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Enzymatic conversion of the most abundant renewable source of organic compounds, cellulose to fermentable sugars is attractive for production of green fuels and chemicals. The major component of industrial enzyme systems, cellobiohydrolase I from Hypocrea jecorina (Trichoderma reesei) (HjCel7A) processively splits disaccharide units from the reducing ends of tightly packed cellulose chains. HjCel7A consists of a catalytic domain (CD) and a carbohydrate-binding module (CBM) separated by a linker peptide. A tunnel-shaped substrate-binding site in the CD includes nine subsites for ß-d-glucose units, seven of which (-7 to -1) precede the catalytic center. Low catalytic activity of Cel7A is the bottleneck and the primary target for improvement. Here it is shown for the first time that, in spite of much lower apparent kcat of HjCel7A at the hydrolysis of ß-1,4-glucosidic linkages in the fluorogenic cellotetra- and -pentaose compared to the structurally related endoglucanase I (HjCel7B), the specificity constants (catalytic efficiency) kcat /Km for both enzymes are almost equal in these reactions. The observed activity difference appears from strong nonproductive substrate binding by HjCel7A, particularly significant for MU-ß-cellotetraose (MUG4 ). Interaction of substrates with the subsites -6 and -5 proximal to the nonconserved Gln101 residue in HjCel7A decreases Km,ap by >1500 times. HjCel7A can be nonproductively bound onto cellulose surface with Kd ≈2-9 nM via CBM and CD that captures six terminal glucose units of cellulose chain. Decomposition of this nonproductive complex can determine the rate of cellulose conversion. MUG4 is a promising substrate to select active cellobiohydrolase I variants with reduced nonproductive substrate binding.

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Discrete sequential information coding is a key mechanism that transforms complex cognitive brain activity into a low-dimensional dynamical process based on the sequential switching among finite numbers of patterns. The storage size of the corresponding process is large because of the permutation capacity as a function of control signals in ensembles of these patterns. Extracting low-dimensional functional dynamics from multiple large-scale neural populations is a central problem both in neuro- and cognitive- sciences. Experimental results in the last decade represent a solid base for the creation of low-dimensional models of different cognitive functions and allow moving toward a dynamical theory of consciousness. We discuss here a methodology to build simple kinetic equations that can be the mathematical skeleton of this theory. Models of the corresponding discrete information processing can be designed using the following dynamical principles: (i) clusterization of the neural activity in space and time and formation of information patterns; (ii) robustness of the sequential dynamics based on heteroclinic chains of metastable clusters; and (iii) sensitivity of such sequential dynamics to intrinsic and external informational signals. We analyze sequential discrete coding based on winnerless competition low-frequency dynamics. Under such dynamics, entrainment, and heteroclinic coordination leads to a large variety of coding regimes that are invariant in time.

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Traditional studies on the interaction of cognitive functions in healthy and disordered brains have used the analyses of the connectivity of several specialized brain networks-the functional connectome. However, emerging evidence suggests that both brain networks and functional spontaneous brain-wide network communication are intrinsically dynamic. In the light of studies investigating the cooperation between different cognitive functions, we consider here the dynamics of hierarchical networks in cognitive space. We show, using an example of behavioural decision-making based on sequential episodic memory, how the description of metastable pattern dynamics underlying basic cognitive processes helps to understand and predict complex processes like sequential episodic memory recall and competition among decision strategies. The mathematical images of the discussed phenomena in the phase space of the corresponding cognitive model are hierarchical heteroclinic networks. One of the most important features of such networks is the robustness of their dynamics. Different kinds of instabilities of these dynamics can be related to 'dynamical signatures' of creativity and different psychiatric disorders. The suggested approach can also be useful for the understanding of the dynamical processes that are the basis of consciousness.

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We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson's disease and Schizophrenia.

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Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories-episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

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The bridge between brain structures as computational devices and the content of mental processes hinges on the solution of several problems: (i) inference of the cognitive brain networks from neurophysiological and imaging data; (ii) inference of cognitive mind networks - interactions between mental processes such as attention and working memory - based on cognitive and behavioral experiments; and (iii) the discovery of general dynamical principles for cognition based on dynamical models. In this opinion article, we focus on the third problem and discuss how it provides the bridge between the solutions to the first two problems. We consider the possibility of creating low-dimensional dynamical models from multidimensional spatiotemporal data and its application to robust sequential cognitive processes in the context of finite processing capacity of the mind.

