways. We examined the association between prenatal exposures and the risk of childhood acute-care clinical encounters for asthma (hospitalizations, emergency department visits, observational stays) using conditional logistic regression with a multivariable smoothing term to model the interaction between continuous variables, adjusted for maternal characteristics and stratified by sex. All births near the New Bedford Harbor (NBH) Superfund site (2000-2006) in New Bedford, Massachusetts, were followed through 2011 using the Massachusetts Pregnancy to Early Life Longitudinal (PELL) Data System to identify children aged 5-11 years with acute-care clinical asthma encounters (265 cases among 7787 children with follow-up). Hazard ratios (HRs) were higher for children living closer to the NBH site with higher umbilical cord blood lead levels than in children living further away from the NBH site with lower lead levels (P <.001). HRs were higher for girls (HR = 4.17; 95% CI, 3.60-4.82) than for boys (HR = 1.72; 95% CI, 1.46-2.02). Our results suggest that prenatal lead exposure in combination with residential proximity to the NBH Superfund site is associated with childhood asthma acute-care clinical encounters. This article is part of a Special Collection on Environmental Epidemiology.

Key HPI axis receptors facilitate light adaptive behavior in larval zebrafish.

The vertebrate stress response (SR) is mediated by the hypothalamic-pituitary-adrenal (HPA) axis and contributes to generating context appropriate physiological and behavioral changes. Although the HPA axis plays vital roles both in stressful and basal conditions, research has focused on the response under stress. To understand broader roles of the HPA axis in a changing environment, we characterized an adaptive behavior of larval zebrafish during ambient illumination changes. Genetic abrogation of glucocorticoid receptor (nr3c1) decreased basal locomotor activity in light and darkness. Some key HPI axis receptors (mc2r [ACTH receptor], nr3c1), but not nr3c2 (mineralocorticoid receptor), were required to adapt to light more efficiently but became dispensable when longer illumination was provided. Such light adaptation was more efficient in dimmer light. Our findings show that the HPI axis contributes to the SR, facilitating the phasic response and maintaining an adapted basal state, and that certain adaptations occur without HPI axis activity.

Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate.

Climate change has well-documented, yet variable, influences on the annual movements of migratory birds. The effects of climate change on fall migration remains understudied compared with spring but appears to be less consistent among species, regions and years. Changes in the pattern and timing of waterfowl migration in particular may result in cascading effects on ecosystem function, and socio-economic and cultural outcomes. We investigated changes in the migration of 15 waterfowl species along a major flyway corridor of continental importance in northeastern North America using 43 years of community-science data. We built spatially- and temporally explicit hierarchical generative additive models for each species and demonstrated that climate, specifically the interaction between minimum temperature and precipitation, significantly influences migration phenology for most species. Certain species' migratory movements responded to specific temperature thresholds (climate migrants) and others reacted more to the interaction of temperature and precipitation (extreme event migrants). There are already significant changes in the fall migration phenology of common waterfowl species with high ecological and economic importance, which may simply increase in the context of a changing climate. If not addressed, climate change could induce mismatches in management, regulations and population surveys which would negatively impact the hunting industry. Our findings highlight the importance of considering species-specific spatiotemporal scales of effect on climate on migration and our methods can be widely adapted to quantify and forecast climate-driven changes in wildlife migration.

Les changements climatiques ont des influences bien documentées, mais variables, sur les mouvements annuels des oiseaux migrateurs. Les effets des changements climatiques sur les migrations automnales demeurent peu étudiés par rapport aux migrations printanières, mais il semble qu'ils soient moins constants d'une espèce, d'une région et d'une année à l'autre. Les changements dans le patron et le calendrier de la migration de la sauvagine en particulier peuvent avoir des effets en chaîne sur la fonction des écosystèmes et des impacts socio­économiques et culturels. Nous avons étudié les changements dans la migration de 15 espèces de sauvagine le long d'un corridor de migration d'importance continentale dans le nord­est de l'Amérique du Nord, en utilisant 43 ans de données scientifiques communautaires. Nous avons construit des modèles additifs généralisés hiérarchiques spatialement et temporellement explicites pour chaque espèce et avons démontré que le climat, en particulier l'interaction entre la température minimale et les précipitations, influence de manière significative la phénologie de la migration pour la plupart des espèces. Les mouvements migratoires de certaines espèces répondent à des seuils de température spécifiques (migrateurs climatiques) et d'autres réagissent davantage à l'interaction entre la température et les précipitations (migrateurs d'événements extrêmes). La phénologie des migrations automnales d'espèces de sauvagine commune qui ont une grande importance écologique et économique connaît déjà des changements importants, qui pourraient simplement s'accentuer dans le cadre des changements climatiques. S'ils ne sont pas pris en compte, les changements climatiques pourraient induire des décalages dans la gestion, les réglementations et les enquêtes de population, ce qui aurait un impact négatif sur l'industrie de la chasse. Nos résultats soulignent l'importance de prendre en compte les échelles spatio­temporelles spécifiques sur la migration et nos méthodes peuvent être largement adaptées pour quantifier et prévoir les changements induits par le climat dans la migration de la faune.

Massachusetts companion program bolsters COVID-19 vaccine rates among seniors.

In parts of the United States, COVID-19 vaccination rates remained low until late in Fall 2021 owing to both limited vaccine access and hesitancy. With colliding epidemics of RSV, flu, and COVID-19 in the winter, the retrospective evaluation of vaccine incentive policies is needed to inform future routine immunization campaigns. The Massachusetts companion program is one example of a policy that could boost vaccine uptake among older populations. Our regression discontinuity analysis suggests that the program was associated with an increase of up to 22 percentage points in the proportion of individuals aged 75 and older who have been fully vaccinated. Going forward, similar intervention strategies could be invaluable in scenarios where household contacts pose the greatest risk of transmission or where social ties can strongly influence individual decision-making.

Recruitment Patterns and Environmental Sensitivity of Glass Eels of Anguilla japonica in the Yangtze Estuary, China.

The decline of Japanese eel (Anguilla japonica) populations in the Yangtze River estuary represents a critical conservation concern. Eleven-years of daily catch data during recruitment periods (i.e., January-April, 2012-2022) indicate that annual catch averaged from 153 to 1108 eels, and show a bimodal pattern in glass eel arrivals. Utilizing seasonal-trend decomposition and generalized additive models, we demonstrated a strong correlation between catch abundance, optimal water temperatures, and lunar cycles. An auto-regressive integrated moving average (ARIMA) model predicts an increase in glass eel numbers for 2023-2024 but also points to a concerning trend of delayed recruitment timing since 2016, attributable to the 0.48 °C per decade rise in sea surface temperatures. This delay correlates with a significant decrease in the average body weight of glass eels, suggesting potential energy deficits that may hinder successful upstream migration. This study not only furthers our understanding of glass eel recruitment dynamics but also underscores the urgent need for targeted conservation measures. Additionally, it highlights the importance of sustained, detailed monitoring to mitigate the detrimental effects of climate change on these eels, vital for preserving the Yangtze River's ecological integrity.

Amplification of annual and diurnal cycles of alpine lightning.

The response of lightning to a changing climate is not fully understood. Historic trends of proxies known for fostering convective environments suggest an increase of lightning over large parts of Europe. Since lightning results from the interaction of processes on many scales, as many of these processes as possible must be considered for a comprehensive answer. Recent achievements of decade-long seamless lightning measurements and hourly reanalyses of atmospheric conditions including cloud micro-physics combined with flexible regression techniques have made a reliable reconstruction of cloud-to-ground lightning down to its seasonally varying diurnal cycle feasible. The European Eastern Alps and their surroundings are chosen as reconstruction region since this domain includes a large variety of land-cover, topographical and atmospheric circulation conditions. The most intense changes over the four decades from 1980 to 2019 occurred over the high Alps where lightning activity doubled in the 2010 s compared to the 1980 s. There, the lightning season reaches a higher maximum and starts one month earlier. Diurnally, the peak is up to 50% stronger with more lightning strikes in the afternoon and evening hours. Signals along the southern and northern alpine rim are similar but weaker whereas the flatlands surrounding the Alps have no significant trend.

