Normal Variables Research Articles

Correlation of Atherogenic Index of Plasma and Atherogenic Coefficient with Cardiovascular Disease Risk assessed by ASCVD Risk Calculator

Introduction: Cardiovascular disease (CVD) is the leading cause of approximately one-third deaths worldwide. Absolute lipid parameters are inadequate in predicting CVD risk and several lipid indices have been introduced namely “Atherogenic index of plasma” (AIP) and “Atherogenic coefficient” (AC). CVD risk of a patient for the upcoming 10 years can be calculated using atherosclerotic cardiovascular disease (ASCVD) risk calculator. Though, this method currently accepts as the gold standard, certain drawbacks have been reported. Therefore, the aim of this study was to investigate the correlation of AIP and AC with CVD risk estimation for the upcoming 10 years calculated by ASCVD risk calculator. Methods: Hundred and fifty-three patients were recruited for the study. Socio-demographic data were collected through interviewer-based questionnaire. Total cholesterol, HDL cholesterol, LDL cholesterol and triglycerides values were obtained from the lipid profile test results of the study participants. AIP and AC were calculated using formulas while 10-year ASCVD risk was calculated by ASCVD risk calculator. Results: There was no significant correlation between AIP and 10-year ASCVD risk (p>0.05) and AC and 10-year ASCVD risk (p>0.05), but a significant correlation was observed between AIP and AC (Pearson’s correlation r=0.425, p<0.05). Conclusions: CVD risk of patients should be assessed routinely especially as it is overlooked in most patients presenting normal lipid profile variables, hence the actual risk remains silent unless it is properly investigated using laboratory investigations

Chemogenetic inhibition of NTS astrocytes normalizes cardiac autonomic control and ameliorate hypertension during chronic intermittent hypoxia

BackgroundObstructive sleep apnea (OSA) is characterized by recurrent episodes of chronic intermittent hypoxia (CIH), which has been linked to the development of sympathoexcitation and hypertension. Furthermore, it has been shown that CIH induced inflammation and neuronal hyperactivation in the nucleus of the solitary tract (NTS), a key brainstem region involved in sympathetic and cardiovascular regulation. Since several studies have proposed that NTS astrocytes may mediate neuroinflammation, we aimed to determine the potential contribution of NTS-astrocytes on the pathogenesis of CIH-induced hypertension.ResultsTwenty-one days of CIH induced autonomic imbalance and hypertension in rats. Notably, acute chemogenetic inhibition (CNO) of medullary NTS astrocytes using Designer Receptors Exclusively Activated by Designers Drugs (DREADD) restored normal cardiac variability (LF/HF: 1.1 ± 0.2 vs. 2.4 ± 0.2 vs. 1.4 ± 0.3, Sham vs. CIH vs. CIH + CNO, respectively) and markedly reduced arterial blood pressure in rats exposed to CIH (MABP: 82.7 ± 1.2 vs. 104.8 ± 4.4 vs. 89.6 ± 0.9 mmHg, Sham vs. CIH vs. CIH + CNO, respectively). In addition, the potentiated sympathoexcitation elicit by acute hypoxic chemoreflex activation in rats exposed to CIH was also completely abolished by chemogenetic inhibition of NTS astrocytes using DREADDs.ConclusionOur results support a role for NTS astrocytes in the maintenance of heightened sympathetic drive and hypertension during chronic exposure to intermittent hypoxia mimicking OSA.

On testing the skew normal distribution by using Shapiro–Wilk test

The skew normal (SN) family of distributions includes the normal distribution as a particular case as well as a wide variety of skew densities. Because of its tractability and flexibility for modeling both symmetric and skew data sets, the SN distribution has plenty of applications in finance, diverse engineering fields and medicine, among others. In this paper, a data transformation to approximately normal variables is used for testing the null hypothesis that a random sample follows a SN distribution with unknown parameters. Formulae obtained by interpolation methods are provided for approximating the critical values of the proposed procedure for a range of sample sizes, avoiding the use of tables and/or resampling methods. The test preserves the nominal test size and turns out to be competitive in terms of power against existing tests for the same problem. Several data sets are considered in order to illustrate the plausibility of the SN distribution for modeling the probability behavior of real data.

