This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework developed herein demonstrates that consciousness exists as a continuous, non-monotonic function across a high-dimensional neurochemical space, with dopamine serving as the primary intensity regulator and serotonin (5-HT2A) as the complexity modulator. This work offers mechanistic explanations for the full spectrum of conscious states, from deep sleep and psychosis to the ultimate collapse in neural death. The theory explains paradoxical phenomena such as prefrontal cortex hypoactivity during seizures, the evolutionary persistence of psychosis-prone individuals, and why controlled administration of classical 5-HT2A agonists shows a comparatively low incidence of serious medical events (< 0.01 % in modern clinical trials), while dopaminergic excess proves rapidly lethal. The framework is tested using 70,290 sleep nights from 242 Parkinson's disease patients, using disease severity (UPDRS) as a proxy for system integrity and medication (LEDD) as a proxy for dopaminergic input. The analysis reveals a significant LEDD x UPDRS interaction (beta=-1.7, p<.0001), confirming the model's prediction of state-dependent, non-linear dynamics.
Jihan K. Zaki, Jakub Tomasik, Jade A. McCune
et al.
Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present challenges of SERS through a novel SERS bio-quantification framework, including spectral processing, analyte quantification, and model explainability. To this end,serotonin quantification in urine media was assessed as a model task with 682 SERS spectra measured in a micromolar range using cucurbit[8]uril chemical spacers. A denoising autoencoder was utilized for spectral enhancement, and convolutional neural networks (CNN) and vision transformers were utilized for biomarker quantification. Lastly, a novel context representative interpretable model explanations (CRIME) method was developed to suit the current needs of SERS mixture analysis explainability. Serotonin quantification was most efficient in denoised spectra analysed using a convolutional neural network with a three-parameter logistic output layer (mean absolute error = 0.15 μM, mean percentage error = 4.67%). Subsequently, the CRIME method revealed the CNN model to present six prediction contexts, of which three were associated with serotonin. The proposed framework could unlock a novel, untargeted hypothesis generating method of biomarker discovery considering the rapid and inexpensive nature of SERS measurements, and the potential to identify biomarkers from CRIME contexts.
Ilse Bollaerts, Jessie Van houcke, Lien Andries
et al.
Damage to the central nervous system (CNS) is one of the leading causes of morbidity and mortality in elderly, as repair after lesions or neurodegenerative disease usually fails because of the limited capacity of CNS regeneration. The causes underlying this limited regenerative potential are multifactorial, but one critical aspect is neuroinflammation. Although classically considered as harmful, it is now becoming increasingly clear that inflammation can also promote regeneration, if the appropriate context is provided. Here, we review the current knowledge on how acute inflammation is intertwined with axonal regeneration, an important component of CNS repair. After optic nerve or spinal cord injury, inflammatory stimulation and/or modification greatly improve the regenerative outcome in rodents. Moreover, the hypothesis of a beneficial role of inflammation is further supported by evidence from adult zebrafish, which possess the remarkable capability to repair CNS lesions and even restore functionality. Lastly, we shed light on the impact of aging processes on the regenerative capacity in the CNS of mammals and zebrafish. As aging not only affects the CNS, but also the immune system, the regeneration potential is expected to further decline in aged individuals, an element that should definitely be considered in the search for novel therapeutic strategies.
Life is a complex biological phenomenon represented by numerous chemical, physical and biological processes performed by a biothermodynamic system/cell/organism. Both living organisms and inanimate objects are subject to aging, a biological and physicochemical process characterized by changes in biological and thermodynamic state. Thus, the same physical laws govern processes in both animate and inanimate matter. All life processes lead to change of an organism's state. The change of biological and thermodynamic state of an organism in time underlies all of three kinds of aging (chronological, biological and thermodynamic). Life and aging of an organism both start at the moment of fertilization and continue through entire lifespan. Fertilization represents formation of a new organism. The new organism represents a new thermodynamic system. From the very beginning, it changes its state by changing thermodynamic parameters. The change of thermodynamic parameters is observed as aging and can be related to change in entropy. Entropy is thus the parameter that is related to all others and describes aging in the best manner. In the beginning, entropy change appears as a consequence of accumulation of matter (growth). Later, decomposition and configurational changes dominate, as a consequence of various chemical reactions (free radical, decomposition, fragmentation, accumulation of lipofuscin-like substances...).
