Characterization of bacterial communities associated with Brassica napus L. growing on a Zn-contaminated soil and their effects on root growth

peerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=bijp20The attached document is the author's final accepted/submitted version of the journal article. You are advised to consult the publisher's version if you wish to cite from it

Simulation of superresolution holography for optical tweezers

Optical tweezers manipulate microscopic particles using foci of light beams. Their performance is therefore limited by diffraction. Using computer simulations of a model system, we investigate the application of superresolution holography for two-dimensional (2D) light shaping in optical tweezers, which can beat the diffraction limit. We use the direct-search and Gerchberg algorithms to shape the center of a light beam into one or two bright spots; we do not constrain the remainder of the beam. We demonstrate that superresolution algorithms can significantly improve the normalized stiffness of an optical trap and the minimum separation at which neighboring traps can be resolved. We also test if such algorithms can be used interactively, as is desirable in optical tweezers

Charged ultrafiltration membranes based on TEMPO-oxidized cellulose nanofibrils/poly(vinyl alcohol) antifouling coating

This study reports the potential of TEMPO-oxidized cellulose nanofibrils (T-CNF)/poly(vinyl alcohol) (PVA) coatings to develop functionalized membranes in the ultrafiltration regime with outstanding antifouling performance and dimensional/pH stability. PVA acts as an anchoring phase interacting with the polyethersulfone (PES) substrate and stabilizing for the hygroscopic T-CNF via crosslinking. The T-CNF/PVA coated PES membranes showed a nano-textured surface, a change in the surface charge, and improved mechanical properties compared to the original PES substrate. A low reduction (4%) in permeance was observed for the coated membranes, attributable to the nanometric coating thickness, surface charge, and hydrophilic nature of the coated layer. The coated membranes exhibited charge specific adsorption driven by electrostatic interaction combined with rejection due to size exclusion (MWCO 530 kDa that correspond to a size of ∼35–40 nm). Furthermore, a significant reduction in organic fouling and biofouling was found for T-CNF/PVA coated membranes when exposed to BSA and E. coli. The results demonstrate the potential of simple modifications using nanocellulose to manipulate the pore structure and surface chemistry of commercially available membranes without compromising on permeability and mechanical stability

Efectos del entrenamiento interválico de alta intensidad durante el periodo transitorio en jugadores de fútbol sub-19

Actualmente existe un creciente interés por la aplicación del entrenamiento interválico de alta intensidad (HIIT) en fútbol (Buchheit et al. 2013). La eficacia mostrada por esta metodología de entrenamiento para mejorar las variables del rendimiento ha provocado una mayor aplicación en deportes colectivos (Iaia et al. 2009). Sin embargo, es escasa la literatura científica acerca de su impacto en fútbol. Por ello, el objetivo del trabajo es comparar los efectos sobre el rendimiento del HIIT versus entrenamiento tradicional durante el periodo transitorio invernal en jugadores de fútbol sub-19

Novel Automation of an Enzyme-Linked Immunosorbent Spot Assay Testing Method: Comparable Diagnostic Performance of the T-SPOT.TB Test Using Manual Density Gradient Cell Isolation versus Automated Positive Selection with the T-Cell Select Kit

The diagnosis of latent tuberculosis (TB) infection (LTBI) is critical to improve TB treatment and control, and the T-SPOT.TB test is a commercial enzyme-linked immunosorbent spot assay used for this purpose. The objective of the study was to increase automation and extend the time between blood collection and processing for the T-SPOT.TB test from 0 to 8 h to 0 to 54 h. The previous maximum time between blood collection and processing for the T-SPOT.TB test is 32 h using T-Cell Xtend. For this, we compared the T-SPOT.TB test using manual peripheral blood mononuclear cell (PBMC) isolation by density gradient separation at 0 to 8 h (reference method, control arm) to an automated PBMC isolation method using magnetic beads (T-Cell Select kit) at 0 to 55 h postcollection. A total of 620 subjects were enrolled from 4 study sites, and blood samples were collected from each volunteer, comprising 1,850 paired samples in total. Overall agreement between both methods was 96.8% (confidence interval [CI], 95.9 to 97.6%), with 95.8% (CI, 93.5 to 97.5%) positive and 97.1% negative agreement (CI, 96.1 to 97.9%). In summary, there was a strong overall agreement between the automated and manual T-SPOT.TB test processing methods. The results suggest that the T-SPOT.TB test can be processed using automated positive selection with magnetic beads using T-Cell Select to decrease hands-on time. Also, this cell isolation method allowed for the time between blood collection and processing to range from 0 to 55 h. Additional studies in larger and diverse patient populations including immunocompromised and pediatric patients are needed

Response of stone wool-insulated building barriers under severe heating exposures

This article presents the experimental results of stone wool-layered sandwich constructions, with either steel or gypsum claddings, tested under four different heating exposures: 7kW/m(2) incident radiant heat flux exposure, 60kW/m(2) incident radiant heat flux exposure, parametric time-temperature curve exposure and ISO 834 standard time-temperature exposure. The test apparatus used were a movable radiant panel system, a mid-scale furnace (1.5m(3)) and a large-scale furnace (15m(3)). The results show that reduced-scale tests are capable of reproducing the heat transferred through the construction at large scale provided there is limited mechanical degradation. The results indicate that the availability of oxygen is fundamental to the fire behaviour of the sandwich composites tested. Reactions occurring in stone wool micro-scale testing, such as oxidative combustion of the binder or crystallisation of the fibres, have a limited effect on the temperature increase when wool is protected from air entrainment

Disentangling environmental correlates of vascular plant biodiversity in a Mediterranean hotspot

