Resultados de investigación

Supplementary Material 2., Peer reviewed

Raw data of manuscript "Dynamics of interaction and internalisation of the antifungal protein PeAfpA into Penicillium digitatum morphotypes", This dataset includes the raw data from the manuscript “Dynamics of interaction and internalisation of the antifungal protein PeAfpA into Penicillium digitatum morphotypes”. This data comprises Excel sheets used to generate all the plots and graphs of the article (.xlsx files), 16-bit, 8-bit and RGB confocal microscopy images (.tif files), statistical analyses (.pzfx and .txt files) and “Readme” files (.txt files) to specificy experimental conditions for data collection and software-specific requirements to visualise each file., This work was supported by grant PID2021-125858OB-I00, and the Severo Ochoa Excellence Program CEX2021-001189-S, funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF, a way of making Europe”. M.G.L. and C.R.P. were recipients of predoctoral grants FPU19/02066 and ACIF/2021/364 funded by MCIN/AEI/10.13039/501100011033 and by ESF-Investing in Your future and by ”Conselleria d’Educació (Generalitat Valenciana, Comunitat Valenciana, Spain), respectively. M.G.L. international exchange was supported by EST23/00233 grant (funded by MCIN/AEI/10.13039/ 501100011033 and by ESF-Investing in Your future) and SEG10314 EMBO Scientific Exchange Grant. S.G. holds a Juan de la Cierva Incorporación grant (IJC2020-042749-I) from the Spanish Ministerio de Ciencia e Innovación, funded by the European Union, NextGenerationEU PRTR. Work undertaken with EMB and DDT was funded by the Medical Research Council Centre for Medical Mycology at the University of Exeter (MR/N006364/2 and MR/V033417/1), that forms part of the National Institute for Health and Care Research (NIHR) Exeter Biomedical Research Centre (BRC), and the BBSRC project grant BB/V017004/1 to EMB., With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2021-001189-S), Figure 1_data: Antifungal activity of the antifungal proteins PeAfpA and PdAfpB against Penicillium digitatum conidia. -- Figure 2_data ROS production in Penicilliumn digitatum germlings, induced by the treatment with antifungal proteins (PeAfpA or PdAfpB).-- Figure 3_data Evaluation of the interaction of PeAfpA and PdAfpB with P. digitaum conidia. Panels show confocal microscopy images of P. digitatum conidia after 15 min or 90 min of treatment with 8 µg/mL PeAfpA (A, B), or after 90 min of treatment with 8 µg/mL PdAfpB (C).-- Figure 4_data Comparison of the PeAfpA interaction with conidia of P. digitatum wild-type strain CECT 20796 (WT) and the KO mutant strains ΔpksP and Δarp2. -- Figure 5_data BODIPY-PeAfpA internalisation dynamics in P. digitatum hyphae in low-dose conditions (2 µg/mL).-- Figure 6_data PeAfpA internalises through hyphal tips and provokes vacuolisation and hyphal burst. Raw images used for each panel are provided.-- Figure 7_data: PeAfpA co-localises with CFW in the fungal cell wall. Raw images used for each panel are provided.-- Figure 8_data: PeAfpA internalisation in fungal hyphae is energy-dependent but not an endocytic process. Raw images used for each panel are provided.-- Supplementary Figure 1_data: Antifungal activity of BODIPY-labelled and -unlabelled antifungal proteins (PeAfpA and PdAfpB) against Penicillium digitatum conidia. Raw OD600 measurements are provided in two separated Excel files. -- Supplementary Figure 2_data: Raw data for the amino acid alignment of the Aspergillus fumigatus RodA protein and the orthologous protein from Penicillium digitatum. Raw pictures for panel B are provided.-- Supplementary Figure 3_data: Raw OD measurements for the dose response curves of P. digitatum CECT 20796 and deletion mutant strains (ΔpksP and Δarp2) treated with PeAfpA and PdAfpB. -- Supplementary Figure 4_data: Evaluation of the colocalization of PeAfpA and CFW in P. digitatum hyphae. Rae images for each panel are provided, as well as raw pixel intensity data.-- Supplementary Figure 5_data: Raw images from insets shown in Figure 5., Peer reviewed

Additional experimental results are available. Structural analysis ((S1) TEM, size distribution, (S2) STEM-HAADF, STEM-EELS, powder XRD patterns, and (S3) Mössbauer of CoFe carbide MNPs). (S4) Magnetic properties of a powder of CoFe carbide MNPs measured at different temperature without magnetic field and under 5 T at 2 K. (S5) Magnetic properties of a powder of CoFe carbide MNPs measured at 2 and 300 K. Study of MNPs concentration and withdrawal speed of the substrate including respectively optical (S6.a and S9.a left) and SEM (S7 and S9.a right) characterizations taken after the deposition at different concentration. (S8 and S10) AFM measurements performed at different MNP concentrations and withdrawal speed. (S12) Optical images taken after the substrates were immersed in suspensions of OA and HDA dissolved in THF as well as pure THF. (S13) High resolution SEM image located between two consecutive stripes when a large excess of HDA (1 equiv) was added to the suspension. (S14) Evolution against the time of the contact angle of cleaned substrates stored in glovebox., Peer reviewed

