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PubMed2003

Adherence and satisfaction of rheumatoid arthritis patients with a long-term intensive dynamic exercise program (RAPIT program).

OBJECTIVE: To evaluate adherence and satisfaction of patients with rheumatoid arthritis (RA) in a long-term intensive dynamic exercise program. METHODS: A total of 146 RA patients started an intensive (strength and endurance training for 75 minutes, twice a week, for 2 years) exercise program (Rheumatoid Arthritis Patients In Training) aimed at improving physical fitness. Program attendance and satisfaction were examined. Additional assessments at baseline were done to find possible predictors of attendance. RESULTS: Median (interquartile range) age and disease duration of the patients were 54 (45-61) and 5 (3-10) years, respectively. After 2 years, 118 (81%) patients still participated in an exercise class. The median attendance rate of all patients was 74%. Low attendance was weakly associated with high disease activity, low functional ability, and low quality of life at baseline but not with the severity of joint damage at baseline. At the end, 78% of all participants would (strongly) recommend the program to other RA patients. CONCLUSION: Adherence and satisfaction of RA patients with an intensive dynamic exercise program over a prolonged time can be high. Disease severity parameters do not strongly predict the compliance of participants in an intensive exercise program.

Adult↗
PubMed1994

On global sequence alignment.

We present a dynamic programming algorithm for computing a best global alignment of two sequences. The proposed algorithm is robust in identifying any of several global relationships between two sequences. The algorithm delivers a best alignment of two sequences in linear space and quadratic time. We also describe a multiple alignment algorithm based on the pairwise algorithm. Both algorithms have been implemented as portable C programs. Experimental results indicate that for a commonly used set of gap penalties, the new programs produce more satisfactory alignments on sequences of various lengths than some existing pairwise and multiple programs based on the dynamic programming algorithm of Needleman and Wunsch.

Algorithms↗
PubMedDate not supplied

Faster algorithms for optimal multiple sequence alignment based on pairwise comparisons.

Multiple Sequence Alignment (MSA) is one of the most fundamental problems in computational molecular biology. The running time of the best known scheme for finding an optimal alignment, based on dynamic programming, increases exponentially with the number of input sequences. Hence, many heuristics were suggested for the problem. We consider a version of the MSA problem where the goal is to find an optimal alignment in which matches are restricted to positions in predefined matching segments. We present several techniques for making the dynamic programming algorithm more efficient, while still finding an optimal solution under these restrictions. We prove that it suffices to find an optimal alignment of the predefined sequence segments, rather than single letters, thereby reducing the input size and thus improving the running time. We also identify "shortcuts" that expedite the dynamic programming scheme. Empirical study shows that, taken together, these observations lead to an improved running time over the basic dynamic programming algorithm by 4 to 12 orders of magnitude, while still obtaining an optimal solution. Under the additional assumption that matches between segments are transitive, we further improve the running time for finding the optimal solution by restricting the search space of the dynamic programming algorithm.

Algorithms↗
PubMed2003

Increased coverage obtained by combination of methods for protein sequence database searching.

MOTIVATION: Sequence alignment methods that compare two sequences (pairwise methods) are important tools for the detection of biological sequence relationships. In genome annotation, multiple methods are often run and agreement between methods taken as confirmation. In this paper, we assess the advantages of combining search methods by comparing seven pairwise alignment methods, including three local dynamic programming algorithms (PRSS, SSEARCH and SCANPS), two global dynamic programming algorithms (GSRCH and AMPS) and two heuristic approximations (BLAST and FASTA), individually and by pairwise intersection and union of their result lists at equal p-value cut-offs. RESULTS: When applied singly, the dynamic programming methods SCANPS and SSEARCH gave significantly better coverage (p=0.01) compared to AMPS, GSRCH, PRSS, BLAST and FASTA. Results ranked by BLAST p-values gave significantly better coverage compared to ranking by BLAST e-values. Of 56 combinations of eight methods considered, 19 gave significant increases in coverage at low error compared to the parent methods at an equal p-value cutoff. The union of results by BLAST (p-value) and FASTA at an equal p-value cutoff gave significantly better coverage than either method individually. The best overall performance was obtained from the intersection of the results from SSEARCH and the GSRCH62 global alignment method. At an error level of five false positives, this combination found 444 true positives, a significant 12.4% increase over SSEARCH applied alone.

Algorithms↗
PubMed1994

An efficient method for multiple sequence alignment.

Multiple sequence alignment has been a useful method in the study of molecular evolution and sequence-structure relationships. This paper presents a new method for multiple sequence alignment based on simulated annealing technique. Dynamic programming has been widely used to find an optimal alignment. However, dynamic programming has several limitations to obtain optimal alignment. It requires long computation time and cannot apply certain types of cost functions. We describe detail mechanisms of simulated annealing for multiple sequence alignment problem. It is shown that simulated annealing can be an effective approach to overcome the limitations of dynamic programming in multiple sequence alignment problem.

Algorithms↗
PubMed1991

The DeSyGNER data access element: a readily reusable component for the construction of data-compatible multimedia programs.

Dynamic assembly of applications from diverse and possibly disparate collections of information components is a key element for powerful information management environments. Traditional methods of application development hinder flexible integration of information components because they treat computer programs as single, isolated units of functionality where inter-application connections and sharing of intra-application information components are achieved only by special cooperation between program authors. Although many programmers strive to develop re-usable program code, it is rare that independently developed components can be readily combined into new applications. One of the major reasons for this is that there is no overall agreement as to the types of segments written, how the segments communicate, or how their data is stored. We have created a multimedia applications development architecture called DeSyGNER (the Decision Systems Group Nucleus of Extensible Resources) that provides a type-independent definition of the fundamental information-bearing unit for the construction of data-compatible applications. By incorporating a common high-level architecture and an intrinsically modular object-oriented design, DeSyGNER applications process information in smaller, isolatable, and more mobile and reusable units than do monolithic computer programs.

Computer Graphics↗
PubMed2002

Performance evaluation of a new algorithm for the detection of remote homologs with sequence comparison.

A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda approximately 1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties.

Algorithms↗
PubMed2005

Significant reduction in risk of falls and back pain in osteoporotic-kyphotic women through a Spinal Proprioceptive Extension Exercise Dynamic (SPEED) program.

OBJECTIVE: To determine the outcome of intervention with a spinal weighted kypho-orthosis (WKO) and a spinal proprioceptive extension exercise dynamic (SPEED) program on the risk of falls in ambulatory community-dwelling persons older than 60 years with osteoporosis-kyphosis at risk for falls. SUBJECTS AND METHODS: The study had 3 stages. At stage 1 (baseline), the 12 women in the kyphotic group were compared with 13 healthy controls to assess the risk of falls and balance disorder in the kyphotic group. At stage 2, the 12 kyphotic women began the SPEED program with a WKO (2 supervised sessions in an outpatient clinic and a 4-week, daily home-based training program). At stage 3, baseline and follow-up data of the kyphotic group were compared to determine the effect of intervention. RESULTS: At baseline, there were significant differences between the osteoporotic-kyphotic group and the control group in balance (P=.002), gait (P<.05), and strength (P<.05). After a 4-week intervention, comparison of the kyphotic group's baseline and follow-up results showed a significant change in balance (P=.003) and several gait parameters (P<.05). Mean back extensor strength improved significantly from baseline (144.0-46.5 N) to follow-up (198.6+/-55.2 N; P<.001). Lower extremity muscle strength was not changed significantly, except for improved left ankle plantar flexors (P=.02). Back pain decreased significantly (P=.001). CONCLUSION: Balance, gait, and risk of falls improved significantly with the 4-week SPEED program.

Accidental Falls↗
PubMed2006

Software news and updates. Carma: a molecular dynamics analysis program.

A computer program has been developed to aid the analysis of molecular dynamics trajectories. The program is tuned for macromolecular large-scale problems and supports features such as removal of global translations-rotations of the solute, calculation of average distance maps and their corresponding standard deviations, calculation of the variance-covariance and cross-correlation matrices, and principal component analysis of trajectories with the added ability to create artificial trajectories based on selected eigenvectors. Limited graphics (trajectory viewing) capabilities are also available.

Computer Simulation↗
PubMed1994

Multiple sequence alignment using simulated annealing.

Multiple sequence alignment is a useful technique for studying molecular evolution and analyzing structure-sequence relationships. Dynamic programming of multiple sequence alignment has been widely used to find an optimal alignment. However, dynamic programming does not allow for certain types of gap costs, and it limits the number of sequences that can be aligned due to its high computational complexity. The focus of this paper is to use simulated annealing as the basis for developing an efficient multiple sequence alignment algorithm. An algorithm called Multiple Sequence Alignment using Simulated Annealing (MSASA) has been developed. The computational complexity of MSASA is significantly reduced by replacing the high-temperature phase of the annealing process by a fast heuristic algorithm. This heuristic algorithm facilitates in minimizing the solution set of the low-temperature phase of the annealing process. Compared to the dynamic programming approach, MSASA can (i) use natural gap costs which can generate better solution, (ii) align more sequences and (iii) take less computation time.

Algorithms↗
PubMed2004

A unified quadratic-programming-based dynamical system approach to joint torque optimization of physically constrained redundant manipulators.

In this paper, for joint torque optimization of redundant manipulators subject to physical constraints, we show that velocity-level and acceleration-level redundancy-resolution schemes both can be formulated as a quadratic programming (QP) problem subject to equality and inequality/bound constraints. To solve this QP problem online, a primal-dual dynamical system solver is further presented based on linear variational inequalities. Compared to previous researches, the presented QP-solver has simple piecewise-linear dynamics, does not entail real-time matrix inversion, and could also provide joint-acceleration information for manipulator torque control in the velocity-level redundancy-resolution schemes. The proposed QP-based dynamical system approach is simulated based on the PUMA560 robot arm with efficiency and effectiveness demonstrated.

Algorithms↗
PubMed1994

Optimally parsing a sequence into different classes based on multiple types of evidence.

We consider the problem of parsing a sequence into different classes of subsequences. Two common examples are finding the exons and introns in genomic sequences and identifying the secondary structure domains of protein sequences. In each case there are various types of evidence that are relevant to the classification, but none are completely reliable, so we expect some weighted average of all the evidence to provide improved classifications. For example, in the problem of identifying coding regions in genomic DNA, the combined use of evidence such as codon bias and splice junction patterns can give more reliable predictions than either type of evidence alone. We show three main results: 1. For a given weighting of the evidence a dynamic programming algorithm returns the optimal parse and any number of sub-optimal parses. 2. For a given weighting of the evidence a dynamic programming algorithm determines the probability of the optimal parse and any number of sub-optimal parses under a natural Boltzmann-Gibbs distribution over the set of possible parses. 3. Given a set of sequences with known correct parses, a dynamic programming algorithm allows one to apply gradient descent to obtain the weights that maximize the probability of the correct parses of these sequences.

Algorithms↗
PubMed1997

Path costs in evolutionary tree reconstruction.

This paper describes a dynamic programming algorithm to solve a family of problems in the reconstruction of evolutionary trees from protein sequence data, that of constructing "minimal" colorings. This dynamic programming formulation can be modified to efficiently enumerate the number of minimal colorings and thereby be used to calculate the average cost of any given edge, where the average is taken over the entire set of minimal colorings. An extension of our dynamic programming formulation allows for the calculation of average path costs amongst all minimal colorings. our results resolve questions raised in (Hendy and penny, 1987); in particular, we develop polynomial time procedures to find the minimum, maximum, and average (expected) cost of an edge, and more generally of a path, for a minimal coloring. Our algorithm is distinguished in its flexibility to address further distribution and statistical questions relating to the minimal colorings. Furthermore, the more general concept of calculating statistics describing the set of optimal solutions may be of interest in other combinatorial problems.

Algorithms↗
PubMed1999

Patient flows and optimal health-care resource allocation at the macro-level: a dynamic linear programming approach.

In this paper we present a deterministic allocation model in which a patient's health-state changes due to health-care interventions. This change in a patient's health-state has a direct effect on the patient's expected future need for health-care. Hence, current allocation decisions determine to some extent the set of possible allocation decisions in the future. In order to take this into account we have formulated a dynamic linear programming version of a patient-flow system. This enables us to describe the effects of using different objective functions on the optimum level and composition of the provision of health services within given resource constraints. The linear programming approach enables the quantification of the health effects and therefore the desirability of the (re-)allocation of health-care resources. We provide some simulation results in order to illustrate the working of the model.

Health Care Rationing↗
PubMed1990

A space-efficient algorithm for local similarities.

Existing dynamic-programming algorithms for identifying similar regions of two sequences require time and space proportional to the product of the sequence lengths. Often this space requirement is more limiting than the time requirement. We describe a dynamic-programming local-similarity algorithm that needs only space proportional to the sum of the sequence lengths. The method can also find repeats within a single long sequence. To illustrate the algorithm's potential, we discuss comparison of a 73,360 nucleotide sequence containing the human beta-like globin gene cluster and a corresponding 44,594 nucleotide sequence for rabbit, a problem well beyond the capabilities of other dynamic-programming software.

Algorithms↗
PubMed2004

The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods.

All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence. In most of these methods, prior to prediction, alignment positions showing a gap in the top sequence are deleted, consequently leading to shrinking of the alignment and loss of position-specific information. In this paper we investigate the effect of this removal of information on secondary structure prediction accuracy. To this end, we have designed SymSSP, an algorithm that post-processes the predicted secondary structure of all sequences in a multiple sequence alignment by (i) making use of the alignment's evolutionary information and (ii) re-introducing most of the information that would otherwise be lost. The post-processed information is then given to a new dynamic programming routine that produces an optimally segmented consensus secondary structure for each of the multiple alignment sequences. We have tested our method on the state-of-the-art secondary structure prediction methods PHD, PROFsec, SSPro2 and JNET using the HOMSTRAD database of reference alignments. Our consensus-deriving dynamic programming strategy is consistently better at improving the segmentation quality of the predictions compared to the commonly used majority voting technique. In addition, we have applied several weighting schemes from the literature to our novel consensus-deriving dynamic programming routine. Finally, we have investigated the level of noise introduced by prediction errors into the consensus and show that predictions of edges of helices and strands are half the time wrong for all the four tested prediction methods.

Algorithms↗
PubMed2004

DPANN: improved sequence to structure alignments following fold recognition.

In fold recognition (FR) a protein sequence of unknown structure is assigned to the closest known three-dimensional (3D) fold. Although FR programs can often identify among all possible folds the one a sequence adopts, they frequently fail to align the sequence to the equivalent residue positions in that fold. Such failures frustrate the next step in structure prediction, protein model building. Hence it is desirable to improve the quality of the alignments between the sequence and the identified structure. We have used artificial neural networks (ANN) to derive a substitution matrix to create alignments between a protein sequence and a protein structure through dynamic programming (DPANN: Dynamic Programming meets Artificial Neural Networks). The matrix is based on the amino acid type and the secondary structure state of each residue. In a database of protein pairs that have the same fold but lack sequences-similarity, DPANN aligns over 30% of all sequences to the paired structure, resembling closely the structural superposition of the pair. In over half of these cases the DPANN alignment is close to the structural superposition, although the initial alignment from the step of fold recognition is not close. Conversely, the alignment created during fold recognition outperforms DPANN in only 10% of all cases. Thus application of DPANN after fold recognition leads to substantial improvements in alignment accuracy, which in turn provides more useful templates for the modeling of protein structures. In the artificial case of using actual instead of predicted secondary structures for the probe protein, over 50% of the alignments are successful.

Amino Acid Sequence↗
PubMed2003

Finding signals for plant promoters.

The strongest signal of plant promoter is searched with the model of single motif with two types. It turns out that the dominant type is the TATA-box. The other type may be called TATA-less signal, and may be used in gene finders for promoter recognition. While the TATA signals are very close for the monocot and the dicot, their TATA-less signals are significantly different. A general and flexible multi-motif model is also proposed for promoter analysis based on dynamic programming. By extending the Gibbs sampler to the dynamic programming and introducing temperature, an efficient algorithm is developed for searching signals in plant promoters.

Algorithms↗