We determined the environmental correlates of vascular plant biodiversity in the Baetic-Rifan region, a plant biodiversity hotspot in the western Mediterranean. A catalog of the whole flora of Andalusia and northern Morocco, the region that includes most of the Baetic-Rifan complex, was compiled using recent comprehensive floristic catalogs. Hierarchical cluster analysis (HCA) and detrended correspondence analysis (DCA) of the different ecoregions of Andalusia and northern Morocco were conducted to determine their floristic affinities. Diversity patterns were studied further by focusing on regional endemic taxa. Endemic and nonendemic alpha diversities were regressed to several environmental variables. Finally, semi-partial regressions on distance matrices were conducted to extract the respective contributions of climatic, altitudinal, lithological, and geographical distance matrices to beta diversity in endemic and nonendemic taxa. We found that West Rifan plant assemblages had more similarities with Andalusian ecoregions than with other nearby northern Morocco ecoregions. The endemic alpha diversity was explained relatively well by the environmental variables related to summer drought and extreme temperature values. Of all the variables, geographical distance contributed by far the most to spatial turnover in species diversity in the Baetic-Rifan hotspot. In the Baetic range, elevation was the most significant driver of nonendemic species beta diversity, while lithology and elevation were the main drivers of endemic beta diversity. Despite the fact that Andalusia and northern Morocco are presently separated by the Atlantic Ocean and the Mediterranean Sea, the Baetic and Rifan mountain ranges have many floristic similarities – especially in their western ranges – due to past migration of species across the Strait of Gibraltar. Climatic variables could be shaping the spatial distribution of endemic species richness throughout the Baetic-Rifan hotspot. Determinants of spatial turnover in biodiversity in the Baetic-Rifan hotspot vary in importance between endemic and nonendemic species

One-Step Isolation of Monoterpene Indole Alkaloids from Psychotria Leiocarpa Leaves and Their Antiviral Activity on Dengue Virus Type-2

The leaf MeOH extract of Psychotria leiocarpa (Rubiaceae) showed in vitro non-cytotoxic and anti-dengue virus serotype 2 (DENV2) activity in human hepatocarcinoma cell lineage (HepG2). A one-step and cost-effective reversed-phase solid-phase extraction method based on high-performance liquid chromatography (HPLC) parameters allowed the isolation, directly from this bioactive extract, of the monoterpene indole alkaloids: N-glucopyranosyl vincosamide (1), vincosamide (2) and strictosidinic acid (3). The chemical structures were characterized based on 1D and 2D nuclear magnetic resonance (NMR), UV and high-resolution mass spectra (HRMS). The methodology has also allowed yielding a polyphenolic-rich fraction that was analyzed by high-performance liquid chromatography coupled to diode array detection and electrospray ionization tandem mass spectrometry (HPLC-DAD-ESI-MS/MS) revealing two fiavonol triglycosides (4, 5) and three caffeoylquinic acid isomers (6-8). Compound 3 is reported for the first time in P leiocarpa and all the phenolic compounds (4-8) are described for the first time in the genus Psychotria. Compounds 1-3 showed to be non-cytotoxic and anti-dengue active towards DENV2, highlighting vincosamide (2).This work was supported by Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), processes No. E-26/111.373/2014 and E-26/203.225/2017. DGL and JOC thank Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), finance code 001, for their fellowships. TW thanks both CAPES and FAPERJ for his fellowships. The authors thank the NMR Lab of the Instituto de Pesquisas em Produtos Naturais, Universidade Federal do Rio de Janeiro for the NMR spectra and MSc Matheus Oliveira, Dr Marcelo M. Pereira and Dr Denise Freire for their support. Technical and staff support provided by SGIker (UPV/EHU, MICINN, GV/EJ, ESF) is also gratefully acknowledged

Integrating an epidemic spread model with remote sensing for Xylella fastidiosa detection

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021.Xylella fastidiosa (Xf) causes plant diseases that lead to massive economic losses in agricultural crops, making it one of the pathogens of greatest concern to agriculture nowadays. Detecting Xf at early stages of infection is crucial to prevent and manage outbreaks of this vector-borne bacterium. Recent remote sensing (RS) studies at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, RS-based forecasting of Xf outbreaks requires tools that account for their spatiotemporal dynamics. Here, we show how coupling a spatial Xf-spread model with the probability of Xf-infection predicted by an RS-driven modeling algorithm based on a Support Vector Machine (RS-SVM) helps detecting the spatial Xf distribution in a landscape. To optimize such model, we investigated which RS plant traits (i.e., pigments, structural or leaf protein content) derived from high-resolution hyperspectral imagery and biophysical modelling are most responsive to Xf infection and damage. For that, we combined a field campaign in almond orchards in Alicante province (Spain) affected by Xf (n=1,426 trees), with an airborne campaign over the same area to acquire high-resolution thermal and hyperspectral images in the visible-near-infrared (400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). We found that coupling the epidemic spread model and the RS-based model increased accuracy by around 5% (OA = 80%, kappa = 0.48 and AUC = 0.81); compared to the best performing RS-SVM model (OA = 75%; kappa = 0.50) that included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator, alongside pigments and structural parameters. The parameters with the greatest explanatory power of the RS model were leaf protein content together with NI (28%), followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. In the subset of almond trees where the presence of Xf was tested by qPCR (n=318 tress), the combined RS-spread model yielded the best performance (OA of 71% and kappa = 0.33). Conversely, the best-performing RS-SVM model and visual inspections produced OA and kappa values of 65% and 0.31, respectively. This study shows for the first time the potential of combining spatial epidemiological models and remote sensing to monitor Xf-disease distribution in almond trees

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain)