Wildfire is a major concern worldwide and particularly in Australia. The 2019–2020 wildfires in Australia became historically significant as they were widespread and extremely severe. Linking climate and vegetation settings to wildfires can provide insightful information for wildfire prediction, and help better understand wildfires behavior in the future. The goal of this research was to examine the relationship between the recent wildfires, various hydroclimatological variables, and satellite-retrieved vegetation indices. The analyses performed here show the uniqueness of the 2019–2020 wildfires. The near-surface air temperature from December 2019 to February 2020 was about 1 °C higher than the 20-year mean, which increased the evaporative demand. The lack of precipitation before the wildfires, due to an enhanced high-pressure system over southeast Australia, prevented the soil from having enough moisture to supply the demand, and set the stage for a large amount of dry fuel that highly favored the spread of the fires.

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.

Objective The aim of the present study was to evaluate, after 5 years, the efficacy of proximal microinvasive sealing of permanent teeth on the risk for caries lesion development. Methods Children aged 8 to 10 y at baseline, at high caries risk, were studied. In the preventive (P) group the children had caries lesions on the distal surface of primary second molars (05d) but sound mesial surfaces of the approximating permanent first molars (6m). In the therapeutic (T) group the children had initial caries lesions on 6m that abutted lesions on 05d. Each child in the two groups had one 05d/6m pair. Using a split-mouth design, one 6m surface in each pair was randomly assigned to receive sealing while the other pair served as an unsealed control. Results Of the 61 children at baseline 42 could be blindly examined clinically and radiographically both at baseline and after 5 years. In the P group, 8 of 28 (28.6%) sealed and 15 of 28 (53.6 %) unsealed sound 6m surfaces had developed caries lesions ( p = 0.04). In the T group, the progression of the carious lesions on 6m was observed in 4 of 14 sealed (28.6%) and 8 of 14 (57.1%) unsealed caries control surfaces ( p = 0.29). Pooling the data from the two groups, the difference between sealed and non-sealed surfaces was significant ( p = 0.013). Conclusion Both preventive and therapeutic sealant to 6m adjacent to a lesion on 05d has effectiveness in caries reduction in high caries risk children Clinical Significance The beneficial effect of sealing is observed for at least 5 years after a single sealant treatment.

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.

The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision‐making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

We introduce an algebra of braids and ties (or bt-algebra) of type B . In analogy to the construction of the bt-algebra of type A, we define this bt–algebra of type B through a framization of the Hecke algebra of type B. We find a basis for it, a faithful tensorial representation, and we prove that it supports a Markov trace, from which we derive invariants of classical links in the solid torus.

Abstract Background Few implementation science (IS) measures have been evaluated for validity, reliability and utility – the latter referring to whether a measure captures meaningful aspects of implementation contexts. We present a real-world case study of rigorous measure development in IS that assesses Barriers and Facilitators in Implementation of Task-Sharing in Mental Health services (BeFITS-MH), with the objective of offering lessons-learned and a framework to enhance measurement utility. Methods We summarize conceptual and empirical work that informed the development of the BeFITS-MH measure, including a description of the Delphi process, detailed translation and local adaptation procedures, and concurrent pilot testing. As validity and reliability are key aspects of measure development, we also report on our process of assessing the measure’s construct validity and utility for the implementation outcomes of acceptability, appropriateness, and feasibility. Results Continuous stakeholder involvement and concurrent pilot testing resulted in several adaptations of the BeFITS-MH measure’s structure, scaling, and format to enhance contextual relevance and utility. Adaptations of broad terms such as “program,” “provider type,” and “type of service” were necessary due to the heterogeneous nature of interventions, type of task-sharing providers employed, and clients served across the three global sites. Item selection benefited from the iterative process, enabling identification of relevance of key aspects of identified barriers and facilitators, and what aspects were common across sites. Program implementers’ conceptions of utility regarding the measure’s acceptability, appropriateness, and feasibility clustered across several common categories. Conclusions This case study provides a rigorous, multi-step process for developing a pragmatic IS measure. The process and lessons learned will aid in the teaching, practice and research of IS measurement development. The importance of including experiences and knowledge from different types of stakeholders in different global settings was reinforced and resulted in a more globally useful measure while allowing for locally-relevant adaptation. To increase the relevance of the measure it is important to target actionable domains that predict markers of utility (e.g., successful uptake) per program implementers’ preferences. With this case study, we provide a detailed roadmap for others seeking to develop and validate IS measures that maximize local utility and impact.

