Final Dataset Research Articles

Year-long buoy-based observations of the air–sea transition zone off the US west coast

Abstract. Two buoys equipped with Doppler lidars owned by the US Department of Energy (DOE) were deployed off the coast of California in autumn of 2020 by Pacific Northwest National Laboratory. The buoys collected data for an entire annual cycle at two offshore locations proposed for offshore wind development by the Bureau of Ocean Energy Management. One of the buoys was deployed approximately 50 km off the coast near Morro Bay in central California in 1100 m of water. The second buoy was deployed approximately 40 km off Humboldt County in northern California in 625 m of water. The buoys provided the first-ever continuous measurements of the air–sea transition zone off the coast of California. The atmospheric and oceanographic characteristics of the area and estimates of annual energy production at both the Morro Bay and Humboldt wind energy areas show that both locations have a high wind energy yield and are prime locations for future floating offshore wind turbines. This article provides a description and comprehensive analysis of the data collected by the buoys, and a final post-processed dataset is uploaded to a data archive maintained by the DOE. Additional analysis was conducted to show the value of the data collected by the DOE buoys. All post-processed data from this study are available on the Wind Data Hub website: https://a2e.energy.gov/data# (last access: 14 September 2023). Near-surface, wave, current, and cloud datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783807 (Krishnamurthy and Sheridan, 2023b) and https://doi.org/10.21947/1959715 (Krishnamurthy and Sheridan, 2023a), respectively. Lidar datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783809 (Krishnamurthy and Sheridan, 2023d) and https://doi.org/10.21947/1959721 (Krishnamurthy and Sheridan, 2023c), respectively.

FAMILY INVOLVEMENT IN EDUCATION OF SPECIAL NEEDS STUDENTS: A SYSTEMATIC LITERATURE REVIEW

Family involvement is an essential element in the education concerning special needs students, and this systematic literature review is focused on investigating its significance and impact. It investigates the factors and elements that influence family involvement, its impact on student outcomes, and strategies for bolstering collaboration between families and educational institutions. While the importance of family involvement is widely recognized, challenges and knowledge gaps remain regarding its best practices and effects. The study utilized the PRISMA approach, conducting a systematic review by scouring academic databases and journals for pertinent articles published within the last five years. The systematic search employed academic databases like Scopus and Web Of Science. After rigorous searching, the final dataset comprised (n=24) studies. Three core themes emerged from this approach: Teacher-Parent Collaboration, Barriers and Challenges, and Effects on the Achievement of Students. The review underscores essential components of family involvement, including communication, collaboration, shared decision-making, and parent-teacher partnerships. It provides an in-depth exploration of how family engagement positively affects students' academic and socio-emotional development while also shedding light on the obstacles and hindrances that can disrupt productive family-school collaborations. In conclusion, family involvement plays a pivotal role in the education of special needs students, significantly contributing to their holistic development. This review offers valuable insights for policymakers, educators, and researchers to further fortify family involvement in special education.

ETHICAL LEADERSHIP PRACTICE IN EDUCATION: A SIGNIFICANT SYSTEMATIC REVIEW

This systematic literature review explores the significance of ethical leadership practices in the context of education. The introduction provides an overview of the importance of ethical leadership within educational settings. However, these leaders contend with complex moral dilemmas, navigating the delicate balance between adhering to laws and potentially disobeying some, which could result in a competitive disadvantage. This study employed the PRISMA methodology, conducting a systematic search across academic databases, specifically Scopus and the Web of Science, resulting in a comprehensive selection of studies, reviews, and articles pertaining to ethical leadership in educational contexts. By utilizing an advanced search strategy involving keywords such as 'ethical leadership,' 'ethical management,' and 'education,' the analysis revealed the pivotal role played by ethical leadership in educational settings and organizations, contributing to higher-quality leadership. The final dataset consisted of 35 items, with the review identifying key themes that were subsequently validated by experts and categorized into three distinct themes: (1) the impact of ethical leadership on workplace behavior, (2) ethical leadership within educational contexts, and (3) ethical leadership across diverse industries and sectors. In conclusion, ethical leadership stands as a foundational element of responsible and effective leadership within the contemporary educational system.

