Additive Model Research Articles

Association between TNF-α, cortisol levels, and exposure to PM10 and PM2.5: a pilot study

PurposeThe most harmful atmospheric pollutant for human health is particulate matter (PM). We analyzed the correlation between short-term lag exposure to PM10 and PM2.5, salivary cortisol and TNF-α level, and methylation levels of the TNF-α promoter.MethodsA pilot study including 20 subjects. Eight salivary samples for each subject at various times of the day were collected for comparing cortisol levels and TNFα detection. TNFα promoter methylation levels on salivary DNA were analyzed. Regression analyses were performed using generalized linear mixed models between the different outcomes and 4, 3, 2 and 1 day’s lag values of PM10/PM2.5.Generalized additive mixed model (GAMM) was used to evaluate any potential deviation from linearity.ResultsArea under the curve with respect to the ground (AUCg) showed a statistically positive association with 4-, 3-, 2-, and 1-day lag of exposure to PM10. Area under the curve with respect to the increase (AUCi) showed a statistically negative association with 4-, 3- and 1-day lag of exposure to PM10. TNFα showed statistically significant association with both exposures, PM10 and PM2.5, at 4-, 3-, 2-, and 1-day lag.ConclusionsRegarding cortisol levels there is an increase of overall hormone levels but a less dynamism of the system to answer to external stressors. Increase of TNF-α may reflect increased levels of oxidative stress and inflammation due to pollution exposure.

Role of land-ocean interactions in stepwise Northern Hemisphere Glaciation

The investigation of triggers causing the onset and intensification of Northern Hemisphere Glaciation (NHG) during the late Pliocene is essential for understanding the global climate system, with important implications for projecting future climate changes. Despite their critical roles in the global climate system, influences of land-ocean interactions on high-latitude ice sheets remain largely unexplored. Here, we present a high-resolution Asian dust record from Ocean Drilling Program Site 1208 in the North Pacific, which lies along the main route of the westerlies. Our data indicate that atmosphere-land-ocean interactions affected aeolian dust emissions through modulating moisture and vegetation in dust source regions, highlighting a critical role of terrestrial systems in initiating the NHG as early as 3.6 Myr ago. Combined with additional multi-proxy and model results, we further show that westerly wind strength was enhanced, mainly at low-to-middle tropospheric levels, during major glacial events at about 3.3 and 2.7 Myr ago. We suggest that coupled responses of Earth’s surface dynamics and atmospheric circulation in the Plio-Pleistocene likely involved feedbacks related to changes in paleogeography, ocean circulation, and global climate.

Jurassic–Cretaceous boundary theropod tracks from the slope of the Fallen Feng, Chicheng, China: review and new observations

ABSTRACT The Tuchengzi Formation represents a Jurassic – Cretaceous boundary regional lithological unit that bridges the temporal gap between the Yanliao biota and the Jehol biota. Tetrapod tracks represent the most significant vertebrate fossil records in the formation, and can be employed to supplement the Late Jurassic fauna and palaeoecological data from northern China. Here, we present a re-analysis of the theropod tracks from the Luofenggou site in northern Hebei Province, utilising an additional 3D model and a locomotory analysis to provide further insights into the ecological context. A morphological analysis indicates that these tracks are more similar to Grallator than to the previously proposed Therangospodus. The latter is diagnosed by coalesced, elongate, oval digital pads lacking a separation in distinct phalangeal pads, whereas these are clearly defined in the presented Tuchengzi material. They can therefore be assigned to the widespread G. ssatoi ichnospecies from the Tuchengzi Formation, although they are longer and more variable in size than other G. ssatoi. A general consideration of speed-related locomotive parameters, trackway morphology, and superposition allows for the speculation of the existence of cooperative or predatory relationships between theropod trackmakers that were differentiated by size or stages of ontogeny, but had similar foot structures.

Drying the mystery: a novel electronic sensor to quantify soft-tissue desiccation and natural mummification for forensic taphonomy

This study investigates the desiccation process of soft-tissue in South Africa, analyzing its interaction with environmental parameters and its implications for estimating the post-mortem interval. Through the examination of four decomposing porcine bodies across two summer seasons and one winter season, the research quantifies desiccation patterns using custom-designed and constructed printed circuit boards to measure the moisture content of body tissue over time. Generalized additive models were used to determine the environmental forces driving desiccation. Tissue resistivity was tested against the environmental predictor variables to determine the amount of variation they account for, and predicted values of the region-specific tissue resistivity variables were measured for each decomposing body. Results reveal distinct desiccation trajectories between summer and winter, with summer conditions conducive to precocious natural mummification. Environmental factors, particularly temperature and solar radiation, emerge as significant drivers of desiccation. This study represents the first quantitative analysis of deep tissue desiccation internationally, but also the first quantitative assessment of desiccation and natural precocious mummification in the Western Cape, South Africa. The exploration of desiccation as a potential indicator for estimating PMI opens new avenues for research and the integration of innovative methodologies and technologies promises to revolutionize forensic taphonomy practices.

Associations between traffic-related air pollution and allergic rhinitis outpatient visits in three cities of Gansu Province.

Emerging evidence indicates an increasing prevalence of allergic rhinitis (AR), potentially linked to air quality. The aim of the present study was to assess the relationship between traffic-related air pollutants (TRAPs) and outpatient visits for AR. Daily outpatient data for AR, air pollutant concentrations, and meteorological data were collected from January 2018 to December 2020 in Dingxi, Longnan, and Tianshui. Utilizing a Quasi-Poisson distribution, a generalized additive model (GAM) was employed in conjunction with distributed lag non-linear models (DLNM) to explore the association and lag effects of TRAPs on AR outpatient visits across the three cities. Stratified analyses based on gender, age, and season were conducted. A total of 11 106 outpatient visits for AR were recorded in the three cities. For an increase of 10 μg m-3 in PM2.5, the effect estimates in Dingxi, Longnan, and Tianshui reached their maximum at lag04, lag06, and lag07, respectively, with relative risk (RR) values of 4.696 (95% CI: 1.890, 11.614), 2.842 (95% CI: 2.102, 4.922), and 1.102 (95% CI: 1.066, 1.140). For NO2, the highest associations were exhibited in Dingxi (RR = 1.262, 95% CI: 1.081, 1.473) at lag07, in Longnan (RR = 2.554, 95% CI: 2.100, 4.805) at lag06, and in Tianshui (RR = 1.158, 95% CI: 1.106, 1.213) at lag07. Meanwhile, the strongest effects observed for a 1 mg m-3 increase in CO were 2.786 (95% CI: 1.467, 5.291) for Dingxi at lag07, 1.502 (95% CI: 1.096, 2.059) for Longnan at lag05, and 1.385 (95% CI: 1.137, 1.686) for Tianshui at lag04. Adults aged 15-64 years appeared to be more susceptible to TRAPs, and the associations were stronger in the cold season. The results of the present study indicate that exposure to TRAPs was positively correlated with outpatient visits for AR.

The association between rs6859 in NECTIN2 gene and Alzheimer’s disease is partly mediated by pTau

IntroductionEmerging evidence suggests a connection between vulnerability to infections and Alzheimer’s disease (AD). The nectin cell adhesion molecule 2 (NECTIN2) gene coding for a membrane component of adherens junctions is involved in response to infections, and its single nucleotide polymorphism (SNP) rs6859 was significantly associated with AD risk in several human cohorts. It is unclear, however, how exactly rs6859 influences the development of AD pathology. The aggregation of hyperphosphorylated tau protein (pTau) is a key pathological feature of neurodegeneration in AD, which may be induced by infections, among other factors, and potentially influenced by genes involved in both AD and vulnerability to infections, such as NECTIN2.Materials and methodsWe conducted a causal mediation analysis (CMA) on a sample of 708 participants in the Alzheimer’s disease Neuroimaging Initiative (ADNI). The relationship between rs6859 and Alzheimer’s disease (AD), with AD (yes/no) as the outcome and pTau-181 levels in the cerebrospinal fluid (CSF) acting as a mediator in this association, was assessed. Adjusted estimates from the probit and linear regression models were used in the CMA model, where an additive model considered an increase in dosage of the rs6859 A allele (AD risk factor).ResultsThe increase in dose of allele A of the SNP rs6859 resulted in about 0.144 increase per standard deviation (SD) of pTau-181 (95% CI: 0.041, 0.248, p < 0.01). When included together in the probit model, the change in A allele dose and each standard deviation change in pTau-181 predicted 6.84% and 9.79% higher probabilities for AD, respectively. In the CMA, the proportion of the average mediated effect was 17.05% and was higher for the risk allele homozygotes (AA), at 19.40% (95% CI: 6.20%, 43.00%, p < 0.01). The sensitivity analysis confirmed the evidence of a robust mediation effect.ConclusionThis study reported a new potential causal relationship between pTau-181 and AD. We found that the association between rs6859 in the NECTIN2 gene and AD is partly mediated by pTau-181 levels in CSF. The rest of this association may be mediated by other factors. Our finding sheds light on the complex interplay between genetic susceptibility, protein aggregation, and neurodegeneration in AD. Further research, using other biomarkers, is needed to uncover the remaining mechanisms of the association between the NECTIN2 gene and AD.

Network resilience of plant-bee interactions in the Eastern Afromontane Biodiversity Hotspot

Interaction network resilience can be defined as the ability of interacting organisms to maintain their functions, processes or populations after experiencing a disturbance. Studies on mutualistic interactions between plants and pollinators along environmental gradients are essential to understand the provision of ecosystem services and the mechanisms challenging their network resilience. However, it remains unknown to what level ecological changes along climatic gradients constrain the network resilience of mutualistic organisms, especially along elevation gradients. We surveyed bee species and recorded their interactions with plants throughout the four major seasons (i.e. long and short rainy, and long and short dry) on 50 study sites positioned along an elevation gradient (525 m to 2,530 m asl) in the Eastern Afromontane Biodiversity Hotspots in Kenya, East Africa. We calculated bee and plant network resilience using the network resilience parameter (βeff) and assessed changes in bee and plant network resilience along the elevation gradient using generalised additive models (gams). We quantified the effects of climate, bee and plant diversity, bee functional traits, network structure, and landscape configuration on bee and plant network resilience using a set of multi-model inference frameworks followed by structural equation models (SEM). We found that bee and plant species exhibited higher levels of network resilience at higher elevations. While bee network resilience increased linearly across the elevation gradient, plant network resilience increased exponentially from ∼1500 m and higher. Bee and plant network resilience increased in areas with reduced mean annual temperature (MAT) and decreased in areas with lower mean annual precipitation (MAP). Our SEM model showed that increasing temperatures indirectly influenced plant network resilience via network modularity and community assemblage of bees. We also found that MAP had a direct positive effect on plant diversity and network resilience, while the fragmentation of habitats reduced richness of plant communities and enhanced network modularity. In conclusion, we revealed that mutualistic networks showed higher network resilience at higher elevations. We also unveiled that climate and habitat fragmentation directly or indirectly influences the network resilience of plants and bees via the modulation of community assemblages and interaction networks. These influences are lower at higher elevations such that these systems seem better able to buffer against extinction cascades. We thus suggest that, management efforts should be geared at consolidating natural habitats. In contrast, restoration efforts should aim at mitigating climate change effects and harnessing the ability of mutualists to reconnect broken links to improve the network resilience and functioning of East-African montane ecosystems.

Spruce bark beetle phenological modelling and drought risk within framework of TANABBO II model

Global warming plays a major role in the disruption of forest ecosystems by bark beetle outbreaks. Decreasing precipitation and more frequent droughts create the conditions for water stress in trees. The Eurasian spruce bark beetle (Ips typographus L.) is more dangerous for Norway spruce (Picea abies (L.) Karst.) forests in Central Europe under these conditions, because even healthy trees under water stress have a lower probability of survival during massive spruce bark beetle outbreaks. This critical issue highlights the importance of targeted research and intervention strategies in affected regions; hence, this study has a focus on the Horní Planá region in Central Europe, which is managed by the Military Forests and Farms of the Czech Republic (VLS).The study presents the results of the drought risk assessment of the operational part of TANABBO II and its validation. TANABBO is a high spatial resolution risk assessment tool for spruce bark beetle infestation in Central European forests, incorporating 30 m spatial resolution and extensive meteorological data, which outperforms existing models in its ability to accurately predict bark beetle activity based on the PHENIPS phenological model. The operational part integrates environmental factors such as global radiation, temperature, precipitation and, in particular, water stress, which is assessed by using a transpiration deficit. The drought risk assessment included the calculation of Drought Indices (DI) and Cumulative Transpiration Deficit Indices (CTDI) on a daily basis, and the estimation of spruce bark beetle swarming time using the PHENIPS model. The risk assessment was validated by comparing it with the number of bark beetles caught in traps and the value of the drought index. Generalised additive models (GAM) were used for validation, with the most effective models incorporating the CTDI. The model with the highest predictive accuracy achieved an adjusted R-squared value of 0.478. The study highlights the critical role of high-resolution environmental data in understanding and managing bark beetle outbreaks, particularly in the context of climate change.

Temporal patterns in dolphin foraging activity in the Mediterranean Sea: insights from vocalisations recorded during the ACCOBAMS Survey Initiative

Marine organisms continually adapt their physiology and behaviour to temporal variations in their environment, resulting in diurnal rhythmic behaviour, particularly when foraging. In delphinids, these rhythms can be studied by recording echolocation clicks, which can provide indicators of foraging activity. The foraging rhythms of delphinids and their relationship to temporal parameters are poorly documented and most studies so far have used moored passive acoustic systems. The present study provides, for the first time, information on the activity rhythms of delphinids investigated in relation with temporal variables at a basin scale from a moving platform, in the western and central Mediterranean Sea. We used passive acoustic recordings collected by hydrophones towed along transect lines during the ACCOBAMS Survey Initiative in the summer 2018. We extracted variables that may influence daily and monthly rhythms, including time of day, lunar cycle, lunar illumination and sea state and fitted generalised additive models. The nycthemeral and lunar cycles were the two main factors influencing dolphin activity rhythms. Echolocation activity was predominant at night, with a maximum of 0.026 acoustic events per minute at 21:00/22:00 compared to as few as 0.0007 events per minute at 11:00. These events were also more frequent during the third quarter of the moon; 0.033 acoustic events on day 22 of the lunar cycle as opposed to 0.0008 on day 8 of the lunar cycle, corresponding to the first quarter of the moon. Variations in the echolocation activity of delphinids in the Mediterranean Sea could reflect variation in their foraging effort and be related to prey density, composition, accessibility and catchability within dolphin foraging depth range. These results should also improve interpretation of passive acoustic monitoring data.

Helmert variance component estimation for mixed additive and multiplicative random error model

With the development of surveying and mapping science, the object of measurement adjustment has been extended from a single observation of the same kind in the past to different precisions of the same kind, or different kinds of observations. There are additive and multiplicative errors related to electronic instrument measurements, which affect the quality of the adjustment results. A single additive error model is difficult to meet the accuracy requirements, so a mixed additive and multiplicative random error model (MAMREM) is needed. Aiming at the problem of inaccurate MAMREM stochastic model, this paper proposes Helmert Variance Component Estimation (VCE) to determine the weight matrix in different types of observations under MAMREM. In this paper, the formula and iterative algorithm of Helmert variance component estimation applied to MAMREM are derived, and the weights of different kinds of observations in the adjustment process are estimated. In order to verify the effectiveness of the method, the digital elevation model experiment and the side network experiment are used to verify the method. The results prove the effectiveness of the method.

