Estado de conservación de la ictiofauna arrecifal en sitios de buceo turístico de cuatro regiones del Archipiélago Sabana-Camagüey, Cuba.

Los peces constituyen uno de los atractivos principales en los arrecifes coralinos para el buceo turístico, por lo cual resulta necesario monitorear su abundancia y valores estéticos. El objetivo de esta investigación fue evaluar el estado de conservación de las asociaciones de peces arrecifales en cuatro de los principales polos turísticos del Archipiélago Sabana-Camagüey, Cuba en el año 2010. Se muestrearon 23 sitios de buceo turísticos en áreas adyacentes a las regiones de Varadero (5 sitios), cayos de Villa Clara (6 sitios), Cayo Coco (6 sitios) y Santa Lucia (6 sitios). En cada uno de ellos se realizaron seis réplicas de censos visuales de todas las especies de peces en recorridos lineales de 50 m de largo por 2 m de ancho, ocupando un área total de 600 m2. El puntaje más alto de conservación lo presentó los cayos de Villa Clara (37), siguiéndole en orden decreciente Cayo Coco (35), y con valores más bajos Varadero (24) y Santa Lucia (22). Los puntajes de las cuatro regiones fueron inferiores a los encontrados en otros arrecifes de Cuba y el Caribe. Los valores de Varadero y Santa Lucia se correspondieron con los de arrecifes de hábitats degradados y sometidos a explotación pesquera. En general, en la mayoría de las regiones predominaron peces de talla pequeña (<20 cm) pertenecientes a las familias Pomacentridae y Labridae y hubo poca abundancia de peces carnívoros y herbívoros de mediana (20-40 cm) y gran talla (>40 cm). ABSTRACT Fish are one of the main attractions on coral reefs for diving tourism; therefore, it is necessary to monitor their condition and aesthetic values. The objective of this research was to evaluate the status of reef fish assemblages in four tourist areas of Sabana-Camaguey Archipelago, Cuba in 2010. Twentythree tourist diving sites were sampled in adjacent areas to regions of Varadero (five sites), Villa Clara keys (6 sites), Coco key (6 sites) and Santa Lucia (6 sites). In each region, six replicated visual census of all fish species were conducted along belt transects of 50 m length and 2 m width, occupying a total area of 600 m2. The highest conservation score was obtained by Villa Clara keys (37), followed in decreasing order by Coco key (35), and with lower values Varadero (24) and St. Lucia (22). The scores of the four regions were lower than the values of other Cuban and Caribbean reefs. The values of Varadero and Santa Lucia corresponded to reefs with degraded habitats and subject to overfishing. In general, small fish (< 20 cm) from families Pomacentridae and Labridae, and low abundance of carnivore and herbivore fish of middle (20-40 cm) and large size (> 40 cm) predominated in most regions.

Applications in adaptive cluster sampling of Gulf of Alaska rockfish

Adaptive cluster sampling (ACS) has been the subject of many publications about sampling aggregated populations. Choosing the criterion value that invokes ACS remains problematic. We address this problem using data from a June 1999 ACS survey for rockfish, specifically for Pacific ocean perch (Sebastes alutus), and for shortraker (S. borealis) and rougheye (S. aleutianus) rockfish combined. Our hypotheses were that ACS would outperform simple random sampling (SRS) for S. alutus and would be more applicable for S. alutus than for S. borealis and S. aleutianus combined because populations of S. alutus are thought to be more aggregated. Three alternatives for choosing a criterion value were investigated. We chose the strategy that yielded the lowest criterion value and simulated the higher criterion values with the data after the survey. Systematic random sampling was conducted across the whole area to determine the lowest criterion value, and then a new systematic random sample was taken with adaptive sampling around each tow that exceeded the fixed criterion value. ACS yielded gains in precision (SE) over SRS. Bootstrapping showed that the distribution of an ACS estimator is approximately normal, whereas the SRS sampling distribution is skewed and bimodal. Simulation showed that a higher criterion value results in substantially less adaptive sampling with little tradeoff in precision. When time-efficiency was examined, ACS quickly added more samples, but sampling edge units caused this efficiency to be lessened, and the gain in efficiency did not measurably affect our conclusions. ACS for S. alutus should be incorporated with a fixed criterion value equal to the top quartile of previously collected survey data. The second hypothesis was confirmed because ACS did not prove to be more effective for S. borealis-S. aleutianus. Overall, our ACS results were not as optimistic as those previously published in the literature, and indicate the need for further study of this sampling method.

Nanostructured carbon films from supersonic cluster beam deposition: Structure and morphology

Nanostructured carbon thin films have been grown by deposition of cluster beams produced by a supersonic expansion. Due to separation effects typical of supersonic beams, films with different nanostructures can be grown by the simple intercepting of different regions of the cluster beam with a substrate. Films show a low-density porous structure, which has been characterized by Raman and Brillouin spectroscopy. Film morphology suggests that growth processes are similar to those occurring in a ballistic deposition regime.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.

Rapid evolution of an X-linked microRNA cluster in primates

MicroRNAs (miRNAs) are a growing class of small RNAs ( about 22 nt) that play crucial regulatory roles in the genome by targeting mRNAs for cleavage or translational repression. Most of the identified miRNAs are highly conserved among species, indicating

Polymorphisms in the C-type lectin genes cluster in chromosome 19 and predisposition to severe acute respiratory syndrome coronavirus (SARS-CoV) infection

Background: Polymorphisms of CLEC4M have been associated with predisposition for infection by the severe acute respiratory syndrome coronavirus (SARS-CoV). DC-SIGNR, a C-type lectin encoded by CLEC4M, is a receptor for the virus. A variable number tandem

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.