Study of Antenna Superstrates Using Metamaterials for Directivity Enhancement Based on Fabry-Perot Resonant Cavity

Metamaterial superstrate is a significant method to obtain high directivity of one or a few antennas. In this paper, the characteristics of directivity enhancement using different metamaterial structures as antenna superstrates, such as electromagnetic bandgap (EBG) structures, frequency selective surface (FSS), and left-handed material (LHM), are unifiedly studied by applying the theory of Fabry-Perot (F-P) resonant cavity. Focusing on the analysis of reflection phase and magnitude of superstrates in presently proposed designs, the essential reason for high-directivity antenna with different superstrates can be revealed in terms of the F-P resonant theory. Furthermore, a new design of the optimum reflection coefficient of superstrates for the maximum antenna directivity is proposed and validated. The optimum location of the LHM superstrate which is based on a refractive lens model can be determined by the F-P resonant distance

Dosage effects of BDNF Val66Met polymorphism on cortical surface area and functional connectivity

The single nucleotide polymorphism (SNP) that leads to a valine-to-methionine substitution at codon 66 (Val66Met) in BDNF is correlated with differences in cognitive and memory functions, as well as with several neurological and psychiatric disorders.MRIstudies have already shown that this genetic variant contributes to changes in cortical thickness and volume, but whether the Val66Met polymorphism affects the cortical surface area of healthy subjects remains unclear. Here, we used multimodal MRI to study whether this polymorphism would affect the cortical morphology and resting-state functional connectivity of a large sample of healthy Han Chinese human subjects. An SNP-wise general linear model analysis revealed a "dosage effect" of the Met allele, specifically a stepwise increase in cortical surface area of the right anterior insular cortex with increasing numbers of the Met allele. Moreover, we found enhanced functional connectivity between the anterior insular and the dorsolateral prefrontal cortices that was linked with the dosage of the Met allele. In conclusion, these data demonstrated a "dosage effect" ofBDNFVal66Met on normal cortical structure and function, suggesting anewpath for exploring the mechanisms underlying the effects of genotype on cognition

DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information

Identifying differentially expressed genes (DEGs) between tumor and normal samples is critical for studying tumorigenesis, and has been routinely applied to identify diagnostic, prognostic, and therapeutic biomarkers for many cancers. It is well-known that solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. However, the tumor purity information is more or less ignored in traditional differential expression analyses, which might decrease the power of differential gene identification or even bias the results. In this paper, we have developed a novel differential gene calling method called DECtp by integrating tumor purity information into a generalized least square procedure, followed by the Wald test. We compared DECtp with popular methods like t-test and limma on nine simulation datasets with different sample sizes and noise levels. DECtp achieved the highest area under curves (AUCs) for all the comparisons, suggesting that cancer purity information is critical for DEG calling between tumor and normal samples. In addition, we applied DECtp into cancer and normal samples of 14 tumor types collected from The Cancer Genome Atlas (TCGA) and compared the DEGs with those called by limma. As a result, DECtp achieved more sensitive, consistent, and biologically meaningful results and identified a few novel DEGs for further experimental validation

Room Temperature Lead-Free Multiaxial Inorganic-Organic Hybrid Ferroelectric.

In recent years, molecular ferroelectrics have received more and more attention. Nevertheless, the study of multiaxial molecular ferroelectrics is relatively rare, which significantly restricts the development of their applications in thin films and other potential fields. Here we demonstrate the characteristics of a room-temperature lead-free multiaxial inorganic-organic hybrid ferroelectric material [(CH3)2NH2] [C6H5CH2NH3]2BiBr6 ( 1 ), which goes through a distinctly reversible phase transition around 386 K and possesses six equivalent ferroelectric directions. At 330 K, the remnant polarization ( P r) of 1 is ∼1.0 μC·cm-2, and the coercive field ( E c) of 1 is 20 kV·cm-1. The multiaxial and switching polarization behaviors of 1 were declared with piezoresponse force microscopy (PFM). Notably, the emergence of six equivalent ferroelectric directions is induced by the easily disordered cations and highly geometrically symmetrical anions, because they usually lead to a large symmetry change in the order-disorder types of ferroelectrics. This work provides an effective approach to construct molecular multiaxial ferroelectrics

Frequency-diverse MIMO metasurface antenna for computational imaging with aperture rotation technique

