Evaluation of Reference Genes in the Polyploid Complex Dianthus broteri (Caryophyllaceae) Using qPCR

Dianthus broteri is an endemic complex which is considered the largest polyploid series within the Dianthus genus. This polyploid species involves four cytotypes (2×, 4×, 6× and 12×) with spatial and ecological segregation. The study of gene expression in polyploid species must be very rigorous because of the effects of duplications on gene regulation. In these cases, real-time polymer-ase chain reaction (qPCR) is the most appropriate technique for determining the gene expression profile because of its high sensitivity. The relative quantification strategy using qPCR requires genes with stable expression, known as reference genes, for normalization. In this work, we evaluated the stability of 13 candidate genes to be considered reference genes in leaf and petal tissues in Dianthus broteri. Several statistical analyses were used to determine the most stable candidate genes: Bayesian analysis, network analysis based on equivalence tests, geNorm and BestKeeper algorithms. In the leaf tissue, the most stable candidate genes were TIP41, TIF5A, PP2A and SAMDC. Similarly, the most adequate reference genes were H3.1, TIP41, TIF5A and ACT7 in the petal tissue. Therefore, we suggest that the best reference genes to compare different ploidy levels for both tissues in D. broteri are TIP41 and TIF5A.Ministerio de Ciencia e Innovación PGC2018-098358-B-I00Junta de Andalucía US-138123

A methodology for the classification of gravel beaches

Beaches are highly flexible structures that can be deformed by several reasons, some natural as wind and swell and others not, as human actions. Gravel, considered as a component of the beach is not always separated from the rest of the materials. It is a part of the coastline sedimentary balance, usually with time and spatial scales much greater than those corresponding to the stretch of the coast under study. The conceptual and experimental difficulties of studying this kind of beach have meant that nowadays they are really unknown. In this paper, methodologies to classify and determinate the most important characteristics in gravel beaches are presented. The authors have studied 34 shingle beaches in the region of Alicante (Spain) from a database with their characteristics. Obtained data corresponds to the morphology of the beach, the materials that take part in its composition and the wave energy, considering its incidence, the wave height, the local period and its influence on the coastline. At the beginning, mathematical models are generated, allowing the expression of the relationships between the slope of berm and the rest of variables. To classify the beaches, a factor analysis has been used on the experimental data matrix, considering all the variables as predictive, obtaining in this way an index for beach classification with similar characteristics. Furthermore, to determine the predictive variables that allow characterizing the 34 beaches, a discriminant analysis has been applied over several sets of variables. In each case, a predictive model of cluster belonging is created, considering a discriminant function, and with the clustering function formed by different clusters. The methodologies developed in this paper will be applied later to other beaches as classification and variable selection methods

Chapter Relationship between shoreline evolution and sediment wear

The main objective to this work is to establish a relationship between shoreline evolution and sediment wear. The shoreline evolution trend is similar to of the results obtained by the accelerated particle wear test (APW). However, the relationship between the number of APW test cycles and the years of shoreline evolution is not clear. In Guardamar beach the ratio (years/cycles) is 9.7, in Marineta Casiana beach (it is 5.6, and in Arenal beach it is 3. Differences may be due to the different mineralogical composition and morphology of the sand particles

Galerkin's formulation of the finite elements method to obtain the depth of closure

Coastal erosion and lack of sediment supply are a serious global problem. It is therefore necessary to determine the depth of closure (DoC) of a beach—key parameter in the calculation of the sand volume and the location of the beach protection elements—in a precise way. For this reason, this work generates a numerical model based on Galerkin's formulation of finite elements that provides sufficient precision for the determination of DoC with a minimum investment. Thus, after the generation of three models in which the difference was the dependent variables, the least complex has been chosen. It is composed of the variables: median sediment size, wave height and period associated with the mean flow, as well as the angle that the mean flow forms with respect to the studied profile in absolute value (α). The selected model has been compared with the most commonly used models currently in use, having an average absolute error of 0.36 m and an average MAPE of 70% over current models. In addition, it presents a high stability, since after the random disturbance of all the input variables (up to 5%), the model error remains stable, increasing the MAPE by a maximum of 7.4% and the average absolute error by 0.15 m. Therefore, it is possible to use the model to infer the DoC in other study areas where the values of the variables are similar to those studied here, although the selected method can be extrapolated to other parts of the world.This work was partially supported by the Universidad de Alicante through the project “Estudio sobre el perfil de equilibrio y la profundidad de cierre en playas de arena” (GRE15-02)

The Simple Non-degenerate Relativistic Gas: Statistical Properties and Brownian Motion

This paper shows a novel calculation of the mean square displacement of a classical Brownian particle in a relativistic thermal bath. The result is compared with the expressions obtained by other authors. Also, the thermodynamic properties of a non-degenerate simple relativistic gas are reviewed in terms of a treatment performed in velocity space.Comment: 6 pages, 2 figure

Clinical text classification in Cancer Real-World Data in Spanish

Healthcare systems currently store a large amount of clinical data, mostly unstructured textual information, such as electronic health records (EHRs). Manually extracting valuable information from these documents is costly for healthcare professionals. For example, when a patient first arrives at an oncology clinical analysis unit, clinical staff must extract information about the type of neoplasm in order to assign the appropriate clinical specialist. Automating this task is equivalent to text classification in natural language processing (NLP). In this study, we have attempted to extract the neoplasm type by processing Spanish clinical documents. A private corpus of 23, 704 real clinical cases has been processed to extract the three most common types of neoplasms in the Spanish territory: breast, lung and colorectal neoplasms. We have developed methodologies based on state-of-the-art text classification task, strategies based on machine learning and bag-of-words, based on embedding models in a supervised task, and based on bidirectional recurrent neural networks with convolutional layers (C-BiRNN). The results obtained show that the application of NLP methods is extremely helpful in performing the task of neoplasm type extraction. In particular, the 2-BiGRU model with convolutional layer and pre-trained fastText embedding obtained the best performance, with a macro-average, more representative than the micro-average due to the unbalanced data, of 0.981 for precision, 0.984 for recall and 0.982 for F1-score.The authors acknowledge the support from the Ministerio de Ciencia e Innovación (MICINN) under project PID2020-116898RB-I00, from Universidad de Málaga and Junta de Andalucía through grants UMA20-FEDERJA-045 and PYC20-046-UMA (all including FEDER funds), and from the Malaga-Pfizer consortium for AI research in Cancer - MAPIC. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech