A spiral structure in the disk of EX Draconis on the rise to outburst maximum

We report on the R-band eclipse mapping analysis of high-speed photometry of the dwarf nova EX Dra on the rise to the maximum of the November 1995 outburst. The eclipse map shows a one-armed spiral structure of ~180 degrees in azimuth, extending in radius from R ~0.2 to 0.43 R_{L1} (where R_{L1} is the distance from the disk center to the inner Lagrangian point), that contributes about 22 per cent of the total flux of the eclipse map. The spiral structure is stationary in a reference frame co-rotating with the binary and is stable for a timescale of at least 5 binary orbits. The comparison of the eclipse maps on the rise and in quiescence suggests that the outbursts of EX Dra may be driven by episodes of enhanced mass-transfer from the secondary star. Possible explanations for the nature of the spiral structure are discussed.Comment: To appear in the Astrophysical Journal Letters; 8 pages, 2 figures; coded with AAS latex styl

Tilted excitation implies odd periodic resonances

This work was supported by the Brazilian agencies FAPESP and CNPq. MSB also acknowledges the Engineering and Physical Sciences Research Council grant Ref. EP/I032606/1. GID thanks Felipe A. C. Pereira for fruitful discussions.Peer reviewedPostprin

Accretion and activity on the post-common-envelope binary RR~Cae

Current scenarios for the evolution of interacting close binaries - such as cataclysmic variables (CVs) - rely mainly on our understanding of low-mass star angular momentum loss (AML) mechanisms. The coupling of stellar wind with its magnetic field, i.e., magnetic braking, is the most promising mechanism to drive AML in these stars. There are basically two properties driving magnetic braking: the stellar magnetic field and the stellar wind. Understanding the mechanisms that drive AML therefore requires a comprehensive understanding of these two properties. RRCae is a well-known nearby (d=20pc) eclipsing DA+M binary with an orbital period of P=7.29h. The system harbors a metal-rich cool white dwarf (WD) and a highly active M-dwarf locked in synchronous rotation. The metallicity of the WD suggests that wind accretion is taking place, which provides a good opportunity to obtain the mass-loss rate of the M-dwarf component. We analyzed multi-epoch time-resolved high-resolution spectra of RRCae in search for traces of magnetic activity and accretion. We selected a number of well-known activity indicators and studied their short and long-term behavior. Indirect-imaging tomographic techniques were also applied to provide the surface brightness distribution of the magnetically active M-dwarf, and reveals a polar feature similar to those observed in fast-rotating solar-type stars. The blue part of the spectrum was modeled using a atmosphere model to constrain the WD properties and its metal enrichment. The latter was used to improve the determination of the mass-accretion rate from the M-dwarf wind. The presence of metals in the WD spectrum suggests that this component arises from accretion of the M-dwarf wind. A model fit to the WD gives Teff=(7260+/-250)K and logg=(7.8+/-0.1) dex with a metallicity of =(-2.8+/-0.1)dex, and a mass-accretion rate of dotMacc=(7+/-2)x1e-16Msun/yr.Comment: 14 pages, 7 Figures, 6 Table

Serotonin Syndrome with Escitolapram and Concomitant Use of Cocaine: A Case Report

Introduction Serotonin syndrome is a potentially life-threatening condition caused by excessive serotonergic activity in the central nervous system. It is characterized by mental status changes (eg, confusion, agitation, lethargy, coma), autonomic instability (eg, hyperthermia, tachycardia, diaphoresis, nausea, vomiting, diarrhea, dilated pupils), and neuromuscular hyperactivity (eg, myoclonus, hyperreflexia, rigidity, trismus). Serotonin syndrome classically occurs in patients receiving two or more serotonergic drugs, but it can occur with monotherapy. We report a case of a 20-year-old man who developed serotonin syndrome resulting from overdose of Escitolapram with concomitant use of cocaine. It is a very important area in medicine as serotonin syndrome should be suspected especially in drug abusers who are being treated with psychotropic agents for mental illnesses

Parameter space of experimental chaotic circuits with high-precision control parameters

ACKNOWLEDGMENTS The authors thank Professor Iberê Luiz Caldas for the suggestions and encouragement. The authors F.F.G.d.S., R.M.R., J.C.S., and H.A.A. acknowledge the Brazilian agency CNPq and state agencies FAPEMIG, FAPESP, and FAPESC, and M.S.B. also acknowledges the EPSRC Grant Ref. No. EP/I032606/1.Peer reviewedPublisher PD

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact on performance. Drug features appeared to be more predictive of drug response. Molecular fingerprint-based drug representations performed slightly better than learned representations, and gene expression data of cancer or drug response-specific genes also improved performance. In general, fully connected feature-encoding subnetworks outperformed other architectures, with DL outperforming other ML methods. Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.Author summary Cancer therapies often fail because tumor cells become resistant to treatment. One way to overcome resistance is by treating patients with a combination of two or more drugs. Some combinations may be more effective than when considering individual drug effects, a phenomenon called drug synergy. Computational drug synergy prediction methods can help to identify new, clinically relevant drug combinations. In this study, we developed several deep learning models for drug synergy prediction. We examined the effect of using different types of deep learning architectures, and different ways of representing drugs and cancer cell lines. We explored the use of biological prior knowledge to select relevant cell line features, and also tested data-driven feature reduction methods. We tested both precomputed drug features and deep learning methods that can directly learn features from raw representations of molecules. We also evaluated whether including genomic features, in addition to gene expression data, improves the predictive performance of the models. Through these experiments, we were able to identify strategies that will help guide the development of new deep learning models for drug synergy prediction in the future.Competing Interest StatementThe authors have declared no competing interest.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit and through a PhD scholarship (SFRH/BD/130913/2017) awarded to Delora Baptista.info:eu-repo/semantics/publishedVersio

Characterisation of Contemporary Slavery through the Analysis of Accommodation Conditions

Slave labour or work in conditions analogous to slavery continues on all continents and sometimes tends to be mistaken for simple violations of labour laws. Therefore, this work aims to identify parameters that allow distinguishing between situations of non-compliance with labour legislation and modern rural slavery in Brazil through the analysis of accommodation conditions. To achieve this objective, a bibliographic research was developed in six databases on sanitary, accommodation and clothing issues of enslaved workers in the 19th century in Brazil. The resulting data were compared with data from a sample of 392 proven cases of neoslavery detected between 2007 and 2017 in Brazil. The analysis focused on the general conditions of the physical structures necessary to protect workers against bad weather, animal attacks, violence, sanitary conditions to support physiological and asepsis needs, as well as the clothing provided and used. Similarities were found in the accommodation conditions between enslaved and neoenslaved workers in Brazil between the 19th and 21st centuries. The availability of sanitary conditions (toilets), rest (bedrooms/dormitories), and the general housing structure are very similar. Future research may point towards identifying other parameters and developing a tool to help authorities unequivocally identify neoslavery situations

Tourist-Communicational Ecosystem in Cirandas of Manacapuru – Amazon

This article presents a partial report of a research on the Brazilian Amazon, entitled Ciranda de Manacapuru as a metaphor for the Amazon: signs of the touristic, communicational, and subjective ecosystem of the region. Ciranda de Manacapuru is a popular dance festival that takes place every year in August in the municipality of Manacapuru, located in the Metropolitan Region of Manaus, in the state of Amazonas, in northern Brazil. This is an event promoted by the State and local government with the participation of three ciranda associations: Flor Matizada, Guerreiros Mura, and Tradicional. This festival is considered the largest Festival of Cirandas in the state, attracting around 50,000 tourists every year. During the Covid19 pandemic, the festival was presented by social media platforms (2020 and 2021). In this cultural manifestation of the city, an important sign of local tourism can be seen, considering the various agents involved, which is capable of attracting a differentiated, expressive, and consumer public. In theoretical terms, the research is being developed with an ecosystemic-complex orientation, which has been marking the studies of AMORCOMTUR! – Study Group on Communication, Tourism, Amouroness, and Autopoiesis. The methodological strategy is the Cartography of Knowledge, qualitative, procedural, and multi-methodological, with development in five investigative paths: We-interlaces, Personal Knowledge, Theoretical Knowledge, Production Plant, and Intuitive Research Dimension. The preliminary results indicated the expressive power of the narratives produced in the Cirandas by the various subjects involved, which makes it possible to reflect on the Tourism-Communication Ecosystemic plot that is established, evaluating the interrelationship of the city of Manacapuru and the cultural manifestation of Ciranda as an autopoietic power.   CITE THIS PAPER: Baptista, Maria L. C.; Santos, Gernei G. (2023). “Tourist-Communicational Ecosystem in Cirandas of Manacapuru – Amazon” Journal of Social Sciences: Transformations & Transitions (JOSSTT) 3(06):27. DOI: https://doi.org/10.52459/josstt3627052