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Abstract Objective This study aims to investigate the anatomical structure of the C6 pedicle and lateral mass in children aged 0–14 years using CT imaging, providing detailed insights into their growth and development. Methods We conducted a comprehensive measurement of C6. Measurements included width, length, and height of the pedicles, as well as the length, width, and thickness of the lateral masses, and several angular metrics. Regression analysis was performed to understand the growth trends, and statistical analyses were carried out to identify differences between age groups, genders, and sides. Results In children younger than four years, the pedicle width exceeds its height, influencing the diameter of the pedicle screws. By age two to three, the pedicle height and lateral mass thickness reaches 3.0 mm, allowing for the use of 3.0 mm diameter screws. The pedicle transverse angle remains stable. Most parameters showed no significant differences between the left and right sides. Size parameters exhibited significant larger in males than females at ages 0–1, 3–7, and 10–12 years. Regression analysis revealed that the growth trends of size parameters follow cubic or polynomial curves. Most angular metrics follow cubic fitting curves without a clear trend of change with age. Conclusion This study provides a detailed analysis of the anatomical development of the C6 pedicle and lateral masses in children, offering valuable insights for pediatric cervical spine surgeries. The findings highlight the importance of considering age-specific anatomical variations when planning posterior surgical fixation, specifically at C6. It is necessary for us to perform thin-layer CT scans on children and carefully measure various indicators before surgery.

Abstract Mine water inrush is a serious threat to mine safety production. It is very important to identify water inrush source types quickly to prevent and control water damage. In this study, the aqueous chemical components Na+ + K+, Ca2+, Mg2+, Cl−, SO4 2− and HCO3− of different aquifers in Pingdingshan coalfield were selected as the characteristic values, and the Surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water were used as the labels. An intelligent water source discrimination model is proposed by combining data mining, classification models, and reinforcement learning. As outlier data in the samples may interfere with the model recognition ability, the data distribution range was analyzed using box plots, and 20 groups of abnormal samples were excluded. The processed water chemistry data were divided into 80% learning samples and 20% test samples, and the learning samples were fed into a light gradient boosting machine (LightGBM) for training. The tree-structured parson estimator (TPE) obtains the optimal values of the main parameters of LightGBM in a very short time. Substituting the hyperparameters back into the model yields a 13.9% improvement in the accuracy of the model, proving the effectiveness of the TPE algorithm. To further validate the performance of the model, TPE-LightGBM is compared and analyzed with a Random Search-Multi Layer Perceptron Machine (RS-MLP) and Genetic Algorithm-Extreme Gradient Boosting Tree (GA-SVM). The accuracy of TPE-LightGBM, RS-MLP, and GA-SVM is 0.931, 0.759, 0.724 in that order, and the generalization error RMSE is 0.415, 1.05, and 1.313 in that order. The results show that TPE-LightGBM is more advantageous in water source identification and is more resistant to overfitting. By calculating and comparing the information gain of each variable, the contribution of Ca2+ is the highest, so it is necessary to pay attention to the change in Ca2+ concentration. TPE-LightGBM’s high accuracy and generalization ability have a good prospect for the identification of sudden water source types.

Abstract Background Tumor morphology, immune function, inflammatory levels, and nutritional status play critical roles in the progression of intrahepatic cholangiocarcinoma (ICC). This multicenter study aimed to investigate the association between markers related to tumor morphology, immune function, inflammatory levels, and nutritional status with the prognosis of ICC patients. Additionally, a novel tumor morphology immune inflammatory nutritional score (TIIN score), integrating these factors was constructed. Methods A retrospective analysis was performed on 418 patients who underwent radical surgical resection and had postoperative pathological confirmation of ICC between January 2016 and January 2020 at three medical centers. The cohort was divided into a training set (n = 272) and a validation set (n = 146). The prognostic significance of 16 relevant markers was assessed, and the TIIN score was derived using LASSO regression. Subsequently, the TIIN-nomogram models for OS and RFS were developed based on the TIIN score and the results of multivariate analysis. The predictive performance of the TIIN-nomogram models was evaluated using ROC survival curves, calibration curves, and clinical decision curve analysis (DCA). Results The TIIN score, derived from albumin-to-alkaline phosphatase ratio (AAPR), albumin–globulin ratio (AGR), monocyte-to-lymphocyte ratio (MLR), and tumor burden score (TBS), effectively categorized patients into high-risk and low-risk groups using the optimal cutoff value. Compared to individual metrics, the TIIN score demonstrated superior predictive value for both OS and RFS. Furthermore, the TIIN score exhibited strong associations with clinical indicators including obstructive jaundice, CEA, CA19-9, Child–pugh grade, perineural invasion, and 8th edition AJCC N stage. Univariate and multivariate analysis confirmed the TIIN score as an independent risk factor for postoperative OS and RFS in ICC patients (p

