A multi-stage machine learning model on diagnosis of esophageal manometry

High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its interpretation and classification includes an initial evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), using a tree-like algorithm. This diagnostic approach on motility disordered using HRM was mirrored using a multi-stage modeling framework developed using a combination of various machine learning approaches. Specifically, the framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. In the swallow-level stage, three models based on convolutional neural networks (CNNs) were developed to predict swallow type, swallow pressurization, and integrated relaxation pressure (IRP). At the study-level stage, model selection from families of the expert-knowledge-based rule models, xgboost models and artificial neural network(ANN) models were conducted, with the latter two model designed and augmented with motivation from the export knowledge. A simple model-agnostic strategy of model balancing motivated by Bayesian principles was utilized, which gave rise to model averaging weighted by precision scores. The averaged (blended) models and individual models were compared and evaluated, of which the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2 predictions. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data. Moreover, the proposed modeling framework could be easily extended to multi-modal tasks, such as diagnosis of esophageal patients based on clinical data from both HRM and functional luminal imaging probe panometry (FLIP)

Dietary intake of benzo(a)pyrene and risk of esophageal cancer in north of Iran

One etiologic factor for high incidence of esophageal squamous cell carcinoma (ESCC) in Golestan (Northeastern Iran) might be exposure to polycyclic aromatic hydrocarbons. We examined whether food and water are major sources of benzo(a)pyrene (BaP) exposure in this population. We used a dietary questionnaire to assess the daily intake of staple food (rice and bread) and water in 3 groups: 40 ESCC Golestan cases, 40 healthy subjects from the same area, and 40 healthy subjects from a low-risk area in Southern Iran. We measured, by high-performance liquid chromatography combined with fluorescence detection, the BaP concentration of bread, rice, and water in samples obtained from these 3 groups and calculated the daily intake of BaP. Mean BaP concentration of staple foods and water was similar and within standard levels in both areas, but the daily intake of BaP was higher in controls from the high-risk area than in controls from the low-risk area (91.4 vs. 70.6 ng/day, P < 0.01). In the multivariate regression analysis, having ESCC had no independent effect on BaP, whereas residence in the low-risk area was associated with a significant decrease in total BaP intake. Polycyclic aromatic hydrocarbons might, along with other risk factors, contribute to the high risk of ESCC in Golestan. Copyright © 2008, Taylor & Francis Group, LLC

Ergodicity, Decisions, and Partial Information

In the simplest sequential decision problem for an ergodic stochastic process X, at each time n a decision u_n is made as a function of past observations X_0,...,X_{n-1}, and a loss l(u_n,X_n) is incurred. In this setting, it is known that one may choose (under a mild integrability assumption) a decision strategy whose pathwise time-average loss is asymptotically smaller than that of any other strategy. The corresponding problem in the case of partial information proves to be much more delicate, however: if the process X is not observable, but decisions must be based on the observation of a different process Y, the existence of pathwise optimal strategies is not guaranteed. The aim of this paper is to exhibit connections between pathwise optimal strategies and notions from ergodic theory. The sequential decision problem is developed in the general setting of an ergodic dynamical system (\Omega,B,P,T) with partial information Y\subseteq B. The existence of pathwise optimal strategies grounded in two basic properties: the conditional ergodic theory of the dynamical system, and the complexity of the loss function. When the loss function is not too complex, a general sufficient condition for the existence of pathwise optimal strategies is that the dynamical system is a conditional K-automorphism relative to the past observations \bigvee_n T^n Y. If the conditional ergodicity assumption is strengthened, the complexity assumption can be weakened. Several examples demonstrate the interplay between complexity and ergodicity, which does not arise in the case of full information. Our results also yield a decision-theoretic characterization of weak mixing in ergodic theory, and establish pathwise optimality of ergodic nonlinear filters.Comment: 45 page

Variability of dayside convection and motions of the cusp/cleft aurora

We present measurements of the ionospheric plasma flow over the range of invariant latitudes 71–76°, observed at 10-second resolution using both the EISCAT radars, with simultaneous observations of the 630 nm cusp/cleft aurora made by a meridian-scanning photometer at Ny Ålesund, Svalbard. A major increase in the trans-auroral voltage from 5 to 40 kV (associated with sunward convection in the early afternoon sector) is found to follow a southward motion of the aurora and coincide with the onset of regular transient auroral breakup events. It is shown that these observations are consistent with recent theoretical work on how ionospheric flows are excited by time-dependent reconnection at the dayside magnetopause

Close-packed dimers on the line: diffraction versus dynamical spectrum

The translation action of \RR^{d} on a translation bounded measure $\omega$ leads to an interesting class of dynamical systems, with a rather rich spectral theory. In general, the diffraction spectrum of $\omega$, which is the carrier of the diffraction measure, live on a subset of the dynamical spectrum. It is known that, under some mild assumptions, a pure point diffraction spectrum implies a pure point dynamical spectrum (the opposite implication always being true). For other systems, the diffraction spectrum can be a proper subset of the dynamical spectrum, as was pointed out for the Thue-Morse sequence (with singular continuous diffraction) in \cite{EM}. Here, we construct a random system of close-packed dimers on the line that have some underlying long-range periodic order as well, and display the same type of phenomenon for a system with absolutely continuous spectrum. An interpretation in terms of `atomic' versus `molecular' spectrum suggests a way to come to a more general correspondence between these two types of spectra.Comment: 14 pages, with some additions and improvement

