Scene Matters: Model-based Deep Video Compression

Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed. These methods typically rely on signal prediction theory to enhance compression performance by designing high efficient intra and inter prediction strategies and compressing video frames one by one. In this paper, we propose a novel model-based video compression (MVC) framework that regards scenes as the fundamental units for video sequences. Our proposed MVC directly models the intensity variation of the entire video sequence in one scene, seeking non-redundant representations instead of reducing redundancy through spatio-temporal predictions. To achieve this, we employ implicit neural representation as our basic modeling architecture. To improve the efficiency of video modeling, we first propose context-related spatial positional embedding and frequency domain supervision in spatial context enhancement. For temporal correlation capturing, we design the scene flow constrain mechanism and temporal contrastive loss. Extensive experimental results demonstrate that our method achieves up to a 20\% bitrate reduction compared to the latest video coding standard H.266 and is more efficient in decoding than existing video coding strategies

Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertainty that can limit further advancements. This uncertainty is traditionally mitigated through different ensemble approaches. However, such ensembles are prohibitively costly with deep networks, which are already large in size. Our work focuses on pushing the performance limits of state-of-the-art methods on Gaussian denoising. We propose a model-agnostic approach for reducing epistemic uncertainty while using only a single pretrained network. We achieve this by tapping into the epistemic uncertainty through augmented and frequency-manipulated images to obtain denoised images with varying error. We propose an ensemble method with two decoupled attention paths, over the pixel domain and over that of our different manipulations, to learn the final fusion. Our results significantly improve over the state-of-the-art baselines and across varying noise levels.Comment: Code and models are publicly available on https://github.com/IVRL/DE

Proving Secure Properties of Cryptographic Protocols with Knowledge Based Approach

Cryptographic protocols have been widely used to protect communications over insecure network environments. Existing cryptographic protocols usually contain flaws. To analyze these protocols and find potential flaws in them, the secure properties of them need be studied in depth. This paper attempts to provide a new framework to analyze and prove the secure properties in these protocols. A number of predicates and action functions are used to model the network communication environment. Domain rules are given to describe the transitions of principals\u27 knowledge and belief states. An example of public key authentication protocols has been studied and analysed

Ameliorative Effect and Underlying Mechanisms of Total Triterpenoids from Psidium guajava Linn (Myrtaceae) Leaf on High-Fat Streptozotocin-induced Diabetic Peripheral Neuropathy in Rats

Purpose: To investigate whether the total triterpenoids extracted from Psidium Guajava leaves (TTPGL) attenuate the development of diabetic peripheral neuropathy in rats by regulating the NF-κB pathway of the inflammatory process and its signaling mediators.Methods: All the Sprague Dawley rats used were maintained in a clean environment on a 12 h light/12h dark cycle. High-fat feeding and intraperitoneal injection of 40 mg/kg streptozotocin (STZ) were used to induce diabetes in the rats. The rats were randomly divided into 5 groups: diabetic mellitus (DM) group; TTPGL - 30 group, TTPGL - 60 group and TTPGL - 120 group treated by intragastric administration (i.g) with 30, 100 and 120 mg/kg/day TTPGL, respectively. The well-established drug, rosiglitazone (RSG, 3 mg/k/d, i.g.), was used as positive control. Normal rats served as control group. Nerve conduction velocity and sensitive tests were measured on weeks 1, 4 and 8. After 8 weeks administration, expression of pro-inflammatory molecules (TNF - α, IL - 6 and iNOS) and tissue proteins (Akt, IKKα, and NF – κB - p65) were evaluated to assess biochemical changes.Results: Compared to DM group, TTPGL (especially 120 mg / kg dose) treatment improved (p < 0.05) physical functions and provided neuronal protection in high - fat/streptozotocin - induced peripheral neuropathy rats. We found that the expressions of several pro - inflammatory factors such as tumor necrosis factor - α (TNF - α), IL - 6 and inducible nitric oxide synthase (iNOS) were highly suppressed (p < 0.05 or p < 0.01) by TTPGL in sciatic nerve. Mechanism analysis indicated that the ameliorative effect of TTPGL, in part, is through suppression of the expression of pro - inflammatory cytokines by NF - κB pathway mediation.Conclusion: TTPGL offers a potential therapeutic approach for the treatment of diabetic peripheral neuropathy.Keywords: Triterpenoids, Psidium Guajava, Diabetic peripheral neuropathy, Pro inflammatory cytokines, NF-κB pathwa

Comparative Transcriptome Analysis Between Resistant and Susceptible Rice Cultivars Responding to Striped Stem Borer (SSB), Chilo suppressalis (Walker) Infestation

The striped stem borer, Chilo suppressalis (Walker), is a notorious pest of rice that causes large losses in China. Breeding and screening of resistance rice cultivars are effective strategies for C. suppressalis management. In this study, insect-resistant traits of 47 rice cultivars were investigated by C. suppressalis artificial infestation (AI) both in field and greenhouse experiments, using the susceptible (S) cultivar 1665 as a control. Results suggest that two rice cultivars, namely 1688 and 1654, are resistant (R) and moderately resistant (MR) to C. suppressalis, respectively. Then, a comparative transcriptome (RNA-Seq) was de novo assembled and differentially expressed genes (DEGs) with altered expression levels were investigated among cultivars 1688, 1654, and 1665, with or without C. suppressalis infestation for 24 h. A total of 2569 and 1861 genes were up-regulated, and 3852 and 1861 genes were down-regulated in cultivars 1688 and 1654, respectively after artificial infestation with C. suppressalis compared to the non-infested control (CK). For the susceptible cultivar 1665, a total of 882 genes were up-regulated and 3863 genes were down-regulated after artificial infestation with C. suppressalis compared to the CK. Twenty four DEGs belong to proteinase inhibitor, lectin and chitinase gene families; plant hormone signal transduction and plant-pathogen interaction pathways were selected as candidate genes to test their possible role in C. suppressalis resistance. RT-qPCR results revealed that 13 genes were significantly up-regulated and 8 were significantly down-regulated in the resistant cultivar 1688 with C. suppressalis artificial infestation (1688AI) compared to the CK. Three genes, LTPL164, LTPL151, and LOC Os11g32100, showed more than a 10-fold higher expression in 1688AI than in 1688CK, suggesting their potential role in insect resistance. Overall, our results provide an important foundation for further understanding the insect resistance mechanisms of selected resistant varieties that will help us to breed C. suppressalis resistant rice varieties

Distinct interactions between fronto-parietal and default mode networks in impaired consciousness

Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients (11 vegetative state/unresponsive wakefulness syndrome, VS/UWS, and 7 minimally conscious state, MCS) and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state

Ferromagnetic and insulating behavior in both half magnetic levitation and non-levitation LK-99 like samples

Finding materials exhibiting superconductivity at room temperature has long been one of the ultimate goals in physics and material science. Recently, room-temperature superconducting properties have been claimed in a copper substituted lead phosphate apatite (Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O, or called LK-99) [1-3]. Using a similar approach, we have prepared LK-99 like samples and confirmed the half-levitation behaviors in some small specimens under the influence of a magnet at room temperature. To examine the magnetic properties of our samples, we have performed systematic magnetization measurements on the as-grown LK-99-like samples, including the half-levitated and non-levitated samples. The magnetization measurements show the coexistence of soft-ferromagnetic and diamagnetic signals in both half-levitated and non-levitated samples. The electrical transport measurements on the as-grown LK-99-like samples including both half-levitated and non-levitated samples show an insulating behavior characterized by the increasing resistivity with the decreasing temperature

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

The genetic variants at the HLA-DRB1 gene are associated with primary IgA nephropathy in Han Chinese

BACKGROUND: Immunoglobulin A nephropathy (IgAN), an immune-complex-mediated glomerulonephritis defined immunohistologically by the presence of glomerular IgA deposits, is the most common primary glomerular disease worldwide and a significant cause of end-stage renal disease. Familial clustering of patients with IgAN suggests a genetic predisposition. METHODS: In this study, 192 patients with IgAN and 192 normal controls in the Sichuan cohort and 935 patients with IgAN and 2,103 normal controls in the Beijing cohort were investigated. HLA-DRB1*01–DRB1*10 specificities were genotyped by the PCR–SSP technique in both cohorts. Based on the HLA-DRB1*04-positive results, the subtypes of HLA-DRB1*04 were analyzed using sequencing-based typing (SBT) in 291 IgAN cases and 420 matched controls. RESULTS: The frequency of HLA-DRB1*04 in the IgAN group was significantly higher than that in the control group (0.129 vs. 0.092, P = 8.29 × 10(-5), odds ratio (OR) =1.381, 95% confidence interval (CI) 1.178–1.619). Other alleles at the HLA-DRB1 locus were observed with no significant differences between the case and control groups. The dominant alleles of the HLA-DRB1*04 subtypes were DRB1*0405 in both cohorts. The frequencies of HLA-DRB1*0405 and 0403 were significantly increased in the patients compared to healthy subjects. CONCLUSION: HLA-DRB1*04 was significantly associated with primary IgAN in Chinese population. This result implies that HLA-DRB1 gene plays a major role in primary IgAN

Clinical Study The Association of Weight Status with Physical Fitness among Chinese Children

Objective. To investigate the association of weight status with physical fitness among Chinese children. Methods. A total of 6929 children aged 6-12 years were selected from 15 primary schools of 5 provincial capital cities in eastern China. The height and fasting body weight were measured. The age-, sex-specific BMI WHO criteria was used to define underweight, overweight and obesity. Physical fitness parameters including standing broad jump, 50 m sprint, and 50 m * 8 shuttle run were tested. Results. The prevalence of underweight, overweight, and obesity was 3.1%, 14.9%, and 7.8%, respectively. Boys performed better than girls, and the older children performed better than their younger counterparts for all physical fitness tests. No significant difference in all three physical fitness tests were found between children with underweight and with normal weight, and they both performed better than their counterparts with overweight and obese in all three physical fitness tests. The likelihood of achieving good performance was much lower among overweight and obese children in comparison with their counterparts with normal weight (OR = 0.13-0.54). Conclusions. An inverse association of obesity with cardiorespiratory fitness, muscle explosive strength, and speed was identified among Chinese children