Car Full View Dataset: Fine-Grained Predictions of Car Orientation from Images

The orientation of objects plays an important role in accurate predictions for the tasks of classification, detection, and trajectory estimation. This is especially important in the automotive domain, where estimating an accurate car orientation can significantly impact the effectiveness of the other prediction tasks. This work presents Car Full View (CFV), a novel dataset for car orientation prediction from images obtained by video recording all possible angles of individual vehicles in diverse scenarios. We developed a tool to semi-automatically annotate all the video frames with the respective car angle based on the walking speed of the recorder and manually annotated key angles. The final dataset contains over 23,000 images of individual cars along with fine-grained angle annotations. We study the performance of three state-of-the-art deep learning architectures on this dataset in three different learning settings: classification, regression, and multi-objective. The top result of 3.39° in circular mean absolute error (CMAE) shows that the model accurately predicts car orientations for unseen vehicles and images. Furthermore, we test the trained models on images from two different datasets and show their generalization capability to realistic images. We release the dataset and the best models while publishing a web service to annotate new images.

Effect of breeding during the peri-vaccination window against foot-and-mouth disease on the pregnancy rate in the cow: A retrospective study

As emerging evidence indicates that FMD vaccination causes variable degrees of pregnancy loss in the beef cow, the study was conducted at NDRI SRS in sub-tropical conditions in the year 2021 to investigate its effects on the pregnancy rate in the dairy cow when they were bred within 14 days of FMD vaccination. Retrospective data on AI done within 14 days post FMD vaccination (dpv -14 to +14) and the corresponding pregnancy outcome were collected for 6 years (January 2016 to January 2021) and each record was categorized into AI pre-vaccination (dpv -14 to -1) or AI post-vaccination (dpv 0 to 14). After excluding repeat breeding cows and pregnant heifers, the final dataset consisted of 47 records derived from crossbred and Deoni cows. The binary outcome variable pregnancy was regressed on the categorical predictor variables such as day of AI with respect to dpv 0, stage of lactation and parity.The results indicated that the odds of pregnancy in the AI post-vaccinated group was 0.47 (95% confidence interval: 0.10-1.88) value) as compared with AI pre-vaccinated group. It can be concluded that FMD vaccination has no significant effect on pregnancy outcome if the animals are bred 14 days before and 14 days after FMD vaccination (irrespective of breed, age, parity, days in milk).

Selective Sweeps in the Austrian Turopolje and Other Commercial Pig Populations.

The goal of our study was to identify signatures of selection in the Turopolje pigs and other commercial pig breeds. We conducted a comprehensive analysis of five datasets, including one local pig breed (Turopolje) and four commercial pig breeds (Large White, Landrace, Pietrain, and Duroc), using strict quality control measures. Our final dataset consisted of 485 individuals and 54,075 single nucleotide polymorphisms (SNPs). To detect selection signatures within these pig breeds, we utilized the XP-EHH and XP-nSL methodologies, which allowed us to identify candidate genes that have been subject to positive selection. Our analysis consistently highlighted the PTBP2 and DPYD genes as commonly targeted by selection in the Turopolje breed. DPYD is associated with muscular development in pigs and other species and PTBP2 emerges as one of the potential genes linked to seminal characteristics. Furthermore, in the Large White breed, a number of genes were detected with the two methods, such as ATP1A1, CASQ2, CD2, IGSF3, MAB21L3, NHLH2, SLC22A15, VANGL1. In the Duroc breed, a different set of genes was detected, such as ARSB, BHMT, BHMT2, DMGDH, JMY. The function of these genes was related to body weight, production efficiency and meat quality, average daily gain, and other similar traits. Overall, our results have identified a number of genomic regions that are under selective pressure between local and commercial pig breeds. This information can help to improve our understanding of the mechanisms underlying pig breeding, and ultimately contribute to the development of more efficient and sustainable pig production practices. Our study highlights the power of using multiple genomic methodologies to detect genetic signatures of selection, and provides important insights into the genetic diversity of pig breeds.

Economic uncertainty and direct property performance: a systematic review using the SPAR-4-SLR protocol

PurposeThis study systematically reviewed existing literature on the impact of economic uncertainty on property performance to highlight focus areas and spur future research amid unprecedented global uncertainty levels. Conceptually, uncertainty levels and environmental dynamism are related to investors' risk judgement and decision-making.Design/methodology/approachPeer-reviewed journal articles published from 2007 to 2022 were assembled and arranged through the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The initial search produced 2,028 results from the Web of Science and Scopus databases, which were rigorously purified for a final dataset of 70 articles. These records were subsequently assessed through content analysis, bibliographic modelling, topic modelling and thematic analysis. Recurring themes were visualised using the VOSviewer software.FindingsThe existing literature suggests that economic uncertainty negatively impacts investment volumes, returns and performance. Research has also increased since 2018, with a strong emphasis on the housing sector and developed property markets. Commercial property and emerging markets account for only 10 and 8% of previous research, respectively.Practical implicationsThese findings highlight the negative impact of economic uncertainties on property performance and investment volumes, which necessitate careful risk assessment. Given the high susceptibility of emerging and commercial property markets to uncertainty, these markets warrant further research amid ongoing uncertainty concerns across the globe.Originality/valueGiven current unprecedented levels of global uncertainty, the effects of economic uncertainty have received renewed interest. This study synthesised the current understanding of how different property markets respond to increased uncertainty and outlined future research directions to enhance understanding. Themes and relationships were also integrated into a conceptual map summarising the reported effects of economic uncertainty on housing, commercial property, investment and behaviour in the property market.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti etal. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose-effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC-220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model-informed development of new investigational agents in SLE.

A novel automated multiplex immunoassay for the ultrasensitive detection of blood‐based biomarkers for neurodegenerative diseases

AbstractBackgroundEfforts to identify accurate blood biomarkers for neurodegenerative disease hallmarks have been hampered by the lack of a proteomic tool that has the required sensitivity to detect very low concentrations of brain‐derived proteins in plasma or serum and the ability to multiplex many analytes in a single assay. Here we evaluated NULISA™, a recently developed novel immunoassay with attomolar level sensitivity and high multiplex capability1, for its ability to detect serum biomarkers associated with Alzheimer’s disease.MethodSerum samples from 31 Alzheimer’s disease (AD) patients and 31 non‐AD controls were analyzed using NULISA with a 200plex inflammation panel targeting a broad spectrum of inflammation‐related cytokines, chemokines and other proteins. Five samples failed quality control and were excluded from further analysis, resulting in 28 AD and 29 control samples in the final dataset. Linear model analysis, including age and sex as covariates, was performed for each target for differential expression between AD patients and controls.ResultTwo significant proteins were identified. The level of glial fibrillary acidic protein (GFAP) was 1.84‐fold higher in AD patients (adjusted p‐value = 0.00014), whereas the level of S100 calcium‐binding protein A12 (S100A12) was 2‐fold higher in non‐AD controls (adjusted p‐value = 0.031). GFAP serum levels correlated strongly with total tau (Spearman rho = 0.69, p = 1.7e‐09), p‐tau181 (rho = 0.69, p = 1.7e‐09) and amyloid beta 42 (rho = ‐0.71, p = 4.7e‐10) levels in cerebrospinal fluid (CSF). S100A12 levels also correlated significantly with total tau (rho = ‐0.39, p = 0.0031), p‐tau181 (rho = ‐0.36, p = 0.0053) and amyloid beta 42 (rho = 0.38, p = 0.0067) CSF levels. Receiver operating characteristic analysis demonstrated that GFAP strongly discriminated AD from controls with an area under the curve (AUC) of 0.93 (95% CI 0.87‐1.0), and S100A12 also exhibited good discrimination with an AUC of 0.72 (95% CI 0.59‐0.86).ConclusionThis exploratory study identified serum GFAP levels as a strong discriminatory biomarker for AD, consistent with previous studies. S100A12 also demonstrated significant associations with AD and CSF biomarkers and thus warrants further study. NULISA holds great promise for the discovery and validation of blood‐based biomarkers for the early detection and monitoring of neurodegenerative diseases.1. Feng, W. et al. bioRxiv 2023.04.09.536130 (2023) https://doi.org/10.1101/2023.04.09.536130.

Abstract A135: Addressing cancer health disparities through global oncology initiatives at a comprehensive cancer center

Abstract Background: The Mayo Clinic Comprehensive Cancer Center (MCCCC) is uniquely positioned to address cancer health disparities globally through the Community Outreach & Engagement (COE) Office and Program’s resources and partnerships in the areas of cancer research, education, and training. The goal of the COE Global Oncology Initiatives (GOI) is to lessen the cancer burden on patients globally by building global capacity through research, education, and training in MCCC impact regions. While MCCCC continues to build institutional relationships worldwide through the GOI, it has specifically increased research collaborations in Africa over the past 5 years. The purpose of this study is to synthesize and report the global oncology reach through scholarly output of collaborative efforts in ongoing research activities with partnering African countries. By analyzing and documenting research outputs within this area, we aim to better understand research priorities and needs, increase inclusivity of African populations, and improve potential avenues for impactful studies in global alliance networks. Methods: Publications were found by using key search terms and by using the map feature on Mayo Clinic’s Elsevier-hosted publication database to locate partner institutions throughout Africa. Only publications in the past 5 years, which included an African researcher as an author, and are relevant to cancer research were included. Duplicate publications were removed from the final dataset. Results: There are 86 unique MCCCC members collaborating with 11 different African countries (Algeria, Egypt, Ghana, Kenya, Morocco, Nigeria, South Africa, Tanzania, Tunisia, Uganda, and Zimbabwe). A total of 50 unique publications have been published, with an African country being represented 72 times. The majority of MCCCC global oncology work is with researchers in Sub-Saharan nations (n=43). Egypt, however, has the largest single country representation (n=22), followed by South Africa (n=14) and Nigeria (n=13). 164 unique African researchers across 133 different institutions have worked with MCCCC members. Prostate and liver cancer were the most represented cancers at 6 instances, respectively. Discussion: Overall, 86 MCCCC members are involved in global research that address cancer health disparities through equal partnership with African researchers, which has resulted in multiple research output. Some of the publications were led by global consortia or cohort studies. These collaborations have been mutually beneficial for MCCCC members and African investigators, elevating cancer research in Africa. This includes increased research scholarship, extramural funding for cancer research in Africa, opening of more clinical trials across the continent, and increase in cancer biomedical workforce. Global research, especially one that is inclusive of the African populations, will foster the understanding of the genetic architecture of cancer, and help shape cancer care and control for Africans and people of African ancestry. Citation Format: Parisa Fathi, Manisha Salinas, Monica Albertie, Folakemi Odedina. Addressing cancer health disparities through global oncology initiatives at a comprehensive cancer center [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A135.

Screening of Mild Cognitive Impairment Subtypes Through the Training of 1D Convolutional Neural Network with QEEG Features

AbstractBackgroundThe pre‐clinical stage of dementia, mild cognitive impairment (MCI) carries various pathological pathways and respective prognoses. The two foremost causes of MCI and dementia that have been identified through autopsy diagnoses are Amyloid plaques and Lewy body, commonly referred to as Alzheimer’s disease (AD) and Lewy body disease (LBD) respectively. Although correct identification of pathological pathways plays a crucial role in effective designing of treatment methods, MCI subtypes are underexploited in the current diagnostic paradigm due to multiple reasons, mainly cost‐effectiveness. Hence, we utilized quantitative electroencephalography (QEEG) that are cheap and convenient to record in the distinguishment of MCI subtypes.MethodA total of 151 MCI patients’ EEG data were aggregated into 3 groups, pure AD (n = 29), pure LBD (n = 88), and mixed (n = 34). The mixed type was further subdivided into main AD (n = 31) and main LBD (n = 3) groups, in accordance with AD/LBD tendency exhibited by the patients. The clinical labelling was brought by the experienced experts of Yonsei Severance hospital, South Korea.1D SE‐ResNet‐based classification model was established for quantitative investigation of AD and LBD propensity. The power spectrum density (PSD) in dB/Hz scale were computed from the EEG data and were used to train the model. Due to the small number of data, augmentation was applied to the training data. The final dataset was split into 8 to 1 to 1 ratio (Train n = 7092: 2274 pure AD + main AD; 3694 pure LBD + main LBD, Validation n = 14: 6 pure AD + main AD; 8 Pure LBD + main LBD, Test n = 15: 6 pure AD + main AD; 9 pure LBD + main LBD).ResultThe classification model showed validation results of 85.7% accuracy, 83.3% AD sensitivity and 87.5% LBD sensitivity. Test results were at 92.8% accuracy, 83.3% AD sensitivity and 88.9% LBD sensitivity.ConclusionQEEG‐based deep learning classifier developed in this study successfully distinguished the two main causes of cognitive degeneration in MCI patients. Our model can help identify subtype‐specific spectral trends, which could also make contributions in the establishment of effective treatment methods for MCI.

