Dynamical phase transition in spike neuronal firing patterns of hippocampal cells.

There is increasing evidence that the brain resides in a state of criticality. The purpose of the present work is to characterize the dynamics of individual hippocampal CA1 pyramidal cells and to investigate how it is influenced by changes in Kv7.2/7.3 (M-channel) ion channel modulation, which is known to be key in determining the neuronal excitability. We show that the resting activity of CA1 neurons exhibit random dynamics with low information content, while changes in M-channel modulation move the neuronal activity near a phase transition to richer non-trivial dynamics. We interpret these results as the basis upon which the state of self-organized criticality is built.

The Standard Genetic Code can Evolve from a Two-Letter GC Code Without Information Loss or Costly Reassignments.

It is widely agreed that the standard genetic code must have been preceded by a simpler code that encoded fewer amino acids. How this simpler code could have expanded into the standard genetic code is not well understood because most changes to the code are costly. Taking inspiration from the recently synthesized six-letter code, we propose a novel hypothesis: the initial genetic code consisted of only two letters, G and C, and then expanded the number of available codons via the introduction of an additional pair of letters, A and U. Various lines of evidence, including the relative prebiotic abundance of the earliest assigned amino acids, the balance of their hydrophobicity, and the higher GC content in genome coding regions, indicate that the original two nucleotides were indeed G and C. This process of code expansion probably started with the third base, continued with the second base, and ended up as the standard genetic code when the second pair of letters was introduced into the first base. The proposed process is consistent with the available empirical evidence, and it uniquely avoids the problem of costly code changes by positing instead that the code expanded its capacity via the creation of new codons with extra letters.

Mucoadhesive Properties of Eudragit®RS100, Eudragit®S100, and Poly(ε-caprolactone) Nanocapsules: Influence of the Vehicle and the Mucosal Surface.

The use of polymers as mucoadhesive materials has been explored in several drug delivery systems. It is well known that the resulting mucoadhesiveness not only depends on the polymers by themselves, but also on the way they are delivered and on the application target. However, little attention has been given to the combined effect of such characteristics. Therefore, the objective of this study is to analyze the mucoadhesion resulting from combined effects of nanocapsules produced with polymers of different ionic properties, Eudragit®RS100, Eudragit®S100, or poly(ε-caprolactone), when they are incorporated into different vehicles (suspension, hydrogel, and powder) and applied on different mucosal surfaces (mucin, porcine vaginal, and buccal mucosa). Mucoadhesion was measured by a tensile stress tester. Our findings show that polymeric self-assembling as nanocapsules improved the mucoadhesion of the polymers. Eudragit®RS100 nanocapsules have the best performance, independently of the vehicle and surface used. Regarding the vehicle, hydrogels showed higher adhesion when compared to suspensions and powders. When considering different types of surfaces, mucin presented a similar pattern like the animal mucosa, but it overestimated the mucoadhesiveness of all formulations. In conclusion, this study demonstrated that the best strategy to achieve high mucoadhesive formulations is by incorporating Eudragit®RS100 nanocapsules in hydrogels. Moreover, mucin is a suitable substrate to compare and screen different formulations but not as a conclusive estimation of the mucoadhesion values that can be achieved. These results are summarized in a decision tree that can help to understand different strategies of combination of these factors and the expected outcomes.

Physiological Network Is Disrupted in Severe COVID-19.

The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism's homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else. The goal of this study is to determine the differences in the relationships that link physiological variables that characterize the elderly and men, and those that characterize fatal outcomes in young men. To accomplish this, we examined a database of patients with moderate to severe COVID-19 (471 men and 277 women) registered at the "Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán" in March 2020. The sample was stratified by outcome, age, and sex. Physiological networks were built using 67 physiological variables (vital signs, anthropometric, hematic, biochemical, and tomographic variables) recorded upon hospital admission. Individual variables and system behavior were examined by descriptive statistics, differences between groups, principal component analysis, and network analysis. We show how topological network properties, particularly clustering coefficient, become disrupted in disease. Finally, anthropometric, metabolic, inflammatory, and pulmonary cluster interaction characterize the deceased young male group.

Criticality in a multisignal system using principal component analysis.

