山水林田湖草沙一体化保护和修复工程与景观生态学

我国山水林田湖草沙生命共同体及其保护和修复工程的理论研究和实践正逐渐开展,需要系统的学科理论支撑,景观生态学作为地理学和生态学的交叉科学,能够以其宏观空间理论和技术体系满足这一需求。本文将景观生态学作为山水林田湖草沙一体化保护和修复工程的支撑学科,首先,明确了山水林田湖草沙生命共同体是镶嵌的异质景观、具有景观的所有特征并遵循景观生态学原理;其次,阐述了景观生态建设理论如何应用于山水林田湖草沙一体化保护和修复工程的规划和评价;最后,总结景观生态建设研究的新趋势,提出待解决的理论和实践问题,并论述山水林田湖草沙一体化保护和修复工程如何为解决这些问题提供广阔的研究空间。景观生态学和山水林田湖草沙一体化保护和修复工程实践相结合,将为实现我国乃至全球生态、经济、社会可持续发展提供极为有效的途径。     

东海带鱼的最适可捕规格

带鱼是中国东海的重要经济渔获种类,其可捕规格会随不同时期的捕捞强度而变化。为确定目前东海带鱼最适可捕规格,根据2016—2020年拖网、帆张网及延绳钓等主捕带鱼作业方式采集的带鱼样品和数据,利用FiSAT II软件对其生长、死亡参数进行估算,进而计算可捕规格。结果表明: 带鱼的拐点肛长、临界肛长、1龄鱼肛长分别为382.84、397.12、216.05 mm,50%带鱼性成熟肛长为230.38 mm,最小可捕肛长为219.23 mm。利用Beverton-Holt模型分析带鱼单位补充量渔获量可知,带鱼当前渔业参考点为:t_c=0.38 a,F=2.11,处于捕捞过度区域,并得出最适可捕肛长为364.64 mm。但过高的可捕规格会导致短期内带鱼产量急剧下降,渔民难以接受,因此,本研究综合考虑采用接近1龄鱼肛长与50%性成熟肛长作为最适可捕规格较为合适,即220 mm。     

茜草属药用植物的研究——Ⅲ.11种茜草属药材的组织形成学研究

本文除对主流商品茜草的生药性状和组织构造作了较全面的描述,还利用图、表及照片列出狭叶茜草P.truppeliana Loes、Loeb、小红参(滇茜草)R.yunnomensis Dtels、中华茜草R.chinensis Regel et maack、大叶茜草R.schumanniana Pritz、按针叶茜草R.lanceolata Hayata、大茜草R.magna Hisao、钩毛茜草R.oncotrucha Hand-Mazz、红花茜草R.podantha Diels、金剑草R.alata Wall、新疆茜草(洋茜草)R.tinctorum L等10种与茜草Rubia cordifolia L的主要区别点,并作药材检索表,以利鉴别。     

植物水分来源稳定氢氧同位素偏移研究进展

稳定氢氧同位素技术能有效计算植物根系水分吸收量,确定植物水分来源贡献,评估植物水分利用策略,是生态水文学探究大气-植被-土壤系统水分传输过程机制的有效工具。然而土壤与木质部水稳定氢氧同位素比值(δ²H和δ¹⁸O)偏移造成植物水分来源贡献率计算偏差,引起氢氧同位素结果差异的原因尚不明晰。该文首先简要介绍氢氧稳定同位素比值偏移现象,其次沿水分在土壤-植物-大气连续体中的传输路径构建梳理框架,系统阐述了3个界面(植物-大气界面、土壤-大气界面和根系-土壤界面)与2个空间(植物体和土壤层)中引起δ²H与δ¹⁸O偏移的自然效应,同时概述了土壤与木质部样品提取与测定技术中引起δ²H与δ¹⁸O偏差的人为效应。最后,根据现有研究进展提出主要问题,从获取同位素时空数据,微尺度同位素偏移原因,提取与测定技术的优化三方面指出未来的发展方向。     