Abstract Aims Dynamin‐2 is a large GTPase, a member of the dynamin superfamily that regulates membrane remodelling and cytoskeleton dynamics. Mutations in the dynamin‐2 gene ( DNM2 ) cause autosomal dominant centronuclear myopathy (CNM), a congenital neuromuscular disorder characterised by progressive weakness and atrophy of the skeletal muscles. Cognitive defects have been reported in some DNM2 ‐linked CNM patients suggesting that these mutations can also affect the central nervous system (CNS). Here we studied how a dynamin‐2 CNM‐causing mutation influences the CNS function. Methods Heterozygous mice harbouring the p.R465W mutation in the dynamin–2 gene (HTZ), the most common causing autosomal dominant CNM, were used as disease model. We evaluated dendritic arborisation and spine density in hippocampal cultured neurons, analysed excitatory synaptic transmission by electrophysiological field recordings in hippocampal slices, and evaluated cognitive function by performing behavioural tests. Results HTZ hippocampal neurons exhibited reduced dendritic arborisation and lower spine density than WT neurons, which was reversed by transfecting an interference RNA against the dynamin‐2 mutant allele. Additionally, HTZ mice showed defective hippocampal excitatory synaptic transmission and reduced recognition memory compared to the WT condition. Conclusion Our findings suggest that the dynamin‐2 p.R465W mutation perturbs the synaptic and cognitive function in a CNM mouse model and support the idea that this GTPase plays a key role in regulating neuronal morphology and excitatory synaptic transmission in the hippocampus.

The binarization of Swarm intelligence continuous metaheuristics is an area of great interest in operations research . This interest is mainly due to the application of binarized metaheuristics to combinatorial problems . In this article we propose a general binarization algorithm called K-means Transition Algorithm (KMTA). KMTA uses K-means clustering technique as learning strategy to perform the binarization process. In particular we apply this mechanism to Cuckoo Search and Black Hole metaheuristics to solve the Set Covering Problem (SCP). A methodology is developed to perform the tuning of parameters. We provide necessary experiments to investigate the role of key ingredients of the algorithm. In addition, with the intention of evaluating the behavior of the binarizations while the algorithms are executed, we use the Page's trend test. Finally to demonstrate the efficiency of our proposal, Set Covering benchmark instances of the literature show that KMTA competes clearly with the state-of-the-art algorithms.

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.

Patients of the Short-stay Child and Adolescent Unit of the Del Salvador Psychiatric Hospital (Valparaíso, Chile) exhibit different clinical and social characteristics compared to literature reports of other national centers, although published data are scarce.To describe the operation of the Unit, the socio-familial and clinical characteristics of its patients and analyze factors associated with their clinical evolution.We performed a cross-sectional study to describe the patients hospitalized over a three-year period. Variables were registered in an anonymized database. Clinical evolution was evaluated over the year following hospital discharge.The Unit's model of care can be described as involving biomedical, psychodynamic, and ecological components. We included 98 patients, of which 70.4% were male, and the average age was 11.5 ± 2.3 years. 82.6% were of low socioeconomic status, and 35.7% did not attend school; 98.9% presented family dysfunction, and 91.8% of parents had a history of psychopathology. The most frequent reason for admission was the risk of harm to self or others. The most frequent discharge diagnoses were behavioral, depressive, and personality development disorders. The average length of stay was 41.8 ± 31.1 days. The most commonly used pharmacological agents were antipsychotics and mood stabilizers. Regarding clinical evolution in the first year post-discharge, 47% were evaluated as positive, 27% regular, and 26% unsatisfactory. The factor associated with an unsatisfactory clinical course was having had in-patient antidepressants. Re-admission during the first year post-discharge was associated with comorbid substance use disorder. Treatment noncompliance was associated with a history of behavioral disorder at hospital discharge and having parents with a history of suicide or consummated suicide.The patient profile is one of low socioeconomic status, severe psychopathology, maladaptive behavior, family dysfunction, and parental psychopathology. Substance use disorder is also associated with readmission.La Unidad de Corta Estadía Infanto-juvenil del Hospital Psiquiátrico del Salvador (Valparaíso, Chile) y sus pacientes poseen características clínicas y sociales diferentes a las reportadas por la escasa literatura nacional al respecto.Describir el funcionamiento la unidad, las características sociofamiliares y clínicas de sus pacientes y analizar los factores asociados a su evolución clínica.Estudio transversal que describió el universo de pacientes hospitalizados durante tres años. Las variables se recogieron desde una base de datos anonimizada. Se evaluó la evolución clínica dentro del año posterior al egreso hospitalario.El modelo de atención de la unidad presenta los componentes biomédico, psicodinámico y ecológico. Se analizaron 98 pacientes, 70,4% hombres, con edad promedio de 11,5 ± 2,3 años. Un 82,6% perteneció al nivel socioeconómico bajo y 35,7% estaba desescolarizado; 98,9% presentó disfunción familiar y 91,8% de los padres portaba alguna psicopatología. Los motivos de ingreso más frecuente fueron riesgo de hetero y autoagresión. Los diagnósticos de egreso más frecuente fueron trastornos de conducta, depresivos y del desarrollo de la personalidad. El tiempo de estadía promedio fue de 41,8 ± 31,1 días. Los fármacos más utilizados fueron antipsicóticos y estabilizadores del ánimo. Un 47% tuvo una buena evolución clínica, 27% regular y 26% insatisfactoria. El único factor que se asoció a esta última fue haber tenido prescrito un antidepresivo durante la hospitalización. El factor que consistentemente se vinculó al reingreso a lo largo de un año, fue el haber presentado trastorno por consumo de sustancias. El abandono a tratamiento se relacionó con haber presentado un trastorno de conducta al egreso hospitalario y a tener padres con antecedente de suicidio o suicidio consumado.El perfil de pacientes de esta unidad corresponde a pacientes de nivel socioeconómico bajo, psicopatología severa, conducta desadaptativa, disfunción familiar y frecuente psicopatología parental. El trastorno por consumo de sustancias es un factor significativamente asociado al reingreso hospitalario.

The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.

Mammography is known as one of the best forms to screen possible breast cancer in women, and recently deep learning models have been developed to assist the radiologist in the diagnosis. However, their lack of interpretability has become a significant drawback to their extended use in clinical practice. This paper introduces a novel approach for detecting and localising pathological findings in mammography exams through the use of a EfficientNet-based deep learning model. The model is trained using cropped segments of labelled pathological findings from Vindr Mammography Dataset. Achieving an average F1-score of 72.7 %, and reaching on mass and suspicious calcifications an F1-Score of 79.9 % and 84.5 % respectively. Using this classifier we propose a method to visualise from local information the regions of interest where pathological findings could be present on the complete image. Plus, we describe the limitations regarding area coverage of these patches on the model's capability of generalization and certainty on its predictions, explaining its functionality.

Sex differences have been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to sex and the conditions that generate these effects. The present work studied the effects of a drill-and-practice mathematical game on primary students. The study focused on an analysis by sex, measuring motivation and learning in the practice activity. Also, two instructional mechanics were considered regarding the question answering to search for possible differences: a multiple-try feedback (MTF) condition and a single-try feedback (STF) condition. A total of 81 students from four courses and two schools participated in the intervention. The study’s main findings were as follows: (a) the girls outperformed the boys in terms of the students’ learning gains; (b) the girls presented lower levels of competence and autonomy than the boys; (c) under MTF, the girls presented lower levels of autonomy but no differences in competence contrasted with the boys; (d) under STF, the girls presented lower levels of competence but no differences in autonomy contrasted with the boys; (e) no sex differences existed in interest, effort, and value, in general, as per the instructional condition. This study enhances the knowledge of sex differences under diverse instructional settings, in particular providing insights into the possible differences by sex when varying the number of attempts provided to students.

Large-pore channels allow the exchange of ions and molecules between the intra- and extracellular compartments. These channels are structures formed by several protein families with little or no evolutionary linkages that include connexins (Cxs), pannexins (Panxs), innexins (Inxs), CALHM1, and LRRC8 proteins. Recently, we have described the unnexins (Unxs) proteins expressed inTrypanosoma cruzi(T. cruzi) that also is like to form large-pore channels at the plasma membrane. In this chapter, we describe a dye uptake method for evaluating the unnexin-formed channel function inT. cruzi, as well as the methods for evaluating their participation in the transformation of trypomastigotes into amastigotes. These methods can facilitate understanding the role of large-pore channels in the parasite’s biology.