The chloroplast protein HCF164 is predicted to be associated with Coffea SH 9 resistance factor against Hemileia vastatrix

This work was funded by Portuguese national funds through FCT—Fundação para a Ciência e Tecnologia, I.P., under the Projects: UID/AGR/04129/2020 of LEAF; UIDP/04378/2020 and UIDB/04378/2020 of UCIBIO; and LA/P/0140/2020 of i4HB and FCT and FEDER funds through PORNorte under the projects: HDT-Coffee (PTDC/ASP-PLA/32429/2017) and CoffeeRES (PTDC/ASP-PLA/29779/2017). H.A. was supported by Portuguese national funds through FCT within the scope of the Stimulus of Scientific Employment—Individual Support (CEECIND/00399/2017/CP1423/CT0004). A.O. was supported at the University of Bristol by Oracle for Research and the Biological and Biotechnological Sciences Research Council ([BB/X009831/1] and [BBW003449/1]). All molecular modelling work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol (http://www.bris.ac.uk/acrc).To explore the connection between chloroplast and coffee resistance factors, designated as SH1 to SH9, whole genomic DNA of 42 coffee genotypes was sequenced, and entire chloroplast genomes were de novo assembled. The chloroplast phylogenetic haplotype network clustered individuals per species instead of SH factors. However, for the first time, it allowed the molecular validation of Coffea arabica as the maternal parent of the spontaneous hybrid “Híbrido de Timor”. Individual reads were also aligned on the C. arabica reference genome to relate SH factors with chloroplast metabolism, and an in-silico analysis of selected nuclear-encoded chloroplast proteins (132 proteins) was performed. The nuclear-encoded thioredoxin-like membrane protein HCF164 enabled the discrimination of individuals with and without the SH9 factor, due to specific DNA variants linked to chromosome 7c (from C. canephora-derived sub-genome). The absence of both the thioredoxin domain and redox-active disulphide center in the HCF164 protein, observed in SH9 individuals, raises the possibility of potential implications on redox regulation. For the first time, the identification of specific DNA variants of chloroplast proteins allows discriminating individuals according to the SH profile. This study introduces an unexplored strategy for identifying protein/genes associated with SH factors and candidate targets of H. vastatrix effectors, thereby creating new perspectives for coffee breeding programs.CoffeeRES CEECIND/00399/2017/CP1423/CT0004, PTDC/ASP-PLA/29779/2017University of Bristol by Oracle for ResearchBiotechnology and Biological Sciences Research Council BB/X009831/1, BBW003449/1 BBSRCUniversity of BristolFundação para a Ciência e a Tecnologia LA/P/0140/2020, UID/AGR/04129/2020, UIDB/04378/2020, UIDP/04378/2020 FCTEuropean Regional Development Fund PTDC/ASP-PLA/32429/2017 ERD

Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning

Emergent application domains (e.g., Edge Computing/Cloud/B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large Variability Models (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems — the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Funding for open access charge: Universidad de Málaga / CBUA. This work is supported by the European Union’s H2020 re search and innovation programme under grant agreement DAEMON H2020-101017109, by the projects IRIS PID2021-12281 2OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/ FEDER, UE), and LEIA UMA18-FEDERIA-157, and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación, Spain

Extended Variability Models, Algebra, and Arithmetic

Although classic variability models have been traditionally used to specify members of a product-line, their level of expressiveness was quite limited. Several extensions have been proposed, like numerical features, complex cardinalities and feature and configuration attributes. However, modern tools often provide limited support to these extensions. Imposing variability modelling restrictions into general theories enables off-the-self automated reasoners to analyse extended variability models. While one could argue that those general theories are less reasoning efficient, in practice happen the same if we extend traditional solvers. In contrast, general theories provide new properties with the potential to a) improve reasoning efficiency above extending traditional solvers, and b) provide exotic analyses that uncover new properties of the variability models and feature and configuration spaces. Examples of this could be the functions commutativity property, (reasoning) functors composition, and the fundamental theorem of calculus applied to feature or configuration space.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Detecting Feature Influences to Quality Attributes in Large and Partially Measured Spaces using Smart Sampling and Dynamic Learning

Publicación Journal First siendo el original: Munoz, D. J., Pinto, M., & Fuentes, L. (2023). Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning. Knowledge-Based Systems, 270, 110558.Emergent application domains (e.g., Edge Computing/Cloud /B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large \textit{Variability Models} (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems -- the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Trabajo financiado por el programa de I+D H2020 de la UE bajo el acuerdo DAEMON 101017109, por los proyectos también co-financiados por fondos FEDER \emph{IRIS} PID2021-122812OB-I00, y \emph{LEIA} UMA18-FEDERIA-157, y la ayuda PRE2019-087496 del Ministerio de Ciencia e Innovación. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Prevalence of caffeine use in elite athletes following its removal from the World Anti-Doping Agency list of banned substances

Abstract: The aim of this investigation was to determine the use of caffeine by athletes after its removal from the World Anti-Doping Agency list. For this purpose, we measured the caffeine concentration in 20 686 urine samples obtained for doping control from 2004 to 2008. We utilized only urine samples obtained after official national and international competitions. Urine caffeine concentration was determined using alkaline extraction followed by gas chromatography-mass spectrometry. The limit of detection (LOD) was set at 0.1 µg·mL -1 . The percentage of urine samples below the LOD was 26.2%; the remaining 73.8% of the urine samples contained caffeine. Most urine samples (67.3%) had urinary caffeine concentrations below 5 µg·mL -1 . Only 0.6% of urine samples exceeded the former threshold for caffeine doping (12 µg·mL -1 ). Triathlon (3.3 ± 2.2 µg·mL -1 ), cycling (2.6 ± 2.0 µg·mL -1 ), and rowing (1.9 ± 1.4 µg·mL -1 ) were the sports with the highest levels of urine caffeine concentration; gymnastics was the sport with the lowest urine caffeine concentration (0.5 ± 0.4 µg·mL -1 ). Older competitors (>30 y) had higher levels of caffeine in their urine than younger competitors (<20 y; p < 0.05); there were no differences between males and females. In conclusion, 3 out of 4 athletes had consumed caffeine before or during sports competition. Nevertheless, only a small proportion of these competitors (0.6%) had a urine caffeine concentration higher than 12 µg·mL -1 . Endurance sports were the disciplines showing the highest urine caffeine excretion after competition. Key words: caffeine, methylxanthine, doping control, endurance, intermittent sports, exercise. % des échantillons d'urine présentent une teneur en caféine supérieure au seuil défini antérieurement comme celui du dopage, soit 12 mg·mL -1 . On observe les plus hauts taux urinaires de caféine au triathlon (3,3 ± 2,2 mg·mL -1 ), au cyclisme (2,6 ± 2,0 mg·mL -1 ) et à l'aviron (1,9 ± 1,4 mg·mL -1 ) et les plus faibles taux à la gymnastique (0,5 ± 0,4 mg·mL -1 ). Les concurrents les plus âgés (>30 ans) présentent de plus hauts taux urinaires de caféine que les plus jeunes (<20 ans; p < 0,05) ; on n'observe pas de différences entre les femmes et les hommes. En conclusion, trois athlètes sur quatre consomment de la caféine avant ou pendant la compétition. Toutefois, une faible proportion de concurrents (0,6 %) présente un taux urinaire de caféine supérieur à 12 mg·mL -1 . C'est dans les sports d'endurance qu'on observe les plus importantes excrétions urinaires de caféine après la compétition

Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes

Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model. In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most commonoperations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept,we present, implement and execute the operations as lambda reasoning for CQL IDE – the state-of-theart CT tool.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081 and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación

Transforming numerical feature models into propositional formulas and the universal variability language

Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features have not only boolean values that satisfy boolean constraints but also have numeric attributes that satisfy arithmetic constraints. An essential operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting and sampling. Nemo (Numbers, features, models) is a tool that supports NFMs by bit-blasting, the technique that encodes arithmetic expressions as boolean clauses. The newest version, Nemo2, translates NFMs to propositional formulas and the Universal Variability Language (UVL). By doing so, products can be counted efficiently by #SAT and Binary Decision Tree solvers, enabling finding near-optimal products. This article evaluates Nemo2 with a large set of synthetic and colossal real-world NFMs, including complex arithmetic constraints and counting and sampling experiments. We empirically demonstrate the viability of Nemo2 when counting and sampling large and complex SPLs.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER, Spain funds LEIA UMA18-FEDERJA-15, IRIS PID2021- 122812OB-I00 (MCI/AEI), and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación. Funding for open access charge: Universidad de Málaga / CBUA

Analysis of doping control test results in individual and team sports from 2003 to 2015.