The data file contains the following columns: Date: numerical variable indicating the “day”/”month”/”year” when the register was generated. User_ID: categorical variable with the identification number of the user who gathered the record. Transect: categorical variable with the identification of the number of the transect. Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect. Photo_label: character string with the identification of the photograph from each record. Register_localization: categorical variable with the identification of the geographic area where the record was done. Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees. Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees. Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS. Habitat_type: categorical variable with the description of the main type of habitat of the sampling location. Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record. Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record. Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record. Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record. Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record. Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record. Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record. Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record. Algae: integer, variable indicating the percentage of algae cover quantified in the record. Soil: integer, variable indicating the percentage of soil visible in the record., Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change. The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded. The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats: Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%. Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%. Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall. Grassland – Habitat characterized by herbaceous plants. Agricultural land – Habitat characterized by temporary or woody crops. Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s. Urban green spaces – Habitat characterized by a landscape in which man-made structures are present. The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers)., Funding/Grants: FECyT FCT-18-13511; AEI PID2019-106840GB-C21 Con la colaboración de la Fundación Española para la Ciencia y la Tecnología - Ministerio de Ciencia e Innovación., Peer reviewed

Supplementary Figure 1. Placement of electrodes within tumour-infiltrated tissue. Electrode locations are represented in red. The outline of each tumour mask is coloured green., Supplementary Figure 2. Time course of high gamma power modulations in tumour-infiltrated tissue for the hard vs. easy contrast. Average time course across all electrodes that showed significant change in power for this contrast, smoothed by a 1000 ms Gaussian kernel (SD=200 ms). Data is average across all trials, cropped to the shortest trial duration. Shaded area indicates SEM., Supplementary Figure 3. The relationship between tumour-DAN connectivity and performance on the Hearts Cancellation task stratified by glioma subtype. Scatterplots of the relationship between overall accuracy on the Hearts Cancellation Task and tumour-DAN connectivity at the preop and latest follow up timepoints, as well as the difference between the follow up and preoperative scores stratified by glioma subtypes indicated in Table 1. The preoperative and follow-up Heart Cancellation Scores were Z-score normalized using descriptive statistics from healthy participants., Supplementary Table 1. Relationship between functional network connectivity and power modulations for hard>easy contrast across different frequency bands. The relationships between functional network connectivity and power modulations were assessed using linear mixed-effects models. Chi-square test statistics are displayed with the corresponding P-values in parentheses. In certain cases, a P-value could not be accurately calculated because of a poorly fit model, indicated in the table as “NA”., Supplementary Table 2. Results of multiple linear regression models predicting Tumour-DAN Connectivity and long-term outcomes on the Heart Cancellation task, excluding participants without a Month 3 or greater follow-up assessment., Peer reviewed

Additional SAXS diagrams, TEM and HRTEM images, TEM–EDS, TGA, and DSC analysis of the precipitate, XRD patterns of the precipitate obtained with different amines, and the evolution of the corresponding interplanar distance., Peer reviewed

This program aims to improve the quality of life and pain in breast cancer survivors. For this purpose, combines two tools (PNE and GEM) that have shown efficacy in other populations with chronic pain1-12. This program is created based on the biopsychosocial13 model and the focused-person14 attendance and tries to empower women regard to their pain management and their health. The program will be applied in a group format., [Introduction] Cancer-related chronic pain is an important sequelae that damages the quality of life of breast cancer survivors. Pain neuroscience education and graded exposure to movement are therapeutic tools that have been shown to be effective in the management of chronic pain in other populations. However, there are no previous studies that combine them after breast cancer., [Objective] To evaluate the effectiveness of an online physiotherapy focused-person program which combines pain neuroscience education and graded exposure to movement for quality of life improvement in breast cancer survivors., [Methodology] This protocol is a randomized controlled trial with a sample size of 40 breast cancer survivors with pain in the last 6 months. Participants will be allocated to the experimental or control group using a fixed size block randomization method. The evaluator and statistician will be blinded to participant allocation. Participants in the experimental group will receive a 12-week intervention based on pain neuroscience education and therapeutic yoga as a graded exposure to movement exercise; participants in the control group will continue with their usual cancer-related symptoms care. Both groups will receive an education booklet. The main outcome will be quality of life, measured by the Functional Assessment of Cancer Therapy – Breast (FACT–B+4); secondary, four outcomes related to pain experience (catastrophising, self-efficacy, kinesiophobia and fear-avoidance behaviors) will be also assessed. All variables will be assessed by two blinded evaluators at four timepoints. A mixed-model analyses of variance ANOVA (2 × 4) will be used to study the effects of the treatment on the dependent variables. All statistical tests will be performed considering a confidence interval of 95%. SPSS program will be used for the data analysis., [Discussion] This research is expected to contribute to breast cancer rehabilitation field. The proposed intervention is also expected to improve self-care skills related to chronic pain and to empower women regarding the management of their symptoms and quality of life., [Clinical trial registration] https://clinicaltrials.gov/, NCT04965909., Peer reviewed

-Additional details for synthesis, HE-XRD experiments, and DFT calculations (PDF). -3D structure modeling (AVI)., Peer reviewed

Details on experimental methods, additional data and analysis., Peer reviewed

Supplementary data: Briefly, lifetime dependence on power (Fig. S1), lifetime fitting components (Fig. S2), emission intensity according with the different measurements necessary to get pure PL QY (Fig. S3), explanation on the error bars of PL QY measurements, TEM images of CNDs (Fig. S4), Raman spectrum (Fig. S5), FTIR (Fig. S6), nitrogen absorption feature in N-doped CNDs (Fig. S7)., Peer reviewed