Study on Estimation of Typhoon Damage Thresholds in Coastal Region

Typhoon damage in coastal areas is a complex disaster characterized by heavy rain, strong winds, and turbulent waves. The strength of these strong winds and waves is determined by the intensity of the typhoon. In this study, a variable representing strong winds and waves was developed. A damage prediction model using rainfall, wind, and the developed variable was then proposed and thresholds for each variable were calculated across cities, counties, and districts located in coastal areas. Two damage prediction models were compared based on variables. Standard normal variables related to strong winds and rainfall by duration were calculated using Empirical Copula. The first logistic regression model was constructed using these variables. The second model used a multiple logistic regression model, considering strong winds and rainfall by duration. Although there are more cities, counties, and districts where the multiple logistic regression model exhibited better accuracy, there have been several instances where the typhoon damage induction threshold suggested by this model was abnormal. On the other hand, a relatively stable critical value was yielded using standard normal variables by Empirical Copula. This is because standard normal variables are calculated based on the probability according to the order of rainfall and strong wind magnitude. Hence, the critical value falls within the minimum and maximum range of observed values. According to the research results, it is possible to predict whether typhoon damage will occur in cities, counties, and districts located in coastal areas using the typhoon information provided by the Korea Meteorological Administration during a typhoon event, which can aid in preparing for typhoon disasters.

A comparative study of CTG monitoring one hour before labor in infants born with and without asphyxia

Background and AimAsphyxia is a condition arising when the infant is deprived of oxygen, causing Fetal brain damage or death, which is associated with hypoxia and hypercapnia. Although fetal Cardiotocography (CTG) can show the Fetal health status during labor, some studies have reported cases of fetal asphyxia despite reassuring CTGs. This study hence aimed to compare FHR Monitoring and uterine contractions in the last hour before delivered between two groups of infants born with and without asphyxia.MethodologyThe study was conducted on 70 pregnant women who delivered Taleghani and Al-Zahra academic teaching hospitals of Tabriz for labor in 2020–2021.ResultsThe study data showed no significant difference between mothers of infants with and without asphyxia in terms of demographics (p > 0.05). The prevalence of asphyxia was significantly higher only in mothers with the gravidity of 3 and 4 (p = 0.003). In terms of the methods for labor induction, the use of oxytocin was more common among mothers of infants with asphyxia (74.3%) than in those of infants without asphyxia (p = 0.015). The results also revealed a significant difference between infants with and without asphyxia in the Apgar score (first, fifth, and tenth minutes), need for neonatal resuscitation, umbilical cord artery Acidosis (pH, bicarbonate, and BE), and severity of HIE between two groups of infants with asphyxia and without asphyxia (p < 0.0001). The comparison of fetal CTG 0 to 20 min before the delivery indicated that normal variability was observed in 71.4% of infants born with asphyxia, whereas this figure for infants born without asphyxia was 91.4% (p = 0.031). However, the results showed no significant difference between the two groups of infants in any of the tstudied indicators at 20 and 40 min before the labor(p > 0.05). There was a significant difference between the two groups of infants in terms of deceleration at 40 and 60 min before the labor, as it was observed in 53.6% of infants born with asphyxia and only 11.1% of those born without asphyxia. The results also demonstrated a significant difference between the two groups in the type of deceleration (p = 0.025). Pearson and Spearman correlation coefficients showed a significant and direct relationship between interpretation the CTG of the three Perinatologists(p < 0.0001, r > 0.8).ConclusionThe study results demonstrated a significant difference between infants born with asphyxia and those born without asphyxia in variability at 0 to 20 min before the labor and deceleration at 40 to 60 min before the labor.

Is Bleeding Gastrointestinal Stromal Tumor Associated With Higher MortalityandMorbidity?

