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This study introduces MIRANDA, a computer vision system, and RELAPP, a complementary force measurement system, developed for characterizing viscoelastic materials. Our aim was to evaluate their combined ability to predict key rheological parameters and demonstrate their utility in material analysis, offering an alternative to traditional methods. We analyzed five distinct flour dough samples, correlating MIRANDA and RELAPP variables with established rheological reference values. Support Vector Machine (SVM) regression models were trained using MIRANDA’s stable TR and elasticity data to predict industrially relevant parameters: baking strength (W), tenacity (P), extensibility (L), and final viscosity (RVU) from Chopin alveograph and viscosimeter. The predictive models showed promising results, with R2 values of 0.594 (p = 0) for W, 0.575 (p = 0) for P, and 0.612 (p = 0.03763) for viscosity, all statistically significant. While these findings are promising, it is important to note that the small sample size may limit the generalizability of these models. The synergy between the systems was evident, exemplified by strong positive correlations, such as between MIRANDA’s Elasticity and RELAPP’s c_exp (parameter ‘c’ of its mathematical model m1, r = 0.858) and final resistive force (r = 0.839). Despite the limited sample size, these findings highlight MIRANDA’s versatility and speed for efficient material characterization. MIRANDA and RELAPP offer significant industrial implications for viscoelastic materials, including accelerating development cycles and enhancing continuous quality control. This approach has strong potential to reduce reliance on slower, traditional methods, warranting further validation with larger datasets.

Background The genera Agathis (Coniferales: Araucariaceae) and Hymenaea (Fabales: Fabaceae) contain resin-producing tree species that are crucial for actuotaphonomic studies. While certain Cretaceous ambers likely originated from Agathis or Agathis -like trees, Hymenaea is the primary source of many Miocene ambers. Field studies were conducted in New Caledonia and Madagascar to collect Defaunation resin (resin produced after 1760 AD (Anno Domini)). Arthropods were collected with yellow sticky and Malaise traps in New Caledonia, Madagascar, and Mexico. Cretaceous and Miocene ambers, copals (2.58 Ma to 1760 AD), and Defaunation resins from various regions were analysed to compare arthropod trapping patterns. Results Actuotaphonomic results show lower number of arthropods trapped in Agathis Defaunation resin, with a non-uniform distribution, compared to the abundant and uniformly distributed arthropods trapped in Hymenaea Defaunation resin. The lower number of arthropod inclusions in the trunk resin of the Agathis trees is attributed to the rapid polymerisation of that resin. Under the same experimental conditions, the arthropods in Agathis Defaunation resin plot far from the arthropods collected in the yellow sticky and Malaise traps, while the arthropods in Hymenaea Defaunation resin plot close to the arthropods in the yellow sticky traps. Conclusions These findings confirm different resin trapping patterns between Agathis and Hymenaea , with significant implications for interpreting the amber record. The fauna trapped by Hymenaea resin closely resembles the arthropod biocoenosis that live in and around the trunks, indicating autochthony and close relationship with the forest ecosystem, unlike Agathis resin. These results improve our understanding of arthropod trapping biases in resin and lead us to reconsider previously proposed interpretations of Cretaceous forest biocoenoses.

Inmersos en la revolución digital, generamos constantemente datos y la mayoría son almacenados. Es lo que se ha denominado los “datos grandes” o Big-data. Junto con el capital y la fuerza de trabajo, los datos se han convertido en un valor añadido para la economía que refleja un futuro con un paradigma revolucionario en el que la sociedad será dirigida por los datos. El futuro está en la investigación, tratamiento y aplicación de los datos que aportarán prosperidad a nuestra sociedad. En el presente artículo se recogen las normas y leyes que existen hoy en día en el Big-data y la regulación existente. Para analizar Big-data la solución pasa por el aprendizaje automático (Machine-Learning) que se ocupa de la construcción y el estudio de los algoritmos que pueden aprender a partir de datos. Existen muchas técnicas, como estadística descriptiva, clasificación o agrupamiento. En este artículo se recoge las tecnologías que se utilizan para almacenar y analizar los “Big-data”.

The objective of the present study was the library designing in the R software which allowed to researchers to standardized the metrics performance to compare to the future results and to know the most discriminating measures by means of automatic process checking of several evaluated metrics on data reducing at random. The library was applied to three classification methods in peripheral blood leukemia cells at Clinic Hospital (Barcelona). [...]it was standardized and proved that the most effective metric for evaluating a classifier in the case of unbalanced classes is the Kappa index. Análisis Lineal Discriminante (LDA), Support Vector Machine (SVM) y Random Forest (RF) aplicados a la clasificación de células leucémicas en sangre periférica de pacientes del Hospital Clínico (Barcelona).

The present study aimed to establish the kinetics of the appearance of coliphage plaques using the double agar layer titration technique to evaluate the feasibility of using traditional coliphage plaque forming unit (PFU) enumeration as a rapid quantification method. Repeated measurements of the appearance of plaques of coliphages titrated according to ISO 10705-2 at different times were analysed using non-linear mixed-effects regression to determine the most suitable model of their appearance kinetics. Although this model is adequate, to simplify its applicability two linear models were developed to predict the numbers of coliphages reliably, using the PFU counts as determined by the ISO after only 3 hours of incubation. One linear model, when the number of plaques detected was between 4 and 26 PFU after 3 hours, had a linear fit of: (1.48 × Counts3 h + 1.97); and the other, values >26 PFU, had a fit of (1.18 × Counts3 h + 2.95). If the number of plaques detected was <4 PFU after 3 hours, we recommend incubation for (18 ± 3) hours. The study indicates that the traditional coliphage plating technique has a reasonable potential to provide results in a single working day without the need to invest in additional laboratory equipment.

High-throughput experimental techniques, such as metagenomics or metatranscriptomics, produce large amounts of data, which interpretation and conversion into understandable knowledge can be challenging and out of reach. We present GANGO, a new algorithm based on the ecological concept of consortium (groups biologically connected) and by using clustering network analysis, gene ontologies and powerful hypothesis test allows the identification and interpretation of complex ecological networks, allowing the identification of the relationship between taxa/genes, the number of groups, their relations and their functionalities using the annotated genes of an organism in a database (e.g. UniProt or Ensembl). Three examples of the use of GANGO are shown: a simulated mixture of fungi and bacteria, alterations in soil fungi communities after a diesel-oil spill and genomic changes in Saccharomyces cerevisae due to abiotic stress. Competing Interest Statement The authors have declared no competing interest.

A comprehensive application designed for the interpretation and visualization of the functional analysis related to KEGG pathways and gene ontologies gives researchers and specialists a tool to get detailed functional information about their data, specifically going deep into biological pathways and gene functions information. By using a variety of techniques and libraries, such as Shiny, htrr, dplyr, tibble, and rvest, we have developed an application that provides a well-designed user-oriented interface with all the facilities to assess their data and start analyzing it directly from scratch through a few steps. The software allows an exhaustive exploration of KEGG pathways and Gene Ontologies, facilitating the analysis of complex biological processes. To achieve this, functions described in the scripts integrate data manipulation methods and web scraping techniques to extract the necessary information from online official databases, Kyoto Encyclopedia of Genes and Genomes (KEGG) and QuickGo. Furthermore, those functions are computed by parallel processing, resulting in efficient petitions to the database servers and allowing the user to get quick results from a large dataset. A crucial feature of BioFunctional is its ability to obtain ancestral information for KEGG pathways and gene ontologies, using the techniques described above. This makes it easier to understand the hierarchy of these ontologies and how each sample in a dataset is classified within them, offering users a way to study the dataset at different taxonomic levels directly from the raw data. Additionally, the app implements the capability to create interactive networks, representing all experimental data to visualize the relationships between groups and ontologies without neglecting the established classification. This is a primary tool for understanding the meaning of the relationships observed within the displayed system. Interactive visualization: Create and explore networks to visualize relationships between groups and ontologies. Hierarchical analysis: Trace ancestral information for KEGG pathways and Gene Ontologies to understand hierarchical classifications. Efficient processing: Employ parallel processing for rapid data analysis, even with large datasets. Intuitive interface: A user-friendly design simplifies data exploration and analysis. Due to these attributes, the software represents a valuable tool for analysts involved in the study of KEGG pathways and Gene Ontologies. By providing an intuitive interface with advanced data processing techniques, it empowers researchers to unravel the intricacies of biological functions and gain insights into the relationships between genes or molecular components. https://github.com/alexrodriguezmena/BIOFunctional

