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Care for low back pain remains a clinical enigma. Its high prevalence and cost to the system warrants attention for improvement. Although, no major recent clinical breakthroughs for resolving back pain have emerged, reducing unnecessary tests, eliminating useless or harmful practices, preventing care dependence, and enhancing coping skills can be useful goals for improving patient outcomes. Quality Improvement (QI) approaches can serve as bridge between clinical intuition and large scale science. Three chiropractic delivery settings including two teaching clinics and one large multidisciplinary spine care center incorporated QI approaches as part of an initiative to improve low back pain care. All clinics were able to implement sustainable process and outcomes improvements.

We explored Kentuckians' attitudes toward children with epilepsy. Questions compared respondents' attitudes about children with epilepsy, asthma, hyperactivity, and AIDS. Random digit dialing led to 617 completed interviews. The key questions asked concerned (a) how a pupil with each illness would alter the classroom environment, and (b) how the condition would affect the child's quality of life (QOL) at age 21 years. Respondents used a 0-10 rating scale (0 = worst, 5 = normal, 10 = best). A dichotomous variable divided respondents into those who rated below and those who rated at or above the norm: 24% predicted a deterioration of the classroom environment with the addition of a pupil with epilepsy (similar to AIDS at 26%); 41% predicted a lessened QOL at age 21 years (a worse rating than either asthma or hyperactivity). We created summary indexes, using difference scores between epilepsy ratings and ratings for the other conditions: Relative Educational Distance (RED) and Relative Quality of Life Distance (RQLD) measures. On the RED index, rural and Appalachian respondents showed the greatest prejudice toward pupils with epilepsy. Conversely, we noted the greatest prejudice in RQLD among urban residents (the best educated group). Our data do not support contentions that prejudices against persons with epilepsy are disappearing.

Covariance models provide excellent accuracy for ncRNA homology search. However, high computational complexity has limited their usefulness. This research improves the covariance model's search efficiency by building combined models for a group of different RNA families, which is selected using a clustering strategy. A series of combined partial covariance models are built from the stem loop structural elements that the ncRNA gene families share. Experimental results suggest that for most RNA gene families investigated, our combination search method successfully provides run time improvement with acceptable accuracy. Although there still exist limitations such as recall loss for a few RNA gene families, this novel combination approach has implications for future studies of reducing covariance model's search complexity.

Aluminium nitride films are deposited by reactive rf sputtering, and a wide variation of deposition parameters is examined. Optical and structural properties dependent on these parameters are studied, and e.g. the optical band gap, the degree of crystallinity, and the effects of hydrogen incorporation are determined. Electronic properties of the films are measured by applying X-ray photoelectron spectroscopy and electron energy loss spectroscopy. From these data an AlN energy level scheme was derived. Depth profiles of AlN on Si are measured mainly by secondary ion mass spectroscopy and photoelectron spectroscopy. They lead to the conclusion that the interfaces are relatively sharp (5 to 10 nm) if the film is deposited at moderate temperatures (

The use of covariance models in finding non-coding RNA gene members in genome sequence databases has been shown quite effective in many studies. However, it has a significant drawback, which is the very large computational burden. A combined covariance model is proposed to reduce the search complexity when a genome sequence is searched for more than one ncRNA gene family. The covariance models that are combined are selected using a hierarchical clustering algorithm. This study shows that when a small number of original covariance models are combined, the combined covariance model can find members from all original ncRNA families thus successfully reducing the search time.

An exploration of the use of multiple secondary structure elements for structural RNA gene finding is conducted. The secondary structure models are combined through a multilayer voting system which first combines the probability output of support vector machines and then combines the results of those votes to predict whether a sequence is a structural RNA gene or not. It is found that the voting in the first layer of the system has significant impact on the performance of individual secondary structure element models with improvements in classification results of up to 56%. Likewise, gains in classification F-measure over 0.6 were seen when two secondary structure element model predictions were voted together. When all the secondary structure element models were used in voting, an accuracy of over 93% was achieved by the secondary structure RNA gene classification system.

This paper explores the use of RNA structural elements for RNA gene finding. A classification experiment is performed in which several support vector machine models, based on the properties of individual RNA secondary structure elements, are trained and tested, revealing the structural elements which have properties useful for RNA gene finding. The study finds that the external loop and structure elements with classification accuracies of over 84% and the stemloop, hairpin, and tail elements with classification accuracies around 70% are the most likely structural elements to be successfully exploited by future RNA gene finders.

Purpose To compare the reproducibility (r) of CT value measurement of pulmonary nodules using volumetry software (LungCare, LC) and manual ROIs (mROI). Materials and methods 54 artificial nodules in a chest phantom were scanned three times with CT. CT values were measured with LC and mROI. The intrascan-r was assessed with three measurements in the first scan, and the interscan-r with measurements in three consecutive scans (one observer). Intrascan-r und interobserver-r (two obs.) were assessed in the first scan and in contrast-enhanced CT of 51 nodules from 15 patients (kernels b50f and b80f). Intrascan-r and interscan-r were described as the mean range and interobserver-r as the mean difference of CT values. The significance of differences was tested using t-test and sign test. Results Reproducibility was significantly higher for volumetry-based measurements in both artificial and patient nodules (range 0.11 vs. 6.16 HU for intrascan-r, 2.22 vs. 7.03 HU for interscan-r, difference 0.11 vs. 18.42 HU for interobserver-r; patients: 1.78 vs. 13.19 HU (b50f-Kernel) and 1.88 vs. 27.4 HU (b80f-Kernel) for intrascan-r, 3.71 vs. 22.43 HU for interobserver-r). Absolute CT values differed significantly between convolution kernels (pat./mROI: 29.3 [b50f] and 151.9 HU [b80f] pat./LC: 5 [b50f] and 147 HU [b80f]). Conclusion The reproducibility of volumetry-based measurements of CT values in pulmonary nodules is significantly higher and should therefore be recommended, e. g. in dynamic chest CT protocols. Reproducibility does not depend on absolute CT values.

Beyond its critical role in protein synthesis, RNA has vital structural, functional, and regulatory roles in the cell. The shape of an RNA molecule primarily determines its function in organic systems, so there is notable interest in the computational prediction of RNA structure. Pseudoknots are relatively rare but important structural elements which are difficult to predict computationally. RnaPredict is an evolutionary algorithm (EA) developed for the prediction of RNA secondary structure. This research evaluates RnaPredict after its enhancement with the thermodynamic model from HotKnots, a model specifically designed to compute free energies of structures containing pseudoknots. The performance of the EA is evaluated against the original HotKnots algorithm. RnaPredict significantly improved upon the sensitivity and specificity of structures predicted by HotKnots.

Recently, there is a resurgence of interest in the RNA secondary structure prediction problem due to the discovery of many new families of non-coding RNAs with a variety of functions. This paper describes and presents a novel algorithm for RNA secondary structure prediction based on an ensemble-based approach. An evaluation of the performance in terms of sensitivity and specificity is made. Experiments were performed on eleven structures from four RNA classes (RNaseP, Group I intron 16S rRNA, Group I intron 23S rRNA and 16S rRNA). Three RNA secondary structure similarity metrics (base pair distance, tree edit distance, and thermodynamic energy distance) and their effects on the clustering algorithm were explored. The significant contribution of this paper is in the examining of the various results from employing different dissimilarity metrics. Overall, the base pair distance dissimilarity metric shows better results with the other two distance metrics (tree edit distance and thermodynamic energy distance). The results presented in this paper demonstrate that SARNA-Ensemble-Predict can give comparable performance to a state-of-the-art algorithm Sfold in terms of sensitivity.

The RNA secondary structure design (SSD) problem is a recently emerging research topic motivated by applications such as customized drug design and the self-assembly of RNA nano-objects. This paper presents a novel local search algorithm, rnaDesign for SSD solving. An evaluation of the algorithm performance in terms of sequence affinity and structure specificity is made through comparison with another algorithm, RNAinverse. Experiments were performed on RNA secondary structures including three biologically existing data sets and one random structure set. Empirical results show that rnaDesign outperforms RNAinverse in terms of structure designability; sequences designed by rnaDesign also exhibit better thermodynamic stability with relatively lower folding energy. Furthermore, we demonstrate through parameter tuning experiments that using a combination of heuristic search strategies leads to better design performance; there also exists a strong correlation between the heuristic values in use and solution quality.

Empirical algorithm study involves tuning various parameter settings in order to achieve an optimal performance. It is also experimentally known that algorithm performance varies across problem instances. In stochastic local search (metaheuristics) paradigm, search efficiency is correlated to the empirical hardness of the underlying combinatorial optimization problem itself. Therefore, investigating these correlations are of crucial importance towards the design of robust algorithmic solutions. To achieve this goal, an accurate prediction of algorithm performance is a prerequisite, since it allows an automatic tuning of parameter settings on a perproblem base. In this work, we investigate using parametric & non-parametric regression models for algorithm performance prediction for the RNA Secondary Structure Design problem (SSD). Empirical results show our non-parametric methods achieve a higher prediction accuracy on biologically existing data, where biological data exhibits a higher degree of local similarity among individual instances. We also found that using a non-parametric regression tree model (CART) provides insight into studying the empirical hardness of solving the SSD problem.

Several factors make stem-loops an attractive sequence signal for a structural RNA gene-finder. Structural RNAs are virtually obligated to form stem-loops on their way to forming stable structures. Also, stem-loops can be identified along a sequence of length n in O(n) time. We postulate that stem-loops found in structural RNA genes may tend to be longer than those found in their genomic counterparts - coding sequences and noncoding DNA. We also postulate that stem-loops may occur in higher frequency in the structural RNA regions.

Much progress has been achieved in recent years in molecular biology and genetics. The sheer volume of data in the form of biological sequences has been enormous and efficient methods for dealing with these huge amounts of data are needed. In addition, the data alone does not provide information on the workings of biological systems; hence much research effort has focused on designing mathematical and computational models to address problems from molecular biology. Often, the terms bioinformatics and computational biology are used to refer to the research fields concerning themselves with designing solutions to molecular problems in biology. However, there is a slight distinction between bioinformatics and computational biology: the former is concerned with managing the enormous amounts of biological data and extracting information from it, while the latter is more concerned with the design and development of new algorithms to address problems such as protein or RNA folding. However, the boundary is blurry, and there is no consistent usage of the terms. We will use the term bioinformatics to encompass both fields. To cover all areas of research in bioinformatics is beyond the scope of this section and we refer the interested reader to [2] for a general introduction. A large part of what bioinformatics is concerned about is evolution and function of biological systems on a molecular level. Evolutionary computation and evolutionary design are concerned with developing computational systems that “mimic” certain aspects of natural evolution (mutation, crossover, selection, fitness). Much of the inner workings of natural evolutionary systems have been copied, sometimes in modified format into evolutionary computation systems. Artificial neural networks mimic the functioning of simple brain cell clusters. Fuzzy systems are concerned with the “fuzzyness” in decision making, similar to a human expert. These three computational paradigms fall into the category of computational intelligence (CI). While biological systems have helped to develop many of the computational paradigms in CI, CI is now returning the favor to help solve some of the most challenging biological mysteries itself. In many cases these probabilistic methods can produce biologically relevant results where exact deterministic methods fail. For an extensive overview of successful applications of CI algorithms to problems in bioinformatics please refer to [1].

Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper presents SARNA-Predict-pk, an algorithm for pseudoknotted RNA secondary structure prediction based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and incorporates a new thermodynamic model into the algorithm, effectively enabling it to predict pseudo-knotted RNA structures. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms. We measured the sensitivity and specificity using five prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. The other two are heuristic algorithms: SARNA-Predict-pk and HotKnots algorithms. An evaluation for the performance of SARNA-Predict-pk in terms of prediction accuracy was verified with native structures. Experiments on ten individual known structures from six RNA classes (tRNA, viral RNA, anti-genomic HDV, telomerase RNA, tmRNA, and RNaseP) were performed. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy.

RNA is central in several stages of protein synthesis, and also has structural, functional, and regulatory roles in the cell. The shape of organic molecules such as RNA largely determines their function within an organic system, thus methods for the computational prediction of structure are sought after. In the ab initio case where only the RNA sequence is known, the currently dominant structure prediction techniques employ minimization of the free energy of a given RNA molecule via a thermodynamic model. However, the minimum free energy structure is rarely the native structure; this is thought to be due to errors in the thermodynamic model parameters, which are experimentally determined. Cluster analysis performed by [6] on a sampling of structures from a Boltzmann weighted ensemble determined that the best cluster centroid had an improved sensitivity and significantly improved positive predictive value over the minimum free energy structure in the ensemble. Based on this result, we investigated the combination of an existing evolutionary algorithm for RNA secondary structure prediction with a clustering algorithm.

Among the most powerful and commonly used methods for finding new members of non-coding RNA gene families in genomic data are covariance models. The parameters of these models are estimated from the observed position-specific frequencies of insertions, deletions, and mutations in a multiple alignment of known non-coding RNA family members. Since the vast majority of positions in the multiple alignment have no observed changes, yet there is no reason to rule them out, some form of prior is applied to the estimate. Currently, observed-frequency priors are generated from non-family members based on model node type and child node type allowing for some differentiation between priors for loops versus helices and between internal segments of structures and edges of structures. In this work it is shown that parameter estimates might be improved when thermodynamic data is combined with the consensus structure/sequence and observed-frequency priors to create more realistic position-specific priors.

Ribonucleic acid, a single stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is key to their function, algorithms for the prediction of RNA structure are of great value. This paper discusses significant improvements made to SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). One major improvement is the incorporation of a sophisticated thermodynamic model (efn2). This model is used by mfold to rank sub-optimal structures, but cannot be used directly by mfold during the structure prediction. Experiments on eight individual known structures from four RNA classes (5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA and 16S rRNA) were performed. The data demonstrate the robustness and the effectiveness of our improved prediction algorithm. The new algorithm shows results which surpass the dynamic programming algorithm mfold in terms of prediction accuracy on all tested structures.

Objective Every promotion committee is challenged by the need to make value judgments on the quantity and quality of peer work. Decisions based upon subjective assessments may not do justice to the applicant's or the institution's needs. The purpose of this article is to (1) describe the process a college promotion committee used to increase the objectivity brought to this activity, (2) present the tools developed that facilitated the collection and evaluation of faculty work, and (3) describe their usage in a promotion cycle. Methods The Professor Promotion Committee met weekly for 6 months engaging in lengthy and comprehensive discourse to capture the breadth of scholastic and service activities normally engaged in by faculty. Results The committee's work culminated in the development of 4 electronic applications soliciting specific evidence aligned with faculty work and 1 scoring rubric tied directly to the e-applications. More than 55 activities were identified, divided into 4 levels of accomplishment using quantitative and qualitative criteria and weighted according to their centrality to faculty work and relative importance to the institution. Each activity was assigned to one of the following categories: teaching/academic support, scholarship/research, service, and professional development. A consensus score based upon the evidence was used to generate promotion discussions. Conclusions The committee believes the online application aids applicants in recognizing the breadth and depth of promotable work. It provides them the opportunity to structure their work in ways that enhance their chances for promotion. The evidence-based rubric helps to reduce subjectivity in the evaluation process.

This paper presents an algorithm for RNA secondary structure prediction based on simulated annealing (SA) and also studies the effect of using different types of annealing schedules. SA is known to be effective in solving many different types of minimization problems and for being able to approximate global minima in the solution space. Based on free energy minimization techniques, this permutation-based SA algorithm heuristically searches for the structure with a free energy value close to the minimum free energy DeltaG for that strand, within given constraints. Other contributions of this paper include the use of permutation-based encoding for RNA secondary structure and the swap mutation operator. Also, a detailed study of the convergence behavior of the algorithm is conducted and various annealing schedules are investigated. An evaluation of the performance of the new algorithm in terms of prediction accuracy is made via comparison with the dynamic programming algorithm mfold for thirteen individual known structures from four RNA classes (5S rRNA, Group I intron 23 rRNA, Group I intron 16S rRNA and 16S rRNA). Although dynamic programming algorithms for RNA folding are guaranteed to give the mathematically optimal (minimum energy) structure, the fundamental problem of this approach seems to be that the thermodynamic model is only accurate within 5-10%. Therefore, it is difficult for a single sequence folding algorithm to resolve which of the plausible lowest-energy structure is correct. The new algorithm showed comparable results with mfold and demonstrated a slightly higher specificity

Presently and even more in the near future more cancer patients will be treated at home especially in the final stage of their disease. For this reason the prehospital emergency system will be confronted with the specific needs of these patients. Palliative care is not part of the German model of post-graduate training regulations for emergency medicine and palliative care teams (PCT) are only involved in the treatment of cancer patients in emergency situations.Over a 12-month period we retrospectively analysed all emergency cases that had been categorised as final cancer stage at 2 emergency sites (one air-based, the other ground-based) involving physicians in an out-of-hospital setting. We analysed all cases for indications of emergency call, prehospital treatment and involvement of a PCT in the treatment of symptoms.For this period we analysed 2,765 emergency documents and identified more than 2.5% as emergency calls by cancer patients or their relatives (the majority of patients had been in the final stage of the disease). Most emergency calls occurred at times when no general practitioner was on duty and acute dyspnoea (42.7%) was the prominent diagnosis. After emergency treatment 61.8% patients had been admitted to hospital. In most settings a PCT was not involved in the treatment of palliative care patients or their relatives (92.7%).Our data demonstrate that care of cancer patients in the final stage of the disease is relevant in emergency medicine. These patients are in need of help based on principles of palliative care. Under these circumstances cooperation of the medical disciplines (emergency and palliative medicine) concerned seems to be necessary. This may increase the possibility for patients to stay at home for the last days of their life. Because of this we are convinced that basic knowledge of palliative care should be integrated into the German model of post-graduate training regulations for emergency care. Combining parts of the curricula (palliative and emergency medicine) it would be possible for emergency physicians to guide their treatment by the ideas and strategies of palliative care. But we are also convinced that the system of PCT should increase and become more involved in prehospital care in emergency cases of palliative care patients.

Covariance models are a powerful description of non-coding RNA (ncRNA) families that can be used to search nucleotide databases for new members of these ncRNA families. Currently, estimation of the parameters of a covariance model (state transition and emission scores) is based only on the observed frequencies of mutations, insertions, and deletions in known ncRNA sequences. For families with very few known members, this can result in rather uninformative models where the consensus sequence has a good score and most deviations from consensus have a fairly uniform poor score. It is proposed here to combine the traditional observed-frequency information with known information about free energy changes in RNA helix formation and loop length changes. More thermodynamically probable deviations from the consensus sequence will then be favored in database search. The thermodynamic information may be incorporated into the models as informative priors that depend on neighboring consensus nucleotides and on loop lengths.

The function of an RNA molecule is primarily established by its physical shape. As current physical structure determination methods are time consuming and expensive, there is great interest in finding computational structure prediction methods. P-RnaPredict is a parallel evolutionary algorithm for RNA secondary structure prediction. Two sets of experiments are performed on 5 known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA, and 16S rRNA). The first determines the actual speedup, and the second evaluates the performance of P-RnaPredict through comparison to mfold. P-RnaPredict succeeds in predicting structures with higher true positive base pair counts and lower false positives than mfold on specific sequences.

