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This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter's signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model's generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm. Our results demonstrate an average reduction of over 90% in the prediction error and 50% in the prediction time achieved by our proposal when compared to the MUSIC algorithm. These results establish DTs as competitive alternatives for DOA estimation in signal reception systems.

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BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition and the most common type of dementia among the elderly. The enzymes acetylcholinesterase (AChE) and nitric oxide synthase (NOS) have a pivotal role in the pathophysiology of this disease. OBJECTIVE: This study aimed to select medicinal plant-derived molecules with reported inhibition of AChE and design optimized molecules that could inhibit not only AChE, but also NOS, potentially increasing its efficacy against AD. METHODS: 24 compounds were selected from the literature based on their known AChE inhibitory activity. Then, we performed molecular orbital calculations, maps of electrostatic potential, molecular docking study, identification of the pharmacophoric pattern, evaluation of pharmacokinetic and toxicological properties of these molecules. Next, ten analogs were generated for each molecule to optimize their effect where the best molecules of natural products had failed. RESULTS: The most relevant correlation was between HOMO and GAP in the correlation matrix of the molecules' descriptors. The pharmacophoric group's derivation found the following pharmacophoric features: two hydrogen bond acceptors and one aromatic ring. The studied molecules interacted with the active site of AChE through hydrophobic and hydrogen bonds and with NOS through hydrogen interactions only but in a meaningful manner. In the pharmacokinetic and toxicological prediction, the compounds showed satisfactory results. CONCLUSION: The design of natural products analogs demonstrated good affinities with the pharmacological targets AChE and NOS, with satisfactory pharmacokinetics and toxicology profiles. Thus, the results could identify promising molecules for treating Alzheimer's disease.

Assuntos

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In vertebrates like mammals and birds, two types of sleep have been identified: rapid eye movement and non-rapid eye movement sleep. Each one is associated with specific electroencephalogram patterns and is accompanied by variations in cardiac and respiratory frequencies. Sleep has been demonstrated only in a handful of invertebrates, and evidence for different sleep stages remains elusive. Previous results show that crayfish sleeps while lying on one side on the surface of the water, but it is not known if this animal has sleep phases. Heart rate and respiratory frequency are modified by diverse changes in the crayfish environment during wakefulness, and previously, we showed that variations in these variables are present during sleep despite that there are no autonomic anatomical structures described in this animal. Here, we conducted experiments to search for sleep phases in crayfish and the relationships between sleep and cardiorespiratory activity. We used the wavelet transform, grouping analysis with k-means clustering, and principal component analysis, to analyze brain and cardiorespiratory electrical activity. Our results show that (a) crayfish can sleep lying on one side or when it is motionless and (b) the depth of sleep (measured as the power of electroencephalographic activity) changes over time and is accompanied by oscillations in cardiorespiratory signal amplitude and power. Finally, we propose that in crayfish there are at least three phases of sleep.

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1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations.2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model.3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat.4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population.5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis.

Assuntos

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The identification of metabolites in complex biological matrices is a challenging task in 1D 1H-NMR-based metabolomics studies. Statistical total correlation spectroscopy (STOCSY) has emerged for aiding the structural elucidation by revealing the peaks that present a high correlation to a driver peak of interest (which would likely belong to the same molecule). However, in these studies, the signals from metabolites are normally present as a mixture of overlapping resonances, limiting the performance of STOCSY. As an alternative to avoid the overlap issue, 2D 1H homonuclear J-resolved (JRES) spectra were projected, in their usual tilted and symmetrized processed form, and STOCSY was applied on these 1D projections (p-JRES-STOCSY). Nonetheless, this approach suffers in cases where the signals are very close. In addition, STOCSY was applied to the whole JRES spectra (also tilted) to identify correlated multiplets, although the overlap issue in itself was not addressed directly and the subsequent search in databases is complicated in cases of higher order coupling. With these limitations in mind, in the present work, we propose a new methodology based on the application of STOCSY on a set of nontilted JRES spectra, detecting peaks that would overlap in 1D spectra of the same sample set. Correlation comparison analysis for peak overlap detection (COCOA-POD) is able to reconstruct projected 1D STOCSY traces that result in more suitable database queries, as all peaks are summed at their f2 resonances instead of the resonance corresponding to the multiplet center in the tilted JRES spectra. (The peak dispersion and resolution enhancement gained are not sacrificed by the projection.) Besides improving database queries with better peak lists obtained from the projections of the 2D STOCSY analysis, the overlap region is examined, and the multiplet itself is analyzed from the correlation trace at 45° to obtain a cleaner multiplet profile, free from contributions from uncorrelated neighboring peaks.

Assuntos

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The modeling of generalized estimating equations used in the analysis of longitudinal data whether in continuous or discrete variables, necessarily requires the prior specification of a correlation matrix in its iterative process in order to obtain the estimates of the regression parameters. Such a matrix is called working correlation matrix and its incorrect specification produces less efficient estimates for the model parameters. Due to this fact, this study aims to propose a selection criterion of working correlation matrix based on the covariance matrix estimates of correlated responses resulting from the limiting values of the association parameter estimates. For validation of the criterion, we used simulation studies considering normal and binary correlated responses. Compared to some criteria in the literature, it was concluded that the proposed criterion resulted in a better performance when the correlation structure for exchangeable working correlation matrix was considered as true structure in the simulated samples and for large samples, the proposed criterion showed similar behavior to the other criteria, resulting in higher success rates.

Assuntos