Standard Z-score Research Articles

NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach.

Non-invasive prenatal testing (NIPT) is a quite popular approach for detecting fetal genomic aneuploidies. However, due to the limitations on sequencing read length and coverage, NIPT suffers a bottleneck on further improving performance and conducting earlier detection. The errors mainly come from reference biases and population polymorphism. To break this bottleneck, we proposed NIPT-PG, which enables the NIPT algorithm to learn from population data. A pan-genome model is introduced to incorporate variant and polymorphic loci information from tested population. Subsequently, we proposed a sequence-to-graph alignment method, which considers the read mis-match rates during the mapping process, and an indexing method using hash indexing and adjacency lists to accelerate the read alignment process. Finally, by integrating multi-source aligned read and polymorphic sites across the pan-genome, NIPT-PG obtains a more accurate z-score, thereby improving the accuracy of chromosomal aneuploidy detection. We tested NIPT-PG on two simulated datasets and 745 real-world cell-free DNA sequencing data sets from pregnant women. Results demonstrate that NIPT-PG outperforms the standard z-score test. Furthermore, combining experimental and theoretical analyses, we demonstrate the probably approximately correct learnability of NIPT-PG. In summary, NIPT-PG provides a new perspective for fetal chromosomal aneuploidies detection. NIPT-PG may have broad applications in clinical testing, and its detection results can serve as a reference for false positive samples approaching the critical threshold.

Abstract 3505: Classification of cancer subtypes by cfDNA fragmentomics analysis

Abstract Introduction Fragmentation patterns of cell-free DNA (cfDNA) from whole genome sequencing offer insights into nucleosome occupancy, providing a tool to infer epigenomic and transcriptomic information. The nucleosome complex, the primary protector of cfDNA, is reflected in the size distribution of cfDNA fragments, with a mode fragment size of 167 bp corresponding to the wrapping of DNA around a single nucleosome. Nucleosome occupancy patterns at transcription factor binding sites (TFBS) exhibit differences between circulating tumor DNA in cancer patients and normal plasma controls, as well as among different tumor subtypes. Therefore, cfDNA fragmentomics can be employed for cancer subtyping analysis. Methods We utilized the Griffin framework to classify tumor subtypes based on nucleosome profiling of cancer-specific TFBS and tumor subtype-specific chromatin accessibility regions from low-pass whole genome sequencing data of cfDNA. For prostate cancer subtyping, AR and ASCL1 binding sites were used to distinguish between androgen receptor-dependent prostate cancer (ARPC) and neuroendocrine prostate cancer (NEPC). ER and ERBB2 were employed for breast cancer subtyping to differentiate between ER-positive and ER-negative tumor subtypes. Results Nucleosome profiling patterns at AR binding sites (ARBS) were compared among over 500 prostate cancer plasma samples and 42 normal plasma backgrounds. The ARBS nucleosome profiling abnormality score (ARBS score) was quantified by comparing its GC-corrected fragment central coverage with the normal background using a standard Z-score.Majority of prostate cancer samples with a high ARBS score could be classified as ARPC. For 12 samples with high tumor fraction but low ARBS score and no AR copy number amplification, we found that 8 had a high ASCL1 nucleosome profiling abnormality score. All these samples also exhibited RB1 copy number loss, suggesting that these 8 samples likely belong to NEPC. Nucleosome profiling of 171 breast cancer plasma samples at breast cancer-specific ER binding sites revealed that majority of samples had a high ERBS abnormality score, indicating likely ER-positive breast cancer. Conclusion Our results demonstrate that nucleosome profiling of tumor cfDNA exhibits distinct patterns across different tumor types. Integrating nucleosome profiling at cancer-specific transcription factor binding sites can facilitate cancer subtyping, highlighting its potential clinical utility for patient stratification. Citation Format: Lisha Zhu, Chao Dai, Shuang Gan, Shidong Jia, Pan Du. Classification of cancer subtypes by cfDNA fragmentomics 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 3505.

Nucleosome profiling at AR binding sites (ARBS) to provide a prognostic tool for metastatic castration-resistant prostate cancer (mCRPC).

42 Background: Prostate cancer is the second leading cause of cancer death for men in the U.S., and metastatic castration-resistant prostate cancer (mCRPC) is the most advanced form. In previous work, we have reported prognostic significance of PTEN-PI3K-AKT pathway and DNA damage response aberrations detected in cell-free DNA (cfDNA) of mCRPC. Here we utilized nucleosome profiling pattern at androgen receptor (AR) binding sites to further investigate AR signaling pathway dysregulation of mCRPC. Methods: Using the next-generation sequencing PredicineScore liquid biopsy assay (low-pass whole genome sequencing, LP-WGS), we profiled cfDNA fragment coverage centered around AR binding sites (ARBS). For 118 mCRPC patient samples, we quantified sample-level ARBS nucleosome profiling abnormality score (ARBS score) by comparing its centric fragment coverage with normal plasma background using standard Z-score. ARBS score was correlated with clinical outcomes including PSA response rate (PSA RR), progression-free survival and overall survival (OS). Results: By comparing ARBS nucleosome profiling and normal plasma background, we found that mCRPC plasma samples had significant ARBS nucleosome depletion, reflective of transcription factor binding and persistent AR signaling. Notably, ARBS nucleosome depletion was prostate cancer specific, and did not occur in other examined cancer types. ARBS score was highly correlated with tumor fraction (R=0.82), indicating that high tumor fraction mCRPC sample had higher AR binding. Importantly, we found that high ARBS score (>2) in pre-treatment samples (n=86) was associated with significantly shorter OS and lower PSA RR compared with low ARBS (≤2) group for patients receiving AR pathway inhibitor (ARPI) or taxane chemotherapy (13.4 vs. 19.4 months, p=7.3×10-3, 45.7% vs. 77.5%, p=5.2×10-3). By integrating genome-wide DNA methylation profiling, we further found that ARBS was hypo-methylated in mCRPC patients, and identified a strong negative correlation between DNA methylation beta value and ARBS score (R=-0.83). Conclusions: ARBS nucleosome profiling abnormality inferred from LP-WGS can serve as a key prognostic tool for mCRPC patients. Patients with high ARBS score had worse clinical outcomes, highlighting the potential clinical utility of cfDNA fragmentomics analysis for mCRPC patient stratification. Integration of mutation, copy number variation and fragmentomics profiling of ARBS may further enhance our understanding of molecular aberrations and treatment outcomes in mCRPC.

Tingkat Kapasitas Ketahanan Komunitas Kasus: Bagian Wilayah Perkotaan Malang Utara

In 2020, the National Disaster Management Agency stated that Malang City is one of the cities with low vulnerability to disasters. Therefore, it is necessary to assess the resilience capacity of communities in the North Malang Urban Area that are vulnerable to social and environmental changes. This study uses the Resilient Capacity Index method to measure the resilience capacity of the community by considering ecological, physical, economic, human, and social aspects. The results of the study, in the form of an overview of aspects of community resilience in the northern Malang City area, can be used to support the development of a more resilient city. By measuring resilience capacity using a standard score or z-score, it is known that the North Malang Urban Area has a moderate community resilience capacity.

Hubungan Sedentary Lifestyle dengan Status Gizi Remaja pada Masa Pandemi Covid-19 di SMPN 14 Jember

More nutritional status occurs because of an imbalance between energy intake and energy expenditure. High energy intake is the result of excessive food consumption, while low energy expenditure is the result of a lack of physical activity. The sedentary lifestyle has increased during the Covid-19 Pandemic. Sedentary lifestyle triggers the risk of overweight and obesity in adolescents. This research uses an analytic observational design with a cross-sectional approach which aims to determine the relationship between sedentary lifestyle and nutritional status of adolescents. Respondents in this study were adolescents aged 13-15 years. The number of samples was 70 respondents used purposive sampling technique. The instrument used was the Adolescent Sedentary Activity Questionnaire (ASAQ) and measurement of nutritional status using standard anthropometric z-score tables to assess nutritional status in children aged 5-18 years. This study obtained the results of the Spearman rank test, obtained a p-value of 0.000 (<0.05) and a correlation coefficient of 0.470, which means that there is a significant and unidirectional relationship with moderate strength between the sedentary lifestyle and the nutritional status of adolescents. It can be concluded that with increasing sedentary lifestyle, nutritional status will also increase. Sedentary lifestyle is a factor associated with overweight status because low physical activity causes fat accumulation due to not burning calories.

Associations of healthy lifestyle and socioeconomic status with cognitive function in U.S. older adults

We investigated the complex relations of socioeconomic status (SES) and healthy lifestyles with cognitive functions among older adults in 1313 participants, aged 60 years and older, from the National Health and Nutrition Examination Survey 2011–2014. Cognitive function was measured using an average of the standardized z-scores of the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning and delayed recall tests, the Animal Fluency Test, and the Digit Symbol Substitution Test. Latent class analysis of family income, education, occupation, health insurance, and food security was used to define composite SES (low, medium, high). A healthy lifestyle score was calculated based on smoking, alcohol consumption, physical activity, and the Healthy-Eating-Index-2015. In survey-weighted multivariable linear regressions, participants with 3 or 4 healthy behaviors had 0.07 (95% CI 0.005, 0.14) standard deviation higher composite cognitive z-score, relative to those with one or no healthy behavior. Participants with high SES had 0.37 (95% CI 0.29, 0.46) standard deviation higher composite cognitive z-score than those with low SES. No statistically significant interaction was observed between healthy lifestyle score and SES. Our findings suggested that higher healthy lifestyle scores and higher SES were associated with better cognitive function among older adults in the United States.

The Correlation between Posyandu Compliance and Feeding Patterns with Stunting Incidence in The Working Area of Puskesmas Kerek Tuban East Java, Indonesia

Stunting is associated with an increased risk of morbidity and mortality as well as retarded motor and mental growth. The research design uses a correlational research method with a cross-sectional approach. This study to know the correlation Between Posyandu Compliance and Feeding Patterns with Stunting Incidents in Toddlers in the Working Area of the Kerek Health Center, Kerek District, Tuban Regency. This type of research is a correlational study with a cross-sectional approach. The population in this study were mothers and toddlers who came to Posyandu in the working area of the Puskesmas, Kerek District, Tuban Regency. Respondents in this study were 30 respondents who were taken by purposive sampling technique. The independent variable in this study was posyandu compliance and feeding patterns and the dependent variable in this study was stunting in toddlers. Data collection using the Child Feeding Questionnaire (CFQ) and KMS toddlers and toddlers' height was measured using a microtoise and converted into a standard Z-score. The results of research conducted on 30 respondents. Then the data were analyzed using the Spearmen's Rho test with a significance level of α = 0.05. The results of this study showed that there was a significant correlation between posyandu compliance with p-value 001 < 0.005 and feeding patterns with the incidence of stunting in toddlers aged 12-24 months with p-value 001 < 0.005. It was concluded that there was a correlation between posyandu compliance and the feeding pattern of stunted toddlers

Combined Model-Based Prediction for Non-Invasive Prenatal Screening

The risk of chromosomal abnormalities in the child increases with increasing maternal age. Although non-invasive prenatal testing (NIPT) is a safe and effective prenatal screening method, the accuracy of the test results needs to be improved owing to various testing conditions. We attempted to achieve a more accurate and robust prediction of chromosomal abnormalities by combining multiple methods. Here, three different methods, namely standard Z-score, normalized chromosome value, and within-sample reference bin, were used for 1698 reference and 109 test samples of whole-genome sequencing. The logistic regression model combining the three methods achieved a higher accuracy than any single method. In conclusion, the proposed method offers a promising approach for increasing the reliability of NIPT.

Application of Deep Learning Network in Bumper Warpage Quality Improvement

Based on the context of Industry 4.0 smart manufacturing and for the prediction of injection molding quality of automobile bumpers, this study proposes a deep learning network that combines artificial neural networks and recognizable performance evaluation methods to better achieve the prediction and control of product quality. A pressure sensor was used to monitor and collect real-time pressure data in the mold cavity of the bumper. The quality indicators reflecting the molding quality were selected, and the correlation between these indicators and the molding quality was evaluated using recognizable performance evaluation methods and Pearson’s correlation coefficient. The standard z-score was used to filter out the abnormal data in the experimental data, and the bumper critical length warpage was converted into different quality levels, and the bumper critical length warpage was defined as either “qualified” and “unqualified” in order to improve the prediction accuracy of the model. Through the experimental study of this research, the monitoring and control of bumper injection molding parameters was completed to control and improve the molding quality of the bumper.

Detecting outliers in a univariate time series dataset using unsupervised combined statistical methods: A case study on surface water temperature

The surface water temperature is a vital ecological and climate variable, and its monitoring is critical. An extensive sensor network measures the ocean, but outliers pervade the monitoring data due to the sudden change in the water surface level. No single algorithm can identify the outlier efficiently. Hence, this work aims to propose and evaluate the performance of three statistical-based outlier detection algorithms for the water surface temperature: 1) the Standard Z-Score method, 2) the Modified Z-Score coupled with decomposition, and 3) the Exponential Moving Average with the Coupled Modified Z-Score and decomposition. A threshold was set to flag the outlier values. The models' performance was evaluated using the F-score method. Results showed that an increase in outlier detection might reduce the precision of identifying the actual outlier. Based on the results, the Exponential Moving Average with the Modified Z-Score gave the highest F-score value (= 0.83) compared to the other two individual methods. Therefore, this proposed algorithm is recommended to detect outliers efficiently in large surface water temperature datasets.

108 Chronic cadmium exposure is associated with cognition among adults over age 60 in a representative US sample

OBJECTIVES/GOALS: To examine the relationship between chronic cadmium exposure and cognitive function in later life, we estimated the association of urinary cadmium concentration on composite cognitive score, an important marker of progression toward dementia, while accounting for diet and key co-pollutants tobacco use and lead exposure. METHODS/STUDY POPULATION: After excluding those missing cognitive data (141) or covariate data (190), we included 760 persons >/= 60 years of age from the National Health and Nutrition Examination Survey (NHANES), 2011-2014. Urinary cadmium reflects prolonged exposure: mean=0.41μg/L (standard deviation (SD)= 0.46μg/L). Concurrent with urine sampling, neuropsychological tests tapping memory, executive function, sustained attention and working memory were combined into a standardized z-score (mean 0, SD 1, 25th and 75th percentiles −0.68 and 0.72, respectively). We used linear models to estimate change in cognition per cadmium interquartile range, incorporating NHANES sampling weights, adjusting for demographic characteristics, diet, lead, and active tobacco use (classified by self-report or serum cotinine levels >10ng/mL). RESULTS/ANTICIPATED RESULTS: A baseline model showed that an IQR (0.38 μg/L) increase in urinary cadmium exposure was associated with a 13% standard deviation lower cognitive z-score (95%CI: −0.19, −0.06), after adjusting for sampling weight and urinary creatinine (measure of urine dilution). This association was attenuated to 7% standard deviation lower cognitive z-score (95% CI −0.13, −0.02) after adjusting additionally for demographic characteristic of sex, age, age^2, race/ethnicity, marital status, education level, and poverty income ratio. Models further adjusted for smoking status (active/former/never), blood lead concentration, and key dietary sources of cadmium showed IQR increase in urinary cadmium exposure associated with 7% standard deviation lower cognitive z-score (95%CI: −0.14, −0.01). DISCUSSION/SIGNIFICANCE: Our findings suggest cadmium exposure is associated with lower cognitive scores even after accounting for confounding influence of diet, tobacco use and lead exposure. Alternate explanations include selection bias due to dropping persons missing needed variables and using concurrent cognitive measures rather than cognitive measures of over time.

Carbamate pesticides exposure and delayed physical development at the age of seven: Evidence from the SMBCS study.

Carbamate pesticides are widely used in agriculture and cause widespread human exposure. The health effect of carbamates on physical development remains unclear. The current study aimed to explore the carbamate's health effect on physical development. Prenatal, 3-year-old, 7-year-old urinary carbofuranphenol concentration was measured by gas chromatography tandem mass spectrometry and adjusted by creatinine. Anthropometric indices were measured by standard method and z-score standardized. Generalized linear models (GLM) were using to assess associations between exposure measurements and anthropometric indices. The generalized estimate equation (GEE) was applied to analyze the association between multiperiod exposure and anthropometric indices, and time-interaction terms were used to exam health effect consistency of exposure in each period. Gender-stratified analysis were conducted according to results of gender-interaction terms to identify gender-specific effects. The gender-interaction term of prenatal exposure with height z-score was significant (β=-0.057; 95% CI: -0.113, -0.001; p=0.045). The 3-year-old carbofuranphenol level showed negative associations with weight z-score (β=-0.019; 95% CI: -0.038, -0.000; p=0.040), height z-score (β=-0.015; 95% CI: -0.028, -0.001; p=0.026), chest circumference (β=-0.086; 95% CI: -0.171, -0.001; p=0.046), and waist circumference (β=-0.128; 95% CI: -0.230, -0.026; p=0.014). No statistically significant trend was found for prenatal and 7-year-old carbofuranphenol levels. In GEEs, carbofuranphenol level was negatively associated with weight z-score (β=-0.103; 95% CI: -0.195, -0.011; p=0.027), height z-score (β=-0.087; 95% CI: -0.152, -0.022; p=0.008), and chest circumference (β=-0.472; 95% CI: -0.918, -0.026; p=0.037). Boy's height z-score was inversely associated with carbamate exposure (β=-0.140; 95% CI: -0.227, -0.053; p=0.001). Prenatal and postnatal carbamate exposure may affect physical developmental process.

