A Framework for Anomaly Identification Applied on Fall Detection

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Integrative multi-kinase approach for the identification of potent antiplasmodial hits

Malaria is a tropical infectious disease that affects over 219 million people worldwide. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new antimalarial drugs is a global health priority. Multi-target drug discovery is a promising and innovative strategy for drug discovery and it is currently regarded as one of the best strategies to face drug resistance. Aiming to identify new multi-target antimalarial drug candidates, we developed an integrative computational approach to select multi-kinase inhibitors for Plasmodium falciparum calcium-dependent protein kinases 1 and 4 (CDPK1 and CDPK4) and protein kinase 6 (PK6). For this purpose, we developed and validated shape-based and machine learning models to prioritize compounds for experimental evaluation. Then, we applied the best models for virtual screening of a large commercial database of drug-like molecules. Ten computational hits were experimentally evaluated against asexual blood stages of both sensitive and multi-drug resistant P. falciparum strains. Among them, LabMol-171, LabMol-172, and LabMol-181 showed potent antiplasmodial activity at nanomolar concentrations (EC50 15 folds. In addition, LabMol-171 and LabMol-181 showed good in vitro inhibition of P. berghei ookinete formation and therefore represent promising transmission-blocking scaffolds. Finally, docking studies with protein kinases CDPK1, CDPK4, and PK6 showed structural insights for further hit-to-lead optimization studies.7CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP405996/2016-0; 400760/2014-2Sem informação2018/05926-2; 2017/02353-9; 2012/16525-2; 2017/18611-7; 2018/07007-4; 2013/13119-6; 2018/24878-9; 2015/20774-

Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.

BACKGROUND:In 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g., brain imaging and behavior). Statistical methods that can integrate such multimodal data, however, are often vulnerable to overfitting, poor generalization, and difficulties in interpreting the results. METHODS:We propose an innovative machine learning framework combining multiple holdouts and a stability criterion with regularized multivariate techniques, such as sparse partial least squares and kernel canonical correlation analysis, for identifying hidden dimensions of cross-modality relationships. To illustrate the approach, we investigated structural brain-behavior associations in an extensively phenotyped developmental sample of 345 participants (312 healthy and 33 with clinical depression). The brain data consisted of whole-brain voxel-based gray matter volumes, and the behavioral data included item-level self-report questionnaires and IQ and demographic measures. RESULTS:Both sparse partial least squares and kernel canonical correlation analysis captured two hidden dimensions of brain-behavior relationships: one related to age and drinking and the other one related to depression. The applied machine learning framework indicates that these results are stable and generalize well to new data. Indeed, the identified brain-behavior associations are in agreement with previous findings in the literature concerning age, alcohol use, and depression-related changes in brain volume. CONCLUSIONS:Multivariate techniques (such as sparse partial least squares and kernel canonical correlation analysis) embedded in our novel framework are promising tools to link behavior and/or symptoms to neurobiology and thus have great potential to contribute to a biologically grounded definition of psychiatric disorders

MARVELS-1: A face-on double-lined binary star masquerading as a resonant planetary system; and consideration of rare false positives in radial velocity planet searches

We have analyzed new and previously published radial velocity observations of MARVELS-1, known to have an ostensibly substellar companion in a ~6- day orbit. We find significant (~100 m/s) residuals to the best-fit model for the companion, and these residuals are naively consistent with an interior giant planet with a P = 1.965d in a nearly perfect 3:1 period commensuribility (|Pb/Pc - 3| < 10^{-4}). We have performed several tests for the reality of such a companion, including a dynamical analysis, a search for photometric variability, and a hunt for contaminating stellar spectra. We find many reasons to be critical of a planetary interpretation, including the fact that most of the three-body dynamical solutions are unstable. We find no evidence for transits, and no evidence of stellar photometric variability. We have discovered two apparent companions to MARVELS-1 with adaptive optics imaging at Keck; both are M dwarfs, one is likely bound, and the other is likely a foreground object. We explore false-alarm scenarios inspired by various curiosities in the data. Ultimately, a line profile and bisector analysis lead us to conclude that the ~100 m/s residuals are an artifact of spectral contamination from a stellar companion contributing ~15-30% of the optical light in the system. We conclude that origin of this contamination is the previously detected radial velocity companion to MARVELS-1, which is not, as previously reported, a brown dwarf, but in fact a G dwarf in a face-on orbit.Comment: ApJ 770, 119. 24 pp emulate ApJ style, 12 figures (One is very large). v2: corrects two (important!) errors: A priori chance of this alignment or worse is 0.1% (not 0.01%) and the primary has THREE total companions (not four

