Estimation of the Spirometric Residual Volume (RV) by a Regression Built from Gower Distances

We estimate the Residual Volume, a spirometric parameter, by use of four continuous and four categorical variables. The estimation is done using distance-based regression, which allows to construct the predicting regression equation from mixed-type explanatory variables. The additionally introduced categorical variables improve essentially the goodness of fit of the regression equation.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。     

Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

1. The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.
2. We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.
3. We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.
4. This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.

     

A quantitative structure-activity relationship study for structurally diverse HIV-1 protease inhibitors: contribution of conformational flexibility to inhibitory activity

In this study, we investigated by linear regression model the SAR data of the 15 HIV-1 protease inhibitors possessing structurally diverse scaffolds. First, a regression model was developed only using the enzyme-inhibitor interaction energy as a term of the model, but did not provide a good correlation with the inhibitory activity (R² = 0.580 and Q² = 0.500). Then, we focused on the conformational flexibility of the inhibitors which may represent the diversity of the inhibitors, and added two conformational parameters into the model, respectively: the number of rotatable bonds of ligands (ΔSrot) and the distortion energy of ligands (ΔElig). The regression model by adding ΔElig successfully improved the quality of the model (R² = 0.771 and Q² = 0.713) while the model with ΔSrot was unsuccessful. The prediction for a training inhibitor by the ΔElig model also showed good agreement with experimental activity. These results suggest that the conformational flexibility of HIV-1 protease inhibitors directly contributes to the enzyme inhibition.     

Selection and Identification of Aptamers Against B Cell Activating Factor

Systemic lupus erythematosus (SLE) is the most common autoimmune disease in China. B cell activating factor (BAFF) is an important target for the treatment and detection of SLE. It is of great significance to develop novel molecular recognition elements with high affinity for BAFF. In this study, artificial nucleic acid aptamers against BAFF were screened from a 78 nt single-stranded DNA random library by systematic evolution of ligands exponential enrichment (SELEX) in vitro based on several selection and amplification steps. Through ten rounds of selection, the aptamers with high specificity and affinity for BAFF were identified. After high-throughput sequencing, several aptamers were selected and further examined for binding affinity and specificity. The investigation by dot blotting, Eastern blotting analyses and enzyme-linked oligonucleotide assay (ELONA) showed that the aptamers Apt 7 and Apt 12 with dissociation constants of 241.00±19.75 nmol/L and 413.51±46.94 nmol/L were able to recognize BAFF specifically. After molecular docking analysis, Apt 7 was truncated to Apt 7～1, and the dissociation constant was 192.10±28.61 nmol/L. A sandwich ELONA using Apt 7～1 and BAFF antibodies was established to detect BAFF. The detection limit was estimated to be 0.227 nmol/L. This study provides new molecular recognition elements for the detection of BAFF and the study of antagonists.     

Identification of Glycosylated Subtypes of Interferon-α Produced by Human Leukocytes

We found that interferon-α produced by human leukocytes contained four subtypes that were glycosylated by N-linkages according to the results of lectin blot analysis. These glycosylated subtypes were type H or type ω and thei sugar moieties had no relation to their biological activities.     

Association of Genetic Structure and Diversity in Iranian Wild Germplasms of Mentha longifolia L. Based on Phenotypical,Biochemical, and Molecular Markers

Mentha longifolia L. is well-known to be one of the most pervasive wild-growing species of the Lamiaceae family, which has extensive beneficial properties in the fields of pharmacology and biological products. In the present study, the correlation between Inter-simple sequence repeat (ISSR) markers and morpho-chemical parameters of twenty different M. longifolia accessions (MLACs) were assessed. The geographic information system (GIS) has been employed to interpret the original habitat of the accessions in Iran. ISSR analysis indicated a remarkable difference in the studied accessions, segregated them into three main groups, constructed by an unweighted pair-group method with arithmetic (UPGMA) and principal coordinate analysis (PCoA). A total of 89 bands were generated by 12 ISSR primers, among which 82 (91.97 %) of them were polymorphic. The cluster analysis based on agro-morphological data scattered MLACs into two main groups. The essential oils (EOs) were analyzed through GC/FID/MS, and four chemotypes were characterized according to the major constituents. Pulegone ranged from 0.17 to 69.50 % was the main oil constituent with the highest content. Also, HPLC-PDA was employed to identify and to quantify the phenolic compounds in the MeOH extracts of MLACs. Heatmap cluster based on phenolic compounds produced three main categories of accessions. The components identified in the extracts were rosmarinic acid, rutin, vanillic acid, ferulic acid, chlorogenic acid, caffeic acid, 3,4-dihydroxybenzoic acid, 2,5-dihydroxybenzoic acid, and p-coumaric acid, which among them rosmarinic acid (RA) varied from 39.16 to 261.55 mg/100 g (DW) as a predominant constituent. Subsequently, multiple regression analyses between ISSR fragments and morpho-chemical data illustrated considerable relationships in the plant materials. The high variation and correlation observed in metabolic and phenotypic traits of MLACs establish an adequate source to conduct reserves conservation programs.     

Efficient approximate k‐fold and leave‐one‐out cross‐validation for ridge regression

In model building and model evaluation, cross‐validation is a frequently used resampling method. Unfortunately, this method can be quite time consuming. In this article, we discuss an approximation method that is much faster and can be used in generalized linear models and Cox’ proportional hazards model with a ridge penalty term. Our approximation method is based on a Taylor expansion around the estimate of the full model. In this way, all cross‐validated estimates are approximated without refitting the model. The tuning parameter can now be chosen based on these approximations and can be optimized in less time. The method is most accurate when approximating leave‐one‐out cross‐validation results for large data sets which is originally the most computationally demanding situation. In order to demonstrate the method's performance, it will be applied to several microarray data sets. An R package penalized, which implements the method, is available on CRAN.