Metasurface antennas have been proposed for computational imaging (CI) systems, which can reconstruct images without using mechanical scanning or large antenna arrays. In a CI system based on metasurface antennas, a variety of different radiation fields, which can be applied to sample the objects, are generated by exciting different frequency points in broadband. According to the compressed sensing theory, the imaging performance of the system is mainly limited by frequency-diversity radiation modes. In general, it is difficult to achieve rich radiation modes; therefore, a special design of metasurface aperture is required. In this paper, we propose a frequency-diversity MIMO metasurface antenna that consists of 2 × 2 sub-apertures with randomly distributed surface impedance. By employing the aperture rotation technique (ART) which rotates the MIMO metasurface antenna around the panel axis, the pseudo-randomness of the radiation fields is utilized. The diversity of the radiation field is improved on the premise of ensuring the relatively low complexity of the system. The ART significantly improves the measurement richness at the cost of increasing the measurement time. The performance of the proposed method is evaluated through simulations and experiments, suggesting that the proposed 2 × 2 MIMO metasurface antenna and the ART are effective to reconstruct high-quality images

Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks

With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important. Statistical or traditional machine learning methods are inefficient, and improved accuracy is needed. The methods based on deep learning cannot directly process non-Euclidean spatial data, such as cell diagrams. In this study, we developed scGAEGAT, a multi-modal model with graph autoencoders and graph attention networks for scRNA-seq analysis based on graph neural networks. Cosine similarity, median L1 distance, and root-mean-squared error were used to measure the gene imputation performance of different methods for comparison with scGAEGAT. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score were used to measure the cell clustering performance of different methods for comparison with scGAEGAT. Experimental results demonstrated promising performance of the scGAEGAT model in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels

DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era

IntroductionThe DNA N4-methylcytosine (4mC) site levels of those suffering from digestive system cancers were higher, and the pathogenesis of digestive system cancers may also be related to the changes in DNA 4mC levels. Identifying DNA 4mC sites is a very important step in studying the analysis of biological function and cancer prediction. Extracting accurate features from DNA sequences is the key to establishing a prediction model of effective DNA 4mC sites. This study sought to develop a new predictive model, DRSN4mCPred, which aimed to improve the performance of the predicting DNA 4mC sites.MethodsThe model adopted multi-scale channel attention to extract features and used attention feature fusion (AFF) to fuse features. In order to capture features information more accurately and effectively, this model utilized Deep Residual Shrinkage Network with Channel-Wise thresholds (DRSN-CW) to eliminate noise-related features and achieve a more precise feature representation, thereby, distinguishing the sites in DNA with 4mC and non-4mC. Additionally, the predictive model incorporated an inverted residual block, a Multi-scale Channel Attention Module (MS-CAM), a Bi-directional Long Short Term Memory Network (Bi-LSTM), AFF, and DRSN-CW.Results and DiscussionThe results indicated the predictive model DRSN4mCPred had extremely good performance in predicting the DNA 4mC sites across different species. This paper will potentially provide support for the diagnosis and treatment of gastrointestinal cancer based on artificial intelligence in the precise medical era

A proton-conducting lanthanide metal-organic framework integrated with a dielectric anomaly and second-order nonlinear optical effect

National Basic 25 Research Program of China (973 Program) [2012CB821700]; National Natural Science Foundation of China [20831002, 20502024]; China Postdoctoral Science Foundation [20100481049]; Scientific Research Program from the Education Department of Shaanxi Provincial Government [2013JK0654]; Priming Scientific Research Foundation [BS1115]; Provincial Innovation Training Project [1399]A multifunctional metal-organic framework generated from chiral tricarboxylate ligands and gadolinium ions has been successfully synthesized and characterized. It shows proton conduction, dielectric anomalous behaviour and a second-order NLO effect

iDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation

Introduction: DNA methylation is a critical epigenetic modification involving the addition of a methyl group to the DNA molecule, playing a key role in regulating gene expression without changing the DNA sequence. The main difficulty in identifying DNA methylation sites lies in the subtle and complex nature of methylation patterns, which may vary across different tissues, developmental stages, and environmental conditions. Traditional methods for methylation site identification, such as bisulfite sequencing, are typically labor-intensive, costly, and require large amounts of DNA, hindering high-throughput analysis. Moreover, these methods may not always provide the resolution needed to detect methylation at specific sites, especially in genomic regions that are rich in repetitive sequences or have low levels of methylation. Furthermore, current deep learning approaches generally lack sufficient accuracy.Methods: This study introduces the iDNA-OpenPrompt model, leveraging the novel OpenPrompt learning framework. The model combines a prompt template, prompt verbalizer, and Pre-trained Language Model (PLM) to construct the prompt-learning framework for DNA methylation sequences. Moreover, a DNA vocabulary library, BERT tokenizer, and specific label words are also introduced into the model to enable accurate identification of DNA methylation sites.Results and Discussion: An extensive analysis is conducted to evaluate the predictive, reliability, and consistency capabilities of the iDNA-OpenPrompt model. The experimental outcomes, covering 17 benchmark datasets that include various species and three DNA methylation modifications (4mC, 5hmC, 6mA), consistently indicate that our model surpasses outstanding performance and robustness approaches