To improve the recycling of solid waste and achieve the secondary utilization of waste resources, this study investigates the preparation of molybdenum tailings composited binders by high temperature calcination using molybdenum tailings, limestone, bauxite, and iron concentrate powder. Besides, to explore the mechanical properties of the new molybdenum tailings composited binders, a molybdenum tailings composited mortar was prepared. The mechanical properties, micro performance and hydration mechanism of the new molybdenum tailings composited binders were studied. Finally, the carbon dioxide emission of green molybdenum tailings composited binders/molybdenum tailings composited mortar was evaluated by the life cycle assessment method. The results show that the compressive strength of molybdenum tailings composited binders gradually increases with the increase of calcination temperature. When the calcination temperature is 1350 °C, the compressive strength and flexural strength at 28 days are 44.17 MPa and 7.56 MPa, respectively, which are 3.65 % and 4.63 % higher than control group made by P·O 42.5 cement. The early strength of molybdenum tailings composited binders increases slowly, and the later strength increases rapidly. In addition, there is more C2S in the matrix, resulting in a slow hydration heat rate and low cumulative hydration heat. Compared with other groups, the hydration kinetic parameters and hydration rate of molybdenum tailings composited binders under 1350 °C were the lowest. The main hydration prodcuts of molybdenum tailings composited binders are C-S-H, calcium hydroxide, and Aft gels. Compared with P·O 42.5 cement paste and mortar, the carbon dioxide emission of molybdenum tailings composited binders/molybdenum tailings composited mortar is reduced by 42.82 % and 29.08 %, respectively, which can indicate that the new binders can produce less carbon dioxide. Overall, this study can provide new ways for the development of the new binder mixed with plenty of molybdenum tailings, and offer a novel way for recycling the molybdenum tailings, and also present some valuable experimental results for the application of molybdenum tailings in infrastructures.

Abstract Background Intrahepatic cholangiocarcinoma (ICC) is a highly malignant tumor with a poor prognosis. This study aimed to investigate whether Hemoglobin, Albumin, Lymphocytes, and Platelets (HALP) score and Tumor Burden Score (TBS) serves as independent influencing factors following radical resection in patients with ICC. Furthermore, we sought to evaluate the predictive capacity of the combined HALP and TBS grade, referred to as HTS grade, and to develop a prognostic prediction model. Methods Clinical data for ICC patients who underwent radical resection were retrospectively analyzed. Univariate and multivariate Cox regression analyses were first used to find influencing factors of prognosis for ICC. Receiver operating characteristic (ROC) curves were then used to find the optimal cut-off values for HALP score and TBS and to compare the predictive ability of HALP, TBS, and HTS grade using the area under these curves (AUC). Nomogram prediction models were constructed and validated based on the results of the multivariate analysis. Results Among 423 patients, 234 (55.3%) were male and 202 (47.8) were aged ≥ 60 years. The cut-off value of HALP was found to be 37.1 and for TBS to be 6.3. Our univariate results showed that HALP, TBS, and HTS grade were prognostic factors of ICC patients (all P

Abstract Background Active surveillance (AS), where treatment is deferred until cancer progression is detected by a biopsy, is acknowledged as a way to reduce overtreatment in prostate cancer. However, a consensus on the frequency of taking biopsies while in AS is lacking. In former studies to optimize biopsy schedules, the delay in progression detection was taken as an evaluation indicator and believed to be associated with the long‐term outcome, prostate cancer mortality. Nevertheless, this relation was never investigated in empirical data. Here, we use simulated data from a microsimulation model to fill this knowledge gap. Methods In this study, the established MIcrosimulation SCreening Analysis model was extended with functionality to simulate the AS procedures. The biopsy sensitivity in the model was calibrated on the Canary Prostate Cancer Active Surveillance Study (PASS) data, and four (tri‐yearly, bi‐yearly, PASS, and yearly) AS programs were simulated. The relation between detection delay and prostate cancer mortality was investigated by Cox models. Results The biopsy sensitivity of progression detection was found to be 50%. The Cox models show a positive relation between a longer detection delay and a higher risk of prostate cancer death. A 2‐year delay resulted in a prostate cancer death risk of 2.46%–2.69% 5 years after progression detection and a 10‐year risk of 5.75%–5.91%. A 4‐year delay led to an approximately 8% greater 5‐year risk and an approximately 25% greater 10‐year risk. Conclusion The detection delay is confirmed as a surrogate for prostate cancer mortality. A cut‐off for a “safe” detection delay could not be identified.

