How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?

The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.

Neural computing in four spatial dimensions

Relationships among near set theory, shape maps and recent accounts of the Quantum Hall effect pave the way to neural networks computations performed in higher dimensions. We illustrate the operational procedure to build a real or artificial neural network able to detect, assess and quantify a fourth spatial dimension. We show how, starting from two-dimensional shapes embedded in a 2D topological charge pump, it is feasible to achieve the corresponding four-dimensional shapes, which encompass a larger amount of information. Synthesis of surface shape components, viewed topologically as shape descriptions in the form of feature vectors that vary over time, leads to a 4D view of cerebral activity. This novel, relatively straightforward architecture permits to increase the amount of available qbits in a fixed volume.

Topological View of Flows Inside the BOLD Spontaneous Activity of the Human Brain

Spatio-temporal brain activities with variable delay detectable in resting-state functional magnetic resonance imaging (rs-fMRI) give rise to highly reproducible structures, termed cortical lag threads, that propagate from one brain region to another. Using a computational topology of data approach, we found that persistent, recurring blood oxygen level dependent (BOLD) signals in triangulated rs-fMRI videoframes display previously undetected topological findings, i.e., vortex structures that cover brain activated regions. Measure of persistence of vortex shapes in BOLD signal propagation is carried out in terms of Betti numbers that rise and fall over time during spontaneous activity of the brain. Importantly, a topology of data given in terms of geometric shapes of BOLD signal propagation offers a practical approach in coping with and sidestepping massive noise in neurodata, such as unwanted dark (low intensity) regions in the neighborhood of non-zero BOLD signals. Our findings have been codified and visualized in plots able to track the non-trivial BOLD signals that appear intermittently in a sequence of rs-fMRI videoframes. The end result of this tracking of changing lag structures is a so-called persistent barcode, which is a pictograph that offers a convenient visual means of exhibiting, comparing, and classifying brain activation patterns.

Removing uncertainty in neural networks

Neuroscientists draw lines of separation among structures and functions that they judge different, arbitrarily excluding or including issues in our description, to achieve positive demarcations that permits a pragmatic treatment of the nervous activity based on regularity and uniformity. However, uncertainty due to disconnectedness, lack of information and absence of objects' sharp boundaries is a troubling issue that prevents these scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial neural networks. Starting from the detection of metamorphoses of shapes inside a Euclidean manifold, we propose a technique to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, that allows the detection of topological holes development and disappearance, makes it possible to solve the problem of uncertainty in the assessment of countless dynamical phenomena, such as cognitive processes, protein homeostasis deterioration, fire propagation, wireless sensor networks, migration flows, and cosmic bodies analysis.

Points and lines inside human brains

Starting from the tenets of human imagination, i.e., the concepts of lines, points and infinity, we provide a biological demonstration that the skeptical claim "human beings cannot attain knowledge of the world" holds true. We show that the Euclidean account of the point as "that of which there is no part" is just a conceptual device produced by our brain, untenable in our physical/biological realm: currently used terms like "lines, surfaces and volumes" label non-existent, arbitrary properties. We elucidate the psychological and neuroscientific features hardwired in our brain that lead us humans to think to points and lines as truly occurring in our environment. Therefore, our current scientific descriptions of objects' shapes, graphs and biological trajectories in phase spaces need to be revisited, leading to a proper portrayal of the real world's events: miniscule bounded physical surface regions stand for the basic objects in a traversal of spacetime, instead of the usual Euclidean points. Our account makes it possible to erase of a painstaking problem that causes many theories to break down and/or being incapable of describing extreme events: the unwanted occurrence of infinite values in equations. We propose a novel approach, based on point-free geometrical standpoints, that banishes infinitesimals, leads to a tenable physical/biological geometry compatible with human reasoning and provides a region-based topological account of the power laws endowed in nervous activities. We conclude that points, lines, volumes and infinity do not describe the world, rather they are fictions introduced by ancient surveyors of land surfaces.

The common features of different brain activities

The term "brain activity" refers to a wide range of mental faculties that can be assessed either on anatomical/functional micro-, meso- and macro- spatiotemporal scales of observation, or on intertwined cortical levels with mutual interactions. Our aim is to show that every brain activity encompassed in a given anatomical or functional level necessarily displays a counterpart in others, i.e., they are "dual". "Duality" refers to the case where two seemingly different physical systems turn out to be equivalent. We describe a method, based on novel topological findings, that makes different manifolds (standing for different brain activities) able to scatter, collide and combine, in order that they merge, condense and stitch together in a quantifiable way. We develop a computational tool which explains how, despite their local cortical functional differences, all mental processes, from perception to emotions, from cognition to mind wandering, may be reduced to a single, general brain activity that takes place in dimensions higher than the classical three-dimensional plus time. This framework permits a topological duality among different brain activities and neuro-techniques, because it holds for all the types of spatio-temporal nervous functions, independent of their cortical location, inter- and intra-level relationships, strength, magnitude and boundaries.

EEG dynamics on hyperbolic manifolds

Biological activities, including cellular metabolic pathways, protein folding and brain function, can be described in terms of curved trajectories in hyperbolic spaces which are constrained by energetic requirements. Here, starting from theorems recently-developed by a deceased Field Medal young mathematician, we show how it is feasible to find and quantify the shortest, energy-sparing functional trajectories taking place in nervous systems' concave phase spaces extracted from real EEG traces. This allows neuroscientists to focus their studies on the few, most prominent functional EEG's paths and loops able to explain, elucidate and experimentally assess the rather elusive mental activity.

Multidimensional brain activity dictated by winner-take-all mechanisms

A novel demon-based architecture is introduced to elucidate brain functions such as pattern recognition during human perception and mental interpretation of visual scenes. Starting from the topological concepts of invariance and persistence, we introduce a Selfridge pandemonium variant of brain activity that takes into account a novel feature, namely, demons that recognize short straight-line segments, curved lines and scene shapes, such as shape interior, density and texture. Low-level representations of objects can be mapped to higher-level views (our mental interpretations): a series of transformations can be gradually applied to a pattern in a visual scene, without affecting its invariant properties. This makes it possible to construct a symbolic multi-dimensional representation of the environment. These representations can be projected continuously to an object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space. Although perceived shapes are 3-dimensional (plus time), the evaluation of shape features (volume, color, contour, closeness, texture, and so on) leads to n-dimensional brain landscapes. Here we discuss the advantages of our parallel, hierarchical model in pattern recognition, computer vision and biological nervous system's evolution.

The informational entropy endowed in cortical oscillations

A two-dimensional shadow may encompass more information than its corresponding three-dimensional object. Indeed, if we rotate the object, we achieve a pool of observed shadows from different angulations, gradients, shapes and variable length contours that make it possible for us to increase our available information. Starting from this simple observation, we show how informational entropies might turn out to be useful in the evaluation of scale-free dynamics in the brain. Indeed, brain activity exhibits a scale-free distribution that leads to the variations in the power law exponent typical of different functional neurophysiological states. Here we show that modifications in scaling slope are associated with variations in Rényi entropy, a generalization of Shannon informational entropy. From a three-dimensional object's perspective, by changing its orientation (standing for the cortical scale-free exponent), we detect different two-dimensional shadows from different perception angles (standing for Rényi entropy in different brain areas). We show how, starting from known values of Rényi entropy (easily detectable in brain fMRIs or EEG traces), it is feasible to calculate the scaling slope in a given moment and in a given brain area. Because changes in scale-free cortical dynamics modify brain activity, this issue points towards novel approaches to mind reading and description of the forces required for transcranial stimulation.

Towards plasma-like collisionless trajectories in the brain

Plasma studies depict collisionless, collective movements of charged particles. In touch with these concepts, originally developed by the far-flung branch of high energy physics, here we evaluate the role of collective behaviors and long-range functional couplingsof charged particlesin brain dynamics. We build a novel, empirically testable, brain model which takes into account collisionless movements of charged particles in a system, the brain, equipped with oscillations. The model is cast in a mathematical fashion with the potential of being operationalized, because it can be assessed in terms of McKean-Vlasov equations, derived from the classical Vlasov equations for plasma. A plasma-like brain also elucidates cortical phase transitions in the context of a brain at the edge of chaos, describing the required order parameters. In sum, showing how the brain might exhibit plasma-like features,we go through the concept of holistic behavior of nervous functions.

