Correlation Between Blood System Impairment and Immune Index in Patients with Primary Sjögren's Syndrome

Objective: To analyze the relationship between blood system impairment and immune indexes, including autoantibody, immunoglobulin and lip gland biopsy in patients with primary Sjogren's syndrome (pSS). Methods: The serological data of patients with Sjogren's syndrome in hospital were collected and divided into hematological system impairment group and normal group. The incidence of hematological system damage in patients with pSS and its correlation with immune indexes were analyzed. Results: 123 patients with pSS were included in this study. There were 57 patients in the blood system involvement group (46%), in which the proportions of leucopenia, anemia and thrombocytopenia were in turn; 17. 89%; 33. 3%; 4. 88%. The antibodies in the blood system affected group were abundant, and the positive rates of anti SSA and Ro-52 antibodies were significantly higher than those in the normal group. The increase of serum IgG in pSS patients accounted for nearly 50%; The levels of serum IgG and complement C3 were significantly different from those in the control group. The positive rate of lip gland biopsy in pSS patients was more than 90%, and there was no significant difference between the two groups. Conclusion: Hematological system involvement was common in PSS patients. The positive rates of anti SSA and Ro-52 antibodies increased significantly, the level of IgG increased and the level of complement C3 decreased; However, there was no significant difference in blood system involvement between high IgG and low IgG groups; The positive rate of lip gland biopsy in PSS patients was more than 90%

beta-Actin messenger RNA localization and protein synthesis augment cell motility

In chicken embryo fibroblasts (CEFs), beta-actin mRNA localizes near an actin-rich region of cytoplasm specialized for motility, the lamellipodia. This localization is mediated by isoform-specific 3\u27-untranslated sequences (zipcodes) and can be inhibited by antizipcode oligodeoxynucleotides (ODNs) (Kislauskis, E.H., X.-C. Zhu, and R.H. Singer. 1994. J. Cell Biol. 127: 441-451). This inhibition of beta-actin mRNA localization resulted in the disruption of fibroblast polarity and, presumably, cell motility. To investigate the role of beta-actin mRNA in motility, we correlated time-lapse images of moving CEFs with the distribution of beta-actin mRNA in these cells. CEFs with localized beta-actin mRNA moved significantly further over the same time period than did CEFs with nonlocalized mRNA. Antizipcode ODN treatment reduced this cell translocation while control ODN treatments showed no effect. The temporal relationship of beta-actin mRNA localization to cell translocation was investigated using serum addition to serum-deprived cultures. beta-actin mRNA was not localized in serum-deprived cells but became localized within minutes after serum addition (Latham, V.M., E.H. Kislauskis, R.H. Singer, and A.F. Ross. 1994. J. Cell Biol. 126:1211-1219). Cell translocation increased over the next 90 min, and actin synthesis likewise increased. Puromycin reduced this cell translocation and blocked this induction in cytosolic actin content. The serum induction of cell movement was also inhibited by antizipcode ODNs. These observations support the hypothesis that beta-actin mRNA localization and consequent protein synthesis augment cell motility

TYK2 promotes malignant peripheral nerve sheath tumor progression through inhibition of cell death

BACKGROUND: Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive sarcomas that arise most commonly in the setting of the Neurofibromatosis Type 1 (NF1) cancer predisposition syndrome. Despite aggressive multimodality therapy, outcomes are dismal and most patients die within 5 years of diagnosis. Prior genomic studies in our laboratory identified tyrosine kinase 2 (TYK2) as a frequently mutated gene in MPNST. Herein, we explored the function of TYK2 in MPNST pathogenesis. METHODS: Immunohistochemistry was utilized to examine expression of TYK2 in MPNSTs and other sarcomas. To establish a role for TYK2 in MPNST pathogenesis, murine and human TYK2 knockdown and knockout cells were established using shRNA and CRISPR/Cas9 systems, respectively. RESULTS: We have demonstrated that TYK2 was highly expressed in the majority of human MPNSTs examined. Additionally, we demonstrated that knockdown of Tyk2/TYK2 in murine and human MPNST cells significantly increased cell death in vitro. These effects were accompanied by a decrease in the levels of activated Stats and Bcl-2 as well as an increase in the levels of Cleaved Caspase-3. In addition, Tyk2-KD cells demonstrated impaired growth in subcutaneous and metastasis models in vivo. CONCLUSION: Taken together, these data illustrate the importance of TYK2 in MPNST pathogenesis and suggest that the TYK2 pathway may be a potential therapeutic target for these deadly cancers

Astaxanthin protects against MPP+-induced oxidative stress in PC12 cells via the HO-1/NOX2 axis

BACKGROUND: Although the etiology of PD remains unclear, increasing evidence has shown that oxidative stress plays an important role in its pathogenesis and that of other neurodegenerative disorders. NOX2, a cytochrome subunit of NOX, transports electrons across the plasma membrane to generate ROS, leading to physiological and pathological processes. Heme oxygenase-1 (HO-1) can be rapidly induced by oxidative stress and other noxious stimuli in the brain or other tissues. Astaxanthin (ATX), a carotenoid with antioxidant properties, is 100–1000 times more effective than vitamin E. The present study investigated the neuroprotective effects of ATX on MPP(+)-induced oxidative stress in PC12 cells. RESULTS: MPP(+) significantly decreased MTT levels in a concentration-dependent manner. Hemin, SnPPIX and ATX didn’t exhibit any cytotoxic effects on PC12 cells. Pretreatment with ATX (5, 10, 20 μM), caused intracellular ROS production in the MPP(+) group to decrease by 13.06%, 22.13%, and 27.86%, respectively. MPP(+) increased NOX2, NRF2 and HO-1 protein expression compared with control (p < 0.05). Co-treatment with hemin or ATX suppressed NOX2 expression (p < 0.01), and greatly increased NRF2 and HO-1 expression (p < 0.01). MPP(+) treatment up-regulated both NOX2 (p < 0.01) and HO-1 (p < 0.01) mRNA levels. Co-treatment with hemin or ATX significantly increased HO-1 mRNA levels (p < 0.01), and decreased NOX2 mRNA levels (p < 0.01). MPP(+) increased NOX2 and HO-1 expression with considerable fluorescence extending out from the perinuclear region toward the periphery; this was attenuated by DPI. Co-treatment with hemin or ATX significantly up-regulated HO-1 expression and decreased NOX2 expression with considerable fluorescence intensity (stronger than the control and MPP(+) groups). CONCLUSIONS: ATX suppresses MPP(+)-induced oxidative stress in PC12 cells via the HO-1/NOX2 axis. ATX should be strongly considered as a potential neuroprotectant and adjuvant therapy for patients with Parkinson’s disease

Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion

3D mesh steganographic algorithms based on geometric modification are vulnerable to 3D steganalyzers. In this paper, we propose a highly adaptive 3D mesh steganography based on feature-preserving distortion (FPD), which guarantees high embedding capacity while effectively resisting 3D steganalysis. Specifically, we first transform vertex coordinates into integers and derive bitplanes from them to construct the embedding domain. To better measure the mesh distortion caused by message embedding, we propose FPD based on the most effective sub-features of the state-of-the-art steganalytic feature set. By improving and minimizing FPD, we can efficiently calculate the optimal vertex-changing distribution and simultaneously preserve mesh features, such as steganalytic and geometric features, to a certain extent. By virtue of the optimal distribution, we adopt the Q-layered syndrome trellis coding (STC) for practical message embedding. However, when Q varies, calculating bit modification probability (BMP) in each layer of Q-layered will be cumbersome. Hence, we contrapuntally design a universal and automatic BMP calculation approach. Extensive experimental results demonstrate that the proposed algorithm outperforms most state-of-the-art 3D mesh steganographic algorithms in terms of resisting 3D steganalysis.Comment: IEEE TVCG major revisio

A distributed anomaly detection system for in-vehicle network using HTM

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall

A sparse Bayesian learning method for structural equation model-based gene regulatory network inference

Gene regulatory networks (GRNs) are underlying networks identified by interactive relationships between genes. Reconstructing GRNs from massive genetic data is important for understanding gene functions and biological mechanism, and can provide effective service for medical treatment and genetic research. A series of artificial intelligence based methods have been proposed to infer GRNs from both gene expression data and genetic perturbations. The accuracy of such algorithms can be better than those models that just consider gene expression data. A structural equation model (SEM), which provides a systematic framework integrating both types of gene data conveniently, is a commonly used model for GRN inference. Considering the sparsity of GRNs, in this paper, we develop a novel sparse Bayesian inference algorithm based on Normal-Equation-Gamma (NEG) type hierarchical prior (BaNEG) to infer GRNs modeled with SEMs more accurately. First, we reparameterize an SEM as a linear type model by integrating the endogenous and exogenous variables; Then, a Bayesian adaptive lasso with a three-level NEG prior is applied to deduce the corresponding posterior mode and estimate the parameters. Simulations on synthetic data are run to compare the performance of BaNEG to some state-of-the-art algorithms, the results demonstrate that the proposed algorithm visibly outperforms the others. What’s more, BaNEG is applied to infer underlying GRNs from a real data set composed of 47 yeast genes from Saccharomyces cerevisiae to discover potential relationships between genes

BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning

Studying backdoor attacks is valuable for model copyright protection and enhancing defenses. While existing backdoor attacks have successfully infected multimodal contrastive learning models such as CLIP, they can be easily countered by specialized backdoor defenses for MCL models. This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses and introduces the \emph{\toolns} attack, which is resistant to backdoor detection and model fine-tuning defenses. To achieve this, we draw motivations from the perspective of the Bayesian rule and propose a dual-embedding guided framework for backdoor attacks. Specifically, we ensure that visual trigger patterns approximate the textual target semantics in the embedding space, making it challenging to detect the subtle parameter variations induced by backdoor learning on such natural trigger patterns. Additionally, we optimize the visual trigger patterns to align the poisoned samples with target vision features in order to hinder the backdoor unlearning through clean fine-tuning. Extensive experiments demonstrate that our attack significantly outperforms state-of-the-art baselines (+45.3% ASR) in the presence of SoTA backdoor defenses, rendering these mitigation and detection strategies virtually ineffective. Furthermore, our approach effectively attacks some more rigorous scenarios like downstream tasks. We believe that this paper raises awareness regarding the potential threats associated with the practical application of multimodal contrastive learning and encourages the development of more robust defense mechanisms.Comment: The paper lacks some work that needs to be cite

Rethinking Data Augmentation in Knowledge Distillation for Object Detection

Knowledge distillation (KD) has shown its effectiveness for object detection, where it trains a compact object detector under the supervision of both AI knowledge (teacher detector) and human knowledge (human expert). However, existing studies treat the AI knowledge and human knowledge consistently and adopt a uniform data augmentation strategy during learning, which would lead to the biased learning of multi-scale objects and insufficient learning for the teacher detector causing unsatisfactory distillation performance. To tackle these problems, we propose the sample-specific data augmentation and adversarial feature augmentation. Firstly, to mitigate the impact incurred by multi-scale objects, we propose an adaptive data augmentation based on our observations from the Fourier perspective. Secondly, we propose a feature augmentation method based on adversarial examples for better mimicking AI knowledge to make up for the insufficient information mining of the teacher detector. Furthermore, our proposed method is unified and easily extended to other KD methods. Extensive experiments demonstrate the effectiveness of our framework and improve the performance of state-of-the-art methods in one-stage and two-stage detectors, bringing at most 0.5 mAP gains.Comment: 8 pages, 5 figure