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BackgroundLeft main coronary artery atresia (LMCAA) is an extremely rare abnormality and only <100 cases have been reported worldwide. We describe the clinical manifestations, imaging features, prognosis, and treatments of LMCAA who were admitted in our department, which aimed to improve the clinical diagnosis and treatments of LMCAA in children.MethodsA retrospective study identified 12 patients diagnosed with congenital left coronary artery atresia at Pediatric Heart Center of Beijing Anzhen Hospital from June 2010 to June 2019. The clinical characteristics, imaging data, and treatment follow-up were analyzed.ResultsAmong the 12 cases, 8 were boys and 4 were girls; the age of onset was 2 months to 2 years old (median age 7 months); the age of diagnosis was 7 months to 6 years old (median age 2 years and 11 months). At the initial diagnosis, there were 4 cases of respiratory tract infection with cardiac murmur, 3 cases of cardiac shadow enlargement, 1 case of recurrent syncope, 2 cases of feeding difficulty with cardiac enlargement, and 2 cases of simple cardiac murmur. In 12 cases of electrocardiogram examination, 7 cases showed pathological Q waves of lead I, AVL and v4–v6; in 12 cases of chest X-ray examination, 8 cases showed cardiac shadow enlargement; in 12 cases of our hospital's first cardiac ultrasound examination, 4 cases were definitely diagnosed, and 8 cases showed the possibility of left coronary artery abnormality; in 5 cases of cardiac coronary CT angiography examination, 2 cases were confirmed, 2 cases reported suspected left coronary artery abnormality, and 1 case did not report abnormality; All cases were definitely diagnosed in 8 cases of angiography. Follow-up was performed from 1 to 8 years; one case died suddenly, one case of syncope after activity was treated by oral medication, 3 cases received open coronary angioplasty and mitral valvuloplasty, recovered well after operation, the rest of the children were treated by oral medication, and the symptoms are stable at present.ConclusionsLeft main coronary artery atresia is difficult to diagnose and can result in heart failure early in life. Timely diagnosis and reasonable treatment are the keys to improve the prognosis.

This study discusses the impact of two different ecological restoration approaches on the distribution of soil particle size and organic carbon, expecting to provide references for research on the effects of ecological restoration on the soil carbon pool in alpine regions. By replacing the method of time sampling with spatial sampling, grasslands enclosed only in the growing season and woodlands enclosed all year round were respectively selected as the research objects. Through centrifugation, the soil samples were classified by grain size into sand (50–2000 μm), silt(2–50 μm), and clay (<2 μm) to analyze the distribution of organic carbon in soil particles of different sizes. The major findings were as follows. First, sand accounted for the largest proportion of all the soil components in the grasslands and woodlands that had been restored for different years, followed by silt and clay. Second, most of the organic carbon in the grasslands and woodlands was from sand and silt. As the restoration years increased, the proportion of organic carbon in clay grew in fluctuation. In short, both ecological restoration approaches have improved the soil structure and raised the content of soil organic carbon (SOC). Specifically, the restoration scheme of the woodlands exerted a more significant influence on the soil components and the distribution of organic carbon than that of the grasslands.

Over the last few years, Contextualized Pretrained Neural Language Models, such as BERT, GPT, have shown significant gains in various NLP tasks. To enhance the robustness of existing pre-trained models, one way is adversarial examples generation and evaluation for conducting data augmentation or adversarial learning. In the meanwhile, gender bias embedded in the models seems to be a serious problem in practical applications. Many researches have covered the gender bias produced by word-level information(e.g. gender-stereotypical occupations), while few researchers have investigated the sentence-level cases and implicit cases.In this paper, we proposed a method to automatically generate implicit gender bias samples at sentence-level and a metric to measure gender bias. Samples generated by our method will be evaluated in terms of accuracy. The metric will be used to guide the generation of examples from Pre-trained models. Therefore, those examples could be used to impose attacks on Pre-trained Models. Finally, we discussed the evaluation efficacy of our generated examples on reducing gender bias for future research.