Alcohol-based adsorption heat pumps using hydrophobic metal-organic frameworks

The building climatization and its influence on energy consumption have consequences on the environment due to the emission of greenhouse gases. Improving the efficiency of this sector is essential to reduce the effect on climate change. In recent years, the interest in porous materials in applications such as heat pumps has increased because of their promising potential. To assess the performance of adsorption heat pumps and cooling systems, here we discuss a multistep approach based on the processing of adsorption data combined with a thermodynamic model. The process provides properties of interest, such as the coefficient of performance, the working capacity, the specific heat or cooling effect, or the released heat upon adsorption and desorption cycles, and it also has the advantage of identifying the optimal conditions for each adsorbent-fluid pair. To test this method, we select several metal-organic frameworks that differ in topology, chemical composition, and pore size, which we validate with available experiments. Adsorption equilibrium curves were calculated using molecular simulations to describe the adsorption mechanisms of methanol and ethanol as working fluids in the selected adsorbents. Then, using a thermodynamic model we calculate the energetic properties combined with iterative algorithms that simultaneously vary all the required working conditions. We discuss the strong influence of operating temperatures on the performance of heat pump devices. Our findings point to the highly hydrophobic metal azolate framework MAF-6 as a very good candidate for heating and cooling applications for its high working capacity and excellent energy efficiency.</p

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features

The relationship between truncation and phosphorylation at the C-terminus of tau protein in the paired helical filaments of Alzheimer's disease

Acknowledgements: Authors want to express their gratitude to Dr. P. Davies (Albert Einstein College of Medicine, Bronx, NY, USA) and Lester I. Binder (NorthWestern, Chicago, IL, USA) for the generous gift of mAbs (TG-3, Alz-50, and MC1), and (TauC-3), respectively, and to M. en C. Ivan J. Galván-Mendoza for his support in confocal microscopy, and Ms. Maricarmen De Lorenz for her secretarial assistance. We also want to express our gratitude to the Mexican Families who donate the brain of their loved ones affected with Alzheimer's disease, and made possible our research. This work was financially supported by CONACyT grant, No. 142293 (For R.M).Peer reviewedPublisher PD

Changes in fire intensity have carry-over effects on plant responses after the next fire in southern California chaparral

n/

Identification of latexin by a proteomic analysis in rat normal articular cartilage

<p>Abstract</p> <p>Background</p> <p>Osteoarthritis (OA) is characterized by degeneration of articular cartilage. Animal models of OA induced are a widely used tool in the study of the pathogenesis of disease. Several proteomic techniques for selective extraction of proteins have provided protein profiles of chondrocytes and secretory patterns in normal and osteoarthritic cartilage, including the discovery of new and promising biomarkers. In this proteomic analysis to study several proteins from rat normal articular cartilage, two-dimensional electrophoresis and mass spectrometry (MS) were used. Interestingly, latexin (LXN) was found. Using an immunohistochemical technique, it was possible to determine its localization within the chondrocytes from normal and osteoarthritic articular cartilage.</p> <p>Results</p> <p>In this study, 147 proteins were visualized, and 47 proteins were identified by MS. A significant proportion of proteins are involved in metabolic processes and energy (32%), as well as participating in different biological functions including structural organization (19%), signal transduction and molecular signaling (11%), redox homeostasis (9%), transcription and protein synthesis (6%), and transport (6%). The identified proteins were assigned to one or more subcellular compartments.</p> <p>Among the identified proteins, we found some proteins already recognized in other studies such as OA-associated proteins. Interestingly, we identified LXN, an inhibitor of mammalian carboxypeptidases, which had not been described in articular cartilage. Immunolabeling assays for LXN showed a granular distribution pattern in the cytoplasm of most chondrocytes of the middle, deep and calcified zones of normal articular cartilage as well as in subchondral bone. In osteoarthritic cartilage, LXN was observed in superficial and deep zones.</p> <p>Conclusions</p> <p>This study provides the first proteomic analysis of normal articular cartilage of rat. We identified LXN, whose location was demonstrated by immunolabeling in the chondrocytes from the middle, deep and calcified zones of normal articular cartilage, and superficial and deep zones of osteoarthritic cartilage.</p

Atomistic insights into the degradation of halide perovskites: a reactive force field molecular dynamics study

Halide perovskites make efficient solar cells due to their exceptional optoelectronic properties, but suffer from several stability issues. The characterization of the degradation processes is challenging because of the limitations in the spatio-temporal resolution in experiments and the absence of efficient computational methods to study the reactive processes. Here, we present the first effort in developing reactive force fields for large scale molecular dynamics simulations of the phase instability and the defect-induced degradation reactions in inorganic CsPbI$_{3}$. We find that the phase transitions are driven by a combination of the anharmonicity of the perovskite lattice with the thermal entropy. At relatively low temperatures, the Cs cations tend to move away from the preferential positions with good contacts with the surrounding metal halide framework, potentially causing its conversion to a non-perovskite phase. Our simulations of defective structures reveal that, although both iodine vacancies and interstitials are very mobile in the perovskite lattice, the vacancies have a detrimental effect on the stability, initiating the decomposition reactions of perovskites to PbI$_{2}$. Our work puts ReaxFF forward as an effective computational framework to study reactive processes in halide perovskites.Comment: 11 pages, 6 figure