Associations between Dengue Incidence, Ecological Factors, and Anthropogenic Factors in Singapore.

Singapore experiences endemic dengue. Vector control remains the primary means to reduce transmission due to the lack of available therapeutics. Resource limitations mean that vector-control tools need to be optimized, which can be achieved by studying risk factors related to disease transmission. We developed a statistical modelling framework which can account for a high-resolution and high-dimensional set of covariates to delineate spatio-temporal characteristics that are associated with dengue transmission from 2014 to 2020 in Singapore. We applied the proposed framework to two distinct datasets, stratified based on the primary type of housing within each spatial unit. Generalized additive models reveal non-linear exposure responses between a large range of ecological and anthropogenic factors as well as dengue incidence rates. At values below their mean, lesser mean total daily rainfall (Incidence rate ratio (IRR): 3.75, 95% CI: 1.00-14.05, Mean: 4.40 mm), decreased mean windspeed (IRR: 3.65, 95% CI: 1.87-7.10, Mean: 4.53 km/h), and lower building heights (IRR: 2.62, 95% CI: 1.44-4.77, Mean: 6.5 m) displayed positive associations, while higher than average annual NO2 concentrations (IRR: 0.35, 95% CI: 0.18-0.66, Mean: 13.8 ppb) were estimated to be negatively associated with dengue incidence rates. Our study provides an understanding of associations between ecological and anthropogenic characteristics with dengue transmission. These findings help us understand high-risk areas of dengue transmission, and allows for land-use planning and formulation of vector control policies.

Association between serum globulin and cognitive impairment in older American adults.

Background and aims: Cognitive impairment is on the rise around the world, with profound economic and social consequences. Serum globulin, a marker of liver function, may also play a role in cognitive function. Unfortunately, no consistent conclusion exists regarding the association between serum globulin and cognitive function. Methods: Data from the 2011 to 2014 National Health and Nutrition Examination Survey were used to assess the association between serum globulin and cognitive impairment. Cognitive function was assessed by three tests: Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Animal Fluency (AF), and Digit Symbol Substitution Test (DSST). Furthermore, the breakthrough point of cognitive impairment correlated with CERAD < 5, AF < 14, and DSST < 34. A weighted multiple logistics regression model was used to verify the association between serum globulin and cognitive impairment. Generalized additive models (GAMs) and a smooth curve fit (penalty spline method) were used to determine a non-linear relationship between serum globulin and cognitive impairment. Finally, subgroup analysis and interaction tests were conducted to further verify the association between serum globulin and cognitive impairment. Results: Data from 2,768 participants aged ≥60 (in accordance with the study design) were collected for the final analysis. Data suggested that serum globulin levels were associated with an elevated cognitive impairment based on the AF [full adjustment, OR = 1.05, 95% CI: 1.01-1.08] and DSST [full adjustment, OR = 1.06, 95% CI: 1.02-1.10] tests. Eventually, the GAM and smooth curve fit model was conducted to confirm that the association between serum globulin and cognitive impairment was non-linear. Moreover, the inflection point was 27 g/L serum globulin based on the CERAD test and 35 g/L serum globulin based on the AF test. Finally, the interaction term between serum globulin and cognitive impairment based on the AF test indicated no significant interactions among all variables (all p for interaction >0.05). Conclusion: The association between serum globulin levels and cognitive impairment is non-linear. A threshold effect exists between serum globulin and cognitive impairment. Large-scale prospective clinical trials are needed to validate our findings.