A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are available at https://github.com/ysyscool/DINe

CBP/p300 bromodomain: new promising epigenetic target

CREB (cAMP responsive element binding protein) binding protein (CBP) and adenovirus E1A-associated 300 kDa protein (p300) are histone acetyltransferases, which are necessary for multiple cellular processes. Thus, CBP/p300 are promising potential antitumor targets. To date, despite various small molecule inhibitors of CBP/p300 bromodomain (BRD) having been reported, no specific inhibitor was approved by U.S. Food and Drug Administration (FDA). In this review, we described the discovery, optimization, binding mode evaluation, selectivity and potency evaluation, and therapeutic opportunities of our CBP/p300 bromodomain inhibitors, aiming to inspire new inhibitor design and advance drug discovery research in this field. One video presents the development of CBP/p300 bromodomain inhibitors

Case Report: Recurrent Autoimmune Hypoglycemia Induced by Non-Hypoglycemic Medications

We present a case of recurrent autoimmune hypoglycemia induced by non-hypoglycemic agents. We review reported cases of autoimmune hypoglycemia related to non-hypoglycemic agents, and discuss the effects of different detection methods for insulin autoantibodies on the results obtained. We aim to provide information for clinicians and a warning for medication usage. Considering the increasing number of clopidogrel-induced AIH cases and the hypoglycemia-induced increase in the risk of cardiovascular events, we recommend that cardiovascular disease patients being treated with clopidogrel be informed of this rare side effect and that clinicians be vigilant for the possibility of autoimmune hypoglycemia in this patient population

ROR-γ drives androgen receptor expression and represents a therapeutic target in castration-resistant prostate cancer.

The androgen receptor (AR) is overexpressed and hyperactivated in human castration-resistant prostate cancer (CRPC). However, the determinants of AR overexpression in CRPC are poorly defined. Here we show that retinoic acid receptor-related orphan receptor γ (ROR-γ) is overexpressed and amplified in metastatic CRPC tumors, and that ROR-γ drives AR expression in the tumors. ROR-γ recruits nuclear receptor coactivator 1 and 3 (NCOA1 and NCOA3, also known as SRC-1 and SRC-3) to an AR-ROR response element (RORE) to stimulate AR gene transcription. ROR-γ antagonists suppress the expression of both AR and its variant AR-V7 in prostate cancer (PCa) cell lines and tumors. ROR-γ antagonists also markedly diminish genome-wide AR binding, H3K27ac abundance and expression of the AR target gene network. Finally, ROR-γ antagonists suppressed tumor growth in multiple AR-expressing, but not AR-negative, xenograft PCa models, and they effectively sensitized CRPC tumors to enzalutamide, without overt toxicity, in mice. Taken together, these results establish ROR-γ as a key player in CRPC by acting upstream of AR and as a potential therapeutic target for advanced PCa

Global land surface temperature influenced by vegetation cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 �g/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72�N and 48�S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature

Electromagnetic Pigging System Based on Sandwich Differential Planar Coil

In-pipeline inspection is an important precontrol method to ensure the safety of oil and gas pipeline transportation. This article proposes an electromagnetic in-pipe detector based on passive resonance-enhanced differential planar coils to detect defects on the inner surface of pipes. Both qualitative and quantitative analyses of pipeline defects and damage are developed. The introduction of passive resonant coils is shown to significantly improve the detection capability of the sensor. This is coupled with the establishment of a theoretical derivation model of the proposed structure. The hardware platform of the laboratory system has been built, and an eddy current internal detector suitable for 8-in-diameter pipes is developed and integrated into the system. Numerical simulations and experimental verifications on flat defects and pipe defects have been undertaken. The obtained results have shown that the real defects have been correctly detected, and the system is effective, reliable, and efficient