Introduction Gastrointestinal stromal tumor (GIST) is the most common primary mesenchymal tumor of the gastrointestinal tract. The clinical presentation of GIST varies widely and ranges from being asymptomatic to being a life-threatening emergency in the form of gastrointestinal bleeding or bowel obstruction. Multiple prognostic factors have been identified for GIST, including, most importantly, larger tumor size (>5 cm), higher mitotic activity (>5 per 50 high-power fields), rupture of the mass, site of the mass, and personal history of GIST. Objective In this paper, we aim to study bleeding in gastrointestinal tumors as a predictor for morbidity and mortality and investigate possible factors influencing bleeding in GIST. Method A retrospective study design was used. Electronic medical records of 39 patients diagnosed with GIST in Prince Sultan Military Medical City in Riyadh between January 2015 and December 2020 were retrieved. Normal variables were presented as mean and standard deviation, while non-normal variables were presented as median and interquartile range. Student t-test or Mann-Whitney test was used to compare quantitative data when appropriate. The distribution of recurrence and survival was plotted with the Kaplan-Meier curveand compared between the two groups (bleeding and non-bleeding groups) with the log-rank test. Factors affecting recurrence and mortality were assessed using univariable Cox regression. Result A total of 39 cases of GIST tumors were included in this study. Patients were categorized according to the presentation into two groups: patients with no bleeding (n= 28) and those presented with bleeding (n= 11). The gender distribution was equal. The mean age was 58.14± 12.46 in patients with non-bleeding GIST and 58.18± 16.01 in patients with bleeding GIST. The most common location of GIST was the stomach (22 cases, 56%). Neither group had any significant differences regarding age (P-value = 0.993), gender (P-value = 0.648), tumor location (P-value = 0.057), size (P-value = 0.250), cluster of differentiation (CD) markers, histological types (P-value = 0.692), and shape (P-value = 0.079). There was no significant difference in recurrence between both groups (log-rank P = 0.972). There was no significant difference in survival between both groups (Log-rank P = 0.506). Conclusion We concluded that bleeding GIST was not a significant predictor for recurrence rate or higher mortality. This can help in the debate of whether bleeding GIST should be added as an independent factor in risk stratification. Despite that, further studies are needed to identify more variables that can add more accuracy to the pre-existing risk stratification systems.

Semiparametric normal transformation joint model of multivariate longitudinal and bivariate time-to-event data.

Joint models for longitudinal and survival data (JMLSs) are widely used to investigate the relationship between longitudinal and survival data in clinical trials in recent years. But, the existing studies mainly focus on independent survival data. In many clinical trials, survival data may be bivariately correlated. To this end, this paper proposes a novel JMLS accommodating multivariate longitudinal and bivariate correlated time-to-event data. Nonparametric marginal survival hazard functions are transformed to bivariate normal random variables. Bayesian penalized splines are employed to approximate unknown baseline hazard functions. Incorporating the Metropolis-Hastings algorithm into the Gibbs sampler, we develop a Bayesian adaptive Lasso method to simultaneously estimate parameters and baseline hazard functions, and select important predictors in the considered JMLS. Simulation studies and an example taken from the International Breast Cancer Study Group are used to illustrate the proposed methodologies.

Compositional and Morphological Analysis of Salt Stress Tolerant Mannitol-1-phosphate Dehydrogenase (mtlD)-Transgenic Potato Plants.

