Identification Of Individuals Research Articles

Variability in Pine Pitch Canker Susceptibility among Scots Pine (Pinus sylvestris) Provenances in Eastern Europe

Pine pitch canker, caused by the ascomycete Fusarium circinatum, poses a substantial threat to pine trees and Douglas firs (Pseudotsuga menziesii), and has been identified as a pervasive issue in forests and nurseries worldwide, particularly in regions where susceptible conifers are cultivated. Given its prevalence in the Iberian Peninsula, assessments of the susceptibility of diverse European provenances of Scots pine (Pinus sylvestris)—specifically those from Poland, Lithuania, and Ukraine—have been conducted. Preliminary evaluations of Polish provenances have raised concerns about the potential threat to Scots pine stands in Poland posed by pitch canker. Under controlled conditions, we examined the impact of F. circinatum inoculation on the survival of seeds and seedlings from ten provenances of Scots pine. In response, the initial assessment of F. circinatum pathogenicity was undertaken in a controlled greenhouse environment. This evaluation uncovered a heightened susceptibility of pine seedlings to pitch canker among the tested provenances. Notably, one Lithuanian provenance demonstrated superior resistance to pitch canker, while two Polish provenances exhibited a higher prevalence of symptomless seedlings. These findings underscore the need for further exploration and identification of resilient individuals within these provenances, offering valuable insights for developing strategies to mitigate the impact of pitch canker on Scots pine in Europe.

Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments

Specific emitter identification is pivotal in both military and civilian sectors for discerning the unique hardware distinctions inherent to various launchers, it can be used to implement security in wireless communications. Recently, a large number of deep learning-based methods for specific emitter identification have been proposed, achieving good performance. However, these methods are trained based on a large amount of data and the data are independently and identically distributed. In actual complex environments, it is very difficult to obtain reliable labeled data. Aiming at the problems of difficulty in data collection and annotation, and the large difference in distribution between training data and test data, a method for individual radiation source identification based on ensemble domain adversarial neural network was proposed. Specifically, a domain adversarial neural network is designed and a Transformer encoder module is added to make the features obey Gaussian distribution and achieve better feature alignment. Ensemble classifiers are then used to enhance the generalization and reliability of the model. In addition, three real and complex migration environments, Alpine–Montane Channel, Plain-Hillock Channel, and Urban-Dense Channel, were constructed, and experiments were conducted on WiFi dataset. The simulation results show that the proposed method exhibits superior performance compared to the other six methods, with an accuracy improvement of about 3%.

Clusters of longitudinal risk profile trajectories are associated with cardiometabolic diseases: Results from the population-based KORA cohort.

Multiple risk factors contribute jointly to the development and progression of cardiometabolic diseases. Therefore, joint longitudinal trajectories of multiple risk factors might represent different degrees of cardiometabolic risk. We analyzed population-based data comprising three examinations (Exam 1: 1999-2001, Exam 2: 2006-2008, Exam 3: 2013-2014) of 976 male and 1004 female participants of the KORA cohort (Southern Germany). Participants were followed up for cardiometabolic diseases, including cardiovascular mortality, myocardial infarction and stroke, or a diagnosis of type 2 diabetes, until 2016. Longitudinal multivariate k-means clustering identified sex-specific trajectory clusters based on nine cardiometabolic risk factors (age, systolic and diastolic blood pressure, body-mass-index, waist circumference, Hemoglobin-A1c, total cholesterol, high- and low-density lipoprotein cholesterol). Associations between clusters and cardiometabolic events were assessed by logistic regression models. We identified three trajectory clusters for men and women, respectively. Trajectory clusters reflected a distinct distribution of cardiometabolic risk burden and were associated with prevalent cardiometabolic disease at Exam 3 (men: odds ratio (OR)ClusterII = 2.0, 95% confidence interval: (0.9-4.5); ORClusterIII = 10.5 (4.8-22.9); women: ORClusterII = 1.7 (0.6-4.7); ORClusterIII = 5.8 (2.6-12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1-15.6); ORClusterIII = 7.5 (2.4-32.7); women: ORClusterII = 5.0 (1.1-34.1); ORClusterIII = 8.0 (2.2-51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham). On a population-based level, distinct longitudinal risk profiles over a 14-year time period are differentially associated with cardiometabolic events. Our results suggest that longitudinal data may provide additional information beyond single time-point measures. Their inclusion in cardiometabolic risk assessment might improve early identification of individuals at risk.

Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease.

Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs). Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACE: fatal and non-fatal myocardial infarction, stroke and coronary artery revascularisation) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC=0.771, p<001, NRI=0.140, and AUC=0.767, p=0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not. Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

Multilingualism and literacy development in interlingual families

Heritage languages are key to shaping the identity of many individuals who grow up in environments where the dominant societal language is different from their home languages. Yet heritage language learners can be incredibly diverse in terms of cultural and language backgrounds, language proficiency, literacy skills, language socialization experiences and in many other ways. Heritage language education and literacy development, in particular, have been examined in both formal and community-based educational settings. Insights drawn from this growing area of research have informed our understanding of challenges faced by heritage language learners in relation to literacy socialization, such as a lack of educational resources and community support. A subset of this research examines the issues faced by mixed-heritage language families in relation to literacy. This article reports on the qualitative phase of a mixed-method study on the language and literacy socialization experiences of interlingual families in Canada with mothers of Japanese descent. The findings highlight the multiple challenges faced by the participants in relation to the development of Japanese literacy. It draws attention to the complexity of their family lives, and how the promotion of multilingualism in the two official languages of Canada comes at the expense of Japanese literacy skills for their children.

Validation of a fall rate prediction model for community-dwelling older adults: a combined analysis of three cohorts with 1850 participants

BackgroundFragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction.MethodsA combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage.ResultsThe coefficient estimates for the prior number of falls were consistent among the three studies. Higgin’s I2 as heterogeneity measure ranged from 0 to 55.39%. The overall coefficient estimates indicated that the expected fall rate increases with an increasing number of previous falls. External model validation revealed that the prediction errors for the data sets were independent of the model to which they were applied.ConclusionThis analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.

Derivation and validation of a mortality risk prediction model in older adults needing home care: Updating the RESPECT (Risk Evaluation for Support: Predictions for Elder-Life in their Communities Tool) algorithm for use with data from the interRAI Home Care Assessment System

Background Despite an increasing number of risk prediction models being developed within the healthcare space, few have been widely adopted and evaluated in clinical practice. RESPECT, a mortality risk communication tool powered by a prediction algorithm, has been implemented in the home care setting in Ontario, Canada, to support the identification of palliative care needs among older adults. We sought to re-estimate and validate the RESPECT algorithm in contemporary data. Methods The study and derivation cohort comprised adults living in Ontario aged 50 years and older with at least 1 interRAI Home Care (interRAI HC) record between April 1, 2018 and September 30, 2019. Algorithm validation used 500 bootstrapped samples, each containing a 5% random selection from the total cohort. The primary outcome was mortality within 6 months following an interRAI HC assessment. We used proportional hazards regression with robust standard errors to account for clustering by the individual. Kaplan–Meier survival curves were estimated to derive the observed risk of death at 6 months for assessment of calibration and median survival. Finally, 61 risk groups were constructed based on incremental increases in the observed median survival. Results The study cohort included 247,377 adults and 35,497 deaths (14.3%). The mean predicted 6-month mortality risk was 18.0% and ranged from 1.5% (95% CI 1.0%–1.542%) in the lowest to 96.0 % (95% CI 95.8%–96.2%) in the highest risk group. Estimated median survival spanned from 36 days in the highest risk group to over 3.5 years in the lowest risk group. The algorithm had a c-statistic of 0.76 (95% CI 0.75-0.77) in our validation cohort. Conclusions RESPECT demonstrates good discrimination and calibration. The algorithm, which leverages routinely-collected information, may be useful in home care settings for earlier identification of individuals who might be nearing the end of life.

Evaluating the performance of semiautomated photographic identification programs for leopard seals

AbstractThe photographic identification (photo‐ID) of individual animals can be time‐consuming and erroneous. Recent efforts to collect photographs of leopard seals (Hydrurga leptonyx) from across their range have necessitated the technological streamlining of photo‐ID. We constructed a dataset containing 595 photographs of the head and body of individuals recorded in New Zealand between 2008 and 2022 to test the performance of semiautomated 3 photo‐ID programs: HotSpotter, Interactive Individual Identification System's Pattern+ (I3S), and Wild‐ID. We classified attributes of photographs (e.g., quality) and individuals (i.e., pelage patterns) to assess their effect on performance. We compared performance using Top20 and Top1 Accuracy, defined as the proportion of test photographs where the highest ranked correct identity was in the top 20 and top 1, respectively, matched reference photographs. HotSpotter outperformed I3S and Wild‐ID in both Top20 and Top1 Accuracy of most assessed attributes. Maximizing HotSpotter's performance may be achieved through several methods, including increasing the number and variety of photographs of individuals in the dataset. HotSpotter will likely perform better with photographs without obstructions (e.g., debris from beaches), such as on the pack ice of their primary Antarctic range. We highlight the viability of HotSpotter in assisting the photo‐ID of leopard seals, and more broadly, other species with similar markings.

Single nucleotide polymorphisms of ANKK1, DDR4, and GRIN2B genes predict behavior in a prospective cohort of Mexican children and adolescents.

Numerous studies have established associations between single nucleotide polymorphisms (SNPs) and various behavioral and neurodevelopmental conditions. This study explores the links between SNPs in candidate genes involved in central nervous system (CNS) physiology and their implications for the behavioral and emotional aspects in children and teenagers. A total of 590 participants, aged 7-15 years, from the Early Life Exposures In Mexico To Environmental Toxicants (ELEMENT) cohort study in Mexico City, underwent genotyping for at least one of 15 CNS gene-related SNPs at different timepoints. We employed multiple linear regression models to assess the potential impact of genetic variations on behavioral and cognitive traits, as measured by the Behavioral Assessment System for Children (BASC) and Conners parent rating scales. Significant associations were observed, including the rs1800497 TC genotype (ANKK1) with the Cognitive Problems/Inattention variable (p value =0.003), the rs1800955 CT genotype (DDR4) with the Emotional Lability Global index variable (p value =0.01), and the rs10492138 GA and rs7970177 TC genotypes (GRIN2B) with the Depression variable (p values 0.007 and 0.012, respectively). These finds suggest potential genetic profiles associated with "risk" and "protective" behaviors for these SNPs. Our results provide valuable insights into the role of genetic variations in neurobehavior and highlight the need for further research in the early identification and intervention in individuals at risk for these conditions.

Impaired olfactory identification in dementia-free individuals is associated with the functional abnormality of the precuneus

ObjectiveOlfactory dysfunction indicates a higher risk of developing dementia. However, the potential structural and functional changes are still largely unknown. MethodsA total of 236 participants were enrolled, including 45 Alzheimer's disease (AD) individuals and 191dementia-free individuals. Detailed study methods, comprising neuropsychological assessment and olfactory identification test (University of Pennsylvania smell identification test, UPSIT), as well as structural and functional magnetic resonance imaging (MRI) were applied in this research. The dementia-free individuals were divided into two sub-groups based on olfactory score: dementia-free with olfactory dysfunction (DF-OD) sub-group and dementia-free without olfactory dysfunction (DF-NOD) sub-group. The results were analyzed for subsequent intergroup comparisons and correlations. The cognitive assessment was conducted again three years later. Results(i) At dementia-free stage, there was a positive correlation between olfactory score and cognitive function. (ii) In dementia-free group, the volume of crucial brain structures involved in olfactory recognition and processing (such as amygdala, entorhinal cortex and basal forebrain volumes) are positively associated with olfactory score. (iii) Compared to the DF-NOD group, the DF-OD group showed a significant reduction in olfactory network (ON) function. (iv) Compared to DF-NOD group, there were significant functional connectivity (FC) decline between PCun_L(R)_4_1 in the precuneus of posterior default mode network (pDMN) and the salience network (SN) in DF-OD group, and the FC values decreased with falling olfactory scores. Moreover, in DF-OD group, the noteworthy reduction in FC were observed between PCun_L(R)_4_1 and amygdala, which was a crucial component of ON. (v) The AD conversion rate of DF-OD was 29.41%, while the DF-NOD group was 12.50%. The structural and functional changes in the precuneus were also observed in AD and were more severe. ConclusionsIn addition to the olfactory circuit, the precuneus is a critical structure in the odor identification process, whose abnormal function underlies the olfactory identification impairment of dementia-free individuals.

Update nutritional management in patients with pancreatic ductal adenocarcinoma: A literature reviews

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. Only 20 % of patients are operable at the time of diagnosis. However, the survival rate varies among patients depending on immune response and nutritional status. Weight loss (WL) is frequent and multifactorial, so anorexia-cachexia syndrome and exocrine pancreatic insufficiency (EPI) should be considered. The nutritional care process must be carried out in all cases from the moment of suspected diagnosis and during the disease. The management of patients with PDAC must be personalized, individualizing behaviors, and from a multidisciplinary perspective at each stage of the disease. The objective of the present review is to offer a tool that facilitates the identification of individuals at nutritional risk and to describe the most appropriate nutritional strategies to improve the quality of care for patients with PDAC.

Gender classification based on gait analysis using ultrawide band radar augmented with artificial intelligence

The identification of individuals based on their walking patterns, also known as gait recognition, has garnered considerable interest as a biometric trait. The use of gait patterns for gender classification has emerged as a significant research domain with diverse applications across multiple fields. The present investigation centers on the classification of gender based on gait utilizing data from Ultra-wide band radar. A total of 181 participants were included in the study, and data was gathered using Ultra-wide band radar technology. This study investigates various preprocessing techniques, feature extraction methods, and dimensionality reduction approaches to efficiently process Ultra-wide band radar data. The data quality is improved through the utilization of a two-pulse canceller and discrete wavelet transform. The hybrid feature dataset is generated through the creation of gray-level co-occurrence matrices and subsequent extraction of statistical features. Principal Component Analysis is utilized for dimensionality reduction, and prediction probabilities are incorporated as features for classification optimization. The present study employs k-fold cross-validation to train and assess machine learning classifiers, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, K-Nearest Neighbors, and Extra Tree Classifier. The Multilayer Perceptron exhibits superior performance, achieving an accuracy of 0.936. The Support Vector Machine and k-Nearest Neighbors classifiers closely trail behind, both achieving an accuracy of 0.934. This research is of the utmost importance due to its capacity to offer solutions to crucial problems in multiple domains. The findings indicate that the utilization of UWB radar data for gait-based gender classification holds promise in diverse domains, including biometrics, surveillance, and healthcare. The present study makes a valuable contribution to the progress of gender classification systems that rely on gait patterns.

