Continuous One-Arm Kettlebell Swing Training on Physiological Parameters in US Air Force Personnel: A Pilot Study.

BACKGROUND: The primary aim of this study was to investigate the effects of continuous one-arm kettlebell (KB) swing training on various US Air Force physical fitness testing components. Thirty trained male (n = 15) and female (n = 15) US Air Force (USAF) personnel volunteered and were sequentially assigned to one of three groups based on 1.5-mile run time: (1) KB one-arm swing training, (2) KB one-arm swing training plus highintensity running (KB + run), and (3) traditional USAF physical training (PT) according to Air Force Instruction 36-2905. METHODS: The following measurements were made before and after 10 weeks of training: 1.5-mile run, 1-minute maximal push-ups, 1-minute maximal situps, maximal grip strength, pro agility, vertical jump, 40-yard dash, bodyweight, and percent body fat. Subjects attended three supervised exercise sessions per week for 10 weeks. During each exercise session, all groups performed a 10-minute dynamic warm-up followed by either (1) 10 minutes of continuous KB swings, (2) 10 minutes of continuous kettlebell swings plus 10 minutes of high-intensity running, or (3) 20 minutes of moderate intensity running plus push-ups and sit-ups. Average and peak heart rate were recorded for each subject after all sessions. Paired t tests were conducted to detect changes from pretesting to posttesting within each group and analysis of variance was used to compare between-group variability (ρ ≤ .05). RESULTS: Twenty subjects completed the study. There were no statistically significant changes in 1.5-mile run time between or within groups. The 40- yard dash significantly improved within the KB swing (ρ ≤ .05) and KB + run group (ρ ≤ .05); however, there were no significant differences in the traditional PT group (ρ ≤ .05) or between groups. Maximal push-ups significantly improved in the KB + run group (ρ ≤ .05) and trends toward significant improvements in maximal push-ups were found in both the KB (ρ = .057) and traditional PT (ρ = .067) groups. CONCLUSIONS: This study suggests that continuous KB swing training may be used by airmen as a high-intensity, low-impact alternative to traditional USAF PT to maintain aerobic fitness and improve speed and maximal push-ups.

Defining scaffold geometries for interacting with proteins: geometrical classification of secondary structure linking regions.

Medicinal chemists synthesize arrays of molecules by attaching functional groups to scaffolds. There is evidence suggesting that some scaffolds yield biologically active molecules more than others, these are termed privileged substructures. One role of the scaffold is to present its side-chains for molecular recognition, and biologically relevant scaffolds may present side-chains in biologically relevant geometries or shapes. Since drug discovery is primarily focused on the discovery of compounds that bind to proteinaceous targets, we have been deciphering the scaffold shapes that are used for binding proteins as they reflect biologically relevant shapes. To decipher the scaffold architecture that is important for binding protein surfaces, we have analyzed the scaffold architecture of protein loops, which are defined in this context as continuous four residue segments of a protein chain that are not part of an α-helix or ß-strand secondary structure. Loops are an important molecular recognition motif of proteins. We have found that 39 clusters reflect the scaffold architecture of 89% of the 23,331 loops in the dataset, with average intra-cluster and inter-cluster RMSD of 0.47 and 1.91, respectively. These protein loop scaffolds all have distinct shapes. We have used these 39 clusters that reflect the scaffold architecture of protein loops as biological descriptors. This involved generation of a small dataset of scaffold-based peptidomimetics. We found that peptidomimetic scaffolds with reported biological activities matched loop scaffold geometries and those peptidomimetic scaffolds with no reported biologically activities did not. This preliminary evidence suggests that organic scaffolds with tight matches to the preferred loop scaffolds of proteins, implies the likelihood of the scaffold to be biologically relevant.

Sampling phylogenetic tree space with the generalized Gibbs sampler.

