The effects of instructional set on the outcome of a stress management program

A multicomponent stress management program, consisting of education about stress, various sorts of relaxation training, and cognitive coping strategies, was administered under two different instructional sets: In the positive demand set the program was touted to have previously been shown to be very effective, while in the neutral demand set the program was described as experimental. The two conditions were compared to a waiting list control. Results showed higher expectations of benefit in the positive demand group. Moreover, this group rated themselves as more able to cope with stress than the other two groups. Finally, the positive demand group showed significantly greater reductions in systolic blood pressure reactivity to laboratory stress tests (mental arithmetic and cold pressor) than the other two groups. The posttest advantages seemed to be mediated by a significantly higher level of home practice in the positive demand group.     

Mining protein sequences for motifs.

We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. The algorithm assumes that a motif is constituted by the presence of a "good" combination of residues in appropriate locations of the motif. The algorithm attempts to compile such good combinations into a "pattern dictionary" by processing an aligned training set of protein sequences. The dictionary is subsequently used to detect motifs in new protein sequences. Statistical significance of the detection results are ensured by statistically determining the various parameters of the algorithm. Based on this approach, we have implemented a program called GYM. The Helix-Turn-Helix motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with existing programs. In addition, the GYM program provides a lot of useful information about a given protein sequence.     

Dynamic use of multiple parameter sets in sequence alignment

The level of conservation between two homologous sequences often varies among sequence regions; functionally important domains are more conserved than the remaining regions. Thus, multiple parameter sets should be used in alignment of homologous sequences with a stringent parameter set for highly conserved regions and a moderate parameter set for weakly conserved regions. We describe an alignment algorithm to allow dynamic use of multiple parameter sets with different levels of stringency in computation of an optimal alignment of two sequences. The algorithm dynamically considers various candidate alignments, partitions each candidate alignment into sections, and determines the most appropriate set of parameter values for each section of the alignment. The algorithm and its local alignment version are implemented in a computer program named GAP4. The local alignment algorithm in GAP4, that in its predecessor GAP3, and an ordinary local alignment program SIM were evaluated on 257716 pairs of homologous sequences from 100 protein families. On 168475 of the 257716 pairs (a rate of 65.4%), alignments from GAP4 were more statistically significant than alignments from GAP3 and SIM.     

Identification of informative features for predicting proinflammatory potentials of engine exhausts

Background

The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment.

Methods

To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm.

Results

A total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively.

Conclusions

The FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

     

基于迭代自学习的操纵子结构预测

原核生物操纵子结构的准确注释对基因功能和基因调控网络的研究具有重要意义,通过生物信息学方法计算预测是当前基因组操纵子结构注释的最主要来源.当前的预测算法大都需要实验确认的操纵子作为训练集,但实验确认的操纵子数据的缺乏一直成为发展算法的瓶颈.基于对操纵子结构的认识,从基因间距离、转录翻译相关的调控信号以及COG功能注释等特征出发,建立了描述操纵子复杂结构的概率模型,并提出了不依赖于特定物种操纵子数据作为训练集的迭代自学习算法.通过对实验验证的操纵子数据集的测试比较,结果表明算法对于预测操纵子结构非常有效.在不依赖于任何已知操纵子信息的情况下,算法在总体预测水平上超过了目前最好的操纵子预测方法,而且这种自学习的预测算法要优于依赖特定物种进行训练的算法.这些特点使得该算法能够适用于新测序的物种,有别于当前常用的操纵子预测方法.对细菌和古细菌的基因组进行大规模比较分析,进一步提高了对基因组操纵子结构的普遍特征和物种特异性的认识.     

Quantitative analysis of single- vs. multiple-set programs in resistance training

The purpose of this study was to examine the existing research on single-set vs. multiple-set resistance training programs. Using the meta-analytic approach, we included studies that met the following criteria in our analysis: (a) at least 6 subjects per group; (b) subject groups consisting of single-set vs. multiple-set resistance training programs; (c) pretest and posttest strength measures; (d) training programs of 6 weeks or more; (e) apparently "healthy" individuals free from orthopedic limitations; and (f) published studies in English-language journals only. Sixteen studies generated 103 effect sizes (ESs) based on a total of 621 subjects, ranging in age from 15-71 years. Across all designs, intervention strategies, and categories, the pretest to posttest ES in muscular strength was (chi = 1.4 +/- 1.4; 95% confidence interval, 0.41-3.8; p < 0.001). The results of 2 x 2 analysis of variance revealed simple main effects for age, training status (trained vs. untrained), and research design (p < 0.001). No significant main effects were found for sex, program duration, and set end point. Significant interactions were found for training status and program duration (6-16 weeks vs. 17-40 weeks) and number of sets performed (single vs. multiple). The data indicated that trained individuals performing multiple sets generated significantly greater increases in strength (p < 0.001). For programs with an extended duration, multiple sets were superior to single sets (p < 0.05). This quantitative review indicates that single-set programs for an initial short training period in untrained individuals result in similar strength gains as multiple-set programs. However, as progression occurs and higher gains are desired, multiple-set programs are more effective.     

