A new method for evaluating air quality using an ideal grey close function cluster correlation analysis method

To scientifically and reasonably evaluate air quality with a large amount of monitored data, this paper proposes a new evaluation method called ideal grey close function cluster correlation analysis (IGCFCCA). Taking the air quality in Ningxia Province, China, as an example, according to China’s air quality standard, SO2, NO2, PM10, PM2.5 and O3 are selected as evaluation indexes to perform the evaluation. The results show that the air quality in this region in 2018 can be divided into three classifications, among which the relatively poor air quality in March, April and May is the first classification, the better air quality in August and September is the third classification, and the air quality in other months falls under the second classification. Correlation analysis is used to qualitatively determine that these three classifications correspond to first-level air quality in China’s air quality standard, and the correlation degree, which is the distance between the three classifications and the first-level air quality, is quantitatively determined. Specifically, the correlation degrees of the first-classification, second-classification and third-classification of air quality are 0.674, 0.697 and 0.71, respectively. The research results indicate potential directions and objectives for air quality management to achieve scientific management.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

LINE-1 and Alu Promote Breast Tumor Progression Via Forming Chimeric Transcripts

Background：LINE-1 (L1) and Alu were reported to regulate tumor development and progression by constituting chimeric transcripts with diverse sequences. The landscape of L1 or Alu chimeric transcripts in breast tumor has not been reported yet, so breast tumor L1 and Alu chimeric transcripts were investigated in this study by analyzing more than 50 billion RNA sequences of breast tumor tissues and adjacent normal tissues from TCGA database. Results：The expression of L1 and Alu in breast tumor tissues were significantly higher than that in the non-tumor tissues. High expression of L1 and Alu in breast tumor predicted poor prognosis. Further exploration demonstrated that L1 and Alu were extensively chimerically expressed with adjacent transcripts. 651 L1-mRNA chimeric transcripts and 1525 Alu-mRNA chimeric transcripts showed significantly different frequencies between non-tumor and tumor tissues. 1009 L1-lncRNA chimeric transcripts and 2575 Alu-lncRNA chimeric transcripts had significantly different frequencies between non-tumor and tumor tissues. Function cluster analysis demonstrated that these differently chimerically expressed genes were involved in multi facets of tumorigenesis, including metabolism, signal transduction, immune reaction, cell cycle and apoptosis, etc. Conclusions：Chimeric expression with different sequences might be an important mechanism for L1 and Alu to promote breast tumor progression.

The Mechanisms of Shcisandrol A in Immune Function Modulation in Immunosuppressed Mice

The population of people with immunodeficiency is increasing due to the accelerating pace of life, increase in work pressure, and lack of exercise, irregularity of diet and rest, and problems of environmental pollution. Chinese herbal medicines have been shown to improve immunity, with little to no side effects. In recent years, studies have shown that Shcisandrol A (Sch A) regulates immune functioning and inhibits inflammation of the nervous system. The current study used gene expression profiling of spleen tissue to screen differentially expressed genes related to Sch A treatment on cyclophosphamide (Cy)-induced immunosuppressed mice. The differentially expressed gene-related pathways were analyzed by gene ontology function cluster analysis and qPCR. Five genes related to immune functioning were found to be regulated by Sch A treatment: Mapk3, Pik3r1, Pik3r5, Ikbkg, and Cd247. qPCR analysis showed that all five genes were significantly down-regulated in mice treated with Sch A compared to untreated immunosuppressed mice. These results suggest potential mechanisms through which Sch A regulates immune functioning.

Cluster strong lensing: a new strategy for testing cosmology with simulations

Comparisons between observed and predicted strong lensing properties of galaxy clusters have been used to claim either tension or consistency with ΛCDM cosmology. However, standard approaches to such tests are unable to quantify the preference for one cosmology over another. We advocate a Bayesian approach whereby the parameters defining the scaling relation between Einstein radii and cluster mass are treated as the observables. We demonstrate a method of estimating the likelihood for observing these parameters under the ΛCDM framework, using the X-ray selected z > 0.5 MACS clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We account for cluster lens triaxiality within the modelling of the likelihood function. Cluster selection criteria is found to play as important a role as the uncertainty related to the description of star formation and feedback.

Integrative Decomposition Procedure and Kappa Statistics for the Distinguished Single Molecular Network Construction and Analysis

Our method concentrates on and constructs the distinguished single gene network. An integrated method was proposed based on linear programming and a decomposition procedure with integrated analysis of the significant function cluster using Kappa statistics and fuzzy heuristic clustering. We tested this method to identify ATF2 regulatory network module using data of 45 samples from the same GEO dataset. The results demonstrate the effectiveness of such integrated way in terms of developing novel prognostic markers and therapeutic targets.