Prediction of Three Dimensional Structure of Calmodulin

Calmodulin (CaM) is an important human protein, which has multiple structures. Numerous researchers studied the CaM structures in the past, and about 50 different structures in complex with fragments derived from CaM-regulated proteins have been discovered. Discovery and analysis of existing and new CaM structures is difficult due to the inherent complexity, i.e. flexibility of 6 loops and a central linker that constitute part of the CaM structure. The extensive interest in CaM structure analysis and discovery calls for a comprehensive study, which based on the accumulated expertise would design a method for prediction and analysis of future and existing CaM structures. It is also important to find the mechanisms by which the protein adjusts its structure with respect to various factors. To this end, this paper analyzes the known CaM structures and finds four factors that influence CaM structure, which include existence of Ca²⁺ binding, different binding segments, measuring surroundings, and sequence mutation. The degree of influence of specific factors on different structural regions is also investigated. Based on the analysis of the relation between the four factors and the corresponding CaM structure a novel method for prediction of the CaM structure in complex with novel segments, given that the surroundings of the complex, is developed. The developed prediction method is tested on a set aside, newest CaM structure. The prediction results provide useful and accurate information about the structure verifying high quality of the proposed prediction method and performed structural analysis.     

Environmental Risk Assessment of Dams in Construction Phase Using a Multi-Criteria Decision-Making (MCDM) Method

This study was conducted to identify environmental and human health risks caused by Balarood Dam, in construction phrase. The first step, all risk-generating factors were identified using a Delphi Questionnaire. Afterwards, the identified criteria were prioritized once using the Analytical Hierarchy Process (AHP) method and then by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Due to the complex and uncertain nature of decision-making in times of risk, it was necessary to use more than one weighting method to ensure accuracy of weights. The results from AHP and TOPSIS revealed a mismatch in priorities; therefore, an integration method was presented blending Mean-Rank, Borda, and Copeland methods. According to the TOPSIS results, factors including cut and fill, explosion, and transportation, were first to third highest-priority risk-generating factors, respectively. Considering the results from the AHP method, factors cut and fill, drilling, and explosion were identified as first to third top-priority risk-generating factors, respectively. The results obtained from the integration method suggested that cut and fill, explosion, and drilling are the most important environmental risks at construction phase. As a general conclusion, different weighting methods can lead to different results by which the fate of a decision may be changed and it is essential to control final scores by applying more than one weighting method.     

Quantification of growth factors in allogenic bone grafts extracted with three different methods

Background Bony allografts are used for defect filling. A reliable sterilization method is the peracetic acid–ethanol sterilization procedure (PES). Several studies showed the antimicrobiological efficacy of this method. Aim of this study was the quantification of growth factors necessary for bone formation in PES sterilized allografts (n = 9). Methods To extract the growth factors from the tissue three different methods were used: (a) use of collagenase 1 for extraction, (b) incubation of the material in a proteinase inhibitor cocktail (Complete), and (c) extraction with guanidine HCl. The supernatants from the different methods were analyzed for the total protein concentration and different growth factors. Results The extraction with guanidine HCl resulted in the highest amount of protein measurable in the supernatants of the samples. For comparison of the individual growth factor values the results were normalized to the protein content. The highest growth factor amount/protein was detectable for BMP-2 using the GndHCL method followed by FGFa, IGF-I, TGF-β1, VEGF, and PDGF. Comparing the three extraction methods, significant differences were measured for the individual growth factor content. Conclusion PES sterilized bony allografts contain several growth factors. Depending on the extraction method, the quantity of the analyzed growth factors varies.     

A computational approach for genome-wide mapping of splicing factor binding sites

Alternative splicing is regulated by splicing factors that serve as positive or negative effectors, interacting with regulatory elements along exons and introns. Here we present a novel computational method for genome-wide mapping of splicing factor binding sites that considers both the genomic environment and the evolutionary conservation of the regulatory elements. The method was applied to study the regulation of different alternative splicing events, uncovering an interesting network of interactions among splicing factors.     

