Post-translational modifications in mammary gland development and mammary tumor progression

Breast cancer is one of the most common cancers in women. Estrogen receptor α (ERα) signaling and p53 signaling have important roles in breast cancer progression. Therefore, post-translational modifications of ERα and p53 play critical roles in breast cancer. The overall aim of this thesis is to characterize the role of RING-finger protein 31 (RNF31) on ERα and p53 signaling and the function of P21-activated kinase 4 (PAK4) on ERα signaling. Moreover, the role of PAK4 in mouse mammary development and mammary tumor progression was also analyzed. In the first study, RNF31 was shown to active and stabilize ERα, and subsequently to increase estrogen-stimulated cell proliferation in breast cancer cells. In breast cancer clinical databases, the gene expression of RNF31 and ERα target genes were correlated. The suggested mechanism is that RNF31 interacts ERα via the RBR domain and facilitate ERα mono-ubiquitination. In the second study, RNF31 depletion was shown to increase the gene expression of p53 target genes. RNF31 depletion caused cycle arrest and cisplatin-induced apoptosis in a p53- dependent manner in breast cancer cells. Depletion of RNF31 increased p53 protein levels and the mRNA levels of its downstream target genes. The suggested mechanism is that RNF31 interacts with the p53/MDM2 complex and stabilizes MDM2 and consequently facilitates p53 poly-ubiquitination and degradation. In the third study, high PAK4 expression level was correlated with poor tamoxifen response in breast cancer patients in clinical databases, based on analysis of available mRNA expression. In MCF-7 cells, PAK4 overexpression promoted tamoxifen resistance, while PAK4 inhibition sensitized tamoxifen-resistant breast cancer cells to tamoxifen. Mechanistically, we identified a regulatory positive feedback loop, where PAK4 acts as a downstream target gene of ERα; while PAK4 can phosphorylate ERα at Ser305, thereby increasing ERα protein stability and activating ERα signaling. In conclusion, PAK4 may be a suitable target for tamoxifen resistance in breast cancer. In the fourth study, we elucidated the function of PAK4 in mammary development and mammary tumor progression in vivo. We observed no difference in mammary gland development between control mice and PAK4 conditional knockout mice. To test the role of PAK4 in mammary tumor development, conditional depletion of PAK4 was introduced in the MMTV-PyMT breast cancer mouse model. Importantly, conditional PAK4 depletion caused an increased tumor latency in MMTV-PyMT mice, indicating a role for PAK4 in early mammary tumor development

Effective Control of Bioelectricity Generation from a Microbial Fuel Cell by Logical Combinations of pH and Temperature

In this study, a microbial fuel cell (MFC) with switchable power release is designed, which can be logically controlled by combinations of the most physiologically important parameters such as “temperature” and “pH.” Changes in voltage output in response to temperature and pH changes were significant in which voltage output decreased sharply when temperature was lowered from 30°C to 10°C or pH was decreased from 7.0 to 5.0. The switchability of the MFC comes from the microbial anode whose activity is affected by the combined medium temperature and pH. Changes in temperature and pH cause reversible activation-inactivation of the bioanode, thus affecting the activity of the entire MFC. With temperature and pH as input signals, an AND logic operation is constructed for the MFC whose power density is controlled. The developed system has the potential to meet the requirement of power supplies producing electrical power on-demand for self-powered biosensors or biomedical devices

Effective Control of Bioelectricity Generation from a Microbial Fuel Cell by Logical Combinations of pH and Temperature

In this study, a microbial fuel cell (MFC) with switchable power release is designed, which can be logically controlled by combinations of the most physiologically important parameters such as “temperature” and “pH.” Changes in voltage output in response to temperature and pH changes were significant in which voltage output decreased sharply when temperature was lowered from 30°C to 10°C or pH was decreased from 7.0 to 5.0. The switchability of the MFC comes from the microbial anode whose activity is affected by the combined medium temperature and pH. Changes in temperature and pH cause reversible activation-inactivation of the bioanode, thus affecting the activity of the entire MFC. With temperature and pH as input signals, an AND logic operation is constructed for the MFC whose power density is controlled. The developed system has the potential to meet the requirement of power supplies producing electrical power on-demand for self-powered biosensors or biomedical devices

SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation

This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train \textbf{SelF-Eval} with a multi-level contrastive learning schema which helps to distinguish different score levels. Experimental results on multiple benchmarks show that SelF-Eval is highly consistent with human evaluations and better than the state-of-the-art models. We give a detailed analysis of the experiments in this paper. Our code is available on GitHub.Comment: 11 pages, 2 figures, 5 table

Poly[[diaqua­hemi-μ4-oxalato-μ2-oxalato-praseodymium(III)] monohydrate]

In the title complex, {[Pr(C2O4)1.5(H2O)2]·H2O}n, the PrIII ion, which lies on a crystallographic inversion centre, is coordinated by seven O atoms from four oxalate ligands and two O atoms from two water ligands; further Pr—O coordination from tetra­dentate oxalate ligands forms a three-dimensional structure. The compound crystallized as a monohydrate, the water mol­ecule occupying space in small voids and being secured by O—H⋯O hydrogen bonding as an acceptor from ligand water H atoms and as a donor to oxalate O-acceptor sites

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML