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Background: Central compartment atopic disease (CCAD) is a subtype of chronic rhinosinusitis (CRS). Research focusing on the endoscopic sinus surgery (ESS) outcomes of CCAD is limited. This study aimed to evaluate the outcomes of ESS in CCAD and compared to 2 following subtypes: chronic rhinosinusitis with nasal polyps (CRSwNP) and concomitant polypoid disease in the central compartment (CRSwNP/CC) and CRSwNP not otherwise specified (CRSwNP NOS). Methods: This case-control study enrolled patients with bilateral CRSwNP who underwent ESS and had at least 1 year of follow-up. Patients were classified into CCAD, CRSwNP/CC, and CRSwNP NOS. The demographic data, preoperative disease severity, and surgery outcomes, including CRS control status, endoscopic score, and symptom scores at 1 year postoperatively, were collected. We defined well controlled and partly controlled as appropriate disease control. Results: This study screened 259 patients and enrolled 138 patients with complete medical records and 1-year follow-up (CCAD N = 51, CRSwNP/CC N = 55, CRSwNP NOS N = 32). Among them, appropriate disease control was achieved in 84.3% of patients (43/51) in the CCAD group, 69.1% (38/55) in the CRSwNP/CC group, and 93.7% (30/32) in the CRSwNP NOS group (P = 0.029). Then we performed post-hoc analysis using appropriate disease control and uncontrolled. There was a significant difference between CRSwNP/CC and CRSwNP NOS (P = 0.007), but no significant difference compared CCAD group to CRSwNP/CC group (P = 0.065) and CRSwNP NOS group (P = 0.199). There were significant differences in endoscopic E-score among groups (P

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.

Background: Type 2 CRSwNP is characterized by severe symptoms, multiple comorbidities, longer recovery course and high recurrence rate. A simple and cost-effective diagnostic model for CRSwNP endotype integrating clinical characteristics and histopathological features is urgently needed. Objective: To establish a clinical diagnostic model of inflammatory endotype in CRSwNP based on the clinical characteristics, pathological characteristics, and cytokines profile in the polyp tissue of patients. Methods: A total of 244 participants with CRSwNP were enrolled at 2 different centers in China and Belgium from 2018 to 2020. IL-5 level of nasal polyp tissue was used as gold standard. Clinical characteristics were used to establish diagnostic models. The area under the receiver operating curve (AUC) was used to evaluate the diagnostic performance. The study was approved by the ethics board of the First Affiliated Hospital of Sun Yat-sen University ([2020] 302), and written informed consent was obtained from all subjects before inclusion. Results: In total, 134 patients from China (training set) and 110 patients from Belgium (validation set) were included. The logistic regression (LR) model in predicting inflammatory endotype of CRSwNP showed the AUC of 83%, which was better than the diagnostic performance of machine learning models (AUC of 61.14%–82.42%), and single clinical variables. We developed a simplified scoring system based on LR model which shows similar diagnostic performance to the LR model (P = 0.6633). Conclusion: The LR model in this diagnostic study provided greater accuracy in prediction of inflammatory endotype of CRSwNP than those obtained from the machine learning model and single clinical variable. This indicates great potential for the use of diagnostic model to facilitate inflammatory endotype evaluation when tissue cytokines are unable to be measured.

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.

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.

