Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mediating Role of Depression in the Association Between Hearing Impairment and Functional Disability Among Middle-Aged and Older Adults in China

Abstract Background and Objectives This study aims to explore the association between hearing impairment (HI) and functional disability and to examine whether depression mediates this association. Research Design and Methods In the study, 11 335 individuals aged 45 years and older were analyzed from the China Health and Retirement Longitudinal Study. The study used logistic regression and Karlson/Holm/Breen’s method to examine the correlation between HI, depression, and functional disability. Functional disability was assessed using activities of daily living and instrumental activities of daily living. Results HI was significantly associated with activities of daily living disability (odds ratio [OR] = 1.34, 95% confidence interval [CI] = 1.21–1.49) and instrumental activities of daily living disability (OR = 1.57, 95% CI = 1.46–1.68). The mediated effect of depression accounted for 22.80% and 15.17% of the total effect of HI on activities of daily living and instrumental activities of daily living disability, respectively. Additionally, depression partially mediated the effects of HI on specific activities of daily living and instrumental activities of daily living tasks, including bathing (33.23%), toileting (27.50%), doing chores (37.36%), preparing meals (28.04%), shopping (25.81%), taking care of finances (11.82%), and taking medicine (12.71%). Discussion and Implications HI increased the likelihood of functional disability partially through depression in middle-aged and older adults, suggesting that emphasizing the mental wellness of these people with HI is necessary to prevent impairments in physical function.
Development and validation of a subjective end-of-life health literacy scale

Personal health literacy is the ability of an individual to find, understand, and use information and services to inform health-related decisions and actions for oneself and others. The end of life is commonly characterized by the occurrence of one or several diseases, the use of many different types of healthcare services, and a need to make complex medical decisions that may involve challenging tradeoffs, such as choices between quality and length of life. Although end-of-life care issues concern most people at some point in life, individuals’ competencies to deal with those questions have rarely been explored. This study aims to introduce, develop, and validate an instrument to measure individuals’ self-assessed competencies to deal with end-of-life medical situations, the Subjective End-Of-Life Health Literacy Scale (S-EOL-HLS), in a sample of older adults aged 50+ living in Switzerland who participated in wave 8 (2019/2020) of the Survey of Health, Ageing, and Retirement in Europe. The S-EOL-HLS uses a series of questions on self-rated difficulties in understanding end-of-life medical jargon, defining in advance which end-of-life medical treatments to receive or refuse, and communicating related choices. Aside from conducting exploratory and confirmatory factor analysis to evaluate the construct validity, we compared measurements from the S-EOL-HLS to respondents’ general health literacy measured with the European Health Literacy Survey questionnaire. We obtained a three-factor model with acceptable fit properties (CFI = 0.993, TLI = 0.992, RMSEA = 0.083, SRMR = 0.061) and high reliability (α = 0.93). The partial associations between the health literacy scores from the two scales and respondents’ sociodemographic characteristics were similar; however, individuals with higher end-of-life health literacy scores appeared to have more positive attitudes towards end-of-life care planning outcomes. The S-EOL-HLS demonstrates reliable and consistent results, making the instrument suitable for older adults in population surveys.
Irregular word reading as a marker of cognitive and semantic decline in Alzheimer’s disease rather than an estimate of premorbid intellectual abilities.

Abstract Background Irregular word reading has been used to estimate premorbid intelligence in Alzheimer’s disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The general aim of this study is to determine whether irregular word reading is a valid estimate of premorbid intelligence, or a marker of cognitive and semantic decline in AD. Method 681 healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer’s Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL). Results EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances. Conclusions Irregular word reading performances decline throughout the AD continuum, and therefore, premorbid intelligence estimates based on the AmNART should not be considered accurate in MCI or AD. Results are consistent with the theory of irregular word reading impairments as an indicator of disease severity and semantic decline.
Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.
Extended theory of planned behavior to explain the influence mechanism of low-speed driving behavior