Measuring bank risk: Forward-looking z-score

While the z-score has been widely used to evaluate bank risk, it is criticized as a backward-looking measure. We propose a forward-looking method to construct the z-score by incorporating analyst forecasts. Empirical results show that the forward-looking z-score can predict the movement of the standard z-score one quarter ahead of time, and its predictive ability on banks' downward risk is better than the standard z-score. Moreover, we find that the predictive ability of the forward-looking z-score improves after the Dodd-Frank Act of 2010, especially for large banks, showing the consequences of strengthened regulation and transparency. The forward-looking z-score is also significantly associated with the probability of default and market-based risk measures and can provide predictive signals for banks' future profitability. Overall, our findings suggest that the forward-looking z-score mitigates the shortcomings of the standard z-score and provides a reliable early warning signal for banks' future risk and performance.

Longitudinal associations between objective and perceived healthy food environment and diet: The Multi-Ethnic Study of Atherosclerosis

IntroductionResearch examining the influence of neighborhood healthy food environment on diet has been mostly cross-sectional and has lacked robust characterization of the food environment. We examined longitudinal associations between features of the local food environment and healthy diet, and whether associations were modified by race/ethnicity. MethodsData on 3634 adults aged 45–84 followed for 10 years were obtained from the Multi-Ethnic Study of Atherosclerosis. Diet quality was assessed using the Alternative Healthy Eating Index at Exam 1 (2000–2002) and Exam 5 (2010–2012). We assessed four measures of the local food environment using survey-based measures (e.g. perceptions of healthier food availability) and geographic information system (GIS)-based measures (e.g. distance to and density of healthier food stores) at Exam 1 and Exam 5. Random effects models adjusted for age, sex, education, moving status, per capita adjusted income, and neighborhood socioeconomic status, and used interaction terms to assess effect measure modification by race/ethnicity. ResultsNet of confounders, one standard z-score higher average composite local food environment was associated with higher average AHEI diet score (β=1.39, 95% CI: 1.05, 1.73) over the follow-up period from Exam 1 to 5. This pattern of association was consistent across both GIS-based and survey-based measures of local food environment and was more pronounced among minoritized racial/ethnic groups. There was no association between changes in neighborhood environment and change in AHEI score, or effect measure modification by race/ethnicity. ConclusionOur findings suggest that neighborhood-level food environment is associated with better diet quality, especially among racially/ethnically minoritized populations.

Implementation of Family Psychoeducation Therapy to Additional Height in Stunting Children

Indonesia is currently still facing serious problems regarding stunting. The high prevalence of stunting in Indonesia is a chronic problem related to poverty, low education, and inadequate environmental and health services. Efforts made are improving the nutritional status of the community. The role of parents is very important in fulfilling children's nutrition. For this reason, treatment is needed through an approach to the family that can have an impact on behavior change within the family in carrying out family functions caring for family members with stunting, one of which is implementing family psychoeducation therapy. The purpose of this study was to determine the effect of family psychoeducation therapy on height gain in stunting children. This study used a quasi experimental research method with a pretest-posttest design. The sampling technique used purposive sampling of 34 respondents consisting of 2 groups, namely 17 respondents in the intervention group and 17 respondents in the control group. Assessment using instruments from the WHO-NCHS Standard deviation Z-Score TB / U. The data analysis used was the dependent sample t 2 test and independent sample t 2 test for bivariate. The results showed that there was an effect of family psychoeducation therapy on height gain and there was a significant difference in the average height increase in stunting children between the intervention group and the control group.

