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Small number of clusters combined with cluster level heterogeneity poses a great challenge for the data analysis. We have published a weighted Jackknife approach to address this issue applying weighted cluster means as the basic estimators. The current study proposes a new version of the weighted delete-one-cluster Jackknife analytic framework, which employs Ordinary Least Squares or Generalized Least Squares estimators as the fundamentals. Algorithms for computing estimated variances of the study estimators have also been derived. Wald test statistics can be further obtained, and the statistical comparison in the outcome means of two conditions is determined using the cluster permutation procedure. The simulation studies show that the proposed framework produces estimates with higher precision and improved power for statistical hypothesis testing compared to other methods. [ABSTRACT FROM AUTHOR]

In a large-scale environmental health population study that is composed of subprojects, often different fractions of participants out of the total enrolled have measures of specific outcomes. It's conceptually reasonable to assume the association study would benefit from utilizing additional exposure information from those with a specific outcome not measured. Partial least squares regression is a practical approach to determine the exposure-outcome associations for mixture data. Like a typical regression approach, however, the partial least squares regression requires that each data observation must have both complete covariate and outcome for model fitting. In this paper, we propose novel adjustments to the general partial least squares regression to estimate and examine the association effects of individual environmental exposure to an outcome within a more complete context of the study population's environmental mixture exposures. The proposed framework takes advantage of the bilinear model structure. It allows information from all participants, with or without the outcome values, to contribute to the model fitting and the assessment of association effects. Using this proposed framework, incorporation of additional information will lead to smaller root mean square errors in the estimation of association effects, and improve the ability to assess the significance of the effects. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore plant-derived peptides as carrier material with the function of anti-hot-melt stickiness of traditional Chinese herbal extracts during spray drying. Crataegi fructus extract (CFE) with a severe hot-melt stickiness problem was used as a model drug, and soy peptides (SPT) were screened as the most effective and suitable carrier material among seven plant-derived proteins and peptides which include soy protein isolate, walnut protein and five kinds of peptides (corn peptides, wheat peptides, walnut peptides, rice peptides and SPT). The addition of 15% SPT also exhibited a strong anti-stickiness effect in other four hot-melt sticky herbal extracts from Chinese herbs (Lycii fructus, Corni fructus, Chaenomelis fructus and Mume fructus) since the obtained powder increased from completely sticking to the wall to more than 50% powder yield. The dynamic surface tensions (DST) of CFE decreased consistently with the increase of SPT from 5% to 20%, and DST1000ms had consistent reduction with the addition of SPT from zero to 20%. Meanwhile, the adhesive force of the single droplet at 28 min decreased below 18.5 N. The softening point of the spray-dried powder of CFE added 15% SPT increased by about 40 °C, and the addition of 15% SPT improved the stability which might be similar with the addition of 50% MD based on scanning electron microscope analysis but more efficiently. These findings confirmed that SPT plant-derived could serve as effective and stable carrier material with good anti-stickiness ability during spray drying. [ABSTRACT FROM AUTHOR]

The rates of SMM have been steadily increasing in Arkansas, a southern rural state, which has the 5th highest maternal death rate among the US states. The aims of the study were to test the functionality of the Bateman index in association to SMM, in clustering the risks of pregnancies to SMM, and to study the predictability of SMM using the Bateman index. From the ANGELS database, 72,183 pregnancies covered by Medicaid in Arkansas between 2013 and 2016 were included in this study. The expanded CDC ICD-9/ICD-10 criteria were used to identify SMM. The Bateman comorbidity index was applied in quantifying the comorbidity burden for a pregnancy. Multivariable logistic regressions, KMeans method, and five widely used predictive models were applied respectively for each of the study aims. SMM prevalence remained persistently high among Arkansas women covered by Medicaid (195 per 10,000 deliveries) during the study period. Using the Bateman comorbidity index score, the study population was divided into four groups, with a monotonically increasing odds of SMM from a lower score group to a higher score group. The association between the index score and the occurrence of SMM is confirmed with statistical significance: relative to Bateman score falling in 0–1, adjusted Odds Ratios and 95% CIs are: 2.1 (1.78, 2.46) for score in 2–5; 5.08 (3.81, 6.79) for score in 6–9; and 8.53 (4.57, 15.92) for score ≥10. Noticeably, more than one-third of SMM cases were detected from the studied pregnancies that did not have any of the comorbid conditions identified. In the prediction analyses, we observed minimal predictability of SMM using the comorbidity index: the calculated c-statistics ranged between 62% and 67%; the Precision-Recall AUC values are <7% for internal validation and <9% for external validation procedures. The comorbidity index can be used in quantifying the risk of SMM and can help cluster the study population into risk tiers of SMM, especially in rural states where there are disproportionately higher rates of SMM; however, the predictive value of the comorbidity index for SMM is inappreciable. [ABSTRACT FROM AUTHOR]

The change of chemical composition of aged asphalt mixture is significant to determine the mechanism of asphalt aging and obtain the direction to produce an advisable asphalt rejuvenator for the specified aged asphalt. For this purpose, this article aims to determine the change of asphaltene after the asphalt aging by centrifugal separation combined with an air stripping method. The result indicates that the content of asphaltene in aged asphalt is approximately 25%, while the content of asphaltene in base asphaltene is about 20%. In addition, compared with traditional distillation-extraction method, the new approach has the advantages of simplicity, rapidity, accuracy and a reduction in pollution to the environment. Furthermore, the analytical result could be provided for preparation and application of asphalt rejuvenators. [ABSTRACT FROM AUTHOR]

We propose a weighted delete-one-cluster Jackknife based framework for few clusters with severe cluster-level heterogeneity. The proposed method estimates the mean for a condition by a weighted sum of estimates from each of the Jackknife procedures. Influence from a heterogeneous cluster can be weighted appropriately, and the conditional mean can be estimated with higher precision. An algorithm for estimating the variance of the proposed estimator is also provided, followed by the cluster permutation test for the condition effect assessment. Our simulation studies demonstrate that the proposed framework has good operating characteristics. [ABSTRACT FROM AUTHOR]

By sequence homology search, the list of all the functions found and the counts of reads being aligned to them present the functional profile of a metagenomic sample. However, a significant obstacle has been observed in this approach due to the short read length associated with many next-generation sequencing technologies. This includes artificial families, cross-annotations, length bias and conservation bias. The widely applied cut-off methods, such as BLAST E-value, are not able to solve the problems. Following the published successful procedures on the artificial families and the cross-annotation issue, we propose in this paper to use zero-truncated Poisson and Binomial (ZTP-Bin) hierarchical modelling to correct the length bias and the conservation bias. Goodness of fit of the modelling and cross-validation for the prediction using a bioinformatic simulated sample show the validity of this approach. Evaluated on an in vitro-simulated data set, the proposed modelling method outperforms other traditional methods. All three steps were then sequentially applied on real-life metagenomic samples to show that the proposed framework will lead to a more accurate functional profile of a short-read metagenomic sample. [ABSTRACT FROM AUTHOR]