Low-speed driving is an underestimated dangerous behavior that may cause safety issues, such as speed dispersion and traffic flow bottlenecks. To investigate the influence mechanism of low-speed driving behavior, this study constructed the low-speed specific model (LSSM) by extending theory of planned behavior (TPB). The LSSM incorporated two factors, namely, risk perception and behavior habit, into the standard TPB components (attitude, subjective norm, perceived behavioral control, and behavior intention). Web-based questionnaires were used to collect data from a valid sample of 374, of which males accounted for 50%. The participants were aged from 18 to 65 years (M = 35.40, SD = 0.88). The structural equation model was applied to calculate and validate the interrelationships among the components of LSSM. Results showed that the LSSM could explain the variance in low-speed driving behavior and behavior intention by 46% and 76%, respectively. Meanwhile, attitude (β = 0.52, p < 0.001) and behavior habit (β = 0.48, p < 0.001) had the strongest positive influence and prediction power over low-speed driving behavior, respectively, whereas subjective norm (β = 0.05, p > 0.01) and perceived behavioral control (β = -0.12, p > 0.01) showed few significant in influencing the intention. LSSM also showed that people who were sensitive to driving risk perception would avoid low-speed driving behaviors and attitudes. Our findings may provide theoretical support for interventions on low-speed driving behavior.
The lncRNA Snhg11, a new candidate contributing to neurogenesis, plasticity and memory deficits in Down syndrome

Down syndrome (DS) stands as the prevalent genetic cause of intellectual disability, yet comprehensive understanding of its cellular and molecular underpinnings remains limited. In this study, we explore the cellular landscape of the hippocampus in a DS mouse model through single-nuclei transcriptional profiling. Our findings demonstrate that trisomy manifests as a highly specific modification of the transcriptome within distinct cell types. Remarkably, we observed a significant shift in the transcriptomic profile of granule cells in the dentate gyrus (DG) associated with trisomy. We identified the downregulation of a specific small nucleolar RNA host gene, Snhg11, as the primary driver behind this observed shift in the trisomic DG. Notably, reduced levels of Snhg11 in this region were also observed in a distinct DS mouse model, the Dp(16)1Yey, as well as in human postmortem tissue, indicating its relevance in Down syndrome. To elucidate the function of this long non-coding RNA (lncRNA), we knocked down Snhg11 in the DG of wild-type mice. Intriguingly, this intervention alone was sufficient to impair synaptic plasticity and adult neurogenesis, resembling the cognitive phenotypes associated with trisomy in the hippocampus. Our study uncovers the functional role of Snhg11 in the DG and underscores the significance of this lncRNA in intellectual disability. Furthermore, our findings highlight the importance of the DG in the memory deficits observed in Down syndrome.
Bioorthogonal Chemical Labeling Probes Targeting Sialic Acid Isomers for N-Glycan MALDI Imaging Mass Spectrometry of Tissues, Cells, and Biofluids

Sialic acid isomers attached in either α2,3 or α2,6 linkage to glycan termini confer distinct chemical, biological, and pathological properties, but they cannot be distinguished by mass differences in traditional mass spectrometry experiments. Multiple derivatization strategies have been developed to stabilize and facilitate the analysis of sialic acid isomers and their glycoconjugate carriers by high-performance liquid chromatography, capillary electrophoresis, and mass spectrometry workflows. Herein, a set of novel derivatization schemes are described that result in the introduction of bioorthogonal click chemistry alkyne or azide groups into α2,3- and α2,8-linked sialic acids. These chemical modifications were validated and structurally characterized using model isomeric sialic acid conjugates and model protein carriers. Use of an alkyne-amine, propargylamine, as the second amidation reagent effectively introduces an alkyne functional group into α2,3-linked sialic acid glycoproteins. In tissues, serum, and cultured cells, this allows for the detection and visualization of N-linked glycan sialic acid isomers by imaging mass spectrometry approaches. Formalin-fixed paraffin-embedded prostate cancer tissues and pancreatic cancer cell lines were used to characterize the numbers and distribution of alkyne-modified α2,3-linked sialic acid N-glycans. An azide-amine compound with a poly(ethylene glycol) linker was evaluated for use in histochemical staining. Formalin-fixed pancreatic cancer tissues were amidated with the azide amine, reacted with biotin-alkyne and copper catalyst, and sialic acid isomers detected by streptavidin-peroxidase staining. The direct chemical introduction of bioorthogonal click chemistry reagents into sialic acid-containing glycans and glycoproteins provides a new glycomic tool set to expand approaches for their detection, labeling, visualization, and enrichment.
Intima-media thickness at the near or far wall of the common carotid artery in cardiovascular risk assessment

