The β3 adrenergic receptor is raising as an important drug target for the treatment of pathologies such as diabetes, obesity, depression, and cardiac diseases among others. Several attempts to obtain selective and high affinity ligands have been made. Currently, Mirabegron is the only available drug on the market that targets this receptor approved for the treatment of overactive bladder. However, the FDA (Food and Drug Administration) in USA and the MHRA (Medicines and Healthcare products Regulatory Agency) in UK have made reports of potentially life-threatening side effects associated with the administration of Mirabegron, casting doubts on the continuity of this compound. Therefore, it is of utmost importance to gather information for the rational design and synthesis of new β3 adrenergic ligands. Herein, we present the first combined 2D-QSAR (two-dimensional Quantitative Structure-Activity Relationship) and 3D-QSAR/CoMSIA (three-dimensional Quantitative Structure-Activity Relationship/Comparative Molecular Similarity Index Analysis) study on a series of potent β3 adrenergic agonists of indole-alkylamine structure. We found a series of changes that can be made in the steric, hydrogen-bond donor and acceptor, lipophilicity and molar refractivity properties of the compounds to generate new promising molecules. Finally, based on our analysis, a summary and a regiospecific description of the requirements for improving β3 adrenergic activity is given.

The existence of mutually correlated thin and rotating planes of satellite galaxies around both the Milky Way (MW) and Andromeda (M31) calls for an explanation. Previous work in Milgromian dynamics (MOND) indicated that a past MW–M31 encounter might have led to the formation of these satellite planes. We perform the first-ever hydrodynamical MOND simulation of the Local Group using phantom of ramses. We show that an MW–M31 encounter at z ≈ 1, with a perigalactic distance of about 80 kpc, can yield two disc galaxies at z = 0 oriented similarly to the observed galactic discs and separated similarly to the observed M31 distance. Importantly, the tidal debris are distributed in phase space similarly to the observed MW and M31 satellite planes, with the correct preferred orbital pole for both. The MW–M31 orbital geometry is consistent with the presently observed M31 proper motion despite this not being considered as a constraint when exploring the parameter space. The mass of the tidal debris around the MW and M31 at z = 0 compare well with the mass observed in their satellite systems. The remnant discs of the two galaxies have realistic radial scale lengths and velocity dispersions, and the simulation naturally produces a much hotter stellar disc in M31 than in the MW. However, reconciling this scenario with the ages of stellar populations in satellite galaxies would require that a higher fraction of stars previously formed in the outskirts of the progenitors ended up within the tidal debris, or that the MW–M31 interaction occurred at z > 1.

In some application fields, series are affected by two different types of effects: abrupt changes (or change-points) and functional effects. We propose here a Bayesian approach that allows us to estimate these two parts. Here, the underlying piecewise-constant part (associated to the abrupt changes) is expressed as the product of a lower triangular matrix by a sparse vector and the functional part as a linear combination of functions from a large dictionary where we want to select the relevant ones. This problem can thus lead to a global sparse estimation and a stochastic search variable selection approach is used to this end. The performance of our proposed method is assessed using simulation experiments. Applications to three real datasets from geodesy, agronomy and economy fields are also presented.

Exchange rates are determined by factors such as interest rates, political stability, confidence, the current account on balance of payments, government intervention, economic growth and relative inflation rates, among other variables. In October 2019, an increased climate of citizen discontent with current social policies resulted in a series of massive protests that ignited important political changes in Chile. This event along with the global COVID-19 pandemic were two major factors that affected the value of the US dollar and produced sudden changes in the typically stable USD/CLP (Chilean Peso) exchange rate. In this paper, we use a Bayesian approach to detect and locate change points in the currency exchange rate process in order to identify and relate these points with the important dates related to the events described above. The implemented method can successfully detect the onset of the social protests, the beginning of the COVID-19 pandemic in Chile and the economic reactivation in the US and Europe. In addition, we evaluate the performance of the proposed MCMC algorithms using a simulation study implemented in Python and R.

