Generalized estimating equations approach for spatial lattice data: A case study in adoption of improved maize varieties in Mozambique

Generalized estimating equations (GEE) are extension of generalized linear models (GLM) widely applied in longitudinal data analysis. GEE are also applied in spatial data analysis using geostatistics methods. In this paper, we advocate application of GEE for spatial lattice data by modeling the spatial working correlation matrix using the Moran's index and the spatial weight matrix. We present theoretical developments and results for simulated and actual data as well. For the former case, 1,000 samples of a random variable (response variable) defined in (0, 1) interval were generated using different values of the Moran's index. In addition, 1,000 samples of a binary and a continuous variable were also randomly generated as covariates. In each sample, three structures of spatial working correlation matrices were used while modeling: The independent, autoregressive, and the Toeplitz structure. Two measures were used to evaluate the performance of each of the spatial working correlation structures: the asymptotic relative efficiency and the working correlation selection criterions. The results showed that both measures indicated that the autoregressive spatial working correlation matrix proposed in this paper presents the best performance in general. For the actual data case, the proportion of small farmers who used improved maize varieties was considered as the response variable and a set of nine variables were used as covariates. Two structures of spatial working correlation matrices were used and the results showed consistence with those obtained in the simulation study.     

Nonparametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-binding Proteins*

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Highlights

• •Method for the analysis of response curves from thermal proteome profiling (TPP).
• •NPARC uses nonparametric statistics and provides false discovery-rate (FDR) control.
• •Increased proteome coverage and sensitivity to identify drug-binding proteins.

     

Quantifying torpor in small mammals non-invasively using infrared thermocouples

Measurements of torpor use are pivotal for many research areas concerning the thermal biology of endotherms. Here, I used infrared thermocouples to non-invasively examine torpor patterns in the small marsupial fat-tailed dunnart (Sminthopsis crassicaudata). Sensors were installed inside the nesting chambers to continuously monitor fur temperature in undisturbed animals. Firstly, to verify the measurements, fur temperature was monitored simultaneously with body temperature using internal radio transmitters (n=6). Secondly, I conducted a food restriction study to demonstrate the reliability of the method within a physiological experiment (n=8). Based on the correspondence of simultaneously measured fur and body temperature during torpor bouts, I was able to confirm that infrared thermocouples provide reliable temporal information on torpor patterns. Furthermore, torpor use was successfully monitored over a 20-day food restriction study. The method can easily be adapted to suit other small mammal or bird species and presents a useful, inexpensive approach for examining torpor patterns remotely and non-invasively in the laboratory.     

Analysis of multivariate recurrent event data with time‐dependent covariates and informative censoring

Multivariate recurrent event data are usually encountered in many clinical and longitudinal studies in which each study subject may experience multiple recurrent events. For the analysis of such data, most existing approaches have been proposed under the assumption that the censoring times are noninformative, which may not be true especially when the observation of recurrent events is terminated by a failure event. In this article, we consider regression analysis of multivariate recurrent event data with both time‐dependent and time‐independent covariates where the censoring times and the recurrent event process are allowed to be correlated via a frailty. The proposed joint model is flexible where both the distributions of censoring and frailty variables are left unspecified. We propose a pairwise pseudolikelihood approach and an estimating equation‐based approach for estimating coefficients of time‐dependent and time‐independent covariates, respectively. The large sample properties of the proposed estimates are established, while the finite‐sample properties are demonstrated by simulation studies. The proposed methods are applied to the analysis of a set of bivariate recurrent event data from a study of platelet transfusion reactions.     

Modeling seasonality in space‐time infectious disease surveillance data

Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one‐step‐ahead predictions if all three components allow for seasonal variation.     

Creating a community resource for protein science

Helen Berman is the recipient of the Protein Society 2012 Carl Branden Award In addition to being one of the early pioneers in protein crystallography, Carl Brändén made significant contributions to science education with his elegant and beautifully illustrated book Introduction to Protein Structure (Brändén and Tooze, New York: Garland, 1991). It is truly an honor to receive this award in their names. This award and the 40th anniversary of the Protein Data Bank (PDB; Berman et al., Structure 2012;20:391–396) have given me an opportunity to reflect on the various components that have contributed to building a resource for protein science and to try to quantify the impact of having PDB data openly available.     

Seasonal changes in circadian rhythms of body temperatures in humans living in a dry tropical climate

Seven volunteers (3 females and 4 males; 3 Caucasians and 4 Africans) participated in two 24 h sessions during the cool dry (CD) and the hot dry (HD) seasons of the sahelian tropical climate. Body temperatures were taken on portable cassette recorders for 24 h. Rectal (Tre) and mean skin (Tsk) temperatures decreased in the HD compared to the CD conditions, meeting one of the criteria for adaptation to heat. No ethnic differences in thermal responses were found. Males and females differed in their body temperature rhythms and in their reactions to heat. Body temperatures were higher in females than in males. Males reacted to heat with a decrease in Tre, without change in the Tre-Tsk gradient. Females showed a decrease in both Tre and Tsk, more marked for Tsk, with an increase in the Tre-Tsk gradient. It was concluded that males showed seasonal acclimatization to heat via a decrease in metabolism confirmed by a decrease in plasma levels of thyroid stimulating hormone (TSH) in the HD condition. Females showed a mixed metabolic and thermolytic type of acclimatization, with an absence of variation in plasma TSH levels. In conclusion, the steady rise in temperature between the CD and HD conditions was sufficient to trigger an acclimatization to heat similar in Caucasian and African subjects, although exposure to the external climate differed widely.     

Competing risks analysis of correlated failure time data

Summary .   We develop methods for competing risks analysis when individual event times are correlated within clusters. Clustering arises naturally in clinical genetic studies and other settings. We develop a nonparametric estimator of cumulative incidence, and obtain robust pointwise standard errors that account for within-cluster correlation. We modify the two-sample Gray and Pepe–Mori tests for correlated competing risks data, and propose a simple two-sample test of the difference in cumulative incidence at a landmark time. In simulation studies, our estimators are asymptotically unbiased, and the modified test statistics control the type I error. The power of the respective two-sample tests is differentially sensitive to the degree of correlation; the optimal test depends on the alternative hypothesis of interest and the within-cluster correlation. For purposes of illustration, we apply our methods to a family-based prospective cohort study of hereditary breast/ovarian cancer families. For women with BRCA1 mutations, we estimate the cumulative incidence of breast cancer in the presence of competing mortality from ovarian cancer, accounting for significant within-family correlation.     

Automated system for gene annotation and metabolic pathway reconstruction using general sequence databases

Despite the growing number of genomes published or currently being sequenced, there is a relative paucity of software for functional classification of newly discovered genes and their assignment to metabolic pathways. Available software for such analyses has a very steep learning curve and requires the installation, configuration, and maintenance of large amounts of complex infrastructure, including complementary software and databases. Many such tools are restricted to one or a few data sources and classification schemes. In this work, we report an automated system for gene annotation and metabolic pathway reconstruction (ASGARD), which was designed to be powerful and generalizable, yet simple for the biologist to install and run on centralized, commonly available computers. It avoids the requirement for complex resources such as relational databases and web servers, as well as the need for administrator access to the operating system. Our methodology contributes to a more rapid investigation of the potential biochemical capabilities of genes and genomes by the biological researcher, and is useful in biochemical as well as comparative and evolutionary studies of pathways and networks.