Nuclear Receptor Coactivator 2 Promotes Human Breast Cancer Cell Growth by Positively Regulating the MAPK/ERK Pathway

As a member of the p160 steroid receptor coactivator (SRC) family, nuclear receptor coactivator 2 (NCOA2) is known to play essential roles in many physiological and pathological processes, including development, endocrine regulation, and tumorigenesis. However, the biological function of NCOA2 in breast cancer is not fully understood. We found that the copy number of the NCOA2 gene was frequently amplified in four breast cancers datasets, varying from 6 to 10%, and the mRNA levels of NCOA2 were also upregulated in 11% of the sequenced cases/patients (TCGA provisional dataset). Next, we confirmed that NCOA2 silencing significantly suppressed cell proliferation in different breast cancer cell lines, by inducing cell cycle arrest and apoptosis. Mechanistically, whole-transcriptome sequencing (RNA-Seq) analysis showed that NCOA2 depletion leads to downregulation of the MAPK/ERK signaling cascade, possibly via downregulating NCOA2's downstream target RASEF. In conclusion, our results suggest NCOA2 as a potential target of therapeutics against breast cancer

GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection

Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language. Existing detection methods typically rely on fusion effectiveness and cross-modal consistency to model the content, complicating understanding how each modality affects prediction accuracy. Additionally, these methods are primarily based on static feature modelling, making it difficult to adapt to the dynamic changes and relationships between different data modalities. This paper develops a significantly novel approach, GAMED, for multimodal modelling, which focuses on generating distinctive and discriminative features through modal decoupling to enhance cross-modal synergies, thereby optimizing overall performance in the detection process. GAMED leverages multiple parallel expert networks to refine features and pre-embed semantic knowledge to improve the experts’ ability in information selection and viewpoint sharing. Subsequently, the feature distribution of each modality is adaptively adjusted based on the respective experts’ opinions. GAMED also introduces a novel classification technique to dynamically manage contributions from different modalities, while improving the explainability of decisions. Experimental results on the Fakeddit and Yang datasets demonstrate that GAMED performs better than recently developed state-of-the-art models

Probing the Shock Breakout Signal of SN 2024ggi from the Transformation of Early Flash Spectroscopy

© 2024. The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We present early-time, hour-to-day cadence spectroscopy of the nearby Type II supernova (SN II) 2024ggi, which was discovered at a phase when the SN shock had just emerged from the red supergiant (RSG) progenitor star. Over the first few days after the first light, SN 2024ggi exhibited prominent narrow emission lines formed through intense and persistent photoionization of the nearby circumstellar material (CSM). In the first 63 hr, spectral lines of He, C, N, and O revealed a rapid rise in ionization as a result of the progressive sweeping up of the CSM by the shock. The duration of the IIn-like spectra indicates a dense and relatively confined CSM distribution extending up to ∼4 × 1014 cm. Spectral modeling reveals that a CSM mass-loss rate at this region exceeding 5 × 10−3 M ⊙ yr−1 is required to reproduce low-ionization emissions, which dramatically exceeds that of an RSG. Analyzing the Hα emission shift implies the velocity of the unshocked outer CSM to be between 20 and 40 km s−1, matching the typical wind velocity of an RSG. The differences between the inner and outer layers of the CSM and an RSG progenitor highlight a complex mass-loss history before the explosion of SN 2024ggi.Peer reviewe

A scientometric analysis of research trends on targeting mTOR in breast cancer from 2012 to 2022

Over the past decade, thousands of articles have been published on the mechanistic target of rapamycin (mTOR) and its role in breast cancer. However, the variability and heterogeneity of academic data may impact the acquisition of published research information. Due to the large number, heterogeneity, and varying quality of publications related to mTOR and breast cancer, sorting out the present state of the research in this area is critical for both researchers and clinicians. Therefore, scientometric techniques and visualization tools were employed to analyze the large number of bibliographic metadata related to the research area of mTOR and breast cancer. The features of relevant publications were searched from 2012 to 2022 to evaluate the present status of research and the evolution of research hotspots in this particular field. Web of Science was utilized to extract all relevant publications from 2012 to 2022. Subsequently, Biblioshiny and VOSviewer were utilized to obtain data on the most productive countries, authors, and institutions, annual publications and citations, the most influential journals and articles, and the most frequently occurring keywords. In total, 1,471 publications were retrieved, comprising 1,167 original articles and 304 reviews. There was a significant rise in publications between 2015 and 2018, followed by a sharp decline in 2019 and a rebound since then. The publication with the highest number of citations was a 2012 review authored by Baselga et al. The United States had the highest number of publications, citations and connections among all countries. Oncotarget had the highest number of published articles among all the journals, and José Baselga had the strongest links with other authors. Excluding the search topics, the most frequently used words were “expression” (n = 297), “growth” (n = 228), “activation” (n = 223), “pathway” (n = 205), and “apoptosis” (n = 195). mTOR is crucially involved in breast cancer pathogenesis, but its exact mechanism of action remains controversial and warrants further investigation. The scientometric analysis provides a distinct overview of the existing state of research and highlights the topical issues that deserve further exploration

Targeting glutaminase is therapeutically effective in ibrutinib-resistant mantle cell lymphoma

