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

A comparative survey of abundance and biomass of Caspian Sea macrobenthos in coastal waters of Mazandaran Province

Caspian Sea macrobenthos was surveyed every two months from December 2007 to October 2008, in the west, east and central parts of Mazandaran province waters. Each area was sampled with 3 replicates at 2 depths of 5 and 10m by Van Veen grab. Five different classes were recognized, including Polychaeta (52.7%), Oligochaeta (27.8%), Bivalvia (12%), Cnistacea (7.5%) and Insects (0.07%). Total mean (LSD) abundance and biomass were 2727± 1303 individual/m2 and 88.9±22.93, respectively. The Polychaeta demonstrated the highest abundance and Bivalvia had the highest biomass. The highest abundance of macrobenthos was found in eastern and the highest biomass in western coasts of Mazandaran. In August 2008, macrobenthos abundance showed higher values. In October, remarkable difference was observed between the abundance of Polychaeta and other macrobenthos organisms. According to Kniskal-Wallis test, abundance and biomass of the entire macrobenthos classes except Insects, showed a significant difference between sampling months (P<0.05). Macrobenthos biomass had no significant difference among the three areas whereas abundance demonstrated a significant difference within these areas (P< 0.05)

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

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

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

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

Statistical pairwise interaction model of stock market

Financial markets are a classical example of complex systems as they comprise many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price of restrictive assumptions on the market dynamics or others are agent-based models with rules designed in order to recover some empirical behaviours. Here we show that the pairwise model is actually a statistically consistent model with observed first and second moments of the stocks orientation without making such restrictive assumptions. This is done with an approach based only on empirical data of price returns. Our data analysis of six major indices suggests that the actual interaction structure may be thought as an Ising model on a complex network with interaction strengths scaling as the inverse of the system size. This has potentially important implications since many properties of such a model are already known and some techniques of the spin glass theory can be straightforwardly applied. Typical behaviours, as multiple equilibria or metastable states, different characteristic time scales, spatial patterns, order-disorder, could find an explanation in this picture.Comment: 11 pages, 8 figure

Potential theranostics of circulating tumor cells and tumor-derived exosomes application in colorectal cancer

Background: At the present time, colorectal cancer (CRC) is still known as a disease with a high mortality rate. Theranostics are flawless scenarios that link diagnosis with therapy, including precision medicine as a critical platform that relies on the development of biomarkers particularly "liquid biopsy". Circulating tumor cells (CTCs) and tumor-derived exosomes (TDEs) in a liquid biopsy approach are of substantial importance in comparison with traditional ones, which cannot generally be performed to determine the dynamics of the tumor due to its wide restriction of range. Thus, recent attempts has shifted towards minimally noninvasive methods. Main text: CTCs and TDEs, as significant signals emitted from the tumor microenvironment, which are also detectable in the blood, prove themselves to be promising novel biomarkers for cancer diagnosis, prognosis, and treatment response prediction. The therapeutic potential of them is still limited, and studies are at its infancy. One of the major challenges for the implementation of CTCs and TDEs which are new trends in translational medicine is the development of isolation and characterization; a standardizable approach. This review highlights and discusses the current challenges to find the bio fluids application in CRC early detection and clinical management. Conclusion: Taken together, CTCs and TDEs as silent drivers of metastasis can serve in the management of cancer patient treatment and it is of the upmost importance to expand our insight into this subject. However, due to the limited data available from clinical trials, further validations are required before addressing their putative application in oncology.Figure not available: see fulltext.. © 2020 The Author(s)

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%