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SUMMARY: Culm development in grasses can be controlled by both miR156 and cytokinin. However, the crosstalk between the miR156‐SPL module and the cytokinin metabolic pathway remains largely unknown. Here, we found CYTOKININ OXIDASE/DEHYDROGENASE4 (PvCKX4) plays a negative regulatory role in culm development of the bioenergy grass Panicum virgatum (switchgrass). Overexpression of PvCKX4 in switchgrass reduced the internode diameter and length without affecting tiller number. Interestingly, we also found that PvCKX4 was always upregulated in miR156 overexpressing (miR156OE) transgenic switchgrass lines. Additionally, upregulation of either miR156 or PvCKX4 in switchgrass reduced the content of isopentenyl adenine (iP) without affecting trans‐zeatin (tZ) accumulation. It is consistent with the evidence that the recombinant PvCKX4 protein exhibited much higher catalytic activity against iP than tZ in vitro. Furthermore, our results showed that miR156‐targeted SPL2 bound directly to the promoter of PvCKX4 to repress its expression. Thus, alleviating the SPL2‐mediated transcriptional repression of PvCKX4 through miR156 overexpression resulted in a significant increase in cytokinin degradation and impaired culm development in switchgrass. On the contrary, suppressing PvCKX4 in miR156OE transgenic plants restored iP content, internode diameter, and length to wild‐type levels. Most strikingly, the double transgenic lines retained the same increased tiller numbers as the miR156OE transgenic line, which yielded more biomass than the wild type. These findings indicate that the miR156‐SPL module can control culm development through transcriptional repression of PvCKX4 in switchgrass, which provides a promising target for precise design of shoot architecture to yield more biomass from grasses. [ABSTRACT FROM AUTHOR]

Purpose: The association between traditional lipid parameters and non-alcoholic fatty liver disease (NAFLD) has been extensively discussed. This study aims to evaluate and compare the lipoprotein combine index (LCI) and traditional lipid parameters [total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C)] to identify NAFLD. Patients and Methods: The analysis included 14,251 participants from the NAfld in the Gifu Area, Longitudinal Analysis (NAGALA). Logistic regression models were employed to calculate standardized odds ratios (ORs) and 95% confidence intervals (CIs) for assessing and comparing the association of LCI and traditional lipid parameters with NAFLD. Additionally, receiver operating characteristic (ROC) curves were used to calculate the area under the curve (AUC) for LCI and traditional lipid parameters in identifying NAFLD. Results: After adjusting for various confounders, we found that LCI was positively associated with NAFLD (OR=2.25, 95% CI 1.92– 2.63), and this association was stronger than that of traditional lipid parameters [OR: TC1.23, TG1.73 LDL-C1.10]. Further subgroup analyses revealed that the association of LCI with NAFLD was stronger than other traditional lipid parameters in all subgroups, including men and women, overweight/obese [body mass index (BMI)≥ 25 kg/m2] and non-obese (BMI< 25 kg/m2), and older (age≥ 45 years) and younger (age< 45 years) participants. Additionally, ROC analysis indicated that LCI (AUC=0.8118) had significantly higher accuracy (All DeLong P< 0.05) in identifying NAFLD compared to traditional lipid parameters (AUC: TC0.6309; TG0.7969; LDL-C0.6941); HDL-C0.7587). Sensitivity analysis further confirmed the robustness of the study findings. Conclusion: This study revealed for the first time a positive correlation between LCI and NAFLD. Compared to traditional lipid parameters, LCI has a higher correlation with NAFLD. Additionally, further ROC analysis demonstrated that LCI had higher accuracy in identifying NAFLD compared to traditional lipid parameters, suggesting that LCI may be a better marker for NAFLD identification than traditional lipid parameters. [ABSTRACT FROM AUTHOR]

Summary: MiRNAs have been reported to be the key regulators involving a wide range of biological processes in diverse plant species, but their functions in switchgrass, an important biofuel and forage crop, are largely unknown. Here, we reported the novel function of miR528, which has expanded to four copies in switchgrass, in controlling biomass trait of tillering number and regrowth rate after mowing. Blocking miR528 activity by expressing short tandem target mimic (STTM) increased tiller number and regrowth rate after mowing. The quadruple pvmir528 mutant lines derived from genome editing also showed such improved traits. Degradome and RNA‐seq analysis, combined with in situ hybridization assay revealed that up‐regulation of two miR528 targets coding for Cu/Zn‐SOD enzymes, might be responsible for the improved traits of tillering and regrowth in pvmir528 mutant. Additionally, natural variations in the miR528‐SOD interaction exist in C3 and C4 monocot species, implying the distinct regulatory strength of the miR528‐SOD module during monocot evolution. Overall, our data illuminated a novel role of miR528 in controlling biomass traits and provided a new target for genetic manipulation‐mediated crop improvement. [ABSTRACT FROM AUTHOR]

Blind Source Separation (BSS) of complex signals composed of radar, communication and jamming signals is the first step in an integrated electronic system, which requires higher accuracy of separation. However, the traditional Single‐Channel Blind Source Separation (SCBSS) method has low separation accuracy and poor robustness. Aiming at this problem, this paper proposes a SCBSS method based on spatial‐temporal fusion deep learning model. This is a deep neural network model, which realizes spatial‐temporal of mixed signals by integrating Convolutional Neural Network (CNN) and Bidirectional Long Short‐Term Memory Network (BiLSTM). Convolutional Neural Network is used to extract spatial features from input sequences, and BiLSTM is used to mine timing rules of signals. A batch normalisation layer and a dropout layer are added to improve stability and prevent overfitting. The experiments show that the average similarity coefficient of the separated signals is above 0.99 and the Signal‐Distortion Ratio (SDR) is up to 27 dB without noise. When the Signal‐Noise Ratio is 0–20 dB and Jamming‐Signal Ratio is 15 dB, the SDR is 5–30 dB higher than the traditional methods and the single network structure deep learning methods. [ABSTRACT FROM AUTHOR]

The integration technology of radar and communication is an effective way to realize resource platform sharing, system advantages complementation and comprehensive performance improvement. Aiming at the shortcomings of the current integrated waveform, an integrated waveform for MIMO radar and communication is proposed in the paper based on orthogonal frequency division complex modulation (OFDCM), which is of favourable performance on the radar and communication. Theoretical analysis and simulation experiments show that the designed waveform not only acquires excellent communication performance with high transmission rate and large transmission capacity, but also features satisfactory performance for radar targets detection such as wide bandwidth, interference rejection, low interception and high doppler tolerance.