University of Talca

The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein-ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein-ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein-ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.

Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities.

Industry 4.0 technologies provide critical perspectives for future innovation and business growth. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, Machine Learning (ML), and other advanced upcoming technologies are being used to implement Industry 4.0. This paper explores how Industry 4.0 technologies help create a sustainable environment in manufacturing and other industries. Industry 4.0 technologies and the crucial interrelationships through advanced technologies should impact the environment positively. In the age of Industry 4.0, manufacturing is tightly interlinked with information and communication systems, making it more scalable, competitive, and knowledgeable. Industry 4.0 provides a range of principles, instructions, and technology for constructing new and existing factories, enabling consumers to choose different models at production rates with scalable robotics, information, and communications technology. This paper aims to study the significant benefits of Industry 4.0 for sustainable manufacturing and identifies tools and elements of Industry 4.0 for developing environmental sustainability. This literature review-based research is undertaken to identify how Industry 4.0 technologies can help to improve environmental sustainability. It also details the capabilities of Industry 4.0 in dealing with environmental aspects. Twenty major applications of Industry 4.0 to create a sustainable environment are identified and discussed. Thus, it gives a better understanding of the production environment, the supply chains, the delivery chains, and market results. Overall, Industry 4.0 technology seems environmentally sustainable while manufacturing goods with better efficiency and reducing resource consumption.

The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.

Molecular docking is the most frequently used computational method for studying the interactions between organic molecules and biological macromolecules. In this context, docking allows predicting the preferred pose of a ligand inside a receptor binding site. However, the selection of the “best” solution is not a trivial task, despite the widely accepted selection criterion that the best pose corresponds to the best energy score. Here, several rigid-target docking methods were evaluated on the same dataset with respect to their ability to reproduce crystallographic binding orientations, to test if the best energy score is a reliable criterion for selecting the best solution. For this, two experiments were performed: (A) to reconstruct the ligand-receptor complex by performing docking of the ligand in its own crystal structure receptor (defined as self-docking), and (B) to reconstruct the ligand-receptor complex by performing docking of the ligand in a crystal structure receptor that contains other ligand (defined as cross-docking). Root-mean square deviation (RMSD) was used to evaluate how different the obtained docking orientation is from the corresponding co-crystallized pose of the same ligand molecule. We found that docking score function is capable of predicting crystallographic binding orientations, but the best ranked solution according to the docking energy is not always the pose that reproduces the experimental binding orientation. This happened when self-docking was achieved, but it was critical in cross-docking. Taking into account that docking is typically used with predictive purposes, during cross-docking experiments, our results indicate that the best energy score is not a reliable criterion to select the best solution in common docking applications. It is strongly recommended to choose the best docking solution according to the scoring function along with additional structural criteria described for analogue ligands to assure the selection of a correct docking solution.

is essential tool to elucidate the phenomenon. The structure-based virtual screening computational approach will be used to filter the best drugs from the literature, the investigate the structural variation of COVID-19 with the interaction of the best inhibitor is a fundamental step to design new drugs and vaccines which can combat the coronavirus. This mini-review will address novel coronavirus structure, mechanism of action, and trial test of antiviral drugs in the lab and patients with COVID-19.

Abstract Climate warming is considered to be among the most serious of anthropogenic stresses to the environment, because it not only has direct effects on biodiversity, but it also exacerbates the harmful effects of other human‐mediated threats. The associated consequences are potentially severe, particularly in terms of threats to species preservation, as well as in the preservation of an array of ecosystem services provided by biodiversity. Among the most affected groups of animals are insects—central components of many ecosystems—for which climate change has pervasive effects from individuals to communities. In this contribution to the scientists' warning series, we summarize the effect of the gradual global surface temperature increase on insects, in terms of physiology, behavior, phenology, distribution, and species interactions, as well as the effect of increased frequency and duration of extreme events such as hot and cold spells, fires, droughts, and floods on these parameters. We warn that, if no action is taken to better understand and reduce the action of climate change on insects, we will drastically reduce our ability to build a sustainable future based on healthy, functional ecosystems. We discuss perspectives on relevant ways to conserve insects in the face of climate change, and we offer several key recommendations on management approaches that can be adopted, on policies that should be pursued, and on the involvement of the general public in the protection effort.

The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.

Antimicrobial resistance (AMR) is one of the biggest threats to the environment and health. AMR rapidly invalidates conventional antibiotics, and antimicrobial nanomaterials have been increasingly explored as alternatives. Interestingly, several antimicrobial nanomaterials show AMR-independent antimicrobial effects without detectable new resistance and have therefore been suggested to prevent AMR evolution. In contrast, some are found to trigger the evolution of AMR. Given these seemingly conflicting findings, a timely discussion of the two faces of antimicrobial nanomaterials is urgently needed. This review systematically compares the killing mechanisms and structure-activity relationships of antibiotics and antimicrobial nanomaterials. We then focus on nano-microbe interactions to elucidate the impacts of molecular initiating events on AMR evolution. Finally, we provide an outlook on future antimicrobial nanomaterials and propose design principles for the prevention of AMR evolution.

Abstract For Abstract see ChemInform Abstract in Full Text.

BACKGROUND AND AIMS: The scope of this working group was to review (1) ecological interactions at the dental biofilm in health and disease, (2) the role of microbial communities in the pathogenesis of periodontitis and caries, and (3) the innate host response in caries and periodontal diseases. RESULTS AND CONCLUSIONS: A health-associated biofilm includes genera such as Neisseria, Streptococcus, Actinomyces, Veillonella and Granulicatella. Microorganisms associated with both caries and periodontal diseases are metabolically highly specialized and organized as multispecies microbial biofilms. Progression of these diseases involves multiple microbial interactions driven by different stressors. In caries, the exposure of dental biofilms to dietary sugars and their fermentation to organic acids results in increasing proportions of acidogenic and aciduric species. In gingivitis, plaque accumulation at the gingival margin leads to inflammation and increasing proportions of proteolytic and often obligately anaerobic species. The natural mucosal barriers and saliva are the main innate defence mechanisms against soft tissue bacterial invasion. Similarly, enamel and dentin are important hard tissue barriers to the caries process. Given that the present state of knowledge suggests that the aetiologies of caries and periodontal diseases are mutually independent, the elements of innate immunity that appear to contribute to resistance to both are somewhat coincidental.

We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs , where nodes are connected by directed, labelled edges, and property graphs , where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions . We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter, we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher, and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher, and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.

Grazing represents the most extensive use of land worldwide. Yet its impacts on ecosystem services remain uncertain because pervasive interactions between grazing pressure, climate, soil properties, and biodiversity may occur but have never been addressed simultaneously. Using a standardized survey at 98 sites across six continents, we show that interactions between grazing pressure, climate, soil, and biodiversity are critical to explain the delivery of fundamental ecosystem services across drylands worldwide. Increasing grazing pressure reduced ecosystem service delivery in warmer and species-poor drylands, whereas positive effects of grazing were observed in colder and species-rich areas. Considering interactions between grazing and local abiotic and biotic factors is key for understanding the fate of dryland ecosystems under climate change and increasing human pressure.

Purpose In recent years, circular economy (CE) has come to prominence as an alternative to the classic approach of “make-use-dispose”. How companies can exploit the opportunities of CE to position themselves better are not well articulated in the literature. The purpose of this paper, therefore, is to identify the barriers and opportunities of CE in the manufacturing sector through a socio-political, economic, legal and environmental perspective. Design/methodology/approach The study adopts a positivist approach, which is deductive in nature. A survey questionnaire was designed and distributed to manufacturing companies operating in the UK and EU. The study used FAME database and social networking platform LinkedIn to identify manufacturing companies. More than 200+ companies were approached for this study and data collection lasted over two months. Findings The study provides a comprehensive review of the CE literature and identifies a number of barriers and opportunities to CE implementation from a socio-political, economic, legal and environmental perspective. The findings highlight key barriers, opportunities and benefits of CE for the manufacturing industries operating in the UK and EU. Research limitations/implications The findings are limited to 63 responses from the survey questionnaire distributed to manufacturing companies in the UK and EU. The present study aims to equip manufacturers with necessary understanding of the key opportunities and barriers to address the challenges encountered during the implementation of CE. Originality/value This study adds to the limited empirical literature on CE barriers and opportunities to manufacturing organisations operating in the UK and EU. The paper also identifies barriers and opportunities of CE from a socio-political, economic, legal and environmental lens.

Metabolic syndrome is estimated to affect more than one in five adults, and its prevalence is growing in the adult and pediatric populations. The most widely recognized metabolic risk factors are atherogenic dyslipidemia, elevated blood pressure, and elevated plasma glucose. Individuals with these characteristics commonly manifest a prothrombotic state and a proinflammatory state as well. Peroxisome proliferator-activated receptors (PPARs) may serve as potential therapeutic targets for treating the metabolic syndrome and its related risk factors. The PPARs are transcriptional factors belonging to the ligand-activated nuclear receptor superfamily. So far, three isoforms of PPARs have been identified, namely, PPAR- α, PPAR-β/δ, and PPAR-γ. Various endogenous and exogenous ligands of PPARs have been identified. PPAR- α and PPAR- γ are mainly involved in regulating lipid metabolism, insulin sensitivity, and glucose homeostasis, and their agonists are used in the treatment of hyperlipidemia and T2DM. Whereas PPAR- β / δ function is to regulate lipid metabolism, glucose homeostasis, anti-inflammation, and fatty acid oxidation and its agonists are used in the treatment of metabolic syndrome and cardiovascular diseases. This review mainly focuses on the biological role of PPARs in gene regulation and metabolic diseases, with particular focus on the therapeutic potential of PPAR modulators in the treatment of thrombosis.

On-farm agricultural innovation through incorporation of new technologies and practices requires access to resources such as knowledge, financial resources, training, and even emotional support, all of which require the support of different actors such as peers, advisors, and researchers. The literature has explored the support networks that farmers use and the overall importance ranking of different support actors, but it has not looked in detail at how these networks may differ for different farmers. This study fills this gap by looking at farmer support network configurations through the lens of the social capital available to them in such configurations. Using a Chilean fruit-farmer case, we examine how different types of social capital (bonding, bridging, and linking) are used to achieve what has been called ‘ambidexterity’. Ambidexterity implies both that open networks (based on linking and bridging social capital) are used to explore and access new knowledge and resources, and that closed networks (based on bonding social capital) are used to successfully implement and exploit new technologies and practices. Our findings show that farmers use all types of social capital – bonding, bridging, and linking – in their support networks, but that they have different configurations, five in this study. These configurations are based on personal motivations, innovation objectives, and resource endowments. Despite the different network configurations and types of social capital – which may be more balanced or less balanced in light of ambidexterity – farmers may achieve the same ambitions and type of innovations. A main theoretical implication is that the configuration of support networks is thus not a one-size-fits-all where each farmer's ranking of support actors for on-farm innovation is the same. This nuances earlier work and calls for more attention to a better understanding of how each support network configuration responds to a certain logic, and hence cannot be identified as superior or inferior.

This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.

Responding to a technical and methodical problem that allows to identify some thematic or intellectual orientation in a means of academic dissemination, the article proposes a theoretical-methodological support, which contributes towards the procedural elaboration of an analysis of thematic and qualitative content for these investigative purposes. The proposal can be applied in studies where it is feasible to constitute an information trajectory longitudinally, expressed in scientific production format, as periodical journals, conference proceedings, library catalogs, etc. The foregoing, to the extent that there is multidisciplinary wealth or a certain thematic diversity contained in these media. In empirical terms, the theoretical-methodological proposal was applied in the Universum journal, that after 30 years of editing, its genesis is characterized by a miscellaneous composition, determining a transit through diverse thematic of study until being indexed scientifically, fact that led to his affiliation to the humanities and social sciences area. The proposal becomes a valid procedure to constitute the thematic orientation of a journal. In light of the results, it is concluded that Universum is predominantly oriented towards four central labels, first a dimension of humanism and letters; secondly, a dimension of social, political and cultural theory; continuing with a dimension in economic development and administration; to end with history of ideas. Finally, the qualitative procedures applied, contribute technically to the construction of labels / dimensions, which once systematized, categorized and re-categorized, give an account of a thematic orientation in this diffusion medium.

Members of the abscisic acid-responsive element binding protein (AREB)/abscisic acid-responsive element binding factor (ABF) subfamily of basic leucine zipper (bZIP) transcription factors have been implicated in abscisic acid (ABA) and abiotic stress responses in plants. Here we describe two members identified in cultivated tomato (Solanum lycopersicum), named SlAREB1 and SlAREB2. Expression of SlAREB1 and SlAREB2 is induced by drought and salinity in both leaves and root tissues, although that of SlAREB1 was more affected. In stress assays, SlAREB1-overexpressing transgenic tomato plants showed increased tolerance to salt and water stress compared to wild-type and SlAREB1-down-regulating transgenic plants, as assessed by physiological parameters such as relative water content (RWC), chlorophyll fluorescence and damage by lipoperoxidation. In order to identify SlAREB1 target genes responsible for the enhanced tolerance, microarray and cDNA-amplified fragment length polymorphism (AFLP) analyses were performed. Genes encoding oxidative stress-related proteins, lipid transfer proteins (LTPs), transcription regulators and late embryogenesis abundant proteins were found among the up-regulated genes in SlAREB1-overexpressing lines, especially in aerial tissue. Notably, several genes encoding defence proteins associated with responses to biotic stress (e.g. pathogenesis-related proteins, protease inhibitors, and catabolic enzymes) were also up-regulated by SlAREB1 overexpression, suggesting that this bZIP transcription factor is involved in ABA signals that participate in abiotic stress and possibly in response to pathogens.

We consider an action for an Abelian gauge field for which the density is given by a power of the Maxwell Lagrangian. In $d$ spacetime dimensions this action is shown to enjoy conformal invariance if the power is chosen as $d/4$. We take advantage of this conformal invariance to derive black hole solutions electrically charged with a purely radial electric field. Since we are considering a power of the Maxwell density, the black hole solutions exist only for dimensions which are multiples of four. The expression for the electric field does not depend on the dimension and corresponds to the four-dimensional Reissner-Nordstr\"om field. Using the Hamiltonian action we identify the mass and the electric charge of these black hole solutions.