Although deep learning has been proven to significantly outperform most traditional methods in the classification of large-scale balanced image datasets, collecting enough samples for defect classification is extremely time-consuming and costly. In this paper, we propose a lightweight defect classification method based on few-shot image generation and self-attention fused convolution features. We constructed a 4-class dataset using welding seam images collected from a solar cell module packaging production line. To address the issue of limited defect samples, especially for classes with less than 10 images, we implemented two strategies. Geometric enhancement techniques were first used to extend the defective images. Secondly, multi-scale feature fusion Generative Adversarial Networks (GANs) were utilized to further enhance the dataset. We then performed the feature-level fusion of convolution neural networks and self-attention networks, achieving a classification accuracy of 98.19%. Our experimental results demonstrate that the proposed model performs well in small sample defect classification tasks. And, it can be effectively applied to product quality inspection tasks in industrial production lines.

Abstract The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

N4-acetylcytidine (ac4C) is one type of RNA modification found in eukaryotes. RNA acetylation modifications are gradually expanding in oncology. However, the role of RNA acetylation modifications in colorectal cancer and its association with colorectal cancer microsatellite status remain unclear. Using public databases and in vitro experiments, we verified the expression and biological function of NAT10, as the key RNA acetylation modification enzyme, in colorectal cancer. The results showed that NAT10 was highly expressed in colorectal cancer, and significantly promoted colorectal cancer cell proliferation. NAT10 was also involved in several aspects of cell homoeostasis such as ion transport, calcium-dependent phospholipid binding, and RNA stability. NAT10 expression positively correlated with immune infiltration in colorectal cancer. We further constructed a risk regression model for mRNA acetylation in colorectal cancer using acetylation-related differential genes. We found that tumour immune infiltration, microsatellite instability (MSI) proportion, tumour immune mutation burden, and patient response to immunotherapy were positively correlated with risk scores. For the first time, our study showed that the level of mRNA acetylation modification level is elevated in colorectal cancer and positively correlates with immune infiltration and microsatellite status of patients. Based on our findings, NAT10 may be a new target for colorectal cancer treatment.

Abstract Background In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. Methods The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. Results The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. Conclusion In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Colon cancer is one of the most prevalent cancers and exhibits high mortality worldwide. Despite the certain success in the immunotherapy of many tumor types, the limited response of colon cancer to immunotherapy remains a difficult problem. Retinoic acid-inducible gene-I (RIG-I) is a crucial component in innate antiviral immunity, but its role in antitumor immunity remains unclear. Here, in this report, we found that silencing RIG-I decreased resistance to tumor cells killed by T cells and attenuated colon tumor growth in immunocompetent mice. Meanwhile, overexpressing RIG-I promoted tumor progression, and high expression of RIG-I sensitized cells to anti-programmed cell death protein-1 (PD-1) therapy in vivo. Interestingly, we found that RIG-I influenced programmed cell death ligand 1 (PD-L1) expression to promote colon cancer immune evasion without relying on type I interferon stimulation. Mechanistically, RIG-I could compete with Speckle Type POZ protein (SPOP) to bind PD-L1, leading to attenuation of the polyubiquitination and proteasomal degradation of PD-L1. Collectively, our work reveals new insights into the contribution of RIG-I to driving immune evasion by maintaining the stability of PD-L1 through post-translational modification and provides a promising biomarker of the efficacy of immunotherapy in colon cancer.

BackgroundThe degree of inflammation and immune status is widely recognized to be associated with intrahepatic cholangiocarcinoma (ICC) and is closely linked to poor postoperative survival. The purpose of this study was to evaluate whether the systemic immune-inflammatory index (SII) and the albumin bilirubin (ALBI) grade together exhibit better predictive strength compared to SII and ALBI separately in patients with ICC undergoing curative surgical resection.MethodsA retrospective analysis was performed on a cohort of 374 patients with histologically confirmed ICC who underwent curative surgical resection from January 2016 to January 2020 at three medical centers. The cohort was divided into a training set comprising 258 patients and a validation set consisting of 116 patients. Subsequently, the prognostic predictive abilities of three indicators, namely SII, ALBI, and SII+ALBI grade, were evaluated. Independent risk factors were identified through univariate and multivariate analyses. The identified independent risk factors were then utilized to construct a nomogram prediction model, and the predictive strength of the nomogram prediction model was assessed through Receiver Operating Characteristic (ROC) survival curves and calibration curves.ResultsUnivariate analysis of the training set, consisting of 258 eligible patients with ICC, revealed that SII, ALBI, and SII+ALBI grade were significant prognostic factors for overall survival (OS) and recurrence-free survival (RFS) (p < 0.05). Multivariate analysis revealed the independent significance of SII+ALBI grade as a risk factor for postoperative OS and RFS (p < 0.05). Furthermore, we conducted an analysis of the correlation between SII, ALBI, SII+ALBI grade, and clinical features, indicating that SII+ALBI grade exhibited stronger associations with clinical and pathological characteristics compared to SII and ALBI. We constructed a predictive model for postoperative survival in ICC based on SII+ALBI grade, as determined by the results of multivariate analysis. Evaluation of the model’s predictive strength was performed through ROC survival curves and calibration curves in the training set and validation set, revealing favorable predictive performance.ConclusionThe SII+ALBI grade, a novel classification based on inflammatory and immune status, serves as a reliable prognostic indicator for postoperative OS and RFS in patients with ICC.

