Spatiotemporal variability of the Persian Gulf and Oman Sea marine heatwaves during 1982-2020.

For the first time, this study explored the dominant features of Marine HeatWaves (MHWs) in the Persian Gulf and Oman Sea (1982-2020). The spatial extent of MHWs has nearly doubled in the last 24 years. Since 1997, the average number of MHW days in the central parts of the Persian Gulf has increased about 19 times compared to the period 1982-1997. The average number of the detected MHW events has increased by about three times. Simultaneously with the increase in MHWs frequency trend, the trend in the average number of MHW days has also increased. Since 1997, the average number of MHW days in the study area has almost increased by 10 times. The mean duration of the detected MHWs ranged from 5 to10 days. On average, in a major part of the Persian Gulf, about 1-2 MHW events occur annually.

Two multivariate online change detection models.

Online change point detection methods monitor changes in the distribution of a data stream. This article discusses two non-parametric online change detection methods based on the energy statistics and Mahalanobis depth. To apply the energy statistic, we use sliding-window algorithm with efficient training and updating procedures. For Mahalanobis depth, we propose an algorithm to train the threshold with desired protective ability against false alarms and discuss factors that have an influence on the threshold. Numerical studies evaluate and compare the performance of the proposed models with three existing methods to detect changes in the mean and variability of a data stream. The methods are applied to detecting changes in the flowing volume of the Mississippi River.

The incidence and outcome of severe hyperlactatemia in critically ill patients.

This study aimed to assess the incidence, persistence, and associated mortality of severe hyperlactatemia in a large cohort of unselected critically ill patients. Also, we evaluated the association between 12 h lactate clearance, the timing of severe hyperlactatemia, and the maximum lactate levels with ICU mortality. In this retrospective, single-center study, we used data from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database. Data extracted to screen 23,598 ICU patients for severe hyperlactatemia. A total of 23,598 critically ill patients were eligible for this study. Overall, ICU mortality in the 23,598 ICU patients was 12.1%. Of these, 760 patients had lactate concentration [Formula: see text] 10 mmol/L and ICU mortality in this group was 65%. Our findings confirm the association between hyperlactatemia and ICU mortality [odds ratio 1.42 (95% CI 1.35; 1.49; P < 0.001)]. Data for 12 h lactate clearance was available for 443 patients (276 nonsurvivable vs. 167 survival). 12 h lactate clearance yielded a high area under the curve (AUC) of 0.78, (95% CI 0.74 and 0.83). Severe hyperlactatemia is associated with extremely high ICU mortality in a heterogeneous ICU population. Lactate derived variables (the timing and persistence of severe hyperlactatemia, maximum level, and 12 h clearance) are shown to be associated with ICU mortality in patients with severe hyperlactatemia. Our results suggest that maximum lactate level and 12 h lactate clearance were clinically useful prognostic parameters for patients with severe hyperlactatemia.

Depth functions and mutidimensional medians on minimal spanning trees.

Based on the minimal spanning tree (MST) of the observed data set, the paper introduces new notions of data depth and medians for multivariate data. The MST of a data set of size n is the MST of the complete weighted undirected graph on n vertices, where the edge weights are the pairwise distances of the data points. We study several properties of the MST-based depth functions. We consider the corresponding multidimensional medians, investigate their robustness and computational complexity. An example illustrates the use of the MST-based depth functions.

Future heat stress arising from climate change on Iran's population health.

Climate change-induced extreme heat events are becoming a major issue in different parts of the world, especially in developing countries. The assessment of regional and temporal past and future change in heat waves is a crucial task for public health strategies and managements. The historical and future heat index (HI) time series are investigated for temporal change across Iran to study the impact of global warming on public health. The heat index is calculated, and the nonparametric trend assessment is carried out for historical time series (1981-2010). The future change in heat index is also projected for 2020-2049 and 2070-2099 periods. A rise in the historical heat index and extreme caution conditions for summer and spring seasons for major parts of Iran are notable for historical (1981-2010) series in this study. Using different climate change scenarios shows that heat index will exceed the critical threshold for human adaptability in the future in the country. The impact of climate change on heat index risk in Iran is significant in the future. To cope with this crucial situation, developing early warning systems and health care strategies to deal with population growth and remarkable socio-economic features in future is essential.

