Effects of Age,Sex, Index Admission,and Predominant Polarity on the Seasonality of Acute Admissions For Bipolar Disorder: A Population-Based Study

Bipolar disorder seasonality has been documented previously, though information on the effect of demographic and clinical variables on seasonal patterns is scant. This study examined effects of age, sex, index admission, and predominant polarity on bipolar disorder seasonality in a nationwide population. An inpatient cohort admitted to hospital exclusively for mental illness was derived from the Taiwan National Health Insurance Research Database for 2002–2007. The authors identified 9619 inpatients with bipolar disorder, who had generated 15 078 acute admission records. An empirical mode decomposition method was used to identify seasonal oscillations in bipolar admission data, and regression and cross-correlation analyses were used to quantify the degree and timing of bipolar admission seasonality. Results for seasonality timing found that manic or mixed episodes peak in spring or summer, and depressive episodes peak in winter. Analysis for degree of seasonality revealed that (1) the polarity of patients' index admission predicted the seasonality of relapse admissions; (2) seasonality was significant in female admissions for depressive episodes and in male admissions for manic episodes; (3) young adults displayed a higher degree of seasonality for acute admissions than middle-aged adults; and (4) patients with predominantly depressive admissions displayed a higher degree of seasonality than patients with predominantly manic admissions. Demographic and clinical variables were found to affect the seasonality of acute admissions for bipolar disorders. These findings highlight the need for research on identification and management of seasonal features in bipolar patients. (Author correspondence: ccyang@physionet.org)     

Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication     

Modelling lung cancer mortality rates from smoking prevalence: Fill in the gap

BackgroundThe objective of this study is to estimate the gap between smoking prevalence and lung cancer mortality and provide predictions of lung cancer mortality based on previous smoking prevalence.Materials and methodsWe used data from the Spanish National Health Surveys (2003, 2006 and 2011) to obtain information about tobacco use and data from the Spanish National Statistics Institute to obtain cancer mortality rates from 1980 to 2013. We calculated the cross-correlation among the historical series of smoking prevalence and lung cancer mortality rate (LCMR) to estimate the most likely time gap between both series. We also predicted the magnitude and timing of the LCMR peak.ResultsAll cross-correlations were statistically significant and positive (all above 0.8). For men, the most likely gap ranges from 20 to 34 years. The age-adjusted LCMR increased by 3.2 deaths per 100,000 people for every 1 unit increase in the smoking prevalence 29 years earlier. The highest rate for men was observed in 1995 (55.6 deaths). For women, the most likely gap ranges from 10 to 37 years. The age-adjusted LCMR increased by 0.28 deaths per 100,000 people for every 1 unit increase in the smoking prevalence 32 years earlier. The maximum rate is expected to occur in 2026 (10.3 deaths).ConclusionThe time series of prevalence of tobacco smoking explains the mortality from lung cancer with a distance (or gap) of around 30 years. According to the lagged smoking prevalence, the lung cancer mortality among men is declining while in women continues to rise (maximum expected in 2026).     

Nonlinear responses of wolverine populations to declining winter snowpack

Understanding the population-level impacts of climate change is critical for effectively managing ecosystems. Predators are important components of many systems because they provide top−down control of community structure. Ecological theory suggests that these species could be particularly susceptible to climate change because they generally occur at low densities and have resource-limited populations. Yet, our understanding of climate-change impacts on predators is hindered by the difficulty in assessing complex, nonlinear dynamics over the large spatial scales necessary to depict a species’ general response to abiotic forcing. Here we use fur-return data to characterize population dynamics of a snow-adapted carnivore, the wolverine, across most of its North American range. Using novel modeling techniques, we simultaneously measured the impact of winter snowpack on wolverine dynamics across critical thresholds in snowpack depth and two domains of population growth. Winter snowpack declined from 1970 to 2004 in nearly the entire region studied, concordant with increases in Northern Hemisphere temperature anomalies. Fur returns have declined in many areas; our models show that snowpack has strong, nonlinear effects on wolverine population dynamics. Importantly, wolverine harvests dropped the fastest in areas where snowpack declined most rapidly and also where snowpack had the greatest effect on population dynamics. Moreover, declining snow cover appears to drive trends in wolverine population synchrony, with important implications for overall persistence. These results illustrate the vulnerability and complex responses of predator populations to climate change. We also suggest that declining snowpack may be an important and hitherto little-analyzed mechanism through which climate change alters high-latitude ecosystems.     

Nonlinear effects in macromolecular assembly and dosage sensitivity

Haploinsufficiency refers to dominant abnormal phenotypes resulting from the absence of substantial activity from one allele at a normally diploid locus. Haploinsufficiency may also result from an altered stoichiometry in a macromolecular complex. Higher-than-diploid levels of a gene product can also induce abnormalities that may even resemble the haploinsufficient phenotype. Here, I explore possible non-linearities in the assembly of multimeric molecules from the perspective of dose effects. I propose that for any oligomer assembly reaction, there may be a set of conditions (initial subunit concentrations and equilibrium constants) such that changing the input concentration of one component (0.5 x or 1.5x) will lead to a minimum and non-proportional change of the final oligomer concentration. This buffer effect is a general property of multimeric systems in equilibrium and can be, in principle, exploited by selection to diminish dosage sensitivity. Other effects involving cooperativity or sequential assembly may also play a role in palliating the effect of changes in input amounts of monomers.     

Non-random mating in classical lekking grouse species: seasonal and diurnal trends

This paper is the first to integrate both field and theoretical approaches to demonstrate that fertility benefits can be a direct benefit to females mating on the classical lek. Field data collected for male sharp-tailed grouse (Tympanuchus phasianellus), a classical lekking species, revealed potential fertility benefits for selective females. Adult males and individuals occupying centrally located territories on the lek were found to have significantly larger testes than juveniles and peripheral individuals. Further, using empirical data from previously published studies of classical lekking grouse species, time-series analysis was employed to illustrate that female mating patterns, seasonal and daily, were non-random. We are the first to show that these patterns coincide with times when male fertility is at its peak. Received: 26 February 1999 / Revised: 13 December 1999 / Accepted: 15 March 2000     

Effect of weather variables on the population fluctuation of muroid Calomys venustus in central Argentina

The effects of weather on population fluctuation patterns of the South American muroid Calomys venustus were studied. Box–Jenkins data series analysis and anova were used to describe the relationship between climatic variables and population. No relation was found between maximum temperatures or precipitation and the C. venustus population. Temperatures below 4 °C appear to affect populations of the overwintered cohort after a time lag of 5 months.     

Analysis of the effect of immunization in rabies time series

The authors analysed time series of rabies cases diagnosed in Hungary between 1967 and 2001. In Transdanubia (West Hungary), an oral immunization program started in 1992 and in East Hungary in 2001. Both long term and seasonal trends were identified in the time series of rabies cases. In order to characterize the underlying processes governing the behaviour of the epidemic, the fluctuations around the trend were analysed before and after immunization separately. It turned out that the tail of the complementary cumulative distribution functions differ. The tail of the distribution follows an inverse power law (IPL) function and describes the distribution of extreme events. The significant difference in the IPL exponents before and after immunization can be explained by the theory of Highly Optimized Tolerance (HOT).     

Climate variations and salmonellosis transmission in Adelaide,South Australia: a comparison between regression models

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990–2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.