Covariate and multinomial: Accounting for distance in movement in capture–recapture analyses

Many biological quantities cannot be measured directly but rather need to be estimated from models. Estimates from models are statistical objects with variance and, when derived simultaneously, covariance. It is well known that their variance–covariance (VC) matrix must be considered in subsequent analyses. Although it is always preferable to carry out the proposed analyses on the raw data themselves, a two‐step approach cannot always be avoided. This situation arises when the parameters of a multinomial must be regressed against a covariate. The Delta method is an appropriate and frequently recommended way of deriving variance approximations of transformed and correlated variables. Implementing the Delta method is not trivial, and there is a lack of a detailed information on the procedure in the literature for complex situations such as those involved in constraining the parameters of a multinomial distribution. This paper proposes a how‐to guide for calculating the correct VC matrices of dependant estimates involved in multinomial distributions and how to use them for testing the effects of covariates in post hoc analyses when the integration of these analyses directly into a model is not possible. For illustrative purpose, we focus on variables calculated in capture–recapture models, but the same procedure can be applied to all analyses dealing with correlated estimates with multinomial distribution and their variances and covariances.     

The influence of impact source on variables associated with strain for impacts in ice hockey

Concussion can occur from a variety of events (falls to ice, collisions etc) in ice hockey, and as a result it is important to identify how these different impact sources affect the relationship between impact kinematics and strain that has been found to be associated to this injury. The purpose of this research was to examine the relationship between kinematic variables and strain in the brain for impact sources that led to concussion in ice hockey. Video of professional ice hockey games was analyzed for impacts that resulted in reported clinically diagnosed concussions. The impacts were reconstructed using physical models/ATDs to determine the impact kinematics and then simulated using finite element modelling to determine maximum principal strain and cumulative strain damage measure. A stepwise linear regression was conducted between linear acceleration, change in linear velocity, rotational acceleration, rotational velocity, and strain response in the brain. The results for the entire dataset was that rotational acceleration had the highest r² value for MPS (r² = 0.581) and change in rotational velocity for cumulative strain damage measure (r² = 450). When the impact source (shoulder, elbow, boards, or ice impacts) was isolated the rotational velocity and acceleration r² value increased, indicating that when evaluating the relationships between kinematics and strain based metrics the characteristics of the impact is an important factor. These results suggest that rotational measures should be included in future standard methods and helmet innovation and design in ice hockey as they have the highest association with strain in the brain tissues.     

A systematic screen for genes expressed in definitive endoderm by Serial Analysis of Gene Expression (SAGE)

Background  

The embryonic definitive endoderm (DE) gives rise to organs of the gastrointestinal and respiratory tract including the liver, pancreas and epithelia of the lung and colon. Understanding how DE progenitor cells generate these tissues is critical to understanding the cause of visceral organ disorders and cancers, and will ultimately lead to novel therapies including tissue and organ regeneration. However, investigation into the molecular mechanisms of DE differentiation has been hindered by the lack of early DE-specific markers.     

Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

Background  

Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio.     

Methods for peptide identification by spectral comparison

Background  

Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies.