Zwanzig-Mori projection operators and EEG dynamics: deriving a simple equation of motion

We present a macroscopic theory of electroencephalogram (EEG) dynamics based on the laws of motion that govern atomic and molecular motion. The theory is an application of Zwanzig-Mori projection operators. The result is a simple equation of motion that has the form of a generalized Langevin equation (GLE), which requires knowledge only of macroscopic properties. The macroscopic properties can be extracted from experimental data by one of two possible variational principles. These variational principles are our principal contribution to the formalism. Potential applications are discussed, including applications to the theory of critical phenomena in the brain, Granger causality and Kalman filters

Deficiency in trefoil factor 1 (TFF1) increases tumorigenicity of human breast cancer cells and mammary tumor development in TFF1-knockout mice

Although trefoil factor 1 (TFF1; previously named pS2) is abnormally expressed in about 50% of human breast tumors, its physiopathological role in this disease has been poorly studied. Moreover, controversial data have been reported. TFF1 function in the mammary gland therefore needs to be clarified. In this study, using retroviral vectors, we performed TFF1 gain- or loss-of-function experiments in four human mammary epithelial cell lines: normal immortalized TFF1-negative MCF10A, malignant TFF1-negative MDA-MB-231 and malignant TFF1-positive MCF7 and ZR75.1. The expression of TFF1 stimulated the migration and invasion in the four cell lines. Forced TFF1 expression in MCF10A, MDA-MB-231 and MCF7 cells did not modify anchorage-dependent or -independent cell proliferation. By contrast, TFF1 knockdown in MCF7 enhanced soft-agar colony formation. This increased oncogenic potential of MCF7 cells in the absence of TFF1 was confirmed in vivo in nude mice. Moreover, chemically induced tumorigenesis in TFF1-deficient (TFF1-KO) mice led to higher tumor incidence in the mammary gland and larger tumor size compared with wild-type mice. Similarly, tumor development was increased in the TFF1-KO ovary and lung. Collectively, our results clearly show that TFF1 does not exhibit oncogenic properties, but rather reduces tumor development. This beneficial function of TFF1 is in agreement with many clinical studies reporting a better outcome for patients with TFF1-positive breast primary tumors

Enhanced Statistical Tests for GWAS in Admixed Populations: Assessment using African Americans from CARe and a Breast Cancer Consortium

While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

YesAlthough many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress