Energy–water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperm species richness in China

Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large‐scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human‐induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human‐induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km² spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (a) all species, (b) endemic species, and (c) nonendemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (a) energy–water, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy–water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat heterogeneity has a better influence on nonendemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.     

The use and misuse of regression models in landscape genetic analyses

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression‐based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression‐like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.     

中老铁路经济廊带生态质量及其与人类活动的关系

区域生态质量很大程度上受人类活动强度影响,制约着区域经济可持续发展。以中老铁路经济廊带为对象,重点研究区内人类活动强度对区域生态环境的影响。选取1999、2009、2019年遥感影像计算遥感生态指数(RSEI),采用空间自相关及局域G统计量探测研究区生态质量的时空演变规律,并结合同期人口密度构建地理加权回归模型,定量分析区内人类活动强度引起的生态效应。研究表明: 1999—2019年,区内生态质量经历了先升后降的变化,RSEI均值由1999年的0.645升为2009年的0.738,随后又降为2019年的0.721,中部地区生态质量波动较大;基于地理加权回归模型(GWR)拟合人口密度与生态质量的效果明显好于最小二乘法,基于GWR的不同时期R²均高于0.7且拟合效果稳定,2019年拟合度最好(R²为0.785),1999、2009年R²分别为0.726、0.754;中老铁路沿线南部万象一带的生态质量对人类活动较敏感,以中度敏感区为主。对高、中、低度敏感区而言,人口密度每增长10、100、1000倍将相应导致RSEI均值降低0.2、0.4、0.6。经济廊带发展会导致人口密度增加,在规划布局过程中应控制人类活动,防止潜在敏感区及现有敏感区生态质量进一步恶化。     

Epidemiological Characteristics of Influenza A and B in Macau, 2010–2018

Virologica Sinica - Influenza is one of the major respiratory diseases in humans. Macau is a tourist city with high density of population and special population mobility. The study on the...     

医学生学习激情现状及影响因素的研究

目的:测量医学生学习激情现状,探索相关人口学变量差异及影响因素作用机制。方法:通过网络问卷对1793名在校医学生进行问卷调查,回收有效问卷1218份,应用SPSS22.0软件对数据进行统计处理。结果:测量对象的学习激情处于中等偏上水平(4.37±0.91);学习激情平均得分在性别(F=9.097,P<0.05)、生源地(F=14.850,P<0.05)、独生子女情况(F=5.470,P<0.05)方面的差异有统计学意义;成绩水平(β=0.094,P<0.01)、发展规划(β=0.105,P<0.01)、生活满意度(β=0.213,P<0.01)和活力(β=0.253,P<0.01)对医学生学习激情的影响有统计学意义。结论:医学生学习激情水平在性别、生源地、独生与否方面存在显著差异;成绩水平、发展规划、生活满意度、活力是影响医学生学习激情的因素。     

Developmental stability and morphological differences in Plantago major L. leaves under contrasting environmental conditions

Abstract

In the present study, an additional combination of end‐points was applied on the natural populations of the common plantain, previously estimated using morphometric assays. Here, besides measuring developmental instability (DI), by determining the level of fluctuating asymmetry (FA) and the total amount of phenotypic variability (PV), we tried to distinguish the three natural populations under contrasting environmental conditions using the morphological data. Results obtained using both FA indices were the same; higher asymmetry levels in the reference than in the polluted environments were detected for leaf width, vein distances within a leaf and lobe length. The one‐way analysis of variance results revealed that there were significant differences in PV values among populations analysed for each character. When all leaf traits were considered together, the PV median value was significantly higher in Crni Lug leaves compared with leaves from other sites. The multivariate analysis of variance results revealed the significant effect of environment on both FA₄ and PV values. The component scores of first factor (PC1) were significantly different between the Karaburma and Crni Lug populations. Besides, component scores of both PC1 and PC2 were significantly different between the Zemun and Crni Lug samples. The stepwise discriminant functional analysis results allowed us to identify a set of four variables, with a sufficient discriminating ability (75%).     

