Prevention of breast cancer by dietary polyphenols—role of cancer stem cells

Abstract

Breast cancer is a common malignancy with poor prognosis. Cancer cells are heterogeneous and cancer stem cells (CSCs) are primarily responsible for tumor relapse, treatment-resistance and metastasis, so for breast cancer stem cells (BCSCs). Diets are known to be associated with carcinogenesis. Food-derived polyphenols are able to attenuate the formation and virulence of BCSCs, implying that these compounds and their analogs might be promising agents for preventing breast cancer. In the present review, we summarized the origin and surface markers of BCSCs and possible mechanisms responsible for the inhibitory effects of polyphenols on BCSCs. The suppressive effects of common dietary polyphenols against BCSCs, such as curcumin, epigallocatechin gallate (EGCG) and related polyphenolic compounds were further discussed.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

PWR堆芯燃料组件非线性梁模型研究

为描述燃料组件的非线性特性，采用有限元方法建立燃料组件横向梁模型，并引入迟滞模型，用于模拟燃料组件横向变形时的非线性效应。计算结果表明:该非线性梁模型能够更准确地获得组件的弯曲变形和受力，有助于开展燃料组件事故分析。      

Achieving Rich and Active Alkaline Hydrogen Evolution Heterostructures via Interface Engineering on 2D 1T‐MoS2 Quantum Sheets

Large‐scale production of hydrogen from water‐alkali electrolyzers is impeded by the sluggish kinetics of hydrogen evolution reaction (HER) electrocatalysts. The hybridization of an acid‐active HER catalyst with a cocatalyst at the nanoscale helps boost HER kinetics in alkaline media. Here, it is demonstrated that 1T–MoS₂ nanosheet edges (instead of basal planes) decorated by metal hydroxides form highly active ${\text{edge}}_{{\text{1T‐MoS}}_{\text{2}}}$/ ${\text{edge}}_{\text{Ni}{(\text{OH})}_{\text{2}}}$ heterostructures, which significantly enhance HER performance in alkaline media. Featured with rich ${\text{edge}}_{{\text{1T‐MoS}}_{\text{2}}}$/ ${\text{edge}}_{\text{Ni}{(\text{OH})}_{\text{2}}}$ sites, the fabricated 1T–MoS₂ QS/Ni(OH)₂ hybrid (quantum sized 1T–MoS₂ sheets decorated with Ni(OH)₂ via interface engineering) only requires overpotentials of 57 and 112 mV to drive HER current densities of 10 and 100 mA cm^?2, respectively, and has a low Tafel slope of 30 mV dec^?1 in 1 m KOH. So far, this is the best performance for MoS₂‐based electrocatalysts and the 1T–MoS₂ QS/Ni(OH)₂ hybrid is among the best‐performing non‐Pt alkaline HER electrocatalysts known. The HER process is durable for 100 h at current densities up to 500 mA cm^?2. This work not only provides an active, cost‐effective, and robust alkaline HER electrocatalyst, but also demonstrates a design strategy for preparing high‐performance catalysts based on edge‐rich 2D quantum sheets for other catalytic reactions.     

RESERVOIR PROPERTIES OF BARREMIAN – APTIAN URGONIAN LIMESTONES,SE FRANCE,PART 1: INFLUENCE OF STRUCTURAL HISTORY ON POROSITY-PERMEABILITY VARIATIONS

Upper Barremian – Lower Aptian inner platform “Urgonian” limestones in the Mont de Vaucluse region, SE France, consist of alternating metre-scale microporous and tight intervals. This paper focuses on the influence of structural deformation on the reservoir properties of the Urgonian limestone succession in a study area near the town of Rustrel. Petrographic, petrophysical and structural data were recovered from five fully-cored boreholes, from the walls of a 100 m long underground tunnel, and from a 50 m long transect at a nearby outcrop. The data allowed reservoir property variations in the Urgonian limestones to be studied from core to reservoir scale. Eleven Reservoir Rock Types (RRTs) were identified based on petrographic features (texture, grain size), reservoir properties (porosity, permeability), and the frequency of structural discontinuities such as fractures, faults and stylolites. Tight and microporous reservoir rock types were distinguished. Tight reservoir rock types were characterised by early cementation of intergranular pore spaces and by the presence of frequent structural discontinuities. By contrast microporous reservoir rock types contained preserved intragranular microporosity and matrix permeability, but had very few structural discontinuities. Observed vertical alternations of microporous and tight rock types are interpreted to have been controlled by the early diagenesis of the Urgonian carbonates. Deformation associated with regional-scale tectonic phases, including Albian – Cenomanian “Durancian” uplift (∼105 to 96 Ma) and Pyrenean compression (∼55 to 25 Ma), resulted in the modification of the initial petrophysical properties of the Urgonian limestones. An early diagenetic imprint conditioned both the intensity of structural deformations and the associated circulations of diagenetic and meteoric fluids. Evolution of the Reservoir Rock Types is therefore linked both to the depositional conditions and to subsequent phases of structural deformation.