Version Of Dataset Research Articles

51 Feasibility of Remote Administration of a Modified UDSv3 Cognitive Battery

Objective:Face-to-face administration is the “gold standard” for both research and clinical cognitive assessments. However, many factors may impede or prevent face-to-face assessments, including distance to clinic, limited mobility, eyesight, or transportation. The COVID19 pandemic further widened gaps in access to care and clinical research participation. Alternatives to face-to-face assessments may provide an opportunity to alleviate the burden caused by both the COVID-19 pandemic and longer standing social inequities. The objectives of this study were to develop and assess the feasibility of a telephone- and video-administered version of the Uniform Data Set (UDS) v3 cognitive batteries for use by NIH-funded Alzheimer’s Disease Research Centers (ADRCs) and other research programs.Participants and Methods:Ninety-three individuals (M age: 72.8 years; education: 15.6 years; 72% female; 84% White) enrolled in our ADRC were included. Their most recent adjudicated cognitive status was normal cognition (N=44), MCI (N=35), mild dementia (N=11) or other (N=3). They completed portions of the UDSv3 cognitive battery, plus the RAVLT, either by telephone or video-format within approximately 6 months (M:151 days) of their annual in-person visit, where they completed the same in-person cognitive assessments. Some measures were substituted (Oral Trails for TMT; Blind MoCA for MoCA) to allow for phone administration. Participants also answered questions about the pleasantness, difficulty level, and preference for administration mode. Cognitive testers provided ratings of perceived validity of the assessment. Participants’ cognitive status was adjudicated by a group of cognitive experts blinded to most recent inperson cognitive status.Results:When results from video and phone modalities were combined, the remote assessments were rated as pleasant as the inperson assessment by 74% of participants. 75% rated the level of difficulty completing the remote cognitive assessment the same as the in-person testing. Overall perceived validity of the testing session, determined by cognitive assessors (video = 92%; phone = 87.5%), was good. There was generally good concordance between test scores obtained remotely and in-person (r = .3 -.8; p < .05), regardless of whether they were administered by phone or video, though individual test correlations differed slightly by mode. Substituted measures also generally correlated well, with the exception of TMT-A and OTMT-A (p > .05). Agreement between adjudicated cognitive status obtained remotely and cognitive status based on in-person data was generally high (78%), with slightly better concordance between video/in-person (82%) vs phone/in-person (76%).Conclusions:This pilot study provided support for the use of telephone- and video-administered cognitive assessments using the UDSv3 among individuals with normal cognitive function and some degree of cognitive impairment. Participants found the experience similarly pleasant and no more difficult than inperson assessment. Test scores obtained remotely correlated well with those obtained in person, with some variability across individual tests. Adjudication of cognitive status did not differ significantly whether it was based on data obtained remotely or in-person. The study was limited by its’ small sample size, large test-retest window, and lack of randomization to test-modality order. Current efforts are underway to more fully validate this battery of tests for remote assessment. Funded by: P30 AG072947 & P30 AG049638-05S1

37 Perceived Financial Exploitation Vulnerability is Associated with Morphometry of the Parahippocampal Gyrus and Entorhinal Cortex in Cognitively Normal Older Adults