Background Osteoporosis, a prevalent orthopedic issue, significantly influences patients’ quality of life and results in considerable financial burden. The objective of this study was to develop and validate a clinical prediction model for osteoporosis risk, utilizing computer algorithms and demographic data. Method In this research, a total of 4,552 residents from Shanghai were retrospectively included. LASSO regression analysis was executed on the sample’s basic characteristics, and logistic regression was employed for analyzing clinical characteristics and building a predictive model. The model’s diagnostic capacity for predicting osteoporosis risk was assessed using R software and computer algorithms. Results The predictive nomogram model for bone loss risk, derived from the LASSO analysis, comprised factors including BMI, TC, TG, HDL, Gender, Age, Education, Income, Sleep, Alcohol Consumption, and Diabetes. The nomogram prediction model demonstrated impressive discriminative capability, with a C-index of 0.908 (training set), 0.908 (validation set), and 0.910 (entire cohort). The area under the ROC curve (AUC) of the model was 0.909 (training set), 0.903 (validation set), and applicable to the entire cohort. The decision curve analysis further corroborated that the model could efficiently predict the risk of bone loss in patients. Conclusion The nomogram, based on essential demographic and health factors (Body Mass Index, Total Cholesterol, Triglycerides, High-Density Lipoprotein, Gender, Age, Education, Income, Sleep, Alcohol Consumption, and Diabetes), offered accurate predictions for the risk of bone loss within the studied population.

Abstract Machine learning is one of the widely used techniques to pattern recognition. Use of the machine learning tools is becoming a more accessible approach for predictive model development in preventing engineering disaster. The objective of the research is to for estimation of water source using the machine learning tools. Random forest classification is a popular machine learning method for developing prediction models in many research settings. The type of mine water in the Pingdingshan coalfield is classified into surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water. Each type of water is encoded with the number 0–4. On the basis of hydrochemical data processing, a random forests model is designed and trained with the hydrochemical data. With respect to the predictive accuracy and robustness, fourfold cross-validation (CV) is adopted for the model training. The results show that the random forests model presented here provides significant guidance for the discrimination of mine water.

Abstract With the increasing depth of coal mining, the geological stress and structure becomes more and more complex. The elevation of No.8 coal floor significantly undulates in the studied coal mine. Even though a lot of boreholes have been drilled, it remains difficult to predict the spatial distribution features and it becomes challenging to plan the tunnel for the working face. Consequently, there has been a great loss of production in the coal mine, therefore, it is of great significance to study the prediction method for coal floor elevation of the working face. The surface spline function can be regarded as an infinite flat plate deformation in pure bending. The fluctuation of the coal floor can be considered to be the result of multi-period tectonic stress applied on the coal seam, so, the prediction of elevation of the coal floor with surface spline method is feasible. An advantage of surface spline method is that any order differentiable smooth surface can be obtained without regular known lattice and boundary derivatives. In the paper, the complete expression of surface spline function is derived, which is used to predict the elevation of No.8 coal floor of the coal mine. The results show that the trend of elevation of the coal floor can be extrapolated by known data, and the maximum error is 20 m, the minimum error is 0 m, and the error of 80% of the data is less than 3 m. The general trend of the coal floor has been predicted well. However, local peaks and valleys could not be predicted correctly, therefore, the first and second order derivative are projected to predict the peaks and valleys in the head of the tunneling.

Worldwide most agroecosystems effort to increase production and yields and leads to damages of a series of non-provisioning ecosystem services (ESs). To fill in the knowledge gaps pertaining to the understanding of complex relationship between agricultural harvests and other ESs, therefore this study aims to estimate the existence of Environmental Kuznets Curve (EKC) for agricultural ESs by incorporating the spatial factors. Based on the test of the spatial autocorrelation of agricultural ESs, the estimation results of spatial model are compared with general regression to explain the spatial effect of agricultural ESs. The results show that (1) contrary to expectation, the curve of the nonlinear relationship between agricultural ESs and annual household income is an inverted U-shape, and not an upright U-shape; (2) compared to non-spatial model, the turning point of the inverted U-shaped curve for agricultural ESs under the direct effect would happen earlier and happen later under the indirect effect; (3) years of formal education, vegetation coverage of field margin and cultivated land area have significantly impact on local agricultural ESs, and local perennial crops has significantly impact on agricultural ESs of neighboring villages. Results of this study have a promising application prospect to promote sustainable development of agriculture.

