Green coffee based CO2 adsorbent with high performance in postcombustion conditions

An environmentally friendly and low cost adsorbent, PPC (patent application filed González (2013)), produced from an abundant residue from the food industry, coffee grounds, is presented and evaluated as CO2 adsorbent in postcombustion conditions. PPC is a high bulk density pelletized carbon with adequate properties for its use in fixed-bed adsorption applications. The equilibrium capacity for CO2 at low partial pressures, relevant for the postcombustion case, in the 25–50 °C temperature range is superior to that of reference carbons, both in mass and volume basis. PPC presents equilibrium selectivity for CO2 over N2, with CO2/N2 equilibrium separation factor values of 15–25 at 50 °C and 130 kPa for CO2 concentrations between 9% and 31%. Moreover, it presents fast adsorption kinetics, which makes it a good candidate for rapid swing adsorption cycles. Different VSA cycle configurations were carried out at 50 °C in the fixed-bed adsorption unit to evaluate the performance of the adsorbent in cyclic operation. The adsorbent did not show any sign of deactivation over extended operation.Work carried out with financial support from the Spanish MINECO (Project ENE2011-23467), co-financed by the European Regional Development Fund (ERDF). M.G.P. acknowledges funding from the CSIC (JAE-Doc program), and A.S.G. acknowledges a contract from the MINECO (FPI program); both programs are co-financed by the European Social Fund.Peer reviewe

Procedimiento de obtención de un adsorbente que utiliza residuos de café postconsumo y su utilización para la adsorción de CO2

La presente invención se refiere a un procedimiento de obtención de un material adsorbente con capacidad para adsorber CO2 , que utiliza residuos de café postconsumo. El procedimiento de obtención comprende el secado del residuo, su conformación, en ausencia de ligantes, y su activación térmica en presencia de un agente activante, que preferentemente es CO2 . Las características texturales y de densidad del material obtenido por el procedimiento de la invención, que también se protege, lo convierten en un candidato ideal para la adsorción de CO2 , y preferentemente en la captura de CO2 postcombustión.Peer reviewedConsejo Superior de Investigaciones CientíficasR Informe sobre el estado de la técnica publicado separadament

Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages

Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (tabea) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (nlp) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous nlp nor online Machine Learning approaches to tabea.Xunta de Galicia | Ref. ED481B-2021-118Xunta de Galicia | Ref. ED481B-2022-093Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Encapsulation of the Antistaphylococcal Endolysin LysRODI in pH-Sensitive Liposomes

© 2020 by the authors.Phage lysins are promising new therapeutics against multidrug-resistant bacteria. These so-called enzybiotics offer, amongst their most notable advantages, high target specificity and low resistance development. Moreover, there are numerous recent and ongoing studies aimed at demonstrating the efficacy and safety of endolysins in animal models or even in clinical trials. Nonetheless, as is the case for other antimicrobials, it is important to assess potential strategies that may broaden their potential applications or improve their stability. Encapsulation, for instance, has given very good results for some antibiotics. This study sought to evaluate the feasibility of encapsulating an endolysin against the opportunistic human pathogen Staphylococcus aureus, one of the most problematic bacteria in the context of the current antibiotic resistance crisis. Endolysin LysRODI has antimicrobial activity against many S. aureus strains from different sources, including methicillin-resistant S. aureus (MRSA) isolates. Here, this protein was encapsulated in pH-sensitive liposomes with an efficacy of approximately 47%, retaining its activity after being released from the nanocapsules. Additionally, the encapsulated endolysin effectively reduced S. aureus cell counts by > 2log units in both planktonic cultures and biofilms upon incubation at pH 5. These results demonstrate the viability of LysRODI encapsulation in liposomes for its targeted delivery under mild acidic conditionsThis study was funded by grants AGL2015-65673-R (MINECO/FEDER, UE), EU ANIWHA ERA-NET (BLAAT ID: 67)/PCIN-2017-001 (AEI/FEDER, UE), Proyecto Intramural CSIC201670E040, Proyecto Intramural CSIC 201770E016. IDI/2018/000119 (Program of Science, Technology and Innovation 2018-2020 and FEDER EU funds, Principado de Asturias, Spain). S.P. has a postdoctoral fellowship CONACYT (México)Peer reviewe

Biodiversity of Bacteriocin-Producing Lactic Acid Bacteria from Mexican Regional Cheeses and their Contribution to Milk Fermentation

The aim of this work was to examine the biodiversity of bacteriocin-producing lactic acid bacteria from homemade cheeses produced in Veracruz (México) and assess their contribution as adjunct cultures in dairy products. Ninety-three presumptive bacteriocinogenic strains were detected by direct antagonism assays and 29 of them were active against Enterococcus faecalis NRRL-B537, Listeria innocua 062 AST, or Listeria monocytogenes ATCC19115 by the well diffusion test using cell-free supernatants, adjusted to pH 6.0 to exclude inhibition by organic acids. Positive isolates were identified by partial sequencing of the 16s rDNA as Pediococcus acidilactici (four isolates), Enterococcus faecium (17 isolates), Lactobacillus plantarum (six isolates) and Lactobacillus fermentum (two isolates). RAPD-PCR discriminated seven groups with a 50% similarity and revealed the presence of the same isolates. The coding genes for the synthesis of plantaricin EF, plantaricin JK, plantaricin N, plantaricin NC8 and the inducing peptide plantaricin A were detected by PCR in L. plantarum. Similarly, enterocin P and pediocin PA-1 genes were amplified from Enterococcus and Pediococcus genomic DNA, respectively. Overall, co-culturing of bacteriocin producing Lactobacillus and Pediococcus strains with the dairy starter Lactococcus lactis IPLA947 did not interfere with milk acidification. Lactose consumption, acidification rate and production of lactic acid were unchanged. Nonetheless, higher levels of acetic acid, ethanol and succinic acid were detected depending on the strain. Our results demonstrate the diversity of bacteriocinogenic species in homemade Mexican cheeses which may be used as adjunct cultures to enhancing safety of this well-appreciated cheese while providing a richer range of metabolites.This work was supported by Tecnológico Nacional de México (5486.14.15-P), the Mexican National Council for Science and Technology (Consejo Nacional de Ciencia y Tecnología -CONACYT) and partially by the Ministry of Economy and Competitiveness of Spain (MINECO) through grant BIO2013-46266-R. Funding by GRUPIN14-139 Plan de Ciencia, Tecnología e Innovación 2013-2017 (Principado de Asturias, Spain), supported by FEDER EU funds, is also acknowledged. S. Portilla-Vázquez held a CONACYT fellowship as well as an i-COOP mobility grant COOPA20015 funded by Consejo Superior de Investigaciones Científicas (CSIC), Spain.Peer reviewe

Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation

Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. They are typically written by market experts who describe stock market events within the context of social, economic and political change. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (NLP) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (LDA) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. Our solution outperformed a rule-based baseline system. We created an experimental data set composed of 2,158 financial news items that were manually labelled by NLP researchers to evaluate our solution. Inter-agreement Alpha-reliability and accuracy values, and ROUGE-L results endorse its potential as a valuable tool for busy investors. The ROUGE-L values for the identification of relevant text and predictions/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with LDA to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text. Our solution may have compelling applications in the financial field, including the possibility of extracting relevant statements on investment strategies to analyse authors’ reputations.Universidade de Vigo/CISUGXunta de Galicia | Ref. ED481B-2021-118Xunta de Galicia | Ref. ED481B-2022-09