Inference of kinetic Ising model on sparse graphs

Based on dynamical cavity method, we propose an approach to the inference of kinetic Ising model, which asks to reconstruct couplings and external fields from given time-dependent output of original system. Our approach gives an exact result on tree graphs and a good approximation on sparse graphs, it can be seen as an extension of Belief Propagation inference of static Ising model to kinetic Ising model. While existing mean field methods to the kinetic Ising inference e.g., na\" ive mean-field, TAP equation and simply mean-field, use approximations which calculate magnetizations and correlations at time $t$ from statistics of data at time $t-1$, dynamical cavity method can use statistics of data at times earlier than $t-1$ to capture more correlations at different time steps. Extensive numerical experiments show that our inference method is superior to existing mean-field approaches on diluted networks.Comment: 9 pages, 3 figures, comments are welcom

Diagnostic and prognostic accuracy of miR-21 in renal cell carcinoma: A systematic review protocol

Introduction: Renal cell carcinoma (RCC) is the most common neoplasm in adult kidneys. One of the most important unmet medical needs in RCC is a prognostic biomarker to enable identification of patients at high risk of relapse after nephrectomy. New biomarkers can help improve diagnosis and hence the management of patients with renal cancer. Thus, this systematic review aims to clarify the prognostic and diagnostic accuracy of miR-21 in patients with RCC. Methods and analysis: We will include observational studies evaluating the diagnostic and prognostic roles of miR-21 in patients with renal cancer. The index test and reference standards should ideally be performed on all patients. We will search PubMed, SCOPUS and ISI Web of Science with no restriction of language. The outcome will be survival measures in adult patients with RCC. Study selection and data extraction will be performed by two independent reviewers. QUADAS-1 will be used to assess study quality. Publication bias and data synthesis will be assessed by funnel plots and Begg's and Egger's tests using Stata software V.11.1. Ethics and dissemination: No ethical issues are predicted. These findings will be published in a peerreviewed journal and presented at national and international conferences. Trail registration number: This systematic review protocol is registered in the PROSPERO International Prospective Register of Systematic Reviews, registration number CRD42015025001

Effect of coupling asymmetry on mean-field solutions of direct and inverse Sherrington-Kirkpatrick model

We study how the degree of symmetry in the couplings influences the performance of three mean field methods used for solving the direct and inverse problems for generalized Sherrington-Kirkpatrick models. In this context, the direct problem is predicting the potentially time-varying magnetizations. The three theories include the first and second order Plefka expansions, referred to as naive mean field (nMF) and TAP, respectively, and a mean field theory which is exact for fully asymmetric couplings. We call the last of these simply MF theory. We show that for the direct problem, nMF performs worse than the other two approximations, TAP outperforms MF when the coupling matrix is nearly symmetric, while MF works better when it is strongly asymmetric. For the inverse problem, MF performs better than both TAP and nMF, although an ad hoc adjustment of TAP can make it comparable to MF. For high temperatures the performance of TAP and MF approach each other

Beyond inverse Ising model: structure of the analytical solution for a class of inverse problems

I consider the problem of deriving couplings of a statistical model from measured correlations, a task which generalizes the well-known inverse Ising problem. After reminding that such problem can be mapped on the one of expressing the entropy of a system as a function of its corresponding observables, I show the conditions under which this can be done without resorting to iterative algorithms. I find that inverse problems are local (the inverse Fisher information is sparse) whenever the corresponding models have a factorized form, and the entropy can be split in a sum of small cluster contributions. I illustrate these ideas through two examples (the Ising model on a tree and the one-dimensional periodic chain with arbitrary order interaction) and support the results with numerical simulations. The extension of these methods to more general scenarios is finally discussed.Comment: 15 pages, 6 figure

Induction of miR-31 causes increased sensitivity to 5-FU and decreased migration and cell invasion in gastric adenocarcinoma

Drug resistance is the main obstacle in the treatment of gastric cancer, the third most common cause of cancer- related death in the world. Due to their small size, easy entrance to cells and multiple targets, microRNAs (miRs) are considered novel and attractive targets. In the current study, parental MKN-45, MKN-45-control vector, and MKN-45-miR-31 populations were compared in terms of cell cycle transitions, migration, cell invasion, and proliferation. In addition, downstream targets of miR-31, including E2F6, and SMUG1 were examined using Real-time RT-PCR and western blotting. MKN-45-miR-31 showed an increased sensitivity to 5-FU, decreased migration and cell invasion compared to the control groups (p = 0.0001, p = 0.01 and p = 0.01, respectively). There was a significant increase in the percentage of cells in G1/pre-G1 phase in MKN-45-miR-31 relative to the control groups (p = 0.001). Induction of miR-31 expression in MKN-45 caused a significant reduction of E2F6 and SMUG1 genes. Our findings indicated that induction of miR-31 expression could increase drug sensitivity, and diminish tumor cell migration and invasion of gastric cancer cells. Therefore, miR-31 can be considered as a potential target molecule in the targeted therapy of gastric cancer. © AEPress s.r.o

