Nuclear speckles (NSs) serve as splicing factor storage sites. In this study, we unexpectedly found that many endogenous intronless mRNAs, which do not undergo splicing, associate with NSs. These ...associations do not require transcription, polyadenylation, or the polyA tail. Rather, exonic splicing enhancers present in intronless mRNAs and their binding partners, SR proteins, promote intronless mRNA localization to NSs. Significantly, speckle targeting of mRNAs promotes the recruitment of the TREX export complex and their TREX-dependent nuclear export. Furthermore, TREX, which accumulates in NSs, is required for releasing intronless mRNAs from NSs, whereas NXF1, which is mainly detected at nuclear pores, is not. Upon NXF1 depletion, the TREX protein UAP56 loses speckle concentration but coaccumulates with intronless mRNAs and polyA RNAs in the nucleoplasm, and these RNAs are trapped in NSs upon UAP56 codepletion. We propose that the export-competent messenger RNP assembly mainly occurs in NSs for intronless mRNAs and that entering NSs serves as a quality control step in mRNA export.
We aimed to evaluate the utility of preoperative D-dimer and plasma fibrinogen (PF) levels as useful markers for predicting the clinical value of patients with osteosarcoma.
145 enrolled patients ...with osteosarcoma were studied retrospectively. We determined the critical values of D-dimer and PF by receiver operating characteristic curve analysis. Cox regression analysis was used to assess prognostic role of the D-dimer and PF levels among osteosarcoma patients.
The critical values of D-dimer and PF were calculated to be 0.46 µg/mL and 3.34 mg/mL, respectively. Upregulation of D-dimer and PF showed positive correlations with a higher clinical stage, tumour metastasis and recurrence. Survival curve results confirmed that osteosarcoma patients with higher levels of D-dimer and PF predicted worse overall survival (OS) and progression-free survival (PFS). Moreover, only a high D-dimer level was associated with a shorter OS (P = 0.013) and PFS (P = 0.042) in both the univariate and multivariate analysis.
Elevated preoperative D-dimer levels are correlated with aggressive clinicopathological features and poor survival outcomes, which indicates that assessment of the D-dimer could be a useful prognostic marker in osteosarcoma.
In realistic road scenarios, two main urban hazards, pedestrians and bicyclists, can approach the radar from any aspect angle which will affect the ability of a radar system to perform target ...identification. However, the identification of these two particular types of targets with different approaching directions has not been investigated thoroughly until now. In this paper, we explore the micro Doppler signature to accurately distinguish pedestrian and the bicyclist. One feature extraction method that is based on the sparse coding of a measured micro Doppler signature is proposed and validated. Further numerical analysis of the sparse matrices was performed to reduce the number of dimensions whilst keeping a sufficient number of features. As on comparison, the two feature extractors, i.e., singular value decomposition and robust principal component analysis are extensively studied and compared with the proposed method. Support vector machine (SVM) and extreme learning machine were adopted to classify the extracted features. Indoor experimental results indicate that the proposed methods can achieve higher identification accuracies than alternative approaches and the outdoor measurement results presented show good performance with realistic targets.
Protein methylation is a functionally important post-translational modification that occurs on diverse amino acid residues. The current proteomics approaches are inefficient to discover the ...methylation on residues other than Arg and Lys, which hinders the deep understanding of the functional role of rare protein methylation. Herein, we present a methyl-specific metabolic labeling approach for global methylome mapping, which enable the acquisition of methylome dataset covering diverse methylation types. Interestingly, of the identified methylation events, His methylation is found to be preferably occurred in C3H1 zinc fingers (ZFs). These His methylation events are determined to be Nπ specific and catalyzed by CARNMT1. The His methylation is found to stabilize the structure of ZFs. U2AF1 is used as a proof-of-concept to highlight the functional importance of His methylation in ZFs in RNA binding and RNA metabolism. The results of this study enable novel understanding of how protein methylation regulates cellular processes.Here the authors developed a methyl-specific metabolic labeling approach for global analysis covering diverse methylation types. His methylation is found in C3H1 zinc fingers. U2AF1 is used to highlight the role of His methylation in ZFs in RNA metabolism.
Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting ...parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources
must fulfill K ≤ ( M 4 - 2 M 3 + 7 M 2 - 6 M ) / 8 . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O ( M 4 ) , where
is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.
Land use/land cover change (LUCC) detection based on optical remote-sensing images is an important research direction in the field of remote sensing. The key to it is to select an appropriate data ...source and detection method. In recent years, the continuous expansion of construction land in urban areas has become the main reason for the increase in LUCC demand. However, due to the complexity and diversity of land-cover types, it is difficult to obtain high-precision classification results. In this article, a 12-month time series NDVI (Normalized Difference Vegetation Index) image of the study area was generated based on the high spatial and temporal resolution PlanetScope satellite images. According to the time series NDVI image, representative land-cover samples were selected, and the changed land samples were selected at the same time. This method could directly obtain the LUCC detection results of the study area through land-cover classification. First, Maximum Likelihood Classification (MLC), a classical machine-learning method, was used for supervised classification, and the samples needed for deep learning were selected according to the classification results. Then, the U-Net model, which can fully identify and explore the deep semantic information of the time series NDVI image, was used for land classification. Finally, this article made a comparative analysis of the two classification results. The results demonstrate that the overall classification accuracy based on time series NDVI is significantly higher than that of single-scene NDVI and mean NDVI. The LUCC detection method proposed in this article can effectively extract changed areas. The overall accuracy of the MLC and U-Net model is 79.38% and 85.26%, respectively. Therefore, the deep-learning method can effectively improve the accuracy of land-cover classification and change detection.
The exosome functions in the degradation of diverse RNA species, yet how it is negatively regulated remains largely unknown. Here, we show that NRDE2 forms a 1:1 complex with MTR4, a nuclear exosome ...cofactor critical for exosome recruitment, via a conserved MTR4-interacting domain (MID). Unexpectedly, NRDE2 mainly localizes in nuclear speckles, where it inhibits MTR4 recruitment and RNA degradation, and thereby ensures efficient mRNA nuclear export. Structural and biochemical data revealed that NRDE2 interacts with MTR4's key residues, locks MTR4 in a closed conformation, and inhibits MTR4 interaction with the exosome as well as proteins important for MTR4 recruitment, such as the cap-binding complex (CBC) and ZFC3H1. Functionally, MID deletion results in the loss of self-renewal of mouse embryonic stem cells. Together, our data pinpoint NRDE2 as a nuclear exosome negative regulator that ensures mRNA stability and nuclear export.
Abstract
External cycling regenerating nitrogen oxides (NO
x
≡ NO + NO
2
) from their oxidative reservoir, NO
z
, is proposed to reshape the temporal–spatial distribution of NO
x
and consequently ...hydroxyl radical (OH), the most important oxidant in the atmosphere. Here we verify the in situ external cycling of NO
x
in various environments with nitrous acid (HONO) as an intermediate based on synthesized field evidence collected onboard aircraft platform at daytime. External cycling helps to reconcile stubborn underestimation on observed ratios of HONO/NO
2
and NO
2
/NO
z
by current chemical model schemes and rationalize atypical diurnal concentration profiles of HONO and NO
2
lacking noontime valleys specially observed in low-NO
x
atmospheres. Perturbation on the budget of HONO and NO
x
by external cycling is also found to increase as NO
x
concentration decreases. Consequently, model underestimation of OH observations by up to 41% in low NO
x
atmospheres is attributed to the omission of external cycling in models.
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