Radiomics was proposed by Lambin et al. in 2012 and since then there has been an explosion of related research. There has been significant interest in developing high-throughput methods that can automatically extract a large number of quantitative image features from medical images for better diagnostic or predictive performance. There have also been numerous radiomics investigations on intrahepatic cholangiocarcinoma in recent years, but no pertinent review materials are readily available. This work discusses the modeling analysis of radiomics for the prediction of lymph node metastasis, microvascular invasion, and early recurrence of intrahepatic cholangiocarcinoma, as well as the use of deep learning. This paper briefly reviews the current status of radiomics research to provide a reference for future studies.

Abstract Background Studies have shown that DAB2IP inhibits cancer progression, while HSP90AA1 promotes cancer progression. However, the specific regulatory mechanism of DAB2IP and HSP90AA1 in colorectal cancer (CRC) is not clear. Our aim is to investigate the role and mechanism of DAB2IP and HSP90AA1 in the development of CRC. Methods We used bioinformation to analyze the interaction between DAB2IP and HSP90AA1 and predict their downstream pathways. Then, a series of in vitro and in vivo experiments were conducted to reveal the role of DAB2IP and HSP90AA1 in the invasion and metastasis of colorectal cancer, and flow cytometry was used to explore their effects on apoptosis. Results Loss of DAB2IP was associated with poor prognosis of CRC. In contrast, elevated expression of HSP90AA1 was associated with the malignant behavior of CRC. The present study demonstrated a negative correlation between DAB2IP and HSP90AA1. Using bioinformatic analysis, we scanned SRP9 which was highly expressed in CRC, as a co-related gene of DAB2IP and HSP90AA1. Mechanistically, DAB2IP promoted apoptosis through HSP90AA1/SRP9/ASK1/JNK signaling axis in CRC. Conclusions These findings provide evidence that DAB2IP-based therapy may enhance the anticancer effect of HSP90AA1 inhibitors, and combined targeting of DAB2IP and HSP90AA1 may be a powerful treatment strategy to combat CRC.

In a complex multiaquifer mine, discriminant approaches that have previously been presented cannot successfully locate water sources. With multiple processing layers, computing models may learn representations of data at various levels of abstraction. Modern technology in many domains has significantly enhanced these models. The problem of distinguishing the source of mine water in mines and tunnels has been addressed by studying the hydrochemical components of the Pingdingshan coalfield and applying the multilayer perceptron neural network (MLP) method to discriminate the source of the mine water. There were five types of mine water in the Pingdingshan coalfield. Each type of water was encoded with the numbers 0 to 4. The one-hot code method was used to encode the numbers, which is the output set. On the basis of hydrochemical data processing, the MLP model was developed using a characteristic ion contrast on aquifers with distinctive chemical properties. The research results show that two hidden layers (with 10 neurons in each hidden layer) for the model were fit for completing the prediction process with a better performance. This approach enabled us to discriminate water sources for the Pingdingshan coalfield and could be tried for other coalfields with similar hydrogeological conditions.

Abstract Dormant cancer cells drive recurrence and drug resistance, which lead to poor prognosis in colorectal cancer (CRC). The mechanisms that regulate the entry of cancer cells into dormancy remain to be extensively studied. Nanog is a master transcription factor to maintain the self-renewal and pluripotency of stem cells. Since dormant cancer cells are similar to quiescent cancer stem cells, the correlation between dormant state and Nanog in CRC is worth to be explored. Serum deprivation is a common method to establish experimental cellular dormancy model. Here, we verified that serum deprivation-induced CRC cells to enter a cellular dormancy state, characterized by no proliferation, no death, no senescence, resistance to chemotherapy, high expression of dormant markers, metabolic suppression, and recovery to active status. Interestingly, we further identified that Nanog was upregulated in dormant CRC cells. Nanog knockdown could destroy the dormant state of serum-deprived CRC cells while Nanog overexpression could induce dormancy in CRC cells. Mechanistically, Nanog was regulated through a fatty acid oxidation (FAO)/ATP citrate lyase (ACLY)-dependent pathway. FAO increased ACLY expression to promote the synthesis of acetyl-CoA, which was transferred by P300 to accelerate H3K27 acetylation of Nanog promoter. Then, Nanog upregulation increased the transcription of P21 and P27, which promoted the dormancy of CRC cells. Our findings revealed that Nanog could induce cellular dormancy in CRC cells and unlocked a specific mechanism to govern the process.

