Showing papers by "University of Alicante published in 2020"

Evolutionary classification of CRISPR-Cas systems: a burst of class 2 and derived variants.

Abstract: The number and diversity of known CRISPR-Cas systems have substantially increased in recent years. Here, we provide an updated evolutionary classification of CRISPR-Cas systems and cas genes, with an emphasis on the major developments that have occurred since the publication of the latest classification, in 2015. The new classification includes 2 classes, 6 types and 33 subtypes, compared with 5 types and 16 subtypes in 2015. A key development is the ongoing discovery of multiple, novel class 2 CRISPR-Cas systems, which now include 3 types and 17 subtypes. A second major novelty is the discovery of numerous derived CRISPR-Cas variants, often associated with mobile genetic elements that lack the nucleases required for interference. Some of these variants are involved in RNA-guided transposition, whereas others are predicted to perform functions distinct from adaptive immunity that remain to be characterized experimentally. The third highlight is the discovery of numerous families of ancillary CRISPR-linked genes, often implicated in signal transduction. Together, these findings substantially clarify the functional diversity and evolutionary history of CRISPR-Cas.

Multiple elements of soil biodiversity drive ecosystem functions across biomes.

Abstract: The role of soil biodiversity in regulating multiple ecosystem functions is poorly understood, limiting our ability to predict how soil biodiversity loss might affect human wellbeing and ecosystem sustainability. Here, combining a global observational study with an experimental microcosm study, we provide evidence that soil biodiversity (bacteria, fungi, protists and invertebrates) is significantly and positively associated with multiple ecosystem functions. These functions include nutrient cycling, decomposition, plant production, and reduced potential for pathogenicity and belowground biological warfare. Our findings also reveal the context dependency of such relationships and the importance of the connectedness, biodiversity and nature of the globally distributed dominant phylotypes within the soil network in maintaining multiple functions. Moreover, our results suggest that the positive association between plant diversity and multifunctionality across biomes is indirectly driven by soil biodiversity. Together, our results provide insights into the importance of soil biodiversity for maintaining soil functionality locally and across biomes, as well as providing strong support for the inclusion of soil biodiversity in conservation and management programmes.

Global ecosystem thresholds driven by aridity

Abstract: Aridity, which is increasing worldwide because of climate change, affects the structure and functioning of dryland ecosystems. Whether aridification leads to gradual (versus abrupt) and systemic (versus specific) ecosystem changes is largely unknown. We investigated how 20 structural and functional ecosystem attributes respond to aridity in global drylands. Aridification led to systemic and abrupt changes in multiple ecosystem attributes. These changes occurred sequentially in three phases characterized by abrupt decays in plant productivity, soil fertility, and plant cover and richness at aridity values of 0.54, 0.7, and 0.8, respectively. More than 20% of the terrestrial surface will cross one or several of these thresholds by 2100, which calls for immediate actions to minimize the negative impacts of aridification on essential ecosystem services for the more than 2 billion people living in drylands.

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/ .

The proportion of soil-borne pathogens increases with warming at the global scale

Abstract: Understanding the present and future distribution of soil-borne plant pathogens is critical to supporting food and fibre production in a warmer world. Using data from a global field survey and a nine-year field experiment, we show that warmer temperatures increase the relative abundance of soil-borne potential fungal plant pathogens. Moreover, we provide a global atlas of these organisms along with future distribution projections under different climate change and land-use scenarios. These projections show an overall increase in the relative abundance of potential plant pathogens worldwide. This work advances our understanding of the global distribution of potential fungal plant pathogens and their sensitivity to ongoing climate and land-use changes, which is fundamental to reduce their incidence and impacts on terrestrial ecosystems globally. Plant pathogens threaten food security and ecosystem health. Projections of potential fungal plant pathogens under different warming and land-use scenarios indicate that warming temperatures under climate change will lead to increases in the relative abundance of such pathogens in most soils worldwide.

The global-scale distributions of soil protists and their contributions to belowground systems

Abstract: Protists are ubiquitous in soil, where they are key contributors to nutrient cycling and energy transfer. However, protists have received far less attention than other components of the soil microbiome. We used amplicon sequencing of soils from 180 locations across six continents to investigate the ecological preferences of protists and their functional contributions to belowground systems. We complemented these analyses with shotgun metagenomic sequencing of 46 soils to validate the identities of the more abundant protist lineages. We found that most soils are dominated by consumers, although parasites and phototrophs are particularly abundant in tropical and arid ecosystems, respectively. The best predictors of protist composition (primarily annual precipitation) are fundamentally distinct from those shaping bacterial and archaeal communities (namely, soil pH). Some protists and bacteria co-occur globally, highlighting the potential importance of these largely undescribed belowground interactions. Together, this study allowed us to identify the most abundant and ubiquitous protists living in soil, with our work providing a cross-ecosystem perspective on the factors structuring soil protist communities and their likely contributions to soil functioning.