The temporal and spatial development law of regional geohazards is the result of the joint action of internal and external geological forces, which are relatively stable within a large area. Therefore, the source, magnitude and occurrence time of earthquakes are the main factors affecting the temporal and spatial development law of regional geohazards. In order to study the impact of "5·12" Wenchuan earthquake on the spatial and temporal development of geohazards in Sichuan Province, based on the data of landslide, collapse, debris flow, earthquake and rainfall from 1998 to 2018, the spatial and temporal characteristics of major geohazards in Sichuan Province around the Wenchuan earthquake were analyzed by mathematical statistics and spatial analysis methods, The results show that: ① Activity in major disasters in Sichuan Province shows cycles, under normal circumstances, it presents a small cycle of about 3 years, after the earthquake geohazards increased year by year, to the peak of 4a, then decreased year by year; the impact is not obvious after 8 years; ② The relative intensity of major geohazards has little change after the earthquake, but the activity and intensity of geohazards are not corresponding before the earthquake. In the geomorphic division, the largest number of geohazards is in Sichuan Basin, and the most active is in Northwest Sichuan Plateau. Compared with that before the earthquake, the occurrence of geohazards in the quaternary system is higher than that in the hard rock area. The geohazards in the tectonic zones have obvious zoning characteristics. The earthquake intensity of the Yangtze quasi-platform is much greater than that of the Songpan-Ganzi geosynclinal fold system before and after the earthquake, but the disaster activity is obviously weaker than that of the Songpan-Ganzi geosynclinal fold system, and the basic structural units have similar characteristics.

The research on the spatial distribution and evolution of ancient settlements from the Neolithic Age to the Bronze Age in Dalian area is of great significance for a profound understanding of the prehistoric human-land relationship in Dalian area. The results showed that: ① The number of settlement sites in Dalian area showed an overall upward trend from the Mid-Neolithic to the Bronze Age. Most sites during the three periods studied were to be found 0–50 m above sea level (asl), with a significant increase in the number of sites located >50 m asl during the Bronze Age. In terms of gradient of slope, all settlements were concentrated on coastal plains or in low-altitude, hilly areas on relatively gentle slopes ranging between 0 and 6°. Settlements from each period faced in all directions, and were mainly located in river valleys or low-altitude mountain valleys with low terrains.② The proximity of ancient settlements to river courses during the Mid Holocene evolved, with Mid and Late Neolithic settlements usually distributed in the range beyond 3 km of the river buffer zone; the distribution of Bronze Age sites was relatively uniform, reflecting an increasing demand for water resources; the density of settlements was clustered between the Mid Neolithic and the Bronze Age, Neolithic sites being both the smallest in number and the most concentrated. ③ The spatiotemporal distribution and evolution of settlements in the Dalian area were influenced by the contemporary climate: the Mid Neolithic climate was warm and humid, and settlements were distributed principally on the various islands near present-day Dalian, and especially Guanglu Island; the Late Neolithic climate became increasingly drier and cooler, stimulating cultural advances and a slight increase in the number of ancient settlements, which gradually developed in the areas in and around present-day Dalian; during the cold and dry Bronze Age, the number of settlements across the Dalian region reached their peak, and it is widely distributed in various areas of Dalian. To a certain extent, this reflected a continuous improvement in the ability of the human population to adapt to the natural environment.

Phytopathogenic Fusarium graminearum poses significant threats to crop health and soil quality. Although our laboratory-cultivated Pseudomonas sp. P13 exhibited potential biocontrol capacities, its effectiveness against F. graminearum and underlying antifungal mechanisms are still unclear. In light of this, our study investigated a significant inhibitory effect of P13 on F. graminearum T1, both in vitro and in a soil environment. Conducting genomic, metabolomic, and transcriptomic analyses of P13, we sought to identify evidence supporting its antagonistic effects on T1. The results revealed the potential of P13, a novel Pseudomonas species, to produce active antifungal components, including phenazine-1-carboxylate (PCA), hydrogen cyanide (HCN), and siderophores [pyoverdine (Pvd) and histicorrugatin (Hcs)], as well as the dynamic adaptive changes in the metabolic pathways of P13 related to these active ingredients. During the logarithmic growth stage, T1-exposed P13 strategically upregulated PCA and HCN biosynthesis, along with transient inhibition of the tricarboxylic acid (TCA) cycle. However, with growth stabilization, upregulation of PCA and HCN synthesis ceased, whereas the TCA cycle was enhanced, increasing siderophores secretion (Pvd and Hcs), suggesting that this mechanism might have caused continuous inhibition of T1. These findings improved our comprehension of the biocontrol mechanisms of P13 and provided the foundation for potential application of Pseudomonas strains in the biocontrol of phytopathogenic F. graminearum. [ABSTRACT FROM AUTHOR]