Selective binding and lateral clustering of α5β1 and αvβ3 integrins: Unraveling the spatial requirements for cell spreading and focal adhesion assembly

ABSTRACT

Coordination of the specific functions of α5β1 and αvβ3 integrins is crucial for the precise regulation of cell adhesion, spreading and migration, yet the contribution of differential integrin-specific crosstalk to these processes remains unclear. To determine the specific functions of αvβ3 and α5β1 integrins, we used nanoarrays of gold particles presenting immobilized, integrin-selective peptidomimetic ligands. Integrin binding to the peptidomimetics is highly selective, and cells can spread on both ligands. However, spreading is faster and the projected cell area is greater on α5β1 ligand; both depend on ligand spacing. Quantitative analysis of adhesion plaques shows that focal adhesion size is increased in cells adhering to αvβ3 ligand at 30 and 60 nm spacings. Analysis of αvβ3 and α5β1 integrin clusters indicates that fibrillar adhesions are more prominent in cells adhering to α5β1 ligand, while clusters are mostly localized at the cell margins in cells adhering to αvβ3 ligand. αvβ3 integrin clusters are more pronounced on αvβ3 ligand, though they can also be detected in cells adhering to α5β1 ligand. Furthermore, α5β1 integrin clusters are present in cells adhering to α5β1 ligand, and often colocalize with αvβ3 clusters. Taken together, these findings indicate that the activation of αvβ3 integrin by ligand binding is dispensable for initial adhesion and spreading, but essential to formation of stable focal adhesions.     

The program structure does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem

Inferences of population structure and more precisely the identification of genetically homogeneous groups of individuals are essential to the fields of ecology, evolutionary biology and conservation biology. Such population structure inferences are routinely investigated via the program structure implementing a Bayesian algorithm to identify groups of individuals at Hardy–Weinberg and linkage equilibrium. While the method is performing relatively well under various population models with even sampling between subpopulations, the robustness of the method to uneven sample size between subpopulations and/or hierarchical levels of population structure has not yet been tested despite being commonly encountered in empirical data sets. In this study, I used simulated and empirical microsatellite data sets to investigate the impact of uneven sample size between subpopulations and/or hierarchical levels of population structure on the detected population structure. The results demonstrated that uneven sampling often leads to wrong inferences on hierarchical structure and downward‐biased estimates of the true number of subpopulations. Distinct subpopulations with reduced sampling tended to be merged together, while at the same time, individuals from extensively sampled subpopulations were generally split, despite belonging to the same panmictic population. Four new supervised methods to detect the number of clusters were developed and tested as part of this study and were found to outperform the existing methods using both evenly and unevenly sampled data sets. Additionally, a subsampling strategy aiming to reduce sampling unevenness between subpopulations is presented and tested. These results altogether demonstrate that when sampling evenness is accounted for, the detection of the correct population structure is greatly improved.     

Dual chamber pacing mode in an atrial antitachycardia pacing device without a ventricular lead – A necessary evil

We present a case of a single chamber atrial pacemaker implanted for sinus node dysfunction and treatment of macroreentrant atrial tachycardias with atrial antitachycardia pacing. The patient presented with sustained atrial tachycardia above the detection rate, however, the device was unable to detect the tachycardia and did not deliver the programmed therapy. We discuss the nuances of the atrial tachyarrhythmia detection algorithms, and the programming strategies to maximize detection of atrial arrhythmias in a single chamber atrial pacemaker.     

Multivariate t linear mixed models for irregularly observed multiple repeated measures with missing outcomes

Missing outcomes or irregularly timed multivariate longitudinal data frequently occur in clinical trials or biomedical studies. The multivariate t linear mixed model (MtLMM) has been shown to be a robust approach to modeling multioutcome continuous repeated measures in the presence of outliers or heavy‐tailed noises. This paper presents a framework for fitting the MtLMM with an arbitrary missing data pattern embodied within multiple outcome variables recorded at irregular occasions. To address the serial correlation among the within‐subject errors, a damped exponential correlation structure is considered in the model. Under the missing at random mechanism, an efficient alternating expectation‐conditional maximization (AECM) algorithm is used to carry out estimation of parameters and imputation of missing values. The techniques for the estimation of random effects and the prediction of future responses are also investigated. Applications to an HIV‐AIDS study and a pregnancy study involving analysis of multivariate longitudinal data with missing outcomes as well as a simulation study have highlighted the superiority of MtLMMs on the provision of more adequate estimation, imputation and prediction performances.     

Applications of Genetic Algorithms in Cluster Optimisation

Abstract

Applications of Genetic Algorithms for optimisation of atomic clusters are reported. It is shown that the genetic algorithms are very useful tools for determining the minimum energy structures of clusters of atoms described by interatomic potential functions containing up to a few hundred atoms. The algorithm generally outperforms other optimisation methods for this task. A number of applications are given including covalent carbon and silicon clusters, close-packed structures such as argon and silver and the two-component C—H system.     

Should artificial neural networks replace linear models in tree ring based climate reconstructions?

Studies focused on tree ring—climate relationships usually use linear methods to find the optimal transfer function. In our study, three sites with three different tree species from the Western Balkan region were selected to compare linear and artificial neural network (ANN) nonlinear models and to see whether linear models can be potentially replaced with ANN in climate reconstruction. For each site, one linear and two different ANN models were calculated. For all analysed sites, we were able to find a better fit using the advanced technique of ANN. All calibration and verification statistics were in favour of ANN models. A climate variable was reconstructed for a selected site using linear and nonlinear ANN methods. We demonstrated that ANN is always a more effective method, which always produce better results than linear models. The key to success is a properly selected training algorithm, which prevents overfitting and is able to find the optimal transfer function, also linear, if that is the case.     

Optimizing Diversity Assessment Protocols for High Canopy Ants in Tropical Rain Forest

The canopies of tropical rain forests support highly diverse, yet poorly known, animal and plant communities. It is vital that researchers who invest the time needed to gain access to the high canopy are able efficiently to survey the animals and plants that they find there. Here, we develop diversity assessment protocols for one of the most ecologically important canopy animal groups, the ants, in lowland dipterocarp rain forest in Sabah, Malaysia. We design and test a novel trap (the purse‐string trap) that can be remotely collected, thus avoiding disturbance to ants. We compare this modified trap with two other methods for surveying canopy ants: precision insecticide fogging and baited pitfall trapping. In total, we collected 39,351 ants belonging to 173 species in 38 genera. Fogging collected the most individuals and species, followed by purse‐string trapping with baited pitfall trapping catching the fewest. Fogging also resulted in samples with a different species composition to purse‐string trapping and baited pitfall trapping, which were not different from one another. Using a ‘greedy algorithm’, which guides the selection of inventory methods in order to maximize new species discovered per researcher‐hour, we show that projects allocating fewer than 132 researcher‐hours to canopy ant collection and identification should sample exclusively using fogging. Those with more time should use a combination of methods. This prioritization technique could be used to accelerate species discovery in future rapid biodiversity assessments. Abstract in Malay is available in the online version of this article.     

Searching Optimal Designs in the Presence of Serially Correlated Errors

Based on simple updating formulaé, a computer algorithm for searching optimal designs when the errors are believed to be serially correlated in the one-dimensional situation is described. The performance of the designs found by the algorithm is compared with the optimal designs available in the literature.