Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis.

Purpose: This paper explores different machine learning (ML) algorithms for analyzing diffusion nuclear magnetic resonance imaging (dMRI) models when analytical fitting shows restrictions. It reviews various ML techniques for dMRI analysis and evaluates their performance on different b-values range datasets, comparing them with analytical methods. Materials and Methods: After standard fitting for reference, four sets of diffusion-weighted nuclear magnetic resonance images were used to train/test various ML algorithms for prediction of diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and kurtosis (K). ML classification algorithms, including extra-tree classifier (ETC), logistic regression, C-support vector, extra-gradient boost, and multilayer perceptron (MLP), were used to determine the existence of diffusion parameters (D, D*, f, and K) within single voxels. Regression algorithms, including linear regression, polynomial regression, ridge, lasso, random forest (RF), elastic-net, and support-vector machines, were used to estimate the value of the diffusion parameters. Performance was evaluated using accuracy (ACC), area under the curve (AUC) tests, and cross-validation root mean square error (RMSECV). Computational timing was also assessed. Results: ETC and MLP were the best classifiers, with 94.1% and 91.7%, respectively, for the ACC test and 98.7% and 96.3% for the AUC test. For parameter estimation, RF algorithm yielded the most accurate results The RMSECV percentages were: 8.39% for D, 3.57% for D*, 4.52% for f, and 3.53% for K. After the training phase, the ML methods demonstrated a substantial decrease in computational time, being approximately 232 times faster than the conventional methods. Conclusions: The findings suggest that ML algorithms can enhance the efficiency of dMRI model analysis and offer new perspectives on the microstructural and functional organization of biological tissues. This paper also discusses the limitations and future directions of ML-based dMRI analysis.

Higher Dose Noninvasive Transcutaneous Auricular Vagus Nerve Stimulation Increases Feeding Volumes and White Matter Microstructural Complexity in Open-Label Study of Infants Slated for Gastrostomy Tube.

OBJECTIVE: To determine whether transcutaneous auricular vagus nerve stimulation (taVNS) paired with twice daily bottle feeding increases the volume of oral feeds and white matter neuroplasticity in term-age-equivalent infants failing oral feeds and determined to need a gastrostomy tube. STUDY DESIGN: In this prospective, open-label study, 21 infants received taVNS paired with 2 bottle feeds for 2 - 3 weeks (2x). We compared 1) increase oral feeding volumes with 2x taVNS and previously reported once daily taVNS (1x) to determine a dose response, 2) number of infants who attained full oral feeding volumes, and 3) diffusional kurtosis imaging and magnetic resonance spectroscopy before and after treatment by paired t tests. RESULTS: All 2x taVNS treated infants significantly increased their feeding volumes compared with 10 days before treatment. Over 50% of 2x taVNS infants achieved full oral feeds but in a shorter time than 1x cohort (median 7 days [2x], 12.5 days [1x], P < .05). Infants attaining full oral feeds showed greater increase in radial kurtosis in the right corticospinal tract at the cerebellar peduncle and external capsule. Notably, 75% of infants of diabetic mothers failed full oral feeds, and their glutathione concentrations in the basal ganglia, a measure of central nervous system oxidative stress, were significantly associated with feeding outcome. CONCLUSIONS: In infants with feeding difficulty, increasing the number of daily taVNS-paired feeding sessions to twice-daily significantly accelerates response time but not the overall response rate of treatment. taVNS was associated with white matter motor tract plasticity in infants able to attain full oral feeds. TRIAL REGISTRATION: Clinicaltrials.gov (NCT04643808).

In Vivo Low-Temperature Plasma Ionization Mass Spectrometry (LTP-MS) Reveals Regulation of 6-Pentyl-2H-Pyran-2-One (6-PP) as a Physiological Variable during Plant-Fungal Interaction.