Sarcopenia and Sarcopenic Obesity and Mortality Among Older People

Sarcopenia and obesity are 2 global concerns associated with adverse health outcomes in older people. Evidence on the population-based prevalence of the combination of sarcopenia with obesity (sarcopenic obesity [SO]) and its association with mortality are still limited. To investigate the prevalence of sarcopenia and SO and their association with all-cause mortality. This large-scale, population-based cohort study assessed participants from the Rotterdam Study from March 1, 2009, to June 1, 2014. Associations of sarcopenia and SO with all-cause mortality were studied using Kaplan-Meier curves, Cox proportional hazards regression, and accelerated failure time models fitted for sex, age, and body mass index (BMI). Data analysis was performed from January 1 to April 1, 2023. The prevalence of sarcopenia and SO, measured based on handgrip strength and body composition (BC) (dual-energy x-ray absorptiometry) as recommended by current consensus criteria, with probable sarcopenia defined as having low handgrip strength and confirmed sarcopenia and SO defined as altered BC (high fat percentage and/or low appendicular skeletal muscle index) in addition to low handgrip strength. The primary outcome was all-cause mortality, collected using linked mortality data from general practitioners and the central municipal records, until October 2022. In the total population of 5888 participants (mean [SD] age, 69.5 [9.1] years; mean [SD] BMI, 27.5 [4.3]; 3343 [56.8%] female), 653 (11.1%; 95% CI, 10.3%-11.9%) had probable sarcopenia and 127 (2.2%; 95% CI, 1.8%-2.6%) had confirmed sarcopenia. Sarcopenic obesity with 1 altered component of BC was present in 295 participants (5.0%; 95% CI, 4.4%-5.6%) and with 2 altered components in 44 participants (0.8%; 95% CI, 0.6%-1.0%). An increased risk of all-cause mortality was observed in participants with probable sarcopenia (hazard ratio [HR], 1.29; 95% CI, 1.14-1.47) and confirmed sarcopenia (HR, 1.93; 95% CI, 1.53-2.43). Participants with SO plus 1 altered component of BC (HR, 1.94; 95% CI, 1.60-2.33]) or 2 altered components of BC (HR, 2.84; 95% CI, 1.97-4.11) had a higher risk of mortality than those without SO. Similar results for SO were obtained for participants with a BMI of 27 or greater. In this study, sarcopenia and SO were found to be prevalent phenotypes in older people and were associated with all-cause mortality. Additional alterations of BC amplified this risk independently of age, sex, and BMI. The use of low muscle strength as a first step of both diagnoses may allow for early identification of individuals at risk for premature mortality.

Confronting Barriers to Equitable Care: A Qualitative Analysis of Factors That Inform People with Mobility Disabilities’ Decisions to Self-Advocate in the Context of Healthcare

Background. One in 5 people in the United States live with disability; however, the public health needs of this community have been largely overlooked. Although U.S. law mandates the availability of accessible medical exam equipment, people with mobility disabilities (PWMD) frequently encounter barriers that require self-advocacy to receive basic primary care. Objective. The purpose of this study was to qualitatively explore the impact of healthcare access barriers—specifically, the experiences of the need to self-advocate and factors that inform decisions to make accommodation requests—for PWMD. Methods. Qualitative semistructured interviews were performed across two phases of data collection with each of the 6 participants. Interpretative phenomenological analysis was used to highlight the essence of participants’ experiences in making decisions to request healthcare accommodation. Results. Participants included a purposive sample of self-advocates with physical disabilities (3 men and 3 women). While individual approaches to self-advocacy varied, participants identified a process of “recognizing the normalization of disability discrimination and disability stigma” which necessitates the development of “agency in self-advocacy.” This process has a lasting impact on people that includes a shared embodied experience of disability, as well as a sense of empowerment based on their collective and individual identities. Conclusions. Findings have implications for supporting individual disability consumer advocacy efforts as well as the need to address the normalization of disability discrimination within healthcare systems levels.

Abstract 1015: Potential of kava in reducing lung cancer risk and associated disparities: Mechanism-based biomarker discovery and analysis