Objective:Prior work suggests financial exploitation vulnerability may be an early behavioral manifestation of Alzheimer’s disease (AD). Brain morphometric measures of the parahippocampal gyrus and entorhinal cortex have been shown to be sensitive to early AD progression. We hypothesized that perceived financial exploitation vulnerability may be associated with morphometric measures of the parahippocampal gyrus and entorhinal cortex in cognitively unimpaired older adults. We secondarily investigated the association of morphometric measures with neuropsychological measures.Participants and Methods:The sample consisted of 39 cognitively unimpaired older adults (mean age = 68.74 ± 6.43, mean education = 16.87 ± 2.35, 77% female). Cognitive impairment was screened using the telephone version of the Montreal Cognitive Assessment (MoCA) and the cut-off was 21 for study participation. Perceived financial exploitation vulnerability was characterized using a 6-item self-report measure derived from the contextual items of the Lichtenberg Financial Rating Scale. Neuropsychological measures included the CVLT-II Long Delay Free Recall (verbal memory), Benson Complex Figure Recall (visual memory), and Verbal Fluency: Phonemic Test from the Alzheimer’s Disease Centers’ Uniform Data Set (UDS) version 3. Brain images were collected on a 7 Tesla Siemens Magnetom with the following parameters: TE=2.95ms, TR=2200ms, 240 sagittal slices, acquired voxel size (avs)=0.7mm x 0.7mm x 0.7mm. Structural brain images were processed using FreeSurfer version 7.2.0. Cortical thickness and volume measures were generated using the Killiany/Desikian parcellation atlas. Regions were averaged across hemispheres to obtain a single value for each region. Volume measures were adjusted for intracranial volume. Bivariate analyses were conducted to assess relationships between each outcome variable and potential confounders (age, sex, and education). Linear regression models were adjusted for any covariates significantly associated with the outcome variable (none for perceived financial exploitation vulnerability; sex and age for verbal memory; education for visual memory; sex for verbal fluency).Results:Smaller entorhinal cortex volume (β = -1275.14, SE = 582.79, p < 0.05) and lower parahippocampal gyrus thickness (β = -3.37, SE = 1.57, p < 0.05) were significantly associated with greater perceived financial exploitation vulnerability. Lower entorhinal cortex thickness was marginally associated with greater perceived financial exploitation vulnerability (β = -2.03, SE = 1.11, p = 0.08). Higher parahippocampal gyrus thickness was associated with better verbal fluency (β = 17.66, SE = 7.01, p < 0.05). Higher entorhinal cortex thickness was associated with better visual memory (β = 4.71, SE = 1.73, p < 0.05). No significant associations were observed between structural brain measures and verbal memory.Conclusions:These results suggest smaller entorhinal cortex volume and lower parahippocampal gyrus thickness are associated with higher perceived financial exploitation vulnerability in cognitively normal older adults. Additionally, parahippocampal gyrus thickness appears to be associated with verbal fluency abilities while entorhinal cortex thickness appears to be associated with visual memory. Taken together, these findings lend support to the notion that financial exploitation vulnerability may serve as an early behavioral manifestation of preclinical AD. Longitudinal studies are needed to better understand the temporal nature of these relationships.

1 Associations Between Alcohol-Related Problems, Neuropsychological Measures, and Financial Exploitation Vulnerability in a Low-Drinking Sample of Cognitively Unimpaired Older Adults

Objective:In recent years, rates of alcohol consumption and alcohol use disorder have steadily increased among adults age 60 and older. Large studies have demonstrated that moderate-to-heavy alcohol consumption (>7 drinks per week) is a risk factor for developing various types of dementias. The effects of alcohol-related problems on cognition are less clear, and are particularly understudied in older adults. Similarly, while there is an established link between worse cognition and financial exploitation vulnerability in older adults, no studies have examined relationships between alcohol-related problems and financial exploitation in this population. The current study therefore explores whether alcohol-related problems are associated with neuropsychological performance and financial exploitation vulnerability in a sample of older adults.Participants and Methods:Participants were a community sample of cognitively unimpaired adults over the age of 50 (N = 55, Age M(SD) = 69.1(6.2), 74.5% female, Years of education M(SD) = 16.8(2.3)). Interested individuals were excluded if they reported current or past substance use disorders. Participants completed a laboratory visit that included a neuropsychological assessment. Measures included the NIH Cognition toolbox, CVLT-II, Digit Span, Trails A/B, Benson Complex Figure Recall, and Verbal Fluency: Phonemic and Semantic, from the Alzheimer’s Disease Centers’ Uniform Data Set (UDS) version 3. Participants completed the CAGE Alcohol Abuse Screening Tool and the Short Michigan Alcohol Screener Test - Geriatric Version (SMAST) to assess alcohol-related problems. Both measures are used as clinical screening tools to measure likelihood of a substance use disorder and produce a summary score (0-4 for CAGE, 010 for SMAST) tabulating symptoms of alcohol-related problems. Participants also completed the Perceived Financial Vulnerability Scale (PFVS) to assess financial exploitation vulnerability. As a significant number of participants reported no drinking and therefore no alcohol-related problems, negative binomial regressions were used to test associations between neuropsychological measures, financial exploitation vulnerability, and alcohol-related problems.Results:After covarying for age and sex, SMAST was negatively associated with NIH toolbox total cognition (B(SE) = -.14(.07), p<.05) and marginally negatively associated with fluid cognition (B(SE) = -.07(.04), p=.06). Neither SMAST nor CAGE scores were significantly associated with performance on any other neuropsychological test (ps = .13-.99). SMAST was positively associated with financial exploitation vulnerability (B(SE) = .31(.16), p = .05); this effect remained significant after covarying for NIH total composite score in a secondary analysis.Conclusions:In a community sample of cognitively unimpaired, low-drinking adults over the age of 50, more alcohol-related problems were associated with worse NIH toolbox cognition scores. Similarly, more alcohol-related problems were associated with greater financial exploitation vulnerability, and this relationship was not driven by worse cognition. These results suggest that even low amounts of drinking and alcohol-related problems may be associated with cognition and financial exploitation vulnerability in cognitively unimpaired older adults. This study also corroborates the use of the SMAST over the CAGE in older adult populations that may be more sensitive to cognitive changes.

