Inherited Photoreceptor Degeneration Causes the Death of Melanopsin-Positive Retinal Ganglion Cells and Increases Their Coexpression of Brn3a

Purpose: To study the population of intrinsically photosensitive retinal ganglion cells (melanopsin-expressing RGCs, m+RGCs) in P23H-1 rats, a rat model of inherited photoreceptor degeneration. Methods: At postnatal (P) times P30, P365, and P540, retinas from P23H dystrophic rats (line 1, rapid degeneration; and line 3, slow degeneration) and Sprague Dawley (SD) rats (control) were dissected as whole-mounts and immunodetected for melanopsin and/or Brn3a. The dendritic arborization of m+RGCs and the numbers of Brn3a+RGCs and m+RGCs were quantified and their retinal distribution and coexpression analyzed. Results: In SD rats, aging did not affect the population of Brn3a+RGCs or m+RGCs or the percentage that showed coexpression (0.27%). Young P23H-1 rats had a significantly lower number of Brn3a+RGCs and showed a further decline with age. The population of m+RGCs in young P23H-1 rats was similar to that found in SD rats and decreased by 22.6% and 28.2% at P365 and P540, respectively, similarly to the decrease of the Brn3a+RGCs. At these ages the m+RGCs showed a decrease of their dendritic arborization parameters, which was similar in both the P23H-1 and P23H-3 lines. The percentage of coexpression of Brn3a was, however, already significantly higher at P30 (3.31%) and increased significantly with age (10.65% at P540). Conclusions: Inherited photoreceptor degeneration was followed by secondary loss of Brn3a+RGCs and m+RGCs. Surviving m+RGCs showed decreased dendritic arborization parameters and increased coexpression of Brn3a and melanopsin, phenotypic and molecular changes that may represent an effort to resist degeneration and/or preferential survival of m+RGCs capable of synthesizing Brn3a.

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 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.