Utilizing artificial intelligence to predict the superplasticizer demand of self-consolidating concrete incorporating pumice, slag, and fly ash powders

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R2), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods

Comparison of quality/quantity mNGS and usual mNGS for pathogen detection in suspected pulmonary infections

Improved metagenomic next-generation sequencing (mNGS), for example, quality/quantity mNGS (QmNGS), is being used in the diagnosis of pulmonary pathogens. There are differences between QmNGS and the usual mNGS (UmNGS), but reports that compare their detection performances are rare. In this prospective study of patients enrolled between December 2021 and March 2022, the bronchoalveolar lavage fluid of thirty-six patients with suspected pulmonary infection was assessed using UmNGS and QmNGS. The sensitivity of QmNGS was similar to that of UmNGS. The specificity of QmNGS was higher than that of UmNGS; however, the difference was not statistically significant. The positive likelihood ratios (+LR) of QmNGS and UmNGS were 3.956 and 1.394, respectively, and the negative likelihood ratios (-LR) were 0.342 and 0.527, respectively. For the co-detection of pathogens, the depth and coverage of the QmNGS sequencing were lower than those of UmNGS, while for the detection of pathogens isolated from patients with pulmonary infection, the concordance rate was 77.2%. In the eleven patients with nonpulmonary infection, only viruses were detected using QmNGS, while UmNGS detected not only viruses but also bacteria and fungi. This study provides a basis for the selection of mNGS for the diagnosis of suspected pulmonary infection

The performance of metagenomic next-generation sequencing in diagnosing pulmonary infectious diseases using authentic clinical specimens: The Illumina platform versus the Beijing Genomics Institute platform

Introduction: Metagenomic next-generation sequencing (mNGS) has been increasingly used to detect infectious organisms and is rapidly moving from research to clinical laboratories. Presently, mNGS platforms mainly include those from Illumina and the Beijing Genomics Institute (BGI). Previous studies have reported that various sequencing platforms have similar sensitivity in detecting the reference panel that mimics clinical specimens. However, whether the Illumina and BGI platforms provide the same diagnostic performance using authentic clinical samples remains unclear.Methods: In this prospective study, we compared the performance of the Illumina and BGI platforms in detecting pulmonary pathogens. Forty-six patients with suspected pulmonary infection were enrolled in the final analysis. All patients received bronchoscopy, and the specimens collected were sent for mNGS on the two different sequencing platforms.Results: The diagnostic sensitivity of the Illumina and BGI platforms was notably higher than that of conventional examination (76.9% vs. 38.5%, p < 0.001; 82.1% vs. 38.5%, p < 0.001; respectively). The sensitivity and specificity for pulmonary infection diagnosis were not significantly different between the Illumina and BGI platforms. Furthermore, the pathogenic detection rate of the two platforms were not significantly different.Conclusion: The Illumina and BGI platforms exhibited similar diagnostic performance for pulmonary infectious diseases using clinical specimens, and both are superior to conventional examinations

Specific N-glycans of Hepatocellular Carcinoma Cell Surface and the Abnormal Increase of Core-α-1, 6-fucosylated Triantennary Glycan via N-acetylglucosaminyltransferases-IVa Regulation

Glycosylation alterations of cell surface proteins are often observed during the progression of malignancies. The specific cell surface N-glycans were profiled in hepatocellular carcinoma (HCC) with clinical tissues (88 tumor and adjacent normal tissues) and the corresponding serum samples of HCC patients. The level of core-α-1,6-fucosylated triantennary glycan (NA3Fb) increased both on the cell surface and in the serum samples of HCC patients (p \u3c 0.01). Additionally, the change of NA3Fb was not influenced by Hepatitis B virus (HBV)and cirrhosis. Furthermore, the mRNA and protein expression of N-acetylglucosaminyltransferase IVa (GnT-IVa), which was related to the synthesis of the NA3Fb, was substantially increased in HCC tissues. Knockdown of GnT-IVa leads to a decreased level of NA3Fb and decreased ability of invasion and migration in HCC cells. NA3Fb can be regarded as a specific cell surface N-glycan of HCC. The high expression of GnT-IVa is the cause of the abnormal increase of NA3Fb on the HCC cell surface, which regulates cell migration. This study demonstrated the specific N-glycans of the cell surface and the mechanisms of altered glycoform related with HCC. These findings lead to better understanding of the function of glycan and glycosyltransferase in the tumorigenesis, progression and metastasis of HCC

Specific N-glycans of Hepatocellular Carcinoma Cell Surface and the Abnormal Increase of Core-α-1, 6-fucosylated Triantennary Glycan via N-acetylglucosaminyltransferases-IVa Regulation

Glycosylation alterations of cell surface proteins are often observed during the progression of malignancies. The specific cell surface N-glycans were profiled in hepatocellular carcinoma (HCC) with clinical tissues (88 tumor and adjacent normal tissues) and the corresponding serum samples of HCC patients. The level of core-α-1,6-fucosylated triantennary glycan (NA3Fb) increased both on the cell surface and in the serum samples of HCC patients (p \u3c 0.01). Additionally, the change of NA3Fb was not influenced by Hepatitis B virus (HBV)and cirrhosis. Furthermore, the mRNA and protein expression of N-acetylglucosaminyltransferase IVa (GnT-IVa), which was related to the synthesis of the NA3Fb, was substantially increased in HCC tissues. Knockdown of GnT-IVa leads to a decreased level of NA3Fb and decreased ability of invasion and migration in HCC cells. NA3Fb can be regarded as a specific cell surface N-glycan of HCC. The high expression of GnT-IVa is the cause of the abnormal increase of NA3Fb on the HCC cell surface, which regulates cell migration. This study demonstrated the specific N-glycans of the cell surface and the mechanisms of altered glycoform related with HCC. These findings lead to better understanding of the function of glycan and glycosyltransferase in the tumorigenesis, progression and metastasis of HCC