As the most prevalent subtype of aortic aneurysm, abdominal aortic aneurysm (AAA) features the apoptosis, extracellular matrix (ECM) disruption, and inflammation response of vascular smooth muscle cells (VSMCs). Noncoding RNAs (ncRNAs) are crucial factors in AAA progression, while the investigations have not been fully explained. miR-191-5p upregulation is found in aortic aneurysm. However, its role in AAA has not been addressed. This research purposed to excavate the possible and associated molecular axis of miR-191-5p in AAA. In our study, miR-191-5p level was detected to be high in the tissues from AAA patients in comparison with the control group. After miR-191-5p expression was enhanced, cell viability was repressed, cell apoptosis was boosted, and ECM disruption and the inflammation response were fortified. Furthermore, the relationship among MIR503HG, miR-191-5p, and phospholipase C delta 1 (PLCD1) in VSMCs was disclosed via mechanism assays. Decreased MIR503HG lacked the inhibition on miR-191-5p targeting PLCD1, resulting in downregulation of PLCD1, which facilitated the progression of AAA. Thus, targeting MIR503HG/miR-191-5p/PLCD1 pathway will provide an additional method for the cure of AAA patients.

BackgroundTuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment.MethodsA total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group.ResultsThe results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644.ConclusionWe used ML methods to screen out the blood-specific factors—PDW, LYM, AST/ALT, BUN, and Na+—of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.

In this study, silage Pennisetum sinese Roxb-based diet was replaced with fermented pineapple residue (FPR) at the replacement ratio of 0% (CON), 25% (T25), and 50% (T50) in fattening Simmental bulls for 30 days to evaluate the effects of FPR on growth performance, serum indexes, and ruminal characteristics. A total of 30 Simmental bulls (546 ± 44 kg initial BW) were allocated to three groups according to a completely randomized design. On day 30, the slaughter performance and meat quality were determined. Rumen fluids were collected for analyzing the rumen fermentation parameters and microbiota composition on day 30. The results showed that the average daily weight gain increased (P < 0.05) as the proportion of FPR rose. Within treatments, the T25 group reached more profit (5.34 RMB per day per bull) than CON while T50 was 3.69. The content of crude fat, cysteine, and proline in the muscle of T50 increased significantly (P < 0.05). The amounts of tyrosine, proline, and phenylalanine were significantly increased in the T25 (P < 0.05). The beta diversity analysis showed significant differences among the rumen bacterial flora of each group (P < 0.05). In the T25 group, the relative abundance of Spirochaetes decreased significantly (P < 0.05). The relative abundance of Lachnospiraceae_bacterium_RM44 was significantly lower (P < 0.05). Thus, FPR could improve the growth performance, economic benefits, and meat quality without adverse effects on ruminal characteristics.

In recent years, inrush water has hampered the regular mining of coal mines, and the proper identification of the source of inrush water is critical to the prevention and management of water hazards in mines. This paper extracts the standard water chemistry discriminating ions Na++K+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− from observed water samples. An improved water source discrimination model is proposed which combines algorithms from data mining, classification models, and learning reinforcement. According to the Pearson correlation coefficient, Na++K+ has a strong correlation with HCO3−. To identify the major metrics, we performed principal component analysis (PCA), and the adaptive differential evolutionary genetic algorithm (GA) was utilized to optimize the depth of the extreme tree (ET) and the number of classifiers. Finally, the model distinguished 25 sets of studied samples from various water sources in the Pingdingshan coalfield. Comparative analysis demonstrated the efficacy of each stage of our work. PCA-GA-ET outperformed the conventional approaches, such as the support vector machine, BP artificial neural network, and random forest. The studies revealed that PCA-GA-ET can eliminate the information overlap between data and simplify the data structure and thereby improve the efficiency and accuracy of water source detection. We discovered that by utilizing the evolutionary algorithm to optimize parameters such as the depth of the extreme trees and the number of decision trees, we could get the model to converge faster and to be more stable and more accurate. The results suggest that PCA-GA-ET has good robustness and accuracy and can meet the needs of water source identification.

In recent years, environmental degradation and the COVID-19 pandemic have seriously affected economic development and social stability. Addressing the impact of major public health events on residents’ willingness to pay for environmental protection (WTPEP) and analyzing the drivers are necessary for improving human well-being and environmental sustainability. We designed a questionnaire to analyze the change in residents’ WTPEP before and during COVID-19 and an established ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multiscale GWR to explore driver factors and scale effects of WTPEP based on the theory of environment Kuznets curve (EKC). The results show that (1) WTPEP is 0–20,000 yuan before COVID-19 and 0–50,000 yuan during COVID-19. Residents’ WTPEP improved during COVID-19, which indicates that residents’ demand for an ecological environment is increasing; (2) The shapes and inflection points of the relationships between income and WTPEP are spatially heterogeneous before and during COVID-19, but the northern WTPEP is larger than southern, which indicates that there is a spatial imbalance in WTPEP; (3) Environmental degradation, health, environmental quality, and education are WTPEP’s significant macro-drivers, whereas income, age, and gender are significant micro-drivers. Those factors can help policymakers better understand which factors are more suitable for macro or micro environmental policy-making and what targeted measures could be taken to solve the contradiction between the growing ecological environment demand of residents and the spatial imbalance of WTPEP in the future.

