Search

Research on the prediction and disaster risk assessment of surface deformation in open-pit mines serves to improve the accuracy of disaster warning and make safety control decisions. In recent years, the development of big data, cloud computing and artificial intelligence methods has provided technical support for the intelligent transformation of traditional mines. This paper summarizes the research progress of surface deformation hazards in open-pit mines from three aspects: intelligent perception, intelligent prediction and disaster risk evaluation of surface deformation. Specifically, by reviewing the intelligent monitoring technologies of mine surface deformation, this study indicates that the choice of intelligent monitoring methods should factor in data accuracy, installation cost and post-processing speed, reviews the intelligent modeling methods of surface deformation prediction regarding the methodological combination of traditional deformation prediction and intelligent optimization, machine learning and deep learning, and summarizes the mechanism behind the typical risk assessment method of mine deformation hazards. Based on the current research progress, we discuss the existing research gap and prospects of intelligent prediction and disaster risk assessment of surface deformation in open-pit mines so as to offer reference to the intelligent upgrading of mine disaster prevention and control.

In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of 'sky', 'air', and 'ground' , as well as integrated collaborative monitoring method of 'sky-air-ground'. The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the 'sky-air-ground' is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.

Objective: To explore the microstructural damage of extrapyramidal system gray matter nuclei in Parkinson disease (PD) using diffusion kurtosis imaging (DKI).Materials and Methods: We enrolled 35 clinically confirmed PD patients and 23 healthy volunteers. All patients underwent MR examination with conventional MRI scan sequences and an additional DKI sequence. We subsequently reconstructed the DKI raw images and analyzed the data. A radiologist in our hospital collected the Mini-Mental State Examination (MMSE) score of all subjects.Results: In the PD group, the mean kurtosis and axial kurtosis level decreased in the red nucleus (RN) and thalamus; the radial kurtosis increased in the substantia nigra (SN) and globus pallidus (GP). Fractional anisotropy decreased in the putamen. The largest area under the ROC curve of mean diffusion in GP was 0.811. Most kurtosis parameters demonstrated a positive correlation with the MMSE score, while several diffusion parameters showed a negative correlation with the same.Conclusion: DKI can qualitatively distinguish PD from healthy controls; furthermore, DKI-derived parameters can quantitatively evaluate the modifications of microstructures in extrapyramidal system gray matter nucleus in PD.

SUMMARY: Cotton seed development and fiber elongation are the inseparable and overlapped development processes requiring the continuous supply of sucrose as the direct carbon source. However, little is known about the molecular mechanism of how sucrose is transported from the source tissues (leaves) into growing cotton seeds. Here, we identify the function of a sucrose transporter gene, Sugars Will Eventually be Exported Transporter 10, GhSWEET10 in cotton seed development. GhSWEET10 encodes a functional sucrose transporter, predominantly expressing in the funiculus, inner seedcoat, and endosperm during fiber elongation. GhSWEET10 RNAi plants (GhSWEET10i) accumulated less sucrose and glucose in growing seeds and that led to shorter fibers and smaller seeds, whereas GhSWEET10 overexpressed plants (GhSWEET10OE) had bigger seeds and longer fibers with more sugar accumulation during fiber elongation. GhSWEET10 gene is transcriptionally controlled by the transcription factor GhDOFD45. GhDOFD45 knockout plants (GhDOFD45‐KO) possessed the phenotypes of smaller seeds and shorter fibers like those of GhSWEET10i plants. Furthermore, GhSWEET10 mainly exports the sucrose from the funiculus into developing seeds according to the mimic‐analysis of sucrose transporting. Collectively, all these findings show that GhDOFD45 positively regulates GhSWEET10 expression to mainly transport sucrose from leaves into developing cotton seeds. Our findings also imply that the sucrose transport into enlarging seeds benefits fiber development, and thus GhSWEET10 can be selected as a target of breeding novel cotton varieties with larger and more vigorous seeds. Significance Statement: The results of this manuscript provide clues and gene candidates for cotton breeding of improving cotton yield (seed and fiber) by directed‐mutation strategy. The regulation of sucrose transportation and cotton seed development is essential for promoting fiber quality and cotton yield. [ABSTRACT FROM AUTHOR]

Purpose: To explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection. Methods: Total 165 HCC patients (ER, n = 96 vs. non-early recurrence (NER), n = 69) were retrospectively collected and divided into a training cohort (n = 132) and a validation cohort (n = 33). From pretreatment CEMR images, a total of 3111 radiomics features were extracted, and radiomics models were constructed using five machine learning classifiers (logistic regression, support vector machine, k-nearest neighbor, extreme gradient Boosting, and multilayer perceptron). DL models were established via three variations of ResNet architecture. The clinical-radiological (CR), radiomics combined with clinical-radiological (RCR), and deep learning combined with RCR (DLRCR) models were constructed. Model discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. The best-performing model was compared with the widely used staging systems and preoperative prognostic indexes. Results: The RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes. Conclusion: Both radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection. [ABSTRACT FROM AUTHOR]

Cotton fiber length is basically determined by well‐coordinated gene expression and phosphatidylinositol phosphates (PIPs) accumulation during fiber elongation but the regulatory mechanism governing PIPs transport remains unknown. Here, we report a MYB transcription factor GhMYB30D04 in Gossypium hirsutum that promotes fiber elongation through modulating the expression of PIP transporter gene GhLTPG1. Knockout of GhMYB30D04 gene in cotton (KO) results in a reduction of GhLTPG1 transcripts with lower accumulation of PIPs, leading to shorter fibers and lower fiber yield. Conversely, GhMYB30D04 overexpression (GhMYB30D04‐OE) causes richer PIPs and longer cotton fibers, mimicking the effects of exogenously applying PIPs on the ovules of GhMYB30D04‐KO and wild type. Furthermore, GhMYB30D04 interacts with GhHD1, the crucial transcription factor of fiber initiation, to form an activation complex stabilized by PIPs, both of which upregulate GhLTPG1 expression. Comparative omics‐analysis revealed that higher and extended expressions of LTPG1 in fiber elongation mainly correlate with the variations of the GhMYB30D04 gene between two cotton allotetraploids, contributing to longer fiber in G. babardense. Our work clarifies a mechanism by which GhHD1–GhMYB30D04 form a regulatory module of fiber elongation to tightly control PIP accumulation. Our work still has an implication that GhMYB30D04–GhHD1 associates with development transition from fiber initiation to elongation. [ABSTRACT FROM AUTHOR]

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC‐diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion‐weighted imaging (DWI), and dynamic contrast‐enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller–Payne G4/5) and nonmajor histological responders (nMHRs, Miller–Payne G1–3). Three b values were used for DWI to derive the S index; ADC‐diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S‐index and ADC‐diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S‐index parameters and ADC‐diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S‐index parameters or ADC‐diff values between progesterone receptor positive and negative or for Ki‐67 tumors (all P > 0.05). No differences were found in the dynamic contrast‐enhanced MRI characteristics between the two groups. HER‐2 expression and kurtosis of the S‐index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC‐diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S‐index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC‐diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials. [ABSTRACT FROM AUTHOR]

Objectives: To find the optimal acceleration factor (AF) of the compressed SENSE (CS) technique for uterine isotropic high-resolution 3D T2-weighted imaging (3D-ISO-T2WI). Methods: A total of 91 female volunteers from the First Affiliated Hospital of Dalian Medical University, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, and The Fourth Hospital of Harbin were recruited. A total of 44 volunteers received uterus sagittal 3D-ISO-T2WI scans on 3.0T MRI device with different CS AFs (including SENSE3, CS3, CS4, CS5, CS6, and CS7), 51 received 3D-ISO-T2WI scans with different degrees of fat suppression (none, light, moderate, and severe), while 4 volunteers received both series of scans. Image quality was subjectively evaluated with a 3-point scoring system. Junction zone signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and myometrial SNR were also calculated. Intraclass correlation coefficients were used to analyse the consistency of the measurement results by 2 observers. Analysis of variance test or Friedman rank sum test was used to compare the differences in subjective scores, SNR, and CNR under different AFs/different degrees of fat suppression. Results: Images by AFs of CS3, CS4, and CS5 had the highest SNR and CNR. Among them, CS5 had the shortest scan time. CS5 also had one of the highest subjective scores. There was no significant difference in SNR and CNR among images acquired with different degrees of fat suppression. Also, images with moderate fat suppression had the highest subjective scores. Conclusion: The CS5 combined with moderate fat suppression is recommended for routine female pelvic 3D-ISO-T2WI scan. Advances in knowledge: The CS5 has the highest image quality and has the shortest scan time, which is the best AF. Moderate fat suppression has the highest subjective scores. The CS5 and moderate fat suppression are the best combination for a female pelvic 3D-ISO-T2WI scan. [ABSTRACT FROM AUTHOR]