The Influence of Radial Stress on Mechanical Properties of Anchorage Structure

The research on the influence of radial stress on the mechanical properties of an anchorage structure has important theoretical and practical significance for optimizing the design of anchorage structures and saving support cost. Firstly, based on the phenomenon of concrete splitting found in the laboratory model test, the influence of radial stress on the mechanical properties of an anchorage structure is analyzed. Secondly, using the criterion of maximum tensile stress and the Mohr–Coulomb criterion, the influence of radial stress on the mechanical properties of rock around the borehole is analyzed. Finally, the influence of radial stress on the shear stress at interface and the ultimate bearing capacity of the anchorage structure is studied by numerical simulation. The results show that the existence of radial stress in the anchorage section greatly improves the bearing capacity of the anchorage structure. With the increase of confining pressure, the maximum value of interfacial shear stress increases obviously. The larger the confining pressure is, the faster the convergence speed of the finite element method (FEM) program is, which shows that the mechanical properties of the anchorage structure are improved obviously. With the increasing confining pressure, the adaptability of the anchorage structure to deformation is stronger and the anchorage structure is less likely to fail. The ultimate bearing capacity of the anchorage structure increases linearly with the increase of confining pressure, and the effect of confining pressure on the ultimate bearing capacity is very significant

Urban Regional Building Energy Planning Model under the Guidance of Network Flow Theory

The satisfactory construction of regional building energy planning models is a key technology in effective energy allocation. At present, the selection of energy planning is only based on artificial judgment criteria, which leads to a high subjectivity in energy planning. This research innovatively introduces the network flow theory into the urban regional building energy planning model. Combined with the actual characteristics of regional building energy planning, the regional building energy planning model was constructed and the regional energy distribution mode was optimized. The model includes the energy supply layer, energy conversion layer, and energy demand layer. At the same time, the minimum cost and maximum flow problem of the model was solved with the help of the BG iterative algorithm. The model includes the energy supply layer, energy conversion layer, and energy demand layer. We used the BG iterative algorithm to solve the minimum cost and maximum flow problem of the model. The accuracy, accuracy, recall rate, and specificity of the four minimum cost maximum flows tended to be stable with the increase of the number of iterations. After the application of BG iterative algorithm, the cost consumption of each part of the regional building energy planning model in summer will be significantly reduced, and the total consumption cost is 929 million dollars. The research results verify the high applicability of introducing the network flow theory and BG iterative algorithm to construct and solve the regional building energy planning model, which can be applied to the rational allocation of resources in the region

The identification of metabolism-related subtypes and potential treatments for idiopathic pulmonary fibrosis

Background: Idiopathic pulmonary fibrosis (IPF) is caused by aberrant repair because of alveolar epithelial injury and can only be effectively treated with several compounds. Several metabolism-related biomolecular processes were found to be involved in IPF. We aimed to identify IPF subtypes based on metabolism-related pathways and explore potential drugs for each subtype.Methods: Gene profiles and clinical information were obtained from the Gene Expression Omnibus (GEO) database (GSE70867 and GSE93606). The enrichment scores for 41 metabolism-related pathways, immune cells, and immune pathways were calculated using the Gene Set Variation Analysis (GSVA) package. The ConsensusClusterPlus package was used to cluster samples. Novel modules and hub genes were identified using weighted correlation network analysis (WGCNA). Receiver operating characteristic (ROC) and calibration curves were plotted, and decision curve analysis (DCA) were performed to evaluate the model in the training and validation cohorts. A connectivity map was used as a drug probe.Results: Two subtypes with significant differences in prognosis were identified based on the metabolism-related pathways. Subtype C1 had a poor prognosis, low metabolic levels, and a unique immune signature. CDS2, LCLAT1, GPD1L, AGPAT1, ALDH3A1, LAP3, ADH5, AHCYL2, and MDH1 were used to distinguish between the two subtypes. Finally, subtype-specific drugs, which can potentially treat IPF, were identified.Conclusion: The aberrant activation of metabolism-related pathways contributes to differential prognoses in patients with IPF. Collectively, our findings provide novel mechanistic insights into subtyping IPF based on the metabolism-related pathway and potential treatments, which would help clinicians provide subtype-specific individualized therapeutic management to patients

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to implement and would enhance research of key asthma variables through data mining techniques. We used routine blood data from healthy individuals to construct a Mahalanobis space (MS). Then, we calculated Mahalanobis distances of the training routine blood data from 355 asthma patients and 1,480 healthy individuals to ensure the efficiency of MS. Orthogonal arrays and signal-to-noise ratios were used to optimize blood biomarker variables. Receiver operating characteristic (ROC) curve was used to determine the threshold value. Ultimately, we validated the system on 182 individuals based on the threshold value. Out of 35 patients with asthma, MTS correctly classified 94.15% of patients. In addition, 97.20% of 147 healthy individuals were correctly classified. The system isolated 7 routine blood biomarkers. Among these biomarkers, platelet distribution width, mean platelet volume, white blood cell count, eosinophil count, and lymphocyte ratio performed well in asthma diagnosis. In brief, MTS shows promise as an accurate method to identify asthma patients based on 7 vital blood biomarker variables and threshold determined by the ROC curve, thus offering the potential to simplify diagnostic complexity and optimize clinical efficiency