Phosphorylation of MITF by AKT affects its downstream targets and causes TP53-dependent cell senescence

Microphthalmia-associated transcription factor (MITF) plays a crucial role in the melanogenesis and proliferation of melanocytes that is dependent on its abundance and modification. Here, we report that epidermal growth factor (EGF) induces senescence and cyclin-dependent kinase inhibitor 1A (CDKN1A) expression that is related to MITF. We found that MITF could bind TP53 to regulate CDKN1A. Furthermore, the interaction between MITF and TP53 is dependent on AKT activity. We found that AKT phosphorylates MITF at S510. Phosphorylated MITF S510 enhances its affinity to TP53 and promotes CDKN1A expression. Meanwhile, the unphosphorylative MITF promotes TYR expression. The levels of p-MITF-S510 are low in 90% human melanoma samples. Thus the level of p-MITF-S510 could be a possible diagnostic marker for melanoma. Our findings reveal a mechanism for regulating MITF functions in response to EGF stimulation and suggest a possible implementation for preventing the over proliferation of melanoma cells.published_or_final_versio

Learning image quality assessment by reinforcing task amenable data selection

In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation. We propose to train simultaneously two neural networks for image selection and a target task using reinforcement learning. A controller network learns an image selection policy by maximising an accumulated reward based on the target task performance on the controller-selected validation set, whilst the target task predictor is optimised using the training set. The trained controller is therefore able to reject those images that lead to poor accuracy in the target task. In this work, we show that the controller-predicted image quality can be significantly different from the task-specific image quality labels that are manually defined by humans. Furthermore, we demonstrate that it is possible to learn effective image quality assessment without using a ``clean'' validation set, thereby avoiding the requirement for human labelling of images with respect to their amenability for the task. Using $6712$, labelled and segmented, clinical ultrasound images from $259$ patients, experimental results on holdout data show that the proposed image quality assessment achieved a mean classification accuracy of $0.94\pm0.01$ and a mean segmentation Dice of $0.89\pm0.02$, by discarding $5\%$ and $15\%$ of the acquired images, respectively. The significantly improved performance was observed for both tested tasks, compared with the respective $0.90\pm0.01$ and $0.82\pm0.02$ from networks without considering task amenability. This enables image quality feedback during real-time ultrasound acquisition among many other medical imaging applications

Adaptable image quality assessment using meta-reinforcement learning of task amenability

The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability, an IQA agent is trained using reinforcement learning (RL) with a simultaneously optimised task predictor, such as a classification or segmentation neural network. In this work, we develop transfer learning or adaptation strategies to increase the adaptability of both the IQA agent and the task predictor so that they are less dependent on high-quality, expert-labelled training data. The proposed transfer learning strategy re-formulates the original RL problem for task amenability in a meta-reinforcement learning (meta-RL) framework. The resulting algorithm facilitates efficient adaptation of the agent to different definitions of image quality, each with its own Markov decision process environment including different images, labels and an adaptable task predictor. Our work demonstrates that the IQA agents pre-trained on non-expert task labels can be adapted to predict task amenability as defined by expert task labels, using only a small set of expert labels. Using 6644 clinical ultrasound images from 249 prostate cancer patients, our results for image classification and segmentation tasks show that the proposed IQA method can be adapted using data with as few as respective 19.7 % % and 29.6 % % expert-reviewed consensus labels and still achieve comparable IQA and task performance, which would otherwise require a training dataset with 100 % % expert labels

Differencing techniques in semi-parametric panel data varying coefficient models with fixed effects: a Monte Carlo study.

Recently, some new techniques have been proposed for the estimation of semi-parametric fixed effects varying coefficient panel data models. These new techniques fall within the class of the so-called differencing estimators. In particular, we consider first-differences and within local linear regression estimators. Analyzing their asymptotic properties it turns out that, keeping the same order of magnitude for the bias term, these estimators exhibit different asymptotic bounds for the variance. In both cases, the consequences are suboptimal non-parametric rates of convergence. In order to solve this problem, by exploiting the additive structure of this model, a one-step backfitting algorithm is proposed. Under fairly general conditions, it turns out that the resulting estimators show optimal rates of convergence and exhibit the oracle efficiency property. Since both estimators are asymptotically equivalent, it is of interest to analyze their behavior in small sample sizes. In a fully parametric context, it is well-known that, under strict exogeneity assumptions the performance of both first-differences and within estimators is going to depend on the stochastic structure of the idiosyncratic random errors. However, in the non-parametric setting, apart from the previous issues other factors such as dimensionality or sample size are of great interest. In particular, we would be interested in learning about their relative average mean square error under different scenarios. The simulation results basically confirm the theoretical findings for both local linear regression and one-step backfitting estimators. However, we have found out that within estimators are rather sensitive to the size of number of time observations

On the nuclear obscuration of H2O maser galaxies

To shed light onto the circumnuclear environment of 22 GHz H2O maser galaxies, we have analyzed some of their multi-wavelength properties, including the far infrared luminosity (FIR), the luminosity of the [O III]\lambda5007 emission line, the nuclear X-ray luminosity, and the equivalent width of the neutral iron Ka emission line (EW (Ka)). Our statistical analysis includes a total of 85 sources, most of them harboring an active galactic nucleus (AGN). There are strong anti-correlations between EW (Ka) and two "optical thickness parameters", i.e. the ratios of the X-ray luminosity versus the presumably more isotropically radiated [O III] and far infrared (FIR) luminosities. Based on these anti-correlations, a set of quantitative criteria, EW (Ka) > 300eV, L_{2-10keV} 600L_{2-10keV} can be established for Compton-thick nuclear regions. 18 H2O maser galaxies belong to this category. There are no obvious correlations between the EW (Ka), the [O III] luminosity and the isotropic H2O maser luminosity. When comparing samples of Seyfert 2s with and without detected H2O maser lines, there seem to exist differences in EW (Ka) and the fraction of Compton-thick nuclei. This should be studied further. For AGN masers alone, there is no obvious correlation between FIR and H2O maser luminosities. However, including masers associated with star forming regions, a linear correlation is revealed. Overall, the extragalactic FIR-H2O data agree with the corresponding relation for Galactic maser sources, extrapolated by several orders of magnitude to higher luminosities.Comment: 32 pages with 5 figures and 2 tables, accepted for publication in Ap

Mitochondrial fission determines cisplatin sensitivity in tongue squamous cell carcinoma through the BRCA1-miR-593-5p-MFF axis

Cisplatin has been widely employed as a cornerstone chemotherapy treatment for a wide spectrum of solid neoplasms; increasing tumor responsiveness to cisplatin has been a topic of interest for the past 30 years. Strong evidence has indicated that mitochondrial fission participates in the regulation of apoptosis in many diseases; however, whether mitochondrial fission regulates cisplatin sensitivity remains poorly understood. Here, we show that MFF mediated mitochondrial fission and apoptosis in tongue squamous cell carcinoma (TSCC) cells after cisplatin treatment and that miR-593-5p was downregulated in this process. miR-593-5p attenuated mitochondrial fission and cisplatin sensitivity by targeting the 3’ untranslated region sequence of MFF and inhibiting its translation. In exploring the underlying mechanism of miR-593-5p downregulation, we observed that BRCA1 transactivated miR-593-5p expression and attenuated cisplatin sensitivity in vitro. The BRCA1-miR-593-5p-MFF axis also affected cisplatin sensitivity in vivo. Importantly, in a retrospective analysis of multiple centers, we further found that the BRCA1-miR-593-5p-MFF axis was significantly associated with cisplatin sensitivity and the survival of patients with TSCC. Together, our data reveal a model for mitochondrial fission regulation at the transcriptional and post-transcriptional levels; we also reveal a new pathway for BRCA1 in determining cisplatin sensitivity through the mitochondrial fission program.published_or_final_versio

The DArk Matter Particle Explorer mission

The DArk Matter Particle Explorer (DAMPE), one of the four scientific space science missions within the framework of the Strategic Pioneer Program on Space Science of the Chinese Academy of Sciences, is a general purpose high energy cosmic-ray and gamma-ray observatory, which was successfully launched on December 17th, 2015 from the Jiuquan Satellite Launch Center. The DAMPE scientific objectives include the study of galactic cosmic rays up to $\sim 10$ TeV and hundreds of TeV for electrons/gammas and nuclei respectively, and the search for dark matter signatures in their spectra. In this paper we illustrate the layout of the DAMPE instrument, and discuss the results of beam tests and calibrations performed on ground. Finally we present the expected performance in space and give an overview of the mission key scientific goals.Comment: 45 pages, including 29 figures and 6 tables. Published in Astropart. Phy