Characterizing unstructured data with the nearest neighbor permutation entropy.

Permutation entropy and its associated frameworks are remarkable examples of physics-inspired techniques adept at processing complex and extensive datasets. Despite substantial progress in developing and applying these tools, their use has been predominantly limited to structured datasets such as time series or images. Here, we introduce the k-nearest neighbor permutation entropy, an innovative extension of the permutation entropy tailored for unstructured data, irrespective of their spatial or temporal configuration and dimensionality. Our approach builds upon nearest neighbor graphs to establish neighborhood relations and uses random walks to extract ordinal patterns and their distribution, thereby defining the k-nearest neighbor permutation entropy. This tool not only adeptly identifies variations in patterns of unstructured data but also does so with a precision that significantly surpasses conventional measures such as spatial autocorrelation. Additionally, it provides a natural approach for incorporating amplitude information and time gaps when analyzing time series or images, thus significantly enhancing its noise resilience and predictive capabilities compared to the usual permutation entropy. Our research substantially expands the applicability of ordinal methods to more general data types, opening promising research avenues for extending the permutation entropy toolkit for unstructured data.

Adaptive exponential integrate-and-fire model with fractal extension.

The description of neuronal activity has been of great importance in neuroscience. In this field, mathematical models are useful to describe the electrophysical behavior of neurons. One successful model used for this purpose is the Adaptive Exponential Integrate-and-Fire (Adex), which is composed of two ordinary differential equations. Usually, this model is considered in the standard formulation, i.e., with integer order derivatives. In this work, we propose and study the fractal extension of Adex model, which in simple terms corresponds to replacing the integer derivative by non-integer. As non-integer operators, we choose the fractal derivatives. We explore the effects of equal and different orders of fractal derivatives in the firing patterns and mean frequency of the neuron described by the Adex model. Previous results suggest that fractal derivatives can provide a more realistic representation due to the fact that the standard operators are generalized. Our findings show that the fractal order influences the inter-spike intervals and changes the mean firing frequency. In addition, the firing patterns depend not only on the neuronal parameters but also on the order of respective fractal operators. As our main conclusion, the fractal order below the unit value increases the influence of the adaptation mechanism in the spike firing patterns.

Non-Markovian Diffusion and Adsorption-Desorption Dynamics: Analytical and Numerical Results.

The interplay of diffusion with phenomena like stochastic adsorption-desorption, absorption, and reaction-diffusion is essential for life and manifests in diverse natural contexts. Many factors must be considered, including geometry, dimensionality, and the interplay of diffusion across bulk and surfaces. To address this complexity, we investigate the diffusion process in heterogeneous media, focusing on non-Markovian diffusion. This process is limited by a surface interaction with the bulk, described by a specific boundary condition relevant to systems such as living cells and biomaterials. The surface can adsorb and desorb particles, and the adsorbed particles may undergo lateral diffusion before returning to the bulk. Different behaviors of the system are identified through analytical and numerical approaches.

Generalized Kinetic Equations with Fractional Time-Derivative and Nonlinear Diffusion: H-Theorem and Entropy.

We investigate the H-theorem for a class of generalized kinetic equations with fractional time-derivative, hyperbolic term, and nonlinear diffusion. When the H-theorem is satisfied, we demonstrate that different entropic forms may emerge due to the equation's nonlinearity. We obtain the entropy production related to these entropies and show that its form remains invariant. Furthermore, we investigate some behaviors for these equations from both numerical and analytical perspectives, showing a large class of behaviors connected with anomalous diffusion and their effects on entropy.

Fractal and fractional SIS model for syphilis data.

This work studies the SIS model extended by fractional and fractal derivatives. We obtain explicit solutions for the standard and fractal formulations; for the fractional case, we study numerical solutions. As a real data example, we consider the Brazilian syphilis data from 2011 to 2021. We fit the data by considering the three variations of the model. Our fit suggests a recovery period of 11.6 days and a reproduction ratio (R0) equal to 6.5. By calculating the correlation coefficient (r) between the real data and the theoretical points, our results suggest that the fractal model presents a higher r compared to the standard or fractional case. The fractal formulation is improved when two different fractal orders with distinguishing weights are considered. This modification in the model provides a better description of the data and improves the correlation coefficient.

Nonlinear Fokker-Planck Equations, H-Theorem and Generalized Entropy of a Composed System.

