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This paper offers a formal account of emotional inference and stress-related behaviour, using the notion of active inference. We formulate responses to stressful scenarios in terms of Bayesian belief-updating and subsequent policy selection; namely, planning as (active) inference. Using a minimal model of how creatures or subjects account for their sensations (and subsequent action), we deconstruct the sequences of belief updating and behaviour that underwrite stress-related responses – and simulate the aberrant responses of the sort seen in post-traumatic stress disorder (PTSD). Crucially, the model used for belief-updating generates predictions in multiple (exteroceptive, proprioceptive and interoceptive) modalities, to provide an integrated account of evidence accumulation and multimodal integration that has consequences for both motor and autonomic responses. The ensuing phenomenology speaks to many constructs in the ecological and clinical literature on stress, which we unpack with reference to simulated inference processes and accompanying neuronal responses. A key insight afforded by this formal approach rests on the trade-off between the epistemic affordance of certain cues (that resolve uncertainty about states of affairs in the environment) and the consequences of epistemic foraging (that may be in conflict with the instrumental or pragmatic value of 'fleeing' or 'freezing'). Starting from first principles, we show how this trade-off is nuanced by prior (subpersonal) beliefs about the outcomes of behaviour – beliefs that, when held with unduly high precision, can lead to (Bayes optimal) responses that closely resemble PTSD. [ABSTRACT FROM AUTHOR]

Human social interactions depend on the ability to resolve uncertainty about the mental states of others. The context in which social interactions take place is crucial for mental state attribution as sensory inputs may be perceived differently depending on the context. In this paper, we introduce a mental state attribution task where a target-face with either an ambiguous or an unambiguous emotion is embedded in different social contexts. The social context is determined by the emotions conveyed by other faces in the scene. This task involves mental state attribution to a target-face (either happy or sad) depending on the social context. Using active inference models, we provide a proof of concept that an agent's perception of sensory stimuli may be altered by social context. We show with simulations that context congruency and facial expression coherency improve behavioural performance in terms of decision times. Furthermore, we show through simulations that the abnormal viewing strategies employed by patients with schizophrenia may be due to (i) an imbalance between the precisions of local and global features in the scene and (ii) a failure to modulate the sensory precision to contextualise emotions. [ABSTRACT FROM AUTHOR]

Neuronal computations rely upon local interactions across synapses. For a neuronal network to perform inference, it must integrate information from locally computed messages that are propagated among elements of that network. We review the form of two popular (Bayesian) message passing schemes and consider their plausibility as descriptions of inference in biological networks. These are variational message passing and belief propagation - each of which is derived from a free energy functional that relies upon different approximations (mean-field and Bethe respectively). We begin with an overview of these schemes and illustrate the form of the messages required to perform inference using Hidden Markov Models as generative models. Throughout, we use factor graphs to show the form of the generative models and of the messages they entail. We consider how these messages might manifest neuronally and simulate the inferences they perform. While variational message passing offers a simple and neuronally plausible architecture, it falls short of the inferential performance of belief propagation. In contrast, belief propagation allows exact computation of marginal posteriors at the expense of the architectural simplicity of variational message passing. As a compromise between these two extremes, we offer a third approach - marginal message passing - that features a simple architecture, while approximating the performance of belief propagation. Finally, we link formal considerations to accounts of neurological and psychiatric syndromes in terms of aberrant message passing. [ABSTRACT FROM AUTHOR]

Active inference is a first principle account of how autonomous agents operate in dynamic, nonstationary environments. This problem is also considered in reinforcement learning, but limited work exists on comparing the two approaches on the same discrete-state environments. In this letter, we provide (1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning, and (2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline. We begin by providing a condensed overview of the active inference literature, in particular viewing the various natural behaviors of active inference agents through the lens of reinforcement learning. We show that by operating in a pure belief-based setting, active inference agents can carry out epistemic exploration—and account for uncertainty about their environment—in a Bayes-optimal fashion. Furthermore, we show that the reliance on an explicit reward signal in reinforcement learning is removed in active inference, where reward can simply be treated as another observation we have a preference over; even in the total absence of rewards, agent behaviors are learned through preference learning. We make these properties explicit by showing two scenarios in which active inference agents can infer behaviors in reward-free environments compared to both Q-learning and Bayesian model-based reinforcement learning agents and by placing zero prior preferences over rewards and learning the prior preferences over the observations corresponding to reward. We conclude by noting that this formalism can be applied to more complex settings (e.g., robotic arm movement, Atari games) if appropriate generative models can be formulated. In short, we aim to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation and demonstrate these behaviors in a OpenAI gym environment, alongside reinforcement learning agents. [ABSTRACT FROM AUTHOR]

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model—an internal estimate of overall model fitness ("subjective fitness"). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses. [ABSTRACT FROM AUTHOR]

In this article, we develop a computational model of obsessive–compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients' own actions and the unfolding of possible events. Clinically, this corresponds both to patients' difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model, including a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e., checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and overresponsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research. [ABSTRACT FROM AUTHOR]

• Low emotional awareness (EA) is associated with multiple clinical conditions. • The neurocomputational processes underlying EA are poorly understood. • We present a deep active inference model of EA that can simulate these processes. • This model illustrates 7 distinct mechanisms whereby aberrant processing produces low EA. • This may offer distinct targets that could inform individualized treatment selection. Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena – such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity – characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals – and provide a principled basis for personalized precision medicine. [ABSTRACT FROM AUTHOR]

• Variational Laplace (VL) is a scheme for Bayesian modelling. • VL is widely used in neuroimaging, in particular DCM. • This paper provides a tutorial explanation of the math and algorithms. • New standalone code is provided to enable re-implementation. • The supplementary materials provide worked derivations. This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations. [ABSTRACT FROM AUTHOR]

This paper characterizes impulsive behavior using a patch-leaving paradigm and active inference—a framework for describing Bayes optimal behavior. This paradigm comprises different environments (patches) with limited resources that decline over time at different rates. The challenge is to decide when to leave the current patch for another to maximize reward. We chose this task because it offers an operational characterization of impulsive behavior, namely, maximizing proximal reward at the expense of future gain. We use a Markov decision process formulation of active inference to simulate behavioral and electrophysiological responses under different models and prior beliefs. Our main finding is that there are at least three distinct causes of impulsive behavior, which we demonstrate by manipulating three different components of the Markov decision process model. These components comprise (i) the depth of planning, (ii) the capacity to maintain and process information, and (iii) the perceived value of immediate (relative to delayed) rewards. We show how these manipulations change beliefs and subsequent choices through variational message passing. Furthermore, we appeal to the process theories associated with this message passing to simulate neuronal correlates. In future work, we will use this scheme to identify the prior beliefs that underlie different sorts of impulsive behavior—and ask whether different causes of impulsivity can be inferred from the electrophysiological correlates of choice behavior. [ABSTRACT FROM AUTHOR]

The free-energy principle is an attempt to explain the structure of the agent and its brain, starting from the fact that an agent exists (Friston and Stephan, 2007; Friston et al., 2010). More specifically, it can be regarded as a systematic attempt to understand the ‘fit’ between an embodied agent and its niche, where the quantity of free-energy is a measure for the ‘misfit’ or disattunement (Bruineberg and Rietveld, 2014) between agent and environment. This paper offers a proof-of-principle simulation of niche construction under the free-energy principle. Agent-centered treatments have so far failed to address situations where environments change alongside agents, often due to the action of agents themselves. The key point of this paper is that the minimum of free-energy is not at a point in which the agent is maximally adapted to the statistics of a static environment, but can better be conceptualized an attracting manifold within the joint agent-environment state-space as a whole, which the system tends toward through mutual interaction. We will provide a general introduction to active inference and the free-energy principle. Using Markov Decision Processes (MDPs), we then describe a canonical generative model and the ensuing update equations that minimize free-energy. We then apply these equations to simulations of foraging in an environment; in which an agent learns the most efficient path to a pre-specified location. In some of those simulations, unbeknownst to the agent, the ‘desire paths’ emerge as a function of the activity of the agent (i.e. niche construction occurs). We will show how, depending on the relative inertia of the environment and agent, the joint agent-environment system moves to different attracting sets of jointly minimized free-energy. [ABSTRACT FROM AUTHOR]

This paper introduces a variational formulation of natural selection, paying special attention to the nature of 'things' and the way that different 'kinds' of 'things' are individuated from—and influence—each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain—and are constrained by—fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses—and identify points of contact with related mathematical formulations of evolution. [ABSTRACT FROM AUTHOR]

This paper provides a concise description of the free energy principle, starting from a formulation of random dynamical systems in terms of a Langevin equation and ending with a Bayesian mechanics that can be read as a physics of sentience. It rehearses the key steps using standard results from statistical physics. These steps entail (i) establishing a particular partition of states based upon conditional independencies that inherit from sparsely coupled dynamics, (ii) unpacking the implications of this partition in terms of Bayesian inference and (iii) describing the paths of particular states with a variational principle of least action. Teleologically, the free energy principle offers a normative account of self-organisation in terms of optimal Bayesian design and decision-making, in the sense of maximising marginal likelihood or Bayesian model evidence. In summary, starting from a description of the world in terms of random dynamical systems, we end up with a description of self-organisation as sentient behaviour that can be interpreted as self-evidencing; namely, self-assembly, autopoiesis or active inference. [Display omitted] • This paper provides a concise derivation of the free energy principle. • The FEP is a normative account of self-organisation and sentient behaviour: • It describes self-organisation as maximizing Bayesian model evidence, • And sentient behaviour as optimal Bayesian design and decision-making. • Altogether, we start from a description of the world as a random dynamical system, • And end with a Bayesian mechanics that can be read as a physics of sentience. [ABSTRACT FROM AUTHOR]

Describes a case of a patient who underwent left total knee replacement for osteoarthrosis. Radiographic evidence of a minimally displaced transverse fracture of right scapula; Treatment management with a sling and pendulum exercises; Mechanism of injury.

How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively. [ABSTRACT FROM AUTHOR]

How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively. [ABSTRACT FROM AUTHOR]

Large storm events can not only increase the runoff mass exports of particulate organic matter (POM) from watersheds, but can also alter the sources, size distribution, and composition of POM. We investigated the quantity, particle size distribution, carbon (C) and nitrogen (N) content, and sources of POM for five locations longitudinally along a forested Piedmont stream. POM was sampled for multiple storm events of varying magnitude and intensity over a two-year period. POM was separated into coarse (CPOM), medium (MPOM), and fine (FPOM) size classes, and sources were estimated using stable isotopes of C and N with a Bayesian mixing model. CPOM largely resembled less-degraded vascular plant material characteristic of forest floor litter, which was estimated to contribute to ~40% of CPOM in upstream locations. FPOM was derived from a more variable mixture of sources with stream beds and stream banks playing a greater role at larger drainage locations (up to ~50 and ~30%, respectively). Contributions from both forest floor litter and humus to CPOM increased with increasing event runoff, and litter contributions increased during events with higher rainfall intensities. Higher C and N content was noted in coarse sediments and finer POM fractions appeared to be more degraded based on C:N and isotope ratios. Climate-change projections predict intensification of large storm events in the Northeastern US. Results of this study suggest that large storms will increase the fluvial exports of coarse, labile, C- and N-rich POM with subsequent impacts on receiving aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Infections caused by antibiotic-resistant pathogens, particularly Gram-negative bacteria, represent significant treatment challenges for physicians resulting in high rates of morbidity and mortality. The outer membrane of Gram-negative bacteria acts as a permeability barrier to many compounds that would otherwise be effective antibacterial agents, including those effective against Gram-positive pathogens. Potentiator molecules disrupt this barrier allowing entry of otherwise impermeant molecules, thus providing a strategy to render multi-drug resistant pathogens susceptible to a broader range of antibiotics. Potentiator molecules are cationic and the mechanism of disruption involves interaction with the negatively charged outer membrane. This physical attribute, along with an often high degree of lipophilicity typically endears these molecules with unacceptable toxicity. Presented herein are examples of advanced potentiator molecules being evaluated for use in combination therapy for the treatment of resistant Gram-negative infections. [ABSTRACT FROM AUTHOR]

Delusions are, by popular definition, false beliefs that are held with certainty and resistant to contradictory evidence. They seem at odds with the notion that the brain at least approximates Bayesian inference. This is especially the case in schizophrenia, a disorder thought to relate to decreased - rather than increased - certainty in the brain's model of the world. We use an active inference Markov decision process model (a Bayes-optimal decision-making agent) to perform a simple task involving social and non-social inferences. We show that even moderate changes in some model parameters - decreasing confidence in sensory input and increasing confidence in states implied by its own (especially habitual) actions - can lead to delusions as defined above. Incorporating affect in the model increases delusions, specifically in the social domain. The model also reproduces some classic psychological effects, including choice-induced preference change, and an optimism bias in inferences about oneself. A key observation is that no change in a single parameter is both necessary and sufficient for delusions; rather, delusions arise due to conditional dependencies that create 'basins of attraction' which trap Bayesian beliefs. Simulating the effects of antidopaminergic antipsychotics - by reducing the model's confidence in its actions - demonstrates that the model can escape from these attractors, through this synthetic pharmacotherapy. [ABSTRACT FROM AUTHOR]

Under the Bayesian brain hypothesis, behavioral variations can be attributed to different priors over generative model parameters. This provides a formal explanation for why individuals exhibit inconsistent behavioral preferences when confronted with similar choices. For example, greedy preferences are a consequence of confident (or precise) beliefs over certain outcomes. Here, we offer an alternative account of behavioral variability using Rényi divergences and their associated variational bounds. Rényi bounds are analogous to the variational free energy (or evidence lower bound) and can be derived under the same assumptions. Importantly, these bounds provide a formal way to establish behavioral differences through an α parameter, given fixed priors. This rests on changes in α that alter the bound (on a continuous scale), inducing different posterior estimates and consequent variations in behavior. Thus, it looks as if individuals have different priors and have reached different conclusions. More specifically, α → 0 + optimization constrains the variational posterior to be positive whenever the true posterior is positive. This leads to mass-covering variational estimates and increased variability in choice behavior. Furthermore, α → + ∞ optimization constrains the variational posterior to be zero whenever the true posterior is zero. This leads to mass-seeking variational posteriors and greedy preferences. We exemplify this formulation through simulations of the multiarmed bandit task. We note that these α parameterizations may be especially relevant (i.e., shape preferences) when the true posterior is not in the same family of distributions as the assumed (simpler) approximate density, which may be the case in many real-world scenarios. The ensuing departure from vanilla variational inference provides a potentially useful explanation for differences in behavioral preferences of biological (or artificial) agents under the assumption that the brain performs variational Bayesian inference. [ABSTRACT FROM AUTHOR]

Ecosystem engineers alter habitat and resource availability within ecosystems, but the magnitude of these effects depends on abiotic context and spatial scale. We examined how the effects of freshwater mussels, an ecosystem engineer, changed with spatial scale. We combined a field enclosure experiment and comparative field study to evaluate associations among mussels and macroinvertebrate communities across three spatial scales: mussel individuals (~ 0.01 m2), patches of mussels (0.25 m2), and large aggregations of many mussel patches (mussel beds, ~ 1000 m2). We used canonical correspondence analysis and variation partitioning to evaluate how mussel abundance, food availability, substrate heterogeneity, and flow influenced macroinvertebrate communities. We found that mussels’ influence on macroinvertebrate communities differed among spatial scales. At the smallest scale, macroinvertebrate density increased on the shells of live mussels, likely due to mussel influences on food availability to grazers. At the patch scale, we found no mussel effects, likely because they were overridden by a flood event. At the mussel bed scale, macroinvertebrate communities were primarily controlled by flow and secondarily by food availability. As such, the continued loss of freshwater mussels means the loss of habitat creation and food provisioning for other aquatic groups, and alteration of facilitation landscapes within streams. [ABSTRACT FROM AUTHOR]

Abstract: In this study, the impact of inoculum density on the growth inhibitory and killing activities of oritavancin and comparators (vancomycin, daptomycin and linezolid) in vitro against four Staphylococcus aureus strains at clinically relevant drug concentrations was studied. Broth microdilution and time–kill assays were performed using a standard inoculum [ca. 105 colony-forming units (CFU)/mL as per Clinical and Laboratory Standards Institute (CLSI) guidelines] and a high inoculum (ca. 107 CFU/mL). Whereas minimal inhibitory concentrations (MICs) of comparators were 2–8-fold higher when tested at high inoculum, oritavancin MICs were 16-fold higher for all strains at the high inoculum relative to the standard inoculum. However, in time–kill assays, when tested at its fCmin [trough concentration of free (non-protein-bound) drug] and fCmax (peak concentration of non-protein-bound drug), oritavancin retained its bactericidal activity against a vancomycin-susceptible, meticillin-susceptible S. aureus (VS-MSSA) strain and a vancomycin-susceptible, meticillin-resistant S. aureus (VS-MRSA) strain both at standard and high inocula. At its fCmax, oritavancin was bactericidal at standard inoculum but not at high inoculum against two vancomycin-intermediate S. aureus (VISA) strains. Against both VISA strains at standard inoculum, oritavancin at its fCmin reduced cell density by between 2 and 3log (bacteriostatic), predicting that it will retain activity against certain VISA infections. However, oritavancin had no substantial growth inhibitory effect against either VISA strain at high inoculum, suggesting that in rare VISA infections with an anticipated high bacterial burden such as endocarditis, alternative oritavancin dosing strategies, including combinations with other agents, may be explored. [Copyright &y& Elsevier]

Biehl et al. (2021) present some interesting observations on an early formulation of the free energy principle. We use these observations to scaffold a discussion of the technical arguments that underwrite the free energy principle. This discussion focuses on solenoidal coupling between various (subsets of) states in sparsely coupled systems that possess a Markov blanket—and the distinction between exact and approximate Bayesian inference, implied by the ensuing Bayesian mechanics. [ABSTRACT FROM AUTHOR]

• We leverage the idea of 'Markov blanket' as a statistical boundary to provide an analysis of partitions in neuronal systems. • We show this partition is applicable to multiple scales, from single neurons, brain regions, and brain-wide networks. • Based on the canonical micro-circuitry, our treatment has practical applications for effective connectivity. • Our proposed partition highlights the limitations of 'modular' proposals considering a single level of description. Recent characterisations of self-organising systems depend upon the presence of a 'Markov blanket': a statistical boundary that mediates the interactions between the inside and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. The notion of effective connectivity depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level of analysis. The nuance afforded by partitioning neural systems in this way highlights certain limitations of 'modular' perspectives of brain function that only consider a single level of description. [ABSTRACT FROM AUTHOR]

Active inference offers a first principle account of sentient behavior, from which special and important cases—for example, reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design—can be derived. Active inference finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on the same footing as reward or value. In brief, active inference replaces value functions with functionals of (Bayesian) beliefs, in the form of an expected (variational) free energy. In this letter, we consider a sophisticated kind of active inference using a recursive form of expected free energy. Sophistication describes the degree to which an agent has beliefs about beliefs. We consider agents with beliefs about the counterfactual consequences of action for states of affairs and beliefs about those latent states. In other words, we move from simply considering beliefs about "what would happen if I did that" to "what I would believe about what would happen if I did that." The recursive form of the free energy functional effectively implements a deep tree search over actions and outcomes in the future. Crucially, this search is over sequences of belief states as opposed to states per se. We illustrate the competence of this scheme using numerical simulations of deep decision problems. [ABSTRACT FROM AUTHOR]

• Computational treatment of biological self-organisation. • Biological self-organisation requires emergence of boundaries, namely Markov blankets. • Hierarchical self-organisation entails emergence of Markov blankets at multiple scale. Biological self-organisation can be regarded as a process of spontaneous pattern formation; namely, the emergence of structures that distinguish themselves from their environment. This process can occur at nested spatial scales: from the microscopic (e.g., the emergence of cells) to the macroscopic (e.g. the emergence of organisms). In this paper, we pursue the idea that Markov blankets – that separate the internal states of a structure from external states – can self-assemble at successively higher levels of organisation. Using simulations, based on the principle of variational free energy minimisation, we show that hierarchical self-organisation emerges when the microscopic elements of an ensemble have prior (e.g., genetic) beliefs that they participate in a macroscopic Markov blanket: i.e., they can only influence – or be influenced by – a subset of other elements. Furthermore, the emergent structures look very much like those found in nature (e.g., cells or organelles), when influences are mediated by short range signalling. These simulations are offered as a proof of concept that hierarchical self-organisation of Markov blankets (into Markov blankets) can explain the self-evidencing, autopoietic behaviour of biological systems. [ABSTRACT FROM AUTHOR]

This paper considers the emergence of a generalised synchrony in ensembles of coupled self-organising systems, such as neurons. We start from the premise that any self-organising system complies with the free energy principle, in virtue of placing an upper bound on its entropy. Crucially, the free energy principle allows one to interpret biological systems as inferring the state of their environment or external milieu. An emergent property of this inference is synchronisation among an ensemble of systems that infer each other. Here, we investigate the implications of neuronal dynamics by simulating neuronal networks, where each neuron minimises its free energy. We cast the ensuing ensemble dynamics in terms of inference and show that cardinal behaviours of neuronal networks – both in vivo and in vitro – can be explained by this framework. In particular, we test the hypotheses that (i) generalised synchrony is an emergent property of free energy minimisation; thereby explaining synchronisation in the resting brain: (ii) desynchronisation is induced by exogenous input; thereby explaining event-related desynchronisation and (iii) structure learning emerges in response to causal structure in exogenous input; thereby explaining functional segregation in real neuronal systems. [ABSTRACT FROM AUTHOR]

Abstract Parkinson's disease (PD) is a neurodegenerative condition in which aberrant oscillatory synchronization of neuronal activity at beta frequencies (15–35 Hz) across the cortico-basal ganglia-thalamocortical circuit is associated with debilitating motor symptoms, such as bradykinesia and rigidity. Mounting evidence suggests that the magnitude of beta synchrony in the parkinsonian state fluctuates over time, but the mechanisms by which thalamocortical circuitry regulates the dynamic properties of cortical beta in PD are poorly understood. Using the recently developed generic Dynamic Causal Modelling (DCM) framework, we recursively optimized a set of plausible models of the thalamocortical circuit (n = 144) to infer the neural mechanisms that best explain the transitions between low and high beta power states observed in recordings of field potentials made in the motor cortex of anesthetized Parkinsonian rats. Bayesian model comparison suggests that upregulation of cortical rhythmic activity in the beta-frequency band results from changes in the coupling strength both between and within the thalamus and motor cortex. Specifically, our model indicates that high levels of cortical beta synchrony are mainly achieved by a delayed (extrinsic) input from thalamic relay cells to deep pyramidal cells and a fast (intrinsic) input from middle pyramidal cells to superficial pyramidal cells. From a clinical perspective, our study provides insights into potential therapeutic strategies that could be utilized to modulate the network mechanisms responsible for the enhancement of cortical beta in PD. Specifically, we speculate that cortical stimulation aimed to reduce the enhanced excitatory inputs to either the superficial or deep pyramidal cells could be a potential non-invasive therapeutic strategy for PD. Highlights • Coupling changes within and between circuit nodes lead to cortical beta enhancement. • Input propagation delays play a crucial role in the up-regulation of cortical beta. • Beta power could be modulated by altering lamina specific inputs. [ABSTRACT FROM AUTHOR]

Animals can play important roles in cycling nutrients [hereafter consumer-driven nutrient dynamics (CND)], but researchers typically simplify animal communities inhabiting dynamic environments into single groups that are tested under relatively static conditions. We propose a conceptual framework and present empirical evidence for CND that considers the potential effects of spatially overlapping animal groups within dynamic ecosystems. Because streams can maintain high biomass of mussels and fish, we were able to evaluate this framework by testing if biogeochemical hotspots generated by stable aggregations of mussels attract fishes. We predicted that spatial overlap between these groups may increase the flux of mineralized nutrients. We quantified how different fish assemblage biomass was between mussel bed reaches and reaches without mussels. We compared fish and mussel biomass at mussel beds to test whether differences in animal biomass mediate their contributions to nutrient cycling through nitrogen and phosphorous excretion. We estimated areal excretion rates for each group by combining biomass estimates with measured excretion rates. Fish biomass was homogeneously distributed, except following a period of low flow when fish were more concentrated at mussel beds. Mussel biomass was consistently an order of magnitude greater than fish biomass and mussel areal excretion rates exceeded fish excretion rates. However, the magnitude of those differences varied spatially and temporally. Mussel excretion stoichiometry varied with changes in assemblage composition, while fish excretion stoichiometry varied little. Biogeochemical hotspots associated with mussels did not generally overlap with fish aggregations, thus, under these conditions, animal processes appear to exert additive ecosystem effects. [ABSTRACT FROM AUTHOR]

Despite decades of research, we do not definitively know how people sometimes see things that are not there. Eight models of complex visual hallucinations have been published since 2000, including Deafferentation, Reality Monitoring, Perception and Attention Deficit, Activation, Input, and Modulation, Hodological, Attentional Networks, Active Inference, and Thalamocortical Dysrhythmia Default Mode Network Decoupling. Each was derived from different understandings of brain organisation. To reduce this variability, representatives from each research group agreed an integrated Visual Hallucination Framework that is consistent with current theories of veridical and hallucinatory vision. The Framework delineates cognitive systems relevant to hallucinations. It allows a systematic, consistent, investigation of relationships between the phenomenology of visual hallucinations and changes in underpinning cognitive structures. The episodic nature of hallucinations highlights separate factors associated with the onset, persistence, and end of specific hallucinations suggesting a complex relationship between state and trait markers of hallucination risk. In addition to a harmonised interpretation of existing evidence, the Framework highlights new avenues of research, and potentially, new approaches to treating distressing hallucinations. • We provide a new Visual Hallucination Framework which integrates eight current models. • All models suggest a key role for visual input. • Variations in factors such as attention or memory may relate to differences in the phenomenology of specific hallucinations. • That hallucinations are episodic suggests different traits are relevant in the onset, persistence and end of hallucinations. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) plays a critical role in the global terrestrial carbon cycle, and a better understanding of soil processes involved in SOM stability is essential to determine how projected climate-driven changes in soil processes will influence carbon dynamics. We used 14 C signature, analytical thermal analysis, and ultrahigh resolution mass spectrometry to determine the influence of deciduous and coniferous forest vegetation type and soil depth on the stability of soil C. The 14 C mean residence time (MRT) of the illuvial B horizon soils averaged 1350 years for the deciduous soils and 795 years for the coniferous soils. The difference of MRT between mineral soils by forest type may be due to the saturation of extractable Fe and Al minerals binding sites by SOM in the coniferous soils, allowing greater transport of modern SOM from the O horizon down the soil profile, as compared with the non-saturated minerals in the deciduous soil profile. The molecular mass distribution of the deciduous water-extractable aromatic SOM fraction was shifted to a lower mass range in the lower portion of B horizon soil compared with the upper portion, indicating preferential sorption of the higher mass aromatic fraction. The shift in the mass distribution of the aromatic fraction in the coniferous soil was much less than in the deciduous soil, which supports the view that the extractable metal minerals had reached saturation. We conclude that greater transport of modern O horizon SOM to the lower mineral B horizons in the coniferous soil profile resulted in its radiocarbon enrichment and shorter estimated MRT. Our findings highlight the importance of forest vegetation type, soil depth and transport mechanisms on SOM stability, and suggest important ecological implications for changes in forest composition on the terrestrial C cycle. [ABSTRACT FROM AUTHOR]

The field of urban stream ecology has evolved rapidly in the last 3 decades, and it now includes natural scientists from numerous disciplines working with social scientists, landscape planners and designers, and land and water managers to address complex, socioecological problems that have manifested in urban landscapes. Over the last decade, stream ecologists have met 3 times at the Symposium on Urbanization and Stream Ecology (SUSE) to discuss current research, identify knowledge gaps, and promote future research collaborations. The papers in this special series on urbanization and stream ecology include both primary research studies and conceptual synthesis papers spurred from discussions at SUSE in May 2014. The themes of the meeting are reflected in the papers in this series emphasizing global differences in mechanisms and responses of stream ecosystems to urbanization and management solutions in diverse urban streams. Our hope is that this series will encourage continued interdisciplinary and collaborative research to increase the global understanding of urban stream ecology toward stream protection and restoration in urban landscapes. [ABSTRACT FROM AUTHOR]

The adsorption of dissolved organic matter (DOM) to metal (oxy)hydroxide mineral surfaces is a critical step for C sequestration in soils. Although equilibrium studies have described some of the factors controlling this process, the molecular-scale description of the adsorption process has been more limited. Chemical force spectroscopy revealed differing adhesion strengths of DOM extracted from three soils and a reference peat soil material to an iron (oxy)hydroxide mineral surface. The DOM was characterized using ultrahigh-resolution negative ion mode electrospray ionization Fourier Transform ion cyclotron resonance mass spectrometry. The results indicate that carboxyl-rich aromatic and N-containing aliphatic molecules of DOM are correlated with high adhesion forces. Increasing molecular mass was shown to decrease the adhesion force between the mineral surface and the DOM. Kendrick mass defect analysis suggests that mechanisms involving two carboxyl groups result in the most stable bond to the mineral surface. We conceptualize these results using a layer-by-layer "onion" model of organic matter stabilization on soil mineral surfaces. [ABSTRACT FROM AUTHOR]

Quantifying spatial and temporal heterogeneity in ecosystem processes presents a challenge for conserving ecosystem function across landscapes. In particular, many ecosystems contain small features that play larger roles in ecosystem processes than their size would indicate; thus, they may represent 'hotspots' of activity relative to their surroundings. Biogeochemical hotspots are characterized as small features within a landscape that show comparatively high chemical reaction rates. In northeastern forests in North America, vernal pools are abundant, small features that typically fill in spring with snow melt and precipitation and dry by the end of summer. Ephemeral flooding alters soil moisture and the depth of the soil's oxic/anoxic boundary, which may affect biogeochemical processes. We studied the effects of vernal pools on leaf-litter decomposition rates, soil enzyme activity, and denitrification in vernal pools to assess whether they function as biogeochemical hotspots. Our results indicate that seasonal inundation enhanced leaf-litter decomposition, denitrification, and enzyme activity in vernal pools relative to adjacent forest sites. Leaves in seasonally flooded areas decomposed faster than leaves in terra firme forest sites. Flooding also influenced the C, N, and P stoichiometry of decomposing leaf litter and explained the variance in microbial extracellular enzyme activity for phosphatase, β- d-glucosidase, and β- N-acetylglucosaminidase. Additionally, denitrification rates were enhanced by seasonal flooding across all of the study pools. Collectively, these data suggest that vernal pool ecosystems may function as hotspots of leaf-litter decomposition and denitrification and play a significant role in decomposition and nutrient dynamics relative to their size. [ABSTRACT FROM AUTHOR]

Organic carbon supply is linked to nitrogen transformation in ecosystems. However, the role of organic carbon quality in nitrogen processing is not as well understood. We determined how the quality of particulate organic carbon (POC) influenced nitrogen transformation in stream sediments by burying identical quantities of varying quality POC (northern red oak ( Quercus rubra) leaves, red maple ( Acer rubrum) leaves, red maple wood) in stream mesocosms and measuring the effects on nitrogen retention and denitrification compared to a control of combusted sand. We also determined how POC quality affected the quantity and quality of dissolved organic carbon (DOC) and dissolved oxygen concentration in groundwater. Nitrate and total dissolved nitrogen (TDN) retention were assessed by comparing solute concentrations and fluxes along groundwater flow paths in the mesocosms. Denitrification was measured by in situ changes in N concentrations (using MIMS) and by acetylene block incubations. POC quality was measured by C:N and lignin:N ratios and DOC quality was assessed by fluorescence excitation emission matrix spectroscopy. POC quality had strong effects on nitrogen processing. Leaf treatments had much higher nitrate retention, TDN retention and denitrification rates than the wood and control treatments and red maple leaf burial resulted in higher nitrate and TDN retention rates than burial of red oak leaves. Leaf, but not wood, burial drove pore water to severe hypoxia and leaf treatments had higher DOC production and different DOC chemical composition than the wood and control treatments. We think that POC quality affected nitrogen processing in the sediments by influencing the quantity and quality of DOC and redox conditions. Our results suggest that the type of organic carbon inputs can affect the rates of nitrogen transformation in stream ecosystems. [ABSTRACT FROM AUTHOR]

Abstract: As therapeutic agents of choice in the treatment of complicated infections, glycopeptide antibiotics are often preferentially used in cases of osteomyelitis, an infection located in bone and notoriously difficult to successfully manage. Yet frequent and heavy doses of these systemically administered antibiotics are conventionally prescribed to obtain higher antibiotic levels in the bone and reduce the high recurrence rates. Targeting antibiotics to the bone after systemic administration would present at least three potential advantages: (i) greater efficacy, by concentrating the therapeutic agent in bone; (ii) greater convenience, through a reduction in the frequency of administration; and (iii) greater safety, by reducing the levels of systemic drug exposure. We present here the design, synthesis and in vitro evaluation of eight prodrugs of the glycopeptide antibacterial agents vancomycin and oritavancin taking advantage of the affinity of the bisphosphonate group for bone for delivery to osseous tissues. [Copyright &y& Elsevier]

Abstract: The activity of oritavancin in vitro against recent clinical isolates of Streptococcus pyogenes, including antibiotic-resistant strains, was characterised by determination of broth microdilution minimal inhibitory concentrations as well as time–kill assays. Ten clinical isolates of S. pyogenes, three of which were resistant to erythromycin, as well as one reference S. pyogenes strain were tested. In the time–kill assays, oritavancin and the comparators vancomycin, teicoplanin, linezolid, penicillin, erythromycin and daptomycin were tested at static concentrations approximating their free peak (fC max) and free trough (fC min) concentrations in plasma when administered at approved doses for skin and skin-structure infections. At its fC max predicted from a 200mg dose in humans, oritavancin exerted bactericidal activity (≥3log kill relative to the starting inoculum) within 15min to 3h against all tested strains. Daptomycin exhibited bactericidal activity at its fC max for all but one strain; time to cidality was between 15min and 6h. At fC min, only oritavancin was bactericidal against all the tested strains. Oritavancin displayed concentration-dependent killing of all isolates in vitro. Oritavancin was more rapidly bactericidal than the comparators at physiologically relevant concentrations against all strains tested. These data support the potential utility of oritavancin in infections with contemporary isolates of S. pyogenes, including drug-resistant strains. [Copyright &y& Elsevier]

