Spatio‑temporal modelling of high‑throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used to characterise growth and developmental processes for large sets of plant genotypes. This dissertation is motivated by the need to accurately estimate genetic effects over time when analysing data from such HTP experiments. The HTP data we deal with here are characterised by phenotypic traits measured multiple times in the presence of spatial and temporal noise and a hierarchical organisation at three levels (populations, genotypes within populations, and plants within genotypes). The challenge is to balance efficient statistical models and com- putational solutions to deal with the complexity and dimensionality of the experimental data. To that aim, we propose two strategies. The first proposal divides the problem into two stages. The first stage (spatial model) focuses on correcting the phenotypic data for experimental design factors and spatial variation, while the second stage (hierarchical longitudinal model) aims to estimate the evolution over time of the genetic signal. The second proposal is to face the problem simultaneously (one-stage approach). That is, mod- elling the longitudinal evolution of the genetic effect on a given phenotypic trait while accounting for the temporal and spatial effects of environmental and design factors (spatio-temporal hierarchical model). We follow the same modelling philosophy throughout our work and propose multidimensional P-spline-based hierarchical approaches. We provide the user with appealing tools that take advantage of the sparse model matrices structure to reduce computational complexity. All our codes are publicly available on the R-package statgenHTP and https://gitlab.bcamath.org/dperez/htp_one_stage_approach. We illustrate the performance of our methods using spatio-temporal simulated data and data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich. In the plant breeding context, we show how to extract new time-independent phenotypes for genomic selection purposes.MTM2017-82379-R BERC 2018-2021 BERC 2022-2025 SEV-2017-0718 CEX2021-001142-S/MICIN/AEI/10.13039/50110001103

Isolation and characterization of phenanthrene degrading bacteria from diesel fuel-contaminated Antarctic soils

Indexación: Scopus.Antarctica is an attractive target for human exploration and scientific investigation, however the negative effects of human activity on this continent are long lasting and can have serious consequences on the native ecosystem. Various areas of Antarctica have been contaminated with diesel fuel, which contains harmful compounds such as heavy metals and polycyclic aromatic hydrocarbons (PAH). Bioremediation of PAHs by the activity of microorganisms is an ecological, economical, and safe decontamination approach. Since the introduction of foreign organisms into the Antarctica is prohibited, it is key to discover native bacteria that can be used for diesel bioremediation. By following the degradation of the PAH phenanthrene, we isolated 53 PAH metabolizing bacteria from diesel contaminated Antarctic soil samples, with three of these isolates exhibiting a high phenanthrene degrading capacity. In particular, the Sphingobium xenophagum D43FB isolate showed the highest phenanthrene degradation ability, generating up to 95% degradation of initial phenanthrene. D43FB can also degrade phenanthrene in the presence of its usual co-pollutant, the heavy metal cadmium, and showed the ability to grow using diesel-fuel as a sole carbon source. Microtiter plate assays and SEM analysis revealed that S. xenophagum D43FB exhibits the ability to form biofilms and can directly adhere to phenanthrene crystals. Genome sequencing analysis also revealed the presence of several genes involved in PAH degradation and heavy metal resistance in the D43FB genome. Altogether, these results demonstrate that S. xenophagum D43FB shows promising potential for its application in the bioremediation of diesel fuel contaminated-Antarctic ecosystems.https://www.frontiersin.org/articles/10.3389/fmicb.2017.01634/ful

Effective description of brane terms in extra dimensions

We study how theories defined in (extra-dimensional) spaces with localized defects can be described perturbatively by effective field theories in which the width of the defects vanishes. These effective theories must incorporate a ``classical'' renormalization, and we propose a renormalization prescription a la dimensional regularization for codimension 1, which can be easily used in phenomenological applications. As a check of the validity of this setting, we compare some general predictions of the renormalized effective theory with those obtained in a particular ultraviolet completion based on deconstruction.Comment: 28 page

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.BERC 2018-2021 BCAM Severo Ochoa accreditation SEV-2017-0718) EU H2020 grant agreement ID 731013 (EPPN2020) PhenoCOOL (project no. 169542)

What factors affect voluntary uptake of community-based health insurance schemes in low- and middle-income countries? A systematic review and meta-analysis

Introduction: This research article reports on factors influencing initial voluntary uptake of community-based health insurance (CBHI) schemes in low- and middle-income countries (LMIC), and renewal decisions. Methods: Following PRISMA protocol, we conducted a comprehensive search of academic and gray literature, including academic databases in social science, economics and medical sciences (e.g., Econlit, Global health, Medline, Proquest) and other electronic resources (e.g., Eldis and Google scholar). Search strategies were developed using the thesaurus or index terms (e.g., MeSH) specific to the databases, combined with free text terms related to CBHI or health insurance. Searches were conducted from May 2013 to November 2013 in English, French, German, and Spanish. From the initial search yield of 15,770 hits, 54 relevant studies were retained for analysis of factors influencing enrolment and renewal decisions. The quantitative synthesis (informed by meta-analysis) and the qualitative analysis (informed by thematic synthesis) were compared to gain insight for an overall synthesis of findings/statements. Results: Meta-analysis suggests that enrolments in CBHI were positively associated with household income, education and age of the household head (HHH), household size, female-headed household, married HHH and chronic illness episodes in the household. The thematic synthesis suggests the following factors as enablers for enrolment: (a) knowledge and understanding of insurance and CBHI, (b) quality of healthcare, (c) trust in scheme management. Factors found to be barriers to enrolment include: (a) inappropriate benefits package, (b) cultural beliefs, (c) affordability, (d) distance to healthcare facility, (e) lack of adequate legal and policy frameworks to support CBHI, and (f) stringent rules of some CBHI schemes. HHH education, household size and trust in the scheme management were positively associated with member renewal decisions. Other motivators were: (a) knowledge and understanding of insurance and CBHI, (b) healthcare quality, (c) trust in scheme management, and (d) receipt of an insurance payout the previous year. The barriers to renewal decisions were: (a) stringent rules of some CBHI schemes, (b) inadequate legal and policy frameworks to support CBHI and (c) inappropriate benefits package. Conclusion and Policy Implications: The demand-side factors positively affecting enrolment in CBHI include education, age, female household heads, and the socioeconomic status of households. Moreover, when individuals understand how their CBHI functions they are more likely to enroll and when people have a positive claims experience, they are more likely to renew. A higher prevalence of chronic conditions or the perception that healthcare is of good quality and nearby act as factors enhancing enrolment. The perception that services are distant or deficient leads to lower enrolments. The second insight is that trust in the scheme enables enrolment. Thirdly, clarity about the legal or policy framework acts as a factor influencing enrolments. This is significant, as it points to hitherto unpublished evidence that governments can effectively broaden their outreach to grassroots groups that are excluded from social protection by formulating supportive regulatory and policy provisions even if they cannot fund such schemes in full, by leveraging people's willingness to exercise voluntary and contributory enrolment in a community-based health insurance

Prevalence of anxiety in the COVID-19 pandemic: An updated meta-analysis of community-based studies

Background: The unprecedented worldwide crisis caused by the rapid spread of COVID-19 and the restrictive public health measures enforced by some countries to slow down its transmission have severely threatened the physical and mental wellbeing of communities globally. Methods: We conducted a systematic review and meta-analysis to determine the prevalence of anxiety in the general population during the COVID-19 pandemic. Two researchers independently searched for cross-sectional community-based studies published between December 1, 2019 and August 23, 2020, using PubMed, WoS, Embase, and other sources (e.g., grey literature, manual search). Results: Of 3049 records retrieved, 43 studies were included. These studies yielded an estimated overall prevalence of anxiety of 25%, which varied significantly across the different tools used to measure anxiety. Consistently reported risk factors for the development of anxiety included initial or peak phase of the outbreak, female sex, younger age, marriage, social isolation, unemployment and student status, financial hardship, low educational level, insufficient knowledge of COVID-19, epidemiological or clinical risk of disease and some lifestyle and personality variables. Conclusions: As the overall global prevalence of anxiety disorders is estimated to be 7.3% normally, our results suggest that rates of anxiety in the general population could be more than 3 times higher during the COVID-19 pandemic. These findings suggest a substantial impact on mental health that should be targeted by individual and population-level strategies

A hybrid semantic approach to building dynamic maps of research communities

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems

Density functional study of Aun_n (n=2-20) clusters: lowest-energy structures and electronic properties

We have investigated the lowest-energy structures and electronic properties of the Au$_n$(n=2-20) clusters based on density functional theory (DFT) with local density approximation. The small Au$_n$ clusters adopt planar structures up to n=6. Tabular cage structures are preferred in the range of n=10-14 and a structural transition from tabular cage-like structure to compact near-spherical structure is found around n=15. The most stable configurations obtained for Au$_{13}$ and Au$_{19}$ clusters are amorphous instead of icosahedral or fcc-like, while the electronic density of states sensitively depend on the cluster geometry. Dramatic odd-even alternative behaviors are obtained in the relative stability, HOMO-LUMO gaps and ionization potentials of gold clusters. The size evolution of electronic properties is discussed and the theoretical ionization potentials of Au$_n$ clusters compare well with experiments.Comment: 6 pages, 7 figure

Expanding graphene properties by a simple S-doping methodology based on cold CS2 plasma

For the first time, graphene has been successfully doped with sulfur via short exposition to CS2 microwave cold plasmas, avoiding high-temperature and time/chemicals-consuming treatments. Different S-doped samples were obtained by varying the duration of plasma treatments, reaching a remarkable 2.3 at % of S content after only 5 min of exposition. The S-doped graphenes present several sulfur containing moieties, among which thioether groups resulted to be predominant. These moieties are covalently bond to graphene layers and exhibit good thermal and water stability. In addition, unlike others more conventional methods, S-doping via CS2 plasmas do not damage the structural order of graphene. The influence of sulfur doping on the graphene properties has been assessed through two different tests: on one side, the capture of Pd2+ ions in aqueous solution, and on the other, the electrocatalytic activity towards the production of oxygen from water (OER process). In both cases, the performance of the pristine graphene was significantly enhanced with S-doping. In addition, the capture of Pd2+ allows the formation of sulfur-Pd nanoclusters supported on the graphene surface, which are very useful in electrochemical devices.The financial support of the MINECO (projects MAT2014-60104- C2-1-R and MAT2014-60104-C2-2-R), FEDER program, Autonomous Regional Government (J. de Andalucía, grupo RNM342) and Programa de Fortalecimiento de la IþDþi from UGR is acknowledged. The technical assistance of Centre of Instrumental Facilities, STI, of the University of Jaen is also acknowledged. The work was also funded by Fundaç~ ao para a Ciencia e a Tecnologia de Portugal ^ (FCT)/MEC under FEDER under Program PT2020 - project UID/QUI/ 50006/2013-POCI/01/0145/FEDER/007265 and project “UniRCell”, with the reference POCI-01-0145-FEDER-016422. Víctor K. Abdelkader Fern andez thanks UniRCell project for the post-doctoral grant

Vector Bosons in the Randall-Sundrum 2 and Lykken-Randall models and unparticles

Unparticle behavior is shown to be realized in the Randall-Sundrum 2 (RS 2) and the Lykken-Randall (LR) brane scenarios when brane-localized Standard Model currents are coupled to a massive vector field living in the five-dimensional warped background of the RS 2 model. By the AdS/CFT dictionary these backgrounds exhibit certain properties of the unparticle CFT at large N_c and strong 't Hooft coupling. Within the RS 2 model we also examine and contrast in detail the scalar and vector position-space correlators at intermediate and large distances. Unitarity of brane-to-brane scattering amplitudes is seen to imply a necessary and sufficient condition on the positivity of the bulk mass, which leads to the well-known unitarity bound on vector operators in a CFT.Comment: 60 pages, 8 figure