Interpreting the flock algorithm from a statistical perspective

We show that the algorithm in the program flock (Duchesne & Turgeon 2009) can be interpreted as an estimation procedure based on a model essentially identical to the structure (Pritchard et al. 2000) model with no admixture and without correlated allele frequency priors. Rather than using MCMC, the flock algorithm searches for the maximum a posteriori estimate of this structure model via a simulated annealing algorithm with a rapid cooling schedule (namely, the exponent on the objective function →∞). We demonstrate the similarities between the two programs in a two‐step approach. First, to enable rapid batch processing of many simulated data sets, we modified the source code of structure to use the flock algorithm, producing the program flockture . With simulated data, we confirmed that results obtained with flock and flockture are very similar (though flockture is some 200 times faster). Second, we simulated multiple large data sets under varying levels of population differentiation for both microsatellite and SNP genotypes. We analysed them with flockture and structure and assessed each program on its ability to cluster individuals to their correct subpopulation. We show that flockture yields results similar to structure albeit with greater variability from run to run. flockture did perform better than structure when genotypes were composed of SNPs and differentiation was moderate ( $F_{\mathrm{ST}}=$ 0.022–0.032). When differentiation was low, structure outperformed flockture for both marker types. On large data sets like those we simulated, it appears that flock 's reliance on inference rules regarding its ‘plateau record’ is not helpful. Interpreting flock 's algorithm as a special case of the model in structure should aid in understanding the program's output and behaviour.     

The effects of resistance training performed in water on muscle strength in the elderly

The purpose of the present study was to compare the effects of a program of resistance training in water-based exercises (RWE) with those of a program without resistance control in water-based exercises (WEs). Twenty-seven women (aged 60-74 years) were randomly assigned to the RWE group (n = 10), WE group (n = 10), or nontraining control (CON) group (n = 7). The RWE and WE groups trained classes with aerobic exercises and localized muscular resistance exercises for 50 minutes, twice a week for 12 weeks. For the RWE group, the program included 4 mesocycles of 3 weeks-respectively, 4 sets of 15 repetitions, 4 sets of 12 repetitions, 5 sets of 10 repetitions, and 5 sets of 8 repetitions-of shoulder horizontal flexion exercise at maximum speed, with the use of resistive equipment. For the WE group, the training was not periodized, and the resistance in the localized muscular exercises was not controlled. One repetition maximum (1RM) was measured on a pectoral fly machine at baseline and after the training period. The level of significance adopted was p ≤ 0.05. The results showed that the only significant increase in 1RM (10.89%, p < 0.001) occurred in the RWE group after training. In conclusion, these findings suggest that WEs with emphasis on resistance training in a periodized program can efficiently increase maximum strength in elderly women. Thus, it is suggested that the strategies used in WE programs be modified to offer suitable stimuli for the development of strength.     

A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups

MOTIVATION: Due to advances in experimental technologies, such as microarray, mass spectrometry and nuclear magnetic resonance, it is feasible to obtain large-scale data sets, in which measurements for a large number of features can be simultaneously collected. However, the sample sizes of these data sets are usually small due to their relatively high costs, which leads to the issue of concordance among different data sets collected for the same study: features should have consistent behavior in different data sets. There is a lack of rigorous statistical methods for evaluating this concordance or discordance. METHODS: Based on a three-component normal-mixture model, we propose two likelihood ratio tests for evaluating the concordance and discordance between two large-scale data sets with two sample groups. The parameter estimation is achieved through the expectation-maximization (E-M) algorithm. A normal-distribution-quantile-based method is used for data transformation. RESULTS: To evaluate the proposed tests, we conducted some simulation studies, which suggested their satisfactory performances. As applications, the proposed tests were applied to three SELDI-MS data sets with replicates. One data set has replicates from different platforms and the other two have replicates from the same platform. We found that data generated by SELDI-MS showed satisfactory concordance between replicates from the same platform but unsatisfactory concordance between replicates from different platforms. AVAILABILITY: The R codes are freely available at http://home.gwu.edu/~ylai/research/Concordance.     

Protein structure comparison using bipartite graph matching and its application to protein structure classification

A measure of protein structure similarity is calculated from the matching of pairs of secondary structure elements between two proteins. The interaction of each pair was estimated from their axial line segments and combined with other geometric features to produce an optimal discrimination between intrafamily and interfamily relationships. The matching used a fast bipartite graph-matching algorithm that avoids the computational complexity of searching for the full subgraph isomorphism between the two sets of interactions. The main algorithm used was the "stable marriage" algorithm, which works on the ranked "preferences" of one interaction for another. The method takes 1/10 of a second for a typical comparison making it suitable as a fast pre-filter for slower, more exhaustive approaches. An application to protein structure classification is described.     

CLUM: A cluster program for analyzing microarray data

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems. Cluster analysis has proven to be a very useful tool for investigating the structure of microarray data. This paper presents a program for clustering microarray data, which is based on the so-called path-distance. The algorithm gives in each step a partition in two clusters and no prior assumptions on the structure of clusters are required. It assigns each object (gene or sample) to only one cluster and gives the global optimum for the function that quantifies the adequacy of a given partition of the sample into k clusters. The program was tested on experimental data sets, showing the robustness of the algorithm.     

CLUM: a cluster program for analyzing microarray data

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems. Cluster analysis has proven to be a very useful tool for investigating the structure of microarray data. This paper presents a program for clustering microarray data, which is based on the so call path-distance. The algorithm gives in each step a partition in two clusters and no prior assumptions on the structure of clusters are required. It assigns each object (gene or sample) to only one cluster and gives the global optimum for the function that quantifies the adequacy of a given partition of the sample into k clusters. The program was tested on experimental data sets, showing the robustness of the algorithm.     

Fuzzy ARTMAP prediction of biological activities for potential HIV-1 protease inhibitors using a small molecular data set

Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4], [5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.     

A biomechanical and spectroscopic study of bone from rats with selenium deficiency and toxicity

Selenium, being an essential mineral in the mammalian diet, is important in providing protection against oxidative damage. Numerous in vitro studies of selenium compounds reveal a very high correlation between catalytic activity of selenium compounds and toxicity. The present study was designed to investigate the effects of dietary selenium on the biomechanical properties of bone. New born rats of both sexes were fed with either a control, or a selenium- and vitamin E-deficient, or a selenium-excess and vitamin E-adequate diet. We obtained the stiffness (modulus of elasticity) of bones (femur and tibia) by tensile test for all groups considered. Both the deficient and the excess groups have decreased biomechanical strength with respect to the control group. To support our biomechanical results for both experimental groups, X-ray diffraction analysis and FTIR spectroscopic study were performed on the femurs and tibiae. The X-ray diffraction analysis showed that the intensities of the peak observed at around 2°=31.820, in the control femur and tibia are stronger than the intensities of the corresponding peak of two experimental groups. In FTIR spectroscopy, the disappearance and/or reduction of the intensities of some carbonate bands in the two experimental groups indicate that there is a decrease in crystallinity and mineral contents which, together with X-ray diffraction analysis, correlate very well with the biomechanical data.     

Efficient algorithms for the discovery of gapped factors

Background

With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters.

Methods

In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA). In FDAFSA, adjacent triplet-pairs (hexamer sequences) were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs) were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM), a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot.

Results

Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity.

Conclusions

We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better) with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem. As such, we expect that PromoBot can be used to help identify new plant promoters. Source codes and analysis results of this work could be provided upon request.     

Effects of a 6-week periodized squat training with or without whole-body vibration upon short-term adaptations in squat strength and body composition

The purpose of this study was to examine the effects of a 6-week, periodized squat training program, with or without whole-body low-frequency vibration (WBLFV), applied before and between sets to 1RM squat strength and body composition. Thirty men aged between 20 and 30 years with at least 6 months of recreational weight training experience completed the study. Subjects were randomly assigned to either 1 of 2 training groups or to an active control group (CON). Group 1 (CON; n = 6) did not participate in the training protocol but participated only in testing sessions. Group 2 (SQTV, n = 13) performed 6 weeks of squat training while receiving WBLFV (50 Hz), before, and in-between sets. The third group (SQT, n = 11) performed 6 weeks of squat training only. Subjects completed 12 workouts with variable loads (55-90% one repetition maximum [1RM]) and sets (), performing squats twice weekly separated by 72 hours. The RM measures were recorded on weeks (W) 1, 3, and 7. During the second workout of a week, the load was reduced by 10-15%, with "speed squats" performed during the final 3 weeks. Rest periods in between sets were set at 240 seconds. The WBLFV was applied while subjects stood on a WBLFV platform holding an isometric quarter squat position (knee angle 135 ± 5°). Initially, WBLFV was applied at 50 Hz for 30 seconds at low amplitude (peak-peak 2-4 mm). A rest period of 180 seconds followed WBLFV exposure before the first set of squats. The WBLFV was then applied intermittently (3 × 10 seconds) at 50 Hz, high amplitude (peak-peak, 4-6 mm) at time points, 60, 120, and 180 seconds into the 240-second rest period. Total body dual x-ray absorptiometry scans were performed at W0 (week before training) and W7 (week after training). Measures recorded included total body mass (kg), total body lean mass (TLBM, kg), trunk lean mass (kg), leg lean mass (kg), total body fat percentage, trunk fat percentage, and leg fat percentage (LF%). Repeated-measures analysis of variance and analysis of covariance revealed 1RM increased significantly between W1-W3, W3-W7, and W1-W7 for both experimental groups but not for control (p = 0.001, effect size [ES] = 0.237, 1 - β = 0.947). No significant differences were seen for %Δ (p > 0.05). Significant group by trial and group effects were seen for TLBM, SQTV > CON at W7 (p = 0.044). A significant main effect for time was seen for LF%, W0 vs. W7 (p = 0.047). No other significant differences were seen (p > 0.05). "Practical trends" were seen favoring "short-term" neuromuscular adaptations for the SQTV group during the first 3 weeks (p = 0.10, ES = 0.157, 1 - β = 0.443, mean diff; SQTV week 3 4.72 kg > CON and 2.53 kg > SQT). Differences in motor unit activation patterns, hypertrophic responses, and dietary intake during the training period could account for the trends seen.     

NETASA: neural network based prediction of solvent accessibility

MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction. RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed. AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.     

Minimum measured-input models for the assessment of motor ability

The problem of assessing the physical functional limitation of a given individual and establishing the relationship between impairment/s and disability using a biomechanical approach is addressed. This endeavour was pursued with reference to the locomotor system and in order to address the following specific clinical issues: prognosis, eligibility for health services, measure of the outcome of a therapy, and therapeutic programming. A thorough biomechanical analysis of selected motor tasks would be effective but awkward to apply for subject-specific evaluation in clinical practice by reason of the complexity of both instrumentation and experimental protocols. In addition, as illustrated in the paper, the adequacy of the accuracy with which this type of analysis provides relevant information may be argued. Therefore, different methods were devised in the attempt to join objectivity with field applicability. These entailed the measurement of a minimum number of biomechanical variables during the execution of the selected motor task and these quantities were acquired using a low cost experimental apparatus least perceivable to the test subject, that is a dynamometric plate. However, since data thus obtained do not necessarily lend themselves to straightforward interpretation in terms of function assessment, models of the musculo-skeletal system that embodied the invariant aspects of both the modelled system and the specific motor task were devised. Using such "minimum measured-input models", physiology-related, and thus easier to interpret, information was obtained. Two different sets of mathematical models are presented: one deals with the lowest level of detail and normally aims at assessing a global physical performance score, the other discloses joint function and segmental mechanics and therefore contributes to establishing a relationship between impairment and disability. The validation of these models, carried out in the laboratory, has shown that they possess a potential for application in clinical practice.     

Estimation of the deformations induced by articulated bodies: Registration of the spinal column

We present a new non-rigid registration algorithm estimating the displacement field generated by articulated bodies. Indeed the bony structures between different patient images may rigidly move while other tissues may deform in a more complex way. Our algorithm tracks the displacement induced in the column by a movement of the patient between two acquisitions. The volumetric deformation field in the whole body is then inferred from those displacements using a linear elastic biomechanical finite element model. We demonstrate in this paper that this method provides accurate results on 3D sets of computed tomography (CT), MR and positron emission tomography (PET) images and that the results of the registration algorithm show significant decreases in the mean, min and max errors.