Robust Symbol-Level Precoding for Massive MIMO Communication Under Channel Aging

This paper investigates the robust design of symbol-level precoding (SLP) for multiuser multiple-input multiple-output (MIMO) downlink transmission with imperfect channel state information (CSI) caused by channel aging. By utilizing the a posteriori channel model based on the widely adopted jointly correlated channel model, the imperfect CSI is modeled as the statistical CSI incorporating the channel mean and channel variance information with spatial correlation. With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing and minimum mean square error (MMSE) problems for robust SLP design. The former targets to maximize the minimum SINR across users, while the latter minimizes the mean square error between the received signal and the target constellation point. When it comes to massive MIMO scenarios, the increment in the number of antennas poses a computational complexity challenge, limiting the deployment of SLP schemes. To address such a challenge, we simplify the objective function of the SINR balancing problem and further derive a closed-form SLP scheme. Besides, by approximating the matrix involved in the computation, we modify the proposed algorithm and develop an MMSE-based SLP scheme with lower computation complexity. Simulation results confirm the superiority of the proposed schemes over the state-of-the-art SLP schemes

Coping with Change: Learning Invariant and Minimum Sufficient Representations for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species. Previous studies always implicitly assume that the training and test data have the same underlying distributions, and that features extracted by modern backbone architectures remain discriminative and generalize well to unseen test data. However, we empirically justify that these conditions are not always true on benchmark datasets. To this end, we combine the merits of invariant risk minimization (IRM) and information bottleneck (IB) principle to learn invariant and minimum sufficient (IMS) representations for FGVC, such that the overall model can always discover the most succinct and consistent fine-grained features. We apply the matrix-based R{\'e}nyi's $\alpha$-order entropy to simplify and stabilize the training of IB; we also design a ``soft" environment partition scheme to make IRM applicable to FGVC task. To the best of our knowledge, we are the first to address the problem of FGVC from a generalization perspective and develop a new information-theoretic solution accordingly. Extensive experiments demonstrate the consistent performance gain offered by our IMS.Comment: Manuscript accepted by CVIU, code is available at Githu

The role of tumor-associated macrophages in glioma cohort: through both traditional RNA sequencing and single cell RNA sequencing

Gliomas are the leading cause in more than 50% of malignant brain tumor cases. Prognoses, recurrences, and mortality are usually poor for gliomas that have malignant features. In gliomas, there are four grades, with grade IV gliomas known as glioblastomas (GBM). Currently, the primary methods employed for glioma treatment include surgical removal, followed by chemotherapy after the operation, and targeted therapy. However, the outcomes of these treatments are unsatisfactory. Gliomas have a high number of tumor-associated macrophages (TAM), which consist of brain microglia and macrophages, making them the predominant cell group in the tumor microenvironment (TME). The glioma cohort was analyzed using single-cell RNA sequencing to quantify the genes related to TAMs in this study. Furthermore, the ssGSEA analysis was utilized to assess the TAM-associated score in the glioma group. In the glioma cohort, we have successfully developed a prognostic model consisting of 12 genes, which is derived from the TAM-associated genes. The glioma cohort demonstrated the predictive significance of the TAM-based risk model through survival analysis and time-dependent ROC curve. Furthermore, the correlation analysis revealed the significance of the TAM-based risk model in the application of immunotherapy for individuals diagnosed with GBM. Ultimately, the additional examination unveiled the prognostic significance of PTX3 in the glioma group, establishing it as the utmost valuable prognostic indicator in patients with GBM. The PCR assay revealed the PTX3 is significantly up-regulated in GBM cohort. Additionally, the assessment of cell growth further confirms the involvement of PTX3 in the GBM group. The analysis of cell proliferation showed that the increased expression of PTX3 enhanced the ability of glioma cells to proliferate. The prognosis of glioblastomas and glioma is influenced by the proliferation of tumor-associated macrophages

Chemical Genetic Analysis and Functional Characterization of Staphylococcal Wall Teichoic Acid 2-Epimerases Reveals Unconventional Antibiotic Drug Targets

Here we describe a chemical biology strategy performed in Staphylococcus aureus and Staphylococcus epidermidis to identify MnaA, a 2-epimerase that we demonstrate interconverts UDP-GlcNAc and UDP-ManNAc to modulate substrate levels of TarO and TarA wall teichoic acid (WTA) biosynthesis enzymes. Genetic inactivation of mnaA results in complete loss of WTA and dramatic in vitro β-lactam hypersensitivity in methicillin-resistant S. aureus (MRSA) and S. epidermidis (MRSE). Likewise, the β-lactam antibiotic imipenem exhibits restored bactericidal activity against mnaA mutants in vitro and concomitant efficacy against 2-epimerase defective strains in a mouse thigh model of MRSA and MRSE infection. Interestingly, whereas MnaA serves as the sole 2-epimerase required for WTA biosynthesis in S. epidermidis, MnaA and Cap5P provide compensatory WTA functional roles in S. aureus. We also demonstrate that MnaA and other enzymes of WTA biosynthesis are required for biofilm formation in MRSA and MRSE. We further determine the 1.9Å crystal structure of S. aureus MnaA and identify critical residues for enzymatic dimerization, stability, and substrate binding. Finally, the natural product antibiotic tunicamycin is shown to physically bind MnaA and Cap5P and inhibit 2-epimerase activity, demonstrating that it inhibits a previously unanticipated step in WTA biosynthesis. In summary, MnaA serves as a new Staphylococcal antibiotic target with cognate inhibitors predicted to possess dual therapeutic benefit: as combination agents to restore β-lactam efficacy against MRSA and MRSE and as non-bioactive prophylactic agents to prevent Staphylococcal biofilm formation.publishe

A Novel Image-Encryption Scheme Based on a Non-Linear Cross-Coupled Hyperchaotic System with the Dynamic Correlation of Plaintext Pixels

Based on a logistic map and Feigenbaum map, we proposed a logistic Feigenbaum non-linear cross-coupled hyperchaotic map (LF-NCHM) model. Experimental verification showed that the system is a hyperchaotic system. Compared with the existing cross-coupled mapping, LF-NCHM demonstrated a wider hyperchaotic range, better ergodicity and richer dynamic behavior. A hyperchaotic sequence with the same number of image pixels was generated by LF-NCHM, and a novel image-encryption algorithm with permutation that is dynamically related to plaintext pixels was proposed. In the scrambling stage, the position of the first scrambled pixel was related to the sum of the plaintext pixel values, and the positions of the remaining scrambled pixels were related to the pixel values after the previous scrambling. The scrambling operation also had a certain diffusion effect. In the diffusion phase, using the same chaotic sequence as in the scrambling stage increased the usage rate of the hyperchaotic sequence and improved the calculation efficiency of the algorithm. A large number of experimental simulations and cryptanalyses were performed, and the results proved that the algorithm had outstanding security and extremely high encryption efficiency. In addition, LF-NCHM could effectively resist statistical analysis attacks, differential attacks and chosen-plaintext attacks

Convolutional Neural Network-Based Fish Posture Classification

Fish killing machines can effectively relieve the workers from the backbreaking labour. Generally, it is necessary to ensure the fish to be in unified posture before being input into the automatic fish killing machine. As such, how to detect the actual posture of fish in real time is a new and meaningful issue. Considering that in the actual situation, we only need to determine the four postures which are related to the head, tail, back, and belly of the fish, and we transfer this task into a four-kind classification problem. As such, the convolutional neural network (CNN) is introduced here to do classification and then to detect the fish’s posture. Before training the network, all sample images are preprocessed to make the fish be horizontal on the image according to the principal component analysis. Meanwhile, the histogram equalization is used to make the grey distribution of different images be close. After that, two kinds of strategies are taken to do classification. The first is a paired binary classification CNN and the second is a four-category CNN. In addition, three kinds of CNN are adopted. By comparison, the four-kind classification can obtain better results with error less than 1/1000

KLF7 regulates super-enhancer-driven IGF2BP2 overexpression to promote the progression of head and neck squamous cell carcinoma

Abstract Background Head and neck squamous carcinoma (HNSCC) is known for its high aggressiveness and susceptibility to cervical lymph node metastasis, which greatly contributes to its poor prognosis. During tumorigenesis, many types of cancer cells acquire oncogenic super-enhancers (SEs) that drive the overexpression of oncogenes, thereby maintaining malignant progression. This study aimed to identify and validate the role of oncogenic SE-associated genes in the malignant progression of HNSCC. Methods We identified HNSCC cell-specific SE-associated genes through H3K27Ac ChIP-seq and overlapped them with HNSCC-associated genes obtained from The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) datasets using weighted gene coexpression network analysis (WGCNA) to identify hub genes. The expression of IGF2BP2 and KLF7 in HNSCC was detected using clinical samples. To determine the biological role of IGF2BP2, we performed CCK-8, colony formation assay, Transwell migration assay, invasion assay, and orthotopic xenograft model experiments. Furthermore, we utilized a CRISPR/Cas9 gene-editing system, small-molecule inhibitors, ChIP-qPCR, and dual-luciferase reporter assays to investigate the molecular mechanisms of IGF2BP2 and its upstream transcription factors. Results Our study identified IGF2BP2 as a hub SE-associated gene that exhibited aberrant expression in HNSCC tissues. Increased expression of IGF2BP2 was observed to be linked with malignant progression and unfavorable prognosis in HNSCC patients. Both in vitro and in vivo experiments confirmed that IGF2BP2 promotes the tumorigenicity and metastasis of HNSCC by promoting cell proliferation, migration, and invasion. Mechanistically, the IGF2BP2-SE region displayed enrichment for H3K27Ac, BRD4, and MED1, which led to the inhibition of IGF2BP2 transcription and expression through deactivation of the SE-associated transcriptional program. Additionally, KLF7 was found to induce the transcription of IGF2BP2 and directly bind to its promoter and SE regions. Moreover, the abundance of KLF7 exhibited a positive correlation with the abundance of IGF2BP2 in HNSCC. Patients with high expression of both KLF7 and IGF2BP2 showed poorer prognosis. Lastly, we demonstrated that the small molecule inhibitor JQ1, targeting BRD4, attenuated the proliferation and metastatic abilities of HNSCC cells. Conclusions Our study reveals the critical role of IGF2BP2 overexpression mediated by SE and KLF7 in promoting HNSCC progression. Targeting SE-associated transcriptional programs may represent a potential therapeutic strategy in managing HNSCC

TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning

Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Existing attention-based models have struggled to learn inferior region features in a single image by solely using unidirectional attention, which ignore the transferability and discriminative attribute localization of visual features. In this paper, we propose a cross attribute-guided Transformer network, termed TransZero++, to refine visual features and learn accurate attribute localization for semantic-augmented visual embedding representations in ZSL. TransZero++ consists of an attribute$\rightarrow$visual Transformer sub-net (AVT) and a visual$\rightarrow$attribute Transformer sub-net (VAT). Specifically, AVT first takes a feature augmentation encoder to alleviate the cross-dataset problem, and improves the transferability of visual features by reducing the entangled relative geometry relationships among region features. Then, an attribute$\rightarrow$visual decoder is employed to localize the image regions most relevant to each attribute in a given image for attribute-based visual feature representations. Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features. By further introducing semantical collaborative losses, the two attribute-guided transformers teach each other to learn semantic-augmented visual embeddings via semantical collaborative learning. Extensive experiments show that TransZero++ achieves the new state-of-the-art results on three challenging ZSL benchmarks. The codes are available at: \url{https://github.com/shiming-chen/TransZero_pp}.Comment: This is an extention of AAAI'22 paper (TransZero). Accepted to TPAMI. arXiv admin note: substantial text overlap with arXiv:2112.0168

Application of Semi-Mechanistic Pharmacokinetic and Pharmacodynamic Model in Antimicrobial Resistance

Antimicrobial resistance is a major public health issue. The pharmacokinetic/pharmacodynamic (PK/PD) model is an essential tool to optimize dosage regimens and alleviate the emergence of resistance. The semi-mechanistic PK/PD model is a mathematical quantitative tool to capture the relationship between dose, exposure, and response, in terms of the mechanism. Understanding the different resistant mechanisms of bacteria to various antibacterials and presenting this as mathematical equations, the semi-mechanistic PK/PD model can capture and simulate the progress of bacterial growth and the variation in susceptibility. In this review, we outline the bacterial growth model and antibacterial effect model, including different resistant mechanisms, such as persisting resistance, adaptive resistance, and pre-existing resistance, of antibacterials against bacteria. The application of the semi-mechanistic PK/PD model, such as the determination of PK/PD breakpoints, combination therapy, and dosage optimization, are also summarized. Additionally, it is important to integrate the PD effect, such as the inoculum effect and host response, in order to develop a comprehensive mechanism model. In conclusion, with the semi-mechanistic PK/PD model, the dosage regimen can be reasonably determined, which can suppress bacterial growth and resistance development