Relationship quality among young couples from an economic and gender perspective

Less than a third of married couple households in the United States are composed of families with one breadwinner. This is a stark contrast to a mere 40 years ago when men were the primary breadwinner for the majority of households. The goal of this study was to determine how the perception of household chores is related to relationship quality. Specifically we wanted to determine how perception of household chores is related to relationship quality reported by partners from a traditional economic and a gender role theory perspective. Using data from the National Longitudinal Survey of Youth, 1986 cohort, results indicate that perceived unfair in household division of chores was predictive of women’s relationship quality, but not men’s. Arguments about affection and money were predictive of relationship quality for both genders

Accuracy of c-KIT in lung cancer prognosis; a systematic review protocol" instead of c-KIT expression in lung cancer prognostic evaluation - A systematic review protocol

Background: Extensive efforts have been made to investigate c-KIT expression in lung cancer specimens and its correlation with clinical outcomes, but the issue remains unresolved. Thus, this study will be conducted to clarify the prognostic value of c-KIT expression in lung cancer patients. Materials and Methods: We will search Pubmed, SCOPUS, and ISI web of sciences with no restriction of language. Studies with any design (except case reports or case series) evaluating correlations of c-KIT expression with survival or outcome in patients with lung cancer will be included. The outcome measures will include all types of survival indexes, including overall survival rate and disease free survival using Kaplan-Meier analysis and hazard ratios. Study selection and data extraction will be performed by two independent researchers. Quality assessment (assessment of risk of bias) and data synthesis will be implemented using Stata software version 11.1. Results: No ethical issues are predicted. These findings will be published in a peer-reviewed journal and presented at national and international conferences. Conclusions: This systematic review protocol is registered in the PROSPERO International Prospective Register of Systematic Reviews, registration number = CRD42015023391

Bump formation in a binary attractor neural network

This paper investigates the conditions for the formation of local bumps in the activity of binary attractor neural networks with spatially dependent connectivity. We show that these formations are observed when asymmetry between the activity during the retrieval and learning is imposed. Analytical approximation for the order parameters is derived. The corresponding phase diagram shows a relatively large and stable region, where this effect is observed, although the critical storage and the information capacities drastically decrease inside that region. We demonstrate that the stability of the network, when starting from the bump formation, is larger than the stability when starting even from the whole pattern. Finally, we show a very good agreement between the analytical results and the simulations performed for different topologies of the network.Comment: about 14 page

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

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

Distribution of KRAS, DDR2, and TP53 gene mutations in lung cancer: An analysis of Iranian patients

Purpose Lung cancer is the deadliest known cancer in the world, with the highest number of mutations in proto-oncogenes and tumor suppressor genes. Therefore, this study was conducted to determine the status of hotspot regions in DDR2 and KRAS genes for the first time, as well as in TP53 gene, in lung cancer patients within the Iranian population. Experimental design The mutations in exon 2 of KRAS, exon 18 of DDR2, and exons 5�6 of TP53 genes were screened in lung cancer samples, including non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) using PCR and sequencing techniques. Results Analysis of the KRAS gene showed only a G12C variation in one large cell carcinoma (LCC) patient, whereas variants were not found in adenocarcinoma (ADC) and squamous cell carcinoma (SCC) cases. The Q808H variation in the DDR2 gene was detected in one SCC sample, while no variant was seen in the ADC and LCC subtypes. Variations in the TP53 gene were seen in all NSCLC subtypes, including six ADC (13.63), seven SCC (15.9) and two LCC (4.54). Forty-eight variants were found in the TP53 gene. Of these, 15 variants were found in coding regions V147A, V157F, Q167Q, D186G, H193R, T211T, F212L and P222P, 33 variants in intronic regions rs1625895 (HGVS: c.672+62A>G), rs766856111 (HGVS: c.672+6G>A) and two new variants (c.560-12A>G and c.672+86T>C). Conclusions In conclusion, KRAS, DDR2, and TP53 variants were detected in 2, 2.17 and 79.54 of all cases, respectively. The frequency of DDR2 mutation is nearly close to other studies, while KRAS and TP53 mutation frequencies are lower and higher than other populations, respectively. Three new putative pathogenic variants, for the first time, have been detected © 2018 Fathi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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

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

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

Action representation in the mouse parieto-frontal network

The posterior parietal cortex (PPC) and frontal motor areas comprise a cortical network supporting goal-directed behaviour, with functions including sensorimotor transformations and decision making. In primates, this network links performed and observed actions via mirror neurons, which fire both when individuals perform an action and when they observe the same action performed by a conspecific. Mirror neurons are believed to be important for social learning, but it is not known whether mirror-like neurons occur in similar networks in other social species, such as rodents, or if they can be measured in such models using paradigms where observers passively view a demonstrator. Therefore, we imaged Ca2+ responses in PPC and secondary motor cortex (M2) while mice performed and observed pellet-reaching and wheel-running tasks, and found that cell populations in both areas robustly encoded several naturalistic behaviours. However, neural responses to the same set of observed actions were absent, although we verified that observer mice were attentive to performers and that PPC neurons responded reliably to visual cues. Statistical modelling also indicated that executed actions outperformed observed actions in predicting neural responses. These results raise the possibility that sensorimotor action recognition in rodents could take place outside of the parieto-frontal circuit, and underscore that detecting socially-driven neural coding depends critically on the species and behavioural paradigm used