Comparison of allometric relationships and morphological differences in Tilia cordata Mill. outer leaves exposed to different environmental conditions

The study is based on four leaf parameters: leaf width (LW), lobe length (LL), leaf size (LS) and leaf shape which is calculated as LW to leaf length (LW/LL) ratio. Under different environmental conditions, LL is an isometric character, LW shows positive allometry, whereas LW/LL shows negative allometry. Regression analysis results indicated that there is no significant difference either in slopes or in regression coefficients between investigated sites. Thus, in this study, we found that allometric relationships between leaf parameters and LS are character specific and that they tended not to differ significantly between Tilia cordata Mill. outer leaves exposed to different environmental conditions. Also, there are no significant interpopulation differences for both principal component PC1 and PC2 scores. The stepwise discriminant functional analysis results allowed us to identify a set of two leaf parameters (LS and LL) with a moderate discriminating ability (59.8%). T. cordata outer leaves are significantly larger and broader in the reference area (R-leaves) than leaves from polluted (P-leaves) site. The data also indicated that there is a relatively larger petiole size in R-leaves than in P-leaves. We found that in P-leaves, LW increased faster with increasing LS than in R-leaves.     

Oxaliplatin induces immunogenic cells death and enhances therapeutic efficacy of checkpoint inhibitor in a model of murine lung carcinoma

Abstract

Background: Platinum compounds are commonly used for lung cancer treatment. However, the severe side effects and relatively poor prognosis limit their therapeutic effect. Therefore, developing novel platinum derivative and treatment strategy are critical for current lung cancer therapy.

Methods: Flow cytometry, HMGB1 and ATP release, and immunoblotting were performed to evaluate the Oxaliplatin-induced immunogenic cell death (ICD) in two lung carcinoma cells. Vaccination approach and subcutaneous tumor models were created to analyze the tumor regression effect of Oxaliplatin. PD-L1 mRNA and protein levels were detected in LLC (Lewis lung carcinoma). Enhanced therapeutic efficacy of LLC was assessed by co-administration Oxaliplatin and aPD-L1 in murine lung tumor model.

Results: Oxaliplatin induced robust ICD in LLC cells, activated dendritic cells (DCs, CD80⁺CD86⁺) and enhanced cytotoxic T cells (CD8⁺) in LLC tumor tissues, which resulted in tumor regression. Co-administration of Oxaliplatin and checkpoint inhibitor, aPD-L1, could enhance the therapeutic efficacy of LLC in murine lung carcinoma.

Conclusion: This study reveals Oxaliplatin can induce robust ICD in tumor tissues and suppress tumor growth by activating DCs and enhancing T-cell infiltration. Notably, the Oxaliplatin-induced ICD provides an immunogenic microenvironment, which enhances the checkpoint inhibitor therapeutic efficacy of LLC.     

MODEL DEVELOPMENT AND PROCESS OPTIMIZATION FOR SOLVENT EXTRACTION OF POLYPHENOLS FROM RED GRAPES USING BOX–BEHNKEN DESIGN

The objective of the present study is to find out the optimum extraction conditions for extraction of polyphenols from red grapes using Box–Behnken design. Red grapes polyphenols were extracted using acid–ethanol solvent at various extraction temperature (40–60°C), extraction time (20–100 min) and different solid–liquid ratio (1:5–1:15 g:ml). The effect (main and interactive) of extraction conditions on total anthocyanin, phenolic and flavonoid content were studied using Box–Behnken design (three factors at three levels). The results showed that the contribution of the quadratic model was significant for all the responses. Second-order mathematical regression models were developed and were found to fit well with observed data. Derringer's desirability function methodology was performed to find out the optimal conditions based on both individual and combinations of all responses (extraction temperature: 57°C, time: 61 min, and solid–liquid ratio: 1:8.7 g:ml) were established. At this optimal condition, the anthocyanin yield, total phenolic and flavonoid content were 73.92 mg/100 g, 221.4 mg GAE/100 g, and 79.08 mg CE/100 g, respectively. A desirability value of 0.902 was achieved at this point.