Sperm vitrification as an alternative approach to conventional cryopreservation (equilibrium freezing) allows quick and low-cost sample preservation and is suitable for small-bodied aquatic species with miniscule testis, fieldwork at remote locations, and small-scale freezing for research purposes. The goal of this present study was to develop operational prototypes of 3-dimensional (3-D) printed vitrification devices with innovative components that can provide comprehensive functionalities for practical repository development for aquatic species. The design featured an elongated loop to suspend a thin film of sperm sample in cryoprotectant, a retractable sleeve to protect the vitrified samples and allow permanent labeling, a handle to facilitate processing and storage, and a shaft with annular grooves to guide positioning of the protective retractable sleeve. To span a wide range of sample capacities and configurations, a total of 39 different configurations (3 loop lengths ×13 loop heights) were fabricated the genetic resources of aquatic species by user groups such as breeders, hatcheries, aquariums, and researchers.Multi-sided facets in polyhedral models and meshes serve to connect regular submeshes (star-configurations) and to start or end quad-strips (T-configurations). Using the polyhedral mesh as control net, recursive subdivision algorithms often yield poor shape for these non-quad configurations. Polynomial surface constructions such as geometrically smooth splines (G-splines) do better, but lack subdivision-like refinability. Such refinability is useful for hierarchical modeling and engineering analysis. This paper introduces a new class of G-splines that generalizes bi-quadratic C1 splines to polyhedral control nets with star- and T-configurations and that is refinable.We first confirmed adolescents diagnosed with disruptive behavior disorders (oppositional defiant, conduct disorder; n = 158) had lower constraint and higher negative emotionality, and greater psychiatric comorbidity and psychosocial dysfunction, relative to adolescents without (n = 755), in a population-based sample enriched for externalizing psychopathology (mean age = 17.90 years; 52% female). We then explored whether different personality types, defined by patterns of personality identified via latent profile analysis, were differently associated with clinical features in adolescents with a disruptive behavior disorder diagnosis. Four distinct personality types ("disinhibited," "high distress," "low distress," "positive") were meaningfully different from one another. learn more Results highlight personality heterogeneity as a means of identifying individuals at greatest risk for the most deleterious forms of externalizing psychopathology.Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to employ the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. This paper asks whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, where one uses sampled ego network data to infer the network features of each context, and then uses the inferred network features as second-level predictors in a hierarchical linear model. We test the validity of this idea in the case of network cohesion. Using two complete datasets as a test, we find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, like attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.With the development of high-throughput technologies, principal component analysis (PCA) in the high-dimensional regime is of great interest. Most of the existing theoretical and methodological results for high-dimensional PCA are based on the spiked population model in which all the population eigenvalues are equal except for a few large ones. Due to the presence of local correlation among features, however, this assumption may not be satisfied in many real-world datasets. To address this issue, we investigate the asymptotic behavior of PCA under the generalized spiked population model. Based on our theoretical results, we propose a series of methods for the consistent estimation of population eigenvalues, angles between the sample and population eigenvectors, correlation coefficients between the sample and population principal component (PC) scores, and the shrinkage bias adjustment for the predicted PC scores. Using numerical experiments and real data examples from the genetics literature, we show that our methods can greatly reduce bias and improve prediction accuracy.Tractions exerted by cells on their surroundings play an important role in many biological processes including stem cell differentiation, tumorigenesis, cell migration, cancer metastasis, and angiogenesis. The ability to quantify these tractions is important in understanding and manipulating these processes. Three-dimensional traction force microscopy (3DTFM) provides reliable means of evaluating cellular tractions by first measuring the displacement of fluorescent beads in response to these tractions in the surrounding matrix, and then using this measurement to compute the tractions. However, most applications of 3DTFM assume that the surrounding extra-cellular matrix (ECM) is non-fibrous, despite the fact that in many natural and synthetic environments the ECM contains a significant proportion of fibrous components. Motivated by this, we develop a computational approach for determining tractions, while accounting for the fibrous nature of the ECM. In particular, we make use of a fiber-based constitutive modant, where using a nonlinear exponential hyperelastic model instead of the fiber-based model, can lead to more than 100% error in the traction field. These results underline the importance of using appropriate constitutive models in 3DTFM, especially in fibrous ECM constructs.