不同蚕区蚕桑生产与气候因子关系的研究

本文采用灰色系统理论和方法,研究了安徽省各蚕区的蚕桑生产与气候因子的变化规律,通过关联分析,研究了影响各蚕区的气候因子,建立了年茧量及亩桑产茧量的灰色静动态模型．研究结果表明,气温、降水、日照是影响各蚕区蚕桑生产的主要因子,但由于不同蚕区气候条件的差异,各蚕区的重点气候因子又各不相同;利用气候因子所建立的方程,与年茧量、亩桑产茧量的实际值及变化规律有很好的拟合度．     

Investigation and comparative characteristic of factors determining human skin elasticity

The results of examining the variation in skin elasticity at its expansion are presented for different skin thickness, different content of collagen and intercellular and intracellular fluids, different venous pressure, and for tension and relaxation of the smooth muscle of skin vessels (vasomotion). Elasticity was determined using the acoustic method by the velocity of the surface shear acoustic wave, the self-resonance method by the frequency of mechanic skin resonance, and the vacuum method by the volume of the skin segment pulled into the aperture of a thin tube by air rarefaction. It is shown that the main factors determining the skin elasticity are its stretching, thickness, and collagen and fluid content. The influence of venous pressure and contractile activity of the vasculature on elasticity is insignificant. This gives grounds for using skin elasticity factors as indicators of systemic and local lesion of connective tissue.     

ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data

MOTIVATION: Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. RESULTS: We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.     

Standardization of the twinning rate

There is a great interest in comparing twinning rates. These comparisons can be performed between different time periods for a specific population, between different regions within the same country, and between different populations. However, there are several factors (maternal age, parity, urbanization, etc.) that influence the twinning rate. The most dominant one is maternal age, and because the age distribution of the mother varies, it is necessary to standardize the data to make these comparisons. If we want to compare the twinning rates in different countries, we have to face the problem that the composition of the data from different countries may differ to a great extent. The applicable method is determined by the data of the lowest quality. Often the available data do not allow the traditional (direct and/or indirect) methods of standardization. Under such circumstances other methods have to be used. Earlier, Fellman and Eriksson (1987) proposed and successfully applied a new method. In this article we discuss the standardization problem in more detail. We suggest different methods and apply them to different data on twinning from Australia, Finland, and Baden-Württemberg (West Germany). The new standardization methods give standardized twinning rates similar to the rates obtained by traditional methods. It is noted that, irrespective of standardization method, changes in maternal age alone cannot explain temporal or regional variations in the twinning rate. Other factors that may raise or lower the twinning rate are decreasing parity, sociodemographic changes with increased communication, which causes the breakup of isolates, and deteriorating physical condition of mothers as a result of increased industrialization and urbanization.     

Method to Estimate Activity of Subcutaneous Thermogenic Structures on Exposure to Stimuli of Different Modalities

A method to estimate activity of subcutaneous thermogenic structures was developed on the basis of dynamical infrared thermography. The gist is comparing the dynamics of skin temperature changes for different body sites on exposure to factors evoking metabolic responses. The method can be used for investigating the mechanisms of changes in body temperature in response to various disturbing factors and, in certain cases, can help to differentiate metabolic and vascular responses.     

MATCHING THE SHAPE OF TIME-INTENSITY CURVES1

Time-Intensity Curves are often summarized by average curves. From these curves TI-parameters are inferred. In this paper a different method is proposed in which the shape of the curves is the central concept. Isotropic scaling factors are computed for each curve. These factors can be tabled and inspected. Often a large number of scaling factors will be computed, which makes it hard to study them in a table. In such cases they can be represented in a plot, for which in this paper PCA is suggested. In this plot a structure in the scaling factors for the different TI-curves may become visible. It is also possible to make a plot in which the homogeneity of the panel of subjects can be studied.     

The role of atmospheric dispersion models and ecosystem sensitivity in the determination of characterisation factors for acidifying and eutrophying emissions in LCIA

Background, aim and scope  The methodological choices and framework to assess environmental impacts in life cycle assessment are still under discussion. Despite intensive developments worldwide, few attempts have been made hitherto to systematically present the role of different factors of characterisation models in life cycle impact assessment (LCIA). The aim of this study is to show how European average and country-dependent characterisation factors for acidifying and eutrophying emissions differ when using (a) acidifying and eutrophying potentials alone, (b) depositions from an atmospheric dispersion model or (c) critical loads in conjunction with those depositions. Furthermore, in the latter case, the contributions of emissions, an atmospheric transport model and critical loads to changes in characterisation factors of NO₂ are studied. In addition, the new characterisation factors based on the accumulated exceedance (AE) method are presented using updated emissions, a new atmospheric transport model and the latest critical loads. Materials and methods  In this study, characterisation factors for acidifying and eutrophying emissions are calculated by three different methods. In the ‘no fate’ (NF) methods, acidifying and eutrophying potentials alone are considered as characterisation factors. In the ‘only above terrestrial environment’ (OT) approach, characterisation factors are based on the deposition of the acidifying or eutrophying substances to terrestrial land surfaces. The third method is the so-called AE method in which critical loads are used in conjunction with depositions. The results of the methods are compared both at the European and the country level using weighted mean, weighted standard deviation, minimum and maximum values. To illustrate the sensitivity of the AE method, changes in European emissions, employed atmospheric dispersion model and the critical loads database are conducted step-by-step, and the differences between the results are analysed. Results and discussion  For European average characterisation factors, the three characterisation methods of acidification produce results in which the contributions of NH₃, NO₂ and SO₂ to the acidification indicator do not differ much within each method when 1 kg of each acidifying substance is emitted. However, the NF methods cannot describe any spatial aspects of environmental problems. Both OT and AE methods show that the spatial aspects play an important role in the characterisation factors. The AE method results in greater differentiations between country-dependent characterisation factors than does the OT method. In addition, the results of the AE and OT methods differ from each other for individual countries. A major shortcoming of the OT approach is that it does not consider the sensitivity of the ecosystems onto which the pollutants are deposited, whereas the AE approach does. In the case of the AE method, a new atmospheric dispersion model, new information on emissions and critical loads have a different influence on the characterisation factors, depending on the country. The results of statistics show that the change in the atmospheric dispersion model has a greatest influence on the results, since ecosystem-specific depositions are taken into account for the first time. Conclusions and recommendations  The simple NF methods can be used in a first approximation to assess the impacts of acidification and terrestrial eutrophication in cases where we do not know where the emissions occur. The OT approach is a more advanced method compared with the NF method, but its capability to describe spatial aspects is limited. The AE factors are truly impact-oriented characterisation factors and the information used here represents the current best knowledge about the assessment practice of acidification and terrestrial eutrophication in Europe. The key message of this study is that there is no shortcut to achieving advanced characterisation of acidification and terrestrial eutrophication: an advanced methodology cannot develop without atmospheric dispersion models and information on ecosystem sensitivity.     

草地生态系统植被变化的自然与人为因素定量区分方法

定量区分导致草地生态系统植被变化的自然和人为因素,是生态系统科学管理和保育的关键。因此,综述当前应用较为广泛的定量区分方法,包括主成分分析法、层次分析法、残差趋势法和模型差值法等,比较不同方法的计算原理、优势及误差来源,进而结合典型区域或典型生态系统,对不同方法进行对比和分析。总体而言,每种方法各有其优势和缺点,当前采用同一方法在不同区域或生态系统类型应用的研究较多,但针对方法本身改进或优化的研究较少。此外,针对同一区域开展的不同区分方法间的结果也存在差异。定量区分的结果除受方法本身算法的局限外,也受数据源的时空连续性及分辨率的影响。未来定量区分方法将强调：(1)在指标的选取上,要兼顾全面、多角度、不冗余等原则;(2)进行多源数据与多时空尺度融合,在更高时空分辨率定量区分自然与人为因素,从单一因子的贡献率区分到更多综合性指标的贡献率区分;(3)对定量区分方法本身的改进,这是当前的重点与难点。以期为生态系统适应性管理与关键生态功能的针对性保育提供科学依据和政策导向。     

不同去趋势方法对树轮气候信号识别的影响

树木生长受到气候因子、随年龄增长的内在生长趋势、环境干扰和其他扰动信号的影响。目前存在不同的去趋势方法对树木年轮进行去趋势以识别树木生长中的气候信号。以往的研究多基于单个方法识别树轮气候信号,而不同去趋势方法识别的树轮气候信号可能会有一定的差别。为了对比不同去趋势方法对树轮气候信号识别的影响,我们基于国际年轮数据库网站获取中国西部地区68个点的树轮宽度数据,采用最常用的"signal-free"方法（SsfCrn）、线性和负指数函数法（std）、67%样条函数法（spline）、firedman方法、以及基于经验模式分解去趋势方法（EEMD）5种去趋势方法分别建立树轮年表,并对比分析同一地点的不同年表对气候响应的异同。结果表明：不同去趋势方法得到的年表对温度、降水以及相对湿度等气候因素的响应具有明显差异。其中,SsfCrn去趋势方法建立的年表对温度（月平均温、月最低温、月平均最低温）响应中相关最高的样点在所有样点中占比最高;EEMD去趋势方法建立的年表对降水量、相对湿度和月最高温响应中相关最高的样点在所有样点中占比最高;firedman去趋势方法建立的年表对月平均最高温响应中相关最高的样点在所有样点中占比最高。研究结果表明SsfCrn,EEMD和firedman方法在识别树轮气候信号方法具有一定的优势。在不同研究区域中,不同去趋势方法建立的年表对不同气候条件响应有差异,因此选择不同的去趋势方法识别树木生长趋势,分析哪种方法可以更好的反应气候变化对树木生长的影响显得尤为重要。     

A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors

MOTIVATION: Time series experiments of cDNA microarrays have been commonly used in various biological studies and conducted under a lot of experimental factors. A popular approach of time series microarray analysis is to compare one gene with another in their expression profiles, and clustering expression sequences is a typical example. On the other hand, a practically important issue in gene expression is to identify the general timing difference that is caused by experimental factors. This type of difference can be extracted by comparing a set of time series expression profiles under a factor with those under another factor, and so it would be difficult to tackle this issue by using only a current approach for time series microarray analysis. RESULTS: We have developed a systematic method to capture the timing difference in gene expression under different experimental factors, based on hidden Markov models. Our model outputs a real-valued vector at each state and has a unique state transition diagram. The parameters of our model are trained from a given set of pairwise (generally multiplewise) expression sequences. We evaluated our model using synthetic as well as real microarray datasets. The results of our experiment indicate that our method worked favourably to identify the timing ordering under different experimental factors, such as that gene expression under heat shock tended to start earlier than that under oxidative stress. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

On estimating number of genes by genotype assay.

The method of genotype assay, proposed by Jinks and Towey (1976) for estimating the number of effective factors in a polygenic system, assumes independent segregation if applied to number of genes. Their results are extended to include the case of linked genes, and the ratio of expected number of effective factors to number of genes is computed for a range of models. Unless all genes are on different chromosomes or many generations of inbreeding are used, the estimate of gene number is biased downwards.     

Rapid colorimetric quantification of lipo-chitooligosaccharides from Mesorhizobium loti and Sinorhizobium meliloti

Nod factors are lipids with a chitinlike headgroup produced by gram-negative Rhizobium bacteria. These lipo-chitooligosaccharides (LCOs) are essential signaling molecules for accomplishing symbiosis between the bacteria and roots of legume plants. Despite their important role in the Rhizobium-legume interaction, no fast and sensitive Nod factor quantification methods exist. Here, we report two different quantification methods. The first is based on the enzymatic hydrolysis of Nod factors to release N-acetylglucosamine (GlcNAc), which can subsequently be quantified. It is shown that the degrading enzyme, glusulase, releases exactly two GlcNAc units per pentameric nodulation factor from Mesorhizobium loti factor, allowing quantification of LCOs from Mesorhizobium loti. The second method is based on a specific type of Nod factors that are sulfated on the reducing GlcNAc, allowing quantification analogous to the quantification of sulfolipids. Here, a two-phase extraction method is used in the presence of methylene blue, which specifically forms an ion pair with sulfated lipids. The blue ion pair partitions into the organic phase, after which the methylene blue signal can be quantified. To enable Nod factor quantification with this method, the organic phase was modified and the partitioning was evaluated using fluorescent and radiolabeled sulfated Nod factors. It is shown that sulfated LCOs can be quantified with this method, using sodium dodecyl sulfate for calibration. Both methods allow Nod factor quantification in parallel enabling a fast and easy detection of nanomole quantities of Nod factors. Accurate Nod factor quantification will be crucial for characterization and cross-comparison of the affinity for Nod factors of newly identified Nod factor binding proteins or putative Nod factor receptors.     

Feature ranking based on synergy networks to identify prognostic markers in DPT-1

Interaction among different risk factors plays an important role in the development and progress of complex disease, such as diabetes. However, traditional epidemiological methods often focus on analyzing individual or a few ‘essential’ risk factors, hopefully to obtain some insights into the etiology of complex disease. In this paper, we propose a systematic framework for risk factor analysis based on a synergy network, which enables better identification of potential risk factors that may serve as prognostic markers for complex disease. A spectral approximate algorithm is derived to solve this network optimization problem, which leads to a new network-based feature ranking method that improves the traditional feature ranking by taking into account the pairwise synergistic interactions among risk factors in addition to their individual predictive power. We first evaluate the performance of our method based on simulated datasets, and then, we use our method to study immunologic and metabolic indices based on the Diabetes Prevention Trial-Type 1 (DPT-1) study that may provide prognostic and diagnostic information regarding the development of type 1 diabetes. The performance comparison based on both simulated and DPT-1 datasets demonstrates that our network-based ranking method provides prognostic markers with higher predictive power than traditional analysis based on individual factors.     

测度森林群落乔木层盖度的一种新方法

乔木层盖度是森林群落的主要数量特征之一。在Matlab6.5的计算环境下,为获得森林群落乔木层盖度的数值提供方便可靠的计算方法。结果表明,与传统的目测估计法相比,该文提供的方法可以在很大程度上避免由于研究者的主观因素造成的估计值差异。