A One-dimensional HEVC video steganalysis method using the Optimality of Predicted Motion Vectors

Among steganalysis techniques, detection against motion vector (MV) domain-based video steganography in High Efficiency Video Coding (HEVC) standard remains a hot and challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis feature based on the optimality of predicted MVs with a dimension of one. Firstly, we point out that the motion vector prediction (MVP) of the prediction unit (PU) encoded using the Advanced Motion Vector Prediction (AMVP) technique satisfies the local optimality in the cover video. Secondly, we analyze that in HEVC video, message embedding either using MVP index or motion vector differences (MVD) may destroy the above optimality of MVP. And then, we define the optimal rate of MVP in HEVC video as a steganalysis feature. Finally, we conduct steganalysis detection experiments on two general datasets for three popular steganography methods and compare the performance with four state-of-the-art steganalysis methods. The experimental results show that the proposed optimal rate of MVP for all cover videos is 100\%, while the optimal rate of MVP for all stego videos is less than 100\%. Therefore, the proposed steganography scheme can accurately distinguish between cover videos and stego videos, and it is efficiently applied to practical scenarios with no model training and low computational complexity.Comment: Submitted to TCSV

The Realizations of Steganography in Encrypted Domain

With the popularization and application of privacy protection technologies in cloud service and social network, ciphertext has been gradually becoming a common platform for public to exchange data. Under the cover of such a plat-form, we propose steganography in encrypted domain (SIED) in this paper to re-alize a novel method to realize secret communication Based on Simmons' model of prisoners' problems, we discuss the application scenarios of SIED. According to the different accesses to the encryption key and decryption key for secret mes-sage sender or receiver, the application modes of SIED are classified into four modes. To analyze the security requirments of SIED, four levels of steganalysis attacks are introduced based on the prior knowledge about the steganography system that the attacker is assumed to obtain in advance. Four levels of security standards of SIED are defined correspondingly. Based on the existing reversible data hiding techniques, we give four schemes of SIED as practical instances with different security levels. By analyzing the embedding and extraction characteris-tics of each instance, their SIED modes, application frameworks and security lev-els are discussed in detail

Two-phase Framework for Automatic Kidney and Kidney Tumor Segmentation

Precise segmentation of kidney and kidney tumor is essential for computer aided diagnosis. Considering the diverse shape, low contrast with surrounding tissues and small tumor volumes, it’s still challenging to segment kidney and kidney tumor accurately. To solve this problem, we proposed a two-phase framework for automatic segmentation of kidney and kidney tumor. In the first phase, the approximate localization of kidney and kidney tumor is predicted by a coarse segmentation network to overcome GPU memory limitation. Taking the coarse prediction from first phase as input, the region of kidney and tumor is cropped and trained in an enhanced two-stage network to achieve a fine-grained segmentation result in the second phase. Besides, our network prediction flows into multiple post-processing steps to remove false positive such as cyst by taking medical prior knowledge into consideration. Data argumentation through registration and ensemble models are used to further enhance performance

Unraveling immunotherapeutic targets for endometriosis: a transcriptomic and single-cell analysis

BackgroundEndometriosis (EMs), a common gynecological disorder, adversely affects the quality of life of females. The pathogenesis of EMs has not been elucidated and the diagnostic methods for EMs have limitations. This study aimed to identify potential molecular biomarkers for the diagnosis and treatment of EMs.MethodsDifferential gene expression (DEG) and functional enrichment analyses were performed using the R language. WGCNA, Random Forest, SVM-REF and LASSO methods were used to identify core immune genes. The CIBERSORT algorithm was then used to analyse the differences in immune cell infiltration and to explore the correlation between immune cells and core genes. In addition, the extent of immune cell infiltration and the expression of immune core genes were investigated using single-cell RNA (scRNA) sequencing data. Finally, we performed molecular docking of three core genes with dienogest and goserelin to screen for potential drug targets.ResultsDEGs enriched in immune response, angiogenesis and estrogen processes. CXCL12, ROBO3 and SCG2 were identified as core immune genes. RT-PCR confirmed that the expression of CXCL12 and SCG2 was significantly upregulated in 12Z cells compared to hESCs cells. ROC curves showed high diagnostic value for these genes. Abnormal immune cell distribution, particularly increased macrophages, was observed in endometriosis. CXCL12, ROBO3 and SCG2 correlated with immune cell levels. Molecular docking suggested their potential as drug targets.ConclusionThis study investigated the correlation between EMs and the immune system and identified potential immune-related biomarkers. These findings provided valuable insights for developing clinically relevant diagnostic and therapeutic strategies for EMs

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the extant and anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) is likely to have greater value as an important diagnostic tool to inform public health. As the widespread adoption of WWS is relatively new at the scale employed for COVID-19, interpretation of data, including the relationship to clinical cases, has yet to be standardized. An in-depth analysis of the metrics derived from WWS is required for public health units/agencies to interpret and utilize WWS-acquired data effectively and efficiently. In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven different cities in Canada over periods ranging from 8 to 21 months. Significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing in these communities. The WC ratio decreased significantly during the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community’s wastewater (40-60% allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community’s wastewater (40-60% allelic proportion). Finally, a rapid and significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant’s greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when vaccine-induced community immunity was high. The WC ratio, used as an additional monitoring metric, complements clinical case counts and wastewater signals as individual metrics in its ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.Ontario's Ministry of Environment, Conservation and Parks||Alberta Healt