Limited genetic structure of Gulf Menhaden (Brevoortia patronus), as revealed by microsatellite markers developed for the genus Brevoortia (Clupeidae)

Long-term sustainable management of wild populations should be based on management actions that account for the genetic structure among populations. Knowledge of genetic structure and of the degree of demographic exchange between discreet [sic] populations allows managers to better define management units. However, adequate gene loci for population assessments are not always available. In this study, variable co-dominant DNA loci in the heavily exploited marine genus Brevoortia were developed with a microsatellite-enriched DNA library for the Gulf Menhaden (Brevoortia patronus). Microsatellite marker discovery was followed by genetic characterization of 4 endemic North American Brevoortia species, by using 14 novel loci as well as 5 previously described loci. Power analysis of these loci for use in species identification and genetic stock structure was used to assess their potential to improve the stock definition in the menhaden fishery of the Gulf of Mexico. These loci could be used to reliably identify menhaden species in the Gulf of Mexico with an estimated error rate of α=0.0001. Similarly, a power analysis completed on the basis of observed allele frequencies in Gulf Menhaden indicated that these markers can be used to detect very small levels of genetic divergence (Fst≈0.004) among simulated populations, with sample sizes as small as n=50 individuals. A cursory analysis of genetic structure among Gulf Menhaden sampled throughout the Gulf of Mexico indicated limited genetic structure among sampling locations, although the available sampling did not reach the target number (n=50) necessary to detect minimal values of significant structure.

Exploiting variable-width features in large vocabulary speech recognition

The use of variable-width features (prosodics, broad structural information etc.) in large vocabulary speech recognition systems is discussed. Although the value of this sort of information has been recognized in the past, previous approaches have not been widely used in speech systems because either they have not been robust enough for realistic, large vocabulary tasks or they have been limited to certain recognizer architectures. A framework for the use of variable-width features is presented which employs the N-Best algorithm with the features being applied in a post-processing phase. The framework is flexible and widely applicable, giving greater scope for exploitation of the features than previous approaches. Large vocabulary speech recognition experiments using TIMIT show that the application of variable-width features has potential benefits.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Variable repetition frequency femtosecond-pulse surface emitting semiconductor laser

We report a femtosecond-pulse vertical-external-cavity surface-emitting laser with a continuous repetition frequency tuning range of 8 near 1 GHz. A constant average output power of 56 ± 1 mW and near-transform-limited pulse duration of 450 ± 20 fs were observed across the entire tuning range. © 2011 American Institute of Physics.

Dose-dependent effects of the dopamine D1 receptor agonists A77636 or SKF81297 on spatial working memory in aged monkeys

With advancing age, monkeys develop deficits in spatial working memory resembling those induced by lesions of the prefrontal cortex (PFC). Aged monkeys also exhibit marked loss of dopamine from the PFC, a transmitter known to be important for proper PFC cognitive function. Previous results suggest that D1 agonist treatment can improve spatial working memory abilities in aged monkeys. However, this research was limited by the use of drugs with either partial agonist actions or significant D2 receptor actions. In our study, the selective dopamine D1 receptor full agonists A77636 and SKF81297 were examined in aged monkeys for effects on the working memory functions of the PFC. Both compounds produced a significant, dose-related effect on delayed response performance without evidence of side effects: low doses improved performance although higher doses impaired or had no effect on performance. Both the improvement and impairment in performance were reversed by pretreatment with the D1 receptor antagonist, SCH23390. These findings are consistent with previous results demonstrating that there is a narrow range of D1 receptor stimulation for optimal PFC cognitive function, and suggest that very low doses of D1 receptor agonists may have cognitive-enhancing actions in the elderly.

Convex relaxation of mixture regression with efficient algorithms

We develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semidefinite matrix variable, we reformulate the problem to eliminate the need for general semidefinite programming. In particular, we provide two reformulations that admit fast algorithms. The first is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form updates. We evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.

Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables

We demonstrate how a prior assumption of smoothness can be used to enhance the reconstruction of free energy profiles from multiple umbrella sampling simulations using the Bayesian Gaussian process regression approach. The method we derive allows the concurrent use of histograms and free energy gradients and can easily be extended to include further data. In Part I we review the necessary theory and test the method for one collective variable. We demonstrate improved performance with respect to the weighted histogram analysis method and obtain meaningful error bars without any significant additional computation. In Part II we consider the case of multiple collective variables and compare to a reconstruction using least squares fitting of radial basis functions. We find substantial improvements in the regimes of spatially sparse data or short sampling trajectories. A software implementation is made available on www.libatoms.org.

Subcellular localization and inductive expression of the dsRNA-dependent protein kinase PKR from Japanese flounder, Paralichthys olivaceus

The double-stranded RNA (dsRNA)-dependent protein kinase (PKR) belongs to the eIF2 alpha kinase family and plays a critical role in interferon (IFN)-mediated antiviral response. Recently, in Japanese flounder (Paralichthys olivaceus), a PKR gene has been identified. In this study, we showed that PoPKR localized to the cytoplasm, and the dsRNA-binding motifs (dsRBMs) played a determinative role in protein localization. In cultured FEC cells, PoPKR was detected at a low level of constitutive expression but was highly induced after treatment with UV-inactivated grass carp hemorrhagic virus, active SMRV and Poly I:C although with different expression kinetics. In flounder, PoPKR was ubiquitously distributed in all tested tissues, and SMRV infection resulted in significant upregulation at mRNA and protein levels. In order to reveal the role of PoPKR in host antiviral response, its expression upon exposure to various inducers was characterized and further compared with that of PoHRI, which is another eIF2 alpha kinase of flounder. Interestingly, expression comparison revealed that all inducers stimulated upregulation of PoHRI in cultured flounder embryonic cells and fish, with a similar kinetics to PoPKR but to a less extent. These results suggest that, during antiviral immune response, both flounder eIF2 alpha kinases might play similar roles and that PoPKR is the predominant kinase. (C) 2009 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.

New type of multimode-interference-type thermo-optic variable optical attenuator

A thermo-optic variable optical attenuator (VOA) based on a Mach-Zehnder interferometer and multimode-interference coupler is fabricated. Not a single-mode but a multimode waveguide is used as the input and output structures of the optical field, which greatly reduces the coupling loss of the VOA with a normal single-mode fiber. The insertion loss of the fabricated VOA is 2.52 to 2.82 dB at the wavelength of 1520 to 1570 nm. The polarization dependent loss is 0.28 to 0.45 dB at the same wavelength range. Its maximum attenuation range is up to 26.3 dB when its power consumption is 369 mW. The response frequency of the fabricated VOA is about 10 kHz. (C) 2004 Society of Photo-Optical Instrumentation Engineers.

Size-Dependent Adhesion Strength of a Single Viscoelastic Fiber

Nano-fibrillar adhesives can adhere strongly to surfaces as a gecko does. The size of each fiber has significant effects on the adhesion enhancement, especially on rough surfaces. In the present study, we report the size effects on the normal and shear strength of adhesion for a single viscoelastic fiber. It is found that there exists a limited region of the critical sizes under which the interfacial normal or tangential tractions uniformly attain the theoretical adhesion strength. The region for a viscoelastic fiber under tension with similar material constants to a gecko's spatula is 135-255 nm and that under torque is 26.5-52 nm. This finding is significant for the development of artificial biomimetic attachment systems.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

Comparison of different methods for variable selection

In chemistry for chemical analysis of a multi-component sample or quantitative structure-activity/property relationship (QSAR/QSPR) studies, variable selection is a key step. In this study, comparisons between different methods were performed. These methods include three classical methods such as forward selection, backward elimination and stepwise regression; orthogonal descriptors; leaps-and-bounds regression and genetic algorithm. Thirty-five nitrobenzenes were taken as the data set. From these structures quantum chemical parameters, topological indices and indicator variable were extracted as the descriptors for the comparisons of variable selections. The interesting results have been obtained. (C) 2001 Elsevier Science B.V. All rights reserved.

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.