From the basic impact of nutrient intake on health maintenance to the psychosocial benefits of mealtime, great advancements in nutritional sciences for support of human space travel have occurred over the past 60 years. Nutrition in space has many areas of impact, including provision of required nutrients and maintenance of endocrine, immune, and musculoskeletal systems. It is affected by environmental conditions such as radiation, temperature, and atmospheric pressures, and these are reviewed. Nutrition with respect to space flight is closely interconnected with other life sciences research disciplines including the study of hematology, immunology, as well as neurosensory, cardiovascular, gastrointestinal, circadian rhythms, and musculoskeletal physiology. Psychosocial aspects of nutrition are also important for more productive missions and crew morale. Research conducted to determine the impact of spaceflight on human physiology and subsequent nutritional requirements will also have direct and indirect applications in Earth-based nutrition research. Cumulative nutritional research over the past five decades has resulted in the current nutritional requirements for astronauts. Realization of the full role of nutrition during spaceflight is critical for the success of extended-duration missions. Long-duration missions will require quantitation of nutrient requirements for maintenance of health and protection against the effects of microgravity. Keywords: Space nutrition, space flight, effects of microgravity, astronauts, space food
Catastrophic regime shifts in complex natural systems may be averted through advanced detection. Recent work has provided a proof-of-principle that many systems approaching a catastrophic transition may be identified through the lens of early warning indicators such as rising variance or increased return times. Despite widespread appreciation of the difficulties and uncertainty involved in such forecasts, proposed methods hardly ever characterize their expected error rates. Without the benefits of replicates, controls, or hindsight, applications of these approaches must quantify how reliable different indicators are in avoiding false alarms, and how sensitive they are to missing subtle warning signs. We propose a model based approach in order to quantify this trade-off between reliability and sensitivity and allow comparisons between different indicators. We show these error rates can be quite severe for common indicators even under favorable assumptions, and also illustrate how a model-based indicator can improve this performance. We demonstrate how the performance of an early warning indicator varies in different data sets, and suggest that uncertainty quantification become a more central part of early warning predictions.
We study the mechanics of a reversible decohesion (unzipping) of an elastic layer subjected to quasi-static end-point loading. At the micro level the system is simulated by an elastic chain of particles interacting with a rigid foundation through breakable springs. Such system can be viewed as prototypical for the description of a wide range of phenomena from peeling of polymeric tapes, to rolling of cells, working of gecko's fibrillar structures and denaturation of DNA. We construct a rigorous continuum limit of the discrete model which captures both stable and metastable configurations and present a detailed parametric study of the interplay between elastic and cohesive interactions. We show that the model reproduces the experimentally observed abrupt transition from an incremental evolution of the adhesion front to a sudden complete decohesion of a macroscopic segment of the adhesion layer. As the microscopic parameters vary the macroscopic response changes from quasi-ductile to quasi-brittle, with corresponding decrease in the size of the adhesion hysteresis. At the micro-scale this corresponds to a transition from a `localized' to a `diffuse' structure of the decohesion front (domain wall). We obtain an explicit expression for the critical debonding threshold in the limit when the internal length scales are much smaller than the size of the system. The achieved parametric control of the microscopic mechanism can be used in the design of new biological inspired adhesion devices and machines.
In this paper I show that, for a class of reaction networks, the discrete stochastic nature of the reacting species and reactions results in qualitative and quantitative differences between the mean of exact stochastic simulations and the prediction of the corresponding deterministic system. The differences are independent of the number of molecules of each species in the system under consideration. These reaction networks are open systems of chemical reactions with no zero-order reaction rates systems. They are characterized by at least two stationary points, one of which is a nonzero stable point, and one unstable trivial solution (stability based on a linear stability analysis of the deterministic system). Starting from a nonzero initial condition, the deterministic system never reaches the zero stationary point due to its unstable nature. In contrast, the result presented here proves that this zero-state is the only stable stationary state for the discrete stochastic system. This result generalizes previous theoretical studies and simulations of specific systems and provides a theoretical basis for analyzing a class of systems that exhibit such inconsistent behavior. This result has implications in the simulation of infection, apoptosis, and population kinetics, as it can be shown that for certain models the stochastic simulations will always yield different predictions for the mean behavior than the deterministic simulations.
Escherichia coli is a motile bacterium that moves up a chemoattractant gradient by performing a biased random walk composed of alternating runs and tumbles. This paper presents calculations of the chemotactic drift velocity vd (the mean velocity up the chemoattractant gradient) of an E. coli cell performing chemotaxis in a uniform, steady shear flow, with a weak chemoattractant gradient at right angles to the flow. Extending earlier models, a combined analytic and numerical approach is used to assess the effect of several complications, namely (i) a cell cannot detect a chemoattractant gradient directly but rather makes temporal comparisons of chemoattractant concentration, (ii) the tumbles exhibit persistence of direction, meaning that the swimming directions before and after a tumble are correlated, (iii) the cell suffers random re-orientations due to rotational Brownian motion, and (iv) the non-spherical shape of the cell affects the way that it is rotated by the shear flow. These complications influence the dependence of vd on the shear rate gamma. When they are all included, it is found that (a) shear disrupts chemotaxis and shear rates beyond gamma = 2/second cause the cell to swim down the chemoattractant gradient rather than up it, (b) in terms of maximising drift velocity, persistence of direction is advantageous in a quiescent fluid but disadvantageous in a shear flow, and (c) a more elongated body shape is advantageous in performing chemotaxis in a strong shear flow.
Spatially explicit models have been widely used in today's mathematical ecology and epidemiology to study persistence and extinction of populations as well as their spatial patterns. Here we extend the earlier work--static dispersal between neighbouring individuals to mobility of individuals as well as multi-patches environment. As is commonly found, the basic reproductive ratio is maximized for the evolutionary stable strategy (ESS) on diseases' persistence in mean-field theory. This has important implications, as it implies that for a wide range of parameters that infection rate will tend maximum. This is opposite with present results obtained in spatial explicit models that infection rate is limited by upper bound. We observe the emergence of trade-offs of extinction and persistence on the parameters of the infection period and infection rate and show the extinction time having a linear relationship with respect to system size. We further find that the higher mobility can pronouncedly promote the persistence of spread of epidemics, i.e., the phase transition occurs from extinction domain to persistence domain, and the spirals' wavelength increases as the mobility increasing and ultimately, it will saturate at a certain value. Furthermore, for multi-patches case, we find that the lower coupling strength leads to anti-phase oscillation of infected fraction, while higher coupling strength corresponds to in-phase oscillation.
Carlos J. Melian, David Alonso, Diego P. Vazquez
et al.
The origin of diversification and coexistence of genes and species have been traditionally studied in isolated biological levels. Ecological and evolutionary views have focused on the mechanisms that enable or constrain species coexistence, genetic variation and the genetics of speciation, but a unified theory linking those approaches is still missing. Here we introduce evolutionary graphs in the context of neutral theories of molecular evolution and biodiversity to provide a framework that simultaneously addresses speciation rate and joint genetic and species diversities. To illuminate this question we also study two models of evolution on graphs with fitness differences, which provide insights on how genetic and ecological dynamics drive the speed of diversification. Neutral evolution generates the highest speed of speciation, species richness (i.e. five times and twice as many species as compared to genetic and ecological graphs, respectively) and genetic--species diversity (i.e., twice as many as genetic and ecological graphs, respectively). Thus the speed of speciation, the genetic--species diversity and coexistence can differ dramatically depending on whether genetic factors versus ecological factors drive the evolution of the system. By linking molecular, sexual and trophic behavior at ecological and evolutionary scales, interacting graphs can illuminate the origin and evolution of diversity and organismal coexistence.
