Biology subjects

Vieira, T.

Publications and source records attributed to Vieira, T..

3 recordsLinked to original sources

bioRxiv2024

The widely used cymoxanil fungicide impairs respiration in Saccharomyces cerevisiae via cytochrome c oxidase inhibition

Cymoxanil (CYM) is a synthetic acetamide fungicide that has been widely used to combat downy mildew diseases in grapevine cultures and late blight diseases in tomato and potato caused by the oomycetes Plasmopara viticola and Phytophthora infestans, respectively. Despite its extensive application, the biochemical mode of action of CYM remains elusive. Previous reports indicate that CYM affects growth, DNA and RNA synthesis in Phytophthora and inhibits cell growth, biomass production and respiration rate in the well-characterized fungal model Saccharomyces cerevisiae. We therefore used this model to further characterize the effect of CYM on mitochondria. We found that CYM inhibits oxygen consumption in whole cells after 3 h of exposure, which persists over time. Using isolated mitochondria, we demonstrated that CYM specifically inhibits cytochrome c oxidase (CcO) activity during oxidative phosphorylation. Based on molecular docking algorithms, we propose that CYM acts by blocking the interaction of cytochrome c (cyt c) with CcO, hampering electron transfer and inhibiting CcO catalytic activity. Although other targets cannot be excluded, our data offer valuable insights into the mode of action of CYM that can be instrumental to drive informed management of the use of this fungicide.

biochemistry↗

bioRxiv2023

Combining high-density electromyography and ultrafast ultrasound to assess individual motor unit properties in vivo

This study aims to compare two methods for the identification of anatomical and mechanical motor unit (MU) properties through the integration of high-density surface electromyography (HDsEMG) and ultrafast ultrasound (UUS). The two approaches rely on a combined analysis of the firing pattern of active MUs, identified from HDsEMG, and tissue velocity sequences of the muscle cross-section, obtained from UUS. The first method is the spike-triggered averaging (STA) of the tissue velocity sequence based on the occurrences of MU firings. The second is a method based on spatio-temporal independent component analysis (STICA) enhanced with the information of single MU firings. We compared the capability of these two approaches to identify the regions where single MU fibers are located within the muscle cross-section (MU displacement area) in vivo. HDsEMG signals and UUS images were detected simultaneously from biceps brachii in ten participants (6 males and 4 females) during low-level isometric elbow flexions. Experimental signals were processed by implementing both STA and STICA approaches. The medio-lateral distance between the estimated MU displacement areas and the centroid of the MU action potential distributions was used to compare the two methods. We found that STICA and STA are able to detect MU displacement areas. However, STICA provides more precise estimations to the detriment of higher computational complexity.

bioengineering↗

bioRxiv2019

Deciphering the many maps of the Xingu, an assessment of deforestation from land cover classifications at multiple scales

Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics, as well as considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (1989-2018) for [~]1.3 million km2 encompassing the Xingu river basin, Brazil, from published, freely accessible remotely sensed classifications. While all datasets showed a decrease in forest extent over time, we found a large range in the total area reported by each product for all time periods. The greatest differences ranged from 9% (year 2000) to 17% of the total area (2014-2018 period). We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. We further show the importance of choosing surface water datasets carefully because they differ greatly in location and amount of surface water mapped between sources. In several of the datasets illustrating the land cover following operationalization of the Belo Monte dam, the large reservoirs are notably absent. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g. Riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate dataset for conservation, taxonomy or policy-making. The differences in forest cover between the datasets examined here are not a failure of the technology, but due to different interpretations of forest and characteristics of the input data (e.g. spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed datasets and the need for users to familiarize themselves with the characteristics and limitations of each chosen data set.

ecology↗