Improvements in diagnosis have changed the incidence of histological types in advanced gastric cancer.

The data on 912 patients with early cancer and 1245 with advanced cancer who were seen between 1971 and 1990 were compared. The incidence of undifferentiated-type cancer increased significantly in patients with advanced gastric cancer, but not in patients with early gastric cancer. When the histological types were compared with regard to sex, age and location in patients with early gastric cancer the undifferentiated type was found to increase only in males, while in patients with advanced gastric cancer the undifferentiated type increased in both sexes as well as in younger patients and in both the upper and middle third of the stomach. These differences in the trends between early and advanced cancers are probably due to the different degrees of diagnostic accuracy for the early detection of histological types

Layer dependent band dispersion and correlations using tunable Soft X-ray ARPES

Soft X-ray Angle-Resolved Photoemission Spectroscopy is applied to study in-plane band dispersions of Nickel as a function of probing depth. Photon energies between 190 and 780 eV were used to effectively probe up to 3-7 layers. The results show layer dependent band dispersion of the Delta_2 minority-spin band which crosses the Fermi level in 3 or more layers, in contrast to known top 1-2 layers dispersion obtained using ultra-violet rays. The layer dependence corresponds to an increased value of exchange splitting and suggests reduced correlation effects in the bulk compared to the surface.Comment: 7 pages, 3 figures Revised text and figur

Bulk screening in core level photoemission from Mott-Hubbard and Charge-Transfer systems

We report bulk-sensitive hard X-ray ($h\nu$ = 5.95 KeV) core level photoemission spectroscopy (PES) of single crystal V$_{1.98}$Cr$_{0.02}$O$_{3}$ and the high-$T_c$ cuprate Bi$_2$Sr$_{2}$CaCu$_{2}$O$_{8+\delta}$ (Bi2212). V$_{1.98}$Cr$_{0.02}$O$_{3}$ exhibits low binding energy "satellites" to the V $2p$ "main lines" in the metallic phase, which are suppressed in the antiferromagnetic insulator phase. In contrast, the Cu $2p$ spectra of Bi2212 do not show temperature dependent features, but a comparison with soft X-ray PES indicates a large increase in the $2p^5 3d^9$ "satellites" or $3d^9$ weight in the bulk. Cluster model calculations, including full multiplet structure and a screening channel derived from the coherent band at the Fermi energy, give very satisfactory agreement with experiments

Incorporating latent variables using nonnegative matrix factorization improves risk stratification in Brugada syndrome

Background: A combination of clinical and electrocardiographic risk factors is used for risk stratification in Brugada syndrome. In this study, we tested the hypothesis that the incorporation of latent variables between variables using nonnegative matrix factorization can improve risk stratification compared with logistic regression. Methods and Results: This was a retrospective cohort study of patients presented with Brugada electrocardiographic patterns between 2000 and 2016 from Hong Kong, China. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation. The external validation cohort included patients from 3 countries. A total of 149 patients with Brugada syndrome (84% males, median age of presentation 50 [38–61] years) were included. Compared with the nonarrhythmic group (n=117, 79%), the spontaneous ventricular tachycardia/ ventricular fibrillation group (n=32, 21%) were more likely to suffer from syncope (69% versus 37%, P=0.001) and atrial fibrillation (16% versus 4%, P=0.023) as well as displayed longer QTc intervals (424 [399–449] versus 408 [386–425]; P=0.020). No difference in QRS interval was observed (108 [98–114] versus 102 [95–110], P=0.104). Logistic regression found that syncope (odds ratio, 3.79; 95% CI, 1.64–8.74; P=0.002), atrial fibrillation (odds ratio, 4.15; 95% CI, 1.12–15.36; P=0.033), QRS duration (odds ratio, 1.03; 95% CI, 1.002–1.06; P=0.037) and QTc interval (odds ratio, 1.02; 95% CI, 1.01–1.03; P=0.009) were significant predictors of spontaneous ventricular tachycardia/ventricular fibrillation. Increasing the number of latent variables of these electrocardiographic indices incorporated from n=0 (logistic regression) to n=6 by nonnegative matrix factorization improved the area under the curve of the receiving operating characteristics curve from 0.71 to 0.80. The model improves area under the curve of external validation cohort (n=227) from 0.64 to 0.71. Conclusions: Nonnegative matrix factorization improves the predictive performance of arrhythmic outcomes by extracting latent features between different variables

Learning from monitoring networks: Few-large vs. many-small plots and multi-scale analysis

In order to learn about broad scale ecological patterns, data from large-scale surveys must allow us to either estimate the correlations between the environment and an outcome and/or accurately predict ecological patterns. An important part of data collection is the sampling effort used to collect observations, which we decompose into two quantities: the number of observations or plots (n) and the per-observation/plot effort (E; e.g., area per plot). If we want to understand the relationships between predictors and a response variable, then lower model parameter uncertainty is desirable. If the goal is to predict a response variable, then lower prediction error is preferable. We aim to learn if and when aggregating data can help attain these goals. We find that a small sample size coupled with large observation effort coupled (few large) can yield better predictions when compared to a large number of observations with low observation effort (many small). We also show that the combination of the two values (n and E), rather than one alone, has an impact on parameter uncertainty. In an application to Forest Inventory and Analysis (FIA) data, we model the tree density of selected species at various amounts of aggregation using linear regression in order to compare the findings from simulated data to real data. The application supports the theoretical findings that increasing observational effort through aggregation can lead to improved predictions, conditional on the thoughtful aggregation of the observational plots. In particular, aggregations over extremely large and variable covariate space may lead to poor prediction and high parameter uncertainty. Analyses of large-range data can improve with aggregation, with implications for both model evaluation and sampling design: testing model prediction accuracy without an underlying knowledge of the datasets and the scale at which predictor variables operate can obscure meaningful results

Bulk Electronic structure of Na0.35_{0.35}CoO2_{2}.1.3H2_{2}O

High-energy (h$\nu$ = 5.95 keV) synchrotron Photoemission spectroscopy (PES) is used to study bulk electronic structure of Na$_{0.35}$CoO$_{2}$.1.3H$_{2}$O, the layered superconductor. In contrast to 3-dimensional doped Co oxides, Co $\it{2p}$ core level spectra show well-separated Co$^{3+}$ and Co$^{4+}$ ions. Cluster calculations suggest low spin Co$^{3+}$ and Co$^{4+}$ character, and a moderate on-site Coulomb correlation energy U$_{dd}\sim$3-5.5 eV. Photon dependent valence band PES identifies Co $\it{3d}$ and O $\it{2p}$ derived states, in near agreement with band structure calculations.Comment: 4 pages 4 figures Revised text added referenc