Spontaneous regression of cancer: New insights

Suppression of oncogene expression and of host- or tumour-expressed growth factors and receptors may precipitate spontaneous regression or dormancy in human cancer. Loss of oncogenes necessary for progressive proliferation can lead to differentiation changes. Both natural factors and chemical agents can trigger such a change, and of the naturally occurring agents, growth factors and immunological factors have been most studied. We may find new clues to biological methods of prolonging arrest of cancer, by looking for cytogenetic abnormalities, alterations in oncogene expression and immunocytological composition, in patients showing prolonged dormancy of cancer.     

拟似然非线性模型中的置信域：几何法

对拟似然非线性模型在欧氏内积实间建立了修改的Ｂａｔｅｓ＆Ｗａｔｔｓ几何结构，基于此几何结构，导出了参数和子集系数的与统计曲率有关的三种近似置信域，进一步推广和发展了Ｈａｍｉｌｔｏｎ ｅｔ ａｌ．（１９８２）。Ｈａｍｉｌｔｏｎ（１９８６）和Ｗｅｉ（１９９４，１９９８）等人的相应结果。     

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.     

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.     

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.     

Bivariate versus multivariate allometry: A note on a paper by Jungers and German

Jungers and German (1981) found differences when they compared 1) coefficients of allometry from bivariate plots of log measurements versus log body weight with 2) those coefficients from the first principal component of the log measurements excluding body weight. It is argued here that an arbitrary choice of unit for “internal size” is all that separates these coefficients. When the unit is chosen to make internal size isometric with body weight the coefficients agree rather well.     

Application of common predictive habitat techniques for post-border weed risk management

Weed risk assessment has become an accepted methodology for examining the likelihood and consequence of a plant species becoming invasive outside of its native range. Weed risk assessment draws upon biological and ecological information to estimate the likelihood and magnitude of the threats posed by introducing non-indigenous plants. In geographical terms, this has traditionally been understood as within a new country following importation of plant material. However, recent risk assessment development has focused more specifically on intracountry risk posed by already-present invasive plants and is referred to as post-border weed risk management. This form of assessment calls for fine-scale predictions of invasive species habitat suitability. This study applies some of the more popular and widely available habitat prediction models that represent a variety of different statistical approaches (linear regression, logistic regression, Bayesian probability, Classification and Regression Trees, Genetic Algorithm for Rule-set Production) to a single invasive plant, the vertebrate-dispersed, fleshy fruited European olive ( Olea europaea L.) in southern Australia. The relationships between the dependant ( O. europaea distribution) and independent (soil and climate) variables are used in the models to produce predictive maps for each model. Accuracy was calculated for each model output as well as a combined surface to examine whether recent calls for ensemble modelling of distributions produces improved predictions. Overall, the combined prediction demonstrated superior accuracy compared to any individual model outputs. The combined outputs can be likened to mapped gradations of predicted habitat suitability. The type of output produced in this study should form a critical component of post-border weed risk management but more importantly, the methodology will add to this important discipline.