Background DNA sequencing is used ubiquitously: from deciphering genomes[1] to determining the primary sequence of small RNAs (smRNAs) [2–5]. The cloning of smRNAs is currently the most conventional method to determine the actual sequence of these important regulators of gene expression. Typical smRNA cloning projects involve the sequencing of hundreds to thousands of smRNA clones that are delimited at their 5' and 3' ends by fixed sequence regions. These primers result from the biochemical protocol used to isolate and convert the smRNA into clonable PCR products. Recently we completed a smRNA cloning project involving tobacco plants, where analysis was required for ~700 smRNA sequences[6]. Finding no easily accessible research tool to enter and analyze smRNA sequences we developed Ebbie to assist us with our study. Results Ebbie is a semi-automated smRNA cloning data processing algorithm, which initially searches for any substring within a DNA sequencing text file, which is flanked by two constant strings. The substring, also termed smRNA or insert, is stored in a MySQL and BlastN database. These inserts are then compared using BlastN to locally installed databases allowing the rapid comparison of the insert to both the growing smRNA database and to other static sequence databases. Our laboratory used Ebbie to analyze scores of DNA sequencing data originating from an smRNA cloning project[6]. Through its built-in instant analysis of all inserts using BlastN, we were able to quickly identify 33 groups of smRNAs from ~700 database entries. This clustering allowed the easy identification of novel and highly expressed clusters of smRNAs. Ebbie is available under GNU GPL and currently implemented on http://bioinformatics.org/ebbie/ Conclusion Ebbie was designed for medium sized smRNA cloning projects with about 1,000 database entries [6–8].Ebbie can be used for any type of sequence analysis where two constant primer regions flank a sequence of interest. The reliable storage of inserts, and their annotation in a MySQL database, BlastN[9] comparison of new inserts to dynamic and static databases make it a powerful new tool in any laboratory using DNA sequencing. Ebbie also prevents manual mistakes during the excision process and speeds up annotation and data-entry. Once the server is installed locally, its access can be restricted to protect sensitive new DNA sequencing data. Ebbie was primarily designed for smRNA cloning projects, but can be applied to a variety of RNA and DNA cloning projects[2, 3, 10, 11].