Abstract Introduction The high number of uncontrollable variables in microbiological systems increases experimental complexity and reduces accuracy, potentially leading to data misinterpretation or uncorrectable errors. During an interlaboratory calibration analysis it was observed that the microbial logarithmic reduction (LR) caused by disinfectants depends not only on the type of disinfectant but also on the initial microbial load in the fabric carriers, which can produce a misinterpretation of the results. Fabric carriers are commonly used in standard tests such as EN16616 and ASTM2274. Objective A method based on statistical calibration is proposed using a regression line between N0 (initial microbial load in the carrier) and LR to eliminate the influence of one on the other. Results An example with Candida albicans is presented. Once the method was applied, the influence of N0 on LR was eliminated and the new LR values can be used for factorial experiments, for example, to check the efficacy of disinfectants or detergents without depending on the microbial load placed in the carrier. Competing Interest Statement The authors have declared no competing interest. Footnotes * Email address: amonleong{at}ub.edu

Abstract Introduction An interlaboratory calibration analysis was carried out to validate a methodology for a European standard for domestic laundry disinfection, using different doses of disinfectant and microorganisms. ISO 5725-2 and ISO 13528 form the basis of interlaboratory validations of quantitative methods, but there is a need for a simple graphical method to detect differences in laboratory behavior in terms of accuracy and variability. Objective A novel multivariate method based on the classical Cochran’s C test, as well as PCA and bootstrapping, which allows the inclusion of different correlated variables, was applied to identify higher variability than expected in factor (e.g. laboratories) levels, and the detection of multivariate outliers in a reduced space. Methods The proposed method is based on resampling, using the same sample many times but removing cases at random and performing Cochran’s C test for all the variables together in a reduced space. Results The method was tested by checking 7 laboratories for high variability in different parameters (logarithmic reduction (LR), cross contamination (RI), and wash water (WW)). After applying the proposed statistical analyses, no reasons were found to reject any of the participating laboratories. Multiple applications of the method are possible and we describe a case study in which the multivariant iterative Cochran’s C test was used: variability detection with multiple microbiological parameters (with high variability) during an interlaboratory ring trial. Competing Interest Statement The authors have declared no competing interest.

Abstract Introduction In an interlaboratory calibration analysis to validate a methodology that will be proposed as a European standard for domestic laundry disinfection, tests were carried out to detect if there are different behaviors in the measurements regarding accuracies and variabilities. Interlaboratory tests using different doses of disinfectant and microorganisms were carried out. ISO 5725-2 and ISO 13528 form the basis of validations of quantitative methods, providing validation specifications for interlaboratory studies. However, a need for a simple graphical method to detect interlaboratory differences in accuracy and variability was observed. Objectives The general goal of this work is to present a new exploratory methodology, graphical and easy to interpret, that can determine the accuracy and variability (precision) of a variable, and compare it to the methodology applied in ISO 5725-2 and ISO 13528. Methods We used confidence probability plots of the multivariate Student’s t-distribution to observe the accuracy and variability of microbiological measures carried out by different laboratories during a ring trial exercise. A function in R was built for this purpose: Miriam.analysis.ellipse(Y, factor_a, eel.plot = “ t-Student”). The different observations of accuracy and variability are represented in the ellipses. If any of the points are outside the ellipse with 95% confidence, we can assume a deviation in accuracy and / or variability. Results Two examples are provided with real microbiological data (logarithmic unit reductions (LR) for Pseudomonas aeruginosa, Escherichia coli, Staphilococcus aureus, Enterococcus hirae, Candida albicans and microbial counts in water (WW)). The proposed new method allowed us to detect possible deviations in the WWMEA variable and we believe it has future application for the rapid control of microbiological measures. Competing Interest Statement The authors have declared no competing interest. Footnotes * Email address: amonleong{at}ub.edu