Abstract Background: Lung cancer is the leading cause of cancer-related deaths. Tobacco smoke is well-recognized as the dominant risk factor. Stress, lung inflammation, air pollution and other factors also contribute to the risk of developing lung cancer Racial/ethnic disparities in lung cancer risks exit as well. The quantitative contributions of different these risk factors to lung cancer and associated disparities, however, have not been systematically characterized. One major barrier is the lack of tools to non-invasively and objectively quantify these risk factors, which precludes the identification of high-risk individuals and the development of effective prevention. Data from our group, particularly results from a pilot clinical trial, suggest that kava, traditionally consumed in the South Pacific Islands as a beverage to reduce stress and promote relaxation, may holistically reduce lung cancer risk by impacting multiple factors that contribute to lung cancer risk. Aim: This study aims to discover mechanism-based non-invasive biomarkers reflective of different lung cancer risk factors and explore their potential roles in lung cancer risk disparities. Such biomarkers are also employed to characterize the holistic effects of kava in reducing lung cancer risk and its potential in mitigating associated disparities. Results and Analysis: One-week of kava significantly reduced the level of nicotine exposure among smokers, quantified by the Total Nicotine Equivalents (TNE) in the 24-h urine sample. Excitingly the reduction in TNE appeared to be higher among African American smokers in comparison to participants of other race/ethnicity. Biomarkers for stress (plasma cortisol and urinary total cortisol equivalents (TCE)) were significantly reduced upon one-week kava use, which may mechanistically contribute to the reductions in tobacco use. The reduction in plasma cortisol and TCE were higher among African American smokers as well. One-week kava exposure also increased urinary excretion of total NNAL and reduced urinary 3-methyladenine (3-mA) in participants, suggestive of its ability to reduce the carcinogenicity of nicotine-derived nitrosamine ketone (NNK). Kava effects on NNAL and 3mA were again more pronounced among African American smokers. Conclusion and future directions: These results demonstrate the potential of mechanism-based biomarkers in investigating lung cancer risks and associated disparities. Such biomarkers may also facilitate the translational development of effective and targeted interventions, such as kava, which may not only reduce lung cancer risk but also mitigate the associated disparities. Additional mechanism-based biomarkers on other tobacco carcinogens and inflammation are currently under development. Two clinical trials are ongoing that the current clinical samples will help validate these discoveries. Citation Format: Chengguo Xing, Breanne Freeman, Jessica Mamallapalli, Allison Lynch, Naomi Fujioka, Ramzi G. Salloum, John Malaty, Frank Orlando, Zhiguang Huo. Potential of kava in reducing lung cancer risk and associated disparities: Mechanism-based biomarker discovery and analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1015.

Plasma biomarkers for Alzheimer's and related dementias: A review and outlook for clinical neuropsychology.

Recent technological advances have improved the sensitivity and specificity of blood-based biomarkers for Alzheimer's disease and related dementias. Accurate quantification of amyloid-ß peptide, phosphorylated tau (pTau) isoforms, as well as markers of neurodegeneration (neurofilament light chain [NfL]) and neuro-immune activation (glial fibrillary acidic protein [GFAP] and chitinase-3-like protein 1 [YKL-40]) in blood has allowed researchers to characterize neurobiological processes at scale in a cost-effective and minimally invasive manner. Although currently used primarily for research purposes, these blood-based biomarkers have the potential to be highly impactful in the clinical setting - aiding in diagnosis, predicting disease risk, and monitoring disease progression. Whereas plasma NfL has shown promise as a non-specific marker of neuronal injury, plasma pTau181, pTau217, pTau231, and GFAP have demonstrated desirable levels of sensitivity and specificity for identification of individuals with Alzheimer's disease pathology and Alzheimer's dementia. In this forward looking review, we (i) provide an overview of the most commonly used blood-based biomarkers for Alzheimer's disease and related dementias, (ii) discuss how comorbid medical conditions, demographic, and genetic factors can inform the interpretation of these biomarkers, (iii) describe ongoing efforts to move blood-based biomarkers into the clinic, and (iv) highlight the central role that clinical neuropsychologists may play in contextualizing and communicating blood-based biomarker results for patients.

A Proposed High-Level Methodology on How OSINT is applied in Blockchain Investigations

The characteristics of blockchain established a desirable platform for entities to innovate and operate in a secure, transparent, and decentralised manner. However, cybercriminals have increasingly found refuge in the decentralised environment of blockchain technology. Cryptocurrencies are increasingly misused in malicious activities that encompass the trade of illicit goods, money laundering, various types of scams and ransomware attacks. The total cryptocurrency value received by illicit addresses reached an all-time high of $20.6 billion in 2022 according to Chainalysis. The inherent privacy and anonymity features of many blockchain networks make it challenging for law enforcement and regulatory agencies to track and apprehend wrongdoers. Consequently, a pressing need arises not only to initiate investigations on the blockchain to identify unlawful activities, but also to discover connections between these activities and the identities of the responsible individuals. Due to blockchain data being publicly available, the application of Open-Source Intelligence (OSINT) techniques is proposed to facilitate these types of investigations. In the context of blockchain, OSINT, together with investigation tools hold the promise of unearthing valuable information that could aid in attributing malicious activities to the individuals responsible for those actions. By analysing and synthesizing data from publicly accessible sources, such as data from blockchain explorers and link analysis tools such Chainalysis, Maltego or Spiderfoot, investigators could potentially unveil valuable clues that assist in building a comprehensive picture of blockchain-related criminal activities. Ultimately, with sufficient information and actionable intelligence collected, the main goal is to link it to Know Your Customer (KYC) data, that could be obtained from cryptocurrency exchanges via a subpoena from law enforcement agencies. This paper delves into the mechanisms of various OSINT tools and techniques, to determine their adaptability to the specific demands of blockchain investigations. This study provides a methodology and recommendations with insights into how these tools can be wielded to bridge the gap between blockchain's pseudonymity and real-world identities.

Risk prediction and interaction analysis using polygenic risk score of type 2 diabetes in a Korean population

Joint modelling of genetic and environmental risk factors can provide important information to predict the risk of type 2 diabetes (T2D). Therefore, to predict the genetic risk of T2D, we constructed a polygenic risk score (PRS) using genotype data of one Korean cohort, KARE (745 cases and 2549 controls), and the genome-wide association study summary statistics of Biobank Japan. We evaluated the performance of PRS in an independent Korean cohort, HEXA (5684 cases and 35,703 controls). Individuals with T2D had a significantly higher mean PRS than controls (0.492 vs. − 0.078, p≈0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 0$$\\end{document}). PRS predicted the risk of T2D with an AUC of 0.658 (95% CI 0.651–0.666). We also evaluated interaction between PRS and waist circumference (WC) in the HEXA cohort. PRS exhibited a significant sub-multiplicative interaction with WC (ORinteraction 0.991, 95% CI 0.987–0.995, pinteraction = 4.93 × 10–6) in T2D. The effect of WC on T2D decreased as PRS increased. The sex-specific analyses produced similar interaction results, revealing a decreased WC effect on T2D as the PRS increased. In conclusion, the risk of WC for T2D may differ depending on PRS and those with a high PRS might develop T2D with a lower WC threshold. Our findings are expected to improve risk prediction for T2D and facilitate the identification of individuals at an increased risk of T2D.

PCOS Disease Prediction Using Machine Learning Algorithms

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting reproductive-aged women, characterized by hormonal imbalances, irregular menstrual cycles, and the presence of cysts on the ovaries. One of the most worrying diseases today is Polycystic Ovarian Syndrome (PCOS) which is highly dangerous to an extent of affecting women's reproductive life to a huge extent. The dataset includes a diverse set of features such as age, body mass index (BMI), hormonal levels, menstrual irregularities, and lifestyle factors. We explore various machine learning algorithms, including linear regression, decision tree, and random forests to identify the most effective model for PCOS prediction. [1] The study focuses on the development of a robust and clinically applicable predictive model that can aid healthcare professionals in early identification of individuals at risk of PCOS. The results obtained from this research have the potential to significantly impact the field of women's health by offering a reliable and efficient tool for PCOS prediction. Early identification of individuals at risk can facilitate timely interventions, personalized treatment plans, and improved outcomes. Furthermore, the study contributes to the growing body of literature on the application of machine learning in healthcare, demonstrating its potential as a valuable tool for predictive modelling in complex endocrine disorders such as PCOS.

Государство и этническая идентичность: противостояние или союз?

Introduction. The article is devoted to the analysis of the relationship between the state and ethnic group, state and ethnic identity. It reveals the mechanisms of state formation based on the development of cultural baggage created within the framework of the ethnic communicative space. Theoretical analysis. One of the distinctive features of modern states is the complex nature of the identities that comprise their citizens. The article shows in what ways and due to what the president of the state asserts the beginning of the state. On the one hand, the state is trying to bring in the best examples of ethnic culture. On the other hand, it seeks to use the possibilities of new types of identity in its manifestations (digital identity). Materials borrowed from other types of identity are improved and adapted to the situation of their use in the process of development. They tend to be integrated into modern mass culture. In the process of development of the state, there is a change in the balance of “I” and “We” identities towards strengthening individual identity. The state is trying to counter this. Conclusion. The state uses ethnic communicative space and ethnic identity as a source of borrowing valuable plots, historical characters, and holidays. It also seeks to attract to its side ethnic activists who are the carriers of this information.