越南槐根的抗HIV活性成分研究

为研究越南槐(Sophora tonkinensis)根的抗HIV蛋白酶活性成分及其分子对接机制,采用硅胶、MCI、Sephadex LH-20等多种色谱分离方法,对越南槐根的化学成分进行分离,采用HIV蛋白酶对化合物进行体外抗HIV活性筛选,运用分子对接手段初步探究活性化合物与HIV-1蛋白酶的结合机制。结果表明,从越南槐中共分离得到8个化合物,根据波谱数据分别鉴定为三叶豆紫檀苷(1)、苦参碱(2)、N-acetylnicotinamide (3)、2′-O-甲基腺苷(4)、毛蕊异黄酮苷(5)、玫瑰花苷(6)、环广豆根素(7)、芒柄花苷(8),此外还分离得到塑化剂衍生物邻苯二甲酸二(2-乙基)己酯(9)和邻苯二甲酸二异丁酯(10)。抗HIV蛋白酶活性测试显示化合物1和2的IC50分别为13.2和38.6μg/m L,分子对接表明其与HIV蛋白酶有一定的结合作用。化合物3～5为首次从该植物中分离得到,化合物1和2显示中等的抗HIV蛋白酶活性。     

Incidence,trends, and survival of oropharyngeal squamous cell cancer in Aotearoa New Zealand, 2006–2020

BackgroundAn increasing trend of oropharyngeal cancer (OPC) has been reported in several countries with different demographic characteristics, and often attributed to increases in human papillomavirus (HPV) infection. The survival of patients with OPC has steadily improved, especially for those with positive HPV status. This study assessed the incidence, trends, and survival of OPC in Aotearoa New Zealand (NZ) by age at diagnosis, sex and ethnicity.MethodsThe study included all 2109 patients resident in NZ with a primary diagnosis of oropharyngeal squamous cell carcinoma from 2006 to 2020, identified from the National Cancer Registry. We assessed age-standardised incidence rate (ASR), annual percent change (APC) and overall and relative survival rates.ResultsThe average annual incidence of OPC was 2.2 per 100,000 population. There was a steady increase of 4.9% per year over 15 years. Although the incidence rates were higher in males over the study period, the overall rate of increase was similar in males (4.9%) and in females (4.3%). The incidence was highest in the 50–69-year group (8.8/100,000 population). This age group had an incidence that increased by 7.5% per year to 2018, and then declined. The main increase in rates was seen between the birth cohort of 1946–50 and that of 1956–60. The increase in incidence was seen in Māori and Pākehā/European populations, but no increase was seen in Pacific or Asian populations. The 5-year overall relative survival rate improved from 69% in 2006‐13 to 78% in 2014–20. Survival rates were lower in older patients, females, and Māori patients.ConclusionThis study confirmed a substantial increase in OPC incidence in NZ, with some evidence to suggest a recent slowing in this increase. Māori and Pākehā/European had the highest incidence, while Pacific and Asian populations showed the lowest rates and no increase over the study period. Survival rates have improved over time, but remained lower in some demographic groups.     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

Insights into circular RNAs: Biogenesis,function and their regulatory roles in cardiovascular disease

As a distinctive member of the noncoding RNA family, circular RNAs (circRNAs) are generated from single-stranded, covalently closed structures and are ubiquitous in mammalian cells and tissues. Due to its atypical circular architecture, it was conventionally deemed insignificant dark matter for a prolonged duration. Nevertheless, studies conducted over the last decade have demonstrated that this abundant, structurally stable and tissue-specific RNA has been increasingly relevant in diverse diseases, including cancer, neurological disorders, diabetes mellitus and cardiovascular diseases (CVDs). Therefore, regulatory pathways controlled by circRNAs are widely involved in the occurrence and pathological processes of CVDs through their function as miRNA sponges, protein sponges and protein scaffolds. To better understand the role of circRNAs and their complex regulatory networks in CVDs, we summarize current knowledge of their biogenesis and function and the latest research on circRNAs in CVDs, with the hope of paving the way for the identification of promising biomarkers and therapeutic strategies for CVDs.     

玉米赤霉烯酮对膨胀青萍G_3生长与发育的影响

玉米赤霉烯酮(Zearalenone,以下简称ZEN)被证明具有动物雌性激素的作用(Mirocha等 1967),并且是某些真菌的一种性激素(Mirocha等1968)。李季伦等首次报道ZEN与高等植物的生长与发育有关(1980)。现已证实ZEN与植物的春化作用(孟繁静等1986)、短日光周期诱导(韩玉珍和孟繁静1990)、以及花器官的发生、分化乃至开花和受精等(阙月美等1990)