Isotopic and in situ DRIFTS study of the CO2 methanation mechanism using Ni/CeO2 and Ni/Al2O3 catalysts

Abstract: The CO2 methanation mechanism was studied for Ni/CeO2 and Ni/Al2O3 catalysts. The higher methanation activity and selectivity of Ni/CeO2 is attributed to: i) Ni/CeO2 combines two types of active sites efficient for CO2 dissociation at the NiO-Ceria interface and for H2 dissociation on Ni0 particles; ii) water desorption is the slowest mechanism step, and, due to the high oxygen mobility throughout the ceria lattice, water is not necessarily formed on the same active sites that chemisorb CO2, i.e., the CO2 chemisorption sites are not blocked by water molecules; iii) the Ni/CeO2 surface does not accumulate carbon-containing species under reaction conditions, which allows faster chemisorption and dissociation of CO2. The Ni/Al2O3 catalyst handicaps are that all the steps of the mechanism take place on the same active sites, and the slow release of water and the accumulation of surface formates on these sites delay the chemisorption of further CO2 molecules.

Blind spots in global soil biodiversity and ecosystem function research.

Abstract: Soils harbor a substantial fraction of the world's biodiversity, contributing to many crucial ecosystem functions. It is thus essential to identify general macroecological patterns related to the distribution and functioning of soil organisms to support their conservation and consideration by governance. These macroecological analyses need to represent the diversity of environmental conditions that can be found worldwide. Here we identify and characterize existing environmental gaps in soil taxa and ecosystem functioning data across soil macroecological studies and 17,186 sampling sites across the globe. These data gaps include important spatial, environmental, taxonomic, and functional gaps, and an almost complete absence of temporally explicit data. We also identify the limitations of soil macroecological studies to explore general patterns in soil biodiversity-ecosystem functioning relationships, with only 0.3% of all sampling sites having both information about biodiversity and function, although with different taxonomic groups and functions at each site. Based on this information, we provide clear priorities to support and expand soil macroecological research.

Recent Trends in the Use of Pectin from Agro-Waste Residues as a Natural-Based Biopolymer for Food Packaging Applications.

Abstract: Regardless of the considerable progress in properties and versatility of synthetic polymers, their low biodegradability and lack of environmentally-friendly character remains a critical issue. Pectin is a natural-based polysaccharide contained in the cell walls of many plants allowing their growth and cell extension. This biopolymer can be extracted from plants and isolated as a bioplastic material with different applications, including food packaging. This review aims to present the latest research results regarding pectin, including the structure, different types, natural sources and potential use in several sectors, particularly in food packaging materials. Many researchers are currently working on a multitude of food and beverage industry applications related to pectin as well as combinations with other biopolymers to improve some key properties, such as antioxidant/antimicrobial performance and flexibility to obtain films. All these advances are covered in this review.

A Review on Deep Learning Techniques for Video Prediction

Abstract: The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We firstly define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions.

ParaCrawl: Web-Scale Acquisition of Parallel Corpora

Abstract: We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems.

Neuromarketing in Haute Cuisine Gastronomic Experiences

Abstract: Gastronomic experiences offer a set of stimuli that affect the customer's perception of chef-designed food. This empirical study aims to analyze the influence on the consumer, at a cerebral level, of the stimuli characteristic of a high-level gastronomic experience, in a Michelin starred restaurant. The presentation by the waiter or chef, the plate design, the dish served, the taste of food, interaction or moment in which the food is served are the variables analyzed. Through the use of neuromarketing techniques - galvanic skin response to register emotional arousal, eye tracking to identify where consumers look, and electroencephalography to interpret emotional reactions - combined with qualitative research technique (In-depth interviews with all consumers), in order to know the natural and suggested memories, the objective of this research is to determine the emotional impact of the variables analyzed against the actual taste of food, obtaining conclusions about each variable in overall experience and allowing the authors to propose a model of order design of dishes, designed by the chef, based on emotions and thereby achieving greater efficiency in results of the experience and the memory of it. Results indicate a favorable influence on emotions when the chef presents the food. Likewise, dishes with special presentation have a greater influence at the level of interest than conventional dishes. It is important to highlight that the levels of emotion and attention fall after the midway point of the experience, due to the duration of the experience. Therefore, the dishes do not have the same emotional impact, despite being as special as at the beginning of the experience.

Direct In Situ Raman Spectroscopic Evidence of Oxygen Reduction Reaction Intermediates at High-Index Pt(hkl) Surfaces.

Abstract: The study of the oxygen reduction reaction (ORR) at high-index Pt(hkl) single crystal surfaces has received considerable interest due to their well-ordered, typical atomic structures and superior c...

Insights into the Oxygen Vacancy Filling Mechanism in CuO/CeO2 Catalysts: A Key Step Toward High Selectivity in Preferential CO Oxidation

Abstract: The preferential CO oxidation (CO-PROX) reaction is paramount for the purification of reformate H2-rich streams, where CuO/CeO2 catalysts show promising opportunities. This work sheds light on the ...

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 paper 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 $97.72\% \pm 0.95 \%$, $86.90\% \pm 3.20\%$, $61.80\% \pm 5.49\%$ in severe, moderate and mild COVID-19 severity levels (Paper accepted for publication in Journal of Biomedical and Health Informatics). 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 this https URL.

The pervasive and multifaceted influence of biocrusts on water in the world's drylands.

Abstract: The capture and use of water are critically important in drylands, which collectively constitute Earth's largest biome. Drylands will likely experience lower and more unreliable rainfall as climatic conditions change over the next century. Dryland soils support a rich community of microphytic organisms (biocrusts), which are critically important because they regulate the delivery and retention of water. Yet despite their hydrological significance, a global synthesis of their effects on hydrology is lacking. We synthesized 2,997 observations from 109 publications to explore how biocrusts affected five hydrological processes (times to ponding and runoff, early [sorptivity] and final [infiltration] stages of water flow into soil, and the rate or volume of runoff) and two hydrological outcomes (moisture storage, sediment production). We found that increasing biocrust cover reduced the time for water to pond on the surface (-40%) and commence runoff (-33%), and reduced infiltration (-34%) and sediment production (-68%). Greater biocrust cover had no significant effect on sorptivity or runoff rate/amount, but increased moisture storage (+14%). Infiltration declined most (-56%) at fine scales, and moisture storage was greatest (+36%) at large scales. Effects of biocrust type (cyanobacteria, lichen, moss, mixed), soil texture (sand, loam, clay), and climatic zone (arid, semiarid, dry subhumid) were nuanced. Our synthesis provides novel insights into the magnitude, processes, and contexts of biocrust effects in drylands. This information is critical to improve our capacity to manage dwindling dryland water supplies as Earth becomes hotter and drier.

Magnetic Two-Dimensional Chromium Trihalides: A Theoretical Perspective.

Abstract: The discovery of ferromagnetic order in monolayer two-dimensional (2D) crystals has opened a new venue in the field of 2D materials. Two-dimensional magnets are not only interesting on their own, but their integration in van der Waals heterostructures allows for the observation of new and exotic effects in the ultrathin limit. The family of chromium trihalides, CrI3, CrBr3, and CrCl3, is so far the most studied among magnetic 2D crystals. In this Mini Review, we provide a perspective of the state of the art of the theoretical understanding of magnetic 2D trihalides, most of which will also be relevant for other 2D magnets, such as vanadium trihalides. We discuss both the well-established facts, such as the origin of the magnetic moment and magnetic anisotropy, and address as well open issues such as the nature of the anisotropic spin couplings and the magnitude of the magnon gap. Recent theoretical predictions on Moire magnets and magnetic skyrmions are also discussed. Finally, we give some prospects about the future interest of these materials and possible device applications.

Long‐term nitrogen loading alleviates phosphorus limitation in terrestrial ecosystems

Abstract: Increased human-derived nitrogen (N) deposition to terrestrial ecosystems has resulted in widespread phosphorus (P) limitation of net primary productivity. However, it remains unclear if and how N-induced P limitation varies over time. Soil extracellular phosphatases catalyze the hydrolysis of P from soil organic matter, an important adaptive mechanism for ecosystems to cope with N-induced P limitation. Here we show, using a meta-analysis of 140 studies and 668 observations worldwide, that N stimulation of soil phosphatase activity diminishes over time. Whereas short-term N loading (≤5 years) significantly increased soil phosphatase activity by 28%, long-term N loading had no significant effect. Nitrogen loading did not affect soil available P and total P content in either short- or long-term studies. Together, these results suggest that N-induced P limitation in ecosystems is alleviated in the long-term through the initial stimulation of soil phosphatase activity, thereby securing P supply to support plant growth. Our results suggest that increases in terrestrial carbon uptake due to ongoing anthropogenic N loading may be greater than previously thought.

The effects of tourism and globalization over environmental degradation in developed countries

Abstract: This paper focuses on long-term evidence on economic growth, international tourism, globalization, energy consumption and carbon dioxide (CO2) emissions in OECD countries for the period of 1994-2014. The empirical analysis reveals that climate change is magnified by energy use, tourism and economic growth. An inverted U-shaped relationship is also found between international tourism and CO2 emissions. The contribution of international tourism to climate change in the early stages of development is thus diminished by globalization in the later stages. In other words, globalization appears to reduce carbon emissions from international tourism. The empirical results provide additional arguments for shaping regulatory frameworks aimed at reversing the current energy mix in OECD countries by facilitating energy efficiency and promoting renewable sources.