Modeling birthweight and gestational age distributions: additive vs. multiplicative processes.

A Botnet is a malicious network that can be leveraged in a wide range of cyber-attacks with possibly catastrophic results. Consequently, botnet detection is a high priority. In the past few years, many studies have been conducted regarding botnet detection of which the proposed approaches focus heavily on machine learning techniques. In this paper, a clustering approach based on Gaussian mixture distributions is proposed that allows accommodating ellipsoidal shaped clusters. Each cluster is associated with a Gaussian distribution by which an overall mixture of Gaussians is obtained. Hereby, an optimization problem is formalized ending up with a cost function based on the entropy of the overall distributions. The optimal solution is determined by using an optimization method. The proposed approach is then applied to bot-generated network traffic to extract models on bots behavior which can thus be leveraged to detect bot-infected hosts. The quality evaluation is performed by using some commonly used criteria that measure the accuracy and detection capability of models generated by the algorithm. The results show that high quality can be achieved by our method.

The size of noctilucent cloud particles above ALOMAR (69N,16E): Optical modeling and method description

Insights from outside BJOG

This paper describes a new low bit-rate formant vocoder. The formant parameters are represented by Gaussian mixture distributions, which are estimated from the discrete Fourier transform (DFT) magnitude spectrum of the speech signal. A voiced/unvoiced classification mechanism has been developed based on the harmonic nature of each formant in the DFT spectrum modulated by the Gaussian mixture distribution. Using a magnitude-only sinusoidal synthesiser, intelligible synthetic speech has been obtained. Vector quantisation of the vocal tract parameters enables this formant vocoder to operate at a bit-rate of 1248 bps.

Data with a multimodal pattern can be analyzed using a mixture model. In a mixture model, the most important step is the determination of the number of mixture components, because finding the correct number of mixture components will reduce the error of the resulting model. In a Bayesian analysis, one method that can be used to determine the number of mixture components is the reversible jump Markov chain Monte Carlo (RJMCMC). The RJMCMC is used for distributions that have location and scale parameters or location-scale distribution, such as the Gaussian distribution family. In this research, we added an important step before beginning to use the RJMCMC method, namely the modification of the analyzed distribution into location-scale distribution. We called this the non-Gaussian RJMCMC (NG-RJMCMC) algorithm. The following steps are the same as for the RJMCMC. In this study, we applied it to the Weibull distribution. This will help many researchers in the field of survival analysis since most of the survival time distribution is Weibull. We transformed the Weibull distribution into a location-scale distribution, which is the extreme value (EV) type 1 (Gumbel-type for minima) distribution. Thus, for the mixture analysis, we call this EV-I mixture distribution. Based on the simulation results, we can conclude that the accuracy level is at minimum 95%. We also applied the EV-I mixture distribution and compared it with the Gaussian mixture distribution for enzyme, acidity, and galaxy datasets. Based on the Kullback–Leibler divergence (KLD) and visual observation, the EV-I mixture distribution has higher coverage than the Gaussian mixture distribution. We also applied it to our dengue hemorrhagic fever (DHF) data from eastern Surabaya, East Java, Indonesia. The estimation results show that the number of mixture components in the data is four; we also obtained the estimation results of the other parameters and labels for each observation. Based on the Kullback–Leibler divergence (KLD) and visual observation, for our data, the EV-I mixture distribution offers better coverage than the Gaussian mixture distribution.

The effect of skewness on hypothesis tests for the existence of a mixture of univariate and bivariate normal distributions is examined through a Monte Carlo study. A likelihood ratio test based on results of the simultaneous estimation of skewness parameters, derived from power transformations, with mixture parameters is proposed. This procedure detects the difference between inherent distributional skewness and the apparent skewness which is a manifestation of the mixture of several distributions. The properties of this test are explored through a simulation study.

We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time spot follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs. We refer to such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions. Specifically, the components of the marginal GMMs are matched by solving an optimal transport problem where the cost between components is the Wasserstein metric for Gaussian distributions. The solution of the optimization problem is a fast approximation to the Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is a semi-metric and can be computed without generating Monte Carlo samples. It is invariant to relabeling or permutation of the states. This distance quantifies the dissimilarity of GMM-HMMs by measuring both the difference between the two marginal GMMs and the difference between the two transition matrices. Our new distance is tested on the tasks of retrieval and classification of time series. Experiments on both synthetic data and real data have demonstrated its advantages in terms of accuracy as well as efficiency in comparison with existing distances based on the Kullback-Leibler divergence.

Ultrasound has been routinely used since late 1979 to diagnose periventricular-intraventricular hemorrhage in infants whose gestational age was 34 weeks or less. During the years 1980 to 1987 the ultrasound scans were interpreted by one person, and a steady decline in incidence of periventricular-intraventricular hemorrhage was observed. Both for infants of 34 weeks or less and for very low birth weight (less than 1500 g) infants, periventricular-intraventricular hemorrhage incidence decreased, respectively, from 34% and 39% in 1980 to 1981 to 19% and 25% in 1986 to 1987. This decrease was confined to true intraventricular hemorrhages, which decreased from 22% in 1980 to 1981 to 7% in 1986 to 1987 for infants of less than or equal to 34 weeks' gestation. These results were not influenced by a change in the distribution of birth weights or gestational ages of the infants evaluated nor by a change in the inborn to outborn ratio. The incidence of periventricular-intraventricular hemorrhage was lower in infants born by cesarean section than those delivered vaginally, but the decrease could not be attributed to an increased number of deliveries by cesarean section. Although there were undoubtedly changes in neonatal care during this time period, no planned intervention occurred. It was concluded that pharmacologic intervention studies must be interpreted with caution.

Gene expression varies stochastically across genders, racial groups, and health statuses. Deciphering these patterns is crucial for identifying informative genes, classifying samples, and understanding diseases like cancer. This study analyzes 11,252 bulk RNA-seq samples to explore expression patterns of 19,156 genes, including 10,512 cancer tissue samples and 740 normal samples. Additionally, 4,884 single-cell RNA-seq samples are examined. Statistical analysis using 16 probability distributions shows that normal samples display a wider range of distributions compared to cancer samples. Cancer samples tend to favor asymmetric distributions such as generalized extreme value, logarithmic normal, and Gaussian mixture distributions. In contrast, certain genes in normal samples exhibit symmetric distributions. Remarkably, more than 95.5% of genes exhibit non-normal distributions, which challenges traditional assumptions. Furthermore, distributions differ significantly between bulk and single-cell RNA-seq data. Many cancer driver genes exhibit distinct distribution patterns across sample types, suggesting potential for gene selection and classification based on distribution characteristics. A novel skewness-based metric is proposed to quantify distribution variation across datasets, showing genes with significant skewness differences have biological relevance. Finally, an improved naïve Bayes method incorporating gene-specific distributions demonstrates superior performance in simulations over traditional methods. This work enhances understanding of gene expression and its application in omics-based gene selection and sample classification.

To evaluate the effects of the September 11, 2001, World Trade Center attacks on birth outcomes. Live singleton births between September 11, 2001, and October 31, 2002, to women enrolled in a World Trade Center Health Registry (the Registry, n=446) were compared with births to women residing more than 5 miles from the World Trade Center (n=49,616). Birth weight, gestational age, low birth weight, and preterm delivery were evaluated using linear and logistic regression. Births before September 11, 2001, were analyzed to assess possible seasonal biases of associations with pregnancy trimester on September 11. Associations of birth outcomes with September 11-related psychologic stress and physical exposures were assessed among births to women within the Registry (n=499). Birth weight and gestational age distributions were similar for births to women enrolled in the Registry and comparison births. Although mean gestational age and birth weight varied with trimester on September 11, a similar association was found among births in previous years, consistent with a seasonal effect not related to exposure. Registry-linked births to mothers with probable posttraumatic stress disorder (n=61) had a higher odds of low birth weight (adjusted odds ratio [OR] 2.49, 95% confidence interval [CI] 1.02-6.08) and preterm delivery (adjusted OR 2.48, 95% CI 1.05-5.84) compared with births to women without posttraumatic stress disorder. Women who lived, worked, or were near the World Trade Center on or soon after September 11 had pregnancy outcomes similar to women residing more than 5 miles away. However, among exposed women, probable posttraumatic stress disorder was associated with low birth weight and preterm delivery. II.