A typology of dietary and anthropometric measures of nutritional need among children across districts and parliamentary constituencies in India, 2016.

BackgroundAnthropometry is the most commonly used approach for assessing nutritional need among children. Anthropometry alone, however, cannot differentiate between the two immediate causes of undernutrition: inadequate diet vs disease. We present a typology of nutritional need by simultaneously considering dietary and anthropometric measures, dietary and anthropometric failures (DAF), and assess its distribution among children in India.MethodsWe used the 2015-16 National Family Health Survey, a nationally representative sample of children aged 6-23 months (n = 67 247), from India. Dietary failure was operationalized using World Health Organization (WHO) standards for minimum dietary diversity. Anthropometric failure was operationalized using WHO child growth reference standard z-score of <-2 for height-for-age (stunting), weight-for-age (underweight) and weight-for-height (wasting). We also created a combined anthropometric measure for children who had any one of these three anthropometric failures. We cross-tabulated dietary and anthropometric failures to produce four combinations: Dietary Failure Only (DFO), Anthropometric Failure Only (AFO), Both Failures (BF), and Neither Failure (NF). We estimated the prevalence and distribution of the four types, nationally, and across 640 administrative districts and 543 Parliamentary Constituencies (PCs) in India.ResultsNationally, 80.3% of children had dietary failure and 53.7% had at least one anthropometric failure. The prevalence for the four DAF types was: 44.0% (BF), 36.3% (DFO), 9.8% (AFO), and 9.9% (NF). Dietary and anthropometric measures were discordant for 46.1% of children; these children had nutritional needs identified by only one of the two measures. Nationally, this translates to 12 181 627 children with DFO and 3 281 913 children with AFO; the nutritional needs of these children would not be captured if using only dietary or anthropometric assessment. Substantial variation was observed across districts and PCs for all DAF types. The interquartile ranges for districts were largest for BF (29.8%-53.0%) and lowest for AFO (5.5%-13.4%).ConclusionsThe current emphasis on anthropometry for measuring nutritional need should be complemented with diet- and food-based measures. By differentiating inadequate food intake from other causes of undernutrition, the DAF typology brings precision in identifying nutritional needs among children. These insights may improve the development and targeting of nutrition interventions.

Standardized Variable Distances: A distance-based machine learning method

Today, machine learning algorithms are an important research area capable of analyzing and modeling data in any field. Information obtained through machine learning methods helps researchers and planners to understand and review systematic problems of their current strategies. Thus, it is very important to work fully in every field that facilitates human life, such as early and correct diagnosis, correct choice, fully functioning autonomous systems. In this paper, a novel machine learning algorithm for multiclass classification is presented. The proposed method is designed based on the Minimum Distance Classifier (MDC) algorithm. The MDC is variance-insensitive because it classifies input vectors by calculating their distances/similarities with respect to class-centroids (average value of input vectors of a class). As it is known, real-world data contains certain proportions of noise. This situation negatively affects the performance of the MDC. To overcome this problem, we developed a variance-sensitive model, which we call Standardized Variable Distances (SVD), considering the standard deviation and z-score (standardized variable) factors. To ensure the accuracy of the SVD, we used Wisconsin Breast Cancer Original (WBCO) and LED Display Domain (led7digit) datasets, which we obtained from UCI machine learning repository, with 5-fold cross validation. It was compared and analyzed classification performance of the SVD with Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (k-NN), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), Support Vector Machine (SVM), and the Minimum Distance Classifier (MDC), which are well-known in the literature. It has also been compared thirteen different studies using the same datasets over the past five years. Our results in the experimental studies have shown that the SVD can classify better than traditional and state-of-the-art methods, compared in this study. The proposed method reached over 97% classification accuracy (CACC), F-measure (FM) and area under the curve (AUC) on the WBCO dataset. On the led7digit dataset, approximately 74% CACC, 75.1% FM and 82.2% AUC scores were obtained. It has been observed that the classification scores obtained with the SVD are higher than other ML algorithms used in the experimental studies.

Relationship between sleep duration and TV time with cardiometabolic risk in adolescents

ObjectiveTo verify the association between sleep duration and television time with cardiometabolic risk and the moderating role of age, gender, and skin color/ethnicity in this relationship among adolescents.MethodsCross-sectional study with 1411 adolescents (800 girls) aged 10 to 17 years. Television time, sleep duration, age, gender, and skin color/ethnicity were obtained by self-reported questionnaire. Cardiometabolic risk was evaluated using the continuous metabolic risk score, by the sum of the standard z-score values for each risk factor: high-density lipoprotein cholesterol, triglycerides, glycemia, cardiorespiratory fitness, systolic blood pressure, and waist circumference. Generalized linear regression models were used.ResultsThere was an association between television time and cardiometabolic risk (β, 0.002; 95% CI, 0.001; 0.003). Short sleep duration (β, 0.422; 95% CI, 0.012; 0.833) was positively associated with cardiometabolic risk. Additionally, age moderated the relationship between television time and cardiometabolic risk (β, − 0.009; 95% CI, − 0.002; − 0.001), suggesting that this relationship was stronger at ages 11 and 13 years (β, 0.004; 95% CI, 0.001; 0.006) compared to 13 to 15 years (β, 0.002; 95% CI, 0.001; 0.004). No association was found in older adolescents (β, 0.001; 95% CI, − 0.002; 0.002).ConclusionsTelevision time and sleep duration are associated with cardiometabolic risk; adolescents with short sleep have higher cardiometabolic risk. In addition, age plays a moderating role in the relationship between TV time and cardiometabolic risk, indicating that in younger adolescents the relationship is stronger compared to older ones.

The Effect of Exchange Rate Volatility on Economic Growth: Case of the CEE Countries

The exchange rate is a key macroeconomic factor that affects international trade and the real economy of each country. The development of international trade creates conditions where volatility comes with the exchange rate. The purpose of this paper is to examine the effect of real effective exchange rate volatility on economic growth in the Central and Eastern European countries. Additionally, the effect, through three channels of influence on economic growth which vary on the measurement of exchange rate volatility, is examined. The study uses annual data for fourteen CEE countries for the period 2002–2018 to examine the nature and extends the impact of such movements on growth. The empirical findings using the fixed effects estimation for panel data reveal that the volatility of the exchange rate has a significant negative effect on real economic growth. The results appear robust with alternative measures of exchange rate volatility such as standard deviation and z-score. This paper suggests that policymakers should adopt different policies to keep the exchange rate stable in order to foster economic growth.

Ancestry prediction efficiency of the software GenoGeographer using a z-score method and the ancestry informative markers in the Precision ID Ancestry Panel

We compared the efficiency of the freely available software GenoGeographer that includes a z-score based analysis with that of a naïve method based on the maximal likelihoods of 164 of the 165 ancestral informative markers (AIM) that are included in the commercially available kit Precision ID Ancestry Panel from Thermo Fisher Scientific. The AIM profiles were obtained by investigations with the Precision ID Ancestry Panel in our laboratory and from SNP data in the literature and publically available databases. We established eight well-defined AIM reference population data sets from 3603 AIM profiles. Six reference populations with profiles from multiple populations (Sub-Saharan Africa, North Africa, Middle East, Europe, South/Central Asia, East Asia), and two populations with individuals with admixed ancestry (Somalia and Greenland). By means of GenoGeographer and naïve calculations of the maximal likelihoods, 566 AIM profiles from individuals that were not included in the reference populations and expected to belong to one of the eight reference populations were tested. An initial standard z-score based test with GenoGeographer demonstrated that 22.4% of the individuals could not be assigned to any of the reference populations. Among the remaining 77.6% of the individuals, 83.6% were assigned to the reference population that was concordant with the specified populations of origin of the individuals, 8.2% had ambiguous assignments because they could belong to both the specified population of origin and one or more of the other populations, and 8.2% were assigned to a reference population that was discordant from the specified population of origin. A naïve assignment based on the maximal likelihood resulted in 78.1% concordant and 21.9% discordant assignments. The results demonstrate that the z-score analysis with GenoGeographer can reduce the error rate with a factor of almost three compared with that of the naïve estimation based on the maximal likelihoods of the AIM profiles. The Precision ID Ancestry Panel is a useful kit for the assignment of ancestry of the eight investigated populations that included two admixed populations. More AIMs with better discrimination and more data on the distribution of AIMs in relevant populations are needed to improve the efficiency of genogeographic prediction with AIMs on a worldwide basis.