The generalized Gibbs sampler (GGS) is a recently developed Markov chain Monte Carlo (MCMC) technique that enables Gibbs-like sampling of state spaces that lack a convenient representation in terms of a fixed coordinate system. This paper describes a new sampler, called the tree sampler, which uses the GGS to sample from a state space consisting of phylogenetic trees. The tree sampler is useful for a wide range of phylogenetic applications, including Bayesian, maximum likelihood, and maximum parsimony methods. A fast new algorithm to search for a maximum parsimony phylogeny is presented, using the tree sampler in the context of simulated annealing. The mathematics underlying the algorithm is explained and its time complexity is analyzed. The method is tested on two large data sets consisting of 123 sequences and 500 sequences, respectively. The new algorithm is shown to compare very favorably in terms of speed and accuracy to the program DNAPARS from the PHYLIP package.

Algorithms for sequence analysis via mutagenesis.

MOTIVATION: Despite many successes of conventional DNA sequencing methods, some DNAs remain difficult or impossible to sequence. Unsequenceable regions occur in the genomes of many biologically important organisms, including the human genome. Such regions range in length from tens to millions of bases, and may contain valuable information such as the sequences of important genes. The authors have recently developed a technique that renders a wide range of problematic DNAs amenable to sequencing. The technique is known as sequence analysis via mutagenesis (SAM). This paper presents a number of algorithms for analysing and interpreting data generated by this technique. RESULTS: The essential idea of SAM is to infer the target sequence using the sequences of mutants derived from the target. We describe three algorithms used in this process. The first algorithm predicts the number of mutants that will be required to infer the target sequence with a desired level of accuracy. The second algorithm infers the target sequence itself, using the mutant sequences. The third algorithm assigns quality values to each inferred base. The algorithms are illustrated using mutant sequences generated in the laboratory.

Unlocking hidden genomic sequence.

Despite the success of conventional Sanger sequencing, significant regions of many genomes still present major obstacles to sequencing. Here we propose a novel approach with the potential to alleviate a wide range of sequencing difficulties. The technique involves extracting target DNA sequence from variants generated by introduction of random mutations. The introduction of mutations does not destroy original sequence information, but distributes it amongst multiple variants. Some of these variants lack problematic features of the target and are more amenable to conventional sequencing. The technique has been successfully demonstrated with mutation levels up to an average 18% base substitution and has been used to read previously intractable poly(A), AT-rich and GC-rich motifs.

A simulated annealing algorithm for finding consensus sequences.

MOTIVATION: A consensus sequence for a family of related sequences is, as the name suggests, a sequence that captures the features common to most members of the family. Consensus sequences are important in various DNA sequencing applications and are a convenient way to characterize a family of molecules. RESULTS: This paper describes a new algorithm for finding a consensus sequence, using the popular optimization method known as simulated annealing. Unlike the conventional approach of finding a consensus sequence by first forming a multiple sequence alignment, this algorithm searches for a sequence that minimises the sum of pairwise distances to each of the input sequences. The resulting consensus sequence can then be used to induce a multiple sequence alignment. The time required by the algorithm scales linearly with the number of input sequences and quadratically with the length of the consensus sequence. We present results demonstrating the high quality of the consensus sequences and alignments produced by the new algorithm. For comparison, we also present similar results obtained using ClustalW. The new algorithm outperforms ClustalW in many cases.

A subset-orientated algorithm for minimizing the number of steps required for synthesis of cyclic-peptide libraries.

Libraries of cyclic peptides are being synthesized using combinatorial chemistry for high throughput screening in the drug discovery process. This paper describes the min_syn_steps.cpp program (available at http://www.imb.uq.edu.au/groups/smythe/tran), which after inputting a list of cyclic peptides to be synthesized, removes cyclic redundant sequences and calculates synthetic strategies which minimize the synthetic steps as well as the reagent requirements. The synthetic steps and reagent requirements could be minimized by finding common subsets within the sequences for block synthesis. Since a brute-force approach to search for optimum synthetic strategies is impractically large, a subset-orientated approach is utilized here to limit the size of the search.