Undesired effects often occur in genetically modified (GM) plants, especially during metabolite engineering. Nevertheless, conducting a comparative study between GM and non-GM plants can identify the unintended alterations and facilitate the risk assessment of GM crops. This research compared the morphology and composition of a transgenic potato plant expressing mannitol-1-phosphate dehydrogenase (mtlD), with its non-transgenic counterpart. The results indicated significant differences in plant height, number of leaves, length and width of leaves, as well as tuber number and weight between the transgenic and non-transgenic plants. However, compositional analysis revealed no significant differences in soluble protein, starch, total sugar, fructose, fiber, and ascorbate contents between mtlD-GM and non-GM potatoes. Nevertheless, sucrose and glucose levels were found to be higher in the transgenic potato tubers and leaves, respectively, when compared to the non-transgenic plants. In addition to ammonium, potassium, chloride, nitrite, and nitrate levels, significant differences were observed in the amino acids asparagine, aspartic acid, glutamic acid, isoleucine, leucine, lysine, serine, and valine between the GM and non-GM plants. Apart from the target gene product, mannitol, all the changes in chemical compositions observed in the transgenic potato plants fell within the ranges of normal variability for potato plants. Moreover, despite some phenotypical differences between the mtlD-GM potato and its non-GM counterpart, it is believed that this variation is a common phenomenon among potato varieties. In conclusion, the morphological and compositional analysis of the mtlD-GM potato plant revealed substantial equivalence with its non-transgenic counterpart.

Predicting cognitive decline in a low-dimensional representation of brain morphology

Identifying early signs of neurodegeneration due to Alzheimer’s disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18–100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits (n ge 2), one year apart. The first embedding’s principal axis was shown to be positively associated (r = 0.65) with participants’ age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.

Abnormal heart rate variability and its application in predicting treatment efficacy in patients with chronic migraine: An exploratory study.

This study aimed to investigate the extent of autonomic nervous system dysfunction in patients with chronic migraine using heart rate variability analysis. In addition, we explored the potential association between heart rate variability and treatment outcomes in patients receiving preventive treatment. In this cross-sectional and prospective study, we compared heart rate variability profiles in 81 preventive-naïve chronic migraine patients and 58 healthy controls. In addition, treatment responses of patients, who received a 12-week treatment with flunarizine, were assessed in relation to baseline heart rate variability. We observed that chronic migraine patients had a reduced heart rate variability, signifying autonomic dysfunction in comparison to healthy controls. Furthermore, patients presenting normal heart rate variability, characterized by a standard deviation exceeding 30 milliseconds in normal-to-normal RR intervals, experienced a superior response to flunarizine treatment. This improvement was exemplified by a significantly larger reduction in monthly headache days for patients with higher heart rate variability compared to those with lower heart rate variability: -9.7 (5.9) vs. -6.2 (6.0) days (p = .026). Autonomic dysfunction occurs in chronic migraine as evaluated by heart rate variability. A preserved function is associated with a better treatment outcome to flunarizine.Trial registration: Neurologic Signatures of Chronic Pain Disorders, NCT02747940. Registered 22 April 2016, https://clinicaltrials.gov/ct2/show/NCT02747940.

Three- and four-wave resonances in the nonlinear quadratic Kelvin lattice

In this paper we investigate analytically and numerically the nonlinear Kelvin lattice, namely a chain of masses and nonlinear springs, as in the α-Fermi-Pasta–Ulam-Tsingou (FPUT) chain, where, in addition, each mass is connected to a nonlinear resonator, i.e., a second mass free to oscillate. Both nonlinearities are quadratic in the equations of motion. This setup represents the simplest prototype of nonlinear wave propagation on a nonlinear metamaterial. In the linear case, we diagonalize the system, and the two branches of the dispersion relation can be found. Using this result, we derive in the nonlinear case the equations of motion for the normal variables in Fourier space, obtaining a system governed by triad interactions among the two branches of the dispersion relation. We find that the transfer of energy between these two branches is ruled by three- and four-wave resonant interactions. We perform numerical simulations of the primitive equations of motion and highlight the role of resonances as an efficient mechanism for transferring energy. Moreover, as predicted by the theory, we provide direct evidence that four-wave resonances appear on a time scale that is longer than the time scale for three-wave resonances. We also assess the recurrence behaviour (usual in the FPUT system) for the nonlinear Kelvin lattice, and we show that, while recurrence is observed if all the energy is placed, at time t=0, in the lowest mode of the acoustical branch, a non-recurrent behaviour is observed if the initial energy is located in the optical branch.

Simulating imprecise data: sine–cosine and convolution methods with neutrosophic normal distribution

ObjectiveThe primary aim of this research paper is to introduce and demonstrate the application of the sine–cosine method and the convolution method for simulating data by utilizing the neutrosophic normal distribution.MethodThe methodological framework presented in this paper elaborates on the incorporation of both the sine–cosine method and the convolution method into the realm of neutrosophic statistics. It also introduces algorithms engineered to produce random variables adhering to the neutrosophic normal distribution.ResultsMoreover, the study furnishes practical tables that encompass neutrosophic random normal variables generated via the sine–cosine method, as well as tables exhibiting neutrosophic random standard normal variables generated using the convolution method.ConclusionThe analysis undertaken in this study conclusively establishes that the proposed sine–cosine and convolution simulation methods yield outcomes presented in the form of intervals. Furthermore, the study's conclusion emphasizes that the extent of indeterminacy significantly influences the characteristics of the random variates.

An inference model gives insights into innate immune adaptation and repertoire diversity

The innate immune system is the body's first line of defense against infection. Natural killer (NK) cells, a vital part of the innate immune system, help to control infection and eliminate cancer. Studies have identified a vast array of receptors that NK cells use to discriminate between healthy and unhealthy cells. However, at present, it is difficult to explain how NK cells will respond to novel stimuli in different environments. In addition, the expression of different receptors on individual NK cells is highly stochastic, but the reason for these variegated expression patterns is unclear. Here, we studied the recognition of unhealthy target cells as an inference problem, where NK cells must distinguish between healthy targets with normal variability in ligand expression and ones that are clear "outliers." Our mathematical model fits well with experimental data, including NK cells' adaptation to changing environments and responses to different target cells. Furthermore, we find that stochastic, "sparse" receptor expression profiles are best able to detect a variety of possible threats, in agreement with experimental studies of the NK cell repertoire. While our study was specifically motivated by NK cells, our model is general and could also apply more broadly to explain principles of target recognition for other immune cell types.

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016–2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented.Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55–64 (serious prediction). The model’s McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables.The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

An example showing that the sum of two normal random variables may not be normal

Two novel proofs show that the sum of a specific pair of normal random variables is not normal are established in this note. This is one of the most often misunderstood facts by first-year students in probability theory and statistics. The first proof is concise using the moment generating function. The second proof checks whether the moments of the sum have the property of normal distribution.

Development of normalized quantitative measures of lumbar disc degeneration.

Lumbar disc degeneration (DD) is widely regarded as a likely contributor to low back pain (LBP), but the association between DD and LBP is relatively weak. No known studies have normalized quantitative measures of DD severity relative to multiple variables such as age, height, and disc level. This study developed normalized quantitative measures (z-scores) of disc signal intensity (DSI) and disc height (DH) to rate relative severity of DD. Raw (unnormalized) quantitative measures of DSI and DH alongside potential normalization variables were acquired from MRI scans and clinical data of 76 patients. The associations between the raw quantitative measures and potential normalization variables were investigated to develop the normalized quantitative measures (z-scores) of DSI and DH. Construct validity was assessed by comparing the normalized measures to an experienced radiologist's subjective measures of relative severity of DSI and DH loss. CSF signal intensity, age, and disc level were significantly associated with raw DSI (R 2 = 0.06, 0.25, and 0.09, respectively). Lumbar height and disc level were significantly associated with raw DH (R 2 = 0.13 and 0.31). Normalizing DSI and DH by these variables resulted in stronger relationships (R 2 = 0.39 and 0.37) than raw DSI and DH (R 2 = 0.24 and 0.31) with the radiologist's subjective measures. Normalized DSI and DH were both normally distributed (p = 0.32 and 0.12). Construct validity and the distributions suggested that normalized quantitative measures of DSI and DH are better than existing measures of DSI and DH at rating relative DD severity. Determining whether normalized quantitative measures are more predictive of clinical outcomes is important future research.

Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring.