Abstract Background Inhibition of angiogenesis in prostatic cancer could be a brand-new method to suppress tumour progression. Nodal/ALK4 has been associated with vascularization in many cancers. However, the relationship between and role of Nodal/ALK4 and miR-185 in human prostatic cancer is still unknown. Methods Prostatic cancer DU145 cells and LNCaP cells were used to investigate the angiogenic effect induced by Nodal and the anti-angiogenic roles of miR-185. Colony formation assay, MTT assay, transwell assay and tube formation assay were used to explore cell proliferation, migration and tube-forming ability, respectively. A luciferase reporter assay confirmed the binding relationship between miR-185 and ALK4. The expression levels of miR-185, ALK4 and VEGF were detected by qRT-PCR and Western blotting. The effects of miR-185 and Nodal in prostate cancer were also investigated in animal experiments. Results VEGF expression was increased in DU145 cells and LNCaP cells after Nodal incubation, and Nodal activated the proliferation ability of prostatic cancer cells and the migration and tube-forming ability of human umbilical vein endothelial cells (HUVECs), which were all inhibited by treatment with the Nodal inhibitor SB431524. Bioinformatics analysis and luciferase assay were used to verify miR-185 as a target of ALK4. Prostatic cancer cell proliferation was inhibited by overexpression of miR-185, which was shown to regulate the migration and angiogenesis of HUVECs by targeting ALK4 for suppression. miR-185 also showed a significant inverse correlation with Nodal treatment and reversed the angiogenic effects induced by Nodal. More importantly, for the first time, xenograft experiments indicated that overexpression of miR-185 suppressed tumour development. Conclusion The Nodal/ALK4 pathway is important in the angiogenesis of prostate cancer and can be inhibited by targeting miR-185 to downregulate ALK4. These findings provide a new perspective on the mechanism of prostate cancer formation.

The deep coal mining in the North China type of coalfield is generally threatened by the underlying limestone water of Taiyuan Formation and Ordovician. The occurrence of water inrush can be avoided effectively by applying grouting reinforcement technology to the coal floor. However, the reinforcement treatment of the coal floor belongs to underground concealment engineering, and it is of great significance for the safe production of the coal mine by scientifically and comprehensively evaluating the technical method of the grouting effect on the working face floor. In this study, the optimal transfer matrix is used to construct the judgment matrix that meets the consistency requirements; the analytic hierarchy process is improved; and the grouting effect of the working face floor is evaluated by fuzzy comprehensive evaluation based on several factors of the grouting effect. Taking the grouting engineering of the 15092 working face of the Guhanshan Mine as an example, the evaluation of the grouting effect based on four evaluatory indices have been refined: dynamic hydrological features, grout amount, grouting inspection hole, and geophysical prospecting have been refined. Based on the improved analytic hierarchy process (AHP), the result of the grouting effect can be divided into four levels: distinction, good, average, and poor. The study would play a very important role in the evaluation of the grouting reinforcement of the working face floor and the practice of coal mine production safety.

Background. Cancer stem cells (CSCs) are typically related to metastasis, recurrence, and drug resistance in malignant tumors. However, the biomarker and mechanism of CSCs need further exploration. This study is aimed at comprehensively depicting the stemness characteristics and identify a potential stemness-associated biomarker in hepatocellular carcinoma (HCC). Methods. The data of HCC patients from The Cancer Genome Atlas (TCGA) were collected and divided based on the mRNA expression-based stemness index (mRNAsi) in this study. Weighted gene coexpression network analysis (WGCNA) and the protein-protein interaction (PPI) network were performed, and the genes were screened through the Cytoscape software. Then, we constructed a prognostic expression signature using the multivariable Cox analysis and verified using the GEO and ICGC databases. Even more importantly, we used the three-dimensional (3D) fibrin gel to enrich the tumor-repopulating cells (TRCs) to validate the expression of the signature in CSCs by quantitative RT-PCR. Results. mRNAsi was significantly elevated in tumor and high-mRNAsi score was associated with poor overall survival in HCC. The positive stemness-associated (blue) module with 737 genes were screened based on WGCNA, and Budding uninhibited by benzimidazoles 1 (BUB1) was identified as the hub gene highly related to stemness in HCC. Then, the prognostic value and stemness characteristics were well validated in the ICGC and GSE14520 cohorts. Further analysis showed the expression of BUB1 was elevated in TRCs. Conclusion. BUB1, as a potential stemness-associated biomarker, could serve as a therapeutic CSCs-target and predicted the clinical outcomes of patients with HCC.