Abstract Aims Current guidelines recommend measuring carotid intima-media thickness (IMT) at the far wall of the common carotid artery (CCA). We aimed to precisely quantify associations of near vs. far wall CCA-IMT with the risk for atherosclerotic cardiovascular disease (CVD, defined as coronary heart disease or stroke) and their added predictive values. Methods and results We analysed individual records of 41 941 participants from 16 prospective studies in the Proof-ATHERO consortium {mean age 61 years [standard deviation (SD) = 11]; 53% female; 16% prior CVD}. Mean baseline values of near and far wall CCA-IMT were 0.83 (SD = 0.28) and 0.82 (SD = 0.27) mm, differed by a mean of 0.02 mm (95% limits of agreement: −0.40 to 0.43), and were moderately correlated [r = 0.44; 95% confidence interval (CI): 0.39–0.49). Over a median follow-up of 9.3 years, we recorded 10 423 CVD events. We pooled study-specific hazard ratios for CVD using random-effects meta-analysis. Near and far wall CCA-IMT values were approximately linearly associated with CVD risk. The respective hazard ratios per SD higher value were 1.18 (95% CI: 1.14–1.22; I² = 30.7%) and 1.20 (1.18–1.23; I² = 5.3%) when adjusted for age, sex, and prior CVD and 1.09 (1.07–1.12; I² = 8.4%) and 1.14 (1.12–1.16; I²=1.3%) upon multivariable adjustment (all P < 0.001). Assessing CCA-IMT at both walls provided a greater C-index improvement than assessing CCA-IMT at one wall only [+0.0046 vs. +0.0023 for near (P < 0.001), +0.0037 for far wall (P = 0.006)]. Conclusions The associations of near and far wall CCA-IMT with incident CVD were positive, approximately linear, and similarly strong. Improvement in risk discrimination was highest when CCA-IMT was measured at both walls.
Urban Air Pollution

Roadway tunnel measurements and their applications perspectives of sustainability in the long-range transport of air pollutants air pollution levels and trends in major urban areas in Belgium importance of atmospheric transport processes in the urban air quality the case of Barcelona influence of emission input data on ozone level predictions for the Upper Rhine Valley phptochemical smog studies in Australian cities air quality in historical perspective - a case study of the Greater Manchester conurbation.
Developing a Charlson Comorbidity Index for the American Indian Population Using the Epidemiologic Data from the Strong Heart Study

Abstract Background The Charlson Comorbidity Index (CCI) is a frequently used mortality predictor based on a scoring system for the number and type of patient comorbidities health researchers have used since the late 1980s. The initial purpose of the CCI was to classify comorbid conditions, which could alter the risk of patient mortality within a one-year time frame. However, the CCI may not accurately reflect risk among American Indians because they are a small proportion of the U.S. population and possibly lack representation in the original patient cohort. A motivating factor in calibrating a CCI for American Indians is that this population, as a whole, experiences a greater burden of comorbidities, including diabetes mellitus, obesity, cancer, cardiovascular disease, and other chronic health conditions, than the rest of the U.S. population. Methods This study attempted to modify the CCI to be specific to the American Indian population utilizing the data from the still ongoing The Strong Heart Study (SHS) - a multi-center population-based longitudinal study of cardiovascular disease among American Indians. A one-year survival analysis with mortality as the outcome was performed using the SHS morbidity and mortality surveillance data and assessing the impact of comorbidities in terms of hazard ratios with the training cohort. A Kaplan-Meier plot for a subset of the testing cohort was used to compare groups with selected mCCI-AI scores. Results A total of 3,038 Phase VI participants from the SHS comprised the study population for whom mortality and morbidity surveillance data were available through December 2019. The weights generated by the SHS participants for myocardial infarction, congestive heart failure, and high blood pressure were greater than Charlson’s original weights. In addition, the weights for liver illness were equivalent to Charlson’s severe form of the disease. Lung cancer had the greatest overall weight derived from a hazard ratio of 8.308. Conclusions The mCCI-AI was a statistically significant predictor of one-year mortality, classifying patients into different risk strata X 2 (8, N = 1,245) = 30.56 (p = .0002). The mCCI-AI exhibited superior performance over the CCI, able to discriminate between participants who died and those who survived 73% of the time.
Quantifying soil moisture impacts on light use efficiency across biomes

