Depression and sickness behavior are Janus-faced responses to shared inflammatory pathways

It is of considerable translational importance whether depression is a form or a consequence of sickness behavior. Sickness behavior is a behavioral complex induced by infections and immune trauma and mediated by pro-inflammatory cytokines. It is an adaptive response that enhances recovery by conserving energy to combat acute inflammation. There are considerable phenomenological similarities between sickness behavior and depression, for example, behavioral inhibition, anorexia and weight loss, and melancholic (anhedonia), physio-somatic (fatigue, hyperalgesia, malaise), anxiety and neurocognitive symptoms. In clinical depression, however, a transition occurs to sensitization of immuno-inflammatory pathways, progressive damage by oxidative and nitrosative stress to lipids, proteins, and DNA, and autoimmune responses directed against self-epitopes. The latter mechanisms are the substrate of a neuroprogressive process, whereby multiple depressive episodes cause neural tissue damage and consequent functional and cognitive sequelae. Thus, shared immuno-inflammatory pathways underpin the physiology of sickness behavior and the pathophysiology of clinical depression explaining their partially overlapping phenomenology. Inflammation may provoke a Janus-faced response with a good, acute side, generating protective inflammation through sickness behavior and a bad, chronic side, for example, clinical depression, a lifelong disorder with positive feedback loops between (neuro)inflammation and (neuro)degenerative processes following less well defined triggers

Nonequilibrium Linear Response for Markov Dynamics, II: Inertial Dynamics

We continue our study of the linear response of a nonequilibrium system. This Part II concentrates on models of open and driven inertial dynamics but the structure and the interpretation of the result remain unchanged: the response can be expressed as a sum of two temporal correlations in the unperturbed system, one entropic, the other frenetic. The decomposition arises from the (anti)symmetry under time-reversal on the level of the nonequilibrium action. The response formula involves a statistical averaging over explicitly known observables but, in contrast with the equilibrium situation, they depend on the model dynamics in terms of an excess in dynamical activity. As an example, the Einstein relation between mobility and diffusion constant is modified by a correlation term between the position and the momentum of the particle

Production and evaluation of (multimodal) answers to medical questions

This paper describes two experiments carried out to investigate the production and evaluation of multimodal answer presentations in the context of a medical question answering system. In a production experiment participants had to produce answers to different types of questions. The results show that about one in four produced answers using multiple media. In an evaluation experiment, users had to evaluate different types of multimodal answer presentations. Answers with an informative visual were evaluated as more informative and more attractive than answers with a mere illustrative visual

Entropy production and Kullback-Leibler divergence between stationary trajectories of discrete systems

The irreversibility of a stationary time series can be quantified using the Kullback-Leibler divergence (KLD) between the probability to observe the series and the probability to observe the time-reversed series. Moreover, this KLD is a tool to estimate entropy production from stationary trajectories since it gives a lower bound to the entropy production of the physical process generating the series. In this paper we introduce analytical and numerical techniques to estimate the KLD between time series generated by several stochastic dynamics with a finite number of states. We examine the accuracy of our estimators for a specific example, a discrete flashing ratchet, and investigate how close is the KLD to the entropy production depending on the number of degrees of freedom of the system that are sampled in the trajectories.Comment: 14 pages, 7 figure

CA 15.3 measurements for separating FDG PET/CT positive from negative findings in breast carcinoma recurrence

In breast cancer CA 15.3 is considered the tumour marker of choice. CA 15.3 is directly related to the disease extent and to hormone status (estrogen receptor ER+/ ER-, progesterone receptor PR+/PR-). This study was designed to assess the impact of disease extent, hormone receptor and HER2-status, and circulating blood volume on the area-under the ROC-curve of CA 15.3 to separate FDG PET positive from negative findings. Patients, methods: We retrospectively evaluated 379 FDG PET/CT examinations performed in 80 patients with breast cancer. Blood volumes were derived using the formulas by Nadler and multiplied by their corresponding CA 15.3 measurement. Results: ROC-curve analysis revealed an AUC of 0.695 (p = 0.0001) for CA 15.3 to separate FDG PET positive from negative findings. AUC measurements to separate normal scan findings from loco-regional disease and metastatic disease were 0.527 (p = 0.587) and 0.732 (p = 0.0001), respectively. AUC measurements for CA 15.3 to separate positive from negative FDG PET findings, in ER+ and ER-patients, were respectively 0.772 (p = 0.0001) and 0.596 (p = 0.143). AUC measurements for CA 15.3 to separate positive from negative FDG PET findings, in PR+ and PR-patients, were respectively 0.675 (p = 0.0001) and 0.694 (p = 0.0001). In HER2-positive and -negative patients, the AUC measurements were respectively 0.594 (p = 0.178) and 0.701 (p = 0.0001) to separate positive from negative FDG PET findings. Conclusion: The AUC for CA 15.3 measurements to separate FDG PET positive from negative findings in breast cancer patients with suspected recurrence proved to be directly related to the extent of the recurrent disease and hormone receptor status and inversely related to HER2-status. Correcting CA 15.3 measurements for blood volumes did not impact the AUC

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity

A meaningful expansion around detailed balance

We consider Markovian dynamics modeling open mesoscopic systems which are driven away from detailed balance by a nonconservative force. A systematic expansion is obtained of the stationary distribution around an equilibrium reference, in orders of the nonequilibrium forcing. The first order around equilibrium has been known since the work of McLennan (1959), and involves the transient irreversible entropy flux. The expansion generalizes the McLennan formula to higher orders, complementing the entropy flux with the dynamical activity. The latter is more kinetic than thermodynamic and is a possible realization of Landauer's insight (1975) that, for nonequilibrium, the relative occupation of states also depends on the noise along possible escape routes. In that way nonlinear response around equilibrium can be meaningfully discussed in terms of two main quantities only, the entropy flux and the dynamical activity. The expansion makes mathematical sense as shown in the simplest cases from exponential ergodicity.Comment: 19 page

Chronic fatigue syndrome: Harvey and Wessely's (bio)psychosocial model versus a bio(psychosocial) model based on inflammatory and oxidative and nitrosative stress pathways

<p>Abstract</p> <p>Background</p> <p>In a recently published paper, Harvey and Wessely put forward a 'biopsychosocial' explanatory model for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), which is proposed to be applicable to (chronic) fatigue even when apparent medical causes are present.</p> <p>Methods</p> <p>Here, we review the model proposed by Harvey and Wessely, which is the rationale for behaviourally oriented interventions, such as cognitive behaviour therapy (CBT) and graded exercise therapy (GET), and compare this model with a biological model, in which inflammatory, immune, oxidative and nitrosative (IO&NS) pathways are key elements.</p> <p>Discussion</p> <p>Although human and animal studies have established that the pathophysiology of ME/CFS includes IO&NS pathways, these abnormalities are not included in the model proposed by Harvey and Wessely. Activation of IO&NS pathways is known to induce fatigue and somatic (F&S) symptoms and can be induced or maintained by viral and bacterial infections, physical and psychosocial stressors, or organic disorders such as (auto)immune disorders. Studies have shown that ME/CFS and major depression are both clinical manifestations of shared IO&NS pathways, and that both disorders can be discriminated by specific symptoms and unshared or differentiating pathways. Interventions with CBT/GET are potentially harmful for many patients with ME/CFS, since the underlying pathophysiological abnormalities may be intensified by physical stressors.</p> <p>Conclusions</p> <p>In contrast to Harvey and Wessely's (bio)psychosocial model for ME/CFS a bio(psychosocial) model based upon IO&NS abnormalities is likely more appropriate to this complex disorder. In clinical practice, we suggest physicians should also explore the IO&NS pathophysiology by applying laboratory tests that examine the pathways involved.</p

Infinite volume limit of the Abelian sandpile model in dimensions d >= 3

We study the Abelian sandpile model on Z^d. In dimensions at least 3 we prove existence of the infinite volume addition operator, almost surely with respect to the infinite volume limit mu of the uniform measures on recurrent configurations. We prove the existence of a Markov process with stationary measure mu, and study ergodic properties of this process. The main techniques we use are a connection between the statistics of waves and uniform two-component spanning trees and results on the uniform spanning tree measure on Z^d.Comment: First version: LaTeX; 29 pages. Revised version: LaTeX; 29 pages. The main result of the paper has been extended to all dimensions at least 3, with a new and simplyfied proof of finiteness of the two-component spanning tree. Second revision: LaTeX; 32 page