We present the second public data release of the Dark Energy Survey, DES DR2, based on optical/near-infrared imaging by the Dark Energy Camera mounted on the 4 m Blanco telescope at Cerro Tololo Inter-American Observatory in Chile. DES DR2 consists of reduced single-epoch and coadded images, a source catalog derived from coadded images, and associated data products assembled from 6 yr of DES science operations. This release includes data from the DES wide-area survey covering ∼5000 deg 2 of the southern Galactic cap in five broad photometric bands, grizY. DES DR2 has a median delivered point-spread function FWHM of g = 1.11″, r = 0.95″, i = 0.88″, z = 0.83″, and Y = 0 90, photometric uniformity with a standard deviation of < 3 mmag with respect to Gaia DR2 G band, a photometric accuracy of ∼11 mmag, and a median internal astrometric precision of ∼27 mas. The median coadded catalog depth for a 1 95 diameter aperture at signal-to-noise ratio = 10 is g = 24.7, r = 24.4, i = 23.8, z = 23.1, and Y = 21.7 mag. DES DR2 includes ∼691 million distinct astronomical objects detected in 10,169 coadded image tiles of size 0.534 deg 2 produced from 76,217 single-epoch images. After a basic quality selection, benchmark galaxy and stellar samples contain 543 million and 145 million objects, respectively. These data are accessible through several interfaces, including interactive image visualization tools, web-based query clients, image cutout servers, and Jupyter notebooks. DES DR2 constitutes the largest photometric data set to date at the achieved depth and photometric precision.

Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems.

For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the postgenomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

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Herein, we report the draft genome sequences of six individual Staphylococcus epidermidis clones, cultivated from blood taken from different preterm neonatal sepsis patients at the Royal Infirmary, Edinburgh, Scotland, United Kingdom.