Objective. Adenocarcinoma with mixed subtypes (AM) is a histological classification based on the WHO classification. We aimed to compare the prognosis among AM, classic adenocarcinoma (CA), mucinous adenocarcinoma (MAC), and signet-ring cell carcinoma (SRCC) in early and advanced gastric cancer (EGC and AGC), respectively. Methods. The Surveillance, Epidemiology, and End Results (SEER) database was queried from 2001 to 2016. Univariate and multivariate Cox analyses were performed to compare prognosis between AM and histologic subtypes of CA, SRCC, and MAC in ECG and ACG. A nomogram was established to predict the cancer-specific survival (CSS) of gastric cancer (GC) patients with AM. C-index, calibration curves, and receiver operating characteristic (ROC) and decision curve analysis (DCA) curves were applied to examine the accuracy and clinical benefits. Results. In the prognosis among these four histological subtypes in EGC patients, there are no differences. For AGC patients, AM had a significantly poorer prognosis compared with CA and MAC (P=0.003, 0.029) but similar prognosis to SRCC. A nomogram based on race, T stage, N stage, M stage, and surgical modalities was proposed to predict 1- and 3-year CSS for GC patients with AM (C-index: training cohort: 0.804, validation cohort: 0.748. 1- and 3-year CSS AUC: training cohort: 0.871 and 0.914, validation cohort: 0.810 and 0.798). 1- and 3-year CSS DCA curves showed good net benefits. Conclusions. EGC patients with AM had similar survival to those with CA, MAC, and SRCC. AM was an independent predictor of poor prognosis in AGC. A nomogram for predicting the prognosis of GC patients with AM was proposed to quantitatively assess the long-term survival.

To build a quantum computing device, which is capable of generating arbitrary input states and performing universal unitary gate operations (UUGOs), is an important goal in the field of quantum information science. However, only a few special quantum computations have been reported by now based on specific input states and well-designed information processors. Here, we demonstrate a flexible scheme for two-qubit quantum computations by employing the polarization and the spatial mode of a single photon. Two-qubit UUGOs both in free-space optics and for arbitrary pure input states consisting of separable states and entangled states are presented. Quantum state tomography and process tomography are used to characterize the fidelity of the output states and the gate operations we considered. Beyond a demonstration, we believe that our work also enriches the techniques of bulk-optics for quantum information study and has a broad application for other fundamental research.

The present study aimed to investigate the colonization process of epithelial bacteria attached to the rumen and intestinal tract tissue during the development of goats after birth. However, this process from fetus to adulthood was very limited. In goats, the rumen and cecum are two important fermentation organs, and it is important to study the acquisition and development of the neonatal microbiome, as well as the difference between these two organs. To characterize the microbial establishment and dynamic changes in the rumen and cecum from fetus to adulthood, we performed 16S rRNA gene sequencing for 106 samples from 47 individuals of nine pregnant mother–fetus pairs and 16 kids from birth up to 6 months. The diversity, structure and composition of the microbial communities were distinct between the rumen and cecum after birth, while they were similar in the fetal period. The study showed a rapid loss and influx of microbes at birth, followed by slight selection after drinking colostrum, and then a strong selection after weaning, suggesting that the establishment and dynamic fluctuations of the gut microbiome undergoes three distinct phases of microbiome progression in life: a conserved phase (during late pregnancy in the fetus), a transitional phase (newborn until weaning), and a stable phase (from weaning to adulthood). The results supported the view that microbes exist in the fetus, and revealed the establishment and dynamic fluctuations of the gut microbiome from fetus to adulthood in goats.

Classical deep learning algorithms have aroused great interest in both academia and industry for their utility in image recognition, language translation, decision-making problems and more. In this work, we have provided a quantum deep learning scheme based on multi-qubit entanglement states, including computation and training of neural network in full quantum process. In the course of training, efficient calculation of the distance between unknown unit vector and known unit vector has been realized by proper measurement based on the Greenberger–Horne–Zeilinger entanglement states. An exponential speedup over classical algorithms has been demonstrated. In the process of computation, quantum scheme corresponding to multi-layer feedforward neural network has been provided. We have shown the utility of our scheme using Iris dataset. The extensibility of the present scheme to different types of model has also been analyzed.

We report on the experimental realization of the analogy of quantum dense coding in classical optical communication using classical optical correlations. Compared to quantum dense coding that uses pairs of photons entangled in polarization, we find that the proposed design exhibits many advantages. Considering that it is convenient to realize in optical communication, the attainable channel capacity in the experiment for dense coding can reach 2 bits, which is higher than that of the usual quantum coding capacity (1.585 bits). This increased channel capacity has been proven experimentally by transmitting ASCII characters in 12 quaternary digitals instead of the usual 24 bits.