We investigate the dynamics of a system composed of two different subsystems when subjected to different nonlinear Fokker-Planck equations by considering the H-theorem. We use the H-theorem to obtain the conditions required to establish a suitable dependence for the system's interaction that agrees with the thermodynamics law when the nonlinearity in these equations is the same. In this framework, we also consider different dynamical aspects of each subsystem and investigate a possible expression for the entropy of the composite system.

Entropy Production in a Fractal System with Diffusive Dynamics.

We study the entropy production in a fractal system composed of two subsystems, each of which is subjected to an external force. This is achieved by using the H-theorem on the nonlinear Fokker-Planck equations (NFEs) characterizing the diffusing dynamics of each subsystem. In particular, we write a general NFE in terms of Hausdorff derivatives to take into account the metric of each system. We have also investigated some solutions from the analytical and numerical point of view. We demonstrate that each subsystem affects the total entropy and how the diffusive process is anomalous when the fractal nature of the system is considered.

Results for Nonlinear Diffusion Equations with Stochastic Resetting.

In this study, we investigate a nonlinear diffusion process in which particles stochastically reset to their initial positions at a constant rate. The nonlinear diffusion process is modeled using the porous media equation and its extensions, which are nonlinear diffusion equations. We use analytical and numerical calculations to obtain and interpret the probability distribution of the position of the particles and the mean square displacement. These results are further compared and shown to agree with the results of numerical simulations. Our findings show that a system of this kind exhibits non-Gaussian distributions, transient anomalous diffusion (subdiffusion and superdiffusion), and stationary states that simultaneously depend on the nonlinearity and resetting rate.

Why Australia needs to define obesity as a chronic condition.

BACKGROUND: In Australia people with a diagnosed chronic condition can be managed on unique funded care plans that allow the recruitment of a multidisciplinary team to assist in setting treatment goals and adequate follow up. In contrast to the World Health Organisation, the North American and European Medical Associations, the Australian Medical Association does not recognise obesity as a chronic condition, therefore excluding a diagnosis of obesity from qualifying for a structured and funded treatment plan. BODY: The Australian guidelines for management of Obesity in adults in Primary Care are structured around a five step process -the '5As': Ask & Assess, Advise, Assist and Arrange'. This article aims to identify the key challenges and successes associated with the '5As' approach, to better understand the reasons for the gap between the high Australian prevalence of overweight and obesity and an actual diagnosis and treatment plan for managing obesity. It argues that until the Australian health system follows the international lead and defines obesity as a chronic condition, the capacity for Australian doctors to diagnose and initiate structured treatment plans will remain limited and ineffective. CONCLUSION: Australian General Practitioners are limited in their ability manage obesity, as the current treatment guidelines only recognise obesity as a risk factor rather than a chronic condition.

Unusual diffusing regimes caused by different adsorbing surfaces.

A confined liquid with dispersed neutral particles is theoretically studied when the limiting surfaces present different dynamics for the adsorption-desorption phenomena. The investigation considers different non-singular kernels in the kinetic equations at the walls, where the suitable choice of the kernel can account for the relative importance of physisorption or chemisorption. We find that even a small difference in the adsorption-desorption rate of one surface (relative to the other) can drastically affect the behavior of the whole system. The surface and bulk densities and the dispersion are calculated when several scenarios are considered and anomalous-like behaviors are found. The approach described here is closely related to experimental situations, and can be applied in several contexts such as dielectric relaxation, diffusion-controlled relaxation in liquids, liquid crystals, and amorphous polymers.

Machine learning partners in criminal networks.

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.

Learning physical properties of liquid crystals with deep convolutional neural networks.

Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties of materials and in simplifying experimental protocols, their usage in liquid crystals research is still limited. This is surprising because optical imaging techniques are often applied in this line of research, and it is precisely with images that machine learning algorithms have achieved major breakthroughs in recent years. Here we use convolutional neural networks to probe several properties of liquid crystals directly from their optical images and without using manual feature engineering. By optimizing simple architectures, we find that convolutional neural networks can predict physical properties of liquid crystals with exceptional accuracy. We show that these deep neural networks identify liquid crystal phases and predict the order parameter of simulated nematic liquid crystals almost perfectly. We also show that convolutional neural networks identify the pitch length of simulated samples of cholesteric liquid crystals and the sample temperature of an experimental liquid crystal with very high precision.

Androgens in Menopausal Women: Not Only Polycystic Ovary Syndrome.

Menopause is the period of a woman's life that is characterized by the permanent cessation of menses associated to hormonal changes, of which the most important is the decrease of estrogen levels. Following menopause, the concentrations of circulating androgens decrease. However, increased concentrations of luteinizing hormone induce androgens secretion from the ovaries and presumably from the adrenal glands. Peripheral conversion of androgens results to the circulating hormonal androgen profile. Some pathological conditions are associated with greater concentrations of androgens after menopause than in controls, with polycystic ovary syndrome (PCOS) being the commonest. These conditions can be distinguished in non-tumorous (adrenal or ovarian) or functional and tumorous (adrenal or ovarian benign or malignant) masses. Apart from PCOS, other non-tumorous (adrenal or ovarian) causes of hyperandrogenism in post-menopausal women are obesity, non-classic congenital adrenal hyperplasia (NCCAH), endocrinopathies, such as Cushing disease or acromegaly; ovarian hyperthecosis, drug use or abuse. Tumorous (adrenal or ovarian) causes include adrenal cortical cancers, adrenal benign adenomas and even incidentalomas, or ovarian tumors such as the sex-cord stromal ovarian tumors and metastases in the ovary. The diagnosis of hyperandrogenism is made through medical history, clinical examination, and laboratory tests. Total testosterone concentration of 150 ng/dL can be used at first to distinguish a malignant from a benign cause of hyperandrogenism. Dehydroepiandrosterone sulfate concentration may support adrenal source of androgens. Imaging techniques are used to localize the source of androgens: computed tomography and magnetic resonance imaging (MRI) for the adrenals and transvaginal ultrasound or MRI for the ovaries. Finally, treatment (etiologic and symptomatic) and long-term effects of hyperandrogenism are developed in this chapter.

Characterizing time series via complexity-entropy curves.

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q-complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

Scale-Adjusted Metrics for Predicting the Evolution of Urban Indicators and Quantifying the Performance of Cities.

More than a half of world population is now living in cities and this number is expected to be two-thirds by 2050. Fostered by the relevancy of a scientific characterization of cities and for the availability of an unprecedented amount of data, academics have recently immersed in this topic and one of the most striking and universal finding was the discovery of robust allometric scaling laws between several urban indicators and the population size. Despite that, most governmental reports and several academic works still ignore these nonlinearities by often analyzing the raw or the per capita value of urban indicators, a practice that actually makes the urban metrics biased towards small or large cities depending on whether we have super or sublinear allometries. By following the ideas of Bettencourt et al. [PLoS ONE 5 (2010) e13541], we account for this bias by evaluating the difference between the actual value of an urban indicator and the value expected by the allometry with the population size. We show that this scale-adjusted metric provides a more appropriate/informative summary of the evolution of urban indicators and reveals patterns that do not appear in the evolution of per capita values of indicators obtained from Brazilian cities. We also show that these scale-adjusted metrics are strongly correlated with their past values by a linear correspondence and that they also display crosscorrelations among themselves. Simple linear models account for 31%-97% of the observed variance in data and correctly reproduce the average of the scale-adjusted metric when grouping the cities in above and below the allometric laws. We further employ these models to forecast future values of urban indicators and, by visualizing the predicted changes, we verify the emergence of spatial clusters characterized by regions of the Brazilian territory where we expect an increase or a decrease in the values of urban indicators.

Rapid screening of environmental chemicals for estrogen receptor binding capacity.

Over the last few years, an increased awareness of endocrine disrupting chemicals (EDCs) and their potential to affect wildlife and humans has produced a demand for practical screening methods to identify endocrine activity in a wide range of environmental and industrial chemicals. While it is clear that in vivo methods will be required to identify adverse effects produced by these chemicals, in vitro assays can define particular mechanisms of action and have the potential to be employed as rapid and low-cost screens for use in large scale EDC screening programs. Traditional estrogen receptor (ER) binding assays are useful for characterizing a chemical's potential to be an estrogen-acting EDC, but they involve displacement of a radioactive ligand from crude receptor preparations at low temperatures. The usefulness of these assays for realistically determining the ER binding interactions of weakly estrogenic environmental and industrial compounds that have low aqueous solubility is unclear. In this report, we present a novel fluorescence polarization (FP) method that measures the capacity of a competitor chemical to displace a high affinity fluorescent ligand from purified, recombinant human ER-[alpha] at room temperature. The ER-[alpha] binding interactions generated for 15 natural and synthetic compounds were found to be similar to those determined with traditional receptor binding assays. We also discuss the potential to employ this FP technology to binding studies involving ER-ss and other receptors. Thus, the assay introduced in this study is a nonradioactive receptor binding method that shows promise as a high throughput screening method for large-scale testing of environmental and industrial chemicals for ER binding interactions.