NALP1 is a transcriptional target for cAMP-response-element-binding protein (CREB) in myeloid leukaemia cells

NALP1 (also called DEFCAP, NAC, CARD7) has been shown to play a central role in the activation of inflammatory caspases and processing of pro-IL1β (pro-interleukin-1β). Previous studies showed that NALP1 is highly expressed in peripheral blood mononuclear cells. In the present study, we report that expression of NALP1 is absent from CD34+ haematopoietic blast cells, and its levels are upregulated upon differentiation of CD34+ cells into granulocytes and to a lesser extent into monocytes. In peripheral blood cells, the highest levels of NALP1 were observed in CD3+ (T-lymphocytes), CD15+ (granulocytes) and CD14+ (monocytes) cell populations. Notably, the expression of NALP1 was significantly increased in the bone marrow blast cell population of some patients with acute leukaemia, but not among tissue samples from thyroid and renal cancer. A search for consensus sites within the NALP1 promoter revealed a sequence for CREB (cAMP-response-element-binding protein) that was required for transcriptional activity. Moreover, treatment of TF1 myeloid leukaemia cells with protein kinase C and protein kinase A activators induced CREB phosphorylation and upregulated the mRNA and protein levels of NALP1. Conversely, ectopic expression of a dominant negative form of CREB in TF1 cells blocked the transcriptional activity of the NALP1 promoter and significantly reduced the expression of NALP1. Thus NALP1 is transcriptionally regulated by CREB in myeloid cells, a mechanism that may contribute to modulate the response of these cells to pro-inflammatory stimuli

Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model

This is a pre-copyedited, author-produced version of an article accepted for publication in [insert journal title] following peer review. The version of record [insert complete citation information here] is available online at: xxxxxxx [insert URL and DOI of the article on the OUP website].[EN] Membrane computing is a natural computing procedure inspired in the compartmental structure of living cells. This approach allows mimicking the complex structure of biological processes, and, when applied to transmissible diseases, can simulate a virtual `epidemic¿ based on interactions between elements within the computational model according to established conditions. General and focused vaccination strategies for controlling SARS-Cov-2 epidemics have been simulated for 2.3 years fromthe emergence of the epidemic in a hypothetical town of 10320 inhabitants in a country with mean European demographics where COVID-19 is imported. The age and immunological-response groups of the hosts and their lifestyles were minutely examined. The duration of natural, acquired immunity influenced the results; the shorter the duration, the more endemic the process, resulting in higher mortality, particularly among elderly individuals. During epidemic valleys between waves, the proportion of infected patients belonging to symptomatic groups (mostly elderly) increased in the total population, a population that largely benefits from standard double vaccination, particularly with boosters. There was no clear difference when comparing booster shots provided at 4 or 6 months after standard doubledose vaccination. Vaccines even of moderate efficacy (short-term protection) were effective in decreasing the number of symptomatic cases. Generalized vaccination of the entire population (all ages) added little benefit to overall mortality rates, and this situation also applied for generalized lockdowns. Elderly-only vaccination and lockdowns, even without general interventions directed to reduce population transmission, is sufficient for dramatically reducing mortality.This work was partially fund by the Fundación del Conocimiento Madri+d from the Madrid' Autonomous Community through a research contract (AVATAR-EPAMEC) within the Health Start Plus Program, promoted by the Carlos III Health Research Institute (ITEMAS), Ministry of Science, Innovation and Universities of Spain.Campos Frances, M.; Sempere Luna, JM.; Galán, JC.; Moya, A.; Cantón, R.; Llorens, C.; Baquero, F. (2022). Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model. microLife. 3:1-13. https://doi.org/10.1093/femsml/uqac018113

The Gln241His polymorphism in the carbohydrate response element binding protein (MLXIPL) gene

Comunicació presentada com a pòster a European Association of Human Genetics Conference, May 23-26, 2009, Vien

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

[EN] Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.MC and FB were sponsored by the Projects COV20 00067 of the Program SARS-COV-2 and COVID-19 infection of the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovacion of Spain, CB06/02/0053 of the Centro de Investigacion Biom edica en Red de Epidemiolog¿a y Salud Publica (CIBERESP), and the Regional Government of Madrid (InGeMICS-B2017/BMD-3691). For JCG, this study was partially founded by the Autonomous Community of Madrid, Spain (COVID-19 Grant, 2020) and the Ramon y Cajal Institute for Health Research (IRYCIS), Madrid, Spain. For AM, this study was supported by grants from the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00), the government of Valencia (project Prometeo/2018/A/133) and cofinanced by the European Regional Development Fund (ERDF).Campos Frances, M.; Sempere Luna, JM.; Galán, JC.; Moya, A.; Llorens, C.; De-Los-Angeles, C.; Baquero-Artigao, F.... (2021). Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model. microLife. 2:1-14. https://doi.org/10.1093/femsml/uqab011S114