A Markovian event-based framework for stochastic spiking neural networks

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks

Evaluating a team-based approach to research capacity building using a matched-pairs study design

Background: There is a continuing need for research capacity building initiatives for primary health care professionals. Historically strategies have focused on interventions aimed at individuals but more recently theoretical frameworks have proposed team-based approaches. Few studies have evaluated these new approaches. This study aims to evaluate a team-based approach to research capacity building (RCB) in primary health using a validated quantitative measure of research capacity in individual, team and organisation domains

Central defect type partial ACL injury model on goat knees: the effect of infrapatellar fat pad excision

BACKGROUND: The mid-substance central defect injury has been used to investigate the primary healing capacity of the anterior cruciate ligament (ACL) in a goat model. The sagittal plane stability on this model has not been confirmed, and possible effects of fat pad excision on healing have not been evaluated. We hypothesize that excising the fat pad tissue results in poorer ligament healing as assessed histologically and decreased tensile strength of the healing ligament. We further hypothesize that the creation of a central defect does not affect sagittal plane knee stability. METHODS: A mid-substance central defect was created with a 4-mm arthroscopic punch in the ACLs of right knees of all the subjects through a medial mini-arthrotomy. Goats were assigned to groups based on whether the fat pad was preserved (group 1, n = 5) or excised completely (group 2, n = 5). The left knees served as controls in each goat. Histopathology of the defect area along with measurement of type I collagen in one goat from each group were performed at 10th week postoperatively. The remaining knees were evaluated biomechanically at the 12th week, by measuring anterior tibial translation (ATT) of the knee joints at 90° of flexion and testing tensile properties (ultimate tensile load (UTL), ultimate elongation (UE), stiffness (S), failure mode (FM)) of the femur-ACL-tibia complex. RESULTS AND DISCUSSION: Histopathology analysis revealed that the central defect area was fully filled macroscopically and microscopically. However, myxoid degeneration and fibrosis were observed in group 2 and increased collagen type I content was noted in group 2. There were no significant differences within and between groups in terms of ATT values (p = 0.715 and p = 0.149, respectively). There were no significance between or within groups in terms of ultimate tensile load and ultimate elongation; however, group 2 demonstrated greater stiffness than group 1 that was correlated with the fibrotic changes detected microscopically (p = 0.043). CONCLUSIONS: The central defect type injury model was confirmed to be biomechanically stable in a goat model. Resection of the fat pad was noted to negatively affect defect healing and increase ligament stiffness in the central defect injury model

Performance of plain and slag-blended cements and mortars exposed to combined chloride-sulphate solution

The durability of reinforced concrete structures exposed to aggressive environments remains a challenge to both researchers and the construction industry. This study investigates the hydration, mechanical properties and durability characteristics of ground granulated blast-furnace slag (GGBS) - blended cements and mortars exposed to a combined sodium chloride - sulphate environment, at temperatures of 20°C and 38°C. The conditions were chosen so as to assess the performance of slag blends under typical temperate and warm tropical marine climatic conditions. Slags, having CaO/SiO2 ratios of 1.05 and 0.94, were blended with CEM I 52.5R at 30% replacement level to study the influence of slag composition and temperature. Parallel control tests were carried out with CEM I 42.5R. Pastes and mortar samples were cast using 0.5 water to binder ratio, pre-cured for 7 days in water before exposure. Flexural strengths were determined once the samples were 7, 28 or 90 days old. Hydration was followed using x-ray diffraction (XRD), thermal analysis, and calorimetry. Also, sorptivity, gas permeability and chloride diffusion tests were carried out on mortar samples to measure transport and durability characteristics. The results show improved mechanical and transport properties for slag blended cements exposed to environments rich in sodium chloride and sulphate

Day-to-Day Test–Retest Variability of CBF, CMRO2, and OEF Measurements Using Dynamic 15O PET Studies

Contains fulltext : 169592.pdf (publisher's version ) (Open Access)PURPOSE: We assessed test-retest variability of cerebral blood flow (CBF), cerebral blood volume (CBV), cerebral metabolic rate of oxygen (CMRO(2)), and oxygen extraction fraction (OEF) measurements derived from dynamic (15)O positron emission tomography (PET) scans. PROCEDURES: In seven healthy volunteers, complete test-retest (15)O PET studies were obtained; test-retest variability and left-to-right ratios of CBF, CBV, OEF, and CMRO(2) in arterial flow territories were calculated. RESULTS: Whole-brain test-retest coefficients of variation for CBF, CBV, CMRO(2), and OEF were 8.8%, 13.8%, 5.3%, and 9.3%, respectively. Test-retest variability of CBV left-to-right ratios was <7.4% across all territories. Corresponding values for CBF, CMRO(2), and OEF were better, i.e., <4.5%, <4.0%, and <1.4%, respectively. CONCLUSIONS: The test-retest variability of CMRO(2) measurements derived from dynamic (15)O PET scans is comparable to within-session test-retest variability derived from steady-state (15)O PET scans. Excellent regional test-retest variability was observed for CBF, CMRO(2), and OEF. Variability of absolute CBF and OEF measurements is probably affected by physiological day-to-day variability of CBF

Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in Internet gaming disorder

Literatures have shown that Internet gaming disorder (IGD) subjects show impaired executive control and enhanced reward sensitivities than healthy controls. However, how these two networks jointly affect the valuation process and drive IGD subjects' online-game-seeking behaviors remains unknown. Thirty-five IGD and 36 healthy controls underwent a resting-states scan in the MRI scanner. Functional connectivity (FC) was examined within control and reward network seeds regions, respectively. Nucleus accumbens (NAcc) was selected as the node to find the interactions between these two networks. IGD subjects show decreased FC in the executive control network and increased FC in the reward network when comparing with the healthy controls. When examining the correlations between the NAcc and the executive control/reward networks, the link between the NAcc - executive control network is negatively related with the link between NAcc - reward network. The changes (decrease/increase) in IGD subjects' brain synchrony in control/reward networks suggest the inefficient/overly processing within neural circuitry underlying these processes. The inverse proportion between control network and reward network in IGD suggest that impairments in executive control lead to inefficient inhibition of enhanced cravings to excessive online game playing. This might shed light on the mechanistic understanding of IGD

New targets for therapy in breast cancer: Farnesyltransferase inhibitors

Current systemic therapies for breast cancer are often limited by their nonspecific mechanism of action, unwanted toxicities on normal tissues, and short-term efficacy due to the emergence of drug resistance. However, identification of the molecular abnormalities in cancer, in particular the key proteins involved in abnormal cell growth, has resulted in development of various signal transduction inhibitor drugs as new treatment strategies against the disease. Protein farnesyltransferase inhibitors (FTIs) were originally designed to target the Ras signal transduction pathway, although it is now clear that several other intracellular proteins are dependent on post-translational farnesylation for their function. Preclinical data revealed that although FTIs inhibit the growth of ras-transformed cells, they are also potent inhibitors of a wide range of cancer cell lines that contain wild-type ras, including breast cancer cells. Additive or synergistic effects were observed when FTIs were combined with cytotoxic agents (in particular the taxanes) or endocrine therapies (tamoxifen). Phase I trials with FTIs have explored different schedules for prolonged administration, and dose-limiting toxicities included myelosuppression, gastrointestinal toxicity and neuropathy. Clinical efficacy against breast cancer was seen for the FTI tipifarnib in a phase II study. Based on promising preclinical data that suggest synergy with taxanes or endocrine therapy, combination clinical studies are now in progress to determine whether FTIs can add further to the efficacy of conventional breast cancer therapies

Adaptive independent sticky MCMC algorithms

Monte Carlo methods have become essential tools to solve complex Bayesian inference problems in different fields, such as computational statistics, machine learning, and statistical signal processing. In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky Markov Chain Monte Carlo (MCMC) algorithms, to sample efficiently from any bounded target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities, which become closer and closer to the target as the number of iterations increases. The proposal pdf is built using interpolation procedures based on a set of support points which is constructed iteratively from previously drawn samples. The algorithm’s efficiency is ensured by a test that supervises the evolution of the set of support points. This extra stage controls the computational cost and the convergence of the proposal density to the target. Each part of the novel family of algorithms is discussed and several examples of specific methods are provided. Although the novel algorithms are presented for univariate target densities, we show how they can be easily extended to the multivariate context by embedding them within a Gibbs-type sampler or the hit and run algorithm. The ergodicity is ensured and discussed. An overview of the related works in the literature is also provided, emphasizing that several well-known existing methods (like the adaptive rejection Metropolis sampling (ARMS) scheme) are encompassed by the new class of algorithms proposed here. Eight numerical examples (including the inference of the hyper-parameters of Gaussian processes, widely used in machine learning for signal processing applications) illustrate the efficiency of sticky schemes, both as stand-alone methods to sample from complicated one-dimensional pdfs and within Gibbs samplers in order to draw from multi-dimensional target distributions

Hydrological legacy determines the type of enzyme inhibition in a peatlands chronosequence

© 2017 The Author(s). Peatland ecosystems contain one-third of the world's soil carbon store and many have been exposed to drought leading to a loss of carbon. Understanding biogeochemical mechanisms affecting decomposition in peatlands is essential for improving resilience of ecosystem function to predicted climate change. We investigated biogeochemical changes along a chronosequence of hydrological restoration (dry eroded gully, drain-blocke

Evolutionarily Conserved Substrate Substructures for Automated Annotation of Enzyme Superfamilies

The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies