Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks

In this paper we consider estimation of common structural breaks in panel data models with interactive fixed effects which are unobservable. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. The Monte Carlo simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data

Association between ERCC1 and TS mRNA levels and disease free survival in colorectal cancer patients receiving oxaliplatin and fluorouracil (5-FU) adjuvant chemotherapy

BACKGROUND: Aim was to explore the association of ERCC1 and TS mRNA levels with the disease free survival (DFS) in Chinese colorectal cancer (CRC) patients receiving oxaliplatin and 5-FU based adjuvant chemotherapy. METHODS: Total 112 Chinese stage II-III CRC patients were respectively treated by four different chemotherapy regimens after curative operation. The TS and ERCC1 mRNA levels in primary tumor were measured by real-time RT-PCR. Kaplan–Meier curves and log-rank tests were used for DFS analysis. The Cox proportional hazards model was used for prognostic analysis. RESULTS: In univariate analysis, the hazard ratio (HR) for the mRNA expression levels of TS and ERCC1 (logTS: HR = 0.820, 95% CI = 0.600 - 1.117, P = 0.210; logERCC1: HR = 1.054, 95% CI = 0.852 - 1.304, P = 0.638) indicated no significant association of DFS with the TS and ERCC1 mRNA levels. In multivariate analyses, tumor stage (IIIc: reference, P = 0.083; IIb: HR = 0.240, 95% CI = 0.080 - 0.724, P = 0.011; IIc: HR < 0.0001, P = 0.977; IIIa: HR = 0.179, 95% CI = 0.012 - 2.593, P = 0.207) was confirmed to be the independent prognostic factor for DFS. Moreover, the Kaplan-Meier DFS curves showed that TS and ERCC1 mRNA levels were not significantly associated with the DFS (TS: P = 0.264; ERCC1: P = 0.484). CONCLUSION: The mRNA expression of ERCC1 and TS were not applicable to predict the DFS of Chinese stage II-III CRC patients receiving 5-FU and oxaliplatin based adjuvant chemotherapy

Activity and expression of ADP-glucose pyrophosphorylase during rhizome formation in lotus (Nelumbo nucifera Gaertn.)

Additional file 7: Figure S6. Comparison of NnAGPS against AGPS of other species

DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes

Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand. Existing methods for such scenes usually involve with spatial warping, geometric supervision from zero-shot normal or depth estimation, or scene division strategies, where the synthesized views are often blurry or fail to meet the requirement of efficient rendering. To address the above challenges, this paper presents a novel framework that learns a density space from the scenes to guide the construction of a point-based renderer, dubbed as DGNR (Density-Guided Neural Rendering). In DGNR, geometric priors are no longer needed, which can be intrinsically learned from the density space through volumetric rendering. Specifically, we make use of a differentiable renderer to synthesize images from the neural density features obtained from the learned density space. A density-based fusion module and geometric regularization are proposed to optimize the density space. By conducting experiments on a widely used autonomous driving dataset, we have validated the effectiveness of DGNR in synthesizing photorealistic driving scenes and achieving real-time capable rendering

Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores

Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of large amount of interactive feedback. This paper presents a new method that uses scores provided by humans, instead of pairwise preferences, to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by human negatively impact the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method on robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores, while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Increased FTO Expression Demethylates XBP1 m6A, Thereby Regulating XBP1-C/EBP&#945; and Promoting Hepatocellular Carcinoma Growth

Background: N6-methyladenosine (m6A) modification predominantly occurs in cancer cells mRNA. The X-box binding protein 1 (XBP1) influences hepatocellular carcinoma (HCC) progression, but its m6A regulatory mechanism remains unclear. Furthermore, the dysregulation of CCAAT/enhancer binding proteins alpha (C/EBP&#945;) in liver cancer is influenced by fat mass and obesity-associated protein (FTO) and acts downstream of XBP1. Therefore, this study aims to investigate how FTO catalyzes XBP1 m6A demethylation in HCC regulation. Methods: Initially, HepG2 cells were used to construct FTO overexpression and knockdown cells. The cells were divided into the FTO overexpression group (oe-FTO), overexpression control group (oe-NC), FTO knocked-down group (sh-FTO), and control of FTO knocked-down group (sh-NC) groups. RNA immunoprecipitation quantitative polymerase chain reaction (RIP-qPCR) was used to determine the interaction between FTO and XBP1. Furthermore, quantitative real time polymerase chain reaction (qRT-PCR) and Western blotting (WB) analysis were utilized to assess the expression levels of XBP1 and C/EBP&#945;. Additionally, subcutaneous transplanted tumor models were constructed and the tumor size, weight, and occurrence time were monitored. Moreover, Hematoxylin-Eosin (H&amp;E) staining was employed to observe the pathological changes of tumors. m6A immunoprecipitation (MeRIP)-qPCR was used to evaluate the XBP1 m6A modification levels. qRT-PCR and WB analysis were used to determine the expression levels of XBP1 and C/EBP&#945;. Results: We observed that FTO specifically binds to XBP1 mRNA in HCC cells, indicating a potential regulatory role at the RNA level. At the cellular level, compared to the sh-NC and oe-NC groups, the m6A methylation level of XBP1 was significantly increased in the sh-FTO group, while it was decreased in the oe-FTO group (p &lt; 0.05). Furthermore, the mRNA and protein expression levels of FTO, XBP1, and C/EBP&#945; were altered following FTO manipulation. Functional assays demonstrated that FTO overexpression promoted cell proliferation and invasion while inhibiting apoptosis. Conversely, FTO knockdown resulted in decreased cell proliferation and invasion and increased apoptosis. In a mouse xenograft tumor model, we observed rapidly growing tumors in the oe-FTO group, whereas sh-FTO tumors exhibited slower growth. Histological analysis revealed distinct patterns of tumor growth and damage. Collectively, these findings suggest that FTO plays a crucial role in HCC progression through its effects on XBP1 and C/EBP&#945;, providing insights into the potential therapeutic intervention of FTO in hepatocellular carcinoma. Conclusion: FTO overexpression leads to m6A demethylation of XBP1, thereby modulating the expression of XBP1-C/EBP&#945; and suppressing cell apoptosis. This, in turn, facilitates the progression of hepatocellular carcinoma by promoting cell growth

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline (http://www.ibeat.cloud), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners