Search

Abstract The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol.

Abstract Inflammatory bowel diseases (IBDs) are complex medical conditions in which the gut microbiota is attacked by the immune system of genetically predisposed subjects when exposed to yet unclear environmental factors. The complexity of this class of diseases makes them suitable to be represented and studied with network science. In this paper, the metagenomic data of control, Crohn’s disease, and ulcerative colitis subjects’ gut microbiota were investigated by representing this data as correlation networks and co-expression networks. We obtained correlation networks by calculating Pearson’s correlation between gene expression across subjects. A percolation-based procedure was used to threshold and binarize the adjacency matrices. In contrast, co-expression networks involved the construction of the bipartite subjects-genes networks and the monopartite genes-genes projection after binarization of the biadjacency matrix. Centrality measures and community detection were used on the so-built networks to mine data complexity and highlight possible biomarkers of the diseases. The main results were about the modules of Bacteroides, which were connected in the control subjects’ correlation network, Faecalibacterium prausnitzii, where co-enzyme A became central in IBD correlation networks and Escherichia coli, whose module has different patterns of integration within the whole network in the different diagnoses.

In September 1777, Ruđer Bošković observed sunspots for six days. Based on these measurements, he used his own methods to calculate the elements of the Sun’s rotation, the longitude of the node, the inclination of the solar equator and the period. He published a description of the methods, the method of observation and detailed instructions for calculations in the second chapter of the fifth part of the Opera in 1785. In this paper, Bošković’s original calculations and repeated calculations by his procedure are published. By analysing the input quantities, procedures, and results, the input quantities of the error, and the calculation results are discussed. The reproduction of Bošković’s calculations is successfully reproduced and we obtained very similar results. The conclusion proposes a relationship of Bošković’s research with mod ern astronomy.

The Jesuit Ferdinand Konšćak (1703–1759) from Varaždin was a Croatian explorer, missionary and cartographer in the Lower California area. Konšćak’s maps were published in 1757 in a work by Miguel Venegas and Marcos Buriel, Noticia de la California. The book appeared in Spanish, and later in English (1759), Dutch (1761/2), French (1766 and 1796/7) and German. In this paper, all the maps attributed to Ferdinand Konšćak are listed and presented for the first time, while the reproductions are taken from the highest quality editions. The places throughout the world where Konšćak’s maps are kept are also listed. In a separate table, the most important information regarding the maps presented in this paper is given in overview. Finally, it is concluded that the life and work of Ferdinand Konšćak have been insufficiently researched, particularly in regard to his cartographic contributions.

Sr. Editor, Los estilos de aprendizaje se conceptúan como características de índole cognitivas y emocionales vinculadas a particularidades biológicas de los estudiantes que están en constante aprehensión de datos1. En la actualidad existen herramientas para determinar los estilos de aprendizaje en población estudiantil, como el cuestionario Honey-Alonso sobre Estilos de Aprendizaje (CHAEA) que define criterios que identifican los estilos de aprendizaje2. Así, los estilos de aprendizaje se definen • Activo: Buscan algo novedoso tanto en experiencias como retos. • Reflexivo: Prefieren tomar un tiempo analizando desde diferentes puntos de vista antes de concluir una afirmación, son prudentes. • Teórico: Buscan el mayor grado de efectividad y de eficiencia. Integran teorías acordes con los hechos basados en raciocinios y juicios objetivos. • Pragmático: Se esmeran por relacionar una afirmación con su aplicación en la realidad, sus conclusiones sobre un tema se basan en la utilidad.