In systems with dynamical transitions, criticality is usually defined by the behavior of suitable individual variables of the system. In the case of time series, the usual procedure involves the analysis of the statistical properties of the selected variable as a function of a control parameter in both the time and frequency domains. An interesting question, however, is how to identify criticality when multiple simultaneous signals are required to provide a reliable representation of the system, especially when the signals exhibit different dynamics and do not individually display the characteristic signs of criticality. In that situation, a technique that analyzes the collective behavior of the signals is necessary. In this work we show that the eigenvalues and eigenvectors obtained from principal components analysis (PCA) can be used as a way to identify collective criticality. To do this, we construct a multilayer Ising model comprised of coupled two-dimensional Ising lattices that have distinct critical temperatures when isolated. We apply PCA to the collection of magnetization signals for a range of global temperatures and study the resulting eigenvalues. We find that there exists a single global temperature at which the eigenvalue spectrum follows a power law, and identify this as an indicator of "multicriticality" for the system. We then apply the technique to electroencephalographic recordings of brain activity, as this is a prime example of multiple signals with distinct individual dynamics. The analysis reveals a power-law eigenspectrum, adding further evidence to the brain criticality hypothesis. We also show that the eigenvectors can be used to distinguish the recordings in the resting state from those during a cognitive task, and that there is important information contained in all eigenvectors, not just the first few dominant ones, establishing that PCA has great utility beyond dimensionality reduction.

Implementing Vertical Integration in the Industry 4.0 Journey: Which Factors Influence the Process of Information Systems Adoption?

One of the key principles of Industry 4.0 is the implementation of vertical integration, which considers the integration of information systems from different hierarchical levels in a company to support decision-making with real-time data flows. Companies face challenges when they want to implement vertical integration, which is not trivial due to the risks inherent to the decision stages of adoption. We investigate the main factors influencing the different stages of adoption of vertical integration to provide a clearer view of what managers should consider at each stage. We adopt a multi-case study approach based on the investigation of ten companies that followed this adoption process. We develop a framework with 22 factors deployed in the three stages of decision (knowledge, persuasion, and final decision) and three main dimensions of analysis: technology, organization, and environment. We analyze the potential tensions between these factors and show how managers should balance such factors during the decision stages.

Cardiovascular dysautonomia in Achalasia Patients: Blood pressure and heart rate variability alterations.

Achalasia is a disease characterized by the inability to relax the esophageal sphincter due to a degeneration of the parasympathetic ganglion cells located in the wall of the thoracic esophagus. Achalasia has been associated with extraesophageal dysmotility, suggesting alterations of the autonomic nervous system (ANS) that extend beyond the esophagus. The purpose of the present contribution is to investigate whether achalasia may be interpreted as the esophageal manifestation of a more generalized disturbance of the ANS which includes alterations of heart rate and/or blood pressure. Therefore simultaneous non-invasive records of the heart inter-beat intervals (IBI) and beat-to-beat systolic blood pressure (SBP) of 14 patients (9 female, 5 male) with achalasia were compared with the records of 34 rigorously screened healthy control subjects (17 female, 17 male) in three different conditions: supine, standing up, and controlled breathing at 0.1 Hz, using a variety of measures in the time and spectral domains. Significant differences in heart rate variability (HRV) and blood pressure variability (BPV) were observed which seem to be due to cardiovagal damage to the heart, i.e., a failure of the ANS, as expected according to our hypothesis. This non-invasive methodology can be employed as an auxiliary clinical protocol to study etiology and evolution of achalasia, and other pathologies that damage ANS.

Sex Differences in the Physiological Network of Healthy Young Subjects.

Within human physiology, systemic interactions couple physiological variables to maintain homeostasis. These interactions change according to health status and are modified by factors such as age and sex. For several physiological processes, sex-based distinctions in normal physiology are present and defined in isolation. However, new methodologies are indispensable to analyze system-wide properties and interactions with the objective of exploring differences between sexes. Here we propose a new method to construct complex inferential networks from a normalization using the clinical criteria for health of physiological variables, and the correlations between anthropometric and blood tests biomarkers of 198 healthy young participants (117 women, 81 men, from 18 to 27 years old). Physiological networks of men have less correlations, displayed higher modularity, higher small-world index, but were more vulnerable to directed attacks, whereas networks of women were more resilient. The networks of both men and women displayed sex-specific connections that are consistent with the literature. Additionally, we carried out a time-series study on heart rate variability (HRV) using Physionet's Fantasia database. Autocorrelation of HRV, variance, and Poincare's plots, as a measure of variability, are statistically significant higher in young men and statistically significant different from young women. These differences are attenuated in older men and women, that have similar HRV distributions. The network approach revealed differences in the association of variables related to glucose homeostasis, nitrogen balance, kidney function, and fat depots. The clusters of physiological variables and their roles within the network remained similar regardless of sex. Both methodologies show a higher number of associations between variables in the physiological system of women, implying redundant mechanisms of control and simultaneously showing that these systems display less variability in time than those of men, constituting a more resilient system.

Phenotype Heritability in Holobionts: An Evolutionary Model.

Many complex diseases are expressed with high incidence only in certain populations. Genealogy studies determine that these diseases are inherited with a high probability. However, genetic studies have been unable to identify the genomic signatures responsible for such heritability, as identifying the genetic variants that make a population prone to a given disease is not enough to explain its high occurrence within the population. This gap is known as the missing heritability problem. We know that the microbiota plays a very important role in determining many important phenotypic characteristics of its host, in particular the complex diseases for which the missing heritability occurs. Therefore, when computing the heritability of a phenotype, it is important to consider not only the genetic variation in the host but also in its microbiota. Here we test this hypothesis by studying an evolutionary model based on gene regulatory networks. Our results show that the holobiont (the host plus its microbiota) is capable of generating a much larger variability than the host alone, greatly reducing the missing heritability of the phenotype. This result strongly suggests that a considerably large part of the missing heritability can be attributed to the microbiome.

Physiological Network From Anthropometric and Blood Test Biomarkers.

Currently, research in physiology focuses on molecular mechanisms underlying the functioning of living organisms. Reductionist strategies are used to decompose systems into their components and to measure changes of physiological variables between experimental conditions. However, how these isolated physiological variables translate into the emergence -and collapse- of biological functions of the organism as a whole is often a less tractable question. To generate a useful representation of physiology as a system, known and unknown interactions between heterogeneous physiological components must be taken into account. In this work we use a Complex Inference Networks approach to build physiological networks from biomarkers. We employ two unrelated databases to generate Spearman correlation matrices of 81 and 54 physiological variables, respectively, including endocrine, mechanic, biochemical, anthropometric, physiological, and cellular variables. From these correlation matrices we generated physiological networks by selecting a p-value threshold indicating statistically significant links. We compared the networks from both samples to show which features are robust and representative for physiology in health. We found that although network topology is sensitive to the p-value threshold, an optimal value may be defined by combining criteria of stability of topological features and network connectedness. Unsupervised community detection algorithms allowed to obtain functional clusters that correlate well with current medical knowledge. Finally, we describe the topology of the physiological networks, which lie between random and ordered structural features, and may reflect system robustness and adaptability. Modularity of physiological networks allows to explore functional clusters that are consistent even when considering different physiological variables. Altogether Complex Inference Networks from biomarkers provide an efficient implementation of a systems biology approach that is visually understandable and robust. We hypothesize that physiological networks allow to translate concepts such as homeostasis into quantifiable properties of biological systems useful for determination and quantification of health and disease.

Atmospheric blockages as trigger of environmental contingencies in Mexico City.

Atmospheric pollution in cities is due to several human factors, for instance the number of cars in circulation, fuel efficiency and industrial waste, as well as orographic and meteorological conditions that determine air circulation. Ozone contingencies cause health disorders on the population, making it important to understand the factors that trigger such contingencies. Here, we analyze meteorological (wind, temperature, relative humidity) and atmospheric composition (ozone, and NOx) data of five atmospheric monitoring stations on Mexico City, from March 2004 to May 2018, comparing normal days with the extreme days in the 90th percentile of ozone. Moreover, we present the synoptic patterns of the seasonal differences of geopotential height at 500 hPa between extreme and control days. We found that, in the dry-hot season (from March to May) an atmospheric blockage with meteorological conditions of almost no wind, low relative humidity, and small temperature fluctuations occurs. Because the air in the city permanently contains large amounts of ozone precursors like NOx, this meteorological scenario raises ozone levels to those of an environmental contingency. Thus, during the dry-hot season on Mexico City, ozone contingencies are triggered by atmospheric blocking. This scenario will be present in cities surrounded by mountains with high levels of Ozone precursors.