Aerial image semantic segmentation based on 3D fits a small dataset of 1D

<p>Time restrictions and lack of precision demand that the initial technique be abandoned. Even though the remaining datasets had fewer identified classes than initially planned for the study, the labels were more accurate. Because of the need for additional data, a single network cannot categorize all the essential elements in a picture, including bodies of water, roads, trees, buildings, and crops. However, the final network gains some invariance in detecting these classes with environmental changes due to the different geographic positions of roads and buildings discovered in the final datasets, which could be valuable in future navigation research. At the moment, binary classifications of a single class are the only datasets that can be used for the semantic segmentation of aerial images. Even though some pictures have more than one classification, images of roads and buildings were only found in a significant number of samples. Then, the building datasets were pooled to produce a larger dataset and for the constructed models to gain some invariance on image location. Because of the massive disparity in sample size, road datasets needed to be integrated.</p>

Alzheimer’s Disease Dementia Classification Through Residual Network and QEEG Image representations

AbstractBackgroundOur everyday actions and decisions involve a complex coordination of various cognitive functions. Severe deterioration of such functions, commonly referred to as dementia, adversely affects quality of our lives. However, through early screening of the disease at its pre‐clinical stage and appropriate treatment, they can be reverted. Current diagnostic criteria for dementia utilize questionnaire‐based cognitive function assessments, such as the mini‐mental state examination. However, it is statistically less robust, where further tests such as positron emission tomography are required for accurate diagnosis. This is laborious, expensive and expose patients to harmful radiation. Therefore, we propose a quantitative electroencephalography (QEEG) based approach that distinguishes Alzheimer’s disease dementia (ADD) from a non‐ADD group that include mild cognitive impairment and subjective cognitive decline data.Method19 channel EEG data employed in the present study were clinically acquired from Chung‐Ang university hospital, in eyes‐closed resting state. Artefacts were eliminated through independent component analysis and bad epoch rejection. Through spectral analysis, the powers of a frequency spectrum (1‐45Hz) were computed with 0.25Hz resolution. Henceforth, the Z‐score of each value were calculated through iMediSync’s age‐ and sex‐differentiated normative database and were mapped into a rectangular arrangement in accordance with channel locations on the scalp. The rearranged Z‐score matrices were visualized, clearly exhibiting both spectral and spatial information. Gamma spectrum (30‐45Hz) was excluded due to its vulnerability to contaminations induced by external noises and muscle movements. The final dataset (N = 732; 137 ADD, 595 non‐ADD) was established, where the 75% of non‐ADD were first excluded as test data due to data imbalance. The remaining data were split into 2 to 8 ratio for testing (N = 503; 27 ADD; 476 non‐ADD) and training (N = 229; 110 ADD; 119 non‐ADD).ResultThe final 18‐layer Residual network model showed test accuracy at 88.5% with ADD sensitivity at 88.9% and specificity at 88.4%. Explainable artificial intelligence algorithm was utilized for the inference and verification of classification criteria.ConclusionThe outstanding classification results of the established model upholds QEEG utility in the distinguishment of dementia from its pre‐clinical stages. Continuous refinement will bolster its potential in the diagnosis several other neurological diseases.

Effect of Different Additives on the Quality of Rehydrated Corn Grain Silage: A Systematic Review

This review aimed to analyze the effects of additives in producing silage from rehydrated corn grains for ruminants. The control treatment studies used in this analysis involved corn grain rehydrated with water only. To be included in the review, the studies needed to follow standardized criteria, including the absence of additives in the control treatment and the silage evaluation of the in animals such as cattle, goats, and sheep. A total of fifteen publications between 2014 and 2023 were included in the final dataset. The PROC ANOVA of SAS was used to compare the results, which included a random effect of comparison within the study, performing a paired comparison. It was observed that additives did not influence the chemical composition, pH, organic acid, ethanol content, microbial population, fermentative losses, aerobic stability, and dry matter in vitro digestibility of rehydrated corn grain silage (p > 0.05). Using additives in corn silage is a promising practice that can significantly benefit silage fermentation. Moisture silage additives mitigate high mycotoxin levels, enhance aerobic stability, improve cell wall digestibility, and increase the efficiency of utilization of silage nitrogen by ruminants. Using fermentation-stimulating additives (Lactobacillus buchneri) can improve the quality of rehydrated corn grain silage. There are still a few studies and more research to elucidate the best additives and the ideal amount to be added to ground corn grain silage.

Composition and micromechanical properties of the femoral neck compact bone in relation to patient age, sex and hip fracture occurrence.

Current clinical methods of bone health assessment depend to a great extent on bone mineral density (BMD) measurements. However, these methods only act as a proxy for bone strength and are often only carried out after the fracture occurs. Besides BMD, composition and tissue-level mechanical properties are expected to affect the whole bone's strength and toughness. While the elastic properties of the bone extracellular matrix (ECM) have been extensively investigated over the past two decades, there is still limited knowledge of the yield properties and their relationship to composition and architecture. In the present study, morphological, compositional and micropillar compression bone data was collected from patients who underwent hip arthroplasty. Femoral neck samples from 42 patients were collected together with anonymous clinical information about age, sex and primary diagnosis (coxarthrosis or hip fracture). The femoral neck cortex from the inferomedial region was analyzed in a site-matched manner using a combination of micromechanical testing (nanoindentation, micropillar compression) together with micro-CT and quantitative polarized Raman spectroscopy for both morphological and compositional characterization. Mechanical properties, as well as the sample-level mineral density, were constant over age. Only compositional properties demonstrate weak dependence on patient age: decreasing mineral to matrix ratio (p = 0.02, R2 = 0.13, 2.6 % per decade) and increasing amide I sub-peak ratio I~1660/I~1683 (p = 0.04, R2 = 0.11, 1.5 % per decade). The patient's sex and diagnosis did not seem to influence investigated bone properties. A clear zonal dependence between interstitial and osteonal cortical zones was observed for compositional and elastic bone properties (p < 0.0001). Site-matched microscale analysis confirmed that all investigated mechanical properties except yield strain demonstrate a positive correlation with the mineral fraction of bone. The output database is the first to integrate the experimentally assessed microscale yield properties, local tissue composition and morphology with the available patient clinical information. The final dataset was used for bone fracture risk prediction in-silico through the principal component analysis and the Naïve Bayes classification algorithm. The analysis showed that the mineral to matrix ratio, indentation hardness and micropillar yield stress are the most relevant parameters for bone fracture risk prediction at 70 % model accuracy (0.71 AUC). Due to the low sample number, further studies to build a universal fracture prediction algorithm are anticipated with the higher number of patients (N > 200). The proposed classification algorithm together with the output database of bone tissue properties can be used for the future comparison of existing methods to evaluate bone quality as well as to form a better understanding of the mechanisms through which bone tissue is affected by aging or disease.

Differential dementia diagnosis using event‐based modelling: Alzheimer’s disease versus Dementia with Lewy Bodies

AbstractBackgroundDifferential diagnosis of dementias in the clinic is challenging, especially in the prodromal phase where early symptoms overlap. Here we prototype a differential diagnosis system based on event‐based modeling of MRI atrophy in two dementias.MethodPublic data from ADNI, NACC, PPMI, PDBP (N = 1978). Inclusion criteria: baseline T1w‐MRI successfully processed by FreeSurfer 7.1.1, CN/AD/DLB/MCI diagnosis. A nearest‐neighbour approach matched disease‐specific control groups to AD and DLB by Age/Sex/scanner (field strength), and Desikan‐Killiany volumes were adjusted for normal trends in Age/Sex/field‐strength/intracranial volume. The final analysis dataset consisted of 91 DLB, 298 AD, and 786 MCI (N = 1175), with matched control groups for each.One event‐based model (EBM) of regional brain atrophy was fit per disease, using Kernel Density Estimation mixture models to quantify biomarker abnormality. Input features were brain volumes having disease signal in both AD and DLB.The EBM patient‐staging tool was adapted to perform differential diagnosis. The tool converts individual data into a probability distribution over stages — stage likelihood — for each model. We define EBM diagnosis as the model having the maximum stage likelihood.ResultFigure 1 shows the event‐based model posterior (positional variance diagram) for AD (left) and DLB (right). AD atrophy starts in a subset of limbic regions before progressing to the temporal cortex and beyond. The DLB atrophy pattern diverges considerably (off‐diagonal density in Figure 1), including involvement of additional limbic regions early on. Figure 2 shows patient staging results for each model, with MCI individuals earlier than patients as expected. Differential diagnosis accuracy was 63%, which is comparable to previous studies.ConclusionWe successfully adapted event‐based modeling of MRI atrophy for differential diagnosis of AD vs DLB. More data for other dementias, especially in the prodromal phase, is needed to test and deploy such a tool in memory clinics.

Differential dementia diagnosis using event‐based modelling: Alzheimer’s disease versus Dementia with Lewy Bodies

AbstractBackgroundDifferential diagnosis of dementias in the clinic is challenging, especially in the prodromal phase where early symptoms overlap. Here we prototype a differential diagnosis system based on event‐based modeling of MRI atrophy in two dementias.MethodPublic data from ADNI, NACC, PPMI, PDBP (N = 1978). Inclusion criteria: baseline T1w‐MRI successfully processed by FreeSurfer 7.1.1, CN/AD/DLB/MCI diagnosis. A nearest‐neighbour approach matched disease‐specific control groups to AD and DLB by Age/Sex/scanner (field strength), and Desikan‐Killiany volumes were adjusted for normal trends in Age/Sex/field‐strength/intracranial volume. The final analysis dataset consisted of 91 DLB, 298 AD, and 786 MCI (N = 1175), with matched control groups for each. One event‐based model (EBM) of regional brain atrophy was fit per disease, using Kernel Density Estimation mixture models to quantify biomarker abnormality. Input features were brain volumes having disease signal in both AD and DLB. The EBM patient‐staging tool was adapted to perform differential diagnosis. The tool converts individual data into a probability distribution over stages — stage likelihood — for each model. We define EBM diagnosis as the model having the maximum stage likelihood.ResultFigure 1 shows the event‐based model posterior (positional variance diagram) for AD (left) and DLB (right). AD atrophy starts in a subset of limbic regions before progressing to the temporal cortex and beyond. The DLB atrophy pattern diverges considerably (off‐diagonal density in Figure 1), including involvement of additional limbic regions early on. Figure 2 shows patient staging results for each model, with MCI individuals earlier than patients as expected. Differential diagnosis accuracy was 63%, which is comparable to previous studies.ConclusionWe successfully adapted event‐based modeling of MRI atrophy for differential diagnosis of AD vs DLB. More data for other dementias, especially in the prodromal phase, is needed to test and deploy such a tool in memory clinics.

Volunteers’ Stewardship Action-Taking Experiences During the COVID-19 Pandemic Predicted by Their Motivation Toward Engagement and Ability to Teach Others

The current study investigates the relationship between volunteer stewardship action-taking, motivation toward engagement in volunteer activities, and the ability to teach others during the COVID-19 pandemic. The final data set included 1,196 responses from the Penn State Extension Master Gardeners and Master Watershed volunteers, which provided a response rate of 39.9%. The results of this study showed that approximately 15.1% of the variation in volunteer stewardship action-taking experiences could be explained by volunteers’ motivation toward engagement in volunteer activities and their ability to teach others. Most findings are in line with previous research. Extension and outreach educators and volunteer coordinators can better prepare their Master Gardeners and Master Watershed volunteers for effective responses to their community needs, especially in times of uncertainty. More research is needed in volunteer stewardship action-taking experiences during times of uncertainty and change.

Association between a maternal vegetarian diet during pregnancy and the occurrence of atopic dermatitis in children.

Atopic dermatitis (AD) is rising globally, with genetics and environmental factors both playing crucial roles. Dietary habits during pregnancy are linked to children's allergic disease risk. However, limited studies have explored the association between maternal vegetarian diets during pregnancy and child AD. Therefore, this study aimed to examine the relationship between maternal vegetarian diets during pregnancy and the occurrence of AD in children. In this study, the Taiwan Birth Cohort Study (TBCS) database was used, comprising a representative national birth cohort of infants born in Taiwan in 2005. Of 24,200 mother-child pairs in the database, 20,172 completed face-to-face interviews at 6 and 18 months. Employing a 1:10 matching strategy based on maternal age, education level, and child sex, 408 mothers who followed a vegetarian diet during pregnancy were matched with 4080 nonvegetarian mothers. This resulted in a final dataset of 4488 subjects. Logistic regression was used to explore the association between maternal vegetarian diets during pregnancy and the occurrence of AD in children. Among the TBCS participants, there were 292 (1.8%) mothers who adhered to lacto-ovo vegetarianism and 116 (0.7%) mothers who adhered to veganism, totaling 408 (2.4%) vegetarians during pregnancy. Compared to children of nonvegetarian mothers, children of mothers who followed a vegetarian diet during pregnancy showed a lower risk of developing AD before 18 months of age (OR = 0.65, 95% CI = 0.45-0.93, p = 0.018). This study suggests that a vegetarian diet during pregnancy may lower the risk of AD in children. It is essential to carry out long-term follow-up to fully understand the impact of a mother's diet on allergic conditions.

Abstract A017: Evaluation of ¡Salud! por la Vida, an educational intervention to increase colorectal cancer screening in federally qualified health centers in Puerto Rico

Abstract Introduction: Colorectal cancer (CRC) is the leading cause of cancer-related deaths in Puerto Rico (PR) and third among Hispanics in the United States (US). Screening rates in PR are relatively low compared to the mainland US. Objective: To evaluate the effectiveness of “¡Salud! por la Vida”, an evidence-based educational intervention designed to increase CRCS. Methods: The educational intervention “¡Salud! por la Vida” (SPLV) was implemented in 10 Federally Qualified Health Clinics (FQHC) in PR to increase CRC screening (CRCS) rates in non-adherent patients aged 50 to 75. SPLV was conducted as a group randomized controlled trial from July 2017 to July 2020. The intervention included a baseline survey collecting 198 variables and a follow-up survey 4 to 6 months later, which included 148 variables to measure the effectiveness of the educational program in increasing CRCS. The initial surveys had some changes to improve the data entry process. Consequently, the database required an updated data analysis consisting of data exploration, data cleaning, and statistical analysis. Prior to calculating results, data cleaning included identifying missing variables, inconsistencies in the data, and missing data. The final dataset for this project concerns participants who completed the follow-up survey, divided into intervention and control groups. The analysis calculated basic demographics in proportions, such as age, education, sex, marital status, annual income, health insurance coverage, and first-degree family history of CRC. Furthermore, the two groups were compared regarding CRCS rates, specifically those of colonoscopy, fecal occult blood test (FOBT), and having completed any CRCS. Results: A total of 445 individuals participated in the study. At baseline, 332 were from the control group and the rest from the intervention group. The follow-up survey was completed by 295 participants in the control group and 78 participants in the intervention group, for a total of 373 at follow-up. The mean age of participants who completed the study was 58.9, with 68.9% of participants being women. 70.5% had an education of high school or less, 45.3% were married or living together, 93% earned less than $20,000 a year, 87.9% had government health insurance, and 88.2% had no first-degree family history of CRC. CRCS tests were completed by 60.2% of those in the intervention group and 38.3% of control group participants (p-value 0.001). FOBTs were done by 55.1% and 36.9% of the intervention and control group participants, respectively (p-value 0.005). In the intervention group, 10% of the participants completed a colonoscopy, while in the control group, 3% had completed a colonoscopy (p-value 0.02). Conclusion: CRCS rates were higher in the intervention groups, for both colonoscopy (p-value 0.008) and FOBT (p-value 0.003), as well as any CRCS test (p-value 0.0004). The SPLV intervention significantly increased CRC screening rates and provided evidence to disseminate this educational effort to FQHCs in PR. Citation Format: Jomarie Jiménez-González, Fernando E. Betancourt-Vélez, Ileska M. Valencia-Torres, Yara Sánchez-Cabrera, Luis R. Pericchi-Guerra, María E. Fernández, Vivian Colón-López. Evaluation of ¡Salud! por la Vida, an educational intervention to increase colorectal cancer screening in federally qualified health centers in Puerto Rico [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A017.