An updated evolutionary classification of CRISPR–Cas systems

The evolution of CRISPR–cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR–cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR–Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

An XMM-Newton view of FeKα in high-mass X-ray binaries

We present a comprehensive analysis of the whole sample of available XMM-Newton observations of high-mass X-ray binaries (HMXBs) until August 2013, focusing on the FeKα emission line. This line is key to better understanding the physical properties of the material surrounding the X-ray source within a few stellar radii (the circumstellar medium). We collected observations from 46 HMXBs and detected FeKα in 21 of them. We used the standard classification of HMXBs to divide the sample into different groups. We find that (1) different classes of HMXBs display different qualitative behaviours in the FeKα spectral region. This is visible especially in SGXBs (showing ubiquitous Fe fluorescence but not recombination Fe lines) and in γ Cass analogues (showing both fluorescent and recombination Fe lines). (2) FeKα is centred at a mean value of 6.42 keV. Considering the instrumental and fits uncertainties, this value is compatible with ionization states that are lower than Fe xviii. (3) The flux of the continuum is well correlated with the flux of the line, as expected. Eclipse observations show that the Fe fluorescence emission comes from an extended region surrounding the X-ray source. (4) We observe an inverse correlation between the X-ray luminosity and the equivalent width of FeKα (EW). This phenomenon is known as the X-ray Baldwin effect. (5) FeKα is narrow (σline< 0.15 keV), reflecting that the reprocessing material does not move at high speeds. We attempt to explain the broadness of the line in terms of three possible broadening phenomena: line blending, Compton scattering, and Doppler shifts (with velocities of the reprocessing material V ~ 1000 km s-1). (6) The equivalent hydrogen column (NH) directly correlates to the EW of FeKα, displaying clear similarities to numerical simulations. It highlights the strong link between the absorbing and the fluorescent matter. (7) The observed NH in supergiant X-ray binaries (SGXBs) is in general higher than in supergiant fast X-ray transients (SFXTs). We suggest two possible explanations: different orbital configurations or a different interaction compact object – wind. (8) Finally, we analysed the sources IGR J16320-4751 and 4U 1700-37 in more detail, covering several orbital phases. The observed variation in NH between phases is compatible with the absorption produced by the wind of their optical companions. The results clearly point to a very important contribution of the donor’s wind in the FeKα emission and the absorption when the donor is a supergiant massive star.