Exposure to di-2-ethylhexyl phthalate and breast neoplasm incidence: A cohort study.

BACKGROUND: Phthalates are ubiquitous environmental endocrine disruptors. As the predominant phthalate, di-2-ethylhexyl phthalate (DEHP) has been considered possibly carcinogenic to humans but large-scale longitudinal evidence is needed to further clarify its carcinogenicity. OBJECTIVES: To examine the association between DEHP exposure and incidence of breast malignant neoplasm, carcinoma in situ and benign neoplasm. METHODS: A total of 273,295 women from UK Biobank cohort were followed up for a median of 13.5 years. Disease information was collected from National Health Service Cancer Registry and National Death Index. Baseline and yearly-average level of DEHP exposure were estimated for each individual by linking chemical monitoring record of European Environment Agency with home address of the participants by Kriging interpolation model. Cox proportional hazard model was employed to estimate the association between DEHP exposure and breast neoplasms. RESULTS: The median (IQR) of baseline and yearly-average DEHP concentration were 8000.25 (interquartile range: 6657.85-11,948.83) and 8000.25 (interquartile range: 1819.93-11,359.55) µg/L. The highest quartile of baseline DEHP was associated with 1.11 fold risk of carcinoma in situ (95 % CI, 1.00, 1.23, p < 0.001) and 1.27 fold risk of benign neoplasm (95 % CI, 1.05, 1.54, p < 0.001). As for yearly-average exposure, each quartile of DEHP was positively associated with higher risk of malignant neoplasm (HR, 1.05; 95 % CI, 1.03, 1.07, p < 0.001), carcinoma in situ (HR, 1.08; 95 % CI, 1.04, 1.11, p < 0.001) and benign neoplasm (HR, 1.13; 95 % CI, 1.07, 1.20, p < 0.001). Stratification analysis showed no significant modification effects on the DEHP-neoplasm relationship by menopausal status or ethnicity but a suggestive higher risk in younger women and those who underwent oral contraceptive pill therapy. In sensitivity analysis, the associations remained when excluding the cases diagnosed within 2 years post baseline. CONCLUSIONS: Real-world level of DEHP exposure was associated with higher risk of breast neoplasms. Because of the health risks associated with DEHP, its release to the environment should be managed.

Frequency specific co-activation pattern analysis via sparse nonnegative tensor decomposition.

BACKGROUND: Traditionally, the diagnosis of Parkinson's disease (PD) has been made based on symptoms. Extensive studies have demonstrated that PD may lead to variation of brain activity in different frequency bands. However, frequency specific dynamic alterations of PD have not yet been explored. NEW METHOD: In order to address this gap, a novel sparse nonnegative tensor decomposition (SNTD) method was used to estimate frequency specific co-activation patterns (CAP). The difference between PD and healthy controls (HC) are investigated with the proposed framework. RESULT: The difference between PD and HC mainly exists at frequency band 0.04-0.1 Hz in basal ganglia. We also found that the average intensity of PD in this frequency band is significantly correlated with the Hoehn and Yahr scale. COMPARISON WITH EXISTING METHODS: Compared with conventional CAP approach, SNTD estimates frequency specific CAPs that show alterations in PD patients. CONCLUSION: SNTD provides an alternative to K-means clustering used in conventional CAP analysis. With the proposed framework, frequency specific CAPs are extracted, and alterations in PD patients are also successfully discovered.

Physical and technofunctional properties of yellow pea flour and bread crumb mixtures processed with low moisture extrusion cooking.

The potential utilization of yellow pea flour and bread crumb blends was investigated to generate nutritionally-dense extruded products with superior physical and/or technofunctional properties. Yellow pea flour mixed with bread crumb at different ratios were processed using low-moisture twin-screw extrusion cooking conditions to examine the effect of blending ratios and feed moisture contents on physical (that is, radial expansion index, extrudate density, microstructure, texture, and color) and technofunctional (that is, emulsifying capacity, emulsifying stability, water solubility [WS], water binding capacity [WBC], oil binding capacity [OBC], and pasting) properties of the final products. Compared to the two feed materials alone, samples produced with yellow pea flour and bread crumb blends showed lower hardness and higher crispiness. Moisture content (12% to 18%) was found to significantly affect physical and technofunctional properties. With an increase in feed moisture content from 12% to 18%, the WBC of the extrudates increased while the WS decreased. Extrudates produced with higher feed moisture content, and higher yellow pea flour content had higher setback viscosity. Among all formulas and feed moisture contents studied, extrudates produced with 50% yellow pea flour and 50% bread crumb at 12% feed moisture content had the highest radial expansion and bigger cells with thinner cell walls. This study has shown that incorporation of yellow pea flour and bread crumb in extrusion cooking process could be used to develop nutritionally-dense foods with improved physical and technofunctional properties.

Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis.

BACKGROUND: Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms. RESULTS: In simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted. CONCLUSION: Based on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.