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[Objectives] The elbow is a common component of ship piping system. Efforts have been made in order to predict the elbow erosion accurately.[Methods] In this paper,the numerical simulation is conducted to calculate and analyze the flow field inside the pipe. From the two aspects,i.e. the influence of the turbulence model on the prediction of the flow field in the elbow and the influence of turbulence model on elbow erosion calculation,intensive research into the method to predict the erosion of elbow by solid and liquid flows.[Results] The results show that the SST k-ω turbulence model,together with the Oka erosion model,can accurately predict the flow field distribution in the elbow. Turbulence model brings a significant impact on the abrasion calculation,where it affects the calculated value of final erosion and also makes a great difference in the distribution of predicted relative erosion.[Conclusions] The obtained results can provide guidance for future pipe erosion prediction.

Babesiosis is a malaria-like disease in humans and animals that is caused by Babesia species, which are tick-transmitted apicomplexan pathogens. Babesia duncani causes severe to lethal infection in humans, but despite the risk that this parasite poses as an emerging pathogen, little is known about its biology, metabolic requirements or pathogenesis. Unlike other apicomplexan parasites that infect red blood cells, B. duncani can be continuously cultured in vitro in human erythrocytes and can infect mice resulting in fulminant babesiosis and death. We report comprehensive, detailed molecular, genomic, transcriptomic and epigenetic analyses to gain insights into the biology of B. duncani. We completed the assembly, 3D structure and annotation of its nuclear genome, and analysed its transcriptomic and epigenetics profiles during its asexual life cycle stages in human erythrocytes. We used RNA-seq data to produce an atlas of parasite metabolism during its intraerythrocytic life cycle. Characterization of the B. duncani genome, epigenome and transcriptome identified classes of candidate virulence factors, antigens for diagnosis of active infection and several attractive drug targets. Furthermore, metabolic reconstitutions from genome annotation and in vitro efficacy studies identified antifolates, pyrimethamine and WR-99210 as potent inhibitors of B. duncani to establish a pipeline of small molecules that could be developed as effective therapies for the treatment of human babesiosis.

To investigate the value of serum eotaxin-2, neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR) in the diagnosis and prognosis of colorectal cancer (CRC). The association between different clinicopathological characteristics and eotaxin-2, NLR, and PLR in different patient groups was evaluated. The combined detection indicator and the combined detection's predictive effect on distant metastasis were examined. The receiver operating characteristic (ROC) curve was drawn to evaluate the efficacy of combined detection. The association between eotaxin-2, inflammation markers, and postoperative complications was assessed. Multivariate analysis to investigate the factors affecting the prognosis of patients with CRC. We detected a marked positive correlation between NLR and PLR (p < 0.05, r= 0.209). The AUC of serum eotaxin-2 combined with inflammation markers was 0.889, which was higher than single diagnosis. Compared with the single eotaxin-2 test, the combined detection of eotaxin-2 and inflammation markers might improve the specificity of CRC assessment. In univariate analysis, age, surgical method, high eotaxin-2, and high NLR were associated with postoperative complications. In multivariate analysis, age (≥ 60 years), high eotaxin-2, and high NLR were independent risk elements influencing postoperative complications of CRC. The distant metastasis, TNM staging -Ⅳ stage, NLR ≥ 3.18, and PLR ≥ 193 were independent factors affecting the prognosis of patients with CRC. The combined detection of eotaxin-2 and inflammatory markers has a particular value in improving the diagnosis of CRC, predicting distant metastasis, and guiding the frequency of reexamination after radical resection of CRC. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technologies have been consistently influencing the progress of education for an extended period, with its impact becoming more significant especially after the launch of ChatGPT-3.5 at the end of November 2022. In the field of physics education, recent research regarding the performance of ChatGPT-3.5 in solving physics problems discovered that its problem-solving abilities were only at the level of novice students, insufficient to cause outstanding alarm in the field of physics education. However, the release of ChatGPT-4 presented substantial improvements in reasoning and conciseness. How does this translate to performance in solving physics problems, and what kind of impact might it have on education? This study undertakes a comprehensive assessment of ChatGPT-4's performance in solving physics problems from the perspective of physics conceptual understanding and reasoning, and compares its performance with that of students. It is concluded that ChatGPT-4's performance in solving physics problems has significantly improved compared to ChatGPT-3.5, and was notably superior to the majority of middle school and high school students. This finding presents both a challenge and an opportunity for physics education and the broader educational field, and triggers immediate considerations for coping with this challenge in future teaching and assessment environments. [ABSTRACT FROM AUTHOR]