Phylogenetic identification of influenza virus candidates for seasonal vaccines.

The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.

Deep clustering of bacterial tree images.

The field of genomic epidemiology is rapidly growing as many jurisdictions begin to deploy whole-genome sequencing (WGS) in their national or regional pathogen surveillance programmes. WGS data offer a rich view of the shared ancestry of a set of taxa, typically visualized with phylogenetic trees illustrating the clusters or subtypes present in a group of taxa, their relatedness and the extent of diversification within and between them. When methicillin-resistant Staphylococcus aureus (MRSA) arose and disseminated widely, phylogenetic trees of MRSA-containing types of S. aureus had a distinctive 'comet' shape, with a 'comet head' of recently adapted drug-resistant isolates in the context of a 'comet tail' that was predominantly drug-sensitive. Placing an S. aureus isolate in the context of such a 'comet' helped public health laboratories interpret local data within the broader setting of S. aureus evolution. In this work, we ask what other tree shapes, analogous to the MRSA comet, are present in bacterial WGS datasets. We extract trees from large bacterial genomic datasets, visualize them as images and cluster the images. We find nine major groups of tree images, including the 'comets', star-like phylogenies, 'barbell' phylogenies and other shapes, and comment on the evolutionary and epidemiological stories these shapes might illustrate. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

Network science inspires novel tree shape statistics.

The shape of phylogenetic trees can be used to gain evolutionary insights. A tree's shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality.

Comparative study of combination therapy with non-steroidal anti inflammatory drugs and different doses of low level laser therapy in acute low back pain.

According to various side effects of medication in low back pain, respecting conservative therapy, owing to the widespread use of laser therapy in recent decades, related therapeutic outcomes were different and contradictory. The current study aimed at comparing the effect of non-steroidal drugs and laser therapy with different doses in patients with acute low back pain. METHODS: The current randomized, placebo-controlled study was conducted on 65 patients randomly assigned to four groups. In group 1(N = 20), only drug therapy and in group 2(N = 15), laser therapy (3 J/cm 2) in addition to medication was administrated to the patients. For group 3(N = 15), a therapeutic plan similar to that of group 2 was given; however, the laser dose was 6 J/cm 2. Finally, drug therapy plus placebo laser therapy was applied to group 4(N = 15). Pain was compared among the groups using visual analogue scale and Oswestry low back pain disability questionnaire. RESULTS: Significant difference between baseline pain scores and those of the weeks 1, 2, 3, and 4 of laser therapy in all intervention groups. Also, the results of the intergroup analyses showed a significant difference between group 1 compared with groups 2 and 3. There was a significant ODI difference between the groups after laser therapy. CONCLUSION: Findings showed that laser therapy plus drug therapy in comparison with drug therapy alone was a more effective method to relieve pain and disability in patient with acute low back pain; however, evidence to support this finding is still inadequate.

Computing the distribution of the Robinson-Foulds distance.

With the exponential growth of genome databases, the importance of phylogenetics has increased dramatically over the past years. Studying phylogenetic trees enables us not only to understand how genes, genomes, and species evolve, but also helps us predict how they might change in future. One of the crucial aspects of phylogenetics is the comparison of two or more phylogenetic trees. There are different metrics for computing the dissimilarity between a pair of trees. The Robinson-Foulds (RF) distance is one of the widely used metrics on the space of labeled trees. The distribution of the RF distance from a given tree has been studied before, but the fastest known algorithm for computing this distribution is a slow, albeit polynomial-time, O(l5) algorithm. In this paper, we modify the dynamic programming algorithm for computing the distribution of this distance for a given tree by leveraging the number-theoretic transform (NTT), and improve the running time from O(l5) to O(l3logl), where l is the number of tips of the tree. In addition to its practical usefulness, our method represents a theoretical novelty, as it is, to our knowledge, one of the rare applications of the number-theoretic transform for solving a computational biology problem.

Predicting the short-term success of human influenza virus variants with machine learning.

Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines' benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71-0.85 and a classifier 'area under the curve' 0.75-0.9.

A new resolution function to evaluate tree shape statistics.

Phylogenetic trees are frequently used in biology to study the relationships between a number of species or organisms. The shape of a phylogenetic tree contains useful information about patterns of speciation and extinction, so powerful tools are needed to investigate the shape of a phylogenetic tree. Tree shape statistics are a common approach to quantifying the shape of a phylogenetic tree by encoding it with a single number. In this article, we propose a new resolution function to evaluate the power of different tree shape statistics to distinguish between dissimilar trees. We show that the new resolution function requires less time and space in comparison with the previously proposed resolution function for tree shape statistics. We also introduce a new class of tree shape statistics, which are linear combinations of two existing statistics that are optimal with respect to a resolution function, and show evidence that the statistics in this class converge to a limiting linear combination as the size of the tree increases. Our implementation is freely available at https://github.com/WGS-TB/TreeShapeStats.

Comparison of non-steroidal anti-inflammatory drugs and knee kinesio taping in early osteoarthritis pain: A randomized controlled trial.

NSAID in knee OA impose many related adverse effects. Kinesio taping was recently suggested as a novel conservative treatment for reducing pain in OA, without adverse effects. This study aimed to compare kinesio taping and NSAID therapy for kn.ee OA. There were 37 participants in the first group (mean age, 53.72 ±â€¯8.91 years), 29 participants in the second group (mean age, 50.24 ±â€¯8.63 years), and 18 participants in the third group (mean age, 53.33 ±â€¯8.50 years). These groups were treated with NSAID therapy and kinesio taping, kinesio taping only, and sham taping with NSAID therapy, respectively. Taping was repeated three times a week at 1-day intervals. Participants reported pain each week, using visual analog scale scores. The main effect of group and the pain-group interaction were not statistically significant (P = 0.88 and 0.15, respectively). Pain reduction in different weeks was statistically significant (P < .001). Cohen's effect size values for pain reduction in the first to third groups were 0.70, 0.55, and 0.48, respectively. Pain reduction was observed in the three groups, but there was no significant difference among the various treatments. Kinesio taping as a pain relief method may reduce pharmacotherapy demands or at least delay NSAID prescriptions in patients with early OA.

Impact of age and obstacle negotiation on timing measures of gait initiation.

INTRODUCTION: The separate and combined impacts of age and walkway obstruction on reaction time, anticipatory postural adjustment phases of gait initiation and cognitive dual task performance are not well understood. This study aimed to a) examine the impact of age and task complexity on reaction and anticipatory postural adjustment phases separately b) examine the impact of age and walkway obstruction on cognitive task performance while walking. METHOD: Nineteen older adults (mean age± SD: 66.73 ± 3.38 years) and twelve younger adults (mean age± SD: 26.5 ± 4.37 years), participated in this study. The tests were performed in four conditions: a smooth walkway; a smooth walkway with concurrent cognitive task; an obstructed walkway; and an obstructed walkway with a concurrent cognitive task. Reaction and anticipatory postural adjustment phases were measured from the recorded center of pressure trajectory data. RESULTS: Reaction time phase was significantly longer in the older group (P = 0.04), but there was no significant impact of task complexity (P = 0.95). Conversely, there was a significant impact of task complexity on anticipatory postural adjustment phase (P = 0.04), but there was no significant difference between the age groups (P = 0.38). Cognitive task response time was not significantly different between age groups or with walkway obstruction (P = 0.19 and P = 0.90 respectively).There were no significant interactions between age group and task complexity. CONCLUSION: Reaction time phase but not anticipatory postural adjustment phase was longer in healthy older than younger adults. Anticipatory postural adjustment phase but not reaction time phase was slower for more complex gait initiation tasks. Cognitive task performance was similar across age groups and conditions.