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Attention is the process of focusing mental resources on a specific cognitive/behavioral task. Such brain dynamics involves different partially overlapping brain functional networks whose interconnections change in time according to the performance stage, and can be stimulus-driven or induced by an intrinsically generated goal. The corresponding activity can be described by different families of spatiotemporal discrete patterns or sequential dynamic modes. Since mental resources are finite, attention modalities compete with each other at all levels of the hierarchy, from perception to decision making and behavior. Cognitive activity is a dynamical process and attention possesses some universal dynamical characteristics. Thus, it is time to apply nonlinear dynamical theory for the description and prediction of hierarchical attentional tasks. Such theory has to include the analyses of attentional control stability, the time cost of attention switching, the finite capacity of informational resources in the brain, and the normal and pathological bifurcations of attention sequential dynamics. In this paper we have integrated today's knowledge, models and results in these directions.

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Psychiatric disorders are often caused by partial heterogeneous disinhibition in cognitive networks, controlling sequential and spatial working memory (SWM). Such dynamic connectivity changes suggest that the normal relationship between the neuronal components within the network deteriorates. As a result, competitive network dynamics is qualitatively altered. This dynamics defines the robust recall of the sequential information from memory and, thus, the SWM capacity. To understand pathological and non-pathological bifurcations of the sequential memory dynamics, here we investigate the model of recurrent inhibitory-excitatory networks with heterogeneous inhibition. We consider the ensemble of units with all-to-all inhibitory connections, in which the connection strengths are monotonically distributed at some interval. Based on computer experiments and studying the Lyapunov exponents, we observed and analyzed the new phenomenon-clustered sequential dynamics. The results are interpreted in the context of the winnerless competition principle. Accordingly, clustered sequential dynamics is represented in the phase space of the model by two weakly interacting quasi-attractors. One of them is similar to the sequential heteroclinic chain-the regular image of SWM, while the other is a quasi-chaotic attractor. Coexistence of these quasi-attractors means that the recall of the normal information sequence is intermittently interrupted by episodes with chaotic dynamics. We indicate potential dynamic ways for augmenting damaged working memory and other cognitive functions.

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Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components-brain modes-participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles-metastable states-connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics.

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The inferior olive (IO) is a neural network belonging to the olivo-cerebellar system whose neurons are coupled with electrical synapses and display subthreshold oscillations and spiking activity. The IO is frequently proposed as the generator of timing signals to the cerebellum. Electrophysiological and imaging recordings show that the IO network generates complex spatio-temporal patterns. The generation and modulation of coherent spiking activity in the IO is one key issue in cerebellar research. In this work, we build a large scale IO network model of electrically coupled conductance-based neurons to study the emerging spatio-temporal patterns of its transient neuronal activity. Our modeling reproduces and helps to understand important phenomena observed in IO in vitro and in vivo experiments, and draws new predictions regarding the computational properties of this network and the associated cerebellar circuits. The main factors studied governing the collective dynamics of the IO network were: the degree of electrical coupling, the extent of the electrotonic connections, the presence of stimuli or regions with different excitability levels and the modulatory effect of an inhibitory loop (IL). The spatio-temporal patterns were analyzed using a discrete wavelet transform to provide a quantitative characterization. Our results show that the electrotonic coupling produces quasi-synchronized subthreshold oscillations over a wide dynamical range. The synchronized oscillatory activity plays the role of a timer for a coordinated representation of spiking rhythms with different frequencies. The encoding and coexistence of several coordinated rhythms is related to the different clusterization and coherence of transient spatio-temporal patterns in the network, where the spiking activity is commensurate with the quasi-synchronized subthreshold oscillations. In the presence of stimuli, different rhythms are encoded in the spiking activity of the IO neurons that nevertheless remains constrained to a commensurate value of the subthreshold frequency. The stimuli induced spatio-temporal patterns can reverberate for long periods, which contributes to the computational properties of the IO. We also show that the presence of regions with different excitability levels creates sinks and sources of coordinated activity which shape the propagation of spike wave fronts. These results can be generalized beyond IO studies, as the control of wave pattern propagation is a highly relevant problem in the context of normal and pathological states in neural systems (e.g., related to tremor, migraine, epilepsy) where the study of the modulation of activity sinks and sources can have a potential large impact.

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Attentional networks that integrate many cortical and subcortical elements dynamically control mental processes to focus on specific events and make a decision. The resources of attentional processing are finite. Nevertheless, we often face situations in which it is necessary to simultaneously process several modalities, for example, to switch attention between players in a soccer field. Here we use a global brain mode description to build a model of attentional control dynamics. This model is based on sequential information processing stability conditions that are realized through nonsymmetric inhibition in cortical circuits. In particular, we analyze the dynamics of attentional switching and focus in the case of parallel processing of three interacting mental modalities. Using an excitatory-inhibitory network, we investigate how the bifurcations between different attentional control strategies depend on the stimuli and analyze the relationship between the time of attention focus and the strength of the stimuli. We discuss the interplay between attention and decision-making: in this context, a decision-making process is a controllable bifurcation of the attention strategy. We also suggest the dynamical evaluation of attentional resources in neural sequence processing.