On the quasi-periodic nature of magnetopause flux transfer events

The recurrence rate of flux transfer events (FTEs) observed near the dayside magnetopause is discussed. A survey of magnetopause observations by the ISEE satellites shows that the distribution of the intervals between FTE signatures has a mode value of 3 min, but is highly skewed, having upper and lower decile values of 1.5 min and 18.5 min, respectively. The mean value is found to be 8 min, consistent with previous surveys of magnetopause data. The recurrence of quasi-periodic events in the dayside auroral ionosphere is frequently used as evidence for an association with magnetopause FTEs, and the distribution of their repetition intervals should be matched to that presented here if such an association is to be confirmed. A survey of 1 year's 15-s data on the interplanetary magnetic field (IMF) suggests that the derived distribution could arise from fluctuations in the IMF Bz component, rather than from a natural oscillation frequency of the magnetosphere-ionosphere system

Human papilloma virus and breast cancer: The role of inflammation and viral expressed proteins

Background: Breast cancer is currently the most common neoplasm diagnosed in women globally. There is a growing body of evidence to suggest that human papillomavirus (HPV) infection may play a key role in invasiveness of breast cancer. The aim of this study was to determine the presence of HPV in patients with breast cancer and its possible association with cancer progression. Methods: Breast specimens were collected from 72 patients with breast cancer and 31 healthy controls. The presence of HPV was investigated by polymerase chain reaction (PCR) and genotyping was performed for positive cases. We also evaluated the viral factors such as E6, E2, and E7 in HPV positive cases. Enzyme-linked immunosorbent assay (ELISA (and Real-time PCR techniques were used to measure the expression level of anti-carcinogenic genes, such as p53, retinoblastoma (RB), breast and ovarian cancer susceptibility gene (BRCA1, BRCA2) and inflammatory cytokines, including tumor necrosis factor α (TNF-α), transforming growth factor β (TGF-β), nuclear factor-kB (NF-kB), and different interleukins ILs (IL-1,IL6, and IL-17). Results: The HPV DNA was detected in 48.6% of breast cancer samples, whereas only 16.1% of controls were positive for HPV. We observed statistically significant differences between breast cancer patients and HPV presence (P = 0.003). HPV type 18 was the most prevalent virus genotype in patients. The expression of P53, RB, BRCA1, and BRCA2 were decreased in patients with HPV-positive breast cancer as compared to HPV-negative breast cancer and healthy controls. (All P-values were less than 0.05). The presence of the HPV was associated with increased inflammatory cytokines (IL-1, IL-6, IL-17, TGF-β, TNF-α, and NF-kB) and tumor progression. Conclusion: The present study demonstrated that HPV infection may implicate in the development of some types of breast cancer. © 2019 The Author(s)

Human papilloma virus and breast cancer: The role of inflammation and viral expressed proteins

Background: Breast cancer is currently the most common neoplasm diagnosed in women globally. There is a growing body of evidence to suggest that human papillomavirus (HPV) infection may play a key role in invasiveness of breast cancer. The aim of this study was to determine the presence of HPV in patients with breast cancer and its possible association with cancer progression. Methods: Breast specimens were collected from 72 patients with breast cancer and 31 healthy controls. The presence of HPV was investigated by polymerase chain reaction (PCR) and genotyping was performed for positive cases. We also evaluated the viral factors such as E6, E2, and E7 in HPV positive cases. Enzyme-linked immunosorbent assay (ELISA (and Real-time PCR techniques were used to measure the expression level of anti-carcinogenic genes, such as p53, retinoblastoma (RB), breast and ovarian cancer susceptibility gene (BRCA1, BRCA2) and inflammatory cytokines, including tumor necrosis factor α (TNF-α), transforming growth factor β (TGF-β), nuclear factor-kB (NF-kB), and different interleukins [ILs] (IL-1,IL6, and IL-17). Results: The HPV DNA was detected in 48.6% of breast cancer samples, whereas only 16.1% of controls were positive for HPV. We observed statistically significant differences between breast cancer patients and HPV presence (P = 0.003). HPV type 18 was the most prevalent virus genotype in patients. The expression of P53, RB, BRCA1, and BRCA2 were decreased in patients with HPV-positive breast cancer as compared to HPV-negative breast cancer and healthy controls. (All P-values were less than 0.05). The presence of the HPV was associated with increased inflammatory cytokines (IL-1, IL-6, IL-17, TGF-β, TNF-α, and NF-kB) and tumor progression. Conclusion: The present study demonstrated that HPV infection may implicate in the development of some types of breast cance