Summary Terrestrial primary productivity and carbon cycle impacts of droughts are commonly quantified using vapour pressure deficit (VPD) data and remotely sensed greenness, without accounting for soil moisture. However, soil moisture limitation is known to strongly affect plant physiology. Here, we investigate light use efficiency, the ratio of gross primary productivity (GPP) to absorbed light. We derive its fractional reduction due to soil moisture (fLUE), separated from VPD and greenness changes, using artificial neural networks trained on eddy covariance data, multiple soil moisture datasets and remotely sensed greenness. This reveals substantial impacts of soil moisture alone that reduce GPP by up to 40% at sites located in sub‐humid, semi‐arid or arid regions. For sites in relatively moist climates, we find, paradoxically, a muted fLUE response to drying soil, but reduced fLUE under wet conditions. fLUE identifies substantial drought impacts that are not captured when relying solely on VPD and greenness changes and, when seasonally recurring, are missed by traditional, anomaly‐based drought indices. Counter to common assumptions, fLUE reductions are largest in drought‐deciduous vegetation, including grasslands. Our results highlight the necessity to account for soil moisture limitation in terrestrial primary productivity data products, especially for drought‐related assessments.
PhagePhisher: a pipeline for the discovery of covert viral sequences in complex genomic datasets

Obtaining meaningful viral information from large sequencing datasets presents unique challenges distinct from prokaryotic and eukaryotic sequencing efforts. The difficulties surrounding this issue can be ascribed in part to the genomic plasticity of viruses themselves as well as the scarcity of existing information in genomic databases. The open-source software PhagePhisher (http://www.putonti-lab.com/phagephisher) has been designed as a simple pipeline to extract relevant information from complex and mixed datasets, and will improve the examination of bacteriophages, viruses, and virally related sequences, in a range of environments. Key aspects of the software include speed and ease of use; PhagePhisher can be used with limited operator knowledge of bioinformatics on a standard workstation. As a proof-of-concept, PhagePhisher was successfully implemented with bacteria–virus mixed samples of varying complexity. Furthermore, viral signals within microbial metagenomic datasets were easily and quickly identified by PhagePhisher, including those from prophages as well as lysogenic phages, an important and often neglected aspect of examining phage populations in the environment. PhagePhisher resolves viral-related sequences which may be obscured by or imbedded in bacterial genomes.
Climatologic and hydrologic influences on the oxygen isotope ratio of tree cellulose in coastal southern California during the late 20th century

The oxygen isotope ratio (δ18O) of precipitation in continental, midlatitude regions is a complex measure of atmospheric dynamics and regional climate variability and can be preserved in geologic archives. However, continuous modern observations of precipitation δ18O in many midlatitude regions, particularly in the coastal western United States, are sparse. Here, tree‐ring cellulose δ18O from southern California is used to assess the potential of this proxy as an indicator of long‐term hydroclimate and atmospheric variability. From 1954 to 2004, we observed that cellulose δ18O was well replicated within a single stand of blue oak (Quercus douglasii) on interannual and decadal time scales. By using a forward mechanistic model, we demonstrate that cellulose δ18O is not driven solely by the oxygen isotope composition of precipitation at the site nor any other single hydroclimate variable. Instead, the interannual variability in cellulose δ18O prior to 1979 is positively correlated with growing season soil water δ18O and after 1979 is negatively correlated with relative humidity. In addition, 2 years (1983 and 1998) of anomalously low cellulose δ18O coincided with the wettest years in California and the strongest El Niño events of the late 20th century. For these years, decreased near surface evaporation and/or increased upper‐level condensation could account for the more depleted cellulose δ18O values. While blue oak cellulose δ18O is sensitive to atmospheric and hydroclimate variability, the varying temporal correlation between the cellulose δ18O and different environmental variables complicates any attempt to use the cellulose oxygen isotopes for reconstructions of climate variability beyond the calibration period.