A Pilot Study of Allostatic Load among Elderly Japanese Living on Hizen-Oshima Island

ABSTRACT: BACKGROUND: Between July and September 2005, a preliminary sampling of the elderly population of Hizen-Oshima Island, Nagasaki Prefecture, Japan was conducted by the local hospital's nursing staff. RESULTS: Reported here are preliminary results from this sample of 27 individuals with an average age of 71 years. Their ages ranged from 51 to 82 years, with a standard deviation (sd) of 7.4 years. In total, 33 aspects of physical and physiological variation were assessed on these 15 women and 12 men. As expected from previous studies of Japanese elders, our sample shows slightly elevated average blood pressure (142/81 mmHg, sd 16/10), but they are relatively lean (waist/hip = .9: sd 0.06) when compared to European or American standards. However, their average total cholesterol (TC = 210 mg/dl, sd = 42.8) is high compared to standards, as is their high-density lipoprotein cholesterol (HDLc = 55.4 mg/dl, sd = 15.1). Means, standard deviations (sd), ranges, and upper bounds for quartile cut-points for all 10 variables used in the calculation of allostatic load (AL) were assessed. The overall average estimate for AL in this sample is 3.1 (sd = 1.58) and ranges from 1-7. CONCLUSION: AL shows variability across men and women, has little correlation with age, and is associated with physiological variation in blood glucose, dopamine, and uric acid.     

基于基准样地法和国产高分数据的湖南省森林植被碳储量估测

为推广国产高分数据在大尺度范围碳储量估测计量的应用,采用覆盖湖南省的206景高分辨率遥感影像,将估测的最小单元固定为由多个像元组合成的面积为0.06 hm²的正方格,通过解译标志的建立和提纯,在森林信息提取上,利用基于像元法和面向对象分类法进行比较;在乔木林碳储量估测上,利用稳健估计、偏最小二乘法和基准样地法(k-NN)估计进行比较,最后实现了对湖南省森林的碳储量估测,并生成了全省的碳密度等级分布图.结果表明: 基于样地自动提取的解译标志在经过提纯后,能进一步增加乔木林提取精度;对于大尺度范围森林植被碳储量估测,无论是在森林信息提取还是乔木林碳储量建模方面,k-NN算法都体现了较大优势,是最佳估测方法;206景影像的平均分类总精度为76.8%,平均均方根误差为8.95 t·hm^-2,平均相对均方根误差为19.1%,湖南省碳储量总量为22.28 Mt.本研究结果为省级及国家级尺度的森林植被碳储量估测计量与监测提供了有效参考.     

A statistical analysis of random mutagenesis methods used for directed protein evolution

We have developed a statistical method named MAP (mutagenesis assistant program) to equip protein engineers with a tool to develop promising directed evolution strategies by comparing 19 mutagenesis methods. Instead of conventional transition/transversion bias indicators as benchmarks for comparison, we propose to use three indicators based on the subset of amino acid substitutions generated on the protein level: (1) protein structure indicator; (2) amino acid diversity indicator with a codon diversity coefficient; and (3) chemical diversity indicator. A MAP analysis for a single nucleotide substitution was performed for four genes: (1) heme domain of cytochrome P450 BM-3 from Bacillus megaterium (EC 1.14.14.1); (2) glucose oxidase from Aspergillus niger (EC 1.1.3.4); (3) arylesterase from Pseudomonas fluorescens (EC 3.1.1.2); and (4) alcohol dehydrogenase from Saccharomyces cerevisiae (EC 1.1.1.1). Based on the MAP analysis of these four genes, 19 mutagenesis methods have been evaluated and criteria for an ideal mutagenesis method have been proposed. The statistical analysis showed that existing gene mutagenesis methods are limited and highly biased. An average amino acid substitution per residue of only 3.15-7.4 can be achieved with current random mutagenesis methods. For the four investigated gene sequences, an average fraction of amino acid substitutions of 0.5-7% results in stop codons and 4.5-23.9% in glycine or proline residues. An average fraction of 16.2-44.2% of the amino acid substitutions are preserved, and 45.6% (epPCR method) are chemically different. The diversity remains low even when applying a non-biased method: an average of seven amino acid substitutions per residue, 2.9-4.7% stop codons, 11.1-16% glycine/proline residues, 21-25.8% preserved amino acids, and 55.5% are amino acids with chemically different side-chains. Statistical information for each mutagenesis method can further be used to investigate the mutational spectra in protein regions regarded as important for the property of interest.     

TO DIVE OR NOT TO DIVE: SCUBA VERSUS ROV SAMPLING OF MACROALGAE AT 30M DEPTH

Spalding, H. L. Moss Landing Marine Laboratories, 8272 Moss Landing, Rd., Moss Landing, CA 95039 USA Remotely Operated Vehicles (ROVs) and enriched air Nitrox SCUBA diving have recently become available to researchers for studying the deep-water environment. Each use a different technique for collecting macroalgal abundance data: ROVs use collections and high-resolution digital video which can be quantified using an integrative laser and computer imagery program (high tech), while divers often count the densities of individuals and use a point contact method for sampling percent (%) cover in situ (low tech). While the types of data collected by both techniques are the same, the effects of the different sampling methods on data resolution are unknown. As part of a larger study on deep-water macroalgae in central California, I compared the abundance of common macroalgae (% cover of macroalgal groups and individuals/m2) collected by divers and the ROV Ventana at a depth of 30m at 3 locations in central California. Generally, there were no significant differences between diver and ROV data in the % cover of coralline Rhodophyta, non-coralline Rhodophyta, and Pleurophycus gardneri/m2. The use of a laser-calibrated computer imagery program and an ROV with user-controlled lighting greatly decreased lab analysis time, and a method for sampling macroalgal layers with the ROV was developed. Thus, ROVs with high-resolution digital video and supplemental macroalgal collections can be used to quantify deep-water algae as accurately as in situ divers, but without the limited dive time, depth limits, and physical demands of the latter.     

A self-calibrating telemetry system for measurement of ventricular pressure-volume relations in conscious, freely moving rats

Using Bluetooth wireless technology, we developed an implantable telemetry system for measurement of the left ventricular pressure-volume relation in conscious, freely moving rats. The telemetry system consisted of a pressure-conductance catheter (1.8-Fr) connected to a small (14-g) fully implantable signal transmitter. To make the system fully telemetric, calibrations such as blood resistivity and parallel conductance were also conducted telemetrically. To estimate blood resistivity, we used four electrodes arranged 0.2 mm apart on the pressure-conductance catheter. To estimate parallel conductance, we used a dual-frequency method. We examined the accuracy of calibrations, stroke volume (SV) measurements, and the reproducibility of the telemetry. The blood resistivity estimated telemetrically agreed with that measured using an ex vivo cuvette method (y=1.09x - 11.9, r2= 0.88, n=10). Parallel conductance estimated by the dual-frequency (2 and 20 kHz) method correlated well with that measured by a conventional saline injection method (y=1.59x - 1.77, r2= 0.87, n=13). The telemetric SV closely correlated with the flowmetric SV during inferior vena cava occlusions (y=0.96x + 7.5, r2=0.96, n=4). In six conscious rats, differences between the repeated telemetries on different days (3 days apart on average) were reasonably small: 13% for end-diastolic volume, 20% for end-systolic volume, 28% for end-diastolic pressure, and 6% for end-systolic pressure. We conclude that the developed telemetry system enables us to estimate the pressure-volume relation with reasonable accuracy and reproducibility in conscious, untethered rats.     

Spotting East African Mammals in Open Savannah from Space

Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite images offer great potential for accurate estimates of animal abundance over large open areas. However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space, and therefore provide a complementary and alternative approach to the conventional wildlife survey techniques.     

Abundance estimation of rocky shore invertebrates at small spatial scale by high-resolution digital photography and digital image analysis

We have tested both the usefulness of high-resolution digital photography for data acquisition and digital image analysis, by non-supervised classification and high pass filter, for recognition and abundance estimation of benthic intertidal organisms. These digital tools were compared with visual scan and photo quadrat conventional methods. The comparison was done using 40 quadrats (10×5 cm) randomly selected along a 5-m transect on the rocky shore of the Pemaquid Point, Maine, USA. ANOVA for repeated measures was used to test differences among methods. Monte Carlo simulation analysis was used to explore differences among methods over a large set of data (n=100, 500, 1000 quadrats). Differences among methods were observed when 40 quadrats were used. Tukey multiple comparison test showed that abundance estimation from visual scan, photo quadrat and digital image analysis by high pass filter do not differ significantly among them but differ from non-supervised classification results. Due to its accurate estimation, high pass filter (Prewitt) method was chosen as the most reliable digital method to estimate species abundance. Monte Carlo simulation of visual scan, photo quadrat and high pass filter results showed significant differences when the number of quadrats was larger. These results showed that the combined use of digital photography and digital image analysis techniques for the acquisition and analysis of recorded data is a powerful method for the study of intertidal benthic organisms. Results produced using these techniques were similar than those produced by conventional methods but were obtained in a much-reduced time.     

Average assignment method for predicting the stability of protein mutants

Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and it will be helpful for designing stable mutants. In this work, we have analyzed the stability of protein mutants using three different data sets of 1791, 1396, and 2204 mutants, respectively, for thermal stability (DeltaTm), free energy change due to thermal (DeltaDeltaG), and denaturant denaturations (DeltaDeltaGH2O), obtained from the ProTherm database. We have classified the mutants into 380 possible substitutions and assigned the stability of each mutant using the information obtained with similar type of mutations. We observed that this assignment could distinguish the stabilizing and destabilizing mutants to an accuracy of 70-80% at different measures of stability. Further, we have classified the mutants based on secondary structure and solvent accessibility (ASA) and observed that the classification significantly improved the accuracy of prediction. The classification of mutants based on helix, strand, and coil distinguished the stabilizing/destabilizing mutants at an average accuracy of 82% and the correlation is 0.56; information about the location of residues at the interior, partially buried, and surface regions of a protein correctly identified the stabilizing/destabilizing residues at an average accuracy of 81% and the correlation is 0.59. The nine subclassifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants to an accuracy of 84-89% for the three data sets. Further, the present method is able to predict the free energy change (DeltaDeltaG) upon mutations within a deviation of 0.64 kcal/mol. We suggest that this method could be used for predicting the stability of protein mutants.     

Forest classification in an Amazonian rainforest landscape using pteridophytes as indicator species

Habitat classification systems are poorly developed for tropical rainforests, where extremely high plant species richness causes numerous methodological difficulties. We used an indicator species approach to classify primary rainforest vegetation for purposes of comparative wildlife habitat studies. We documented species composition of pteridophytes (ferns and fern allies) in 635 plots (2×100 m) along 8 transects within a continuous rainforest landscape in northeastern Peruvian Amazonia. Considerable floristic variation was found when the data were analyzed using multivariate methods. The obtained forest classification was interpreted with the help of indicator value analysis and known soil preferences of the pteridophyte species. The final classification included four forest types: 1) inundated forests, 2) terrace forests, 3) intermediate tierra firme forests and 4) Pebas Formation forests. This rapid and relatively simple vegetation classification technique offers a practical, quantitative method for large-scale vegetation inventory in complex rainforest landscapes.     

On the statistical assessment of classifiers using DNA microarray data

Background

In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia – Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data.

Results

We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and Support Vector Machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set size increases, reaching its best performances with 35 training examples. In this case, RLS and SVM have error rates of e = 14% (p = 0.027) and e = 11% (p = 0.019). Concerning the number of genes, we found about 6000 genes (p < 0.05) correlated with the pathology, resulting from the signal-to-noise statistic. Moreover the performances of RLS and SVM classifiers do not change when 74% of genes is used. They progressively reduce up to e = 16% (p < 0.05) when only 2 genes are employed. The biological relevance of a set of genes determined by our statistical analysis and the major roles they play in colorectal tumorigenesis is discussed.

Conclusions

The method proposed provides statistically significant answers to precise questions relevant for the diagnosis and prognosis of cancer. We found that, with as few as 15 examples, it is possible to train statistically significant classifiers for colon cancer diagnosis. As for the definition of the number of genes sufficient for a reliable classification of colon cancer, our results suggest that it depends on the accuracy required.     

On the relationship between the protein structure and protein dynamics

Recently, we have developed a method (Shih et al., Proteins: Structure, Function, and Bioinformatics 2007;68: 34-38) to compute correlation of fluctuations of proteins. This method, referred to as the protein fixed-point (PFP) model, is based on the positional vectors of atoms issuing from the fixed point, which is the point of the least fluctuations in proteins. One corollary from this model is that atoms lying on the same shell centered at the fixed point will have the same thermal fluctuations. In practice, this model provides a convenient way to compute the average dynamical properties of proteins directly from the geometrical shapes of proteins without the need of any mechanical models, and hence no trajectory integration or sophisticated matrix operations are needed. As a result, it is more efficient than molecular dynamics simulation or normal mode analysis. Though in the previous study the PFP model has been successfully applied to a number of proteins of various folds, it is not clear to what extent this model will be applied. In this article, we have carried out the comprehensive analysis of the PFP model for a dataset comprising 972 high-resolution X-ray structures with pairwise sequence identity or=0.5. Our result shows that the fixed-point model is indeed quite general and will be a useful tool for high throughput analysis of dynamical properties of proteins.     

Coupling 16S-ITS rDNA clone libraries and automated ribosomal intergenic spacer analysis to show marine microbial diversity: development and application to a time series

We outline an approach to simultaneously assess multilevel microbial diversity patterns utilizing 16S-ITS rDNA clone libraries coupled with automated ribosomal intergenic spacer analysis (ARISA). Sequence data from 512 clones allowed estimation of ARISA fragment lengths associated with bacteria in a coastal marine environment. We matched 92% of ARISA peaks (each comprising >1% total amplified product) with corresponding lengths from clone libraries. These peaks with putative identification accounted for an average of 83% of total amplified community DNA. At 16S rDNA similarities <98%, most taxa displayed differences in ARISA fragment lengths >10 bp, readily detectable and suggesting ARISA resolution is near the 'species' level. Prochlorococcus abundance profiles from ARISA were strongly correlated (r2=0.86) to Prochlorococcus cell counts, indicating ARISA data are roughly proportional to actual cell abundance within a defined taxon. Analysis of ARISA profiles for 42 months elucidated patterns of microbial presence and abundance providing insights into community shifts and ecological niches for specific organisms, including a coupling of ecological patterns for taxa within the Prochlorococcus, the Gamma Proteobacteria and Actinobacteria. Clade-specific ARISA protocols were developed for the SAR11 and marine cyanobacteria to resolve ambiguous identifications and to perform focused studies. 16S-ITS data allowed high-resolution identification of organisms by ITS sequence analysis, and examination of microdiversity.     

Α comparison of alternative techniques for prediction of the fauna of running-water sites in Great Britain

1. Early versions of the river invertebrate prediction and classification system (RIVPACS) used TWINSPAN to classify reference sites based on the macro-invertebrate fauna, followed by multiple discriminant analysis (MDA) for prediction of the fauna to be expected at new sites from environmental variables. This paper examines some alternative methods for the initial site classification and a different technique for prediction. 2. A data set of 410 sites from RIVPACS II was used for initial screening of seventeen alternative methods of site classification. Multiple discriminant analysis was used to predict classification group from environmental variables. 3. Five of the classification–prediction systems which showed promise were developed further to facilitate prediction of taxa at species and at Biological Monitoring Working Party (BMWP) family level. 4. The predictive capability of these new systems, plus RIVPACS II, was tested on an independent data set of 101 sites from locations throughout Great Britain. 5. Differences between the methods were often marginal but two gave the most consistently reliable outputs: the original TWINSPAN method, and the ordination method semi-strong hybrid multidimensional scaling (SSH) followed by K-means clustering. 6. Logistic regression, an alternative approach to prediction which does not require the prior development of a classification system, was also examined. Although its performance fell within the range offered by the other five systems tested, it conveyed no advantages over them. 7. This study demonstrated that several different multivariate methods were suitable for developing a reliable system for predicting expected probability of occurrence of taxa. This is because the prediction system involves a weighted average smoothing across site groupings. 8. Hence, the two most promising procedures for site classification, coupled to MDA, were both used in the exploratory analyses for RIVPACS III development, which utilized over 600 reference sites.     

Multi-attribute ecological river typology for assessing ecological condition and conservation planning

A river classification framework is needed to make good management and planning decisions about river health and biodiversity. We developed a multi-attribute ecological river typology to address this need in the Australian State of New South Wales (801,428 km²). Multivariate patterns in data collected from 322 reference sites were used to define river types for each attribute: abiotic features (10 types), fish assemblages (6 types) and macroinvertebrate assemblages from river edges (8 types) and riffle zones (5 types). We used classification tree analysis to define broad regions for each attribute and then to construct identification keys for river types within each region using slope, elevation, maximum distance from source, latitude and mean annual rainfall. These keys allow the mapping of the likely spatial extent of river types and the assignment of a multi-attribute river-type identity to a river reach anywhere in the State. We used the average dissimilarity distances among the river types and the rates of misclassification of reference sites to assess the reliability of the assignments for different attributes in different regions. This approach to river classification can be applied anywhere in the world, resulting in simple to highly complex typologies depending on data availability. In data-poor areas it may result in a single attribute typology based on remotely derived variables and a coarsely defined reference condition. In data-rich areas the typology may have a large number of attributes using very large datasets with high resolution. Handling editor: D. Dudgeon     

A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors

MOTIVATION: The analysis of gene expression data in its chromosomal context has been a recent development in cancer research. However, currently available methods fail to account for variation in the distance between genes, gene density and genomic features (e.g. GC content) in identifying increased or decreased chromosomal regions of gene expression. RESULTS: We have developed a model-based scan statistic that accounts for these aspects of the complex landscape of the human genome in the identification of extreme chromosomal regions of gene expression. This method may be applied to gene expression data regardless of the microarray platform used to generate it. To demonstrate the accuracy and utility of this method, we applied it to a breast cancer gene expression dataset and tested its ability to predict regions containing medium-to-high level DNA amplification (DNA ratio values >2). A classifier was developed from the scan statistic results that had a 10-fold cross-validated classification rate of 93% and a positive predictive value of 88%. This result strongly suggests that the model-based scan statistic and the expression characteristics of an increased chromosomal region of gene expression can be used to accurately predict chromosomal regions containing amplified genes. AVAILABILITY: Functions in the R-language are available from the author upon request. CONTACT: fcouples@umich.edu.     

Probabilistic classifiers with high-dimensional data

For medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement and develop corresponding evaluation measures. We evaluated several published high-dimensional probabilistic classifiers and developed 2 extensions of the Bayesian compound covariate classifier. Based on simulation studies and analysis of gene expression microarray data, we found that proper probabilistic classification is more difficult than deterministic classification. It is important to ensure that a probabilistic classifier is well calibrated or at least not "anticonservative" using the methods developed here. We provide this evaluation for several probabilistic classifiers and also evaluate their refinement as a function of sample size under weak and strong signal conditions. We also present a cross-validation method for evaluating the calibration and refinement of any probabilistic classifier on any data set.     