An associative network with spatially organized connectivity

We investigate the properties of an autoassociative network of threshold-linear units whose synaptic connectivity is spatially structured and asymmetric. Since the methods of equilibrium statistical mechanics cannot be applied to such a network due to the lack of a Hamiltonian, we approach the problem through a signal-to-noise analysis, that we adapt to spatially organized networks. The conditions are analyzed for the appearance of stable, spatially non-uniform profiles of activity with large overlaps with one of the stored patterns. It is also shown, with simulations and analytic results, that the storage capacity does not decrease much when the connectivity of the network becomes short range. In addition, the method used here enables us to calculate exactly the storage capacity of a randomly connected network with arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA

U.S. stock market interaction network as learned by the Boltzmann Machine

We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented analysis shows that binarization preserves market correlation structure. Properties of distributions of external fields and couplings as well as industry sector clustering structure are studied for different historical dates and moving window sizes. We found that a heavy positive tail in the distribution of couplings is responsible for the sparse market clustering structure. We also show that discrepancies between the model parameters might be used as a precursor of financial instabilities.Comment: 15 pages, 17 figures, 1 tabl

Prediction and optimization of the Fenton process for the treatment of landfill leachate using an artificial neural network

In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H2O2:Fe2+ ratio, Fe2+ concentration, pH and process reaction time were studied closely. The results showed that the highest and lowest predicted chemical oxygen demand (COD) removal efficiency were 78.9% and 9.3%, respectively. The overall prediction error using the developed ANN model was within -0.625%. The derived model was adequate in predicting responses (R2 = 0.9896 and prediction R2 = 0.6954). The initial pH, H2O2:Fe2+ ratio and Fe2+ concentrations had positive effects, whereas coagulation pH had no direct effect on COD removal. Optimized conditions under specified constraints were obtained at pH = 3, Fe2+ concentration = 781.25 mg/L, reaction time = 28.04 min and H2O2:Fe2+ ratio = 2. Under these optimized conditions, 100% COD removal was predicted. To confirm the accuracy of the predicted model and the reliability of the optimum combination, one additional experiment was carried out under optimum conditions. The experimental values were found to agree well with those predicted, with a mean COD removal efficiency of 97.83%

Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

Evidence for embryonic stem-like signature and epithelial-mesenchymal transition features in the spheroid cells derived from lung adenocarcinoma

Identification of the cellular and molecular aspects of lung cancer stem cells (LCSCs) that are suggested to be the main culprit of tumor initiation, maintenance, drug resistance, and relapse is a prerequisite for targeted therapy of lung cancer. In the current study, LCSCs subpopulation of A549 cells was enriched, and after characterization of the spheroid cells, complementary DNA (cDNA) microarray analysis was applied to identify differentially expressed genes (DEGs) between the spheroid and parental cells. Microarray results were validated using quantitative real-time reverse transcription-PCR (qRT-PCR), flow cytometry, and western blotting. Our results showed that spheroid cells had higher clonogenic potential, up-regulation of stemness gene Sox2, loss of CD44 expression, and gain of CD24 expression compared to parental cells. Among a total of 160 genes that were differentially expressed between the spheroid cells and the parental cells, 104 genes were up-regulated and 56 genes were down-regulated. Analysis of cDNA microarray revealed an embryonic stem cell-like signature and over-expression of epithelial-mesenchymal transition (EMT)-associated genes in the spheroid cells. cDNA microarray results were validated at the gene expression level using qRT-PCR, and further validation was performed at the protein level by flow cytometry and western blotting. The embryonic stem cell-like signature in the spheroid cells supports two important notions: maintenance of CSCs phenotype by dedifferentiating mechanisms activated through oncogenic pathways and the origination of CSCs from embryonic stem cells (ESCs). PI3/AKT3, as the most common up-regulated pathway, and other pathways related to aggressive tumor behavior and EMT process can confer to the spheroid cells� high potential for metastasis and distant seeding. © 2016, International Society of Oncology and BioMarkers (ISOBM)