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There is no "gold standard" treatment for femoral mid-shaft fractures near the tip of a hip implant. Moreover, no study has quantified the changes in a femur's mechanical properties from injury through to healing. The present aim was to predict overall stiffness and peak bone stress in the same femur after injury, repair, and healing with respect to its intact condition. Stage 1 was an intact femur. Stage 2 mimicked a femur with a hip stem. Stage 3 had a 5-mm fracture gap repaired with a plate and screws. Stage 4 represented complete fracture union. Experiments were done on a synthetic femur with strain gages and subjected to 1500 N of axial force. Finite element (FE) models were validated against experiments and then re-analyzed using a clinical-level force of 3000 N. At 1500 N, FE vs. experimental strains had excellent linear agreement (R=0.94; slope=0.97). At 3000 N, FE stiffnesses were 2167 N/mm (Stage 1), 2359 N/mm (Stage 2), 973 N/mm (Stage 3), and 3348 N/mm (Stage 4), showing that Stage 3 was the least stable compared to Stage 1. At 3000 N, FE bone stresses yielded peaks of 75.7 MPa at the load application point (Stage 1), 29.0 MPa near the hip implant tip (Stage 2), 126.3 MPa at the distal portion of the plate (Stage 3), and 69.3 MPa at the proximal portion of the plate (Stage 4), showing that Stage 3 was most vulnerable to re-injury compared to Stage 1. Stress shielding and high stresses were present not only after hip implantation and plating, but also after healing.

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Extracellular fungal flavocytochrome cellobiose dehydrogenase (CDH) is a promising enzyme for both bioelectronics and lignocellulose bioconversion. A selective high-throughput screening assay for CDH in the presence of various fungal oxidoreductases was developed. It is based on Prussian Blue (PB) in situ formation in the presence of cellobiose (<0.25 mM), ferric acetate, and ferricyanide. CDH induces PB formation via both reduction of ferricyanide to ferrocyanide reacting with an excess of Fe³âº (pathway 1) and reduction of ferric ions to Fe²âº reacting with the excess of ferricyanide (pathway 2). Basidiomycetous and ascomycetous CDH formed PB optimally at pH 3.5 and 4.5, respectively. In contrast to the holoenzyme CDH, its FAD-containing dehydrogenase domain lacking the cytochrome domain formed PB only via pathway 1 and was less active than the parent enzyme. The assay can be applied on active growing cultures on agar plates or on fungal culture supernatants in 96-well plates under aerobic conditions. Neither other carbohydrate oxidoreductases (pyranose dehydrogenase, FAD-dependent glucose dehydrogenase, glucose oxidase) nor laccase interfered with CDH activity in this assay. Applicability of the developed assay for the selection of new ascomycetous CDH producers as well as possibility of the controlled synthesis of new PB nanocomposites by CDH are discussed.

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Timing and dynamics of information in the brain is a hot field in modern neuroscience. The analysis of the temporal evolution of brain information is crucially important for the understanding of higher cognitive mechanisms in normal and pathological states. From the perspective of information dynamics, in this review we discuss working memory capacity, language dynamics, goal-dependent behavior programming and other functions of brain activity. In contrast with the classical description of information theory, which is mostly algebraic, brain flow information dynamics deals with problems such as the stability/instability of information flows, their quality, the timing of sequential processing, the top-down cognitive control of perceptual information, and information creation. In this framework, different types of information flow instabilities correspond to different cognitive disorders. On the other hand, the robustness of cognitive activity is related to the control of the information flow stability. We discuss these problems using both experimental and theoretical approaches, and we argue that brain activity is better understood considering information flows in the phase space of the corresponding dynamical model. In particular, we show how theory helps to understand intriguing experimental results in this matter, and how recent knowledge inspires new theoretical formalisms that can be tested with modern experimental techniques.

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In the last few decades several concepts of dynamical systems theory (DST) have guided psychologists, cognitive scientists, and neuroscientists to rethink about sensory motor behavior and embodied cognition. A critical step in the progress of DST application to the brain (supported by modern methods of brain imaging and multi-electrode recording techniques) has been the transfer of its initial success in motor behavior to mental function, i.e., perception, emotion, and cognition. Open questions from research in genetics, ecology, brain sciences, etc., have changed DST itself and lead to the discovery of a new dynamical phenomenon, i.e., reproducible and robust transients that are at the same time sensitive to informational signals. The goal of this review is to describe a new mathematical framework - heteroclinic sequential dynamics - to understand self-organized activity in the brain that can explain certain aspects of robust itinerant behavior. Specifically, we discuss a hierarchy of coarse-grain models of mental dynamics in the form of kinetic equations of modes. These modes compete for resources at three levels: (i) within the same modality, (ii) among different modalities from the same family (like perception), and (iii) among modalities from different families (like emotion and cognition). The analysis of the conditions for robustness, i.e., the structural stability of transient (sequential) dynamics, give us the possibility to explain phenomena like the finite capacity of our sequential working memory - a vital cognitive function -, and to find specific dynamical signatures - different kinds of instabilities - of several brain functions and mental diseases.