Abstract: The increasing frequency of Enterococcus faecium isolates with multidrug resistance is a serious clinical problem given the severely limited number of therapeutic options available to treat these infections. Oritavancin is a promising new alternative in clinical development that has potent antimicrobial activity against both staphylococcal and enterococcal vancomycin-resistant pathogens. Using solid-state NMR to detect changes in the cell-wall structure and peptidoglycan precursors of whole cells after antibiotic-induced stress, we report that vancomycin and oritavancin have different modes of action in E. faecium. Our results show the accumulation of peptidoglycan precursors after vancomycin treatment, consistent with transglycosylase inhibition, but no measurable difference in cross-linking. In contrast, after oritavancin exposure, we did not observe the accumulation of peptidoglycan precursors. Instead, the number of cross-links is significantly reduced, showing that oritavancin primarily inhibits transpeptidation. We propose that the activity of oritavancin is the result of a secondary binding interaction with the E. faecium peptidoglycan. The hypothesis is supported by results from 13C{19F} rotational-echo double-resonance (REDOR) experiments on whole cells enriched with l-[1-13C]lysine and complexed with desleucyl [19F]oritavancin. These experiments establish that an oritavancin derivative with a damaged d-Ala–d-Ala binding pocket still binds to E. faecium peptidoglycan. The 13C{19F} REDOR dephasing maximum indicates that the secondary binding site of oritavancin is specific to nascent and template peptidoglycan. We conclude that the inhibition of transpeptidation by oritavancin in E. faecium is the result of the large number of secondary binding sites relative to the number of primary binding sites. [Copyright &y& Elsevier]

Abstract: Human serum albumin (HSA) did not affect oritavancin MICs against non–vancomycin-intermediate Staphylococcus aureus (non-VISA) strains. In time–kill assays, oritavancin bactericidal activity in the presence of HSA was significantly more rapid than comparators against non-VISA strains. HSA increased oritavancin MICs by 4-fold for VISA strains, reflective of reduced oritavancin activity in time–kill assays with HSA. [Copyright &y& Elsevier]

Abstract: Osteomyelitis is a difficult to treat bacterial infection of the bone. Delivering antibacterial agents to the bone may overcome the difficulties in treating this illness by effectively concentrating the antibiotic at the site of infection and by limiting the toxicity that may result from systemic exposure to the large doses conventionally used. Using bisphosphonates as osteophilic functional groups, different forms of fluoroquinolone esters were synthesized and evaluated for their ability to bind bone and to release the parent antibacterial agent. Bisphosphonated glycolamide fluoroquinolone esters were found to present a profile consistent with effective and rapid bone binding and efficient release of the active drug moiety. They were assessed for their ability to prevent bone infection in vivo and were found to be effective when the free fluoroquinolones were not. [Copyright &y& Elsevier]

In this treatment of random dynamical systems, we consider the existence—and identification—of conditional independencies at nonequilibrium steady-state. These independencies underwrite a particular partition of states, in which internal states are statistically secluded from external states by blanket states. The existence of such partitions has interesting implications for the information geometry of internal states. In brief, this geometry can be read as a physics of sentience, where internal states look as if they are inferring external states. However, the existence of such partitions—and the functional form of the underlying densities—have yet to be established. Here, using the Lorenz system as the basis of stochastic chaos, we leverage the Helmholtz decomposition—and polynomial expansions—to parameterise the steady-state density in terms of surprisal or self-information. We then show how Markov blankets can be identified—using the accompanying Hessian—to characterise the coupling between internal and external states in terms of a generalised synchrony or synchronisation of chaos. We conclude by suggesting that this kind of synchronisation may provide a mathematical basis for an elemental form of (autonomous or active) sentience in biology. [ABSTRACT FROM AUTHOR]

Abstract: A 9-laboratory M23-A2 quality control (QC) study was performed to evaluate reproducibility of oritavancin MICs against reference strains of Staphylococcus aureus, Enterococcus faecalis, and Streptococcus pneumoniae using broth microdilution assays in the presence of polysorbate 80. Polysorbate 80 has previously been shown to be required for accurate measurement of oritavancin broth microdilution MICs. Greater than 95% of replicate results (n = 270/organism) fell within the following QC ranges (in micrograms per milliliter): S. aureus ATCC 29213, 0.015 to 0.12; E. faecalis ATCC 29212, 0.008 to 0.03; and S. pneumoniae ATCC 49619, 0.001 to 0.004. Oritavancin MIC QC ranges were, thus, narrow and reproducible. Parameters affecting testing results in the presence of polysorbate 80 were also evaluated. Oritavancin MICs were equivalent to or within 1 doubling dilution of those obtained under standard Clinical and Laboratory Standards Institute testing conditions, regardless of incubation time (18, 24, or 48 h), Ca2+ concentration, pH, or frozen panel storage time (up to 6 months). [Copyright &y& Elsevier]

Abstract: Solid-state NMR measurements performed on intact whole cells of Staphylococcus aureus labeled selectively in vivo have established that des-N-methylleucyl oritavancin (which has antimicrobial activity) binds to the cell-wall peptidoglycan, even though removal of the terminal N-methylleucyl residue destroys the d-Ala-d-Ala binding pocket. By contrast, the des-N-methylleucyl form of vancomycin (which has no antimicrobial activity) does not bind to the cell wall. Solid-state NMR has also determined that oritavancin and vancomycin are comparable inhibitors of transglycosylation, but that oritavancin is a more potent inhibitor of transpeptidation. This combination of effects on cell-wall binding and biosynthesis is interpreted in terms of a recent proposal that oritavancin-like glycopeptides have two cell-wall binding sites: the well-known peptidoglycan d-Ala-d-Ala pentapeptide stem terminus and the pentaglycyl bridging segment. The resulting dual mode of action provides a structural framework for coordinated cell-wall assembly that accounts for the enhanced potency of oritavancin and oritavancin-like analogues against vancomycin-resistant organisms. [Copyright &y& Elsevier]

Abstract: The RNA polymerase holoenzyme is a proven target for antibacterial agents. A high-throughput screening program based on this enzyme from Staphylococcus aureus had previously identified a 2-ureidothiophene-3-carboxylate as a low micromolar inhibitor. An investigation of the relationships between the structures of this class of compounds and their inhibitory- and antibacterial activities is described here, leading to a set of potent RNA polymerase inhibitors with antibacterial activity. Characterization of this bioactivity, including studies of the mechanism of action, is provided, highlighting the power of the reverse chemical genetics approach in providing tools to inhibit the bacterial RNA polymerase. [Copyright &y& Elsevier]

