有机物生物降解性评价方法综述

评价有机物生物降解性的方法和文献很多,但缺乏系统性介绍评价方法及指导方法选择的综述性文献.本文围绕有机物的生物降解性评价,简要介绍了评价有机物生物降解性的指示性参数及厌、好氧有机物生物降解性评价方法,着重介绍了评价方法选择的标准和原则,并详细分析了不同试验方法使用过程中产生有机物生物降解率差异的原因.最后,以世界经济合作与发展组织(OECD)制定的标准为例,全面介绍了该评价方法的选择使用过程并整合这一评价体系绘制了方法选择路线图,指出了各项试验方法的优越性.     

量子化学在生物分子领域的研究进展

量子化学的方法对生物分子研究的发展提供了全新的思路和方法。本文介绍了量子化学的基本计算方法,并综述了近两年来量子化学方法在生物分子领域的研究进展。     

上下法在急性毒性试验中的应用进展

随着全球对动物福利的关注,各种传统急性毒性试验的替代方法得到了人们越来越广泛的关注。上下法作为OECD修订发布的急性毒性试验方法之一,是传统急性毒性试验的一个替代方法,有着巨大的应用价值和发展前景。本文简要介绍了该方法的发展历程和在实际应用中的注意事项,评价了上下法与传统方法及其他替代方法相比的优缺点,并综述了近年来该方法在国内外的应用进展。     

人类基因组结构变异检测方法

本研究介绍了基因组结构变异检测的生物信息学基本方法和前沿技术。对基于第二代测序技术的四种检测方法(读对方法,读深方法,分裂片段方法和序列拼接方法)的原理和特点进行了详细解读,分析了第二代测序技术应用在检测结构变异上的特点与发展趋势。最后介绍了三代测序、Linked-reads和光学物理图谱等新技术在基因组结构变异检测中的应用,论述了融合新技术的结构变异检测方法的特点与优势。     

生物医学数据分析中的深度学习方法应用

生物医学数据的积累速度史无前例,为生物医学研究带来机遇的同时,也让传统数据分析技术面临巨大挑战.本文综述了深度学习方法应用在生物医学数据分析中的最新研究进展.首先阐述了深度学习方法,列举深度学习方法的主要实现模型,随后总结了目前生物医学数据分析中的深度学习方法应用情况,分析了在数据处理、模型构建和训练方法等方面共有问题的解决方法,最后给出了深度学习方法应用于生物医学数据分析时可能存在的问题及建议.     

森林生态系统碳平衡估测方法及其研究进展

综述了全球范围内森林生态系统碳平衡估测的 2种主要方法 ,即测定表面通量的微气象学方法 (涡相关法 )和生物量清单统计方法。指出了每种方法的优缺点及综合运用各种方法的重要性。简要介绍了应用上述方法对森林生态系统碳平衡研究的进展情况 ,并对今后森林生态系统碳平衡研究的发展趋势进行了探讨。     

抗氧化活性检测方法的研究进展

本文综述了常用的抗氧化活性检测方法,讨论了各种方法的优缺点.     

蛋白质组学的研究方法

近年来,蛋白质组学的研究在生物医学领域正逐步深入,研究方法也正逐步完善。本文综述了蛋白质组学的几种研究方法,指出了各种方法的优点及局限性,并且对蛋白质组学的研究方法和前景进行了展望。     

结构模型中预报方法的研究及其应用

本文对结构模型中未知真值在两种不同条件下的最佳预报方法进行了研究.其次,对因子模型预报方法进行了探讨.最后,分别举例说明了这些方法的应用.     

基于物种的大尺度生物多样性热点研究方法

生物多样性热点是建立保护区、制定保护决策的依据,是生物保护研究的热点问题之一。基于物种的研究方法是大尺度陆地生物多样性热点的主要研究方法,但数据的缺乏限制了直接根据物种丰富度确定热点的方法,因此研究中经常采用其他方法间接的反映物种情况,介绍了4种主要的基于物种的替代方法:指示种、高级分类单元、环境模型和景观异质性,详细阐述了各种方法存在的利弊,并从数据的可获取性、操作的便捷性和对物种特征的反映3个方面对各种方法进行了评价。任何单一的方法都无法准确反映出生物多样性热点的真实分布。合适的研究方法是权衡研究目的、时间和资金的结果,建议选择优势互补的多种方法。     

放线菌和霉菌菌种保藏研究报告

用砂管法室温保藏放线菌244株29年。其存活率(%)为:6年96;9年91;11年88;14年78;15年76;17年74;20年70;22年59;25年47;27年40;29年37。保藏29年尚存活的放线菌的抗曾活性与保藏前基本相同。用麸曲法室温保藏木霉、曲霉和青霉等12属共216株,存活率因种而异。保藏12年,不同种属霉菌的存活率为0—100%;保藏至16年,全部失活。     

Genetic parameter estimates in a clonally replicated progeny test of teak (Tectona grandis Linn. f.)

Teak (Tectona grandis Linn. f.) has been planted extensively in the tropics for its highly valued timber. We analysed data from a 3.5-year-old teak progeny test with clonal replication located in northern Australia. Additive and non-additive genetic variances were estimated for commercially important traits. Trees originating from seedlings were on average 2% taller and 4% straighter than those of the same genotype originating from cuttings. Non-additive genetic variance represented 35–50% of total genetic variance for growth traits and 63% of total genetic variance for incidence of flowering. Narrow-sense heritability was 0.22 for diameter, 0.18 for height and volume, 0.07 for stem straightness, 0.05 for insect defoliation, 0.03 for epicormic sprouts and 0.30 for incidence of flowering (estimated on an assumed underlying continuous scale). Broad-sense heritability was 0.37 for diameter, 0.28 for height, 0.35 for volume, 0.12 for stem straightness, 0.06 for insect defoliation, 0.12 for epicormic sprouts and 0.71 for incidence of flowering. Positive correlations were found between tree volume and flowering and between tree volume and stem straightness. The presence of sizeable non-additive variance supports the selection and deployment of clones to capture the full extent of genetic variation in commercially important traits.     

十种一碳代谢关键产物的HPLC-MS/MS 定量检测方法

目的:利用HPLC-MS/MS方法对十种一碳代谢相关产物进行定量分析。方法:采用Aglient ZORBAX SB-AQ C18柱(2.1mm×100 mm,3.5 m)、电喷雾离子源(ESI),以多离子反应监测方式(MRM)进行正离子检测。对游离叶酸(FA)、5-甲酰四氢叶酸(5-FT)、5-甲基四氢叶酸(5-MT)、S-腺苷蛋氨酸(SAM)、S-腺苷同型半胱氨酸(SAH)、胱硫醚(CYSTA)、组氨酸(HIS)、丝氨酸(SER)、蛋氨酸(MET)、同型半胱氨酸(HCY)进行定量分析。结果:FA、5-FT、5-MT、SAM、SAH、CYSTA、HIS、SER、MET、HCY的检测限分别为0.1 ng.L-1、0.25 ng.L-1、0.1 ng.L-1、0.1 ng.L-1、0.25 ng.L-1、0.25 ng.L-1、0.1 ng.L-1、0.025 ng.L-1、0.1 ng.L-1、0.1 ng.L-1。FA、5-FT、5-MT、SAH、CYSTA浓度测定方法线性范围为2~50 ng.L-1,SER、SAM浓度测定方法线性范围20~500 ng.L-1,MET、HCY浓度测定方法线性范围200~5000 ng.L-1,HIS浓度测定方法线性范围为400~10000 ng.L-1,r均在0.993以上,全部涵盖了已报道的血清中指标的含量范围。结论:建立了HPLC-MS/MS方法,可同时分析十种一碳代谢通路的关键产物,所需样品量少,检测速度快,同时实现分项检测,可为多种代谢性疾病系统性地检测一碳代谢中间产物体液分析方法建立实验条件基础。     

First nuclear DNA C-values for 28 angiosperm genera

This paper reports first DNA C-values for 28 angiosperm genera. These include first DNA C-values for 25 families, of which 16 are monocots. Overall familial representation is 47.2 % for angiosperms, but is now much higher for monocots (75 %) and basal angiosperms (73.1 %) than for eudicots (38.7 %). Chromosome counts are reported for 22 taxa, including first records for six genera plus seven species. Unrepresented families will become increasingly enriched for monotypic taxa from obscure locations that are harder to access. Thus, completing familial representation for genome size for angiosperms may prove impossible in any short period, and progress towards this goal will become slower.     

Revised and extended serum cotinine cut-offs to classify smokers and non-smokers

Purpose: To revise and extend the previously published serum cotinine cut offs to classify smokers and non-smokers for US adolescents and adults.

Materials and methods: Cross-sectional data (N?=?10171) from National Health and Nutrition Examination Survey for 2011–2014 were used to compute serum cotinine cut-offs to classify smokers and non-smokers for US adults aged ≥20?years and 2007–2014 (N?=?4583) data were used to compute serum cotinine cut-offs for US adolescents aged 12–19?years.

Results: Specificities and sensitivities for the cut-offs among adults were ≥95% and ≥75% among adolescents. For adults, serum cotinine cut-offs in ng/mL to classify smokers from non-smokers were 3.3 for the total population, 4.13 for males, 2.99 for females, 4.03 for non-Hispanic whites, 8.85 for non-Hispanic blacks, 0.377 for Mexican Americans, 1.72 for other Hispanics and 1.41 for non-Hispanic Asians. For adolescents, serum cotinine cut-offs in ng/mL to classify smokers from non-smokers were 0.765 for the total population, 1.1 for males, 0.408 for females, 1.2 for non-Hispanic whites, 1.98 for non-Hispanic blacks, 0.215 for Mexican Americans and 0.321 for other Hispanics.

Conclusions: Serum cotinine cut-offs to distinguish smokers from non-smokers for US adults and adolescents were developed.     

RFLP mapping of QTLs for yield and related characters in rice (Oryza sativa L.)

Quantitative triat loci (QTLs) for yield and related traits in rice were mapped based on RFLP maps from two indica/indica F₂ populations, Tesanai 2/CB and Waiyin 2/CB. In Tesanai 2/CB, 14 intervals carrying QTLs for eight traits were detected, including 3 for grain weight per plant (GWT), 2 for number of panicles per plant (NP), 2 for number of grains per panicle (NG), 1 for total number of spikelets per panicle (TNS), 1 for spikelet fertility (SF), 3 for 1000-grain weight (TGWT), 1 for spikelet density (SD), and 1 for number of first branches per main panicle. The 3 QTLs for GWT were located on chromosomes 1, 2, and 4, with 1 in each chromosome. The additive effect of the single locus ranged from 2.0 g to 9.1 g. A major gene (np4) for NP was detected on chromosome 4 within the interval of RG143–RG214, about 4cM for RG143, and this locus explained 26.1% of the observed phenotypic variance for NP. The paternal allele of this locus was responsible for reduced panicles per plant (3 panicles per plant). In another population, Waiyin 2/CB, 12 intervals containing QTLs for six of the above-mentioned traits were detected, including 3 for GWT, 2 for each of NP, TNS, TGWT and SD, 1 for SF. Three QTLs for GWT were located on chromosome 1, 4, and 5, respectively. The additive effect of the single locus for GWT ranged from 6.7 g to 8.8 g, while the dominance effect was 1.7–11.5 g. QTL mapping in two populations with a common male parent is compared and discussed.     

A gas chromatography/mass spectrometry assay to measure estradiol, catecholestradiols, and methoxyestradiols in plasma

We have developed a gas chromatography/mass spectrometry (GC/MS) assay to measure 17beta-estradiol (E) and its biologically active metabolites 2-hydroxyestradiol (2OHE) and 4-hydroxyestradiol (4OHE), and 2-methoxyestradiol (2MEOE) and 4-methoxyestradiol (4MEOE) in rat plasma. All analytes are well separated and show a linear relationship between concentration (0.25-5 pg/microl) and signal, and coefficients of variation (CVs) are low. Intra-assay CV for the lowest quality control samples (QCs) (0.375 pg/microl) were on average for 17beta-estradiol 20.5%, for 2-hydroxyestradiol 15.6%, for 4-hydroxyestradiol 16.5%, for 2-methoxyestradiol 16.5%, and for 4-methoxyestradiol 11.5%. The inter-assay CVs for the lowest QCs were for 17beta-estradiol 12.1%, for 2-hydroxyestradiol 7.1%, for 4-hydroxyestradiol 15.5%, for 2-methoxyestradiol 16.7%, and for 4-methoxyestradiol 9.7%. The highest sensitivity for this assay was observed for hydroxyestradiols followed by the methoxyestradiols and 17beta-estradiol. In summary, we describe a convenient, sensitive, and specific assay to measure 17beta-estradiol and its biologically active metabolites.     

A preliminary investigation of some Maruthamalai forest plants for phytochemical compounds

Twenty five plant species from the Maruthamalai hills, Coimbatore, were screened for the presence of phytoconstituents. Among 25 plants, 19 gave positive tests for flavonoids, 23 for alkaloids, 20 for saponins, 25 for tannins, 22 for steroids and terpenoids, 23 for phenolic compounds, and tests indicated the presence of rotenoids in all the plants.     

Communalities for rates of diaphyseal elongation of short bones of the hand of children with Down syndrome

Communality indices for rates of elongation of diaphyses of short bones of the hand were computed from serial data for children with Down syndrome, 7 to 14 years of age. Communalities were larger for adjacent than for nonadjacent bones and also larger for bones grouped in rows rather than rays of the hand. This pattern is similar to that reported for normal children. Communality indices for rates of diaphyseal elongation for girls with Down syndrome were lower than those of boys with Down syndrome and normal children.     

Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat–protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.