There is no greater embarrassment to the world's wealthiest nation than the fact that over 44 million of its own citizens are uninsured and, at least, another 60 million are “under-insured.” Therefore, over one-third of Americans are ill prepared for the financial hardships resulting from mounting medical problems. For many, emergency departments—often cramped and understaffed—are the only access to health care. With inordinate waiting periods and some patients only presenting when the disease is intolerable (which is oftentimes a reliable marker for an advanced state of the disease), the current state of health care in the United States is not sustainable or ethical. Equal access to health care is one of the inherent responsibilities of this great nation. This country spends over a trillion dollars per year for health care, which no other nation can even approach. The health care industry is the only trillion-dollar industry in the United States. With $1.7 trillion being spent for health care last year, it is unlikely that more dollars would effectively address the disparity issue. In fact, this is, perhaps, one of the few times when “how” money is used rather than “how much” is at the crux of a better and more equitable system. Based on the premise that universal access to basic medical care is a right, meaningful health care reform—a critical element in combating racial disparity—cannot be based on a person's socioeconomic status. With the first national health care disparities report demonstrating racial disparity in surgical complications, the authors explored possible causes using New York State Hospital data. The purpose of the study was to investigate possible racial disparity (African American versus Caucasian) with regard to surgical complications in the state of New York. Hospital discharge data obtained from 1998–2000 in New York was used in the analysis. The data included information on 145,833 African Americans and 865,293 Caucasian patients. The authors highlight that previous studies have shown that African Americans have had higher complication rates than Caucasians. The present study, unlike the earlier ones, controls for patient comorbidity. It also controls for such hospital characteristics as hospital size, academic affiliation, hospital specialization (using the Herfindahl index) and other hospital demographics. Also, hospital ecological characteristics were controlled for such factors as percent of African American patients discharged in the past year, percent of Medicaid patients discharged, and Metropolitan Statistical Area sizes. The primary statistical method used in the data analysis was the hierarchical logistic regression, which controlled sequentially for patient and hospital characteristics. The first logistic regression model included race and gender and age, with the second adding the presence or absence of comorbidities and length of stay. The third added patient social and economic factors, including insurance, HMO status, and admission type (elective nor not). The fourth and fifth added hospital characteristics from the AHA file and hospital ecological characteristics, respectively. Tables 1 and 21 contain the main results. However, it should be noted that for Table 1, which has African Americans and Caucasians compared on outcome variables, patient demographics, patient morbidity indicators, patient social factors, hospital characteristics, and hospital ecology, there is no mention as to the statistical methodology used to produce the P values. Therefore, no determination can be made regarding the appropriateness of the analysis. Also, due to the extremely large sample sizes, virtually any difference between the 2 study groups (African Americans and Caucasians) will be statistically significant. Whether these differences are important in any practical sense remains to be answered. For example, the authors noted that the unadjusted complication rate for African Americans is 1.8% and 1.4% for Caucasians. This difference is 0.4% or 4 people out of a 1000. Although statistically significant, this difference cannot be considered “large” by any reasonable definition. The major portion of this paper addressed whether this 0.4% difference could be eliminated after controlling for several independent variables. The authors ultimately conclude that the higher rates of surgical complications among African Americans, as compared with Caucasians, are primarily explained by patient comorbidities and secondarily by hospital characteristics. Unfortunately, with comorbidities in African Americans so closely linked with health care disparity, particularly with respect to suboptimal access to medical management, the authors cannot readily uncouple the surgical complications from a more systematic racial and socioeconomic bias.

