Multi-scale distribution of coal fractures based on CT digital core deep learning

In order to realize high-precision and high-efficiency identification of multi-scale distribution characteristics of coal fractures, carry out the study of multi-scale distribution characteristics identification methods based on CT digital core deep learning. Industrial CT scanning system is used to collect a large number of coal original CT digital core information array, the CT digital core information array is converted into a two-dimensional gray-scale image and then it is divided into square images of different scales and the image brightness is enhanced to different levels as training samples, Finally, the construction and optimization of model parameters of AlexNet, ResNet-18, GoogLeNet and Inception-V3 models for the identification of CT-containing fractures are realized by Matlab platform. Study the recognition accuracy and verification accuracy of different model training under different number of training samples; Study the accuracy, calculation efficiency and training time of different models for images with different scales and brightness under the same training sample, obtain the optimal model for calculating the fractal dimension of two-dimensional CT images with fractures, then, the fractal distribution characteristics of each fracture image are calculated according to the statistical method of box-counting dimension, compared with the traditional binarization method and human eye recognition method, The applicability of the multi-scale distribution characteristics identification method of coal fractures based on CT digital core deep learning is verified. The result shows: ① ResNet-18 model is the optimal model for calculating the fractal dimension of two-dimensional CT images with cracks when the image sample is brightness 4 and the scale is 3.5 mm to 21 mm, the model has high accuracy and short training time in calculating the fractal dimension of two-dimensional CT fracture images. ② Compared with the traditional binarization method, the multi-scale recognition method of coal fracture based on CT digital core deep learning has the advantages of fast speed, high accuracy and is not easily affected by impurities in coal

Who is the main caregiver of the mother during the doing-the-month : is there an association with postpartum depression?

Background: To examine the relationship between the main caregiver during the “doing-the-month” (a traditional Chinese practice which a mother is confined at home for 1 month after giving birth) and the risk of postpartum depression (PPD) in postnatal women. Methods: Participants were postnatal women stayed in hospital and women who attended the hospital for postpartum examination, at 14–60 days after delivery from November 1, 2013 to December 30, 2013. Postpartum depression status was assessed using the Edinburgh Postnatal Depression Scale. Univariate and multivariable logistic regressions were used to identify the associations between the main caregiver during “doing-the-month” and the risk of PPD in postnatal women. Results: One thousand three hundred twenty-five postnatal women with a mean (SD) age of 28 (4.58) years were included in the analyses. The median score (IQR) of PPD was 6.0 (2, 10) and the prevalence of PPD was 27%. Of these postnatal women, 44.5% were cared by their mother-in-law in the first month after delivery, 36.3% cared by own mother, 11.1% by “yuesao” or “maternity matron” and 8.1% by other relatives. No association was found between the main caregivers and the risk of PPD after multiple adjustments. Conclusions: Although no association between the main caregivers and the risk of PPD during doing-the-month was identified, considering the increasing prevalence of PPD in Chinese women, and the contradictions between traditional culture and latest scientific evidence for some of the doing-the-month practices, public health interventions aim to increase the awareness of PPD among caregivers and family members are warranted

Pathogenic mechanisms and therapeutic implications of extracellular matrix remodelling in cerebral vasospasm

Abstract Cerebral vasospasm significantly contributes to poor prognosis and mortality in patients with aneurysmal subarachnoid hemorrhage. Current research indicates that the pathological and physiological mechanisms of cerebral vasospasm may be attributed to the exposure of blood vessels to toxic substances, such as oxyhaemoglobin and inflammation factors. These factors disrupt cerebral vascular homeostasis. Vascular homeostasis is maintained by the extracellular matrix (ECM) and related cell surface receptors, such as integrins, characterised by collagen deposition, collagen crosslinking, and elastin degradation within the vascular ECM. It involves interactions between the ECM and smooth muscle cells as well as endothelial cells. Its biological activities are particularly crucial in the context of cerebral vasospasm. Therefore, regulating ECM homeostasis may represent a novel therapeutic target for cerebral vasospasm. This review explores the potential pathogenic mechanisms of cerebral vasospasm and the impacts of ECM protein metabolism on the vascular wall during ECM remodelling. Additionally, we underscore the significance of an ECM protein imbalance, which can lead to increased ECM stiffness and activation of the YAP pathway, resulting in vascular remodelling. Lastly, we discuss future research directions

Quantitative Assessment and Diagnosis for Regional Agricultural Drought Resilience Based on Set Pair Analysis and Connection Entropy

Assessment and diagnosis of regional agricultural drought resilience (RADR) is an important groundwork to identify the shortcomings of regional agriculture to resist drought disasters accurately. In order to quantitatively assess the capacity of regional agriculture system to reduce losses from drought disasters under complex conditions and to identify vulnerability indexes, an assessment and diagnosis model for RADR was established. Firstly, this model used the improved fuzzy analytic hierarchy process to determine the index weights, then proposed an assessment method based on connection number and an improved connection entropy. Furthermore, the set pair potential based on subtraction was used to diagnose the vulnerability indexes. In addition, a practical application had been carried out in the region of the Huaibei Plain in Anhui Province. The evaluation results showed that the RADR in this area from 2005 to 2014 as a whole was in a relatively weak situation. However, the average grade values had decreased from 3.144 to 2.790 during these 10 years and the RADR had an enhanced tendency. Moreover, the possibility of RADR enhancement for six cities in this region decreased from east to west, and the drought emergency condition was the weak link of the RADR in the Huaibei Plain

Development of a nomogram to predict the incidence of acute kidney injury among ischemic stroke individuals during ICU hospitalization

Background: Limited clinical prediction models exist to assess the likelihood of acute kidney injury (AKI) occurrence in ischemic stroke individuals. In this retrospective study, our aim was to construct a nomogram that utilizes commonly available clinical features to predict the occurrence of AKI during intensive care unit hospitalization among this patient population. Methods: In this study, the MIMIC-IV database was utilized to investigate potential risk factors associated with the incidence of AKI among ischemic stroke individuals. A predictive nomogram was developed based on these identified risk factors. The discriminative performance of the constructed nomogram was assessed. Calibration analysis was utilized to evaluate the calibration performance of the constructed model, assessing the agreement between predicted probabilities and actual outcomes. Furthermore, decision curve analysis (DCA) was employed to assess the clinical net benefit, taking into account the potential risks and benefits associated with different decision thresholds. Results: A total of 2089 ischemic stroke individuals were included and randomly allocated into developing (n = 1452) and verification cohorts (n = 637). Risk factors for AKI incidence in ischemic stroke individuals, determined through LASSO and logistic regression. The constructed nomogram had good performance in predicting the occurrence of AKI among ischemic stroke patients and provided significant improvement compared to existing scoring systems. DCA demonstrated satisfactory clinical net benefit of the constructed nomogram in both the validation and development cohorts. Conclusions: The developed nomogram exhibits robust predictive performance in forecasting AKI occurrence in ischemic stroke individuals

Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine

Carbon emissions and environmental protection issues have become the pressure from the international community during the current transitional stage of China’s energy transformation. China has set a macro carbon emission target, which will reduce carbon emissions per unit of Gross Domestic Product (GDP) by 40% in 2020 and 60–65% in 2030 than that in 2005. To achieve the emission reduction target, the industrial structure must be adjusted and upgraded. Furthermore, it must start from a high-pollution and high-emission industry. Therefore, it is of practical significance to construct a low-carbon sustainability and green operation benefits of power generation enterprises to save energy and reduce emissions. In this paper, an intuitionistic fuzzy comprehensive analytic hierarchy process based on improved dynamic hesitation degree (D-IFAHP) and an improved extreme learning machine algorithm optimized by RBF kernel function (RELM) are proposed. Firstly, we construct the evaluation indicator system of low-carbon sustainability and green operation benefits of power generation enterprises. Moreover, during the non-dimensional processing, the evaluation index system is determined. Secondly, we apply the evaluation indicator system by an empirical analysis. It is proved that the D-IFAHP evaluation model proposed in this paper has higher accuracy performance. Finally, the RELM is applied to D-IFAHP to construct a combined evaluation model named D-IFAHP-RELM evaluation model. The D-IFAHP evaluation results are used as the input of the training sets of the RELM algorithm, which simplifies the comprehensive evaluation process and can be directly applied to similar projects

Musashi-1 and miR-147 Precursor Interaction Mediates Synergistic Oncogenicity Induced by Co-Infection of Two Avian Retroviruses

Synergism between avian leukosis virus subgroup J (ALV-J) and reticuloendotheliosis virus (REV) has been reported frequently in co-infected chicken flocks. Although significant progress has been made in understanding the tumorigenesis mechanisms of ALV and REV, how these two simple oncogenic retroviruses induce synergistic oncogenicity remains unclear. In this study, we found that ALV-J and REV synergistically promoted mutual replication, suppressed cellular senescence, and activated epithelial-mesenchymal transition (EMT) in vitro. Mechanistically, structural proteins from ALV-J and REV synergistically activated the expression of Musashi-1(MSI1), which directly targeted pri-miR-147 through its RNA binding site. This inhibited the maturation of miR-147, which relieved the inhibition of NF-κB/KIAA1199/EGFR signaling, thereby suppressing cellular senescence and activating EMT. We revealed a synergistic oncogenicity mechanism induced by ALV-J and REV in vitro. The elucidation of the synergistic oncogenicity of these two simple retroviruses could help in understanding the mechanism of tumorigenesis in ALV-J and REV co-infection and help identify promising molecular targets and key obstacles for the joint control of ALV-J and REV and the development of clinical technologies

Genome-wide survey of potato MADS-box genes reveals that StMADS1 and StMADS13 are putative downstream targets of tuberigen StSP6A

Abstract Background MADS-box genes encode transcription factors that are known to be involved in several aspects of plant growth and development, especially in floral organ specification. To date, the comprehensive analysis of potato MADS-box gene family is still lacking after the completion of potato genome sequencing. A genome-wide characterization, classification, and expression analysis of MADS-box transcription factor gene family was performed in this study. Results A total of 153 MADS-box genes were identified and categorized into MIKC subfamily (MIKCC and MIKC*) and M-type subfamily (Mα, Mβ, and Mγ) based on their phylogenetic relationships to the Arabidopsis and rice MADS-box genes. The potato M-type subfamily had 114 members, which is almost three times of the MIKC members (39), indicating that M-type MADS-box genes have a higher duplication rate and/or a lower loss rate during potato genome evolution. Potato MADS-box genes were present on all 12 potato chromosomes with substantial clustering that mainly contributed by the M-type members. Chromosomal localization of potato MADS-box genes revealed that MADS-box genes, mostly MIKC, were located on the duplicated segments of the potato genome whereas tandem duplications mainly contributed to the M-type gene expansion. The potato MIKC subfamily could be further classified into 11 subgroups and the TT16-like, AGL17-like, and FLC-like subgroups found in Arabidopsis were absent in potato. Moreover, the expressions of potato MADS-box genes in various tissues were analyzed by using RNA-seq data and verified by quantitative real-time PCR, revealing that the MIKCC genes were mainly expressed in flower organs and several of them were highly expressed in stolon and tubers. StMADS1 and StMADS13 were up-regulated in the StSP6A-overexpression plants and down-regulated in the StSP6A-RNAi plant, and their expression in leaves and/or young tubers were associated with high level expression of StSP6A. Conclusion Our study identifies the family members of potato MADS-box genes and investigate the evolution history and functional divergence of MADS-box gene family. Moreover, we analyze the MIKCC expression patterns and screen for genes involved in tuberization. Finally, the StMADS1 and StMADS13 are most likely to be downstream target of StSP6A and involved in tuber development