A multi-task learning CNN for image steganalysis

Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN

Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images

This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images. Compared to natural images, objects in aerial images show smaller sizes and larger quantities per image, increasing the difficulty of manual annotation. Meanwhile, the advanced SSOD technique can train superior detectors by leveraging limited labeled data and massive unlabeled data, saving annotation costs. However, as an understudied task in aerial images, SSOD suffers from a drastic performance drop when facing a large proportion of small objects. By analyzing the predictions between small and large objects, we identify three imbalance issues caused by the scale bias, i.e., pseudo-label imbalance, label assignment imbalance, and negative learning imbalance. To tackle these issues, we propose a novel Scale-discriminative Semi-Supervised Object Detection (S^3OD) learning pipeline for aerial images. In our S^3OD, three key components, Size-aware Adaptive Thresholding (SAT), Size-rebalanced Label Assignment (SLA), and Teacher-guided Negative Learning (TNL), are proposed to warrant scale unbiased learning. Specifically, SAT adaptively selects appropriate thresholds to filter pseudo-labels for objects at different scales. SLA balances positive samples of objects at different scales through resampling and reweighting. TNL alleviates the imbalance in negative samples by leveraging information generated by a teacher model. Extensive experiments conducted on the DOTA-v1.5 benchmark demonstrate the superiority of our proposed methods over state-of-the-art competitors. Codes will be released soon

Activation of AMPK sensitizes medulloblastoma to Vismodegib and overcomes Vismodegib‐resistance

Vismodegib, a Smoothened antagonist, is clinically approved for treatment of human basal cell carcinoma (BCC), in the clinical trials of medulloblastoma (MB) and other cancers. However, a significant proportion of these tumors fail to respond to Vismodegib after a period of treatment. Here, we find that AMPK agonists, A769662, and Metformin, can inhibit GLI1 activity and synergize with Vismodegib to suppress MB cell growth in vitro and in vivo. Furthermore, combination of AMPK agonists with Vismodegib is effective in overcoming Vismodegib‐resistant MB. This is the first report demonstrating that combining AMPK agonist (Metformin) and SHH pathway inhibitor (Vismodegib) confers synergy for MB treatment and provides an effective chemotherapeutic regimen that can be used to overcome resistance to Vismodegib in SHH‐driven cancers

Supplemental File.docx

the supplemental file of the artical"A high-concentrate diet induces an inflammatory response and oxidativestress and depresses milk fat synthesis in the mammary gland of dairy cows

GABAA Receptor/STEP61 Signaling Pathway May Be Involved in Emulsified Isoflurane Anesthesia in Rats

(1) Background: Emulsified isoflurane (EISO) is a type of intravenous anesthetic. How emulsified isoflurane works in the brain is still unclear. The aim of this study was to explore whether epigenetic mechanisms affect anesthesia and to evaluate the anesthetic effects of emulsified isoflurane in rats. (2) Methods: Rats were randomly divided into four groups (n = 8/group): The tail vein was injected with normal saline 0.1 mL·kg−1·min−1 for the control (Con) group, with intralipid for the fat emulsion (FE) group, with EISO at 60 mg·kg−1·min−1 for the high-concentration (HD) group, and 45 mg·kg−1·min−1 for the low-concentration (LD) group. The consciousness state, motor function of limbs, and response to nociceptive stimulus were observed after drug administration. (3) Results: Using real-time polymerase chain reaction (PCR) to assess the promoter methylation of ion channel proteins in the cerebral cortex of rats anesthetized by EISO, we demonstrated that the change in the promoters’ methylation of the coding genes for gamma-aminobutyric acid A receptor α1 subunit (GABAAα1), N-methyl-D-aspartate receptor subunit 1 (NMDAR1), and mu opioid receptor 1 (OPRM1) was accompanied by the change in messenger ribonucleic acid (mRNA) and protein expression by these genes. (4) Conclusion: These data suggest that the epigenetic factors’ modulation might offer a novel approach to explore the anesthetic mechanism of EISO.</jats:p