COVID-19 pandemic-related drugs and microplastics from mask fibers jointly affect soil functions and processes.

The COVID-19 pandemic has led to an unprecedented increase in pharmaceutical drug consumption and plastic waste disposal from personal protective equipment. Most drugs consumed during the COVID-19 pandemic were used to treat other human and animal diseases. Hence, their nearly ubiquitous presence in the soil and the sharp increase in the last 3years led us to investigate their potential impact on the environment. Similarly, the compulsory use of face masks has led to an enormous amount of plastic waste. Our study aims to investigate the combined effects of COVID-19 drugs and microplastics from FFP2 face masks on important soil processes using soil microcosm experiments. We used three null models (additive, multiplicative, and dominative models) to indicate potential interactions among different pharmaceutical drugs and mask MP. We found that the multiple-factor treatments tend to affect soil respiration and FDA hydrolysis more strongly than the individual treatments. We also found that mask microplastics when combined with pharmaceuticals caused greater negative effects on soil. Additionally, null model predictions show that combinations of high concentrations of pharmaceuticals and mask MP have antagonistic interactions on soil enzyme activities, while the joint effects of low concentrations of pharmaceuticals (with or without MP) on soil enzyme activities are mostly explained by null model predictions. Our study underscores the need for more attention on the environmental side effects of pharmaceutical contamination and their potential interactions with other anthropogenic global change factors.

Efficient adsorptive separation of nitrotoluene isomers via a stable and low-cost metal–organic framework with descending affinity order of meta-/ortho-/para-isomers

Obtaining purified nitrotoluenes from their mixture is significant but challenging due to their similar molecular sizes and physical properties. To overcome this challenge, here we report a stable, inexpensive and green-synthesized aluminium-based metal–organic framework (MIL-160) to separate the nitrotoluene mixtures. The appropriate pore size and pore environment permitted MIL-160 to exhibit a rare meta-nitrotoluene (m-NT) preference and always maintain the meta- > ortho- > para-nitrotoluene (m- > o- > p-NT) adsorption order in dynamic breakthrough and pulse modes as well as batch-mode adsorption. Theoretical calculations revealed that the abundant H/O atoms in the confined nanospace provided strong binding sites for selectively capturing nitrotoluene isomers. Specifically, due to difference in shape matching, the strongest host–guest interactions occurred in m-NT@MIL-160, followed by o-NT@MIL-160, and last p-NT@MIL-160. Notably, breakthrough experiments confirmed that a variety of binary and ternary mixtures can be separated via continuous adsorption. The dynamic adsorption stage of ternary mixture directly produced 0.33 mmol g−1 of pure p-NT, and the eluent in the desorption stage contained a high content of m-NT. Additional dynamic adsorption model fitting results indicated that the outstanding dynamic separation performance was also achieved from kinetic factors. These results lay the foundation for the effective separation of nitrotoluene mixtures.

Gene-Environment Interactions Significantly Alter the Obesity Risk of SH2B1 rs7498665 Carriers.

Src homology 2 B adaptor protein 1 (SH2B1) gene and variants have been found to be associated with common obesity. We aimed to investigate the association between the common missense variant SH2B1 rs7498665 and common obesity risk as well as interactions with lifestyle variables in an Israeli population. An adult cohort (n=3,070; ≥18 years) with the SH2B1 rs7498665 variant and lifestyle, behavior (online questionnaire), and blood glucose data was analyzed. Associations between this variant, obesity risk (body mass index [BMI] ≥25 and ≥30 kg/m2), and interactions with behavioral and lifestyle factors (stress levels, eating habits score (EHS), physical activity (PA) and wine consumption) were investigated. Association and gene-environment interactions were analyzed using binary logistic regressions with interaction (SPSS version 29.0). SH2B1 rs7498665 carriers were significantly (P<0.05) more likely to be overweight (BMI ≥25 kg/m2) or obese (BMI ≥30 kg/m2) in recessive (odds ratio [OR], 1.9 and 1.36, respectively), additive (OR, 1.24 and 1.14, respectively), and codominant (OR, 2 and 1.41, respectively) genetic models. SH2B1 rs7498665 interacted with lifestyle and behavioral factors as well as glucose levels. PA and moderate wine consumption (1 to 3 drinks/week) reduced obesity risk (OR, 0.35 and 0.71, respectively). Conversely, carriers of two risk alleles who reported high stress levels, had ≥median EHS, and who had a fasting glucose level ≥90 mg/dL had a significantly increased obesity risk (OR, 3.63 and 5.82, respectively). Carrying SH2B1 rs7498665 significantly elevates the risk of obesity. Actionable lifestyle and behavioral factors significantly modulate the rs7498665 genetic predisposition to obesity; PA and moderate wine consumption attenuate the risk, while high stress, EHS, and fasting glucose level increase the obesity risk.

Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies.

Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. While past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. Here, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semi-parametric generalized additive model (GAM) on fitting tumor volume data from over 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line-derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models, the latter is often considered the standard growth model. On the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that eGaIT, a GAM derived efficacy metric, is equivalent to eGR, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on Paclitaxel, anti-PD-1 antibody, Cetuximab, Irinotecan, and Sorafenib, we show that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared eGR-based association analysis and exponential modeling approach in biomarker discovery and found they complement each other. Modeling methods herein are implemented in an open-source R package freely available at https://github.com/hjzhou988/TuGroMix.

Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.

Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.

BARMPy: Bayesian additive regression models Python package

BARMPy: Bayesian additive regression models Python package

Numerical investigation of scramjet inlet models for side spillage reduction

This study introduces and evaluates five models of Mach 7 scramjet inlets designed to reduce side spillage. The baseline model is characterized as a 2D-wedge type inlet without sidewalls. To mitigate interference from expansion waves, an alternative model featuring a converging angle identical to the freestream Mach angle is proposed. Another inlet model presents each ramp's converging angle matched to the local Mach angle of its respective ramp. Subsequently, an inlet model with sidewalls attached to the baseline model is introduced, and an additional inlet model is presented with reduced-height sidewalls. Three-dimensional Reynolds-Averaged Navier-Stokes simulations are conducted for each inlet model, and the performance of the inlets is compared. The mass flow averaged properties at the isolator exit for each inlet model are computed. To evaluate the performance of engines equipped with each inlet model, thrust and specific impulse are determined using thermodynamic cycle analysis. Ultimately, net thrust is calculated by subtracting drag from thrust, and the results are compared. The inlet with converging ramps, which has the largest captured area, indicates the lowest net thrust, amounting to 38% of that of the baseline model. In contrast, the inlet with reduced height sidewalls represents the highest net thrust, which is 3.5 times that of the inlet with converging ramps. These results demonstrate that for scramjet inlet design, using sidewalls to reduce drag is a more effective method for reducing side spillage compared to employing converging ramps with a large captured area.

SPATIAL REGRESSION APPROACH TO MODELLING POVERTY IN JAVA ISLAND 2022

Geographically Weighted Regression (GWR) model is a powerful tool for analyzing spatial patterns in data. However, the standard form of a spatial model that uses a single bandwidth calibration may be unrealistic because the response-predictor relationship may be either linear or nonlinear. To address this issue, the Multiscale GWR (MSGWR) model offers improved model performance by employing Generalized Additive Model (GAM) with varying bandwidth or smoothing function for each covariate in the model. This research aims to analyze the Percentage of Poor Population (PPP) on Java Island in 2022 using the geospatial models and related socioeconomic and demographic attributes, such as Open Unemployment Rate, Human Development Index, Labor Force Participation Rate, and GRDP Per capita to identify the best model in explaining the spatial pattern and to find out the determinant of PPP on Java Island in 2022. This study uses secondary data from Statistics Indonesia. The findings reveal that the MSGWR model provides the highest R2 and smallest AICc value compared to single bandwidth models, specifically the GWR and MXGWR models. Furthermore, the MSGWR model indicates that HDI has a significant negative effect on PPP, whereas LFPR has a significant positive effect on PPP across all districts in Java Island in 2022.