Volatile organic compounds (VOCs) comprises a broad class of small molecules (up to ~300 g/mol) produced by biological and non-biological sources. VOCs play a vital role in an organism's metabolism during its growth, defense, and reproduction. The well-known 6-pentyl-α-pyrone (6-PP) molecule is an example of a major volatile biosynthesized by Trichoderma atroviride that modulates the expression of PIN auxin-transport proteins in primary roots of Arabidopsis thaliana during their relationship. Their beneficial relation includes lateral root formation, defense induction, and increased plant biomass production. The role of 6-PP has been widely studied due to its relevance in this cross-kingdom relationship. Conventional VOCs measurements are often destructive; samples require further preparation, and the time resolution is low (around hours). Some techniques enable at-line or real-time analyses but are highly selective to defined compounds. Due to these technical constraints, it is difficult to acquire relevant information about the dynamics of VOCs in biological systems. Low-temperature plasma (LTP) ionization allows the analysis of a wide range of VOCs by mass spectrometry (MS). In addition, LTP-MS requires no sample preparation, is solvent-free, and enables the detection of 6-PP faster than conventional analytical methods. Applying static statistical methods such as Principal Component Analysis (PCA) and Discriminant Factorial Analysis (DFA) leads to a loss of information since the biological systems are dynamic. Thus, we applied a time series analysis to find patterns in the signal changes. Our results indicate that the 6-PP signal is constitutively emitted by T. atroviride only; the signal shows high skewness and kurtosis. In A. thaliana grown alone, no signal corresponding to 6-PP is detected above the white noise level. However, during T. atroviride-A. thaliana interaction, the signal performance showed reduced skewness and kurtosis with high autocorrelation. These results suggest that 6-PP is a physiological variable that promotes homeostasis during the plant-fungal relationship. Although the molecular mechanism of this cross-kingdom control is still unknown, our study indicates that 6-PP has to be regulated by A. thaliana during their interaction.

Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram.

The pandemic COVID-19 brings with it the need for studies and tools to help those in charge make decisions. Working with classical time series methods such as ARIMA and SARIMA has shown promising results in the first studies of COVID-19. We advance in this branch by proposing a risk factor map induced by the well-known Pearson diagram based on multivariate kurtosis and skewness measures to analyze the dynamics of deaths from COVID-19. In particular, we combine bootstrap for time series with SARIMA modeling in a new paradigm to construct a map on which one can analyze the dynamics of a set of time series. The proposed map allows a risk analysis of multiple countries in the four different periods of the pandemic COVID-19 in 55 countries. Our empirical evidence suggests a direct relationship between the multivariate skewness and kurtosis. We observe that the multivariate kurtosis increase leads to the rise of the multivariate skewness. Our findings reveal that the countries with high risk from the behavior of the number of deaths tend to have pronounced skewness and kurtosis values.

Kurtosis-based projection pursuit analysis to evaluate South American rapadura.

Rapadura is an artisanal candy obtained from concentrated sugarcane juice. In this study, a differentiation between South American rapadura producers has been tried using a Kurtosis-based projection pursuit analysis (kPPA) concerning essential minerals, acrylamide, moisture contents, pH, and color. These parameters revealed significant inter- and intra-country differences. Based on the employed measurements, a multivariate exploration with kPPA extracted information from rapadura even though it is a very artisanal product and was effective in separating classes, especially Brazilian and Ecuadorian rapadura, where principal component analysis failed. Moreover, ellipse confidence regions showed significant differences between non-organic and organic rapadura from Colombia and Peru in granulated form. From a chemometric point of view, the application of kPPA can be used in cases when other metrics (as based on the variance) fail and can be useful in the exploratory analysis of complex multivariate chemical data.

A Comparative Study of Corrosion AA6061 and AlSi10Mg Alloys Produced by Extruded and Additive Manufacturing.

The aim of this work was to evaluate the corrosion behavior of the AA6061 and AlSi10Mg alloys produced by extruded and additive manufacturing (selective laser melting, SLM). Alloys were immersed in two electrolytes in H2O and 3.5 wt. % NaCl solutions at room temperature and their corrosion behavior was studied by electrochemical noise technique (EN). Three different methods filtered EN signals, and the statistical analysis was employed to obtain Rn, the localization index (LI), Kurtosis, skew, and the potential spectral density analysis (PSD). The Energy Dispersion Plots (EDP) of wavelets method was employed to determine the type of corrosion and the Hilbert-Huang Transform (HHT), analyzing the Hilbert Spectra. The result indicated that the amplitude of the transients in the time series in potential and current is greater in the AlSi10Mg alloy manufactured by additive manufacturing. The amplitude of the transients decreases in both alloys (AA6061 and AlSi10Mg) as time increases.

Using an Algorithm to Fit a GAMLSS Model on Dry Matter Data from Brachiaria brizantha.

<b>Background and Objective:</b> Forage production in the tropics is generally asymmetrically distributed. Hence the need to use more complex models, especially when multiple comparisons are made and there are very large deviations from normality. The objective of this research is to fit a Generalized Additive Model for Location, Scale and Shape (GAMLSS) model on accumulated dry matter data from <i>Brachiaria brizantha</i> using a model selection algorithm. <b>Materials and Methods:</b> A Box-Cox Power Exponential (BCPE) distribution was adjusted on the dry matter from <i>Brachiaria brizantha</i> data implementing GAMLSS in R (programming language). The accumulated dry matter data for <i>B. brizantha</i> were obtained from a study carried out on a farm in the state of Portuguesa, Venezuela. The explanatory covariate x was the interval between cuts (21, 28, 35 and 42 days). <b>Results:</b> The dependent variable (dry matter) exhibited both skewness and kurtosis. GAMLSS allowed flexible modeling of both the distribution of the dry matter yield from <i>B. brizantha</i> and the dependence of all the parameters of the distribution on intervals between cuttings. For the dry matter yield from <i>B. brizantha</i>, which exhibited skewness and leptokurtosis, the BCPE distribution, provided the best fit. <b>Conclusion:</b> The interval between cuttings showed an effect that is reflected in the average yield of dry matter from <i>B. brizantha</i>. The interval between cuts affected the skewness and the kurtosis of the distribution.

Tree seedling trait optimization and growth in response to local-scale soil and light variability.

At local scales, it has been suggested that high levels of resources lead to increased tree growth via trait optimization (highly peaked trait distribution). However, this contrasts with (1) theories that suggest that trait optimization and high growth occur in the most common resource level and (2) empirical evidence showing that high trait optimization can be also found at low resource levels. This raises the question of how are traits and growth optimized in highly diverse plant communities. Here, we propose a series of hypotheses about how traits and growth are expected to be maximized under different resource levels (low, the most common, and high) in tree seedling communities from a subtropical forest in Puerto Rico, USA. We studied the variation in the distribution of biomass allocation and leaf traits and seedlings growth rate along four resource gradients: light availability (canopy openness) and soil K, Mg, and N content. Our analyses consisted of comparing trait kurtosis (a measurement of trait optimization), community trait means, and relative growth rates at three resource levels (low, common, and high). Trait optimization varied across the three resource levels depending on the type of resource and trait, with leaf traits being optimized under high N and in the most common K and Mg conditions, but not at any of the light levels. Also, seedling growth increased at high-light conditions and high N and K but was not related to trait kurtosis. Our results indicate that local-scale variability of soil fertility and understory light conditions result in shifts in species ecological strategies that increase growth despite a weak trait optimization, suggesting the existence of alternative phenotypes that achieve similar high performance. Uncovering the links between abiotic factors, functional trait diversity and performance is necessary to better predict tree responses to future changes in abiotic conditions.

Vibrations in CDFW.

Continuous drive friction welding is a solid-state welding process that has been experimentally proven to be a fast and reliable method. This is a complex process; deformations in the viscosity of a material alter the friction between the surfaces of the pieces. All these dynamics cause changes in the vibration signals; the interpretation of these signals can reveal important information. The vibration signals generated during the friction and forging stages are measured on the stationary part of the structure to determine the influence of the manipulated variables on the time domain statistical characteristics (root mean square, peak value, crest factor, and kurtosis). In the frequency domain, empirical mode decomposition is used to characterize frequencies. It was observed that it is possible to identify the effects of the manipulated variables on the calculated statistical characteristics. The results also indicate that the effect of manipulated variables is stronger on low-frequency signals.

