Model selection method based on maximal information coefficient of residuals

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this article, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coefficient (MIC), a recently proposed dependence measure, captures a wide range of associations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient (PMIC) is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.     

Novel selection schemes for harmony search

Selection is a vital component used in Evolutionary Algorithms (EA) where the fitness value of the solution has influence on the evolution process. Normally, any efficient selection method makes use of the Darwinian principle of natural selection (i.e., survival of the fittest). Harmony search (HS) is a recent EA inspired by musical improvisation process to seek a pleasing harmony. Originally, two selection methods are used in HS: (i) memory consideration selection method where the values of the decision variables are randomly selected from the population (or solutions stored in harmony memory (HM)) to generate a new harmony, and (ii) selecting a new solution in HM whereby a greedy selection is used to update the HM. The memory consideration selection, the focal point of this paper, is not based on natural selection principle which draws heavily on random selection. In this paper, novel selection schemes which replace the random selection scheme in memory consideration are investigated, comprising global-best, fitness-proportional, tournament, linear rank and exponential rank. The proposed selection schemes are individually altered and incorporated in the process of memory consideration and each adoption is realized as a new HS variation. The performance of the proposed HS variations are evaluated and a comparative study is conducted. The experimental results using benchmark functions show that the selection schemes incorporated in memory consideration directly affect the performance of HS algorithm. Finally, a parameter sensitivity analysis of the proposed HS variations is analyzed.     

Multi-segmental representations and approximation of set-valued functions with 1D images

In this work univariate set-valued functions (SVFs, multifunctions) with 1D compact sets as images are considered. For such a continuous SFV of bounded variation (CBV multifunction), we show that the boundaries of its graph are continuous, and inherit the continuity properties of the SVF. Based on these results we introduce a special class of representations of CBV multifunctions with a finite number of ‘holes’ in their graphs. Each such representation is a finite union of SVFs with compact convex images having boundaries with continuity properties as those of the represented SVF. With the help of these representations, positive linear operators are adapted to SVFs. For specific positive approximation operators error estimates are obtained in terms of the continuity properties of the approximated multifunction.     

Display Selection of a Hybrid Foldamer–Peptide Macrocycle

Expanding the chemical diversity of peptide macrocycle libraries for display selection is desirable to improve their potential to bind biomolecular targets. We now have implemented a considerable expansion through a large aromatic helical foldamer inclusion. A foldamer was first identified that undergoes flexizyme-mediated tRNA acylation and that is capable of initiating ribosomal translation with yields sufficiently high to perform an mRNA display selection of macrocyclic foldamer–peptide hybrids. A hybrid macrocyclic nanomolar binder to the C-lobe of the E6AP HECT domain was selected that showed a highly converged peptide sequence. A crystal structure and molecular dynamics simulations revealed that both the peptide and foldamer are helical in an intriguing reciprocal stapling fashion. The strong residue convergence could be rationalized based on their involvement in specific interactions with the target protein. The foldamer stabilizes the peptide helix through stapling and through contacts with key residues. These results altogether represent a significant extension of the chemical space amenable to display selection and highlight possible benefits of inserting an aromatic foldamer into a peptide macrocycle for the purpose of protein recognition.     

西藏“一江两河”流域主栽品种青稞对土壤重金属的积累特性研究

农业环境重金属污染会带来潜在的农产品质量安全风险,不同品种的作物在重金属积累能力方面存在显著的差异,通过种植低富集品种可以一定程度阻断有毒重金属向人体转移的途径。西藏“一江两河”流域部分农田的重金属污染问题是当地迫切需要解决的问题,同时青稞是西藏地区主要的农作物之一。因此本研究选用目前“一江两河”流域广泛种植的9种不同的青稞品种为筛选对象,以汞(Hg)、铬(Cr)、砷(As)、铅(Pb)、镉(Cd)为主要参数对象,利用电感耦合等离子体质谱(ICP-MS)方法进行检测。通过该流域土壤中As的高含量证实了该地区存在的重金属污染情况,利用富集系数和转运系数分析分别筛选出一些低富集或低转运能力的青稞品种,最后结合聚类分析,确定藏青320品种是最适合“一江两河”流域推广种植,且具备根部重金属高积累,茎、叶、籽粒低积累特性的青稞品种。