The El Gordo galaxy cluster challenges {\Lambda}CDM for any plausible collision velocity

El Gordo (ACT-CL J0102-4915) is an extraordinarily large and bright galaxy cluster collision. In a previous study, we found that El Gordo is in $6.2\sigma$ tension with the $\Lambda$CDM standard model when assuming the nominal mass and infall velocity values from the hydrodynamical simulations of Zhang et al. ($M_{200} = 3.2 \times 10^{15} M_{\odot}$ and $V_{\textrm{infall}} = 2500~\textrm{km~s}^{-1}$, respectively). The recent weak lensing study of Kim et al. showed that the mass of El Gordo is actually $2.13^{+0.25}_{-0.23} \times 10^{15} M_{\odot}$. Here we explore the level of tension between El Gordo and $\Lambda$CDM for the new mass estimate, assuming several $V_{\textrm{infall}}$ values. We find that in order to reduce the tension below the $5\sigma$ level, the El Gordo subclusters should have $V_{\textrm{infall}} < 2300~\textrm{km~s}^{-1}$ ($V_{\textrm{infall}} < 1800~\textrm{km~s}^{-1}$ when considering the combined tension with the Bullet Cluster). To the best of our knowledge, the El Gordo hydrodynamical simulations conducted so far require $V_{\textrm{infall}} \geq 2500~\textrm{km~s}^{-1}$ to simultaneously reproduce its morphology and its high X-ray luminosity and temperature. We therefore conclude that El Gordo still poses a significant challenge to $\Lambda$CDM cosmology. Whether the properties of El Gordo can be reconciled with a lower $V_{\textrm{infall}}$ should be tested with new hydrodynamical simulations that explore different configurations of the interaction.Comment: 8 pages, 1 figure. Accepted for publication in The Astrophysical Journal in this for

PARATACHARDINA PSEUDOLOBATA (HEMIPTERA: COCCOIDEA: KERRIIDAE): A NEW INVASIVE SCALE INSECT IN PUERTO RICO

PARATACHARDINA PSEUDOLOBATA (HEMIPTERA: COCCOIDEA: KERRIIDAE): A NEW INVASIVE SCALE INSECT IN PUERTO RIC

The role of cyanoprokaryota in the rhizospheres of gypsophytes and effects of drought and water pulses on microbial functionality

In a Mediterranean environment plants are subject to water stress and lack of nutrients, among others. The adaptations that they can present are very varied and depend on the type of soil and the climate itself. In gypsiferous soils the adaptations of gypsophytes are numerous and unclear. Cyanoprokaryota had a relevant role in the rhizospheres of gypsophytes in drought conditions. We detect the responses to the availability of water in the rhizospheres of three gypsophytes and in non-rhizospheric soil during a summer drought and during spring. Water retention and water loss were studied. We were obtained the highest values in drought conditions due to the association of Cyanoprokaryota with the rhizospheres. The results are also explained by two water pulses that occurred before the samplings. Several parameters, whose values changed markedly due to the microbiological activation just after the drought and water pulses, are proposed as indicators of this activation: microbial biomass carbon and basal respiration rate, together with urease and protease. However, it was the dehydrogenase activity in spring that best reflected the microbiology associated with the carbon cycle, together with β-glucosidase. The interrelationships between carbon and nitrogen were shown through the indices: water soluble nitrogen and water soluble carbon. We propose three functional adaptation mechanisms of these plants associated with the Cyanoprokaryota in their rhizospheres and related to the water availability as determined by drought and water pulse effects. Financing: This study was funded by the Spanish National Government (CICYTCGL2009-12582-C02-02) and by the Valencian Autonomous Government (AICO/2019/258

A novel tree-based algorithm to discover seismic patterns in earthquake catalogs

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Junta de Andalucía P12-TIC-1728Instituto Ramón y Cajal (RYC) RYC-2012-1198

Experiential Learning with Social Action Entrepreneurs before and during COVID-19

Across the country, the struggles of universities for market-place relevancy and financial stability has worsened due to the shocks of the COVID-19 crisis. While institutional leaders are evaluating a variety of business models to determine the best options for operating in the “new normal” that will be both financially viable and safe for the campus community, professors are busy adapting their teaching approaches for mixed method delivery. We, interdisciplinary professors and students, argue that new experiential learning opportunities lead to personal growth and development and may be the key to enhancing students’ job readiness. Furthermore, these opportunities may lead to improved evaluations by critics of higher education and result in improved financial stability for universities. In this paper, we, learning partners, share the experiential learning opportunities we experienced while collaborating and working in a physical, mixed model, and 100% online social action entrepreneurship lab. During the chaos of the pandemic, we transitioned our program to fully online and successfully built a learning environment that was viewed as an oasis of inspiring and productive calm. It is our goal to inspire others to use our experiences as a blueprint to design and deliver similar opportunities at their institutions

The use of cerium compounds as antimicrobials for biomedical applications

Cerium and its derivatives have been used as remedies for wounds since the early 20th century. Cerium nitrate has attracted most attention in the treatment of deep burns, followed later by reports of its antimicrobial properties. Its ability to mimic and replace calcium is presumed to be a major mechanism of its beneficial action. However, despite some encouraging results, the overall data are somewhat confusing with seemingly the same compounds yielding opposing results. Despite this, cerium nitrate is currently used in wound treatment in combination with silver sulfadiazine as Flammacérium. Cerium oxide, especially in nanoparticle form (Nanoceria), has lately captured much interest due to its antibacterial properties mediated via oxidative stress, leading to an increase of published reports. The properties of Nanoceria depend on the synthesis method, their shape and size. Recently, the green synthesis route has gained a lot of interest as an alternative environmentally friendly method, resulting in production of effective antimicrobial and antifungal nanoparticles. Unfortunately, as is the case with antibiotics, emerging bacterial resistance against cerium-derived nanoparticles is a growing concern, especially in the case of bacterial biofilm. However, diverse strategies resulting from better understanding of the biology of cerium are promising. The aim of this paper is to present the progress to date in the use of cerium compounds as antimicrobials in clinical applications (in particular wound healing) and to provide an overview of the mechanisms of action of cerium at both the cellular and molecular level

A decision tree-based method for protein contact map prediction

In this paper, we focus on protein contact map prediction. We describe a method where contact maps are predicted using decision tree-based model. The algorithm includes the subsequence information between the couple of analyzed amino acids. In order to evaluate the method generalization capabilities, we carry out an experiment using 173 non-homologous proteins of known structures. Our results indicate that the method can assign protein contacts with an average accuracy of 0.34, superior to the 0.25 obtained by the FNETCSS method. This shows that our algorithm improves the accuracy with respect to the methods compared, especially with the increase of protein lengt

Short-Range Interactions and Decision Tree-Based Protein Contact Map Predictor

In this paper, we focus on protein contact map prediction, one of the most important intermediate steps of the protein folding prob lem. The objective of this research is to know how short-range interac tions can contribute to a system based on decision trees to learn about the correlation among the covalent structures of a protein residues. We propose a solution to predict protein contact maps that combines the use of decision trees with a new input codification for short-range in teractions. The method’s performance was very satisfactory, improving the accuracy instead using all information of the protein sequence. For a globulin data set the method can predict contacts with a maximal accu racy of 43%. The presented predictive model illustrates that short-range interactions play the predominant role in determining protein structur