Minimum Area Confidence Set Optimality for Simultaneous Confidence Bands for Percentiles in Linear Regression

Simultaneous confidence bands (SCBs) for percentiles in linear regression are valuable tools with many applications. In this paper, we propose a novel criterion for comparing SCBs for percentiles, termed the Minimum Area Confidence Set (MACS) criterion. This criterion utilizes the area of the confidence set for the pivotal quantities, which are generated from the confidence set of the unknown parameters. Subsequently, we employ the MACS criterion to construct exact SCBs over any finite covariate intervals and to compare multiple SCBs of different forms. This approach can be used to determine the optimal SCBs. It is discovered that the area of the confidence set for the pivotal quantities of an asymmetric SCB is uniformly and can be very substantially smaller than that of the corresponding symmetric SCB. Therefore, under the MACS criterion, exact asymmetric SCBs should always be preferred. Furthermore, a new computationally efficient method is proposed to calculate the critical constants of exact SCBs for percentiles. A real data example on drug stability study is provided for illustration.Comment: 26 pages, 6 figure

Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA

BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM

Multiple-use calibration for all future values and exact two-sided simultaneous tolerance intervals in linear regression

Multiple-use calibration using regression is an important statistical tool. Confidence sets for the x-values associated with all future y-values should guarantee a key property, which can be satisfied by simultaneous tolerance intervals (STI’s), and so multiple-use calibration requires the construction ofSTI’s. In this paper, exact two-sided STI’s have been constructed for polynomial regression over any given covariate interval. There is a misconception that two-sided pointwise tolerance intervals (PTI’s) can be employed for multiple-use calibration. This paper shows that the confidence sets based on the two-sided PTI’s do not satisfy the key property and so should not be used. Real-world data examples are given in this paper for illustration.<br/

Multiple-use calibration for all future values and exact two-sided simultaneous tolerance intervals in linear regression

Multiple-use calibration using regression is an important statistical tool. Confidence sets for the x-values associated with all future y-values should guarantee a key property, which can be satisfied by simultaneous tolerance intervals (STI's), and so multiple-use calibration requires the construction of STI's. In this paper, exact two-sided STI's have been constructed for polynomial regression over any given covariate interval. There is a misconception that two-sided pointwise tolerance intervals (PTI's) can be employed for multiple-use calibration. This paper shows that the confidence sets based on the two-sided PTI's do not satisfy the key property and so should not be used. Real-world data examples are given in this paper for illustration

Data_Sheet_1_Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA.docx

BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.</p

circRNF10 Regulates Tumorigenic Properties and Natural Killer Cell-Mediated Cytotoxicity against Breast Cancer through the miR-934/PTEN/PI3k-Akt Axis

Circular RNA (circRNA), a type of non-coding RNA, has received a great deal of attention with regard to the initiation and progression of tumors. However, the molecular mechanism and function of circRNAs in breast cancer (BC) remain unclear. In the current study, we discovered that hsa_circ_0028899 (also called circRNF10) was significantly reduced in BC tissues, and a higher level of circRNF10 was markedly related to a favorable prognosis. The results of CCK8, colony formation, Transwell, ELISA, and NK cell-mediated cytotoxicity assays indicated that increased circRNF10 expression could significantly repress the proliferation, invasion, and migration of BC cells and enhance the killing efficiency of NK cells against BC cells. According to these biological functions, the possible role and molecular mechanism of circRNF10 in BC cells were further investigated. We used bioinformatics prediction tools to predict circRNF10-bound miRNAs, which were verified by many experimental studies, including FISH, luciferase reporter assays, RIP, and Western blots. These data suggest that circRNF10 serves as a molecular sponge for miR-934 to further regulate PTEN expression and PI3k/Akt/MICA signaling in vitro and tumor growth in vivo. Altogether, these findings reveal that circRNF10 functions as a novel anti-oncogene in BC via sponging miR-934 and suppressing the PI3K/Akt/MICA pathway

Comparative transcriptome among Euscaphis konishii Hayata tissues and analysis of genes involved in flavonoid biosynthesis and accumulation

Abstract Bachground Euscaphis konishii Hayata, a member of the Staphyleaceae Family, is a plant that has been widely used in Traditional Chinese Medicine and it has been the source for several types of flavonoids. To identify candidate genes involved in flavonoid biosynthesis and accumulation, we analyzed transcriptome data from three E. konishii tissues (leaf, branch and capsule) using Illumina Hiseq 2000 platform. Results A total of 91.7, 100.3 and 100.1million clean reads were acquired for the leaf, branch and capsule, respectively; and 85,342 unigenes with a mean length of 893.60 bp and N50 length of 1307 nt were assembled using Trinity program. BLASTx analysis allowed to annotate 40,218 unigenes using public protein databases, including NR, KOG/COG/eggNOG, Swiss-Prot, KEGG and GO. A total of 14,291 (16.75%) unigenes were assigned to 128 KEGG pathways, and 900 unigenes were annotated into 22 KEGG secondary metabolites, including flavonoid biosynthesis. The structure enzymes involved in flavonoid biosynthesis, such as phenylalanine ammonia lyase, cinnamate 4-hydroxylase, 4-coumarate CoA ligase, shikimate O-hydroxycinnamoyltransferase, coumaroylquinate 3′-monooxygenase, caffeoyl-CoA O-methyltransferase, chalcone synthase, chalcone isomerase, flavanone 3-hydroxylase, flavonoid 3′-hydroxylase, flavonoid 3′,5′-hydroxylase, flavonolsynthese, dihydroflavonol 4-reductase, anthocyanidinreductase, leucoanthocyanidin dioxygenase, leucoanthocyanidin reductase, were identified in the transcriptome data, 40 UDP-glycosyltransferase (UGT), 122 Cytochrome P450 (CYP) and 25 O-methyltransferase (OMT) unigenes were also found. A total of 295 unigenes involved in flavonoid transport and 220 transcription factors (97 MYB, 84 bHLH and 39 WD40) were identified. Furthermore, their expression patterns among different tissues were analyzed by DESeq, the differentially expressed genes may play important roles in tissues-specific synthesis, accumulation and modification of flavonoids. Conclusion We present here the de novo transcriptome analysis of E. konishii and the identification of candidate genes involved in biosynthesis and accumulation of flavonoid. In general, these results are an important resource for further research on gene expression, genomic and functional genomics in E. konishii and other related species