Analysis of properties of Ising and Kuramoto models that are preserved in networks constructed by visualization algorithms.

Recently it has been shown that building networks from time series allows to study complex systems to characterize them when they go through a phase transition. This give us the opportunity to study this systems from a entire new point of view. In the present work we have used the natural and horizontal visualization algorithms to built networks of two popular models, which present phase transitions: the Ising model and the Kuramoto model. By measuring some topological quantities as the average degree, or the clustering coefficient, it was found that the networks retain the capability of detecting the phase transition of the system. From our results it is possible to establish that both visibility algorithms are capable of detecting the critical control parameter, as in every quantity analyzed (the average degree, the average path and the clustering coefficient) there is a minimum or a maximum value. In the case of the natural visualization algorithm, the average path results are much more noisy than in the other quantities in the study. Specially for the Kuramoto Model, which in this case does not allow a detection of the critical point at plain sight as for the other quantities. The horizontal visualization algorithm has proven to be more explicit in every quantity, as every one of them show a clear change of behavior before and after the critical point of the transition.

The Role of the Autonomic Nervous System on Cardiac Rhythm during the Evolution of Diabetes Mellitus Using Heart Rate Variability as a Biomarker.

Heart rate variability (HRV) is highly influenced by the Autonomic Nervous System (ANS). Several illnesses have been associated with changes in the ANS, thus altering the pattern of HRV. However, the variability of the heart rhythm is originated within the Sinus Atrial Node (SAN) which has its own variability. Still, although both oscillators produce HRV, the influence of the SAN on HRV has not yet been exhaustively studied. On the other hand, the complications of diabetes mellitus (DM), for instance, nephropathy, retinopathy, and neuropathy, increase cardiovascular morbidity and mortality. Traditionally, these complications are diagnosed only when the patient is already suffering from the negative symptoms these complications implicate. Consequently, it is of paramount importance to develop new techniques for early diagnosis prior to any deterioration on healthy patients. HRV has been proved to be a valuable, noninvasive clinical evidence for evaluating diseases and even for describing aging and behavior. In this study, several ECGs were recorded and their RR and PP intervals were analyzed to detect the interpotential interval (ii) of the SAN. Additionally, HRV reduction was quantified to identify alterations in the nervous system within the nodal tissue via measuring the SD1/SD2 ratio in a Poincaré plot. With 15 years of DM development, the data showed an age-dependent increase in HRV due to the axon retraction of ANS neurons from its effectors. In addition, these alterations modify the heart rhythm-producing fatal arrhythmias. Therefore, it is possible to avoid the consequences of DM identifying alterations in SAN previous to its symptomatic appearance. This could be used as an early diagnosis indicator.

Modeling the Role of the Microbiome in Evolution.

There is undeniable evidence showing that bacteria have strongly influenced the evolution and biological functions of multicellular organisms. It has been hypothesized that many host-microbial interactions have emerged so as to increase the adaptive fitness of the holobiont (the host plus its microbiota). Although this association has been corroborated for many specific cases, general mechanisms explaining the role of the microbiota in the evolution of the host are yet to be understood. Here we present an evolutionary model in which a network representing the host adapts in order to perform a predefined function. During its adaptation, the host network (HN) can interact with other networks representing its microbiota. We show that this interaction greatly accelerates and improves the adaptability of the HN without decreasing the adaptation of the microbial networks. Furthermore, the adaptation of the HN to perform several functions is possible only when it interacts with many different bacterial networks in a specialized way (each bacterial network participating in the adaptation of one function). Disrupting these interactions often leads to non-adaptive states, reminiscent of dysbiosis, where none of the networks the holobiont consists of can perform their respective functions. By considering the holobiont as a unit of selection and focusing on the adaptation of the host to predefined but arbitrary functions, our model predicts the need for specialized diversity in the microbiota. This structural and dynamical complexity in the holobiont facilitates its adaptation, whereas a homogeneous (non-specialized) microbiota is inconsequential or even detrimental to the holobiont's evolution. To our knowledge, this is the first model in which symbiotic interactions, diversity, specialization and dysbiosis in an ecosystem emerge as a result of coevolution. It also helps us understand the emergence of complex organisms, as they adapt more easily to perform multiple tasks than non-complex ones.

Assessing sustainability in North America's ecosystems using criticality and information theory.

Sustainability is a key concept in economic and policy debates. Nevertheless, it is usually treated only in a qualitative way and has eluded quantitative analysis. Here, we propose a sustainability index based on the premise that sustainable systems do not lose or gain Fisher Information over time. We test this approach using time series data from the AmeriFlux network that measures ecosystem respiration, water and energy fluxes in order to elucidate two key sustainability features: ecosystem health and stability. A novel definition of ecosystem health is developed based on the concept of criticality, which implies that if a system's fluctuations are scale invariant then the system is in a balance between robustness and adaptability. We define ecosystem stability by taking an information theory approach that measures its entropy and Fisher information. Analysis of the Ameriflux consortium big data set of ecosystem respiration time series is contrasted with land condition data. In general we find a good agreement between the sustainability index and land condition data. However, we acknowledge that the results are a preliminary test of the approach and further verification will require a multi-signal analysis. For example, high values of the sustainability index for some croplands are counter-intuitive and we interpret these results as ecosystems maintained in artificial health due to continuous human-induced inflows of matter and energy in the form of soil nutrients and control of competition, pests and disease.

Evolving Ecosystems: Inheritance and Selection in the Light of the Microbiome.

The importance of microorganisms in human biology is undeniable. The amount of research that supports that microbes have a fundamental role in animal and plant physiology is substantial and increasing every year. Even though we are only beginning to comprehend the broadness and complexity of microbial communities, evolutionary theories need to be recast in the light of such discoveries to fully understand and incorporate the role of microbes in our evolution. Fundamental evolutionary concepts such as diversity, heredity, selection, speciation, etc., which constitute the modern synthesis, are now being challenged, or at least expanded, by the emerging notion of the holobiont, which defines the genetic and metabolic networks of the host and its microbes as a single evolutionary unit. Several concepts originally developed to study ecosystems, can be used to understand the physiology and evolution of such complex systems that constitute "individuals." In this review, we discuss these ecological concepts and also provide examples that range from squids, insects and koalas to other mammals and humans, suggesting that microorganisms have a fundamental role not only in physiology but also in evolution. Current evolutionary theories need to take into account the dynamics and interconnectedness of the host-microbiome network, as animals and plants not only owe their symbiogenetic origin to microbes, but also share a long evolutionary history together.

The Human Microbiome and the Missing Heritability Problem.

The "missing heritability" problem states that genetic variants in Genome-Wide Association Studies (GWAS) cannot completely explain the heritability of complex traits. Traditionally, the heritability of a phenotype is measured through familial studies using twins, siblings and other close relatives, making assumptions on the genetic similarities between them. When this heritability is compared to the one obtained through GWAS for the same traits, a substantial gap between both measurements arise with genome wide studies reporting significantly smaller values. Several mechanisms for this "missing heritability" have been proposed, such as epigenetics, epistasis, and sequencing depth. However, none of them are able to fully account for this gap in heritability. In this paper we provide evidence that suggests that in order for the phenotypic heritability of human traits to be broadly understood and accounted for, the compositional and functional diversity of the human microbiome must be taken into account. This hypothesis is based on several observations: (A) The composition of the human microbiome is associated with many important traits, including obesity, cancer, and neurological disorders. (B) Our microbiome encodes a second genome with nearly a 100 times more genes than the human genome, and this second genome may act as a rich source of genetic variation and phenotypic plasticity. (C) Human genotypes interact with the composition and structure of our microbiome, but cannot by themselves explain microbial variation. (D) Microbial genetic composition can be strongly influenced by the host's behavior, its environment or by vertical and horizontal transmissions from other hosts. Therefore, genetic similarities assumed in familial studies may cause overestimations of heritability values. We also propose a method that allows the compositional and functional diversity of our microbiome to be incorporated to genome wide association studies.