34 Machine Learning Predicts Time to Dementia Conversion in Cognitively Normal Subjects

Objective:Identification of pre-clinical Alzheimer’s disease (AD) is necessary for the development of future disease-modifying treatments, which would ideally target pre-clinical stages to mitigate functional loss. Despite advanced in biomarker development, clinical trials are still without a non-invasive and cost-effective means of identifying pre-symptomatic subjects who are at high risk for eventual conversion to AD. In previous work, we developed a machine learning algorithm using neuropsychological test scores and health history to identify subjects at high risk for eventual conversion. Here, we examine the performance of a similar algorithm in predicting the timing of that conversion in years.Participants and Methods:Data were obtained from the National Alzheimer’s Coordination Center (NACC) Uniform Data Set (UDS) version 3.0. Subjects with normal cognition at baseline were used to train a multi-class Random Forest classifier to predict conversion to AD. Each subject could be classified as a short-, mid-, or long-term converter (0-3 years, 4 to 6 years, and 7 to 9 years, respectively) or as a non-converter, if no dementia diagnosis was given within ten years of baseline. Predictors included baseline demographics, basic medical history, and neuropsychological test results. Algorithms were evaluated using standard, cross-validated performance metrics.Results:Multi-class Matthews correlation coefficient between predicted time to diagnosis and the ground truth averaged 0.26 +/- 0.06 across 100 cross validation splits. Prediction accuracy exceeded 0.67 in all cases, when computed for each class individually, and was greatest for the short-term (0.75) and nonconverter (0.78) classes.Conclusions:Machine-learning algorithms applied to neuropsychological, demographic, and medical history information were able to predict the eventual timing of conversion to dementia in cognitively healthy adults significantly better than chance. Results were most accurate when predicting shorter time to conversion. Results illustrate the potential of this data analytic approach for targeted recruitment in clinical trials.

97 Evaluation of Video and Telephone-Based Administration of the Uniform Data Set Version 3 (UDS v3.0) Teleneuropsychological Measures