The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr

Photobiomodulation reduces the cytokine storm syndrome associated with Covid-19 in the zebrafish model

Although the exact mechanism of the pathogenesis of COVID-19 is not fully understood, oxidative stress and the release of pro-inflammatory cytokines have been highlighted as playing a vital role in the pathogenesis of the disease. In this sense, alternative treatments are needed to reduce the inflammation caused by COVID-19. Therefore, this study aimed to investigate the potential effect of red PBM as an attractive therapy to downregulate the cytokine storm caused by COVID-19 from a zebrafish model. RT-PCR analyses and protein-protein interaction prediction among SARS-CoV-2 and Danio rerio proteins showed that rSpike was responsible for generating systemic inflammatory processes with significantly increased pro-inflammatory (il1b, il6, tnfa, and nfkbiab), oxidative stress (romo1) and energy metabolism (slc2a1a, coa1) mRNA markers, with a pattern like those observed in COVID-19 cases in humans. On the other hand, PBM treatment decreased the mRNA levels of these pro-inflammatory and oxidative stress markers compared with rSpike in various tissues, promoting an anti-inflammatory response. Conversely, PBM promotes cellular and tissue repair of injured tissues and significantly increases the survival rate of rSpike-inoculated individuals. Additionally, metabolomics analysis showed that the most impacted metabolic pathways between PBM and the rSpike-treated groups were related to steroid metabolism, immune system, and lipids metabolism. Together, our findings suggest that the inflammatory process is an incisive feature of COVID-19, and red PBM can be used as a novel therapeutic agent for COVID-19 by regulating the inflammatory response. Nevertheless, the need for more clinical trials remains, and there is a significant gap to overcome before clinical trials.publishedVersio

Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Funding: Data collection was largely funded by the UK Natural Environment Research Council (NERC) project TREMOR (NE/N004655/1) to D.G., E.G. and O.P., with further funds from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES, finance code 001) to J.V.T. and a University of Leeds Climate Research Bursary Fund to J.V.T. D.G., E.G. and O.P. acknowledge further support from a NERC-funded consortium award (ARBOLES, NE/S011811/1). This paper is an outcome of J.V.T.’s doctoral thesis, which was sponsored by CAPES (GDE 99999.001293/2015-00). J.V.T. was previously supported by the NERC-funded ARBOLES project (NE/S011811/1) and is supported at present by the Swedish Research Council Vetenskapsrådet (grant no. 2019-03758 to R.M.). E.G., O.P. and D.G. acknowledge support from NERC-funded BIORED grant (NE/N012542/1). O.P. acknowledges support from an ERC Advanced Grant and a Royal Society Wolfson Research Merit Award. R.S.O. was supported by a CNPq productivity scholarship, the São Paulo Research Foundation (FAPESP-Microsoft 11/52072-0) and the US Department of Energy, project GoAmazon (FAPESP 2013/50531-2). M.M. acknowledges support from MINECO FUN2FUN (CGL2013-46808-R) and DRESS (CGL2017-89149-C2-1-R). C.S.-M., F.B.V. and P.R.L.B. were financed by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES, finance code 001). C.S.-M. received a scholarship from the Brazilian National Council for Scientific and Technological Development (CNPq 140353/2017-8) and CAPES (science without borders 88881.135316/2016-01). Y.M. acknowledges the Gordon and Betty Moore Foundation and ERC Advanced Investigator Grant (GEM-TRAITS, 321131) for supporting the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk), within which some of the field sites (KEN, TAM and ALP) are nested. The authors thank Brazil–USA Collaborative Research GoAmazon DOE-FAPESP-FAPEAM (FAPESP 2013/50533-5 to L.A.) and National Science Foundation (award DEB-1753973 to L. Alves). They thank Serrapilheira Serra-1709-18983 (to M.H.) and CNPq-PELD/POPA-441443/2016-8 (to L.G.) (P.I. Albertina Lima). They thank all the colleagues and grants mentioned elsewhere [8,36] that established, identified and measured the Amazon forest plots in the RAINFOR network analysed here. The authors particularly thank J. Lyod, S. Almeida, F. Brown, B. Vicenti, N. Silva and L. Alves. This work is an outcome approved Research Project no. 19 from ForestPlots.net, a collaborative initiative developed at the University of Leeds that unites researchers and the monitoring of their permanent plots from the world’s tropical forests [61]. The authros thank A. Levesley, K. Melgaço Ladvocat and G. Pickavance for ForestPlots.net management. They thank Y. Wang and J. Baker, respectively, for their help with the map and with the climatic data. The authors acknowledge the invaluable help of M. Brum for kindly providing the comparison of vulnerability curves based on PAD and on PLC shown in this manuscript. They thank J. Martinez-Vilalta for his comments on an early version of this manuscript. The authors also thank V. Hilares and the Asociación para la Investigación y Desarrollo Integral (AIDER, Puerto Maldonado, Peru); V. Saldaña and Instituto de Investigaciones de la Amazonía Peruana (IIAP) for local field campaign support in Peru; E. Chavez and Noel Kempff Natural History Museum for local field campaign support in Bolivia; ICMBio, INPA/NAPPA/LBA COOMFLONA (Cooperativa mista da Flona Tapajós) and T. I. Bragança-Marituba for the research support.Tropical forests face increasing climate risk1,2, yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, Ψ50) and hydraulic safety margins (for example, HSM50) are important predictors of drought-induced mortality risk3-5, little is known about how these vary across Earth's largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters Ψ50 and HSM50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both Ψ50 and HSM50 influence the biogeographical distribution of Amazon tree species. However, HSM50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM50 are gaining more biomass than are low HSM50 forests. We propose that this may be associated with a growth-mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM50 in the Amazon6,7, with strong implications for the Amazon carbon sink.Publisher PDFPeer reviewe

Factors influencing terrestriality in primates of the Americas and Madagascar

Among mammals, the order Primates is exceptional in having a high taxonomic richness in which the taxa are arboreal, semiterrestrial, or terrestrial. Although habitual terrestriality is pervasive among the apes and African and Asian monkeys (catarrhines), it is largely absent among monkeys of the Americas (platyrrhines), as well as galagos, lemurs, and lorises (strepsirrhines), which are mostly arboreal. Numerous ecological drivers and species-specific factors are suggested to set the conditions for an evolutionary shift from arboreality to terrestriality, and current environmental conditions may provide analogous scenarios to those transitional periods. Therefore, we investigated predominantly arboreal, diurnal primate genera from the Americas and Madagascar that lack fully terrestrial taxa, to determine whether ecological drivers (habitat canopy cover, predation risk, maximum temperature, precipitation, primate species richness, human population density, and distance to roads) or species-specific traits (bodymass, group size, and degree of frugivory) associate with increased terrestriality. We collated 150,961 observation hours across 2,227 months from 47 species at 20 sites in Madagascar and 48 sites in the Americas. Multiple factors were associated with ground use in these otherwise arboreal species, including increased temperature, a decrease in canopy cover, a dietary shift away from frugivory, and larger group size. These factors mostly explain intraspecific differences in terrestriality. As humanity modifies habitats and causes climate change, our results suggest that species already inhabiting hot, sparsely canopied sites, and exhibiting more generalized diets, are more likely to shift toward greater ground use

<i>In vitro</i> antiviral activity of the anti-HCV drugs daclatasvir and sofosbuvir against SARS-CoV-2, the aetiological agent of COVID-19

BackgroundCurrent approaches of drug repurposing against COVID-19 have not proven overwhelmingly successful and the SARS-CoV-2 pandemic continues to cause major global mortality. SARS-CoV-2 nsp12, its RNA polymerase, shares homology in the nucleotide uptake channel with the HCV orthologue enzyme NS5B. Besides, HCV enzyme NS5A has pleiotropic activities, such as RNA binding, that are shared with various SARS-CoV-2 proteins. Thus, anti-HCV NS5B and NS5A inhibitors, like sofosbuvir and daclatasvir, respectively, could be endowed with anti-SARS-CoV-2 activity.MethodsSARS-CoV-2-infected Vero cells, HuH-7 cells, Calu-3 cells, neural stem cells and monocytes were used to investigate the effects of daclatasvir and sofosbuvir. In silico and cell-free based assays were performed with SARS-CoV-2 RNA and nsp12 to better comprehend the mechanism of inhibition of the investigated compounds. A physiologically based pharmacokinetic model was generated to estimate daclatasvir's dose and schedule to maximize the probability of success for COVID-19.ResultsDaclatasvir inhibited SARS-CoV-2 replication in Vero, HuH-7 and Calu-3 cells, with potencies of 0.8, 0.6 and 1.1 μM, respectively. Although less potent than daclatasvir, sofosbuvir alone and combined with daclatasvir inhibited replication in Calu-3 cells. Sofosbuvir and daclatasvir prevented virus-induced neuronal apoptosis and release of cytokine storm-related inflammatory mediators, respectively. Sofosbuvir inhibited RNA synthesis by chain termination and daclatasvir targeted the folding of secondary RNA structures in the SARS-CoV-2 genome. Concentrations required for partial daclatasvir in vitro activity are achieved in plasma at Cmax after administration of the approved dose to humans.ConclusionsDaclatasvir, alone or in combination with sofosbuvir, at higher doses than used against HCV, may be further fostered as an anti-COVID-19 therapy

SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way Galaxy, and Extra-Solar Planetary Systems

Building on the legacy of the Sloan Digital Sky Survey (SDSS-I and II), SDSS-III is a program of four spectroscopic surveys on three scientific themes: dark energy and cosmological parameters, the history and structure of the Milky Way, and the population of giant planets around other stars. In keeping with SDSS tradition, SDSS-III will provide regular public releases of all its data, beginning with SDSS DR8 (which occurred in Jan 2011). This paper presents an overview of the four SDSS-III surveys. BOSS will measure redshifts of 1.5 million massive galaxies and Lya forest spectra of 150,000 quasars, using the BAO feature of large scale structure to obtain percent-level determinations of the distance scale and Hubble expansion rate at z<0.7 and at z~2.5. SEGUE-2, which is now completed, measured medium-resolution (R=1800) optical spectra of 118,000 stars in a variety of target categories, probing chemical evolution, stellar kinematics and substructure, and the mass profile of the dark matter halo from the solar neighborhood to distances of 100 kpc. APOGEE will obtain high-resolution (R~30,000), high signal-to-noise (S/N>100 per resolution element), H-band (1.51-1.70 micron) spectra of 10^5 evolved, late-type stars, measuring separate abundances for ~15 elements per star and creating the first high-precision spectroscopic survey of all Galactic stellar populations (bulge, bar, disks, halo) with a uniform set of stellar tracers and spectral diagnostics. MARVELS will monitor radial velocities of more than 8000 FGK stars with the sensitivity and cadence (10-40 m/s, ~24 visits per star) needed to detect giant planets with periods up to two years, providing an unprecedented data set for understanding the formation and dynamical evolution of giant planet systems. (Abridged)Comment: Revised to version published in The Astronomical Journa