An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets.

MOTIVATION: We have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression. However, when the number of datasets increases, the model parameter space increases exponentially due to the component combination from different datasets. RESULTS: In this study, motivated by the well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the concordant components and assume that the proportions of non-concordant components follow a special structure. We discuss the exchangeable, multiset coefficient and autoregressive structures for model reduction, and their related expectation-maximization (EM) algorithms. Then, the parameter space is linear with the number of datasets. In our previous study, we have applied the general mixture model to three microarray datasets for lung cancer studies. We show that more gene sets (or pathways) can be detected by the reduced mixture model with the exchangeable structure. Furthermore, we show that more genes can also be detected by the reduced model. The Cancer Genome Atlas (TCGA) data have been increasingly collected. The advantage of incorporating the concordance feature has also been clearly demonstrated based on TCGA RNA sequencing data for studying two closely related types of cancer. AVAILABILITY AND IMPLEMENTATION: Additional results are included in a supplemental file. Computer program R-functions are freely available at http://home.gwu.edu/∼ylai/research/Concordance. CONTACT: ylai@gwu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting discordance enrichment among a series of two-sample genome-wide expression data sets.

BACKGROUND: With the current microarray and RNA-seq technologies, two-sample genome-wide expression data have been widely collected in biological and medical studies. The related differential expression analysis and gene set enrichment analysis have been frequently conducted. Integrative analysis can be conducted when multiple data sets are available. In practice, discordant molecular behaviors among a series of data sets can be of biological and clinical interest. METHODS: In this study, a statistical method is proposed for detecting discordance gene set enrichment. Our method is based on a two-level multivariate normal mixture model. It is statistically efficient with linearly increased parameter space when the number of data sets is increased. The model-based probability of discordance enrichment can be calculated for gene set detection. RESULTS: We apply our method to a microarray expression data set collected from forty-five matched tumor/non-tumor pairs of tissues for studying pancreatic cancer. We divided the data set into a series of non-overlapping subsets according to the tumor/non-tumor paired expression ratio of gene PNLIP (pancreatic lipase, recently shown it association with pancreatic cancer). The log-ratio ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). Our purpose is to understand whether any gene sets are enriched in discordant behaviors among these subsets (when the log-ratio is increased from negative to positive). We focus on KEGG pathways. The detected pathways will be useful for our further understanding of the role of gene PNLIP in pancreatic cancer research. Among the top list of detected pathways, the neuroactive ligand receptor interaction and olfactory transduction pathways are the most significant two. Then, we consider gene TP53 that is well-known for its role as tumor suppressor in cancer research. The log-ratio also ranges from a negative value (e.g. more expressed in non-tumor tissue) to a positive value (e.g. more expressed in tumor tissue). We divided the microarray data set again according to the expression ratio of gene TP53. After the discordance enrichment analysis, we observed overall similar results and the above two pathways are still the most significant detections. More interestingly, only these two pathways have been identified for their association with pancreatic cancer in a pathway analysis of genome-wide association study (GWAS) data. CONCLUSIONS: This study illustrates that some disease-related pathways can be enriched in discordant molecular behaviors when an important disease-related gene changes its expression. Our proposed statistical method is useful in the detection of these pathways. Furthermore, our method can also be applied to genome-wide expression data collected by the recent RNA-seq technology.

Comparison of tests of contingency tables.

We explore the use of bootstrap for testing independence of two categorical variables. We develop a theoretical justification for bootstrapping a contingency table and provide more accurate inference for small sample sizes. We also study the effect of equalized marginals on tests of independence. The small sample properties of the proposed and existing tests of independence are examined using Monte Carlo simulations. It is shown that the Fisher exact test and the Chi-squared test with continuity correction are very conservative and cannot be recommended to test independence with small sample sizes.

Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.

BACKGROUND: Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment. METHODS: We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets. RESULTS: We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method. CONCLUSIONS: This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada.

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56% of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

Modeling seasonal variation of hip fracture in Montreal, Canada.

The investigation of the association of the climate variables with hip fracture incidences is important in social health issues. This study examined and modeled the seasonal variation of monthly population based hip fracture rate (HFr) time series. The seasonal ARIMA time series modeling approach is used to model monthly HFr incidences time series of female and male patients of the ages 40-74 and 75+ of Montreal, Québec province, Canada, in the period of 1993-2004. The correlation coefficients between meteorological variables such as temperature, snow depth, rainfall depth and day length and HFr are significant. The nonparametric Mann-Kendall test for trend assessment and the nonparametric Levene's test and Wilcoxon's test for checking the difference of HFr before and after change point are also used. The seasonality in HFr indicated sharp difference between winter and summer time. The trend assessment showed decreasing trends in HFr of female and male groups. The nonparametric test also indicated a significant change of the mean HFr. A seasonal ARIMA model was applied for HFr time series without trend and a time trend ARIMA model (TT-ARIMA) was developed and fitted to HFr time series with a significant trend. The multi criteria evaluation showed the adequacy of SARIMA and TT-ARIMA models for modeling seasonal hip fracture time series with and without significant trend. In the time series analysis of HFr of the Montreal region, the effects of the seasonal variation of climate variables on hip fracture are clear. The Seasonal ARIMA model is useful for modeling HFr time series without trend. However, for time series with significant trend, the TT-ARIMA model should be applied for modeling HFr time series.

Spatial memory during progressive disorientation.

Human spatial representations of object locations in a room-sized environment were probed for evidence that the object locations were encoded relative not just to the observer (egocentrically) but also to each other (allocentrically). Participants learned the locations of 4 objects and then were blindfolded and either (a) underwent a succession of 70 degrees and 200 degrees whole-body rotations or (b) were fully disoriented and then underwent a similar sequence of 70 degrees and 200 degrees rotations. After each rotation, participants pointed to the objects without vision. Analyses of the pointing errors suggest that as participants lost orientation, represented object directions generally "drifted" off of their true directions as an ensemble, not in random, unrelated directions. This is interpreted as evidence that object-to-object (allocentric) relationships play a large part in the human spatial updating system. However, there was also some evidence that represented object directions occasionally drifted off of their true directions independently of one another, suggesting a lack of allocentric influence. Implications regarding the interplay of egocentric and allocentric information are considered.

Cryptosporidiosis susceptibility and risk: a case study.

Regional estimates of cryptosporidiosis risks from drinking water exposure were developed and validated, accounting for AIDS status and age. We constructed a model with probability distributions and point estimates representing Cryptosporidium in tap water, tap water consumed per day (exposure characterization); dose response, illness given infection, prolonged illness given illness; and three conditional probabilities describing the likelihood of case detection by active surveillance (health effects characterization). The model predictions were combined with population data to derive expected case numbers and incidence rates per 100,000 population, by age and AIDS status, borough specific and for New York City overall in 2000 (risk characterization). They were compared with same-year surveillance data to evaluate predictive ability, assumed to represent true incidence of waterborne cryptosporidiosis. The predicted mean risks, similar to previously published estimates for this region, overpredicted observed incidence-most extensively when accounting for AIDS status. The results suggest that overprediction may be due to conservative parameters applied to both non-AIDS and AIDS populations, and that biological differences for children need to be incorporated. Interpretations are limited by the unknown accuracy of available surveillance data, in addition to variability and uncertainty of model predictions. The model appears sensitive to geographical differences in AIDS prevalence. The use of surveillance data for validation and model parameters pertinent to susceptibility are discussed.