High-efficiency Algorithm for the Most Unfavourable Load Case Combinations of Multilayered Frame-Type Wharf Structure

The wharf, which was built in the Three Gorges Reservoir of China, is constructed as a layered frame-type structure for adapting to large water level fluctuations that exceed 30 m. These large fluctuations cause the frame-type structure to exhibit a considerably higher number of load case combinations than traditional marine high-piled wharfs. To estimate the most adverse combined internal force and the corresponding unfavourable load case combinations of significant components for multilayered frame-type wharf structures in the Three Gorges Reservoir of China, a high-efficiency algorithm is developed in this study. This algorithm can skilfully transform the computational mode of load case combinations into a matrix operations process by computer programming. By applying the proposed algorithm, the number of load case combinations for eight significant components of frame-type wharf, including piles, columns, beams, braces and berthing components, are resolved to a total of 21 from the original quantity of more than six billion. This high-efficiency algorithm can provide powerful technical support for evaluating the bearing capability of multilayered frame-type wharfs in the Three Gorges Reservoir of China

Identification and validation of prognostically relevant gene signature in melanoma

Background. Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis. Method. The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis. Results. We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients. Conclusions. We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients

Recent Advances in Hypertrophic Cardiomyopathy: A System Review

Hypertrophic cardiomyopathy (HCM) is a common genetic cardiovascular disease present in 1 in 500 of the general population, leading to the most frequent cause of sudden death in young people (including trained athletes), heart failure, and stroke. HCM is an autosomal dominant inheritance, which is associated with a large number of mutations in genes encoding proteins of the cardiac sarcomere. Over the last 20 years, the recognition, diagnosis, and treatment of HCM have been improved dramatically. And moreover, recent advancement in genomic medicine, the growing amount of data from genotype-phenotype correlation studies, and new pathways for HCM help the progress in understanding the diagnosis, mechanism, and treatment of HCM. In this chapter, we aim to outline the symptoms, complications, and diagnosis of HCM; update pathogenic variants (including miRNAs); review the treatment of HCM; and discuss current treatment and efforts to study HCM using induced pluripotent stem cell–derived cardiomyocytes and gene editing technologies. The authors ultimately hope that this chapter will stimulate further research, drive novel discoveries, and contribute to the precision medicine in diagnosis and therapy for HCM

LncRNA BASP1-AS1 interacts with YBX1 to regulate Notch transcription and drives the malignancy of melanoma

Melanoma is a fatal skin malignant tumor with a poor prognosis. We found that long noncoding RNA BASP1-AS1 is essential for the development and prognosis of melanoma. The methylation, RNA sequencing, copy number variation, mutation data, and sample follow-up information of melanoma from The Cancer Genome Atlas (TCGA) were analyzed using weighted gene co-expression network analysis and 366 samples common to the three omics were selected for multigroup clustering analysis. A four-gene prognostic model (BASP1-AS1, LOC100506098, ARHGAP27P1, and LINC01532) was constructed in the TCGA cohort and validated using the GSE65904 series. The expression of BASP1-AS1 was upregulated in melanoma tissues and various melanoma cell lines. Functionally, the ectopic expression of BASP1-AS1 promoted cell proliferation, migration, and invasion in both A375 and SK-MEL-2 cells. Mechanically, BASP1-AS1 interacted with YBX1 and recruited it to the promoter of NOTCH3, initiating its transcription process. The activation of the Notch signaling then resulted in the transcription of multiple oncogenes, including c-MYC, PCNA, and CDK4, which contributed to melanoma progression. Thus, BASP1-AS1 could act as a potential biomarker for cutaneous malignant melanoma

A 5-year review of invasive fungal infection at an academic medical center

Background: Invasive fungal infection (IFI) is one of the most common nosocomial infections. However, data on the epidemiology of IFI and susceptibility to antifungal agents in China are quite limited, and in particular, no current data exist on the microbiological, and clinical characteristics of IFI patients in Northeast China. Objectives: The purpose of this study was to provide a retrospective review of the clinical characteristics, laboratory test results, and risk factor predictions of inpatients diagnosed with IFI. Multivariate regression analysis was used to assess prognostic factors associated with the mortality of these patients. Methods: We retrospectively analyzed the results from 509 patients with IFI extracted from the First Hospital of China Medical University from January 2013 to January 2018. Results: Neutrophil numbers, total bilirubin, length of stay in the ICU, renal failure, use of immunosuppressants within the past 30 days, stomach tube placement and septic shock were risk factors for death from IFI. Recent surgery (within 2 weeks) and drainage tube placement did not increase mortality in these IFI patients. Increased serum levels of PCT (AUC 0.601, 95% CI 0.536–0.665, P = 0.003) and CRP (AUC 0.578, 95% CI 0.512–0.644, P = 0.020) provided effective predictors of 30-day mortality rates. Conclusions: We report for the first time epidemiological data on invasive fungal infections in Northeast China over the past 5 years. Despite the limited available clinical data, these findings will greatly aid clinical health care workers with regard to the identification, prevention, and treatment of IFI in hospitalized patients

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis

Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52–71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia

Machine‑learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study

Background Invasive candidal infection combined with bacterial bloodstream infection is one of the common nosocomial infections that is also the main cause of morbidity and mortality. The incidence of invasive Candidal infection with bacterial bloodstream infection is increasing year by year worldwide, but data on China is still limited. Methods We included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 2013 to January 2018; we collected and analysed the relevant epidemiological information and used machine learning methods to find prognostic factors related to death (training set and test set were randomly allocated at a ratio of 7:3). Results Of the 246 patients with invasive candidal infection complicated with a bacterial bloodstream infection, the median age was 63 years (53.25–74), of which 159 (64.6%) were male, 109 (44.3%) were elderly patients (> 65 years), 238 (96.7%) were hospitalised for more than 10 days, 168 (68.3%) were admitted to ICU during hospitalisation, and most patients had records of multiple admissions within 2 years (167/246, 67.9%). The most common blood index was hypoproteinemia (169/246, 68.7%), and the most common inducement was urinary catheter use (210/246, 85.4%). Moreover, the most frequently infected fungi and bacteria were Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis by machine learning method are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, C-Reactive protein (CRP), leukocyte count, neutrophil count, Procalcitonin (PCT), and total bilirubin level. Conclusion Our results showed that the most common candida and bacteria infections were caused by Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, CRP, leukocyte count, neutrophil count, PCT and total bilirubin level

A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data

MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without a priori knowledge, will vastly enhance the understanding of microorganisms’ role in ecology and human health

Construction and application of Digital Twin model of pipeline

According to the concept of intelligent pipeline network and the current status of pipeline enterprises, it was put forward that Digital Twin was the basic method and theoretical basis to realize the intelligent pipeline network. Thereby, based on the theory and the five-dimensional model of Digital Twin, a Digital Twin model of pipeline, including the physical pipeline, the virtual pipeline, the pipeline service system and the pipeline twin data, was constructed, and the connotation of the model, the relationship between various elements, the operation mechanism and the concrete application of key technologies such as cloud computing, big data, artificial intelligence and Internet of Things, were elaborated. In addition, the interaction and fusion between the physical and the virtual pipeline was analyzed, and the application of the pipeline Digital Twin model in the pipeline design, construction, operation, maintenance and other scenarios was discussed. By applying the Digital Twin in the full life cycle of pipeline, the resource allocation was optimized and the level of safety pre-warning was enhanced through data sharing, information visualization and intelligent analysis, with the decision-making ability improved and supports provided for the informatization and intelligent transformation of pipeline industry

Comparison test of factors affecting wax deposition of waxy crude oil

In order to study the influence of different wax deposition factors on the wax deposition law of Daqing Crude Oil, the influence factors such as different wax deposition time, oil wall temperature difference, temperature range and shear rate were controlled with a single variable method, and the comparison test of wax deposition for Daqing Crude Oil was conducted with a new dynamic wax deposition testing apparatus. The results show that: In case the shear rate is constant, the amount of wax deposition will increase with the wax deposition time increase, while the wax deposition rate will decrease. In case the wall temperature of the wax deposition barrel is kept unchanged and lower than the wax appearance temperature, the amount of oil deposition will increase with temperature rising. In case the temperature difference between oil and pipe wall is maintained constant and the temperature range rises gradually, the amount of wax deposition will decrease. For shear stress is of nonlinear relationship with the amount of sheared and stripped wax, the wax deposition will be washed down by the oil flow when the shear force reaches a critical value. The research results could provide reference and technical support for pipeline transportation of waxy crude oil in future