Bioinformatics can be defined as the application of computational tools to manage and analyze biological data, typically at the molecular level. The field of Computational Biology aims at the mathematical or computational modeling of biological processes, such as protein folding or protein-protein interaction. Many books that include the title bioinformatics or computational biology (BCB) actually present a mixture of the two fields. It is sometimes not possible to separate the two as for a single problem such as protein folding both bioinformatics techniques such as similarity search of existing protein databases as well as computational biology is used in the form of mathematically modeling the protein structure via thermodynamic models that are optimized via a computational technique. Books in BCB can be categorized broadly into application tools books, which describe existing applications (typically available on the WWW) to solve a biological problem [1]. There are also books that do not offer any computational solutions, but should be considered valuable, as they describe a biological domain [2]. The remaining category offers detailed mathematical, statistical, and computational approaches for problems in BCB [3,4] and books that focus on specific computational approaches [5]. The book reviewed here is different in the sense that it focuses on a single domain, namely protein bioinformatics. Ten problems in this domain are identified and discussed in 10 Chapters. The book starts with an introduction to proteins and their structure which is helpful for the uninitiated. Chapter 1 discusses the basic problem of Protein Sequence Alignment and provides a biological motivation based on the evolution of proteins. The standard algorithms for global (Needleman Wunsch) and local alignment (Smith Waterman) are presented. Chapter 2 discusses Feature Prediction of proteins including features for secondary structure, active sites, binding ligands or sites being recognized by enzymes. In this context hidden markov models, neural networks and support vector machines are discussed. The important principles of specificity and sensitivity are also introduced. In Chapter 3 the challenges of Protein Function prediction are discussed including the problem of absence of standards in new protein naming and protein function annotation. Another point of caution raised is the issue of error propagation in biological networks due to transferring functional annotations by similarity. Protein Structure Prediction is discussed in Chapter 4, which covers energy based modeling of protein structures and the limitations of such approaches, mainly based on the imperfections due to the classical approximation of quantum-mechanical interactions between protein atoms. In addition, a number of computational approaches to search the protein conformational space are discussed. Chapter 5 reviews Membrane Proteins and is more biological and less technically oriented than previous chapters. The topic of Chapter 6 is Functional Site Identification and the detection of functional sites in proteins and structural superposition are presented. Chapter 7 discusses Protein-Protein Interaction and explores sequence-based, structure based and experimental methods for detecting protein-protein interactions. The scoring of docking solutions and the CAPRI experiment are discussed as well. A related problem is presented in Chapter 8, which focuses on Protein-Small Molecule Interaction. A number of methods are presented and the implications to the virtual screening of drug compounds for the drug discovery process are discussed. Chapter 9 elaborates on Protein Design to synthesize proteins that do not necessarily exist in nature. This is also known as the inverse protein folding problem. Intuitive design, lattice models and automatic models are touched upon. The book concludes with a discussion of Protein Engineering in Chapter 10, which deals with engineering novel properties into naturally occurring proteins. Each chapter offers an introduction to the problem and discusses a number of solutions or solution attempts. It is nice that for proposed solutions either the biological rationale is given, or it is explained how the model's deviation from the biological reality affects the proposed solution. A summary of the reliability of the present methods, suggestions for promising avenues and a list of suggested readings conclude each chapter. The author needs to be commended for fairly discussing a great variety of computational approaches including dynamic programming, hidden markov models, monte carlo methods, simulated annealing, genetic algorithms, neural networks, and heuristic based methods including "intuitive design" as well as for discussing their benefits and drawbacks on selected problems. One of the clear strengths of the book is that it cautions us to accurately test the reliability of methods, assess the appropriateness of approximations and to be aware of the limitations of data produced in wet-lab experiments. This book is geared towards graduate students or bioinformatics researchers at an intermediate level. It is useful to people from either a life science background or a computational background and a bit of exposure to both areas is helpful. The book is comprehensive and succinct; people on busy schedules can read it in a short period of time. Of course, due to being succinct, it does not provide all the technical details of all discussed computational methods, but I do not see this as a drawback, because it does a great job at drawing interconnections between the related problems and related approaches. One reservation to be noted is that not all illustrations are of the same high quality. Particularly the high quality illustrations from the color insert are reproduced in grayscale where they are discussed in the text and this renders many of them hard to interpret. I believe that it would have been better to simply refer the reader to the color insert. Another issue is that the number of human genes is given as 50,000 (p. 53) and this is dated as more recent estimates are closer to below 30,000 [6]. Nevertheless, these are minor issues, and I believe that it is a great book. I will definitely include it as a recommended text for one of my Bioinformatics Graduate courses. I highly recommend this text to anyone interested in protein bioinformatics.

In order for structure prediction researchers to better understand the results of their algorithms and to enable life science researchers to interpret RNA structure easily, it is helpful to provide them with a flexible and powerful tool for RNA secondary structure visualization. jViz.Rna is a multi-platform visualization tool capable of displaying RNA secondary structures encoded in a variety of file formats. A single structure can be shown using the linear Feynman, circular Feynman, dot plot, and classical structure visualization models. The resulting drawings are dynamic and can easily be further modified by the user. Any of the drawings produced can be saved to disk enabling easy dissemination. The unique usage of a spring model for classical structure drawing allows for clear visualization of pseudoknots with minimal overlaps. The addition of a locality tool allows for the isolation of pseudoknotted regions or other regions of interest. Availability: http://jviz.research.iat.sfu.ca

This paper presents an extension to P-RnaPredict, a parallel evolutionary algorithm (EA) for RNA folding. The impact of three pseudorandom number generators (PRNGs) on the EA's performance is evaluated. The generators tested included the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). P-RnaPredict was implemented using the message passing interface (MPI) and tested on a 128 node Beowulf cluster. The PRNG comparison testing was performed with four known structures that are 118, 122, 543, and 556 nucleotides in length. PRNGs effects were investigated and predicted structures compared to known structures

