Zee electrical interconnect

An interconnect, having some length, that reliably connects two conductors separated by the length of the interconnect when the connection is made but in which one length if unstressed would change relative to the other in operation. The interconnect comprises a base element an intermediate element and a top element. Each element is rectangular and formed of a conducting material and has opposed ends. The elements are arranged in a generally Z-shape with the base element having one end adapted to be connected to one conductor. The top element has one end adapted to be connected to another conductor and the intermediate element has its ends disposed against the other end of the base and the top element. Brazes mechanically and electrically interconnect the intermediate element to the base and the top elements proximate the corresponding ends of the elements. When the respective ends of the base and the top elements are connected to the conductors, an electrical connection is formed therebetween, and when the conductors are relatively moved or the interconnect elements change length the elements accommodate the changes and the associated compression and tension forces in such a way that the interconnect does not mechanically fatigue

Rhabdovirus Matrix Protein Structures Reveal a Novel Mode of Self-Association

The matrix (M) proteins of rhabdoviruses are multifunctional proteins essential for virus maturation and budding that also regulate the expression of viral and host proteins. We have solved the structures of M from the vesicular stomatitis virus serotype New Jersey (genus: Vesiculovirus) and from Lagos bat virus (genus: Lyssavirus), revealing that both share a common fold despite sharing no identifiable sequence homology. Strikingly, in both structures a stretch of residues from the otherwise-disordered N terminus of a crystallographically adjacent molecule is observed binding to a hydrophobic cavity on the surface of the protein, thereby forming non-covalent linear polymers of M in the crystals. While the overall topology of the interaction is conserved between the two structures, the molecular details of the interactions are completely different. The observed interactions provide a compelling model for the flexible self-assembly of the matrix protein during virion morphogenesis and may also modulate interactions with host proteins

Towards a framework for attention cueing in instructional animations: Guidelines for research and design

This paper examines the transferability of successful cueing approaches from text and static visualization research to animations. Theories of visual attention and learning as well as empirical evidence for the instructional effectiveness of attention cueing are reviewed and, based on Mayer’s theory of multimedia learning, a framework was developed for classifying three functions for cueing: (1) selection—cues guide attention to specific locations, (2) organization—cues emphasize structure, and (3) integration—cues explicate relations between and within elements. The framework was used to structure the discussion of studies on cueing in animations. It is concluded that attentional cues may facilitate the selection of information in animations and sometimes improve learning, whereas organizational and relational cueing requires more consideration on how to enhance understanding. Consequently, it is suggested to develop cues that work in animations rather than borrowing effective cues from static representations. Guidelines for future research on attention cueing in animations are presented

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

<div><p>Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published <i>in vivo</i> measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical <i>in vivo</i> mouse studies.</p></div

Statistical analysis of the optimized parameter sets.

<p>Standard ANOVA analysis followed by pairwise comparisons was used to determine whether the sets of optimized parameter values were statistically different. <b>A,</b> upper triangle: <i>k</i>_<i>0</i>; lower triangle: <i>k</i>_<i>1</i>. <b>B,</b> upper triangle: <i>Ang</i>_<i>0</i>; lower triangle: <i>k</i>_<i>0</i>/<i>k</i>_<i>1</i>. <b>C,</b> upper triangle: RTV for bevacizumab dose of 2 mg/kg; lower triangle: RTV for dose of 10 mg/kg. The color and asterisks indicate log₁₀(<i>p</i>-value): <b>***</b>, (<i>p</i>-value ≤ 0.001); <b>**</b>, (0.001 < <i>p</i>-value ≤ 0.01); <b>*</b>, (0.01 < <i>p</i>-value < 0.05).</p

Predicted response to anti-VEGF treatment.

<p>The whole-body mouse model, including the dynamic tumor compartment whose volume is predicted using Eq (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.e001" target="_blank">1</a>), was used to simulate bevacizumab treatment at a dose of <b>A,</b> 2 mg/kg or <b>B,</b> 10 mg/kg. The relative tumor volume (RTV) predicted by the model is shown. Horizontal bar indicates the median of the predicted RTV for the best fits from each dataset.</p

Estimated model parameters obtained from fitting.

<p>The whole-body mouse model was used to fit measurements of tumor xenograft volumes, and the tumor growth kinetic parameters were estimated. The estimated parameter values from the best fits are plotted for each dataset. <b>A,</b> <i>k</i>_<i>0</i>. <b>B,</b> <i>k</i>_<i>1</i>. <b>C,</b> <i>Ang</i>_<i>0</i>. <b>D,</b> <i>k</i>_<i>0</i>/<i>k</i>_<i>1</i>. Horizontal bar indicates the median of the best fits obtained from fitting the model to each dataset; error bars indicate the 95% confidence interval. Statistical comparison of the estimated parameter sets is given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.g005" target="_blank">Fig 5</a>.</p

Results from multivariate analysis.

<p>PLSR analysis quantifies how the tumor growth parameters influence the response to treatment (RTV). <b>A,</b> PLSR model to predict RTV for two dosage levels of the anti-VEGF. The optimal PLSR model includes two components. Decreasing in component 1 or increasing in component 2 corresponds to higher efficacy of the anti-VEGF treatment. <b>B,</b> VIP scores for the model inputs; a score greater than one indicate variables that are important for predicting the RTV. <b>C,</b> Scores of the model output, revealing how tumors from each dataset compare in their responsiveness to treatment. <b>D</b>, Loadings of the model inputs, indicating how the model inputs (fitted parameters) correspond to sensitivity to anti-VEGF treatment.</p

Model fit and validation using full tumor growth time course for fitting.

<p>The whole-body mouse model was used to fit measurements of tumor xenograft volumes, and the tumor growth kinetic parameters were estimated. The predicted tumor volume over time is shown for the six datasets. <b>A,</b> Roland [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.ref034" target="_blank">34</a>]. <b>B,</b> Zibara [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.ref035" target="_blank">35</a>]. <b>C,</b> Tan [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.ref036" target="_blank">36</a>]. <b>D,</b> Volk 2008 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.ref037" target="_blank">37</a>]. <b>E,</b> Volk 2011a [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.ref038" target="_blank">38</a>]. <b>F,</b> Volk 2011b [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005874#pcbi.1005874.ref038" target="_blank">38</a>]. The model is able to reproduce experimental data for tumor growth without treatment and predict validation data not used in parameter fitting. Blue triangles and purple squares are control and treatment experimental data points, respectively. Shading indicates the 95% confidence interval. Note different scales on both axes.</p