A timeless biology

Contrary to claims that physics is timeless while biology is time-dependent, we take the opposite standpoint: physical systems' dynamics are constrained by the arrow of time, while living assemblies are time-independent. Indeed, the concepts of "constraints" and "displacements" shed new light on the role of continuous time flow in life evolution, allowing us to sketch a physical gauge theory for biological systems in long timescales. In the very short timescales of biological systems' individual lives, time looks like "frozen" and "fixed", so that the second law of thermodynamics is momentarily wrecked. The global symmetries (standing for biological constrained trajectories, i.e. the energetic gradient flows dictated by the second law of thermodynamics in long timescales) are broken by local "displacements" where time is held constant, i.e., modifications occurring in living systems. Such displacements stand for brief local forces, able to temporarily "break" the cosmic increase in entropy. The force able to restore the symmetries (called "gauge field") stands for the very long timescales of biological evolution. Therefore, at the very low speeds of life evolution, time is no longer one of the four phase space coordinates of a spacetime Universe: it becomes just a gauge field superimposed to three-dimensional biological systems. We discuss the implications in biology: when assessing living beings, the underrated role of isolated "spatial" modifications needs to be emphasized, living apart the evolutionary role of time.

Relationships between short and fast brain timescales

Brain electric activity exhibits two important features: oscillations with different timescales, characterized by diverse functional and psychological outcomes, and a temporal power law distribution. In order to further investigate the relationships between low- and high- frequency spikes in the brain, we used a variant of the Borsuk-Ulam theorem which states that, when we assess the nervous activity as embedded in a sphere equipped with a fractal dimension, we achieve two antipodal points with similar features (the slow and fast, scale-free oscillations). We demonstrate that slow and fast nervous oscillations mirror each other over time via a sinusoid relationship and provide, through the Bloch theorem from solid-state physics, the possible equation which links the two timescale activities. We show that, based on topological findings, nervous activities occurring in micro-levels are projected to single activities at meso- and macro-levels. This means that brain functions assessed at the higher scale of the whole brain necessarily display a counterpart in the lower ones, and vice versa. Our topological approach makes it possible to assess brain functions both based on entropy, and in the general terms of particle trajectories taking place on donut-like manifolds. Condensed brain activities might give rise to ideas and concepts by combination of different functional and anatomical levels. Furthermore, cognitive phenomena, as well as social activity can be described by the laws of quantum mechanics; memories and decisions exhibit holographic organization. In physics, the term duality refers to a case where two seemingly different systems turn out to be equivalent. This topological duality holds for all the types of spatio-temporal brain activities, independent of their inter- and intra-level relationships, strength, magnitude and boundaries, allowing us to connect the physiological manifestations of consciousness to the electric activities of the brain.

Cellular Gauge Symmetry and the Li Organization Principle: A Mathematical Addendum. Quantifying energetic dynamics in physical and biological systems through a simple geometric tool and geodetic curves

The present Addendum complements the accompanying paper "Cellular Gauge Symmetry and the Li Organization Principle"; it illustrates a recently-developed geometrical physical model able to assess electronic movements and energetic paths in atomic shells. The model describes a multi-level system of circular, wavy and zigzag paths which can be projected onto a horizontal tape. This model ushers in a visual interpretation of the distribution of atomic electrons' energy levels and the corresponding quantum numbers through rather simple tools, such as compasses, rulers and straightforward calculations. Here we show how this geometrical model, with the due corrections, among them the use of geodetic curves, might be able to describe and quantify the structure and the temporal development of countless physical and biological systems, from Langevin equations for random paths, to symmetry breaks occurring ubiquitously in physical and biological phenomena, to the relationships among different frequencies of EEG electric spikes. Therefore, in our work we explore the possible association of binomial distribution and geodetic curves configuring a uniform approach for the research of natural phenomena, in biology, medicine or the neurosciences.

From abstract topology to real thermodynamic brain activity

Recent approaches to brain phase spaces reinforce the foremost role of symmetries and energy requirements in the assessment of nervous activity. Changes in thermodynamic parameters and dimensions occur in the brain during symmetry breakings and transitions from one functional state to another. Based on topological results and string-like trajectories into nervous energy landscapes, we provide a novel method for the evaluation of energetic features and constraints in different brain functional activities. We show how abstract approaches, namely the Borsuk-Ulam theorem and its variants, may display real, energetic physical counterparts. When topology meets the physics of the brain, we arrive at a general model of neuronal activity, in terms of multidimensional manifolds and computational geometry, that has the potential to be operationalized.

Topodynamics of metastable brains

The brain displays both the anatomical features of a vast amount of interconnected topological mappings as well as the functional features of a nonlinear, metastable system at the edge of chaos, equipped with a phase space where mental random walks tend towards lower energetic basins. Nevertheless, with the exception of some advanced neuro-anatomic descriptions and present-day connectomic research, very few studies have been addressing the topological path of a brain embedded or embodied in its external and internal environment. Herein, by using new formal tools derived from algebraic topology, we provide an account of the metastable brain, based on the neuro-scientific model of Operational Architectonics of brain-mind functioning. We introduce a "topodynamic" description that shows how the relationships among the countless intertwined spatio-temporal levels of brain functioning can be assessed in terms of projections and mappings that take place on abstract structures, equipped with different dimensions, curvatures and energetic constraints. Such a topodynamical approach, apart from providing a biologically plausible model of brain function that can be operationalized, is also able to tackle the issue of a long-standing dichotomy: it throws indeed a bridge between the subjective, immediate datum of the naïve complex of sensations and mentations and the objective, quantitative, data extracted from experimental neuro-scientific procedures. Importantly, it opens the door to a series of new predictions and future directions of advancement for neuroscientific research.

Cracking the barcode of fullerene-like cortical microcolumns

Artificial neural systems and nervous graph theoretical analysis rely upon the stance that the neural code is embodied in logic circuits, e.g., spatio-temporal sequences of ON/OFF spiking neurons. Nevertheless, this assumption does not fully explain complex brain functions. Here we show how nervous activity, other than logic circuits, could instead depend on topological transformations and symmetry constraints occurring at the micro-level of the cortical microcolumn, i.e., the embryological, anatomical and functional basic unit of the brain. Tubular microcolumns can be flattened in fullerene-like two-dimensional lattices, equipped with about 80 nodes standing for pyramidal neurons where neural computations take place. We show how the countless possible combinations of activated neurons embedded in the lattice resemble a barcode. Despite the fact that further experimental verification is required in order to validate our claim, different assemblies of firing neurons might have the appearance of diverse codes, each one responsible for a single mental activity. A two-dimensional fullerene-like lattice, grounded on simple topological changes standing for pyramidal neurons' activation, not just displays analogies with the real microcolumn's microcircuitry and the neural connectome, but also the potential for the manufacture of plastic, robust and fast artificial networks in robotic forms of full-fledged neural systems.

Bold-Independent Computational Entropy Assesses Functional Donut-Like Structures in Brain fMRI Images

We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. This approach befits the geometric character of fMRIs. The main purpose of this study is to provide a computable framework for fMRI that not only facilitates analyses, but also provides an easily decipherable visualization of structures. This framework commands attention because it is easily implemented using conventional software systems. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimension's distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Rényi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain.

Building a minimum frustration framework for brain functions over long time scales

The minimum frustration principle (MFP) is a computational approach stating that, over the long time scales of evolution, proteins' free energy decreases more than expected by thermodynamical constraints as their amino acids assume conformations progressively closer to the lowest energetic state. This Review shows that this general principle, borrowed from protein folding dynamics, can also be fruitfully applied to nervous function. Highlighting the foremost role of energetic requirements, macromolecular dynamics, and above all intertwined time scales in brain activity, the MFP elucidates a wide range of mental processes from sensations to memory retrieval. Brain functions are compared with trajectories that, over long nervous time scales, are attracted toward the low-energy bottom of funnel-like structures characterized by both robustness and plasticity. We discuss how the principle, derived explicitly from evolution and selection of a funneling structure from microdynamics of contacts, is unlike other brain models equipped with energy landscapes, such as the Bayesian and free energy principles and the Hopfield networks. In summary, we make available a novel approach to brain function cast in a biologically informed fashion, with the potential to be operationalized and assessed empirically. © 2016 Wiley Periodicals, Inc.

