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FAM129B, an antioxidative protein, reduces chemosensitivity by competing with Nrf2 for Keap1 binding.
BackgroundThe transcription factor Nrf2 is a master regulator of antioxidant response. While Nrf2 activation may counter increasing oxidative stress in aging, its activation in cancer can promote cancer progression and metastasis, and confer resistance to chemotherapy and radiotherapy. Thus, Nrf2 has been considered as a key pharmacological target. Unfortunately, there are no specific Nrf2 inhibitors for therapeutic application. Moreover, high Nrf2 activity in many tumors without Keap1 or Nrf2 mutations suggests that alternative mechanisms of Nrf2 regulation exist.MethodsInteraction of FAM129B with Keap1 is demonstrated by immunofluorescence, colocalization, co-immunoprecipitation and mammalian two-hybrid assay. Antioxidative function of FAM129B is analyzed by measuring ROS levels with DCF/flow cytometry, Nrf2 activation using luciferase reporter assay and determination of downstream gene expression by qPCR and wester blotting. Impact of FAM129B on in vivo chemosensitivity is examined in mice bearing breast and colon cancer xenografts. The clinical relevance of FAM129B is assessed by qPCR in breast cancer samples and data mining of publicly available databases.FindingsWe have demonstrated that FAM129B in cancer promotes Nrf2 activity by reducing its ubiquitination through competition with Nrf2 for Keap1 binding via its DLG and ETGE motifs. In addition, FAM129B reduces chemosensitivity by augmenting Nrf2 antioxidative signaling and confers poor prognosis in breast and lung cancer.InterpretationThese findings demonstrate the important role of FAM129B in Nrf2 activation and antioxidative response, and identify FMA129B as a potential therapeutic target. FUND: The Chang Gung Medical Foundation (Taiwan) and the Ministry of Science and Technology (Taiwan)
Sparse Fr\'echet Sufficient Dimension Reduction with Graphical Structure Among Predictors
Fr\'echet regression has received considerable attention to model
metric-space valued responses that are complex and non-Euclidean data, such as
probability distributions and vectors on the unit sphere. However, existing
Fr\'echet regression literature focuses on the classical setting where the
predictor dimension is fixed, and the sample size goes to infinity. This paper
proposes sparse Fr\'echet sufficient dimension reduction with graphical
structure among high-dimensional Euclidean predictors. In particular, we
propose a convex optimization problem that leverages the graphical information
among predictors and avoids inverting the high-dimensional covariance matrix.
We also provide the Alternating Direction Method of Multipliers (ADMM)
algorithm to solve the optimization problem. Theoretically, the proposed method
achieves subspace estimation and variable selection consistency under suitable
conditions. Extensive simulations and a real data analysis are carried out to
illustrate the finite-sample performance of the proposed method
Res2Net: A New Multi-scale Backbone Architecture
Representing features at multiple scales is of great importance for numerous
vision tasks. Recent advances in backbone convolutional neural networks (CNNs)
continually demonstrate stronger multi-scale representation ability, leading to
consistent performance gains on a wide range of applications. However, most
existing methods represent the multi-scale features in a layer-wise manner. In
this paper, we propose a novel building block for CNNs, namely Res2Net, by
constructing hierarchical residual-like connections within one single residual
block. The Res2Net represents multi-scale features at a granular level and
increases the range of receptive fields for each network layer. The proposed
Res2Net block can be plugged into the state-of-the-art backbone CNN models,
e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these
models and demonstrate consistent performance gains over baseline models on
widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies
and experimental results on representative computer vision tasks, i.e., object
detection, class activation mapping, and salient object detection, further
verify the superiority of the Res2Net over the state-of-the-art baseline
methods. The source code and trained models are available on
https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure
Associations of parental bonding and adolescent internet addiction symptoms with depression and anxiety in parents of adolescents with attention deficit/hyperactivity disorder
Objectives: The aim of the present study was to evaluate the associations of parental bonding and adolescents’ Internet addiction symptoms with depression and anxiety in parents of adolescents with attention deficit/hyperactivity disorder (ADHD). Methods: Parental depression and anxiety symptoms, parental bonding, and adolescents’ Internet addiction symptoms were assessed in 46 parent-child dyads using the Center for Epidemiological Studies Depression Scale, State-Trait Anxiety Inventory, Parental Bonding Instrument (PBI), and Chen Internet Addiction Scale, respectively. Forward stepwise multiple regression analysis was used to examine the associations of parental bonding and adolescents’ Internet addiction symptoms with parental depression and anxiety. Results: Low care/affection on the PBI was significantly associated with parental depression, and overprotection on the PBI and adolescents’ Internet addiction were significantly associated with parental anxiety. Discussion: Parental bonding and adolescents’ Internet addiction are related to depression and anxiety in parents of adolescents with ADHD
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