Multi-Year Spatial Variability of the Impact of Sociodemographic, Behavioural, and Health Factors on Depression of Older Adults

The mental health of older adults has become a critical issue with the rising suicide rate in older adults in South Korea. Various factors related to depression can make heterogeneous impacts in different regions. Yet, such spatial perspectives have been rarely integrated with the mental health studies in South Korea. This study aims to explore 1) how differently each factor of sociodemographic characteristics, social interactions, and health-related behaviors is associated with depression of older adults throughout different regions in South Korea, and 2) how those relationships change across five survey years (2008–2016) for a long term. Spatially local regression and small-multiple map visualization were applied to analyze a longitudinal panel survey dataset named KLoSA, collected in South Korea. It is found that age, marital status, in-person social contact frequency, and perceived physical health are significantly correlated with depression in more regions than other variables. The local regression coefficients and significance vary by region and year.

Modeling and analyzing predictive monthly survival in females diagnosed with gynecological cancers

Cancer ranks as a leading cause of death worldwide; an estimated 1.7 million new diagnoses were reported in 2021. Ovarian cancer, the most lethal of gynecological malignancies, has no effective screening with over 70% of patients being diagnosed in an advanced stage. The aim of this study was to determine the most statistically significant contributing factors through a multivariate regression into the severity of female gynecological cancers. Data from the surveillance, epidemiology, and end results program (SEER) cancer database were utilized in this study. Several attempted multivariate linear regressions were implemented with further reduced models; however, a linear model could not be properly fit to the data. Because of unmet assumptions, a nonparametric moving, local regression, locally estimated scatterplot smoothing (LOESS), was performed. After smoothing factors were included to reduced-models, residual information was minimized although few conclusions can be drawn from the resulting statistics. These issues were prevalent mainly because of the massive variability in the data and inherent lack of linearity. This can be a significant issue with clinical data that does not dive deeper into cancer-dependent factors including genetic expression and cell surface receptor overexpression. General patient demographic data and diagnostic information alone does not provide enough detail to make a definite conclusion or prediction on patient survivability. Increased attention to the acquisition of tumor tissue for genomic and proteomic analysis in addition to next-generation sequencing methods can lead to significant improvements in prognostic predictions.

Spatiotemporal Distribution Patterns and Local Driving Factors of Regional Development in Java

Although uneven regional development has long been an issue in Java, most parts of the territory experienced an increased level of development over the last two decades. Due to the variance in local background and spatial heterogeneity, the driving factors of the development level should, theoretically, vary over space. Therefore, in this study, we aim to investigate the local factors that influence the development level of Java’s regions. We used the spatiotemporal pattern analysis, ordinary least squares (OLS) regression, and geographically weighted regression (GWR), utilizing the regional development index as the predicted variable, and the social level, economy, infrastructure, land use, and environmental barriers as predictors. As per our results, it was found that the level of development in Java has improved over the past two decades. Metropolitan areas continued to lead this improvement. All the predictors that we examined significantly affected regional development. However, the spatial pattern of the local regression coefficients of Human Development Index (HDI), landslide, paddy conversion, and crime shifted due to changes in the spatial concentration of development activities.

Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea

The Seoul metropolitan area is one of the most populated metropolitan areas in the world; hence, Seoul’s COVID-19 cases are highly concentrated. This study identified local demographic and socio-economic characteristics that affected SARS-CoV-2 transmission to provide locally targeted intervention policies. For the effective control of outbreaks, locally targeted intervention policies are required since the SARS-CoV-2 transmission process is heterogeneous over space. To identify the local COVID-19 characteristics, this study applied the geographically weighted lasso (GWL). GWL provides local regression coefficients, which were used to account for the spatial heterogeneity of SARS-CoV-2 outbreaks. In particular, the GWL pinpoints statistically significant regions with specific local characteristics. The applied explanatory variables involving demographic and socio-economic characteristics that were associated with higher SARS-CoV-2 transmission in the Seoul metropolitan area were as follows: young adults (19~34 years), older population, Christian population, foreign-born population, low-income households, and subway commuters. The COVID-19 case data were classified into three periods: the first period (from January 2020 to July 2021), the second period (from August to November 2020), and the third period (from December 2020 to February 2021), and the GWL was fitted for the entire period (from January 2020 to February 2021). The result showed that young adults, the Christian population, and subway commuters were the most significant local characteristics that influenced SARS-CoV-2 transmissions in the Seoul metropolitan area.

Abstract 12398: Cardiopulmonary Resuscitation Duration Determines Phenotype of Post-Arrest Brain Injury

Introduction: Patients resuscitated from out-of-hospital cardiac arrest (OHCA) have variable severity of brain injury. Signatures of severe injury on brain imaging and EEG including diffuse cerebral edema and burst suppression with identical bursts (BSIB). Current therapies for these patterns of injury are inadequate and patient outcomes are poor. Hypothesis: We hypothesize distinct phenotypes of brain injury are associated with increasing CPR duration. Methods: We identified from our prospective registry OHCA patients treated between January 2010 to July 2019. We abstracted CPR duration, best neurological examination < 6 hours from OHCA, initial brain CT grey-to-white ratio (GWR), and initial EEG pattern. We defined cerebral edema as GWR <1.20. We defined BSIB according to American Clinical Neurophysiology Society guidelines. We considered four phenotypes on presentation: awake; comatose with neither BSIB nor cerebral edema; BSIB; and cerebral edema. BSIB and cerebral edema were considered as non-mutually exclusive outcomes. We compared duration of CPR across groups using Kruskal-Wallis tests with Bonferroni correction for multiple hypothesis testing. We report the probability of presenting phenotype at the median CPR duration for each group using local regression. Results: We included 2,721 patients, of whom 582 (21%) were awake, 1,428 (52%) had coma without BSIB or edema, 372 (14%) had BSIB and 356 (13%) had cerebral edema. Only 47 (2%) had both BSIB and edema. Median CPR duration was 16 [IQR 8-28] minutes overall. Median CPR duration increased in a stepwise manner across groups: awake 6 [3-12] minutes; comatose without BSIB or edema 16 [9-27] minutes; BSIB 21 [14-30] minutes; cerebral edema 32 [22-46] minutes (all P <0.001). The probability of observing each phenotype at the median CPR duration for each was: awake (0.42); comatose without BSIB or edema (0.72); BSIB (0.34); cerebral edema (0.29). Conclusions: The brain injury phenotype is related to CPR duration, which is a surrogate for severity of ischemic injury. The sequence of most likely brain injury phenotype with progressively longer CPR duration is awake, coma without BSIB or edema, BSIB, and finally cerebral edema.

A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression

Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime.

Comparative Analysis of COVID-19 Prevalence in Coastal Versus Inland Populations of North Carolina

Statement of Significance: This study aimed to assess geographic trends in COVID-19 cases and deaths across North Carolina (NC). Our study found that population-adjusted COVID-19 cases and deaths were lower in the coastal region of NC during the study period, independent of demographic composition and population-density within the region. This represents an interesting finding regarding COVID-19 transmission that deserves further investigation. One possible explanation for this finding is differing environmental conditions between the inland and coastal region. Background: Existing literature has explored the geographic and spatial variations in COVID-19 prevalence. Some studies suggest that the transmission and total prevalence of COVID-19 is diminished in areas with low levels of air pollution, high humidity, and more sunlight. The coastal regions of NC are more likely to have these environmental characteristics than the inland regions. Given these trends, we analyzed and compared population-adjusted COVID-19 case and death counts in the coastal and inland regions of NC. Methods: Time series data displaying the prevalence of population adjusted COVID-19 case and death counts from 15 March 2020 to 15 August 2020 were plotted for a variety of North Carolina regional and population density classifications. A local regression analysis was computed to further assess the observed relationships. Basic demographic characteristics were also compared for the coastal versus inland region. Results: There were fewer population-adjusted COVID-19 cases and deaths in the coastal region (889 cases/100,000; 12.5 deaths/100,000) than in the inland region (1426 cases/100,000; 23.5 deaths/100,000) at the endpoint of this study. This trend is observed even when controlling for population density, and in the absence of significant demographic differences between the two regions. Conclusions: The prevalence of population-adjusted COVID-19 cases and deaths was lower in coastal versus inland NC during this study period. Given that the NC coastal region is associated with lower pollution, higher humidity, and more exposure to sunlight, our findings suggest that more research should be done to explore the correlation between environmental variables and the spread of COVID-19.

BENCHMARKING VIRTUAL PERMEABILITY PREDICTIONS OF REAL FIBROUS MICROSTRUCTURE

ABSTRACT For fast and complete impregnation in Liquid Composite Molding, knowledge about the permeability of the fibrous reinforcement is required. While development of experimental methods continues, a parallel benchmark effort to numerically characterize permeability is being pursued. The approach was to send out the images of a real fibrous microstructure to a number of participants, in order for them to apply their methods for virtual permeability prediction. Via resin transfer molding a plate was manufactured, using the glass woven fabric Hexcel 01102 (295 g/m²) at a fiber volume content of 54% and a thermoset resin. From this plate, a specimen was scanned using a 3D x-ray microscope at a scan size of 1000 x 1000 x 1000 μm³ and a resolution of 0.521 μm³ per voxel. The sample extracted for the simulations with a size of 523 x 65 x 507 μm³ contains about 400 fibers of a single tow. It revealed a variation of filament diameters between 7.5-9.3 μm and a fiber volume content in average of 56.46% with a variation of 54 - 59% in the individual 2D-slices transverse to the fiber direction. The image segmentation was performed by 2D-slices, to which a Hough transform was applied to detect fiber centers and cross-sections. Then fiber paths were tracked through-out the slices by the closest neighbor algorithm. Finally, fiber paths were smoothened by means of the local regression using weighted linear least squares and a 1st degree polynomial model. The participants received a stack of 973 segmented (binary) 2D-images and a corresponding segmented 3D volume raw-file. They were asked to calculate the full permeability tensor components and fill out a detailed questionnaire including questions e.g. on applied flow models and conditions, numerical discretization and approximation methods, fluid properties etc. The received results scatter considerably over two orders of magnitude, although the participants were provided an already segmented image structure, thus eliminating from the beginning a significant source of variation that could have come from image processing. Model size, meshing and many other sources of variation were identified, allowing further specification of the guidelines for the next step.

Plasma p-tau181 Level Predicts Neurodegeneration and Progression to Alzheimer's Dementia: A Longitudinal Study

Background: Plasma-based biomarkers would be potential biomarkers for early diagnosis of Alzheimer's disease (AD) because they are more available and cost-effective than cerebrospinal fluid (CSF) or neuroimaging. Therefore, we aimed to evaluate whether phosphorylated tau181 (p-tau181) in plasma could be an accurate AD predictor.Methods: Participants from the ADNI database included 185 cognitively unimpaired subjects with negative Aβ (CU–), 66 subjects with pre-clinical AD (CU with positive Aβ), 164 subjects with mild cognitive impairment with negative Aβ (MCI–), 254 subjects with prodromal AD (MCI with positive Aβ), and 98 subjects with dementia. Multiple linear regression models, linear mixed-effects models, and local regression were used to explore cross-sectional and longitudinal associations of plasma p-tau181 with cognition, neuroimaging, or CSF biomarkers adjusted for age, sex, education, and APOE genotype. Besides, Kaplan–Meier and adjusted Cox-regression model were performed to predict the risk of progression to dementia. Receiver operating characteristic analyses were performed to evaluate the predictive value of p-tau181.Results: Plasma p-tau181 level was highest in AD dementia, followed by prodromal AD and pre-clinical AD. In pre-clinical AD, plasma p-tau181 was negatively associated with hippocampal volume (β = −0.031, p-value = 0.017). In prodromal AD, plasma p-tau181 was associated with decreased global cognition, executive function, memory, language, and visuospatial functioning (β range −0.119 to −0.273, p-value &lt; 0.05) and correlated with hippocampal volume (β = −0.028, p-value &lt; 0.005) and white matter hyperintensity volume (WMH) volume (β = 0.02, p-value = 0.01). In AD dementia, increased plasma p-tau181 was associated with worse memory. In the whole group, baseline plasma p-tau181 was significantly associated with longitudinal increases in multiple neuropsychological test z-scores and correlated with AD-related CSF biomarkers and hippocampal volume (p-value &lt; 0.05). Meanwhile, CU or MCI with high plasma p-tau181 carried a higher risk of progression to dementia. The area under the curve (AUC) of the adjusted model (age, sex, education, APOE genotype, and plasma p-tau181) was 0.78; that of additionally included CSF biomarkers was 0.84.Conclusions: Plasma p-tau181 level is related to multiple AD-associated cognitive domains and AD-related CSF biomarkers at the clinical stages of AD. Moreover, plasma p-tau181 level is related to the change rates of cognitive decline and hippocampal atrophy. Thus, this study confirms the utility of plasma p-tau181 as a non-invasive biomarker for early detection and prediction of AD.