We consider a novel class of semiparametric joint models for multivariate longitudinal and survival data with dependent censoring. In these models, unknown-fashion cumulative baseline hazard functions are fitted by a novel class of penalized-splines (P-splines) with linear constraints. The dependence between the failure time of interest and censoring time is accommodated by a normal transformation model, where both nonparametric marginal survival function and censoring function are transformed to standard normal random variables with bivariate normal joint distribution. Based on a hybrid algorithm together with the Metropolis-Hastings algorithm within the Gibbs sampler, we propose a feasible Bayesian method to simultaneously estimate unknown parameters of interest, and to fit baseline survival and censoring functions. Intensive simulation studies are conducted to assess the performance of the proposed method. The use of the proposed method is also illustrated in the analysis of a data set from the International Breast Cancer Study Group.

A New R-Function to Estimate the PDF of the Product of Two Uncorrelated Normal Variables

This paper analyses the implementation of a procedure using the software R to calculate the Probability Density Function (PDF) of the product of two uncorrelated Normally Distributed Random Variables. The problem of estimating the distribution of the product of two random variables has been solved for some particular cases, but there is no unique expression for all possible situations. In our study, we chose Rohatgi’s theorem as a basis for approximating the product of two uncorrelated Normally Distributed Random Variables. The numerical approximation of the product PDF was calculated using a function that we implemented in R. Several numerical examples show that the approximations obtained in R fit the theoretical values of the product distributions. The results obtained with our R function are very positive when we compare them with the Monte Carlo Simulation of the product of the two variables.

Cardiometabolic Risk Assessment through Comprehensive Screening for Early Detection

Background: The development of cardiovascular disease (CVD) is influenced by many risk factors, such as tobacco use, an unhealthy diet, and physical inactivity, that could result in obesity, hypertension, dyslipidemia, and diabetes mellitus. Continuing exposure to these risk factors can be prevented by routine screening for populations without any clinical symptoms. This study was conducted to obtain data on cardiometabolic profiles of people who participated in the Community Service Program of the Department of Cardiology and Vascular Disease, University of Sumatera Utara. Method: This research was a descriptive study with a cross-sectional research design. The study aims to screen for the cardiometabolic profile in the people of Tebing Tinggi City. Cardiovascular risk factors were obtained from interviewing the participants and on-the-spot examination with validated measurement tools. All data were processed and analyzed statistically using SPSS ver.26. Categorical variables are presented with frequency (n) and percentage (%). Numeric variables are presented with mean and standard deviation (SD) values for normally distributed data. As for the normal non-distributed data numerical variables are presented with the middle value (median) and the interquartile range. Results: All subjects in this study (n= 102) are 39 male and 63 female. Most participants were in the 4th to 5th decade, with normal heart rates and normal sinus rhythm. Most female participants had a low risk for obstructive sleep apnea (OSA) and the male had intermediate risk. Most participants had normal random blood sugar and total cholesterol but mostly had obesity. Near 20-30% of participants were grade I and II systolic hypertensives but normally had diastolic blood pressure. Conclusion: In this study, most of the cardiometabolic risks of patients in the Community Service program were obesity, followed by hypertension.

Techniques to produce and evaluate realistic multivariate synthetic data

Data modeling requires a sufficient sample size for reproducibility. A small sample size can inhibit model evaluation. A synthetic data generation technique addressing this small sample size problem is evaluated: from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; synthetic data can be generated from this class with univariate kernel density estimation (KDE); and synthetic samples are statistically like their respective samples. Three samples (n = 667) were investigated with 10 input variables (X). KDE was used to augment the sample size in X. Maps produced univariate normal variables in Y. Principal component analysis in Y produced uncorrelated variables in T, where the probability density functions were approximated as normal and characterized; synthetic data was generated with normally distributed univariate random variables in T. Reversing each step produced synthetic data in Y and X. All samples were approximately multivariate normal in Y, permitting the generation of synthetic data. Probability density function and covariance comparisons showed similarity between samples and synthetic samples. A class of samples has a latent normal characteristic. For such samples, this approach offers a solution to the small sample size problem. Further studies are required to understand this latent class.