Stereoconservative and stereoselective syntheses of rare and non-naturalα-amino acids from (S)-aspartic acid and (S)-malic acid

Summary A new strategy for stereoconservative and stereoselective syntheses of several types of amino acids starting from-functional carboxylic acids employing hexafluoroacetone as protecting and activating reagent is described. Outstanding features of this new method are the mild reaction conditions and the high yields for introduction and cleavage of the protective group allowing sensitive functional groups in the side chain to survive. Furthermore, the new concept results in saving of synthetic steps.     

轻稀土离子对钙调蛋白激活的磷酸二酯酶活力作用的影响

研究了轻稀土离子（Ｌｎ３＋）对钙调蛋白（ＣａＭ）调控的磷酸二酯酶（ＰＤＥ）活力的影响。结果表明,在无Ｃａ２＋的ＣａＭ（Ａｐｏ—ＣａＭ）体系中,由ＣａＭ调节的ＰＤＥ的活力随Ｌｎ３＋浓度的变化曲线是双相效应,即在高浓度时,Ｌｎ３＋具有抑制ＣａＭ调节ＰＤＥ活力的能力;低浓度的Ｌｎ３＋可以提高ＣａＭ调节ＰＤＥ活力的能力。在Ｃａ２＋４－ＣａＭ－ＰＤＥ体系中,高浓度的Ｌｎ３＋的加入能抑制ＣｈＭ调节ＰＤＥ活力的能力,其抑制程度因其离子不同而异。ＣａＭ的两类拮抗剂ＪｕＡ（非竞争性抑制剂）和ＴＦＰ（竞争性抑制剂）都能抑制ＣａＭ－Ｌｎ３＋－ＰＤＥ系统的活性。最后对Ｌｎ３＋和ＣａＭ相互作用的分子机制进行了初步的讨论。     

稀土离子对带3蛋白阴离子转运活性及血影膜流动性的影响

测定了Ｌａ２＋、Ｇｄ３＋、Ｔｂ３＋及Ｙｂ３＋四种稀土离子对带３蛋白阴离子转运活性及对血影膜脂流动性的影响。结果如下：（１）稀土离子可强烈抑制带３蛋白的阴离子转运活性,抑制程度随稀土离子浓度增加而增加,最高达到６３．７％。（２）不同的稀土离子对带３蛋白的抑制程度不同,抑制程度从大到小的顺序为Ｙｂ＞Ｔｂ＞Ｇｄ＞Ｌａ。（３）稀土离子可显著降低血影膜流动性,并且在脂双层的全部厚度内都降低,降低的程度随稀土浓度的增大而增大。（４）不同的稀土离子对血影膜流动性的影响不同,作用从大到小的顺序与它们对带３蛋白活性抑制程度大小的顺序一致。（５）稀土离子对血影膜流动性的影响特征与稀土离子抑制带３蛋白活性的特征完全相符,带３蛋白中承担阴离子转运功能的部分是贯穿性膜蛋白,并且在阴离子转运过程中要发生显著的构象变化,因此稀土离子可能是通过作用于膜脂再影响带３蛋白活性的。     

镱和铕诱导金属硫蛋白生成的初步研究

本文研究了不同剂量的稀土元素镱和铕,通过皮下注射对兔肝中金属硫蛋白生成的影响。用ＳｅｐｈａｄｅｘＧ５０柱对兔肝提取液进行分离,收集的组分用紫外分光光度法、原子吸收光谱法和ＥＬＩＳＡ法进行分析。实验结果表明,低剂量（１－３ｐｐｍ）的镱和铕不能明显地诱导兔肝内金属硫蛋白的合成,而高剂量（１０－４０ｐｐｍ）的镱和铕可以诱导兔肝金属硫蛋白合成。     

湖北保康天然蜡梅资源分布及其生态环境初探

蜡梅是我国的珍稀植物。本文根据1984—1985年间进行的实地考察,论述了保康蜡梅资源的分布和生态环境;根据形态学特征和花粉扫描电镜观察,对保康蜡梅分类学问题做了讨论。从资源保护和开发利用角度,提出了保护保康蜡梅资源的建议。     

Do human disturbance variables influence more on fish community structure and function than natural variables in the Wei River basin,China?

Quantifying how human disturbance affects biotic communities in the context of other natural and spatial factors is a vital precursor to develop environmental management strategies to effectively conserve and restore ecosystem. This is particularly so for freshwater ecosystems in heavily populated and increasingly disturbed regions such as the Wei River basin in north-west China. In this paper, we employed both species’ abundance and functional metrics (displaying species diversity, habitat, trophic level, tolerance and abundance) to quantify the effect of human disturbance using canonical correspondence analysis. The results showed that spatial, natural and human disturbance variables can explain 40.1% and 33.7% of the total variation in fish abundance without rare species and with all species, respectively. 37.1% and 37.9% variation can be explained in fish metrics without rare species and with all species. Human disturbance variables explained most of the total variations in the four fish matrices. Inclusion or exclusion of rare species will influence the total explained variation from three factors in fish metrics, but little in fish abundance. Rare species will highlight this relationship with human disturbance gradient. For fish metrics, removing rare species will reflect some wrong information for human disturbance gradient. We recommend to include rare species for fish metrics to assess the impact of environment.     

Rare breast tumors: Review of the literature

Breast cancer tumors have different morphological phenotypes and specific histopathological types with particular prognostic and clinical characteristics. The treatment of rare malignant lesions is frequently controversial due to the absence of trials to determine the optimal managements. This review describes the spectrum of rare breast tumors indicating the clinical, epidemiological and treatment characteristics.     

Identifying rare and common disease associated variants in genomic data using Parkinson’s disease as a model

Background

Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson’s disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects.

Results

We propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson’s disease case–control dataset as a model to demonstrate the application of our method.Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinson’s disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson’s disease.

Conclusions

Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson’s disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-014-0088-9) contains supplementary material, which is available to authorized users.     

Soil seed-bank composition reveals the land-use history of calcareous grasslands

We compared soil seed banks and vegetation of recent (established on abandoned arable fields) and ancient (continuously managed as pastures at least since 1830) calcareous grasslands if there is any impact of former arable field use. The study was carried out in two regions of Southern Germany with well-preserved dry grassland vegetation: the western Jurassic mountains (Kaltes Feld) and the climatically drier eastern part of Southern Germany (Kallmünz).Total number of species in the seed bank was similar in both regions, but species composition partly differed, reflecting phytogeographical differences between the regions. The total number of emerged seedlings showed a large disparity (5457 compared to 2523 seedlings/m² in Kaltes Feld and Kallmünz, respectively).Though there were differences in seed bank composition and size, we found a uniform pattern of plant traits (affiliation to phytosociological groups, Raunkiaer plant life-forms and seed longevity), which depended on the age of the grassland.The main conclusion is that seed banks in contemporary calcareous grasslands still reflect the history of former land use – in this case arable cultivation, even though it occurred a long time ago (up to 150 years). Indicators of former arable fields are germinable seeds of weeds which have persisted in the soil to the present. By contrast, weedy species are completely absent from the seed banks of ancient grasslands. Soil seed banks of recent grasslands may be of substantial conservation importance because they may store seeds of rare and endangered weed species such as Kickxia spuria, Silene noctiflora and Stachys annua, the majority of which have already gone extinct from the current vegetation of the study sites.     

A new approach for efficient genotype imputation using information from relatives

Background

Genotype imputation can help reduce genotyping costs particularly for implementation of genomic selection. In applications entailing large populations, recovering the genotypes of untyped loci using information from reference individuals that were genotyped with a higher density panel is computationally challenging. Popular imputation methods are based upon the Hidden Markov model and have computational constraints due to an intensive sampling process. A fast, deterministic approach, which makes use of both family and population information, is presented here. All individuals are related and, therefore, share haplotypes which may differ in length and frequency based on their relationships. The method starts with family imputation if pedigree information is available, and then exploits close relationships by searching for long haplotype matches in the reference group using overlapping sliding windows. The search continues as the window size is shrunk in each chromosome sweep in order to capture more distant relationships.

Results

The proposed method gave higher or similar imputation accuracy than Beagle and Impute2 in cattle data sets when all available information was used. When close relatives of target individuals were present in the reference group, the method resulted in higher accuracy compared to the other two methods even when the pedigree was not used. Rare variants were also imputed with higher accuracy. Finally, computing requirements were considerably lower than those of Beagle and Impute2. The presented method took 28 minutes to impute from 6 k to 50 k genotypes for 2,000 individuals with a reference size of 64,429 individuals.

Conclusions

The proposed method efficiently makes use of information from close and distant relatives for accurate genotype imputation. In addition to its high imputation accuracy, the method is fast, owing to its deterministic nature and, therefore, it can easily be used in large data sets where the use of other methods is impractical.