A novel radar signal recognition method based on a deep restricted Boltzmann machine

Radar signal recognition is of great importance in the field of electronic intelligence reconnaissance. To deal with the problem of parameter complexity and agility of multi-function radars in radar signal recognition, a new model called radar signal recognition based on the deep restricted Boltzmann machine (RSRDRBM) is proposed to extract the feature parameters and recognize the radar emitter. This model is composed of multiple restricted Boltzmann machines. A bottom-up hierarchical unsupervised learning is used to obtain the initial parameters, and then the traditional back propagation (BP) algorithm is conducted to fine-tune the network parameters. Softmax algorithm is used to classify the results at last. Simulation and comparison experiments show that the proposed method has the ability of extracting the parameter features and recognizing the radar emitters, and it is characterized with strong robustness as well as highly correct recognition rate

Sequential Wnt Agonist then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis

Tissue fibrosis compromises organ function and occurs as a potential long-term outcome in response to acute tissue injuries. Currently, lack of mechanistic understanding prevents effective prevention and treatment of the progression from acute injury to fibrosis. Here, we combined quantitative experimental studies with a mouse kidney injury model and a computational approach to determine how the physiological consequences are determined by the severity of ischemia injury, and to identify how to manipulate Wnt signaling to accelerate repair of ischemic tissue damage while minimizing fibrosis. The study reveals that Wnt-mediated memory of prior injury contributes to fibrosis progression, and ischemic preconditioning reduces the risk of death but increases the risk of fibrosis. Furthermore, we validated the prediction that sequential combination therapy of initial treatment with a Wnt agonist followed by treatment with a Wnt antagonist can reduce both the risk of death and fibrosis in response to acute injuries

RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery

Collected and annotated datasets, which are obtained through extensive efforts, are effective for training Deep Neural Network (DNN) models. However, these datasets are susceptible to be misused by unauthorized users, resulting in infringement of Intellectual Property (IP) rights owned by the dataset creators. Reversible Adversarial Exsamples (RAE) can help to solve the issues of IP protection for datasets. RAEs are adversarial perturbed images that can be restored to the original. As a cutting-edge approach, RAE scheme can serve the purposes of preventing unauthorized users from engaging in malicious model training, as well as ensuring the legitimate usage of authorized users. Nevertheless, in the existing work, RAEs still rely on the embedded auxiliary information for restoration, which may compromise their adversarial abilities. In this paper, a novel self-generation and self-recovery method, named as RAEDiff, is introduced for generating RAEs based on a Denoising Diffusion Probabilistic Models (DDPM). It diffuses datasets into a Biased Gaussian Distribution (BGD) and utilizes the prior knowledge of the DDPM for generating and recovering RAEs. The experimental results demonstrate that RAEDiff effectively self-generates adversarial perturbations for DNN models, including Artificial Intelligence Generated Content (AIGC) models, while also exhibiting significant self-recovery capabilities

Overexpression of an isoform of AML1 in acute leukemia and its potential role in leukemogenesis

AML1/RUNX1 is a critical transcription factor in hematopoietic cell differentiation and proliferation. From the _AML1_ gene, at least three isoforms, _AML1a_, _AML1b_ and _AML1c_, are produced through alternative splicing. AML1a interferes with the function of AML1b/1c, which are often called AML1. In the current study, we found a higher expression level of _AML1a_ in ALL patients in comparison to the controls. Additionally, AML1a represses transcription from promotor of macrophage-colony simulating factor receptor (M-CSFR) mediated by AML1b, indicating that AML1a antagonized the effect of AML1b. In order to investigate the role of _AML1a_ in hematopoiesis and leukemogenesis _in vivo_, bone marrow mononuclear cells (BMMNCs) from mice were transduced with AML1a and transplanted into lethally irradiated mice, which develop lymphoblastic leukemia after transplantation. Taken together, these results indicate that overexpression of AML1a may be an important contributing factor to leukemogenesis

Identification of a nuclear localization motif in the serine/arginine protein kinase PSRPK of physarum polycephalum

<p>Abstract</p> <p>Background</p> <p>Serine/arginine (SR) protein-specific kinases (SRPKs) are conserved in a wide range of organisms, from humans to yeast. Studies showed that SRPKs can regulate the nuclear import of SR proteins in cytoplasm, and regulate the sub-localization of SR proteins in the nucleus. But no nuclear localization signal (NLS) of SRPKs was found. We isolated an SRPK-like protein PSRPK (GenBank accession No. <ext-link ext-link-id="DQ140379" ext-link-type="gen">DQ140379</ext-link>) from <it>Physarum polycephalum </it>previously, and identified a NLS of PSRPK in this study.</p> <p>Results</p> <p>We carried out a thorough molecular dissection of the different domains of the PSRPK protein involved in its nuclear localization. By truncation of PSRPK protein, deletion of and single amino acid substitution in a putative NLS and transfection of mammalian cells, we observed the distribution of PSRPK fluorescent fusion protein in mammalian cells using confocal microscopy and found that the protein was mainly accumulated in the nucleus; this indicated that the motif contained a nuclear localization signal (NLS). Further investigation with truncated PSPRK peptides showed that the NLS (³¹⁸PKKGDKYDKTD³²⁸) was localized in the alkaline Ω-loop of a helix-loop-helix motif (HLHM) of the C-terminal conserved domain. If the ³¹⁸PKKGDK³²²sequence was deleted from the loop or K³²⁰was mutated to T³²⁰, the PSRPK fluorescent fusion protein could not enter and accumulate in the nucleus.</p> <p>Conclusion</p> <p>This study demonstrated that the ³¹⁸PKKGDKYDKTD³²⁸peptides localized in the C-terminal conserved domain of PSRPK with the Ω-loop structure could play a crucial role in the NLS function of PSRPK.</p

Therapeutic potential of tumor treating fields for malignant brain tumors

BACKGROUND: Malignant brain tumors are among the most threatening diseases of the central nervous system, and despite increasingly updated treatments, the prognosis has not been improved. Tumor treating fields (TTFields) are an emerging approach in cancer treatment using intermediate-frequency and low-intensity electric field and can lead to the development of novel therapeutic options. RECENT FINDINGS: A series of biological processes induced by TTFields to exert anti-cancer effects have been identified. Recent studies have shown that TTFields can alter the bioelectrical state of macromolecules and organelles involved in cancer biology. Massive alterations in cancer cell proteomics and transcriptomics caused by TTFields were related to cell biological processes as well as multiple organelle structures and activities. This review addresses the mechanisms of TTFields and recent advances in the application of TTFields therapy in malignant brain tumors, especially in glioblastoma (GBM). CONCLUSIONS: As a novel therapeutic strategy, TTFields have shown promising results in many clinical trials, especially in GBM, and continue to evolve. A growing number of patients with malignant brain tumors are being enrolled in ongoing clinical studies demonstrating that TTFields-based combination therapies can improve treatment outcomes