Detection, imputation, and association analysis of small deletions and null alleles on oligonucleotide arrays

Copy-number variation (CNV) is a major contributor to human genetic variation. Recently, CNV associations with human disease have been reported. Many genome-wide association (GWA) studies in complex diseases have been performed with sets of biallelic single-nucleotide polymorphisms (SNPs), but the available CNV methods are still limited. We present a new method (TriTyper) that can infer genotypes in case-control data sets for deletion CNVs, or SNPs with an extra, untyped allele at a high-resolution single SNP level. By accounting for linkage disequilibrium (LD), as well as intensity data, calling accuracy is improved. Analysis of 3102 unrelated individuals with European descent, genotyped with Illumina Infinium BeadChips, resulted in the identification of 1880 SNPs with a common untyped allele, and these SNPs are in strong LD with neighboring biallelic SNPs. Simulations indicate our method has superior power to detect associations compared to biallelic SNPs that are in LD with these SNPs, yet without increasing type I errors, as shown in a GWA analysis in celiac disease. Genotypes for 1204 triallelic SNPs could be fully imputed, with only biallelic-genotype calls, permitting association analysis of these SNPs in many published data sets. We estimate that 682 of the 1655 unique loci reflect deletions; this is on average 99 deletions per individual, four times greater than those detected by other methods. Whereas the identified loci are strongly enriched for known deletions, 61% have not been reported before. Genes overlapping with these loci more often have paralogs (p = 0.006) and biologically interact with fewer genes than expected (p = 0.004).     

Enhanced membrane protein topology prediction using a hierarchical classification method and a new scoring function

The prediction of transmembrane (TM) helix and topology provides important information about the structure and function of a membrane protein. Due to the experimental difficulties in obtaining a high-resolution model, computational methods are highly desirable. In this paper, we present a hierarchical classification method using support vector machines (SVMs) that integrates selected features by capturing the sequence-to-structure relationship and developing a new scoring function based on membrane protein folding. The proposed approach is evaluated on low- and high-resolution data sets with cross-validation, and the topology (sidedness) prediction accuracy reaches as high as 90%. Our method is also found to correctly predict both the location of TM helices and the topology for 69% of the low-resolution benchmark set. We also test our method for discrimination between soluble and membrane proteins and achieve very low overall false positive (0.5%) and false negative rates (0 to approximately 1.2%). Lastly, the analysis of the scoring function suggests that the topogeneses of single-spanning and multispanning TM proteins have different levels of complexity, and the consideration of interloop topogenic interactions for the latter is the key to achieving better predictions. This method can facilitate the annotation of membrane proteomes to extract useful structural and functional information. It is publicly available at http://bio-cluster.iis.sinica.edu.tw/~bioapp/SVMtop.     

A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules

Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macromolecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate alignments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orientations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimensional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large (60S/50S) and the small (40S/30S) subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolution single particle analysis of macromolecular complexes with dynamic conformations.     

Combined feature selection and cancer prognosis using support vector machine regression

Prognostic prediction is important in medical domain, because it can be used to select an appropriate treatment for a patient by predicting the patient's clinical outcomes. For high-dimensional data, a normal prognostic method undergoes two steps: feature selection and prognosis analysis. Recently, the L?-L?-norm Support Vector Machine (L?-L? SVM) has been developed as an effective classification technique and shown good classification performance with automatic feature selection. In this paper, we extend L?-L? SVM for regression analysis with automatic feature selection. We further improve the L?-L? SVM for prognostic prediction by utilizing the information of censored data as constraints. We design an efficient solution to the new optimization problem. The proposed method is compared with other seven prognostic prediction methods on three realworld data sets. The experimental results show that the proposed method performs consistently better than the medium performance. It is more efficient than other algorithms with the similar performance.