Predicting knee osteoarthritis risk in injured populations

Background Individuals who suffered a lower limb injury have an increased risk of developing knee osteoarthritis. Early diagnosis of osteoarthritis and the ability to track its progression is challenging. This study aimed to explore links between self-reported knee osteoarthritis outcome scores and biomechanical gait parameters, whether self-reported outcome scores could predict gait abnormalities characteristic of knee osteoarthritis in injured populations and, whether scores and biomechanical outcomes were related to osteoarthritis severity via Spearman's correlation coefficient. Methods A cross-sectional study was conducted with asymptomatic participants, participants with lower-limb injury and those with medial knee osteoarthritis. Spearman rank determined relationships between knee injury and outcome scores and hip and knee kinetic/kinematic gait parameters. K-Nearest Neighbour algorithm was used to determine which of the evaluated parameters created the strongest classifier model. Findings Differences in outcome scores were evident between groups, with knee quality of life correlated to first and second peak external knee adduction moment (0.47, 0.55). Combining hip and knee kinetics with quality of life outcome produced the strongest classifier (1.00) with the least prediction error (0.02), enabling classification of injured subjects gait as characteristic of either asymptomatic or knee osteoarthritis subjects. When correlating outcome scores and biomechanical outcomes with osteoarthritis severity only maximum external hip and knee abduction moment (0.62, 0.62) in addition to first peak hip adduction moment (0.47) displayed significant correlations. Interpretation The use of predictive models could enable clinicians to identify individuals at risk of knee osteoarthritis and be a cost-effective method for osteoarthritis screening

MODELLING SCAPULAR BIOMECHANICS TO ENHANCE INTERPRETATION OF KINEMATICS AND PERFORMANCE DATA IN ROWING

Rowing involves repetitive, high intensity loading on the glenohumeral joint. Shoulder pain is associated with muscle weakness and imbalance, resulting in long-lasting overuse injuries. The goal of this study was to explore three-dimensional shoulder biomechanics during rowing to identify parameters that influence technique. Eleven athletes had their movement recorded by motion capture while using an instrumented ergometer. Kinetics and kinematics drove a computational model which output joint and muscle forces across the shoulder. Results suggest that subtle muscular changes identified by the model can be sensitively mapped to performance variables. When evaluated alongside ergometer-derived power metrics, biomechanics parameters can provide athletes and coaches a fuller picture of performance potential, injury risk, and training program efficacy

Coral microatoll reconstructions of El Niño-southern oscillation: new windows on seasonal and interannual processes

Porites corals are the most commonly used genus for reconstructing El Niño- Southern Oscillation (ENSO). This hermatypic coral is found in all tropical reef environments(Veron 2000) with a variety of growth forms. Climate reconstructions of a century or more have been obtained from the most common, dome-shaped Porites growth form, whereby the colonies, beginning from the substrate, grow outward and upward towards the ocean surface(Knutson et al. 1972). Domed structures, however, are not the only Porites growth form. © 2013, Authors

FATIGUE LEADS TO ALTERED SPINAL KINEMATICS DURING HIGH PERFORMANCE ERGOMETER ROWING

Low back injuries in rowing are attributed to intense, repetitive, loading through the spine. Good technique and postural control are essential to maximize performance and minimize injury risk. This motion capture study recorded 3D spinal kinematics of 14 athletes during rowing at varying speeds on an instrumented ergometer and correlated motion with power metrics and athlete demographics. Sagittal plane rotation decreases in the lumbar spine and increases in the thoracic spine as speed increases. Transverse and frontal planes have little influence on force output. Declining postural control can be seen within each trial and worsened with higher rate. Assessments of form differences across athletes using relative motion between spine segments at critical stroke points show greater lumbar flexion (compared to thoracic) at the catch and neutral alignment at max handle force

Assessing the potential for dimethylsulfide enrichment at the sea surface and its influence on air–sea flux

The flux of dimethylsulfide (DMS) to the atmosphere is generally inferred using water sampled at or below 2 m depth, thereby excluding any concentration anomalies at the air–sea interface. Two independent techniques were used to assess the potential for near-surface DMS enrichment to influence DMS emissions and also identify the factors influencing enrichment. DMS measurements in productive frontal waters over the Chatham Rise, east of New Zealand, did not identify any significant gradients between 0.01 and 6 m in sub-surface seawater, whereas DMS enrichment in the sea-surface microlayer was variable, with a mean enrichment factor (EF; the concentration ratio between DMS in the sea-surface microlayer and in sub-surface water) of 1.7. Physical and biological factors influenced sea-surface microlayer DMS concentration, with high enrichment (EF > 1.3) only recorded in a dinoflagellate-dominated bloom, and associated with low to medium wind speeds and near-surface temperature gradients. On occasion, high DMS enrichment preceded periods when the air–sea DMS flux, measured by eddy covariance, exceeded the flux calculated using National Oceanic and Atmospheric Administration (NOAA) Coupled-Ocean Atmospheric Response Experiment (COARE) parameterized gas transfer velocities and measured sub-surface seawater DMS concentrations. The results of these two independent approaches suggest that air–sea emissions may be influenced by near-surface DMS production under certain conditions, and highlight the need for further study to constrain the magnitude and mechanisms of DMS production in the sea-surface microlayer

Economic, social and demographic impacts of drought on treatment adherence among people living with HIV in rural South Africa: A qualitative analysis

The 2015 El Niño-triggered drought in Southern Africa caused widespread economic and livelihood disruption in South Africa, imposing multiple physical and health challenges for rural populations including people living with HIV (PLHIV). We examined the economic, social and demographic impacts of drought drawing on 27 in-depth interviews in two cohorts of PLHIV in Hlabisa, uMkhanyakude district, KwaZulu-Natal. Thematic analysis revealed how drought-enforced soil water depletion, dried-up rivers, and dams culminated in a continuum of events such as loss of livestock, reduced agricultural production, and insufficient access to water and food which was understood to indirectly have a negative impact on HIV treatment adherence. This was mediated through disruptions in incomes, livelihoods and food systems, increased risk to general health, forced mobility and exacerbation of contextual vulnerabilities linked to poverty and unemployment. The systems approach, drawn from interview themes, hypothesises the complex pathways of plausible networks of impacts from drought through varying socioeconomic factors, exacerbating longstanding contextual precarity, and ultimately challenging HIV care utilisation. Understanding the multidimensional relationships between climate change, especially drought, and poor HIV care outcomes through the prism of contextual vulnerabilities is vital for shaping policy interventions

Observational Evidence of For-Profit Delivery and Inferior Nursing Home Care: When Is There Enough Evidence for Policy Change?

This research was financially supported by the Social Sciences and Humanities Research Council

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p

By hook or by crook? Morphometry, competition and cooperation in rodent sperm

Background Sperm design varies enormously across species and sperm competition is thought to be a major factor influencing this variation. However, the functional significance of many sperm traits is still poorly understood. The sperm of most murid rodents are characterised by an apical hook of the sperm head that varies markedly in extent across species. In the European woodmouse Apodemus sylvaticus (Muridae), the highly reflected apical hook of sperm is used to form sperm groups, or “trains,” which exhibited increased swimming velocity and thrusting force compared to individual sperm. Methodology/Principal Findings Here we use a comparative study of murine rodent sperm and demonstrate that the apical hook and sperm cooperation are likely to be general adaptations to sperm competition in rodents. We found that species with relatively larger testes, and therefore more intense sperm competition, have a longer, more reflected apical sperm hook. In addition, we show that sperm groups also occur in rodents other than the European woodmouse. Conclusions Our results suggest that in rodents sperm cooperation is more widespread than assumed so far and highlight the importance of diploid versus haploid selection in the evolution of sperm design and function

High Resolution MEMS Accelerometers to Estimate VO2 and Compare Running Mechanics between Highly Trained Inter-Collegiate and Untrained Runners

BACKGROUND: The purposes of this study were to determine the validity and reliability of high resolution accelerometers (HRA) relative to VO(2) and speed, and compare putative differences in HRA signal between trained (T) and untrained (UT) runners during treadmill locomotion. METHODOLOGY: Runners performed 2 incremental VO(2max) trials while wearing HRA. RMS of high frequency signal from three axes (VT, ML, AP) and the Euclidean resultant (RES) were compared to VO(2) to determine validity and reliability. Additionally, axial rms relative to speed, and ratio of axial accelerations to RES were compared between T and UT to determine if differences in running mechanics could be identified between the two groups. PRINCIPAL FINDINGS: Regression of RES was strongly related to VO(2), but T was different than UT (r = 0.96 vs 0.92; p<.001) for walking and running. During walking, only the ratio of ML and AP to RES were different between groups. For running, nearly all acceleration parameters were lower for T than UT, the exception being ratio of VT to RES, which was higher in T than UT. All of these differences during running were despite higher VO(2), O(2) cost, and lower RER in T vs UT, which resulted in no significant difference in energy expenditure between groups. CONCLUSIONS/SIGNFICANCE: These results indicate that HRA can accurately and reliably estimate VO(2) during treadmill locomotion, but differences exist between T and UT that should be considered when estimating energy expenditure. Differences in running mechanics between T and UT were identified, yet the importance of these differences remains to be determined