Objective:Telecommunication-assisted neuropsychological assessment (teleNP) has become more widespread, particularly in response to the COVID-19 pandemic. However, comparatively few studies have evaluated in-home teleNP testing and none, to our knowledge, have evaluated the National Alzheimer’s Coordinating Center’s (NACC) Uniform Data Set version 3 tele-adapted test battery (UDS v3.0 t-cog). The current study compares in-home teleNP administration of the UDS v3.0, acquired while in-person activities were suspended due to COVID-19, with a prior in-person UDS v3.0 evaluation.Participants and Methods:210 participants from the Michigan Alzheimer’s Disease Research Center’s longitudinal study of memory and aging completed both an in-person UDS v3.0 and a subsequent teleNP UDS v3.0 evaluation. The teleNP UDS v3.0 was administered either via video conference (n = 131), telephone (n = 75), or hybrid format (n = 4) with approximately 16 months between evaluations (mean = 484.7 days; SD = 122.4 days; range = 320-986 days). The following clinical phenotypes were represented at the initial assessment period (i.e., the most recent in-person UDS v3.0 evaluation prior to the teleNP UDS v3.0): cognitively healthy (n = 138), mild cognitive impairment (MCI; n = 60), dementia (n = 11), and impaired not MCI (n = 1). Tests included both the in-person and teleNP UDS v3.0 measures, as well as the Hopkins Verbal Learning Test-Revised (HVLT-R) and Letter “C” Fluency.Results:We calculated intraclass correlation coefficients (ICC) with raw scores from each time point for the entire sample. Sub-analyses were conducted for each phenotype among participants with an unchanged consensus research diagnosis: cognitively healthy (n = 122), MCI (n = 47), or cognitively impaired (i.e., MCI, dementia, and impaired not MCI) (n = 66). Test-retest reliability across modalities and clinical phenotypes was, in general, moderate. The poorest agreement was associated with the Trail Making Test (TMT) - A (ICC = 0.00; r = 0.027), TMT - B (ICC = 0.26; r = 0.44), and Number Span Backward (ICC = 0.49). The HVLT-R demonstrated moderate reliability overall (ICC = 0.51-0.68) but had notably weak reliability for cognitively healthy participants (ICC = 0.12-0.36). The most favorable reliability was observed in Craft Story 21 Recall - Delayed (ICC = 0.77), Letter Fluency (C, F, and L) (ICC = 0.74), Multilingual Naming Test (MINT) (ICC = 0.75), and Benson Complex Figure – Delayed (ICC = 0.79).Conclusions:Even after accounting for the inherent limitations of this study (e.g., significant lapse of time between testing intervals), our findings suggest that the UDS v3.0 teleNP battery shows only modest relationships with its in-person counterpart. Particular caution should be used when interpreting measures showing questionable reliability, though we encourage further investigation of remote vs. in-person testing under more controlled conditions.

Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Movies are the better source of entertainment. Every year, a great percentage of movies are released. People comment on movies in the form of reviews after watching them. Since it is difficult to read all of the reviews for a movie, summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews. Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data. Opinion mining involves identifying and extracting the opinions of individuals, which can be positive, neutral, or negative. The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews. Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie. The summary of all reviews can give a general idea about the movie. This study compares baseline techniques, Logistic Regression, Random Forest Classifier, Decision Tree, K-Nearest Neighbor, Gradient Boosting Classifier, and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0. Before applying these classifiers, in pre-processing both datasets are cleaned, duplicate data is dropped and chat words are treated for better results. On the IMDB Dataset of 50K reviews, Linear Support Vector Machines achieve the highest accuracy of 89.48%, and after hyperparameter tuning, the Passive Aggressive Classifier achieves the highest accuracy of 90.27%, while Multinomial Nave Bayes achieves the highest accuracy of 70.69% and 71.04% after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0. This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.

A Version Control System for Point Clouds

This paper presents a novel version control system for point clouds, which allows the complete editing history of a dataset to be stored. For each intermediate version, this system stores only the information that changes with respect to the previous one, which is compressed using a new strategy based on several algorithms. It allows undo/redo functionality in memory, which serves to optimize the operation of the version control system. It can also manage changes produced from third-party applications, which makes it ideal to be integrated into typical Computer-Aided Design workflows. In addition to automated management of incremental versions of point cloud datasets, the proposed system has a much lower storage footprint than the manual backup approach for most common point cloud workflows, which is essential when working with LiDAR (Light Detection and Ranging) data in the context of spatial big data.

Potentially Inappropriate Prescribing in Long-Term Care and its Relationship With Probable Delirium

ObjectivesThis study examined potentially inappropriate prescribing (PIP) of medication and its association with probable delirium among long-term care (LTC) residents in Ontario, Canada. DesignPopulation-based cross-sectional study using provincial health administrative data, including LTC assessment data via the Resident Assessment Instrument–Minimum Dataset version 2.0 (RAI-MDS 2.0). Setting and ParticipantsLTC residents in Ontario between January 1, 2016, and December 31, 2019. MethodsWe used residents’ first RAI-MDS 2.0 assessment in the study period as the index assessment. Probable delirium was identified via the delirium Clinical Assessment Protocol. Medication use in the 2 weeks preceding assessment was captured using medication claims data. PIP was measured using the STOPP/START criteria and 2015 Beers criteria, with residents classified as having 0, 1, 2, or 3+ instances of PIP. Relationships between PIP and probable delirium was assessed via bivariate and multivariable logistic regression models. ResultsThe study population included 171,190 LTC residents (mean age 84.5 years, 66.8% female, 62.9% with dementia). More than half (51.8%) of residents had 1+ instances of PIP and 21% had 3+ instances of PIP according to the STOPP/START criteria; PIP prevalence was slightly lower when assessed using Beers criteria (36.5% with 1+, 11.1% with 3+). Overall, 3.7% of residents had probable delirium. The prevalence of probable delirium increased as the number of instances of PIP increased, with residents with 3+ instances of STOPP/START PIP being 1.66 times more likely (95% CI 1.56-1.77) to have probable delirium compared to those with no instances of PIP. Similar findings were observed when PIP was measured using the Beers criteria. Central nervous system (CNS)-related PIP criteria showed a stronger association with probable delirium than non–CNS-related PIP criteria. Conclusions and ImplicationsThis population-based study highlighted that PIP was highly prevalent in long-term care residents and was associated with an increased prevalence of probable delirium.

Fake News Detection in Low Resource Languages using SetFit Framework

Social media has become an integral part of people’s lives, resulting in a constant flow of information. However, a concerning trend has emerged with the rapid spread of fake news, attributed to the lack of verification mechanisms. Fake news has far-reaching consequences, influencing public opinion, disrupting democracy, fuelingsocial tensions, and impacting various domains such as health, environment, and the economy. In order to identify fake news with data sparsity, especially with low resources languages such as Arabic and its dialects, we propose a few-shot learning fake news detection model based on sentence transformer fine-tuning, utilizing no crafted prompts and language model with few parameters. The experimental results prove that the proposed method can achieve higher performances with fewer news samples. This approach provided 71% F1 score on the Algerian dialect fake news dataset and 70% F1 score on the Modern Standard Arabic (MSA) version of the same dataset, which proves that the approach can work on the standard Arabic and its dialects. Therefore, the proposed model can identify fake news in several domains concerning the Algerian community such as politics, COVID-19, tourism, e-commerce, sport, accidents, and car prices.

A Resource-Efficient Keyword Spotting System Based on a One-Dimensional Binary Convolutional Neural Network

This paper proposes a resource-efficient keyword spotting (KWS) system based on a convolutional neural network (CNN). The end-to-end KWS process is performed based solely on 1D-CNN inference, where features are first extracted from a few convolutional blocks, and then the keywords are classified using a few fully connected blocks. The 1D-CNN model is binarized to reduce resource usage, and its inference is executed by employing a dedicated engine. This engine is designed to skip redundant operations, enabling high inference speed despite its low complexity. The proposed system is implemented using 6895 ALUTs in an Intel Cyclone V FPGA by integrating the essential components for performing the KWS process. In the system, the latency required to process a frame is 22 ms, and the spotting accuracy is 91.80% in an environment where the signal-to-noise ratio is 10 dB for Google speech commands dataset version 2.

Regional differences in the link between water exchange rate across the blood–brain barrier and cognitive performance in normal aging

The blood–brain barrier (BBB) undergoes functional changes with aging which may contribute to cognitive decline. A novel, diffusion prepared arterial spin labeling-based MRI technique can measure the rate of water exchange across the BBB (kw) and may thus be sensitive to age-related alterations in water exchange at the BBB. However, studies investigating relationships between kw and cognition have reported different directions of association. Here, we begin to investigate the direction of associations between kw and cognition in different brain regions, and their possible underpinnings, by evaluating links between kw, cognitive performance, and MRI markers of cerebrovascular dysfunction and/or damage. Forty-seven healthy older adults (age range 61–84) underwent neuroimaging to obtain whole-brain measures of kw, cerebrovascular reactivity (CVR), and white matter hyperintensity (WMH) volumes. Additionally, participants completed uniform data set (Version 3) neuropsychological tests of executive function (EF) and episodic memory (MEM). Voxel-wise linear regressions were conducted to test associations between kw and cognitive performance, CVR, and WMH volumes. We found that kw in the frontoparietal brain regions was positively associated with cognitive performance but not with CVR or WMH volumes. Conversely, kw in the basal ganglia was negatively associated with cognitive performance and CVR and positively associated with regional, periventricular WMH volume. These regionally dependent associations may relate to different physiological underpinnings in the relationships between kw and cognition in neocortical versus subcortical brain regions in older adults.

Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites

Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies.

A performance analysis of transformer-based deep learning models for Arabic image captioning

Image captioning has become a fundamental operation that allows the automatic generation of text descriptions of images. However, most existing work focused on performing the image captioning task in English, and only a few proposals exist that address the image captioning task in Arabic. This paper focuses on understanding the factors that affect the performance of machine learning models performing Arabic image captioning (AIC). In particular, we focus on transformer-based models for AIC and study the impact of various text-preprocessing methods: CAMeL Tools, ArabertPreprocessor, and Stanza. Our study shows that using CAMeL Tools to preprocess text labels improves the AIC performance by up to 34–92% in the BLEU-4 score. In addition, we study the impact of image recognition models. Our results show that ResNet152 is better than EfficientNet-B0 and can improve BLEU scores performance by 9–11%. Furthermore, we investigate the impact of different datasets on the overall AIC performance and build an extended version of the Arabic Flickr8k dataset. Using the extended version improves the BLEU-4 score of the AIC model by up to 148%. Finally, utilizing our results, we build a model that significantly outperforms the state-of-the-art proposals in AIC by up to 196–379% in the BLUE-4 score.

Evaluation of the Electronic Clinical Dementia Rating for Dementia Screening

The Clinical Dementia Rating (CDR) is a well-validated instrument widely used to detect and stage dementia due to Alzheimer disease. The digital Electronic Clinical Dementia Rating (eCDR) can be remotely self-administered and automatically scored, with potential to facilitate efficient dementia screening and staging. To evaluate the association of the eCDR with the CDR and other in-clinic assessments for screening older adults for cognitive impairment. This multisite, cross-sectional study used baseline data from a longitudinal, observational study from 2020 to 2023, including up to 3 years of follow-up. Participants were enrolled from 3 Alzheimer Disease Research Centers and the Brain Health Registry. Participants (aged ≥55 years, with a study partner, and no acute or unstable major medical conditions) were recruited during in-clinic visits or by automated emails. Participants completed the Uniform Data Set Version 3 (UDS; including the CDR) in supervised clinical research settings, and then completed the eCDR remotely, online and unsupervised, using their own device. The primary outcomes were eCDR scores (item; categorical box and global; continuous box and global), CDR scores (item; categorical box and global), and UDS assessment scores. Associations were evaluated using linear and logistic regressions. A total of 3565 participants were contacted, and 288 were enrolled. Among 173 participants with item-level data (mean [SD] age, 70.84 [7.65] years; 76 women [43.9%]), eCDR to CDR concordance was 90% or higher for 33 items (63%) and 70% to 89% for 13 items (25%). Box (domain) level concordance ranged from 80% (memory) to 99% (personal care). The global score concordance rate was 81%. κ statistics were fair to moderate. Among 206 participants with box and global scores (mean [SD] age, 71.34 [7.68] years; 95 women [46.1%]), eCDR continuous global score was associated with CDR global (categorical) score with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.70-0.87). Correlations between eCDR and in-clinic UDS assessments were similar to those between CDR sum of box scores and the same in-clinic assessments. These findings suggest that the eCDR is valid and has potential use for screening and assessment of older adults for cognitive and functional decline related to Alzheimer disease. Instrument optimization and validation in diverse cohorts in remote settings are crucial for evaluating scalability and eCDR utility in clinical research, trials, and health care settings.

Acute Antipsychotic Use and Presence of Dysphagia Among Older Veterans with Heart Failure.

Examine whether new antipsychotic (AP) exposure is associated with dysphagia in hospitalized patients with heart failure (HF). Retrospective cohort. AP-naïve Veterans hospitalized with HF and subsequently discharged to a skilled nursing facility (SNF) between October 1, 2010, and November 30,2019. We linked Veterans Health Administration (VHA) electronic medical records with Centers for Medicare & Medicaid (CMS) Minimum Data Set (MDS) version 3.0 assessments and CMS claims. The exposure variable was administration of ≥1 dose of a typical or atypical AP during hospitalization. Our main outcome measure was dysphagia presence defined by (1) inpatient dysphagia diagnosis codes and (2) the SNF admission MDS 3.0 swallowing-related items to examine post-acute care dysphagia status. Inverse probability of treatment weighting was used for risk adjustment. The analytic cohort consisted of 29,591 Veterans (mean age 78.5±10.0years; female 2.9%; n= 865). Acute APs were administered to 9.9% (n= 2941). Those receiving APs had differences in prior dementia [37.1%, n= 1091, vs 22.3%, n= 5942; standardized mean difference (SMD)= 0.33] and hospital delirium diagnoses (7.7%, n= 227 vs 2.8%, n= 754; SMD= 0.22). Acute AP exposure was associated with nearly double the risk for hospital dysphagia diagnosis codes [adjusted (adj.) relative risk (RR) 1.9, 95% CI 1.8, 2.1]. At the SNF admission MDS assessment, acute AP administration during hospitalization was associated with an increased dysphagia risk (adj. RR 1.2, 95% CI 1.0, 1.5) both in the oral (adj. RR 1.7, 95% CI 1.2, 2.0) and pharyngeal phases (adj. RR 1.3, 95% CI 1.0, 1.7). In this retrospective study, AP medication exposure was associated with increased dysphagia coding and MDS assessment. Considering other adverse effects, acute AP should be cautiously administered during hospitalization, particularly in those with dementia. Swallowing function is critical to hydration, nutrition, and medical management of HF; therefore, when acute APs are initiated, a swallow evaluation should be considered.

High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Associations between dementia staging, neuropsychiatric behavioral symptoms, and divorce or separation in late life: A case control study.

Dementia can be difficult for married couples for many reasons, including the introduction of caregiving burden, loss of intimacy, and financial strain. In this study, we investigated the impact of dementia staging and neuropsychiatric behavioral symptoms on the likelihood of divorce or separation for older adult married couples. For this case-control study, we used data from the National Alzheimer's Coordinating Center (NACC) Uniform dataset (UDS) versions 2 and 3. This dataset was from 2007 to 2021 and contains standardized clinical information submitted by NIA/NIH Alzheimer's Disease Research Centers (ADRCs) across the United States (US). This data was from 37 ADRCs. We selected participants who were married or living as married/domestic partners at their initial visit. Cases were defined by a first divorce/separation occurring during the follow-up period, resulting in 291 participants. We selected 5 controls for each married/living as married case and matched on age. Conditional logistic regression estimated the association between overall Neuro Psychiatric Inventory (NPI) score and severity of individual symptoms of the NPI with case/control status, adjusted for education, the CDR® Dementia Staging Instrument score, living situation, symptom informant, sex, and race. Separate analyses were conducted for each symptom. Multiple comparisons were accounted for with the Hochberg method. Later stage of dementia was negatively associated with divorce/separation with an adjusted odds ratio (AOR) = 0.68 (95%CI = 0.50 to 0.93). A higher overall NPI score was positively associated with divorce/separation AOR = 1.08 (95% CI = 1.03 to 1.12,). More severe ratings of agitation/aggression, depression/dysphoria, disinhibition, and elation/euphoria were associated with greater odds of divorce/separation. Among older adults in the US, a later stage of dementia is associated with a lower likelihood of divorce or separation, while having more severe neuropsychiatric behavioral symptoms of agitation/aggression, depression/dysphoria, disinhibition, and elation/euphoria are associated with a higher likelihood of divorce or separation.

Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification

Biometric and identification patterns have gained extensive research and application, particularly in iris recognition. The iris harbors a wealth of individual-specific information, making it a vital element in biometric authentication. This article presents a comprehensive study encompassing iris segmentation and identification. We introduce the Weighted Adaptive Hough Ellipsopolar Transform Convolutional Neural Network (WAHET-CNN) as a novel approach for classifying pattern images. Our experimental outcomes demonstrate a commendable 90% accuracy achieved by the proposed WAHET-CNN on the CASIA dataset Version 4.

A Climatological Comparison of the Arctic Environment and Arctic Cyclones between Periods of Low and High Forecast Skill of the Synoptic-Scale Flow

Abstract The prediction of weather conditions in the Arctic is important to human activities in the Arctic. Arctic cyclones (ACs), which are extratropical cyclones that originate within the Arctic or move into the Arctic from lower latitudes, can be associated with hazardous weather conditions that may adversely affect human activities. The purpose of this study is to increase understanding of processes that influence the forecast skill of the synoptic-scale flow over the Arctic and of ACs. The 11-member NOAA Global Ensemble Forecast System (GEFS) reforecast dataset, version 2, is utilized to identify periods of low and high forecast skill of the synoptic-scale flow over the Arctic, hereinafter referred to as low-skill and high-skill periods, respectively, during the summers of 2007–17, and to evaluate the forecast skill of ACs during these respective periods. The ERA-Interim dataset is used to examine characteristics of the Arctic environment and characteristics of ACs during low-skill and high-skill periods. The Arctic environment tends to be characterized by more vigorous baroclinic processes and latent heating during low-skill periods relative to high-skill periods. ACs occur more frequently over much of the Arctic; tend to be stronger; and tend to be located in regions of larger lower-tropospheric baroclinicity, lower-to-midtropospheric Eady growth rate (EGR), and latent heating during low-skill periods relative to high-skill periods. ACs during low-skill periods that are characterized by low forecast skill of intensity tend to be relatively strong and tend to be located in regions of relatively large lower-tropospheric baroclinicity, lower-to-midtropospheric EGR, and latent heating.

Traumatic Cervical spine injury epidemiology: a tertiary care center perspective in Nepal

Background: The epidemiology of traumatic cervical spine injury is not well documented in developing/ underdeveloped countries. There are a few single-centered studies on the epidemiology of cervical spine injury in Nepal. Objectives: This study aimed to explore the epidemiology of traumatic cervical spine injuries presented at Dhulikhel Hospital, Kathmandu University Hospital, Nepal. Methods: This was a retrospective study of individuals with traumatic cervical spine injuries admitted to the Department of Orthopedics from 2015 January to 2021 December. Data collection was done by using the International Spinal Cord Injury core data set (version 2) and International Spinal Cord Injury Spinal Column Injury Basic Data Set (version 1.0). Results: One hundred and forty-four patients were included of which the majority (68.8%) were males. The mean age of the patients was 47.89 ± 17 years. Fall injury was the most common etiology (79.2%). C2 was the most common level injured (28.5%). Patients with ASIA A neurology was 18% and surgical treatment was given to nearly 60%. The patients had a median hospital stay of 9 days (4- 16) and a mortality of 4.2%. Conclusion: There was a male predominance with a recent increase in the female proportion for traumatic cervical spine injury. Fall was the most common etiology with the majority of patients in their 30s and 40s. Surgical treatment is more preferred recently. The mortality rate was relatively low which needs further investigation.