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Cellobiose dehydrogenase (CDH) is an extracellular fungal flavocytochrome specifically oxidizing cellooligosaccharides and lactose to corresponding (-lactones by a variety of electron acceptors. In contrast to basidiomycetous CDHs, CDHs of ascomycetes also display certain activity toward glucose. The objective of this study was to establish the structural reasons of such an activity of CDH from mesophilic ascomycete Chaetomium sp. INBI 2-26 (ChCDH). The complete amino acid sequence of ChCDH displayed high levels of similarity with the amino acid sequences of CDHs from the thermophilic fungi Thielavia heterotallica and Myriococcum thermophilum. Peptide mass fingerprinting of purified ChCDH provided evidence for the oxidation of methionine residues in the FAD-domain. Comparative homology modeling of the structure of the ChCDH FAD-domain in complex with the transition state analog based on the structure of the same complex of basidiomycetous CDH (1NAA) as template indicated possible structural reasons for the enhanced activity of ascomycetous CDHs toward glucose at neutral pH, which is a prerequisite for application of CDH in a variety of biocompatible biosensors and biofuel cells.

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Putative cellobiose dehydrogenase (CDH) genes are frequently discovered in various fungi by genome sequencing projects. The expression of CDH, an extracellular flavocytochrome, is well studied in white rot basidiomycetes and is attributed to extracellular lignocellulose degradation. CDH has also been reported for plant-pathogenic or saprotrophic ascomycetes, but the molecular and catalytic properties of these enzymes are currently less investigated. This study links various ascomycetous cdh genes with the molecular and catalytic characteristics of the mature proteins and suggests a differentiation of ascomycete class II CDHs into two subclasses, namely, class IIA and class IIB, in addition to the recently introduced class III of hypothetical ascomycete CDHs. This new classification is based on sequence and biochemical data obtained from sequenced fungal genomes and a screening of 40 ascomycetes. Thirteen strains showed CDH activity when they were grown on cellulose-based media, and Chaetomium atrobrunneum, Corynascus thermophilus, Dichomera saubinetii, Hypoxylon haematostroma, Neurospora crassa, and Stachybotrys bisbyi were selected for detailed studies. In these strains, one or two cdh-encoding genes were found that stem either from class IIA and contain a C-terminal carbohydrate-binding module or from class IIB without such a module. In several strains, both genes were found. Regarding substrate specificity, class IIB CDHs show a less pronounced substrate specificity for cellobiose than class IIA enzymes. A pH-dependent pattern of the intramolecular electron transfer was also observed, and the CDHs were classified into three groups featuring acidic, intermediate, or alkaline pH optima. The pH optimum, however, does not correlate with the CDH subclasses and is most likely a species-dependent adaptation to different habitats.

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Emotion (i.e., spontaneous motivation and subsequent implementation of a behavior) and cognition (i.e., problem solving by information processing) are essential to how we, as humans, respond to changes in our environment. Recent studies in cognitive science suggest that emotion and cognition are subserved by different, although heavily integrated, neural systems. Understanding the time-varying relationship of emotion and cognition is a challenging goal with important implications for neuroscience. We formulate here the dynamical model of emotion-cognition interaction that is based on the following principles: (1) the temporal evolution of cognitive and emotion modes are captured by the incoming stimuli and competition within and among themselves (competition principle); (2) metastable states exist in the unified emotion-cognition phase space; and (3) the brain processes information with robust and reproducible transients through the sequence of metastable states. Such a model can take advantage of the often ignored temporal structure of the emotion-cognition interaction to provide a robust and generalizable method for understanding the relationship between brain activation and complex human behavior. The mathematical image of the robust and reproducible transient dynamics is a Stable Heteroclinic Sequence (SHS), and the Stable Heteroclinic Channels (SHCs). These have been hypothesized to be possible mechanisms that lead to the sequential transient behavior observed in networks. We investigate the modularity of SHCs, i.e., given a SHS and a SHC that is supported in one part of a network, we study conditions under which the SHC pertaining to the cognition will continue to function in the presence of interfering activity with other parts of the network, i.e., emotion.

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