An improved high-resolution and deep $A_{K_{s}}$ foreground dust extinction map is presented for the Galactic disc area within 295° ≲ l ≲ 350°, −1.0° ≲ b ≲ +1.0°. At some longitudes the map reaches up to |b| ∼ 2.25°, for a total of ∼148 deg2. The map was constructed via the Rayleigh–Jeans colour excess (RJCE) technique based on deep near-infrared (NIR) and mid-infrared (MIR) photometry. The new extinction map features a maximum bin size of 1 arcmin, and relies on NIR observations from the Two Micron All-Sky Survey (2MASS) and new data from ESO’s Vista Variables in the Vía Láctea (VVV) survey, in concert with MIR observations from the Galactic Legacy Infrared Mid-Plane Survey Extraordinaire. The VVV photometry penetrates ∼4 mag fainter than 2MASS, and provides enhanced sampling of the underlying stellar populations in this heavily obscured region. Consequently, the new results supersede existing RJCE maps tied solely to brighter photometry, revealing a systematic underestimation of extinction in prior work that was based on shallower data. The new high-resolution and large-scale extinction map presented here is readily available to the community through a web query interface.

Hot subdwarf B (sdB) stars are evolved, subluminous, helium-burning stars that most likely form when red giant stars loose their hydrogen envelope via interactions with close companions. They play an important role in our understanding of binary evolution, stellar atmospheres, and interiors. Only a small fraction of the sdB population is known to exhibit pulsations. Pulsating sdBs have typically been discovered serendipitously in various photometric surveys because specific selection criteria for the sample are lacking. Consequently, while individual properties of these stars are well known, a comprehensive understanding of the entire population remains elusive, and many related questions remain unanswered. The Gaia mission has presented an exceptional chance to create an unbiased sample by employing precise criteria and ensuring a high degree of completeness. The progression of high-precision and high-duty cycle photometric monitoring facilitated by space missions such as Kepler/K2 and the Transiting Exoplanet Survey Satellite (TESS) has yielded an unparalleled wealth of data for pulsating sdBs. We created a dataset of confirmed pulsating sdB stars by combining information from various ground- and space-based photometric surveys. With this dataset, we present a thorough approach to search for pulsating sdB stars based on the current Gaia DR3 sample. Based on TESS photometry, we discovered 61 new pulsating sdB stars and 20 variable sdBs whose source of variability remains to be determined through future spectroscopic follow-up observations.

Insufficient oxygen supply represents a relevant issue in several fields of human physiology and medicine. It has been suggested that the implantation of photosynthetic cells can provide oxygen to tissues in the absence of a vascular supply. This approach has been demonstrated to be successful in several in vitro and in vivo models; however, no data is available about their safety in human patients. Here, an early phase-1 clinical trial (ClinicalTrials.gov identifier: NCT03960164, https://clinicaltrials.gov/ct2/show/NCT03960164) is presented to evaluate the safety and feasibility of implanting photosynthetic scaffolds for dermal regeneration in eight patients with full-thickness skin wounds. Overall, this trial shows that the presence of the photosynthetic microalgae Chlamydomonas reinhardtii in the implanted scaffolds did not trigger any deleterious local or systemic immune responses in a 90 days follow-up, allowing full tissue regeneration in humans. The results presented here represent the first attempt to treat patients with photosynthetic cells, supporting the translation of photosynthetic therapies into clinics.

In mammals, temperature-sensitive TRP channels make membrane conductance of cells extremely temperature dependent, allowing the detection of temperature ranging from noxious cold to noxious heat. We progressively deleted the distal carboxyl terminus domain (CTD) of the cold-activated melastatin receptor channel, TRPM8. We found that the enthalpy change associated with channel gating is proportional to the length of the CTD. Deletion of the last 36 amino acids of the CTD transforms TRPM8 into a reduced temperature-sensitivity channel (Q10 ∼4). Exposing the intracellular domain to a denaturing agent increases the energy required to open the channel indicating that cold drives channel gating by stabilizing the folded state of the CTD. Experiments in the presence of an osmoticant agent suggest that channel gating involves a change in solute-inaccessible volume in the CTD of ∼1,900 Å3. This volume matches the void space inside the coiled coil according to the cryogenic electron microscopy structure of TRPM8. The results indicate that a folding–unfolding reaction of a specialized temperature-sensitive structure is coupled to TRPM8 gating.

Background This work presents a forecast model for non-typhoidal salmonellosis outbreaks. Method This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA). Results The forecast model was validated by analyzing the cases of Salmonella enterica serovar Enteritidis in Sydney Australia (2014–2016), the environmental conditions and the consumption of high-risk food as predictive variables. Conclusions The prediction of cases of Salmonella enterica serovar Enteritidis infections are included in a forecast model based on fitted values of time series modeled by GSARIMA, for an early alert of future outbreaks caused by this pathogen, and associated to high-risk food. In this context, the decision makers in the epidemiology field can led to preventive actions using the proposed model.

