Evasive maneuver subsequent to CSM/LM ejection from the S-4B in earth orbit - Project Apollo

Apollo 9 evasive maneuver after command service module/lunar module ejection from Saturn S-4B stage in earth orbi

The influence of social network size on speech perception

Infants and adults learn new phonological varieties better when exposed to multiple rather than a single speaker. This article tests whether having a larger social network similarly facilitates phonological performance. Experiment 1 shows that people with larger social networks are better at vowel perception in noise, indicating that the benefit of laboratory exposure to multiple speakers extends to real life experience and to adults tested in their native language. Furthermore, the experiment shows that this association is not due to differences in amount of input or to cognitive differences between people with different social network sizes. Follow-up computational simulations reveal that the benefit of larger social networks is mostly due to increased input variability. Additionally, the simulations show that the boost that larger social networks provide is independent of the amount of input received but is larger if the population is more heterogeneous. Finally, a comparison of “adult” and “child” simulations reconciles previous conflicting findings by suggesting that input variability along the relevant dimension might be less useful at the earliest stages of learning. Together, this article shows when and how the size of our social network influences our speech perception. It thus shows how aspects of our lifestyle can influence our linguistic performance

Inferring meta-covariates in classification

This paper develops an alternative method for gene selection that combines model based clustering and binary classification. By averaging the covariates within the clusters obtained from model based clustering, we define “meta-covariates” and use them to build a probit regression model, thereby selecting clusters of similarly behaving genes, aiding interpretation. This simultaneous learning task is accomplished by an EM algorithm that optimises a single likelihood function which rewards good performance at both classification and clustering. We explore the performance of our methodology on a well known leukaemia dataset and use the Gene Ontology to interpret our results

Detection of elliptical shapes via cross-entropy clustering

The problem of finding elliptical shapes in an image will be considered. We discuss the solution which uses cross-entropy clustering. The proposed method allows the search for ellipses with predefined sizes and position in the space. Moreover, it works well for search of ellipsoids in higher dimensions

Cysteine-rich protein 1 (CRP1) regulates actin filament bundling

BACKGROUND: Cysteine-rich protein 1 (CRP1) is a LIM domain containing protein localized to the nucleus and the actin cytoskeleton. CRP1 has been demonstrated to bind the actin-bundling protein α-actinin and proposed to modulate the actin cytoskeleton; however, specific regulatory mechanisms have not been identified. RESULTS: CRP1 expression increased actin bundling in rat embryonic fibroblasts. Although CRP1 did not affect the bundling activity of α-actinin, CRP1 was found to stabilize the interaction of α-actinin with actin bundles and to directly bundle actin microfilaments. Using confocal and photobleaching fluorescence resonance energy transfer (FRET) microscopy, we demonstrate that there are two populations of CRP1 localized along actin stress fibers, one associated through interaction with α-actinin and one that appears to bind the actin filaments directly. Consistent with a role in regulating actin filament cross-linking, CRP1 also localized to the membrane ruffles of spreading and PDGF treated fibroblasts. CONCLUSION: CRP1 regulates actin filament bundling by directly cross-linking actin filaments and stabilizing the interaction of α-actinin with actin filament bundles

Adult attachment style across individuals and role-relationships: Avoidance is relationship-specific, but anxiety shows greater generalizability

A generalisability study examined the hypotheses that avoidant attachment, reflecting the representation of others, should be more relationship-specific (vary across relationships more than across individuals), while attachment anxiety, reflecting self-representation, should be more generalisable across a person’s relationships. College students responded to 6-item questionnaire measures of these variables for 5 relationships (mother, father, best same-gender friend, romantic partner or best opposite-gender friend, other close person), on 3 (N = 120) or 2 (N = 77) occasions separated by a few weeks. Results supported the hypotheses, with the person variance component being larger than the relationship-specific component for anxiety, and the opposite happening for avoidance. Anxiety therefore seems not to be as relationship-specific as previous research suggested. Possible reasons for discrepancies between the current and previous studies are discussed

Model selection in High-Dimensions: A Quadratic-risk based approach

In this article we propose a general class of risk measures which can be used for data based evaluation of parametric models. The loss function is defined as generalized quadratic distance between the true density and the proposed model. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a nonnegative definite kernel and a bandwidth parameter. Using asymptotic results for the quadratic distances we build a quick-to-compute approximation for the risk function. Its derivation is analogous to the Akaike Information Criterion (AIC), but unlike AIC, the quadratic risk is a global comparison tool. The method does not require resampling, a great advantage when point estimators are expensive to compute. The method is illustrated using the problem of selecting the number of components in a mixture model, where it is shown that, by using an appropriate kernel, the method is computationally straightforward in arbitrarily high data dimensions. In this same context it is shown that the method has some clear advantages over AIC and BIC.Comment: Updated with reviewer suggestion

Latent class analysis variable selection

We propose a method for selecting variables in latent class analysis, which is the most common model-based clustering method for discrete data. The method assesses a variable's usefulness for clustering by comparing two models, given the clustering variables already selected. In one model the variable contributes information about cluster allocation beyond that contained in the already selected variables, and in the other model it does not. A headlong search algorithm is used to explore the model space and select clustering variables. In simulated datasets we found that the method selected the correct clustering variables, and also led to improvements in classification performance and in accuracy of the choice of the number of classes. In two real datasets, our method discovered the same group structure with fewer variables. In a dataset from the International HapMap Project consisting of 639 single nucleotide polymorphisms (SNPs) from 210 members of different groups, our method discovered the same group structure with a much smaller number of SNP

Model-based clustering via linear cluster-weighted models

A novel family of twelve mixture models with random covariates, nested in the linear $t$ cluster-weighted model (CWM), is introduced for model-based clustering. The linear $t$ CWM was recently presented as a robust alternative to the better known linear Gaussian CWM. The proposed family of models provides a unified framework that also includes the linear Gaussian CWM as a special case. Maximum likelihood parameter estimation is carried out within the EM framework, and both the BIC and the ICL are used for model selection. A simple and effective hierarchical random initialization is also proposed for the EM algorithm. The novel model-based clustering technique is illustrated in some applications to real data. Finally, a simulation study for evaluating the performance of the BIC and the ICL is presented