Landscape and Environmental Factors Influencing Stage Persistence and Abundance of the Bamboo Mosquito, Tripteroides bambusa (Diptera: Culicidae), across an Altitudinal Gradient.

The bamboo mosquito, Tripteroides bambusa (Yamada) (Diptera: Culicidae), is a common insect across East Asia. Several studies have looked at the ecology of Tr. bambusa developmental stages separately, but little is known about the factors associated with the persistence (how often) and abundance (how many individuals) of Tr. bambusa stages simultaneously studied across a heterogeneous landscape. Here, we ask what environmental and landscape factors are associated with the persistence and abundance of Tr. bambusa stages across the altitudinal gradient of Mt. Konpira, Nagasaki City, Japan. During a season-long study we counted 8065 (7297 4th instar larvae, 670 pupae and 98 adults) Tr. bambusa mosquitoes. We found that persistence and abundance patterns were not associated among stages, with the exception of large (4th instar) and small (1st to 3rd instars) larvae persistence, which were positively correlated. We also found that relative humidity was associated with the persistence of Tr. bambusa aquatic stages, being positively associated with large and small larvae, but negatively with pupae. Similarly, landscape aspect changed from positive to negative the sign of its association with Tr. bambusa pupae and adults, highlighting that environmental associations change with life stage. Meanwhile, Tr. bambusa abundance patterns were negatively impacted by more variable microenvironments, as measured by the negative impacts of kurtosis and standard deviation (SD) of environmental variables, indicating Tr. bambusa thrives in stable environments, suggesting this mosquito species has a finely grained response to environmental changes.

Quantification of alignment of vascular smooth muscle cells.

Vascular smooth muscle cells (VSMCs) are essential components that keep the tonus of the arterial network, which is the channel used to conduct the blood from the heart to the peripheral areas of the body. It is known that mechanical and architectural changes in VSMCs may lead to functional modifications in the cardiovascular system; therefore, the quantitative characterization of these changes can help to elucidate questions that remain unclear in pathological situations, such as hypertension, vasospasm, vascular hypertrophy, and atherosclerosis. In this work, we have developed a new framework of image processing using the Sobel operator, associated with statistical analysis, to determine the degree of local alignment of actin filaments, which we found to be directly related with the distensibility of the arterial wall. We have also compared these results with the rigidity of the cytoskeleton of VSMCs. The results suggest that the alignment degree increases from peripheral arteries, such as carotid and femoral, to central arteries, as well coronary and thoracic aorta, which can indicate that the level of local alignment of the actin fibers in VSMCs is related with the mechanical behavior of the arterial wall. © 2018 International Society for Advancement of Cytometry.

Desempenho de testes de normalidade multivariados avaliado por simulação Monte Carlo / Monte Carlo evaluation of the performance of multivariate normality tests

Neste trabalho, objetivou-se avaliar a performance do teste multivariado de normalidade de Shapiro-Wilk implementado no R comparando o seu desempenho com os testes de assimetria e curtose de Mardia (1970, 1974, 1975) utilizando simulação Monte Carlo. Foram mensuradas e comparadas as taxas de erro tipo I e poderes dos testes. Pode-se concluir que o teste de Shapiro-Wilk multivariado do programa R, função mshapiro.test do pacote mvnormtest, tem fraco desempenho (liberal) e não é recomendado para uso rotineiro.

This work aimed to evaluate the performance of the multivariate normality test of Shapiro-Wilk implemented in R in the library mvnormtest and to compare it with the asymmetry and kurtosis normality test proposed by Mardia (1970, 1974, 1975) using Monte Carlo simulation. The multivariate normality test of Shapiro-Wilk is not recommended for regular use.