Clustering gene expression data based on predicted differential effects of GV interaction

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.     

Identification of Immunity-related Genes in Arabidopsis and Cassava Using Genomic Data

Recent advances in genomic and post-genomic technologies have provided the opportu- nity to generate a previously unimaginable amount of information. However, biological knowledge is still needed to improve the understanding of complex mechanisms such as plant immune responses. Better knowledge of this process could improve crop production and management. Here, we used holistic analysis to combine our own microarray and RNA-seq data with public genomic data from Arabidopsis and cassava in order to acquire biological knowledge about the relationships between proteins encoded by immunity-related genes （IRGs） and other genes. This approach was based on a kernel method adapted for the construction of gene networks. The obtained results allowed us to propose a list of new IRGs. A putative function in the immunity pathway was predicted for the new IRGs. The analysis of networks revealed that our predicted IRGs are either well documented or recognized in previous co-expression studies. In addition to robust relationships between IRGs, there is evidence suggesting that other cellular processes may be also strongly related to immunity.     

Kinematic Analysis and Experimental Verification on the Locomotion of Gecko

This paper presents a kinematic analysis of the locomotion of a gecko,and experimental verification of the kinematicmodel.Kinematic analysis is important for parameter design,dynamic analysis,and optimization in biomimetic robot research.The proposed kinematic analysis can simulate,without iteration,the locomotion of gecko satisfying the constraint conditionsthat maintain the position of the contacted feet on the surface.So the method has an advantage for analyzing the climbing motionof the quadruped mechanism in a real time application.The kinematic model of a gecko consists of four legs based on 7-degreesof freedom spherical-revolute-spherical joints and two revolute joints in the waist.The motion of the kinematic model issimulated based on measurement data of each joint.The motion of the kinematic model simulates the investigated real gecko’smotion by using the experimental results.The analysis solves the forward kinematics by considering the model as a combinationof closed and open serial mechanisms under the condition that maintains the contact positions of the attached feet on the ground.The motions of each joint are validated by comparing with the experimental results.In addition to the measured gait,three othergaits are simulated based on the kinematic model.The maximum strides of each gait are calculated by workspace analysis.Theresult can be used in biomimetic robot design and motion planning.     

Dynamical behaviors of the generalized hematopoiesis model with discontinuous harvesting terms

This paper is concerned with the generalized hematopoiesis model with discontinuous harvesting terms.Under the framework of Filippov solution,by means of the differential inclusions and the topological degree theory in set-valued analysis,we have established the existence of the bounded positive periodic solutions for the addressed models.After that,based on the nonsmooth analysis theory w让 h Lyapunov-like approach,we employ a novel argument and derive some new criteria on the uniqueness,global exponential stability of the addressed models and convergence of the corresponding autonomous case of the addressed models.Our results extend previous works on hematopoiesis model to the discontinuous harvesting terms and some corresponding results in the literature can be enriched and extended.In addition,typical examples with numerical simulations are given to illustrate the feasibility and validity of obtained results.     

A hybrid SOM-SVM approach for the zebrafish gene expression analysis

Microarray technology can be employed to quantitatively measure the expression of thousands of genes in a single experiment. It has become one of the main tools for global gene expression analysis in molecular biology research in recent years. The large amount of expression data generated by this technology makes the study of certain complex biological problems possible, and machine learning methods are expected to play a crucial role in the analysis process. In this paper, we present our results from integrating the self-organizing map （SOM） and the support vector machine （SVM） for the analysis of the various functions of zebrafish genes based on their expression. The most distinctive characteristic of our zebrafish gene expression is that the number of samples of different classes is imbalanced. We discuss how SOM can be used as a data-filtering tool to improve the classification performance of the SVM on this data set.     

Delayed information induces oscillations in a dynamical model for infectious disease

During disease outbreak,it has been observed that information about the disease prevalence induces the individuafs behavioral changes.This information is usually assumed to be generated by the density of infective individuals and active mass media.The delay in reporting of these infective individuals may have its impact on generated information.Hence,to study the impact of delay on information generation,and therefore on the disease dynamics,a delay differential equation model is proposed and analyzed.The dynamics of information with delay effect is also modeled by a separate rate equation.Model analysis is performed and a unique infected equilibrium is obtained when the basic reproduction number(R0)is greater than one,whereas the disease free equilibrium always exists.When R0<1,the disease free equilibrium is found to be locally stable independent of delay effect.The unique infected equilibrium is found to be locally stable till delay reaches a threshold value.The global stability of the unique infected equilibrium is also established under some parametric conditions by constructing a suitable Lyapunov function.The occurrence of Hopf bifurcation is observed when the delay in information crosses the threshold value.Analytically,the direction and stability of bifurcating periodic solutions is established.Further,we observed the occurrence of Hopf-Hopf bifurcation at two different delays.At first delay threshold,the endemic equilibrium loses its st ability and produces periodic oscillations via Hopf bifurcation.It further regains its stability at second delay threshold via another Hopf bifurcation.Hence,the delay effect on information shows possibility of stability switches.Numerical experiments are carried out to support the obtained analytical results.Our study infers that the disease will show persistent oscillations if there is a significant time lag in reporting of infective after the disease outbreak.Thus,the delay in dissemination of information shows rich and complex dynamics in the model and provides important insights.We also observe numerically that the saturation in information plays a significant role on stability of infected equilibrium in presence of delay.     

A Phase-Dependent Hypothesis for Locomotor Functions of Human Foot Complex

The human foot is a very complex structure comprising numerous bones, muscles, ligaments and synovial joints. As the only component in contact with the ground, the foot complex delivers a variety of biomechanical functions during human locomotion, e.g. body support and propulsion, stability maintenance and impact absorption. These need the human foot to be rigid and damped to transmit ground reaction forces to the upper body and maintain body stability, and also to be compliant and resilient to moderate risky impacts and save energy. How does the human foot achieve these apparent conflicting functions？ In this study, we propose a phase-dependent hypothesis for the overall locomotor functions of the human foot complex based on in-vivo measurements of human natural gait and simulation results of a mathematical foot model. We propse that foot functions are highly dependent on gait phase, which is a major characteristics of human locomotion. In early stance just after heel strike, the foot mainly works as a shock absorber by moderating high impacts using the viscouselastic heel pad in both vertical and horizontal directions. In mid-stance phase （-80% of stance phase）, the foot complex can be considered as a springy rocker, reserving external mechanical work using the foot arch whilst moving ground contact point forward along a curved path to maintain body stability. In late stance after heel off, the foot complex mainly serves as a force modulator like a gear box, modulating effective mechanical advantages of ankle plantiflexor muscles using metatarsal-phalangeal joints. A sound under- standing of how diverse functions are implemented in a simple foot segment during human locomotion might be useful to gain insight into the overall foot locomotor functions and hence to facilitate clinical diagnosis, rehabilitation product design and humanoid robot development.     

In silico Analysis of Sequential,Structural and Functional Diversity of Wheat Cystatins and Its Implication in Plant Defense

Phytocystatins constitute a multigene family that regulates the activity of endogenous and/or exogenous cysteine proteinases.Cereal crops like wheat are continuously threatened by a multitude of pathogens,therefore cystatins offer to play a pivotal role in deciding the plant response.In order to study the need of having diverse specificities and activities of various cystatins,we conducted comparative analysis of six wheat cystatins(WCs) with twelve rice,seven barley,one sorghum and ten corn cystatin sequences employing different bioinformatics tools.The obtained results identified highly conserved signature sequences in all the cystatins considered.Several other motifs were also identified,based on which the sequences could be categorized into groups in congruence with the phylogenetic clustering.Homology modeling of WCs revealed 3D structural topology so well shared by other cystatins.Protein-protein interaction of WCs with papain supported the notion that functional diversity is a consequence of existing differences in amino acid residues in highly conserved as well as relatively less conserved motifs.Thus there is a significant conservation at the sequential and structural levels;however,concomitant variations maintain the functional diversity in this protein family,which constantly modulates itself to reciprocate the diversity while counteracting the cysteine proteinases.     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis.Microarray data contains many sources of error.In order to glean information about mRNA expression levels,the true signal must first be segregated from noise.This research focuses on the variation that can be captured at the spot level in cDNA microarray images.Variation at other levels,due to differences at the array,dye,and block levels,can be corrected for by a variety of existing normalization procedures.Two signal quality estimates that capture the reliability of each spot printed on a microarray are described.A parametric estimate of within-spot vari ance,referred to here as σ s2pot,assumes that pixels follow a normal distribution and are spatially correlated.A non-parametric estimate of error,called the mean square prediction error(MSPE),assumes that spots of high quality possess pixels that are similar to their neighbors.This paper will provide a framework to use either spot quality measure in downstream analysis,specifically as weights in regression models.Using these spot quality estimates as weights can result in greater efficiency,in a statistical sense,when modeling microarray data.     

Quantitative Profiling of Polar Metabolites in Herbal Medicine Injections for Multivariate Statistical Evaluation Based on Independence Principal Component Analysis

Botanical primary metabolites extensively exist in herbal medicine injections (HMIs), but often were ignored to control. With the limitation of bias towards hydrophilic substances, the primary metabolites with strong polarity, such as saccharides, amino acids and organic acids, are usually difficult to detect by the routinely applied reversed-phase chromatographic fingerprint technology. In this study, a proton nuclear magnetic resonance (¹H NMR) profiling method was developed for efficient identification and quantification of small polar molecules, mostly primary metabolites in HMIs. A commonly used medicine, Danhong injection (DHI), was employed as a model. With the developed method, 23 primary metabolites together with 7 polyphenolic acids were simultaneously identified, of which 13 metabolites with fully separated proton signals were quantified and employed for further multivariate quality control assay. The quantitative ¹H NMR method was validated with good linearity, precision, repeatability, stability and accuracy. Based on independence principal component analysis (IPCA), the contents of 13 metabolites were characterized and dimensionally reduced into the first two independence principal components (IPCs). IPC1 and IPC2 were then used to calculate the upper control limits (with 99% confidence ellipsoids) of χ² and Hotelling T² control charts. Through the constructed upper control limits, the proposed method was successfully applied to 36 batches of DHI to examine the out-of control sample with the perturbed levels of succinate, malonate, glucose, fructose, salvianic acid and protocatechuic aldehyde. The integrated strategy has provided a reliable approach to identify and quantify multiple polar metabolites of DHI in one fingerprinting spectrum, and it has also assisted in the establishment of IPCA models for the multivariate statistical evaluation of HMIs.     

Heritability of and correlations among genotype-by-environment stability statistics for grain yield in bread wheat

Several genotype-by-environment stability measures are in use, but little information exists about their inheritance or genetic inter-relationships. Among those measures in common use are the linear regression coefficient (b), deviations from regression (s_b), coefficient of determination (R²), coefficient of phenotypic variation (CPV) and, more recently, interaction principal components (IPCA) of the additive-main-effect-and-multiplicative-interaction (AMMI) model. Because of the factorial structure of the data, the diallel cross is well suited to study these parameters and their relationship to quantitative traits. For this study a complete diallel cross, derived by mating eight lines from a broad based bread wheat breeding population, was grown for several growing seasons at two Ugandan locations, one of which was prone to yellow rust. Stability parameters and grain yield were measured for each cross. CPV had the highest narrow-sense heritability (h²=0.522) followed by IPCA1 of the AMMI (h²=0.461). Lowest narrow-sense heritabilities were calculated for b and R² (h²=0.150 and 0.100 respectively). There were high additive genetic correlations (r_A) between grain yield and CPV (r_A=−0.933), grain yield and IPCA1 (r_A=0.707), and grain yield and IPCA2 (r_A=0.751). The genetic association between CPV and IPCA1 was also high and negative (r_A= −0.934). These results suggest that it may be possible to select simultaneously for high and stable grain yield in this broad-based bread wheat breeding pool by selecting outyielders that exhibit a low CPV. Received: 25 July 2000 / Accepted: 7 December 2000     

Seedling and adult stage resistance to net form of net blotch (NFNB) in spring barley and stability of adult stage resistance to NFNB in Morocco

This study was conducted to identify stable resistance to net form of net blotch (NFNB) in spring barley in Moroccan environments. Seedling resistance to NFNB was evaluated by inoculating 336 barley genotypes with two NFNB isolates LDNH04Ptt-19 and TD-10 in the greenhouse. These genotypes were evaluated for adult plant resistance to NFNB under seven environments in Morocco in 2015 and 2016. The disease severity was estimated at GS 77–87 on barley leaves using a double-digit scale. To investigate stability of resistance, 149 barley genotypes were subjected to AMMI analysis. At the seedling stage, differential responses of barley genotypes to different NFNB isolates were identified, whereas genotypes had variable stability to NFNB resistance at the adult stages. Five genotypes, AM-68, AM-95, AM-250, AM-267 and AM-322, were resistant to both NFNB isolates at the seedling stage. There were significant (p < .001) effects of genotype (G) and G × E interaction on NFNB severity for barley genotypes at the adult stage. The principal components, IPCA1 and IPCA2, accounted for 48.4% and 18.7% variation for NFNB severity, respectively. The AMMI stability values (ASVs) ranged from 0.01 to 15.5, and fifty-nine barley genotypes had stable responses (ASV ≤ 0.05) across all seven environments. Specifically, two stable genotypes, AM-187 and AM-244, had lower mean NFNB severities across all environments, suggesting a quantitative resistance in these genotypes. Divergent environmental responses of NFNB severity were measured in Sidi El Ayedi 2015 and Sidi Allal Tazi 2016, suggesting that these environments may be suitable to capture resistance to diverse pathotypes. These stable genotypes are valuable resources for introgression of both qualitative resistance and quantitative resistance to NFNB in future.     

Global analysis of the thermal and chemical denaturation of the N-terminal domain of the ribosomal protein L9 in H2O and D2O. Determination of the thermodynamic parameters, deltaH(o), deltaS(o), and deltaC(o)p and evaluation of solvent isotope effects.

The stability of the N-terminal domain of the ribosomal protein L9, NTL9, from Bacillus stearothermophilus has been monitored by circular dichroism at various temperatures and chemical denaturant concentrations in H2O and D2O. The basic thermodynamic parameters for the unfolding reaction, deltaH(o), deltaS(o), and deltaC(o)p, were determined by global analysis of temperature and denaturant effects on stability. The data were well fit by a model that assumes stability varies linearly with denaturant concentration and that uses the Gibbs-Helmholtz equation to model changes in stability with temperature. The results obtained from the global analysis are consistent with information obtained from individual thermal and chemical denaturations. NTL9 has a maximum stability of 3.78 +/- 0.25 kcal mol(-1) at 14 degrees C. DeltaH(o)(25 degrees C) for protein unfolding equals 9.9 +/- 0.7 kcal mol(-1) and TdeltaS(o)++(25 degrees C) equals 6.2 +/- 0.6 kcal mol(-1). DeltaC(o)p equals 0.53 +/- 0.06 kcal mol(-1) deg(-1). There is a small increase in stability when D2O is substituted for H2O. Based on the results from global analysis, NTL9 is 1.06 +/- 0.60 kcal mol(-1) more stable in D2O at 25 degrees C and Tm is increased by 5.8 +/- 3.6 degrees C in D2O. Based on the results from individual denaturation experiments, NTL9 is 0.68 +/- 0.68 kcal mol(-1) more stable in D2O at 25 degrees C and Tm is increased by 3.5 +/- 2.1 degrees C in D2O. Within experimental error there are no changes in deltaH(o) (25 degrees C) when D2O is substituted for H2O.     

Testing uniformity of mutants of the Lathyrus sativus L. (grasspea) using Bennet's method

In this paper interest is focused on uniformity of individual mutants. Because before the registration of a new variety the criteria for DUS (distinctness, uniformity and stability) are checked, so you should also check whether the mutant meets them. The aim of created mutants, in many genetic experiments, is to expand variation of the initial material. This is achieved through use of the mutagen or combination of mutagens on different stages of plants growth. The experimenter should choose some of obtained mutants (for example these which showed better property as regards the same trait). Calculations were done on the basis of results of test with variety Krab of the Lathyrus sativus L. (grasspea) species and 17 mutants getting from this variety. Uniformity was checked with the use of the Bennett's method which is based on testing of equality of coefficients of variation. After analysis the mutants with codes K3, K37, K63 and K64 were detected as uniform at the same level as variety Krab.     

Quantification of the non-planarity of the human carotid bifurcation

The carotid bifurcation has been a region of particular interest due to its predilection for clinically significant atherosclerosis. It has been shown that the vessel geometry is a major determinant of the local haemodynamic properties which are believed to be associated with the location of atherosclerotic lesions. Current knowledge of the geometry of the carotid bifurcation is insufficient and restricted to basic geometric parameters. To provide some means of quantifying the degree of complexity of the 3D shape of the bifurcation, we made an initial attempt by evaluating the non-planarity of an arterial bifurcation based upon the singular value decomposition theorem.In this paper we present our results obtained on the right carotid bifurcations of six normal subjects, each of whom was scanned twice using the 2D time-of-flight MR sequence. The acquired 2D cross sectional images were processed by using our in-house software which comprises 2D segmentation, 3D reconstruction and smoothing. The centroids of each transverse slices were determined and used as input data for the non-planarity analysis. Our results using the singular value decomposition method have demonstrated discernible differences in non-planarity among individuals. Comparisons with the planarity definition proposed by other investigators suggest that the singular value decomposition method offers more information about the linearity and planarity of the bifurcation. However, it is also realised that a single measure of non-planarity can never fully characterise a bifurcation owing to the great variety of geometries.     

Determining the modern carbon content of biobased products using radiocarbon analysis

In support of the USDA Federal Biobased Products Preferred Procurement Program (FB4P), Iowa State University is coordinating testing to determine the "biobased content" of manufactured products. These tests are part of a process to "designate" items that qualify for preferential procurement status with federal agencies. Biobased content determinations are being performed using three radiocarbon dating procedures specified in ASTM D 6866-05. Test results obtained thus far indicate that the AMS and benzene synthesis methods provide comparable results. Data from the CO(2) cocktail method did not agree as well with the data from the other two methods, but were still in reasonably good agreement with those data. Radiocarbon analysis is shown to be a reliable and valuable tool for verifying the biobased content of a wide variety of biobased products. Based on inter- and intra-laboratory comparisons, a reasonable uncertainty to associate with the analyses would be +/-3% (absolute) for the AMS and benzene synthesis methods. Because of limited data availability, additional work is needed to establish the uncertainty of the CO(2) cocktail method for analyzing biobased products.