“Enthusiastic Admiration Is the First Principle of Knowledge and Its Last”: A Qualitative Study of Admiration for the Famous

The concept of fame has been associated with celebrities, wealth, attractiveness, and social recognition. Nevertheless, people have admiration for the famous who may not be celebrities. Admiration is regarded as one of the emotions of appreciation, or moral emotions, triggered by positive appraisals of excellence. It is present when seeing extraordinary displays of skills talent or achievement. However, theoretical and empirical research on admiration and its psychological effects on people are scarce. In this article, we discuss a qualitative study that explores a collection of experiences of admiration for the famous. Based on 26 in-depth interviews with residents in southern England, we explored why people admire famous individuals and how the experience may produce positive attitudes and behaviors. We found that through admiring famous individuals who are perceived to share similar interests and attributes, people may develop positive thinking about their own lives and may be more active in seeking new opportunities or engaging in self-growth. We also discuss the potential problems of admiration. This exploratory research contributes to the literature of positive psychology and has implications for furthering the understanding of people’s well-being

Multi-Layer Feature Boosting Framework for Pipeline Inspection using an Intelligent Pig System

As pipelines take an increasingly important role in energy transportation, their health management is necessary. In-pipe inspection is a common pipeline life maintenance method. The signal obtained through internal inspection contains strong noise and interference where the internal environment of the pipeline is extremely complicated. Thus, it is challenging to accurately identify the defect signal. In this paper, a defect detection framework based on feature boosting is proposed by using the multi sensing pipeline pig as the detection signals. Through boosting construction of features and hierarchical classification, the framework can not only correctly classify various signals in the internal detection signals but also realize the accurate identification of defect signals. Concurrently, in order to demonstrate the high flexibility and robustness of the detection framework, experiments and verifications have been carried out on specimens in three different environments i.e., laboratory environment, simulated environment and actual environment. In the classification of actual environmental detection signals, quantitative evaluation with different algorithms have been undertaken using the F-score to demonstrate the effectiveness of the proposed framework

The relationship between red blood cell distribution width at admission and post-stroke fatigue in the acute phase of acute ischemic stroke

IntroductionPost-stroke fatigue (PSF) is a common complication in the patients with acute ischemic stroke (AIS). This prospective study aimed to investigate the relationship between red blood cell distribution width (RDW) at admission and PSF in the acute phase.MethodsThe AIS patients were enrolled in Nantong Third People's Hospital, consecutively. PSF in the acute phase was scored according to the Fatigue Severity Scale. Levels of RDW were measured at admission. The associations were analyzed using multivariate regression and restricted cubic splines (RCS).ResultsFrom April 2021 to March 2022, a total of 206 AIS patients (mean age, 69.3 ± 10.7 years; 52.9% men) were recruited. After the adjustment for potential confounding factors, RDW at admission remained the independent associated factor with PSF in the acute phase (OR [odds ratio], 1.635; 95% CI [confidence interval], 1.153–2.318; P = 0.006). The linear dose-response associations of RDW with PSF in the acute phase were found, based on the RCS model (P for non-linearity = 0.372; P for linearity = 0.037). These results remained significant in other models.ConclusionsRDW at admission could serve as a novel biomarker of PSF in the acute phase of AIS

Toward a natural classification of Botryosphaeriaceae : a study of the type specimens of Botryosphaeria sensu lato

The genus Botryosphaeria includes more than 200 epithets, but only the type species, Botryosphaeria dothidea and a dozen or more other species have been identified based on DNA sequence data. The taxonomic status of the other species remains unconfirmed because they lack either morphological information or DNA sequence data. In this study, types or authentic specimens of 16 “Botryosphaeria” species are reassessed to clarify their identity and phylogenetic position. nuDNA sequences of four regions, ITS, LSU, tef1-a and tub2, are analyzed and considered in combination with morphological characteristics. Based on the multigene phylogeny and morphological characters, Botryosphaeria cruenta and Botryosphaeria hamamelidis are transferred to Neofusicoccum. The generic status of Botryosphaeria aterrima and Botryosphaeria mirabile is confirmed in Botryosphaeria. Botryosphaeria berengeriana var. weigeliae and B. berengeriana var. acerina are treated synonyms of B. dothidea. Botryosphaeria mucosa is transferred to Neodeightonia as Neodeightonia mucosa, and Botryosphaeria ferruginea to Nothophoma as Nothophoma ferruginea. Botryosphaeria foliicola is reduced to synonymy with Phyllachorella micheliae. Botryosphaeria abuensis, Botryosphaeria aesculi, Botryosphaeria dasylirii, and Botryosphaeria wisteriae are tentatively kept in Botryosphaeria sensu stricto until further phylogenetic analysis is carried out on verified specimens. The ordinal status of Botryosphaeria apocyni, Botryosphaeria gaubae, and Botryosphaeria smilacinina cannot be determined, and tentatively accommodate these species in Dothideomycetes incertae sedis. The study demonstrates the significance of a polyphasic approach in characterizing type specimens, including the importance of using of DNA sequence data.Supplementary Figure 1 | Botryosphaeria gaubae (W 1992-05937, holotype). (A,B) Ascomata erumpent through the lower side of the leaf. (C) Squash showing cylindrical or broadly cylindrical asci in cotton blue. (D) Part of the peridium. (E) Septate pseudoparaphyses in cotton blue. (F-H) Aseptate, fusiform to ellipsoid ascospores in cotton blue. Scale bars: (A) = 1 mm, (B) = 200 mm, (C) = 50 mm, (E) = 20 mm, (D,F-H) = 10 m m.Supplementary Figure 2 | Laestadia apocyni (MICH 14281, isotype). (A) Ascomata erumpent through a piece of twig epidermis. (B) Released, hyaline, 1-septate ascospores. (C) Ascus in water. (D) Line drawing of ascus in water. Scale bars: (A) = 200 mm, (B-D) = 20 m m.Supplementary Figure 3 | Sphaeria smilacinina (NYS f2818, holotype). (A) Ascomata erumpent through the twig epidermis. (B,C) Immature asci. (D) Released ascospores. (E) Line drawing of broadly clavate ascus. Scale bars: (A) = 500 mm, (B-D) = 20 mm, (E) = 40 m m.Supplementary Figure 4 | One of the most parsimonious trees obtained from combined ITS, LSU, tub2, and tef1-a sequence data of Botryosphaeria spp. Outgroup taxa are Neofusicoccum luteum and Neofusicoccum parvum. Maximum parsimony (MP) support values above 70% and Bayesian posterior probabilities (PP) support above 80% are shown with MP bootstrap followed by Bayesian PP (MP/PP) values at the nodes. The species characterized in this study are in boldface.Supplementary Figure 5 | One of the most parsimonious trees obtained based on combined ITS, tef1-a, and tub2 sequence data of Neofusicoccum spp. Outgroup taxon are Botryosphaeria dothidea and B. corticis. Maximum parsimony (MP) support values above 60% and Bayesian posterior probabilities (PP) support above 80% are shown with MP/PP, values at the nodes. The species characterized in this study are in boldface.Supplementary Figure 6 | One of the most parsimonious trees obtained from LSU sequence dataset of Neofusicoccum spp. Outgroup taxa are Botryosphaeria corticis and B. dothidea. Maximum parsimony (MP) support values above 70% and Bayesian posterior probabilities (PP) support above 80% are shown with MP bootstrap followed by Bayesian PP (MP/PP) values at the nodes. The species characterized in this study are in boldface.Supplementary Figure 7 | One of the most parsimonious trees obtained from ITS and LSU sequence dataset of Nothophoma spp. Outgroup taxa is Didymella calidophila. Maximum likelihood (ML) support values above 50%, Maximum parsimony (MP) support values above 50%, and Bayesian posterior probabilities (PP) support above 95% are shown with ML and MP bootstrap followed by Bayesian PP (MP/PP/ML) values at the nodes. The species characterized in this study are in boldface.Supplementary Table 1 | Species, specimens and GenBank accession numbers of sequences used in this study (newly generated sequences are indicated in bold).The National Natural Science Foundation of China and NSFC Projects of International Cooperation and Exchanges.http://www.frontiersin.org/Microbiologyam2022Forestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant Patholog

High Expression of Cancer-Derived Glycosylated Immunoglobulin G Predicts Poor Prognosis in Pancreatic Ductal Adenocarcinoma: Erratum.

[This corrects the article DOI: 10.7150/jca.39800.]

High Expression of Cancer-Derived Glycosylated Immunoglobulin G Predicts Poor Prognosis in Pancreatic Ductal Adenocarcinoma.

Background: Cancer-derived immunoglobulin G (CIgG) has been detected in various cancers and plays important roles in carcinogenesis. The present study aimed to investigate its clinical significance in pancreatic ductal adenocarcinoma (PDAC). Methods: Using tissue microarrays (TMAs) and immunohistochemistry, we assessed CIgG expression in 326 patients who underwent surgical resection for PDAC. The associations between CIgG expression and clinicopathological features and clinical outcomes were analyzed. Functional experiments were also performed to investigate the effect of CIgG on PDAC cells. Results: High CIgG expression was related to poor tumor differentiation and metastasis during follow-up and was associated with poor disease-free survival (DFS) and overall survival (OS). A multivariate Cox regression analysis identified high CIgG expression as an independent prognostic factor for DFS and OS. The incorporation of CIgG expression improved the accuracy of an established prognosis prediction model for 1-year OS and 2-year OS. In vitro studies showed that knocking down CIgG profoundly suppressed the proliferation, migration, and invasion capacity of PDAC cells. Conclusions: CIgG contributes to the malignant behaviors of PDAC and offers a powerful prognostic predictor for these patients

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCCrelated genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumorinfiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area