Mantle cell lymphoma (MCL) is an incurable B-cell non-Hodgkin lymphoma characterized by frequent relapses. The development of resistance to ibrutinib therapy remains a major challenge in MCL. We previously showed that glutaminolysis is associated with resistance to ibrutinib. In this study, we confirmed that glutaminase (GLS), the first enzyme in glutaminolysis, is overexpressed in ibrutinib-resistant MCL cells, and that its expression correlates well with elevated glutamine dependency and glutaminolysis. Furthermore, we discovered that GLS expression correlates with MYC expression and the functioning of the glutamine transporter ASCT2. Depletion of glutamine or GLS significantly reduced cell growth, while GLS overexpression enhanced glutamine dependency and ibrutinib resistance. Consistent with this, GLS inhibition by its specific inhibitor telaglenastat suppressed MCL cell growth both in vitro and in vivo. Moreover, telaglenastat showed anti-MCL synergy when combined with ibrutinib or venetoclax in vitro, which was confirmed using an MCL patient-derived xenograft model. Our study provides the first evidence that targeting GLS with telaglenastat, alone or in combination with ibrutinib or venetoclax, is a promising strategy to overcome ibrutinib resistance in MCL

Reinforcement trading for multi-market portfolio with crisis avoidance

The global financial market comes to a new crisis in 2020 triggered by the COVID-19 pandemic. During such a period, it is crucial for a portfolio manager to adopt policies that can preserve the value of the portfolio. Although innovations in computational finance using Machine Learning emerge rapidly, many of the works are using off-line supervised learning that is dependent on the training data in a specific period, and the model is not capable of direct trading. Additionally, many other works using Reinforcement Learning approaches are built with in-house tools and is lack of extensibility. As such, these models are neither transferrable to greater markets in a longer time range, nor are they capable to handle the black swan or grey rhino events that reappear almost every decade. In this paper, we proposed a Reinforcement Learning trading framework with a crisis avoidance algorithm. The framework adopts the open-sourced OpenAI Gym standard and Stable Baseline model that are open for third-party tools and future extension. We invented a Reinforcement Learning Environment to describe the market behavior with technical analysis and finite rule-based action sets. The framework further implements a crisis detection and avoidance algorithm. The experiment result shows that the models trained by the framework performed as well as buy-and-hold strategy benchmark in the bullish period of 2015-2019. Furthermore, very much accredited to the crisis avoidance algorithm, the models acted 17% better than buy-and-hold during all testing windows no less than 5 years in 2000-2019.Bachelor of Engineering (Computer Science

Harnessing Rayleigh-Plateau Instability in Polymer Melts

In this dissertation, I study the breakup of liquid threads and generation of liquid droplets within an immiscible fluid using an embedded 3-dimensional (3D) printing system. Firstly, we develop a robust fluid-mediated route for the rapid fabrication of soft elastomers architected with liquid inclusions. Our approach consists of depositing water drops at the surface of an immiscible liquid elastomer bath. As the elastomer cures, the drops are encapsulated in the polymer and impart shape and function to the newly formed elastic matrix. Using the framework of fluid mechanics, we show how this type of composite material can be tailored. In the second part, I study the droplet forming instability of a thin jet extruded from a nozzle moving horizontally below the surface of an iso-viscous immiscible fluid bath. While this interfacial instability is a classic problem in fluid mechanics, it has never been studied in the context of the deposition of a thread into a reservoir, an open-sky version of microfluidics. As the nozzle translates through the reservoir, drops may form at the nozzle (dripping) or further downstream (jetting). We first focus on rectilinear printing paths and derive a scaling law to rationalize the transition between dripping and jetting. We then leverage the flexibility of our system and study the dynamics of breakup when printing sinusoidal paths. We unravel a methodology to control both the size of the drops formed by the instability and the distance that separates them. Finally, we show that the breakup of closely spaced liquid threads sequentially printed in an immiscible bath locks into crystal-like lattices of droplets. We rationalize the hydrodynamics at the origin of this previously unknown phenomenon. We leverage this knowledge to tune the lattice pattern via the control of injection flow rate and nozzle translation speed. We further demonstrate that the drop crystals have the ability to self-correct and a simple mechanism is proposed to describe the convergence towards a uniform pattern of drops. Our printing techniques can serve as a new pathway for the fabrication of droplet patterns, which could be adapted to the existing droplet-based technologies and open up previously unexplored opportunities in additive manufacturing

Instability mediated self-templating of drop crystals

International audienceThe breakup of liquid threads into droplets is prevalent in engineering and natural settings. While drop formation in these systems has a long-standing history, existing studies typically consider axisymmetric systems. Conversely, the physics at play when multiple threads are involved and the interaction of a thread with a symmetry breaking boundary remain unexplored. Here, we show that the breakup of closely spaced liquid threads sequentially printed in an immiscible bath locks into crystal-like lattices of droplets. We rationalize the hydrodynamics at the origin of this previously unknown phenomenon. We leverage this knowledge to tune the lattice pattern via the control of injection flow rate and nozzle translation speed, thereby overcoming the limitations in structural versatility typically seen in existing fluid manipulations paradigms. We further demonstrate that these drop crystals have the ability to self-correct and propose a simple mechanism to describe the convergence toward a uniform pattern of drops

Printing on liquid elastomers

In recent years the research community has paid significant attention to geometrically engineered materials. These materials derive their unique properties from their structure rather than their chemistry alone. Despite their success in the laboratory, the assembly of such soft functional materials remains an outstanding challenge. Here, we propose a robust fluid-mediated route for the rapid fabrication of soft elastomers architected with liquid inclusions. Our approach consists of depositing water drops at the surface of an immiscible liquid elastomer bath. As the elastomer cures, the drops are encapsulated in the polymer and impart shape and function to the newly formed elastic matrix. Using the framework of fluid mechanics, we show how this type of composite material can be tailored