The nematode Caenorhabditis elegans responds to an impressive range of chemical, mechanical and thermal stimuli and is extensively used to investigate the molecular mechanisms that mediate chemosensation, mechanotransduction and thermosensation. The main behavioral output of these responses is manifested as alterations in animal locomotion. Monitoring and examination of such alterations requires tools to capture and quantify features of nematode movement. In this paper, we introduce Nemo (nematode movement), a computationally efficient and robust two-dimensional object tracking algorithm for automated detection and analysis of C. elegans locomotion. This algorithm enables precise measurement and feature extraction of nematode movement components. In addition, we develop a Graphical User Interface designed to facilitate processing and interpretation of movement data. While, in this study, we focus on the simple sinusoidal locomotion of C. elegans, our approach can be readily adapted to handle complicated locomotory behaviour patterns by including additional movement characteristics and parameters subject to quantification. Our software tool offers the capacity to extract, analyze and measure nematode locomotion features by processing simple video files. By allowing precise and quantitative assessment of behavioral traits, this tool will assist the genetic dissection and elucidation of the molecular mechanisms underlying specific behavioral responses.
Yet in spite of advances in hospital treatment, hospitals continue to be a breeding ground for several airborne diseases and for diseases that are transmitted through close contacts like SARS, methicillin-resistant Staphylococcus aureus (MRSA), norovirus infections and tuberculosis (TB). Here we extract contact networks for up to 295,108 inpatients for durations up to two years from a database used for administrating a local public healthcare system serving a population of 1.9 million individuals. Structural and dynamical properties of the network of importance for the transmission of contagious diseases are then analyzed by methods from network epidemiology. The contact networks are found to be very much determined by an extreme (age independent) variation in duration of hospital stays and the hospital structure. We find that that the structure of contacts between in-patients exhibit structural properties, such as a high level of transitivity, assortativity and variation in number of contacts, that are likely to be of importance for the transmission of less contagious diseases. If these properties are considered when designing prevention programs the risk for and the effect of epidemic outbreaks may be decreased.
We present for the first time a complex network approach to the study of the electrical properties of single protein devices. In particular, we consider an electronic nanobiosensor based on a G-protein coupled receptor. By adopting a coarse grain description, the protein is modeled as a complex network of elementary impedances. The positions of the alpha-carbon atoms of each amino acid are taken as the nodes of the network. The amino acids are assumed to interact electrically among them. Consequently, a link is drawn between any pair of nodes neighboring in space within a given distance and an elementary impedance is associated with each link. The value of this impedance can be related to the physical and chemical properties of the amino acid pair and to their relative distance. Accordingly, the conformational changes of the receptor induced by the capture of the ligand, are translated into a variation of its electrical properties. Stochastic fluctuations in the value of the elementary impedances of the network, which mimic different physical effects, have also been considered. Preliminary results concerning the impedance spectrum of the network and its fluctuations are presented and discussed for different values of the model parameters.
Mining large-scale high-throughput tandem mass spectrometry data sets is a very important problem in mass spectrometry based protein identification. One of the fundamental problems in large scale mining of spectra is to design appropriate metrics and algorithms to avoid all-pair-wise comparisons of spectra. In this paper, we present a general framework based on vector spaces to avoid pair-wise comparisons. We first robustly embed spectra in a high dimensional space in a novel fashion and then apply fast approximate near neighbor algorithms for tasks such as constructing filters for database search, indexing and similarity searching. We formally prove that our embedding has low distortion compared to the cosine similarity, and, along with locality sensitive hashing (LSH), we design filters for database search that can filter out more than 989% of peptides (118 times less) while missing at most 0.29% of the correct sequences. We then show how our framework can be used in similarity searching, which can then be used to detect tight clusters or replicates. On an average, for a cluster size of 16 spectra, LSH only misses 1 spectrum and admits only 1 false spectrum. In addition, our framework in conjunction with dimension reduction techniques allow us to visualize large datasets in 2D space. Our framework also has the potential to embed and compare datasets with post translation modifications (PTM).
During the HIV infection several quasispecies of the virus arise, which are able to use different coreceptors, in particular the CCR5 and CXCR4 coreceptors (R5 and X4 phenotypes, respectively). The switch in coreceptor usage has been correlated with a faster progression of the disease to the AIDS phase. As several pharmaceutical companies are starting large phase III trials for R5 and X4 drugs, models are needed to predict the co-evolutionary and competitive dynamics of virus strains. We present a model of HIV early infection which describes the dynamics of R5 quasispecies and a model of HIV late infection which describes the R5 to X4 switch. We report the following findings: after superinfection or coinfection, quasispecies dynamics has time scales of several months and becomes even slower at low number of CD4+ T cells. The curve of CD4+ T cells decreases, during AIDS late stage, and can be described taking into account the X4 related Tumor Necrosis Factor dynamics. Phylogenetic inference of chemokine receptors suggests that viral mutational pathway may generate R5 variants able to interact with chemokine receptors different from CXCR4. This may explain the massive signaling disruptions in the immune system observed during AIDS late stages and may have relevance for vaccination and therapy.
David R. B. Stockwell, James H. Beach, Aimee Stewart
et al.
Lifemapper (http://www.lifemapper.org) is a predictive electronic atlas of the Earth's biological biodiversity. Using a screensaver version of the GARP genetic algorithm for modeling species distributions, Lifemapper harnesses vast computing resources through 'volunteers' PCs similar to SETI@home, to develop models of the distribution of the worlds fauna and flora. The Lifemapper project's primary goal is to provide an up to date and comprehensive database of species maps and prediction models (i.e. a fauna and flora of the world) using available data on species' locations. The models are developed using specimen data from distributed museum collections and an archive of geospatial environmental correlates. A central server maintains a dynamic archive of species maps and models for research, outreach to the general community, and feedback to museum data providers. This paper is a case study in the role, use and justification of a genetic algorithm in development of large-scale environmental informatics infrastructure.
Many biological networks have been labelled scale-free as their degree distribution can be approximately described by a powerlaw distribution. While the degree distribution does not summarize all aspects of a network it has often been suggested that its functional form contains important clues as to underlying evolutionary processes that have shaped the network. Generally determining the appropriate functional form for the degree distribution has been fitted in an ad-hoc fashion. Here we apply formal statistical model selection methods to determine which functional form best describes degree distributions of protein interaction and metabolic networks. We interpret the degree distribution as belonging to a class of probability models and determine which of these models provides the best description for the empirical data using maximum likelihood inference, composite likelihood methods, the Akaike information criterion and goodness-of-fit tests. The whole data is used in order to determine the parameter that best explains the data under a given model (e.g. scale-free or random graph). As we will show, present protein interaction and metabolic network data from different organisms suggests that simple scale-free models do not provide an adequate description of real network data.
Biologists use random transposon mutagenesis to construct knockout libraries for bacteria. Random mutagenesis offers cost and efficiency benefits over the standard site directed mutagenesis, but one can no longer ensure that all the nonessential genes will appear in the library. In random libraries for haploid organisms, there is always a class of genes for which knockout clones have not been made, and the members of this class are either essential or nonessential. One requires statistical methods to estimate the number of essential genes. Two groups of researchers, Blades and Broman and Jacobs et al., independently and simultaneously developed methods to do this. Blades and Broman used a Gibbs sampler and Jacobs et al. used a parametric bootstrap. We compare the performance of these two methods and find that they both depend on having an accurate probabilistic model for transposon insertion or on having a library with a large number of clones. At this point, we do not have good enough probabilistic models so we must build libraries that have at least five clones per open reading frame to accurately estimate the number of essential genes.
We simulate the electrical properties of a device realized by a G protein coupled receptor (GPCR), embedded in its membrane and in contact with two metallic electrodes through which an external voltage is applied. To this purpose, recently, we have proposed a model based on a coarse graining description, which describes the protein as a network of elementary impedances. The network is built from the knowledge of the positions of the C-alpha atoms of the amino acids, which represent the nodes of the network. Since the elementary impedances are taken depending of the inter-nodes distance, the conformational change of the receptor induced by the capture of the ligand results in a variation of the network impedance. On the other hand, the fluctuations of the atomic positions due to thermal motion imply an impedance noise, whose level is crucial to the purpose of an electrical detection of the ligand capture by the GPCR. Here, in particular, we address this issue by presenting a computational study of the impedance noise due to thermal fluctuations of the atomic positions within a rhodopsin molecule. In our model, the C-alpha atoms are treated as independent, isotropic, harmonic oscillators, with amplitude depending on the temperature and on the position within the protein (alpha-helix or loop). The relative fluctuation of the impedance is then calculated for different temperatures.