University of Alicante

About: University of Alicante is a education organization based out in Alicante, Spain. It is known for research contribution in the topics: Catalysis & Population. The organization has 8681 authors who have published 22690 publications receiving 476064 citations. The organization is also known as: Universitat d'Alacant & Universidad de Alicante.

Membrane fluidity determines sensitivity of filamentous fungi to chitosan

Abstract: The antifungal mode of action of chitosan has been studied for the last 30 years, but is still little understood. We have found that the plasma membrane forms a barrier to chitosan in chitosan-resistant but not chitosan-sensitive fungi. The plasma membranes of chitosan-sensitive fungi were shown to have more polyunsaturated fatty acids than chitosan-resistant fungi, suggesting that their permeabilization by chitosan may be dependent on membrane fluidity. A fatty acid desaturase mutant of Neurospora crassa with reduced plasma membrane fluidity exhibited increased resistance to chitosan. Steady-state fluorescence anisotropy measurements on artificial membranes showed that chitosan binds to negatively charged phospholipids that alter plasma membrane fluidity and induces membrane permeabilization, which was greatest in membranes containing more polyunsaturated lipids. Phylogenetic analysis of fungi with known sensitivity to chitosan suggests that chitosan resistance may have evolved in nematophagous and entomopathogenic fungi, which naturally encounter chitosan during infection of arthropods and nematodes. Our findings provide a method to predict the sensitivity of a fungus to chitosan based on its plasma membrane composition, and suggests a new strategy for antifungal therapy, which involves treatments that increase plasma membrane fluidity to make fungi more sensitive to fungicides such as chitosan.

Multicomponent Synthesis of 1,2,3‐Triazoles in Water Catalyzed by Copper Nanoparticles on Activated Carbon

Abstract: Copper nanoparticles on activated carbon have been found to effectively catalyze the multicomponent synthesis of 1,2,3-triazoles from different azide precursors, such as organic halides, diazonium salts, anilines and epoxides in water. The first one-pot transformation of an olefin into a triazole is also described. The catalyst is easy to prepare, very versatile and reusable at a low copper loading.

Paleobiology and comparative morphology of a late Neandertal sample from El Sidrón, Asturias, Spain

Abstract: Fossil evidence from the Iberian Peninsula is essential for understanding Neandertal evolution and history. Since 2000, a new sample ≈43,000 years old has been systematically recovered at the El Sidron cave site (Asturias, Spain). Human remains almost exclusively compose the bone assemblage. All of the skeletal parts are preserved, and there is a moderate occurrence of Middle Paleolithic stone tools. A minimum number of eight individuals are represented, and ancient mtDNA has been extracted from dental and osteological remains. Paleobiology of the El Sidron archaic humans fits the pattern found in other Neandertal samples: a high incidence of dental hypoplasia and interproximal grooves, yet no traumatic lesions are present. Moreover, unambiguous evidence of human-induced modifications has been found on the human remains. Morphologically, the El Sidron humans show a large number of Neandertal lineage-derived features even though certain traits place the sample at the limits of Neandertal variation. Integrating the El Sidron human mandibles into the larger Neandertal sample reveals a north–south geographic patterning, with southern Neandertals showing broader faces with increased lower facial heights. The large El Sidron sample therefore augments the European evolutionary lineage fossil record and supports ecogeographical variability across Neandertal populations.

COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images

Abstract: Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building COVID-19 triage systems and detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This article is three-fold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Clinico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from normal with Positive RT-PCR, Mild, Moderate to Severe. COVIDGR-1.0 contains 426 positive and 426 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. Our approach reaches good and stable results with an accuracy of $\text{97.72}\% \pm \text{0.95}\%$ , $\text{86.90}\% \pm \text{3.20}\%$ , $\text{61.80}\% \pm \text{5.49}\%$ in severe, moderate and mild COVID-19 severity levels. Our approach could help in the early detection of COVID-19. COVIDGR-1.0 along with the severity level labels are available to the scientific community through this link https://dasci.es/es/transferencia/open-data/covidgr/ .