This article aims to present a Free Dialogic Logic [FDL] as a general framework for hypothesis generation in the practice of modelling in science. Our proposal is based on the idea that the inferential function that models fulfil during the modelling process (surrogate reasoning) should be carried out without ontological commitments. The starting point to achieve our objective is that the scientific consideration of models without a target is a symptom that, on the one hand, the Applicability of Logic should be considered among the conditions of adequacy that should take into account all modeling process and, on the other, that the inferential apparatus at the base of the surrogate reasoning process must be rid of realistic assumptions that lead to erroneous conclusions. In this sense, we propose as an alternative an ontologically neutral inferential system in the perspective of dialogical pragmatism.

Despite the importance of the role of small and medium enterprises (SMEs) in developing and growing economies, little is known regarding the use of management control tools in them. In management control in SMEs, a holistic system needs to be modeled to enable a careful study of how each lever (belief systems, boundary systems, interactive control systems, and diagnostic control systems) affects the organizational performance of SMEs. In this article, a fuzzy logic approach is proposed for the decision-making system in management control in small and medium enterprises. C. Mamdani fuzzy inference system (MFIS) was applied as a decision-making technique to explore the influence of the use of management control tools on the organizational performance of SMEs. Perceptions data analysis is obtained through empirical research.

Chile has become the first country in the world where an equal number of men and women will draft the new Constitution due a parity law that was passed in March 2020. In addition, this historic opportunity will take place during one of the worst health pandemics in recorded history, COVID-19, which has revealed deep gender inequalities. The new Chilean Constitution, drafted with gender parity, will have a unique opportunity to grant a right to a universal basic income (UBI), which has been targeted to address some of the worst consequences of the pandemic: the increase in poverty, unemployment, and vulnerability of women. This article reviews the theories developed to justify a UBI and the feminist critics who argue that not all UBI is equally advantageous to women. The misconception that a ‘morally neutral’ model is sufficient and women-friendly disregards the way in which it encourages stereotypes that feminists have fought for centuries. We argue for the development of public policies with a gender focus, especially the right to a ‘gendered UBI’. This means a UBI that meets two basic requirements: first, that every citizen or resident be guaranteed the same amount of income from birth; second, that caregivers be provided with management rights to turn the UBI into a compensatory income that can also promote changes in gender roles, encouraging men to become caregivers.

Establishing a node importance ranking is a problem that has attracted the attention of many researchers in recent decades. For unweighted networks where the edges do not have any attached weight, many proposals have been presented, considering local or global information of the networks. On the contrary, it occurs in undirected edge-weighted networks, where the proposals to address this problem have been more scarce. In this paper, a ranking method of node importance for undirected and edge-weighted is provided, generalizing the measure of line importance (DIL) based on the centrality degree proposed by Opsahl. The experimentation was done on five real networks and the results illustrate the benefits of our proposal.

The merger of two or more galaxies can enhance the inflow of material from galactic scales into the close environments of active galactic nuclei (AGNs), obscuring and feeding the supermassive black hole (SMBH). Both recent simulations and observations of AGN in mergers have confirmed that mergers are related to strong nuclear obscuration. However, it is still unclear how AGN obscuration evolves in the last phases of the merger process. We study a sample of 60 luminous and ultra-luminous IR galaxies (U/LIRGs) from the GOALS sample observed by NuSTAR. We find that the fraction of AGNs that are Compton thick (CT; NH ≥ 1024 cm−2) peaks at 74+14−19 per cent at a late merger stage, prior to coalescence, when the nuclei have projected separations (dsep) of 0.4–6 kpc. A similar peak is also observed in the median NH [(1.6 ± 0.5) × 1024 cm−2]. The vast majority (85+7−9 per cent) of the AGNs in the final merger stages (dsep 10 kpc) are heavily obscured (NH ≥ 1023 cm−2), and the median NH of the accreting SMBHs in our sample is systematically higher than that of local hard X-ray-selected AGN, regardless of the merger stage. This implies that these objects have very obscured nuclear environments, with the NH ≥ 1023 cm−2 gas almost completely covering the AGN in late mergers. CT AGNs tend to have systematically higher absorption-corrected X-ray luminosities than less obscured sources. This could either be due to an evolutionary effect, with more obscured sources accreting more rapidly because they have more gas available in their surroundings, or to a selection bias. The latter scenario would imply that we are still missing a large fraction of heavily obscured, lower luminosity (L2−10 1043 erg s−1) AGNs in U/LIRGs.

Motivated by two seminal models proposed to explain the Universe acceleration, this paper is devoted to study a hybrid model which is constructed through a generalized Chaplygin gas with the addition of a bulk viscosity. We call the model a viscous generalized Chaplygin gas (VGCG) and its free parameters are constrained through several cosmological data like the Observational Hubble Parameter, Type Ia Supernovae, Baryon Acoustic Oscillations, Strong Lensing Systems, HII Galaxies and using Joint Bayesian analysis. In addition, we implement a Om-diagnostic to analyze the VGCC dynamics and its difference with the standard cosmological model. The hybrid model shows important differences when compared with the standard cosmological model. Finally, based on our Joint analysis we find that the VGCG could be an interesting candidate to alleviate the well-known Hubble constant tension.

Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

We present Atacama Large Millimeter/Submillimeter Array (ALMA) continuum observations of a sample of nine star-forming galaxies at redshifts 1.47 and 2.23 selected from the High-z Emission Line Survey (HiZELS). Four galaxies in our sample are detected at high significance by ALMA at a resolution of 0${_{.}^{\prime\prime}}$25 at rest-frame 355 μm. Together with the previously observed H α emission, from adaptive optics-assisted integral-field-unit spectroscopy (∼0${_{.}^{\prime\prime}}$15 resolution), and F606W and F140W imaging from the Hubble Space Telescope (∼0${_{.}^{\prime\prime}}$2 resolution), we study the star formation activity, stellar and dust mass in these high-redshift galaxies at ∼kpc-scale resolution. We find that ALMA detection rates are higher for more massive galaxies (M* > 1010.5 M⊙) and higher [N ii]/H α ratios (>0.25, a proxy for gas-phase metallicity). The dust extends out to a radius of 8 kpc, with a smooth structure, even for those galaxies presenting clumpy H α morphologies. The half-light radii (Rdust) derived for the detected galaxies are of the order ∼4.5 kpc, more than twice the size of submillimetre-selected galaxies at a similar redshift. Our global star formation rate estimates – from far-infrared and extinction-corrected H α luminosities – are in good agreement. However, the different morphologies of the different phases of the interstellar medium suggest complex extinction properties of the high-redshift normal galaxies.

Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported several approaches to the development of models for the early prediction of at-risk students. However, maximizing the results obtained by predictions is a considerable challenge. In this work, we developed a solution using only students’ interactions with the virtual learning environment and its derivative features for early predict at-risk students in a Brazilian distance technical high school course that is 103 weeks in duration. To maximize results, we developed an elitist genetic algorithm based on Darwin’s theory of natural selection for hyperparameter tuning. With the application of the proposed technique, we predicted the student at risk with an Area Under the Receiver Operating Characteristic Curve (AUROC) above 0.75 in the initial weeks of a course. The results demonstrate the viability of applying interaction count and derivative features to generate prediction models in contexts where access to demographic data is restricted. The application of a genetic algorithm to the tuning of hyperparameters classifiers can increase their performance in comparison with other techniques.

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.

Obscuration and confusion conspire to limit our knowledge of the inner Milky Way. Even at moderate distances, the identification of stellar systems becomes compounded by the extremely high density of background sources. Here, we provide a very revealing example of these complications by unveiling a large, massive, young cluster in the Sagittarius arm that has escaped detection until now despite containing more than 30 stars brighter than G = 13. By combining Gaia DR2 astrometry, Gaia and 2MASS photometry, and optical spectroscopy, we find that the new cluster, which we name Valparaiso 1, located at ∼ 2.3 kpc, is about 75 Ma old and includes a large complement of evolved stars, among which we highlight the 4 d classical Cepheid CM Sct and an M-type giant that probably represents the first detection of an asymptotic giant branch star in a Galactic young open cluster. Although strong differential reddening renders accurate parameter determination unfeasible with the current data set, direct comparison to clusters of similar age suggests that Valparaiso 1 was born as one of the most massive clusters in the solar neighbourhood, with an initial mass close to 104 M.