Improving the Gilbert-Varshamov Bound by Graph Spectral Method

We improve Gilbert-Varshamov bound by graph spectral method. Gilbert graph $G_{q,n,d}$ is a graph with all vectors in $\mathbb{F}_q^n$ as vertices where two vertices are adjacent if their Hamming distance is less than $d$. In this paper, we calculate the eigenvalues and eigenvectors of $G_{q,n,d}$ using the properties of Cayley graph. The improved bound is associated with the minimum eigenvalue of the graph. Finally we give an algorithm to calculate the bound and linear codes which satisfy the bound

On the Weight Distribution of Weights Less than 2wmin⁡2w_{\min} in Polar Codes

The number of low-weight codewords is critical to the performance of error-correcting codes. In 1970, Kasami and Tokura characterized the codewords of Reed-Muller (RM) codes whose weights are less than $2w_{\min}$, where $w_{\min}$ represents the minimum weight. In this paper, we extend their results to decreasing polar codes. We present the closed-form expressions for the number of codewords in decreasing polar codes with weights less than $2w_{\min}$. Moreover, the proposed enumeration algorithm runs in polynomial time with respect to the code length

On the Weight Spectrum Improvement of Pre-transformed Reed-Muller Codes and Polar Codes

Pre-transformation with an upper-triangular matrix (including cyclic redundancy check (CRC), parity-check (PC) and polarization-adjusted convolutional (PAC) codes) improves the weight spectrum of Reed-Muller (RM) codes and polar codes significantly. However, a theoretical analysis to quantify the improvement is missing. In this paper, we provide asymptotic analysis on the number of low-weight codewords of the original and pre-transformed RM codes respectively, and prove that pre-transformation significantly reduces low-weight codewords, even in the order sense. For polar codes, we prove that the average number of minimum-weight codewords does not increase after pre-transformation. Both results confirm the advantages of pre-transformation

SHP-2-induced M2 polarization of tumor associated macrophages via IL-4 regulate colorectal cancer progression

ObjectiveTo explore the effect and molecular mechanism of Src homology region 2 domain-containing protein tyrosine phosphatase-2 (SHP-2) in tumor-associated macrophages (TAMs) repressing the migration and invasion of colorectal cancer (CRC) cells.MethodsThe relevant data sets of human colon specimens were obtained from GEO database, and then the performed correlation analysis, principal component analysis and differentially expressed gene (DEGs) analysis on the samples were conducted. GO and KEGG enrichment analysis were performed on the common DEGs, and then functional interaction prediction was performed to verify the gene regulatory circuit of SHP-2. Furthermore, western blot was used to detect the effect of low expression of SHP-2 on related proteins, including the markers of promoting M2 polarization and exosome secretion, and keys proteins of the PI3K pathway. The relationship between SHP-2 and PI3K pathway was further verified by adding PI3K inhibitor LY294002. Finally, the effect of SHP-2 on the function of colon cancer cells was confirmed by wound healing assay and Transwell assay.ResultsThrough bioinformatics analysis, SHP-2 was screened as a possible key gene affecting CRC. The low expression of SHP-2 promoted the protein levels of Arginase-1 and IL-10 in IL-4 induced M2 macrophages, while inhibited the protein levels of IL-1β and TNF-α. Meanwhile, low expression of SHP-2 was found to similarly promote the expression of p-PI3K, p-AKT, and the release of exosomes. Interestingly, the promotion was suppressed after the addition of the PI3K inhibitor LY294002. In terms of cellular behavior, wound healing and transwell data showed that low expression of SHP-2 enhanced the migration and invasion abilities of CRC cells.ConclusionThe low expression of SHP-2 induced by PHPS1 may regulate M2 polarization of TAMs and release of exosomes through PI3K/AKT pathway, thereby enhancing the migration and invasion ability of CRC cells

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks?outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care?which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling

Distamycin A Inhibits HMGA1-Binding to the P-Selectin Promoter and Attenuates Lung and Liver Inflammation during Murine Endotoxemia

Background: The architectural transcription factor High Mobility Group-A1 (HMGA1) binds to the minor groove of AT-rich DNA and forms transcription factor complexes (“enhanceosomes”) that upregulate expression of select genes within the inflammatory cascade during critical illness syndromes such as acute lung injury (ALI). AT-rich regions of DNA surround transcription factor binding sites in genes critical for the inflammatory response. Minor groove binding drugs (MGBs), such as Distamycin A (Dist A), interfere with AT-rich region DNA binding in a sequence and conformation-specific manner, and HMGA1 is one of the few transcription factors whose binding is inhibited by MGBs. Objectives: To determine whether MGBs exert beneficial effects during endotoxemia through attenuating tissue inflammation via interfering with HMGA1-DNA binding and modulating expression of adhesion molecules. Methodology/Principal Findings: Administration of Dist A significantly decreased lung and liver inflammation during murine endotoxemia. In intravital microscopy studies, Dist A attenuated neutrophil-endothelial interactions in vivo following an inflammatory stimulus. Endotoxin induction of P-selectin expression in lung and liver tissue and promoter activity in endothelial cells was significantly reduced by Dist A, while E-selectin induction was not significantly affected. Moreover, Dist A disrupted formation of an inducible complex containing NF-κB that binds an AT-rich region of the P-selectin promoter. Transfection studies demonstrated a critical role for HMGA1 in facilitating cytokine and NF-κB induction of P-selectin promoter activity, and Dist A inhibited binding of HMGA1 to this AT-rich region of the P-selectin promoter in vivo. Conclusions/Significance: We describe a novel targeted approach in modulating lung and liver inflammation in vivo during murine endotoxemia through decreasing binding of HMGA1 to a distinct AT-rich region of the P-selectin promoter. These studies highlight the ability of MGBs to function as molecular tools for dissecting transcriptional mechanisms in vivo and suggest alternative treatment approaches for critical illness

The Explosion Resistance of Double-layer Honeycomb Sandwich Panel Under Blast Load

In order to deeply analyze the influence of core layer size parameters on the anti-explosion performance of sandwich panel under explosive load, on the basis of the fiber-reinforced three-layer honeycomb sandwich structure, the dynamic response of the double-layer gradient sandwich panel made of fiber-reinforced aluminum alloy panel and aluminum alloy honeycomb core layer under explosive load was studied by experiment and numerical simulation. First, the deformation failure modes of the double-layer gradient sandwich panel in the experiment were analyzed: large deformation of the whole sandwich structure, failure of the front panel in the central area and compression buckling of the core layer, and penetration failure of the front panel and the core layer in the central area. Second, the influence of the change in the hole length and wall thickness on the explosion resistance performance of the upper core layer was examined without changing the geometric size of the lower core layer by numerical simulation. The results show that the specimen with the same hole side length of the upper and lower core layer had better explosion resistance performance, and when the density ratio of the upper and lower core layer was 3∶1 while the side length of the upper and lower core layer was the same, the structure had the best anti-explosion performance

Multi-Responsive Nanocarriers Based on β-CD-PNIPAM Star Polymer Coated MSN-SS-Fc Composite Particles

A temperature, glutathione (GSH), and H2O2 multi-responsive composite nanocarrier (MSN-SS-Fc@β-CD-PNIPAM) based on β-cyclodextrin-poly(N-isopropylacrylamide) (β-CD-PNIPAM) star polymer capped ferrocene modified mesoporous silica nanoparticles (MSN-SS-Fc) was successfully prepared. The surface of the mesoporous silica was first modified by ferrocene (Fc) via a disulfide bond (–SS–) to form an oxidizing and reducing site and then complexed with a β-CD-PNIPAM star shaped polymer through host–guest interactions as a nano-valve to provide temperature responsive characteristics. The structure and properties of the complex nanoparticles were studied by FTIR, TGA, EDS, Zeta potential, and elemental analysis. Doxorubicin (DOX) and Naproxen (NAP), as model drugs, were loaded into nanocarriers to assess drug loading and release behaviour. The release of drugs from nanocarriers was enhanced with an increase of the GSH, H2O2 concentration, or temperatures of the solution. The kinetics of the release process were studied using different models. This nanocarrier presents successful multi-stimuli responsive drug delivery in optimal stimuli and provides potential applications for clinical treatment