The dissociation rate of estrogen receptor alpha from the consensus estrogen response element.

The rate of dissociation of recombinant, purified human estrogen receptor alpha (ERalpha) from a fluorescein-labeled DNA containing the consensus vitellogenin ERE sequence (F-vitERE) was determined in real time using fluorescence anisotropy. The complex of estradiol-occupied ERalpha with F-vitERE had an apparent dissociation rate of 1.48+/-0.06x10(-2) s(-1) and a half-life of 46.6 s at room temperature. The dissociation rate was characterized by a single exponential decay, suggesting that ER dissociates from the DNA as a preformed dimer, rather than as two individual monomers. The association rate of estradiol-occupied ERalpha for the F-vitERE was calculated as 7x10(6) M(-1) s(-1) based on the dissociation rate measured and previous determinations of the equilibrium dissociation constant (Kd) in similar assay conditions (Ozers et al., 1997). In buffer containing various concentrations of salt, the rate of dissociation of estradiol-occupied ERalpha from F-vitERE was accelerated by increasing salt concentrations. Compared to estradiol-occupied ERalpha, the rate of dissociation of unoccupied ERalpha from the F-vitERE was very similar, indicating that estradiol occupancy does not affect the dissociation rate of ERalpha from the ERE.

Development and application of fluorescence polarization assays in drug discovery.

Fluorescence polarization technology has been used in basic research and commercial diagnostic assays for many decades, but has begun to be widely used in drug discovery only in the past six years. Originally, FP assays for drug discovery were developed for single-tube analytical instruments, but the technology was rapidly converted to high-throughput screening assays when commercial plate readers with equivalent sensitivity became available. This review will discuss fluorescence polarization assays in current use in drug discovery research as well as those in development that will likely be used in the near future. These assays include targets such as kinases, phosphatases, proteases, G-protein coupled receptors, and nuclear receptors.

Issues and implications in the selection of blood glucose monitoring technologies.

Over the last 2 decades assay technology originating in the laboratory has been adapted for the special situation of in vitro blood glucose monitoring in the home, at work or play, or at the bedside. The availability of blood glucose monitoring devices has had a significant impact on the treatment of diabetes, especially with respect to involving the patients in their treatment. The unique requirements of this type of testing have led to novel developments in sample acquisition techniques, analyte detection, measurement techniques, and error detection. The performance of these in vitro devices in terms of accuracy and imprecision is largely dependent on factors that contribute to variation in response that are related to testing with blood samples outside of the laboratory. These factors include, for example, variations in environmental conditions, the variability of hematocrit and oxygen concentrations of the blood, and the fact that the blood is used undiluted. Therefore, the technologies used have been selected, developed, optimized, and calibrated to minimize the impact of these factors. The technologies also must be capable of providing accurate, reproducible results over the large range of clinical interest from the hypoglycemic range to glucose concentrations 10 to 15 to 20 times greater. However, when selecting a technology there are invariably some trade-offs to consider. Thus, the products must be optimized to balance performance, reliability, and cost. Examples are discussed.

Data analyst technician: an innovative role for the pharmacy technician.

The development of an innovative role for the pharmacy technician is described. The role of the pharmacy technician was based on a needs assessment and the expertise of the pharmacy technician selected. Initial responsibilities of the technician included chart reviews, benchmarking surveys, monthly financial impact analysis, initiative assessment, and quality improvement reporting. As the drug-use and disease-state management (DUDSM) program expanded, pharmacist activities increased, requiring the expansion of data analyst technician (DAT) duties. These new responsibilities included participation in patient assessment, data collection and interpretation, and formulary enforcement. Most recently, technicians' expanded duties include maintenance of a physician compliance profiling database, quality improvement reporting and graphing, active role in patient risk assessment and database management for adult vaccination, and support of financial impact monitoring for other institutions within the health system. This pharmacist-technician collaboration resulted a threefold increase in patient assessments completed per day. In addition, as the DUDSM program continues to expand across the health system, an increase in DAT resources from 0.5 to 1.0 full-time equivalent was obtained. The role of the DAT has increased the efficiency of the DUDSM program and has provided an innovative role for the pharmacy technician.