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TGF-β/HDAC7 axis curbs TCA cycle metabolism within kidney cancer malignancy.
Condition monitoring is a well-established field of research; however, for industrial applications, one may find some challenges. They are mostly related to complex design, a specific process performed by the machine, time-varying load/speed conditions, and the presence of non-Gaussian noise. #link# A procedure for vibration analysis from the sieving screen used in the raw material industry is proposed in the paper. It is more for pre-processing than the damage detection procedure. The idea presented here is related to identification and extraction of two main types of components (i) deterministic (D)-related to the unbalanced shaft(s) and (ii) high amplitude, impulsive component randomly (R) appeared in the vibration due to pieces of ore falling down of moving along the deck. If we could identify these components, then we will be able to perform classical diagnostic procedures for local damage detection in rolling element bearing. As deterministic component may be AM/FM modulated and each impulse may appear with dient and interpret thus the method may be used in practice in a commercial system.Helicobacter pylori (Hp)-induced inflammatory reaction leads to a persistent disturbance of gastric mucosa and chronic gastritis evidenced by deregulation of tissue self-renewal and local fibrosis with the crucial role of epithelial-mesenchymal transition (EMT) in this process. As we reported before, Hp activated gastric fibroblasts into cells possessing cancer-associated fibroblast properties (CAFs), which secreted factors responsible for EMT process initiation in normal gastric epithelial RGM1 cells. Here, we showed that the long-term incubation of RGM1 cells in the presence of Hp-activated gastric fibroblast (Hp-AGF) secretome induced their shift towards plastic LGR5+/Oct4high/Sox-2high/c-Mychigh/Klf4low phenotype (l.t.EMT+RGM1 cells), while Hp-non-infected gastric fibroblast (GF) secretome prompted a permanent epithelial-myofibroblast transition (EMyoT) of RGM1 cells favoring LGR - /Oct4high/Sox2low/c-Myclow/Klf4high phenotype (l.t.EMT - RGM1 cells). TGFβ1 rich secretome from Hp-reprogrammed fibroblasts prompted phenotypic plasticity and EMT of gastric epithelium, inducing pro-neoplastic expansion of post-EMT cells in the presence of low TGFβR1 and TGFβR2 activity. In turn, TGFβR1 activity along with GF-induced TGFβR2 activation in l.t.EMT - RGM1 cells prompted their stromal phenotype. Collectively, our data show that infected and non-infected gastric fibroblast secretome induces alternative differentiation programs in gastric epithelium at least partially dependent on TGFβ signaling. Hp infection-activated fibroblasts can switch gastric epithelium microevolution towards cancer stem cell-related differentiation program that can potentially initiate gastric neoplasm.In many developing countries, the existence of the uncertified recycler seriously hinders the healthy development of the waste electrical and electronic equipment (WEEE or e-waste) recycling industry. As a result, how the government can regulate the uncertified recycler to improve environment and public health during the recycling processes has become a critical issue. To help tackle this issue, we build an evolutionary game model to study the interactions between the government and the uncertified recycler. We conduct stability analysis of each participant and obtain four asymptotically stable states. Furthermore, we conduct numerical simulations for comparative analysis based on the current situation of the Chinese e-waste recycling industry. Our results are as follows. First, there exist multiple asymptotically stable states for the government and the uncertified recycler, namely (no-governance, maintaining status quo), (governance, maintaining status quo), (governance, industrial upgrading), and (no-goverrding to the asymptotically stable state (no-governance, industrial upgrading), the government should prepare to withdraw from the market when the uncertified recycler chooses industrial upgrading.The calibration of any sophisticated model, and in particular a constitutive relation, is a complex problem that has a direct impact in the cost of generating experimental data and the accuracy of its prediction capacity. In GS-5734 cell line , we address this common situation using a two-stage procedure. In order to evaluate the sensitivity of the model to its parameters, the first step in our approach consists of formulating a meta-model and employing it to identify the most relevant parameters. In the second step, a Bayesian calibration is performed on the most influential parameters of the model in order to obtain an optimal mean value and its associated uncertainty. We claim that this strategy is very efficient for a wide range of applications and can guide the design of experiments, thus reducing test campaigns and computational costs. Moreover, the use of Gaussian processes together with Bayesian calibration effectively combines the information coming from experiments and numerical simulations. The framework described is applied to the calibration of three widely employed material constitutive relations for metals under high strain rates and temperatures, namely, the Johnson-Cook, Zerilli-Armstrong, and Arrhenius models.Virtual Try-on is the ability to realistically superimpose clothing onto a target person. Due to its importance to the multi-billion dollar e-commerce industry, the problem has received significant attention in recent years. To date, most virtual try-on methods have been supervised approaches, namely using annotated data, such as clothes parsing semantic segmentation masks and paired images. These approaches incur a very high cost in annotation. Even existing weakly-supervised virtual try-on methods still use annotated data or pre-trained networks as auxiliary information and the costs of the annotation are still significantly high. Plus, the strategy using pre-trained networks is not appropriate in the practical scenarios due to latency. In this paper we propose Unsupervised VIRtual Try-on using disentangled representation (UVIRT). After UVIRT extracts a clothes and a person feature from a person image and a clothes image respectively, it exchanges a clothes and a person feature. Finally, UVIRT achieve virtual try-on.
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