Electric vehicle and supply equipment adoption dynamics in the United States

In the present era of climate change concerns, plug-in electric vehicles (PEV) and their associated infrastructure are strategic investments by many governments. Investment discussions often include the dynamic supply-demand relationship between PEV adoption and charging station deployment, but rarely is it given an empirical treatment. We make use of publicly available charging station installation and PEV vehicle registration data by U.S. County between the years 2012 and 2021 to examine the phasing and causal relationship between adoption trends in these two markets. Non-linear Granger causality tests suggest a feedback relationship between PEV vehicle adoption and charging station installations. It can be further explained that when individuals adopt PEV, the demand for the charging infrastructure increases to meet the demand for PEV. Conversely, when more charging station installations become available, it encourages adoption of this technology making it convenient for individuals to have access to these charging facilities. It is observed in aggregate that PEVs are penetrating across the U.S., but the penetration rate differs across states and counties. We examine this interaction using a generalized propensity score (GPS) identification strategy for the effect of charging station installations on PEV registrations, coupled with a generalized additive model (GAM) to construct a causal dose-response curve. We observe statistically significant effects on per capita charging station rates for income and total population. We suggest a policy package that includes charging infrastructure investments but also provides wider adoption of subsidies for low-income households across all states.

Glycated Hemoglobin A1c Time in Range and Dementia in Older Adults With Diabetes

Individuals with diabetes commonly experience Alzheimer disease and related dementias (ADRD). Factors such as hypoglycemia, hyperglycemia, and glycemic variability have been associated with increased risk of ADRD. Traditional glycemic measures, such as mean glycated hemoglobin A1c (HbA1c), may not identify the dynamic and complex pathophysiologic factors in the association between diabetes and ADRD. The HbA1c time in range (TIR) is a previously developed measure of glycemic control that expresses HbA1c stability over time within specific ranges. This measure may inform the current understanding of the association between glucose levels over time and ADRD incidence. To examine the association between HbA1c TIR and incidence of ADRD in older veterans with diabetes. The study sample for this cohort study was obtained from administrative and health care utilization data from the Veterans Health Administration and Medicare from January 1, 2004, to December 31, 2018. Veterans 65 years or older with diabetes were assessed. Participants were required to have at least 4 HbA1c tests during the 3-year baseline period, which could start between January 1, 2005, and December 31, 2014. Data analysis was conducted between July and December 2023. Hemoglobin A1c TIR was calculated as the percentage of days during baseline in which HbA1c was in individualized target ranges based on clinical characteristics and life expectancy, with higher HbA1c TIR viewed as more favorable. The association between HbA1c TIR and ADRD incidence was estimated. Additional models considered ADRD incidence in participants who were above or below HbA1c target ranges most of the time. The study included 374 021 veterans with diabetes (mean [SD] age, 73.2 [5.8] years; 369 059 [99%] male). During follow-up of up to 10 years, 41 424 (11%) developed ADRD. Adjusted Cox proportional hazards regression models showed that lower HbA1c TIR was associated with increased risk of incident ADRD (HbA1c TIR of 0 to <20% compared with ≥80%: hazard ratio, 1.19; 95% CI, 1.16-1.23). Furthermore, the direction of out-of-range HbA1c levels was associated with incident ADRD. Having greater time below range (≥60%, compared with ≥60% TIR) was associated with significantly increased risk (hazard ratio, 1.23; 95% CI, 1.19-1.27). Findings remained significant after excluding individuals with baseline use of medications associated with hypoglycemia risk (ie, insulin and sulfonylureas) or with hypoglycemia events. In this study of older adults with diabetes, increased HbA1c stability within patient-specific target ranges was associated with a lower risk of ADRD. Lower HbA1c TIR may identify patients at increased risk of ADRD.