A topological approach unveils system invariances and broken symmetries in the brain

Symmetries are widespread invariances underscoring countless systems, including the brain. A symmetry break occurs when the symmetry is present at one level of observation but is hidden at another level. In such a general framework, a concept from algebraic topology, namely, the Borsuk-Ulam theorem (BUT), comes into play and sheds new light on the general mechanisms of nervous symmetries. The BUT tells us that we can find, on an n-dimensional sphere, a pair of opposite points that have the same encoding on an n - 1 sphere. This mapping makes it possible to describe both antipodal points with a single real-valued vector on a lower dimensional sphere. Here we argue that this topological approach is useful for the evaluation of hidden nervous symmetries. This means that symmetries can be found when evaluating the brain in a proper dimension, although they disappear (are hidden or broken) when we evaluate the same brain only one dimension lower. In conclusion, we provide a topological methodology for the evaluation of the most general features of brain activity, i.e., the symmetries, cast in a physical/biological fashion that has the potential to be operationalized. © 2016 Wiley Periodicals, Inc.

Towards a fourth spatial dimension of brain activity

Current advances in neurosciences deal with the functional architecture of the central nervous system, paving the way for general theories that improve our understanding of brain activity. From topology, a strong concept comes into play in understanding brain functions, namely, the 4D space of a "hypersphere's torus", undetectable by observers living in a 3D world. The torus may be compared with a video game with biplanes in aerial combat: when a biplane flies off one edge of gaming display, it does not crash but rather it comes back from the opposite edge of the screen. Our thoughts exhibit similar behaviour, i.e. the unique ability to connect past, present and future events in a single, coherent picture as if we were allowed to watch the three screens of past-present-future "glued" together in a mental kaleidoscope. Here we hypothesize that brain functions are embedded in a imperceptible fourth spatial dimension and propose a method to empirically assess its presence. Neuroimaging fMRI series can be evaluated, looking for the topological hallmark of the presence of a fourth dimension. Indeed, there is a typical feature which reveal the existence of a functional hypersphere: the simultaneous activation of areas opposite each other on the 3D cortical surface. Our suggestion-substantiated by recent findings-that brain activity takes place on a closed, donut-like trajectory helps to solve long-standing mysteries concerning our psychological activities, such as mind-wandering, memory retrieval, consciousness and dreaming state.

Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports

BACKGROUND

Accumulating quantitative outcome parameters may contribute to constructing a healthcare organization in which outcomes of clinical procedures are reproducible and predictable. In imaging studies, measurements are the principal category of quantitative para meters.

OBJECTIVES

The purpose of this work is to develop and evaluate two natural language processing engines that extract finding and organ measurements from narrative radiology reports and to categorize extracted measurements by their "temporality".

METHODS

The measurement extraction engine is developed as a set of regular expressions. The engine was evaluated against a manually created ground truth. Automated categorization of measurement temporality is defined as a machine learning problem. A ground truth was manually developed based on a corpus of radiology reports. A maximum entropy model was created using features that characterize the measurement itself and its narrative context. The model was evaluated in a ten-fold cross validation protocol.

RESULTS

The measurement extraction engine has precision 0.994 and recall 0.991. Accuracy of the measurement classification engine is 0.960.

CONCLUSIONS

The work contributes to machine understanding of radiology reports and may find application in software applications that process medical data.

Ethology-Based Approximate Adaptive Learning

The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for machine learning combines near sets and ethology. It leads to novel forms of Q-learning algorithm that have practical applications in the controlling the behavior of machines, which learn to adapt to changing environments. Both traditional and new forms of adaptive learning theory and applications are considered in this chapter. A complete framework for an ethology-based approximate adaptive learning is established by using near sets.

Stand-alone performance of a computer-assisted detection prototype for detection of acute pulmonary embolism: a multi-institutional comparison

OBJECTIVE

To assess whether the performance of a computer-assisted detection (CAD) algorithm for acute pulmonary embolism (PE) differs in pulmonary CT angiographies acquired at various institutions.

METHODS

In this retrospective study, we included 40 consecutive scans with and 40 without PE from 3 institutions (n = 240) using 64-slice scanners made by different manufacturers (General Electric; Philips; Siemens). CAD markers were classified as true or false positive (FP) using independent evaluation by two readers and consultation of a third chest radiologist in discordant cases. Image quality parameters were subjectively scored using 4/5-point scales. Image noise and vascular enhancement were measured. Statistical analysis was done to correlate image quality of the three institutions with CAD stand-alone performance.

RESULTS

Patient groups were comparable with respect to age (p = 0.22), accompanying lung disease (p = 0.12) and inpatient/outpatient ratio (p = 0.67). The sensitivity was 100% (34/34), 97% (37/38) and 92% (33/36), and the specificity was 18% (8/44), 15% (6/41) and 13% (5/39). Neither significantly differed between the institutions (p = 0.21 and p = 0.820, respectively). The mean number of FP findings (4.5, 6.2 and 3.7) significantly varied (p = 0.02 and p = 0.03), but median numbers (2, 3 and 3) were comparable. Image quality parameters were significantly associated with the number of FP findings (p<0.05) but not with sensitivity. After correcting for noise and vascular enhancement, the number of FPs did not significantly differ between the three institutions (p = 0.43).

CONCLUSIONS

CAD stand-alone performance is independent of scanner type but strongly related to image quality and thus scanning protocols.

Therapeutic musculoskeletal injection: what is current practice? What is the evidence?

Physicians are encountering an increasing number of patients with musculoskeletal injuries and conditions. Although conservative approaches such as rest, ice, elevation, analgesic medications, and therapeutic exercise are usually the first line of treatment, they do not provide relief for some patients. For those patients, injectable agents may be useful in relieving pain and promoting healing. This article describes older and newer injectable agents and reviews the evidence regarding their use and effectiveness.

Characterization of force chains in granular material

It has been observed that the majority of particles in a granular material carries less than the average load and that the number of particles carrying larger than the average load decreases exponentially with increasing contact force. The particles carrying above average load appear to form a strong network of forces while the majority of particles belong to a weak network. The strong network of forces appear to have a spatial characteristic whereby the stronger forces are carried though chainlike particle groups referred to as force chains. There is a strong case for a connection between force chains of the discrete medium and the trajectory of the most compressive principal stress in its continuous idealization. While such properties seem obvious from descriptive analysis of physical and numerical experiments in granular media, progress in quantification of the force chain statistics requires an objective description of what constitutes a force chain. A procedure to quantify the occurrence of force chains is built on a proposed definition having two parts: first, the chain is a quasilinear arrangement of three or more particles, and second, along the chain, stress concentration within each grain is characterized by the vector delineating the most compressive principal stress. The procedure is incorporated into an algorithm that can be applied to large particle assemblies to compile force chain statistics. The procedure is demonstrated on a discrete element simulation of a rigid punch into a half space. It was found that only approximately half of the particles within the group of so-called strong network particles are part of force chains. Throughout deformation, the average length of force chains varied slightly but the number of force chains decreased as the punch advanced. The force chain lengths follow an exponential distribution. The procedure provides a tool for objective analysis of force chains, although future work is required to incorporate branching of force chains into the analysis.

Canadian families into the year 2000

"Canadian families are pluralistic and varied, with a mix of traditional, modern, and post-modern characteristics. This paper looks at families into the immediate future.... Consideration is given to ethnicity, cohabitation, fertility, childrearing, sexuality, family policy, adolescence, and general family life. The state will continue to influence family life. Non-familial associations will affect family values and activity."

Enhanced dispersion in cylindrical packed beds

The effective longitudinal dispersion constant, D(L)(eff), in cylindrical packed beds is larger than in the bulk due to the existence of radial inhomogeneities induced by the cylinder walls. For dense random packed beds, D(L)(eff) can be several times larger than the bulk value, even for arbitrarily large cylinder radius, R. The time-scale for attaining asymptotic dispersion rates in a cylindrical geometry is neither the convective nor the diffusive time-scale, but rather D(T)/R(2), where D(T) is the bulk transverse dispersion rate. Similar effects are predicted for packed beds confined in ducts of any cross-sectional geometry. The case of a rectangular duct, compared with an infinite slit, provides an intuitive model for the influence of walls in the limit as R goes to infinity.

X-Ray waveguiding studies of ordering phenomena in confined fluids

We have determined the structure of a colloidal fluid confined in a gap between two walls by making use of the waveguiding properties of the gap at x-ray wavelengths. The method is based on an analysis of the coupling of waveguide modes induced by the density variations in the confined fluid. Studies on suspensions confined within gaps of a few hundred nanometers showed strongly selective mode coupling effects, indicative of an ordering of the colloidal particles in layers parallel to the confining walls.

Fuzzy Control Stability Analysis Using a Generalized Fuzzy Petri Net Model

Fuzzy inference is used to describe nonlinear input- output relationships using fuzzy if-then rules. Continuous values of input and output are converted into granules by fuzzy sets, and each granule is labeled with a symbol. Fuzzy inference has a multigranular architecture consisting of continuous values and symbols that has worked well incorporating expert knowledge into fuzzy control. An important issue in fuzzy control is guaranteeing fuzzy control stability. We applied Petri nets to fuzzy control stability analysis and derived a theory on asymptotic stability for symbolic representation of control. We present new bridging between symbolic stability analysis and actual control behavior numerically. We use a generalized fuzzy Petri net model and its neural network representation. Conditions for validity of granularized control stability analysis are derived from the movement of tokens in neural network representation. Simulations are used to study derived conditions.

Dynamic posturography in the diagnosis and management of dizziness and balance disorders

This article reviews the basic concepts underlying the balance system, describes the information provided by dynamic posturography, and explains how the technique complements and expands on the information provided by traditional tests of vestibular function.

Isolation of the human chromosomal band Xq28 within somatic cell hybrids by fragile X site breakage

The chromosomal fragile-site mapping to Xq27.3 is associated with a frequent form of mental retardation and is prone to breakage after induced deoxyribonucleotide pool perturbation. The human hypoxanthine phosphoribosyltransferase (HPRT) and glucose-6-phosphate dehydrogenase (G6PD) genes flank the fragile X chromosome site and can be used to monitor integrity of the site in human-hamster somatic cell hybrids deficient in the rodent forms of these activities. After induction of the fragile X site, negative selection for HPRT and positive enrichment for G6PD resulted in 31 independent colonies of HPRT-,G6PD+ phenotype. Southern blot analysis demonstrated the loss of all tested markers proximal to the fragile X site with retention of all tested human Xq28 loci in a majority of the hybrids. In situ hybridization with a human-specific probe demonstrated the translocation of a small amount of human DNA to rodent chromosomes in these hybrids, suggesting chromosome breakage at the fragile X site and the subsequent translocation of Xq28. Southern blot hybridization of hybrid-cell DNA, resolved by pulsed-field gel electrophoresis, for human-specific repetitive sequences revealed abundant CpG-islands within Xq28, consistent with its known gene density. The electrophoretic banding patterns of human DNA among the hybrids were remarkably consistent, suggesting that fragile X site breakage is limited to a relatively small region in Xq27-28. These somatic cell hybrids, containing Xq27.3-qter as the sole human DNA, will aid the search for DNA associated with the fragile X site and will augment the high resolution genomic analysis of Xq28, including the identification of candidate genes for genetic-disease loci mapping to this region.

Strategy for molecular cloning of the fragile X site DNA

Fragile X syndrome is a common form of mental retardation associated with a fragile site on the human X chromosome. We have recently demonstrated that the fragile X chromosome, when isolated within a somatic cell hybrid, often participates in translocations involving rodent chromosome arms. Cytogenetic and molecular evidence strongly suggests that the human breakpoint of these translocations is within the fragile X sequence. Hence, the joining of heterologous DNA (i.e. from two species) may permit the molecular cloning of the fragile X site. We describe here the cloning approach employed to enhance the isolation of interspecific chromosome translocation junctions. The human portion of the translocation junction should be derived from the fragile X site sequence.

The fragile X site in somatic cell hybrids: an approach for molecular cloning of fragile sites

Fragile X syndrome is a common form of mental retardation associated with a fragile site on the human X chromosome. Although fragility at this site is usually evident as a nonstaining chromatid gap, it remains unclear whether or not actual chromosomal breakage occurs. By means of somatic cell hybrids containing either a normal human X or a fragile X chromosome and utilizing two genes that flank the fragile site as markers of chromosome integrity, segregation of these markers was shown to be more frequent if they encompass the fragile site under appropriate culture conditions. Hybrid cells that reveal marker segregation were found to contain rearranged X chromosomes involving the region at or near the fragile site, thus demonstrating true chromosomal breakage within this area. Two independent translocation chromosomes were identified involving a rodent chromosome joined to the human X at the location of the fragile site. DNA analysis of closely linked, flanking loci was consistent with the position of the breakpoint being at or very near the fragile X site. Fragility at the translocation junctions was observed in both hybrids, but at significantly lower frequencies than that seen in the intact X of the parental hybrid. This observation suggests that the human portion of the junctional DNA may contain part of a repeated fragility sequence. Since the translocation junctions join heterologous DNA, the molecular cloning of the fragile X sequence should now be possible.

Non-suppressibility by room light of pineal N-acetyltransferase activity and melatonin levels in two diurnally active rodents, the Mexican ground squirrel (Spermophilus mexicanus) and the eastern chipmunk (Tamias striatus)

The rhythms in pineal N-acetyltransferase (NAT) activity and melatonin levels were studied in the diurnally active Mexican ground squirrel and Eastern chipmunk. In the ground squirrel, both NAT activity and melatonin levels exhibited a marked nocturnal rise; these increases were not prevented by the exposure of these animals to a light irradiance of 200 microW/cm2 throughout the night. In the Eastern chipmunk, darkness at night was also associated with a marked rise in both the activity of the acetylating enzyme as well as the levels of melatonin. Again, these rhythms were not suppressed by the exposure of these animals to a light irradiance of 200 microW/cm2 for either 1 night or for 7 nights; exposure of chipmunks to light for 7 consecutive days did, however, reduce the rise in melatonin normally associated with darkness. The non-suppressibility of pineal NAT and melatonin by a 200 microW/cm2 light irradiance may relate either to the activity pattern of these animals, i.e., diurnal, or to their previous lighting history.

Respiratory pauses and apnea during daytime sleep in normal infants during the first year of life: longitudinal observations

Respiratory pauses (3-10 sec in duration), apnea (less than 10 sex in duration), and periodic respiration observed in thoracic respirograms were measured in 226 polysomnograms obtained on 17 normal infants during the first year of life. All subjects had one or more respiratory pauses in a majority of recordings; 35% had respiratory pauses in all recordings; 75% of respiratory pauses were associated with body movement. There is marked intersubject and even intrasubject variability in respiratory pause rates. The range of mean respiratory pause rates among subjects was 2.0 - 14.4/h, and for single recordings was 0.0 - 43.6/h. Their occurrence was directly related to the occurrence of periodic respiration. Rates were higher during REM and indeterminate sleep than during slow wave sleep. There was no significant trend toward increase or decrease in respiratory pause rate during the first year post term. Apnea occurred in only one of the 226 recordings (0.4%). Periodic respiration occurred in 8 of 17 subjects (47%), and in 25 of 226 recordings (11%). Its occurrence was unrelated to sleep stage. The following conclusions are considered valid on the basis of the data presented and reports in the literature: (1) Rates of respiratory interruption are higher before than after 40 weeks conceptional age. (2) There is considerable intersubject variability in rates of respiratory interruptions. (3) Respiratory pauses are common during sleep in normal human infants. (4) Respiratory pauses occur more frequently with movements than in their absence. (5) Respiratory pause rates are higher during REM sleep than during slow wave sleep. (6) Apneas of greater than 10-15 sec duration do occur in normal infants, but are rare. From the clinical viewpoint, respiratory pauses (less than 15 sec) of the central type, regardless of abundance, and periodic respiration cannot by themselves be used as evidence that a baby is at risk of anything. The occurrence of apneas (greater than 15 sec duration), especially if any are of the obstructive or mixed types (and perhaps respiratory pauses of the obstructive and mixed types), and/or if associated with bradycardia or decrease in oxygen saturation, indicate sleep apnea syndrome and suggest risk of sudden infant death.