The use of chitosan as cationic coating or gel vehicle for polymeric nanocapsules: Increasing penetration and adhesion of imiquimod in vaginal tissue.

The human papillomavirus (HPV) infection, which is strongly related to cervical cancer, can be reduced by the topical application of imiquimod. Some strategies have been used to increase the adhesion and penetration of drugs through the vaginal mucosa. Two of them are the development of mucoadhesive semisolid formulations and the development of polymeric nanocarriers. In this paper, we hypothesize that the combined use of these two strategies results in a better performance of the formulation to retain imiquimod into the vaginal tissue. Aiming this, two different systems are proposed: (a) chitosan-coated poly(ε-caprolactone)-nanocapsules incorporated into hydroxyethylcellulose gel (HEC-NCimiq-chit), and (b) poly(ε-caprolactone)-nanocapsules incorporated into chitosan hydrogel (CHIT-NCimiq). These formulations were submitted to three main tests: mucoadhesivity by interaction, permeation and washability test (or retention test). We developed an integrative index that allows comparing the global performance of the proposed formulations by considering jointly the results of these three tests. Thus, when considered the integrative indexes for the formulations, our results show that CHIT-NCimiq presents the best performance for the treatment of HPV.

Enhancement of early warning properties in the Kuramoto model and in an atrial fibrillation model due to an external perturbation of the system.

When a complex dynamical system is externally disturbed, the statistical moments of signals associated to it can be affected in ways that depend on the nature and amplitude of the perturbation. In systems that exhibit phase transitions, the statistical moments can be used as Early Warnings (EW) of the transition. A natural question is thus to wonder what effect external disturbances have on the EWs of system. In this work we study the impact of external noise added to the system on the EWs, with particular focus on understanding the importance of the amplitude and complexity of the noise. We do this by analyzing the EWs of two computational models related to biology: the Kuramoto model, which is a paradigm of synchronization for biological systems, and a cellular automaton model of cardiac dynamics which has been used as a model for atrial fibrillation. For each model we first characterize the EWs. Then, we introduce external noise of varying intensity and nature to observe what effect this has on the EWs. In both cases we find that the introduction of noise amplified the EWs, with more complex noise having a greater effect. This both offers a way to improve the chance of detection of EWs in real systems and suggests that natural variability in the real world does not have a detrimental effect on EWs, but the opposite.

Loss of Breathing Modulation of Heart Rate Variability in Patients with Recent and Long Standing Diabetes Mellitus Type II.

Healthy subjects under rhythmic breathing have heart interbeat intervals with a respiratory band in the frequency domain that can be an index of vagal activity. Diabetes Mellitus Type II (DM) affects the autonomic nervous system of patients, thus it can be expected changes on the vagal activity. Here, the influence of DM on the breathing modulation of the heart rate is evaluated by analyzing in the frequency domain heart interbeat interval (IBI) records obtained from 30 recently diagnosed, 15 long standing DM patients, and 30 control subjects during standardized clinical tests of controlled breathing at 0.1 Hz, supine rest and standing upright. Fourier spectral analysis of IBI records quantifies heart rate variability in different regions: low-frequencies (LF, 0.04-0.15 Hz), high-frequencies (HF, 0.15-0.4 Hz), and a controlled breathing peak (RP, centered around 0.1 Hz). Two new parameters are introduced: the frequency radius rf (square root of the sum of LF and HF squared) and ß (power of RP divided by the sum of LF and HF). As diabetes evolves, the controlled breathing peak loses power and shifts to smaller frequencies, indicating that heart rate modulation is slower in diabetic patients than in controls. In contrast to the traditional parameters LF, HF and LF/HF, which do not show significant differences between the three populations in neither of the clinical tests, the new parameters rf and ß, distinguish between control and diabetic subjects in the case of controlled breathing. Sympathetic activity that is driven by the baroreceptor reflex associated with the 0.1 Hz breathing modulations is affected in DM patients. Diabetes produces not only a rigid heartbeat with less autonomic induced variability (rf diminishes), but also alters the coupling between breathing and heart rate (reduced ß), due to a progressive decline of vagal and sympathetic activity.