We use particle filtering for correcting the erroneous motion vectors (MVs), which are derived from the boundary matching algorithm (BMA) in packet video communications. Assuming a two-state Markov channel model for transmission, the error of the extracted MVs by BMA is shown to be modeled by the Gaussian mixture distribution. Formulating the problem in the state space, we deploy particle filtering for denoising the erroneous MVs. The main challenge of using particle filters is high computational complexity that is directly related to the number of particles. The proposed particle filtering scheme is efficient even if the number of particles is decreased. Experimental results are provided to show the efficiency of this filtering approach compared to a recent scheme based on Kalman filtering. The experiments show meaningful increase in the quality of the recovered video sequences in terms of PSNR up to 3 dB compared with the other error concealment techniques. Also, the computational complexity of the proposed scheme is discussed.

Catechol-O-methyltransferase (COMT) activity has been reported to be higher in African Americans (AA) than Caucasians (Cau). COMT converts 2- and 4-hydroxy (OH) estrogens to 2- and 4-methoxyestrogens, respectively, and can increase estrogenic milieu locally in tissues. To assess whether the increased incidence of preterm birth (PTB) among AA women is associated with single-nucleotide polymorphism (SNP) in the COMT gene, we examined variations in maternal and fetal COMT genes and their association with pregnancy outcomes (term vs preterm pregnancies) using 4 functional SNPs: rs4633, rs4680, rs4818, and rs6269 in both AA and Cau. We analyzed samples from 267 AA women (191 term and 76 preterm pregnancies) and 339 Cau (194 term and 145 preterm pregnancies) in this study. The results showed a significant difference (P < .05) in allele and genotype frequencies between term and preterm AA and Cau women in 3 SNPs in both maternal and fetal DNA. The analysis revealed that in AA fetal COMT genes, SNP rs4818 is associated with PTB at the allele (C; P < .001), genotype (C/C; P < .01), and 2- (P < .03) and 3 (P < .04)-window haplotype levels. Multidimensionality reduction analysis also showed a significant (P < .01) association between rs4818 and PTB. In conclusion, our study demonstrated that a synonymous polymorphism, rs4818 in the fetal COMT gene, is associated with PTB in AA.

We often find skewed distributions such as the power law distribution and the lognormal distribution in studies of complex systems. Emergence of lognormal distribution is explained based on multiplicative stochastic processes, while power-law distribution can be explained by the multiplicative stochastic process with a modification. Thus, multiplicative process and lognormal distribution can serve as a starting point to discuss the statistical aspect of complex systems. In this paper, we show some economic and social phenomena in which lognormal and its related distributions can be observed to discuss their origins in terms of multiplicative growth.

Prematurity is more prevalent in African Americans than in European Americans. We investigated the contribution of a functional SNP in the promoter of the SERPINH1 gene, enriched among those of African ancestry, to preterm premature rupture of membranes (PPROM), the leading identifiable cause of preterm birth. SERPINH1 encodes heat-shock protein 47, a chaperone essential for collagen synthesis. The SERPINH1 -656 minor T allele had a greater frequency in African populations and African Americans than in European Americans (7.4% [corrected] vs. 4.1%). The -656 T allele displayed significantly reduced promoter activity compared to the major -656 C allele in amnion fibroblasts, which lay down the fibrillar collagen that gives tensile strength to the amnion. An initial case-control study demonstrated that the -656 T allele is significantly more frequent in African-American neonates (P < 0.0009) born from pregnancies complicated by PPROM compared with controls (odds ratio of 3.22, 95% confidence interval 1.50, 7.22). There was no significant difference in ancestry among cases and controls using a dihybrid model based on 29 ancestry-informative markers. Adjusting the results of the case-control study for admixture still yielded a statistically significant association between the -656 T allele and PPROM (P < 0.002). A follow-up case-control study gave similar results. The combined case-control findings showed a highly significant (P < 0.0000045) association between the -656 T allele and PPROM. The SERPINH1 -656 T allele is the first example of an ancestry-informative marker associated with preterm birth in African Americans.