Characterising mechanisms of aberrant mutant androgen receptor signalling in advanced prostate cancer

PhD ThesisProstate cancer (PC) is the most commonly diagnosed disease in the UK which causes approximately 10,000 deaths annually. Although an initially effective response to androgen deprivation therapy (ADT) occurs in most patients, the tumour normally recurs in a more aggressive form of the disease termed castrate resistant PC (CRPC) and is largely untreatable at this stage. In many cases, disease is driven by inappropriate androgen receptor (AR) signalling. It is therefore vital to have better understanding of mechanisms that re-activate AR and promote ADT resistance in the clinic and hence better treatments for advanced tumour. Activation of AR by testosterone is crucial for prostate growth and transformation. Antiandrogens, the second most common PC therapy after surgery, antagonise ligand binding to the receptor and hence deactivate AR signalling. In 2012, enzalutamide, a more potent agents in terms of availability to block AR was approved by the FDA and ENA as a second-generation antiandrogen for clinical usage. Although it demonstrated several advantages over its pervious counterpart bicalutamide, response rates of just 50% in CRPC patients and subsequent resistance observed in responders haslimited its effectiveness. Critically,several lines of evidence from preclinical models and patient samples indicate that one particular resistant mechanism is the emergence of AR mutant(s), in part, driven by a specific AR mutation F876L that enables the compound to act as an agonist. Importantly, the same mutant was later detected in metastatic PC patient had been treated with apalutamide. Evidently, novel therapies emerging into clinical treatment of advance disease have the added challenge of being efficacious in the background of mutant AR and thus developing model systems to test this is of paramount importance. In order to enable more physiological modelling of aberrant ARF876L activity that would highlight potentially distinct mechanisms that could be exploited in future therapies, this project aimed to generate CRISPR-edited LNCaP and CWR22Rv1 cell lines expressing the enzalutamide-activated ARF876L mutant. Meanwhile, a part of project has also focused on generation of a physiologically relevant AR rescue/replacement in vitro cell line model (LNCaP- ARF876L cells) which permits ability forstudying ARF876L directly regulated gene expression profiles by effectively knockdown endogenous AR without impacting on the ectopically expressed mutant. Furthermore, by using 3 Illumina Human HT-12 arrays analysing LNCaP-ARF876L cells revealed a comprehensive transcriptomic data-set to provide an insight into how an enzalutamide-activated AR mutant can drive a distinct gene-set in advanced PC. This is important as it may enable distinct biomarker discovery in enzalutamide-resistance disease and has highlighted interplay between the ARF876L mutation and the glucocorticoid receptor. Lastly, the LNCaP- ARF876L cell lines was utilised to demonstrate that aberrantly-functioning receptor is sensitive to BET inhibitors. In all, the work hasshown that the ARF876L mutant drives a distinct transcriptional programme to the endogenous AR in LNCaP cells and the model can be utilised effectively to indicate sensitivities of the receptor to clinically-relevant compounds

Experimental Study on the Influence of KDL Physical and Health Education Curriculum on Primary School Students\u27 Physical Fitness

Physical and Health Education Curriculum (KDL) is a curriculum based on China\u27s Sports and Health Curriculum Standards and China\u27s Health Sports Curriculum Model. This study aims to explore the influence of KDL on primary school students\u27 physical fitness. A total of 91 primary school students participated in this study, including 47 in the experimental group and 44 in the control group. The experiment lasted for 18 weeks. During the experiment, the PE teacher of experimental group used KDL to teach, with specific requirements: (1) The activity time of each class was more than 75%, and the intensity, measured with average heart rates, was above 140-160 beats/min; (2) Each class had about 10 minutes of physical exercise; and (3) each class focused on activities and competitions. The control group was given routine physical education lessons without intervention. Before and after the experiment, both groups participated in physical fitness tests, including 50-meter running, vital capacity, seat forward flexion, and 1-minute rope jumping. SPSS was used to analyze the physical fitness of both groups. Before the experiment, there was no significant difference in physical fitness between the two groups. After the experiment, the experiment group outperformed the control group in the 1-minute rope skipping (t = 10.77, p \u3c 0.05) and exceled in the vital capacity (t = 0.04, p \u3c 0.05). There was no significant difference in other physical fitness tests between the two groups. This study shows that KDL curriculum has a significant positive impact on physical fitness of primary school students, mainly reflected in vital capacity and 1-minute rope skipping. The effect may be related to the high time on task and appropriate intensity advocated by KDL curriculum. We recommend KDL physical curriculum to be promoted in primary and secondary schools

Aggregation signature for small object tracking

Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://github.com/bczhangbczhang/.Comment: IEEE Transactions on Image Processing, 201

Environmental Sustainable Development: Study on the Value Realization Mechanism and Diversified Realization Path of Ecological Products under the Background of "Double Carbon"

Under the background of carbon neutrality and common prosperity, the importance of carbon sinks is constantly highlighted. Realizing the value of carbon sink ecological products is not only conducive to the realization of the goal of carbon neutrality, but also an effective way to promote the endogenous development of rural areas and promote common prosperity. Broadening the value transformation channel of carbon sink ecological products and realizing the sustainable transformation from "green water and green hills" to "Jinshan and Yinshan" provide a new way to achieve the goal of carbon neutrality and common prosperity. Based on the theoretical analysis of the traditional connotation, formation mechanism and value of carbon sink ecological products, this paper summarizes the main ways and existing problems of realizing carbon sink ecological value in China, systematically analyzes the two-way promotion relationship between the double carbon target and the realization of carbon sink ecological product value, and emphasizes the important role of carbon sink ecological value realization and participation in carbon market transactions in carbon emission reduction. It also summarizes the experience of international typical cases. Finally, suggestions and reflections were put forward for redistributing the supply of ecological products based on carbon sinks, improving the basic system for calculating the value of ecological products, strengthening the government's guiding role, improving the ecological rights trading market, and innovating financial models, providing reference for optimizing the innovative mechanism and path for realizing the value of ecological products in China under the "dual carbon" goal

ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting

Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence. In this paper, we propose a plug-and-play module named Action Prompt Module (APM) that effectively mines different kinds of action clues for 3D HPE. The highlight is that, the mining scheme of APM can be widely adapted to different frameworks and bring consistent benefits. Specifically, we first present a novel Action-related Text Prompt module (ATP) that directly embeds action labels and transfers the rich language information in the label to the pose sequence. Besides, we further introduce Action-specific Pose Prompt module (APP) to mine the position-aware pose pattern of each action, and exploit the correlation between the mined patterns and input pose sequence for further pose refinement. Experiments show that APM can improve the performance of most video-based 2D-to-3D HPE frameworks by a large margin.Comment: 6 pages, 4 figures, 2023ICM

Prospects for shale gas production in China: Implications for water demand

AbstractDevelopment of shale gas resources is expected to play an important role in China's projected transition to a low-carbon energy future. The question arises whether the availability of water could limit this development. The paper considers a range of scenarios to define the demand for water needed to accommodate China's projected shale gas production through 2020. Based on data from the gas field at Fuling, the first large-scale shale gas field in China, it is concluded that the water intensity for shale gas development in China (water demand per unit lateral length) is likely to exceed that in the US by about 50%. Fuling field would require a total of 39.9–132.9Mm3 of water to achieve full development of its shale gas, with well spacing assumed to vary between 300 and 1000m. To achieve the 2020 production goal set by Sinopec, the key Chinese developer, water consumption is projected to peak at 7.22Mm3 in 2018. Maximum water consumption would account for 1% and 3%, respectively, of the available water resource and annual water use in the Fuling district. To achieve China's nationwide shale gas production goal set for 2020, water consumption is projected to peak at 15.03Mm3 in 2019 in a high-use scenario. It is concluded that supplies of water are adequate to meet demand in Fuling and most projected shale plays in China, with the exception of localized regions in the Tarim and Jungger Basins

Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation

There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topology in the self-attention mechanism. To tackle this issue, in this paper, we propose a Pose-Oriented Transformer (POT) with uncertainty guided refinement for 3D HPE. Specifically, we first develop novel pose-oriented self-attention mechanism and distance-related position embedding for POT to explicitly exploit the human skeleton topology. The pose-oriented self-attention mechanism explicitly models the topological interactions between body joints, whereas the distance-related position embedding encodes the distance of joints to the root joint to distinguish groups of joints with different difficulties in regression. Furthermore, we present an Uncertainty-Guided Refinement Network (UGRN) to refine pose predictions from POT, especially for the difficult joints, by considering the estimated uncertainty of each joint with uncertainty-guided sampling strategy and self-attention mechanism. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods with reduced model parameters on 3D HPE benchmarks such as Human3.6M and MPI-INF-3DHPComment: accepted by AAAI202