Radar Signal Modulation Recognition Based on Sep-ResNet

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%

Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation

With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characteristic matrix can well construct the semantic features of different pulse streams of radar signals. The Sep-RefineNet semantic segmentation network can complete pixel-level segmentation of the frequency characteristic matrix and finally uses position decoding and verification to obtain the position in the original pulse stream to complete radar signals deinterleaving. The proposed method avoids the processing of threshold judgment and pulse sequence search in traditional methods. The results of the experiment show that this algorithm improves the deinterleaving accuracy and has a good against-noise ability of aliasing pulses and missing pulses

Associations between the Epithelial-Mesenchymal Transition Phenotypes of Circulating Tumor Cells and the Clinicopathological Features of Patients with Colorectal Cancer

In this study, we identified CTCs using the previously reported CanPatrol CTC enrichment technique from peripheral blood samples of 126 patients with colorectal cancer (CRC) and found that CTCs could be classified into three subpopulations based on expression of epithelial cell adhesion molecule (EpCAM) (E-CTCs), the mesenchymal cell marker vimentin (M-CTCs), or both EpCAM and vimentin (biphenotypic E/M-CTCs). Circulating tumor microemboli (CTMs) were also identified in peripheral blood samples. Meanwhile, E-CTCs, M-CTCs, E/M-CTCs, and CTMs were detected in 76.98%, 42.06%, 56.35%, and 36.51% of the 126 patients, respectively. Interestingly, the presence of CTMs and each CTC subpopulation was significantly associated with blood lymphocyte counts and tumor-node-metastasis stage (P<0.001). Lymphocyte counts and the neutrophil-to-lymphocyte ratio (NLR) in patients lacking CTCs were significantly different from those in patients testing positive for CTMs and each CTC subpopulation (P<0.001). Our results indicate that tumor metastasis is more significantly associated with the presence of CTMs and M-CTCs than with other CTC subpopulations and suggest that EMT may be involved in CTC evasion of lymphocyte-mediated clearance

Analysis of Non-Genetic Factors Affecting Wood’s Model of Daily Milk Fat Percentage of Holstein Cattle

This research paper aimed to explore the characteristics of Holstein cattle’s milk fat percentage lactation curve and its influencing factors. The Wood model was used for fitting the lactation curve of 398,449 DHI test-day milk fat percentage records of Holstein cows from 2018 to 2020 in 12 dairy farms in Jiangsu province, and the influencing factors—including farm size, parity, calving season, calving interval, and 305-days milk production—on the parameters of the lactation curve were analyzed. The results showed that the non-genetic factors such as dairy farm size, calving season, parity, calving interval, and 305-days milk yield have a significant impact on milk fat percentage (p R2 of the daily milk fat percentage curve was 0.9699; the lowest milk fat percentage was 3.54%; the time to reach the lowest milk fat percentage was 126 days; and the persistence of milk fat percentage was 3.59%. All of these factors explored in this study fit at different levels above 0.96. The Wood model performed well in the fitting and analysis of the milk fat percentage curve of Holstein cattle in Jiangsu Province. This study provides a reference for improving the milk fat percentage of Holstein cattle

MIR221HG Is a Novel Long Noncoding RNA that Inhibits Bovine Adipocyte Differentiation

Adipogenesis is a complicated but precisely orchestrated process mediated by a series of transcription factors. Our previous study has identified a novel long noncoding RNA (lncRNA) that was differentially expressed during bovine adipocyte differentiation. Because this lncRNA overlaps with miR-221 in the genome, it was named miR-221 host gene (MIR221HG). The purpose of this study was to clone the full length of MIR221HG, detect its subcellular localization, and determine the effects of MIR221HG on bovine adipocyte differentiation. The 5&prime; rapid amplification of cDNA ends (RACE) and 3&prime; RACE analyses demonstrated that MIR221HG is a transcript of 1064 nucleotides, is located on the bovine X chromosome, and contains a single exon. Bioinformatics analyses suggested that MIR221HG is an lncRNA and the promoter of MIR221HG includes the binding consensus sequences of the forkhead box C1 (FOXC1) and kr&uuml;ppel-like factor5 (KLF5). The semi-quantitative PCR and quantitative real-time PCR (qRT-PCR) of nuclear and cytoplasmic fractions revealed that MIR221HG mainly resides in the nucleus. Inhibition of MIR221HG significantly increased adipocyte differentiation, as indicated by a dramatic increment in the number of mature adipocytes and in the expression of the respective adipogenic markers, peroxisome proliferator-activated receptor &gamma; (PPAR&gamma;), CCAAT/enhancer-binding protein &alpha; (C/EBP&alpha;), and fatty acid binding protein 4 (FABP4). Our results provide a basis for elucidating the mechanism by which MIR221HG regulates adipocyte differentiation

Polymorphisms of the ACSL1 Gene Influence Milk Production Traits and Somatic Cell Score in Chinese Holstein Cows

Improving the quality of milk is a challenge for zootechnicians and dairy farms across the globe. Long-chain acyl-CoA synthetase 1 (ACSL1) is a significant member of the long-chain acyl-CoA synthetase gene family. It is widely found in various organisms and influences the lactation performance of cows, including fat percentage, milk protein percentage etc. Our study was aimed to investigate the genetic effects of single nucleotide polymorphisms (SNPs) in ACSL1 on milk production traits. Twenty Chinese Holstein cows were randomly selected to extract DNA from their blood samples for PCR amplification and sequencing to identify SNPs of the bovine ACSL1 gene, and six SNPs (5&rsquo;UTR-g.20523C&gt;G, g.35446C&gt;T, g.35651G&gt;A, g.35827C&gt;T, g.35941G&gt;A and g.51472C&gt;T) were discovered. Then, Holstein cow genotyping (n = 992) was performed by Sequenom MassARRAY based on former SNP information. Associations between SNPs and milk production traits and somatic cell score (SCS) were analyzed by the least-squares method. The results showed that SNP g.35827C&gt;T was in high linkage disequilibrium with g.35941G&gt;A. Significant associations were found between SNPs and test-day milk yield (TDMY), fat content (FC), protein content (PC) and SCS (p &lt; 0.05). Among these SNPs, SNP 5&rsquo;UTR-g.20523C&gt;G showed an extremely significant effect on PC and SCS (p &lt; 0.01). The SNP g.35446C&gt;T showed a statistically significant effect on FC, PC, and SCS (p &lt; 0.01), and also TDMY (p &lt; 0.05). The SNP g.35651G&gt;A had a statistically significant effect on PC (p &lt; 0.01). The SNP g.35827C&gt;T showed a highly significant effect on TDMY, FC, and SCS (p &lt; 0.01) and significantly influenced PC (p &lt; 0.05). Lastly, SNP g.51472C&gt;T was significantly associated with TDMY, FC, and SCS (p &lt; 0.05). In summary, the pleiotropic effects of bovine ACSL1 for milk production traits were found in this paper, but further investigation will be required on the intrinsic correlation to provide a theoretical basis for the research on molecular genetics of milk quality traits of Holstein cows