This paper presents a comparison of two types of algorithms for RNA secondary structure prediction: an implementation of Nussinov's dynamic programming algorithm (DPA), and P-RnaPredict, a parallel evolutionary algorithm (EA). The research presented here builds on previous work and examines the results from tests of three RNA sequences that are 118, 543, and 784 nucleotides in length. A variety of EA parameter settings were employed based on previous experimentation. Predicted structures were compared to those generated by the Nussinov DPA and to known structures to determine relative accuracy. Results indicate that the EA demonstrated high prediction accuracy and outperformed the Nussinov DPA on all tested sequences

Kinetics of the dark relaxation of variable chlorophyll fluorescence, Fv, were studied after brief illumination of dark-adapted barley leaves in order to understand the rapid reversibility of pulse-induced fluorescence increases, which is observed even when fast linear electron transport to an external electron acceptor is not possible. Four kinetically distinct components were observed which reveal complexity in the oxidation of the reduced primary quinone acceptor of Photosystem II, Q (A) (-) : the slowest component accounted for 4-5% of maximal Fv and had a life-time of several seconds. It is suggested to represent a minor population of inactive Photosystem II centers. The other three components displayed first-order kinetics with half-time of 6-8 ms ('fast' component), 60-80 ms ('middle' component) and 650-680 ms ('slow' component). The fast component dominated Fv when methyl viologen or far-red light accelerated oxidation of plastohydroquinone. It shows rapid oxidation of Q (A) (-) during electron flow to plastoquinone commensurate with maximum linear electron flow through the electron transport chain. The other two components were observed under conditions of restricted electron flow and excessive reduction of electron carriers. Unexpectedly, the slow component, which is interpreted to reflect the recombination between Q (A) (-) and an intermediate on the oxidizing side of Photosystem II, saturated already at low irradiances of actinic light when plastoquinone was not yet strongly reduced suggesting that dark-adaptation of leaves results not only in the loss of activity of light-regulated enzymes of the carbon cycle but affects also electron flow from Q(A) (-) to plastoquinone. KCN poisoning or high temperature treatment of leaves produced a nonexponential pattern of slow Fv relaxation. This effect was largely (heat treatment) or even completely (KCN) abolished by far-red light.

This paper presents a fully parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented on a 128-node Beowulf cluster. The PRNG comparison tests were performed with known structures whose sequences are 118, 122, 468, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures. Results indicate that P-RnaPredict demonstrated good prediction accuracy, particularly so for shorter sequences.

This paper presents a parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented. The PRNG comparison tests were performed with known structures that are 118, 122, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures.

This work builds on previous research from an EA used to predict secondary structure of RNA molecules. The EA has the goal of predicting which canonical base pairs will form hydrogen bonds and helices. The addition of stacking energies, through INN and INN-HB, to our thermodynamic model has enhanced our predictions. We test three RNA sequences of lengths 118, 543, and 784 nucleotides using a variety of previously successful operators and parameter settings. The accuracy of the predicted structures are compared against those generated by the Nussinov DPA and also to known structures. The EA showed high accuracy of prediction especially on short sequences. On all tested sequences, the EA outperforms the Nussinov DPA.

This work presents a coarse-grained distributed genetic algorithm (GA) for RNA secondary structure prediction. This research builds on previous work and contains two new thermodynamic models, INN and INN-HB, which add stacking-energies using base pair adjacencies. Comparison tests were performed against the original serial GA on known structures that are 122, 543, and 784 nucleotides in length on a wide variety of parameter settings. The effects of the new models are investigated, the predicted structures are compared to known structures and the GA is compared against a serial GA with identical models. Both algorithms perform well and are able to predict structures with high accuracy for short sequences.

This paper presents a new coarse-grained distributed genetic algorithm (GA) for the prediction of the secondary structure of RNA molecules, based largely on a serial permutation-based GA. The benefits of the distributed GA over our existing serial GA are analyzed and demonstrated. We also analyze the impact of the keep-best reproduction (KBR) and roulette wheel selection (STDS) GA replacement techniques. Finally, we verify the increase in convergence speed of our distributed GA. Tests was performed on 241 and 785 nucleotide sequences. Overall, the distributed GA is found to improve upon the serial GA performances, with a much more pronounced impact on the STDS selection strategy. There is also a notable acceleration in convergence speed.