Background Determining the prevalence of doping in sport might be useful for anti-doping authorities to gauge the effectiveness of anti-doping policies implemented to prevent positive attitudes toward doping. Using questionnaires and personal interviews, previous investigations have found that the prevalence of doping might be different among different sports disciplines; however, there is no sport-specific information about the proportion of adverse and atypical findings in samples used for doping control. The aim of the present investigation was to assess the differences in the frequency of adverse analytical and atypical findings among sports using the data made available by the World Anti-Doping Agency. Method The data included in this investigation were gathered from the Testing Figures Reports made available annually from 2003 to 2015 by WADA. These Testing Figures Reports include information about the number of samples analyzed, the number of adverse and atypical findings reported, and the most commonly found drugs in the urine and blood samples analyzed. A total of 1,347,213 samples were analyzed from the individual sports selected for this investigation, and 698,371 samples were analyzed for disciplines catalogued as team sports. Results In individual sports, the highest proportions of adverse and atypical findings (AAF) were 3.3% ± 1.0% in cycling, 3.0% ± 0.6% in weightlifting and 2.9% ± 0.6% in boxing. In team sports, the highest proportions of AAF were 2.2% ± 0.5% in ice hockey, 2.0% ± 0.5% in rugby and 2.0% ± 0.5% in basketball. Gymnastics and skating had the lowest proportions ≤ 1.0%) for individual sports, while field hockey, volleyball and football had the lowest proportions for team sports (<1.5%). Conclusion As suggested by the analysis, the incidence of AAF was not uniform across all sports disciplines, with the different proportions pointing to an uneven use of banned substances depending on the sport. This information might be useful for increasing the strength and efficacy of anti-doping policies in those sports with the highest prevalence in the use of banned substances.pre-print1193 K

Sistema inteligente de monitorización para la auscultación de tuberías mediante un robot

En este artículo se presenta el diseño, desarrollo e implementación de un sistema de monitorización para un robot de auscultación de tuberías en agujeros profundos. El cometido de estas tuberías es actuar como canalizaciones a la hora de inyectar materiales endurecedores del terreno, como paso previo a la realización de obras en infraestructuras subterráneas, como son los túneles. El diseño y la implementación de un sistema de monitorización tienen una serie de restricciones debido a su pequeño tamaño, condiciones de alta humedad y baja luminosidad. La tarea principal del sistema de monitorización es mejorar el seguimiento y localización del robot en el interior de tuberías de hasta 36 mm y realizar la monitorización mediante imágenes de modo que permita la trazabilidad de las labores realizadas en las infraestructuras. Para ello, en este trabajo se presenta el desarrollo de un sistema inteligente de visión e iluminación compuesto por una cámara de alta definición y un sistema de iluminación LED, el cual permite monitorizar el estado de la tubería y el recorrido llevado a cabo por el robot en el interior de la misma. Como corroboración experimental, los resultados serán comparados con los obtenidos mediante las mediciones realizadas con otro tipo de sensores, tales como inclinómetros, acelerómetros, etc. El robot ha sido probado en condiciones extremas realizando tareas de vigilancia y localización, obteniéndose unos resultados muy prometedores. El desarrollo de este robot pretende generar unas bases científicas y técnicas que ayuden a mejorar, e incluso sustituir, los sistemas comerciales existentes para la comprobación de la calidad y el cumplimiento de tolerancias en agujeros profundos para tuberías de poco diámetro llevados a cabo en obras para infraestructuras subterráneas, como son los túneles

Sport-Specific Use of Doping Substances: Analysis of World Anti-Doping Agency Doping Control Tests between 2014 and 2017.

Background: In recent years, there has been a solid effort across all sports organizations to reduce the prevalence and incidence of doping in sport. However, the efficacy of current strategies to fight against doping might be improved by using anti-doping polices tailored to the features of doping in each sport. Objectives: The aim of this investigation was to analyze the substances more commonly found in doping control tests in individual and team sports. Material and Methods: The publicly accessible Testing Figures Reports made available by the World Anti-Doping Agency, were analyzed from 2014 to 2017. Results: The most commonly detected groups of banned substances were anabolic agents and stimulants but the distribution of adverse findings per drug class was very different depending on the sports discipline. Weightlifting, athletics, rugby, hockey and volleyball presented abnormally high proportions of anabolic agents (p = 2.8 × 10−11). Cycling, athletics and rugby presented atypically elevated proportions of peptide hormones and growth factors (p = 1.4 × 10−1). Diuretics and masking agents were more commonly found in boxing, wrestling, taekwondo, judo, shooting, and gymnastics than in other sports (p = 4.0 × 10−68). Cycling, rowing, aquatics, tennis, gymnastics and ice hockey presented abnormally high proportions of stimulants (p = 1.8 × 10−5). Conclusions: These results indicate that the groups of banned substances more commonly detected in anti-doping control tests were different depending on the sports discipline. These data suggest the prohibited substances used as doping agents might be substantially different depending on the type of sport and thus, sports-specific anti-doping policies should be implemented to enhance the efficacy of anti-doping testing.pre-print523 K