Abstract: Screening of a chemical library in a DNA helicase assay involving the Pseudomonas aeruginosa DnaB helicase provided a triaminotriazine inhibitor with good antibacterial activity but associated cytotoxicity toward mammalian cells. Synthesis of analogs provided a few inhibitors that retained antibacterial activity and demonstrated a significant reduction in cytotoxicity. The impact of serum and initial investigations toward a mode of action highlight several features of this class of compounds as antibacterials. [Copyright &y& Elsevier]

Background SPR859 (tebipenem) is the active form of the orally bioavailable prodrug SPR994. Tebipenem is an oral carbapenem with a broad-spectrum activity against Gram-positive and -negative bacteria. SPR994 is being developed as an oral option for the treatment of complicated urinary tract infections (cUTI). The goal of these studies was to characterize the PK-PD of tebipenem against a diverse panel of Enterobacteriaceae. Methods 24-hour dose-fractionation and dose-ranging studies were carried out utilizing the one-compartment in vitro infection model. Bacteria (1 × 106 CFU/mL) were exposed to tebipenem concentrations that mimicked human healthy volunteer free-drug plasma (f) concentration–time profiles following oral drug administration. For the dose-fractionation studies, 7 total daily dose levels (f AUC range, 0.11 to 19.0 mg hours/L) were fractionated in equal divided doses administered every 4, 8 or 12 hours (q4h, q8h and q12h, respectively). A single challenge isolate, E. coli ATCC 25922 (MIC = 0.015 mg/L), was evaluated in the dose-fractionation studies. In the dose-ranging studies, 13 Enterobacteriaceae clinical isolates (MIC values from 0.008 to 0.25 mg/L) were exposed to doses ranging from 4.69 to 1200 mg administered q8h (f AUC ranging from 0.14 to 37.2 mg hours/L). Relationships between change in log10 CFU from baseline at 24 h and each of f Cmax:MIC ratio, f %T>MIC, and f AUC:MIC ratio, with and without adjustment for dosing interval tau for the latter (f AUC:MIC ratio·1/τ), were fit using Hill-type models. Results Dose-fractionation study results demonstrated the activity of tebipenem to be time-dependent, with both f %T>MIC and f AUC:MIC ratio·1/τ similarly describing the PK-PD of tebipenem. The relationship between change in log10 CFU from baseline at 24 hours and f AUC:MIC ratio·1/τ, as assessed using pooled data for 13 Enterobacteriaceae isolates evaluated in the dose-ranging studies, described the PK-PD of tebipenem well (Figure 1). The magnitude of f AUC:MIC ratio·1/τ associated with net bacterial stasis and 1- and 2-log10 CFU reductions from baseline was 7.23, 13.1, and 32.4, respectively. Conclusion These data will be useful to design other pre-clinical studies and support tebipenem dose selection for clinical studies in patients with cUTI. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

Organic matter sequestration is influenced by both biotic and abiotic factors, which determine the rate of decomposition and result into distinguish soil forming processes and different humus types. Previous observations indicated that podzol soils and mor humus form typically develop under spruce forest, while under beech forest the higher mineralization rate leads to moder humus and the development of dystric cambisol. This study was designed to observe the forming of the different microbial communities participating in diverse decomposition processes reflecting the soil types developed under naturally occurring beech and artificially planted spruce forests. A litterbag experiment was performed with beech and spruce litter placed respectively to beech and spruce forests. In the beech forest, the observed litter decomposition rate reached the exponential faze after 15 months, while in the spruce forest a steeper decrease was noted only 29 months after the litter burial. Thus, the study focused on the period between 15 and 29 months to observe the exponential stages of recalcitrant organic matter transformation. In this period, the chemical composition of two litters was distinguished by higher contents of Mn and Ca in beech and higher content of Fe, S, N and P in spruce. On top of that, the beech litter released higher amount of dissolved organic carbon and its associated bacterial community was enriched with r-selected taxa that correlated positively with organic acids and cations. In contrast, the more acidic spruce litter was dominated by K-selected acidophilic community and its turnover rate was slower, resulting in increased carbon sequestration. The higher pH and humus quality of beech cambisol also correlates with its observed higher resilience to disturbances by acidification, pests or climate change in mountainous environments. [Display omitted] • Spruce and beech litter differ by chemistry and microbial community structure but not abundance at a temperate forest site. • Beech litter inhabited enriched in DOC, oxalate and K decomposed faster than spruce litter enriched in P. • Spruce but not beech biochemical characteristics promoted soil podzolization process. [ABSTRACT FROM AUTHOR]

This paper introduces active listening, as a unified framework for synthesising and recognising speech. The notion of active listening inherits from active inference, which considers perception and action under one universal imperative: to maximise the evidence for our (generative) models of the world. First, we describe a generative model of spoken words that simulates (i) how discrete lexical, prosodic, and speaker attributes give rise to continuous acoustic signals; and conversely (ii) how continuous acoustic signals are recognised as words. The 'active' aspect involves (covertly) segmenting spoken sentences and borrows ideas from active vision. It casts speech segmentation as the selection of internal actions, corresponding to the placement of word boundaries. Practically, word boundaries are selected that maximise the evidence for an internal model of how individual words are generated. We establish face validity by simulating speech recognition and showing how the inferred content of a sentence depends on prior beliefs and background noise. Finally, we consider predictive validity by associating neuronal or physiological responses, such as the mismatch negativity and P300, with belief updating under active listening, which is greatest in the absence of accurate prior beliefs about what will be heard next. • Describes a generative model for synthesising and recognising speech. • Considers speech segmentation (placing word boundaries) as an active process. • Treats speech segmentation and lexical inferences as complementary. • Associates neural mismatch responses (e.g., MMN) with belief updating. [ABSTRACT FROM AUTHOR]