Shoes that restrict metatarsophalangeal dorsiflexion cause proximal joint compensations

To describe barefoot, shod and in-shoe kinematics during stance phase of walking gait in a normal arched adult population. An equal sample of males and females (n = 24) was recruited. In order to quantify the effect of footwear independent of technical design features, an ASICS shoe (Onitsuka Tiger-Mexico 66, Japan) was used in this study. Markers were applied to three conditions; barefoot, shod, and in-shoe. The calibration markers were used to define static pose. The order of testing was randomised. Participants completed five trials in each condition. Kinematic data were captured using a 12 camera VICON MX40 motion capture system at 100 Hz and processed in Visual3D. A previously developed model was used to describe joint angles [1]. A univariate two-way ANOVA was used to identify any differences between the pairs of conditions. Post-hoc Sheffé tests were used to further interrogate the data for differences. At peak hallux dorsiflexion (Figure 1), during propulsion, the metatarsophalangeal joint (MPTJ) was significantly more dorsiflexed in the barefoot condition compared to the shod condition (p = 0.004). At the same gait event, the tibiocalcaneal joint (TCJ) was significantly more plantarflexed than both the shod and in-shoe conditions (p < 0.001), and the tarsometatarsal joint (TMTJ) was significantly less dorsiflexed in the barefoot condition compared to the shod and in-shoe conditions (p < 0.001). The findings of the current study demonstrate that footwear has significant effects on sagittal plane MPTJ joint dorsiflexion at peak hallux dorsiflexion, which results in compensations at proximal foot joints

The development of a kinematic model to quantify in-shoe foot motion

A multi-segment foot model was used to develop an accurate and reliable kinematic model to describe in-shoe foot kinematics during gait

A systematic review of kinematic models used in foot & ankle biomechanics

Over the past decade our understanding of foot function has increased significantly. Our understanding of foot and ankle biomechanics appears to be directly correlated to advances in models used to assess and quantify kinematic parameters in gait. These advances in models in turn lead to greater detail in the data. However, we must consider that the level of complexity is determined by the question or task being analysed. This systematic review aims to provide a critical appraisal of commonly used marker sets and foot models to assess foot and ankle kinematics in a wide variety of clinical and research purposes

Livestock vocalisation classification in farm soundscapes

Livestock vocalisations have been shown to contain information related to animal welfare and behaviour. Automated sound detection has the potential to facilitate a continuous acoustic monitoring system, for use in a range Precision Livestock Farming (PLF) applications. There are few examples of automated livestock vocalisation classification algorithms, and we have found none capable of being easily adapted and applied to different species' vocalisations. In this work, a multi-purpose livestock vocalisation classification algorithm is presented, utilising audio-specific feature extraction techniques, and machine learning models. To test the multi-purpose nature of the algorithm, three separate data sets were created targeting livestock-related vocalisations, namely sheep, cattle, and Maremma sheepdogs. Audio data was extracted from continuous recordings conducted on-site at three different operational farming enterprises, reflecting the conditions of real deployment. A comparison of Mel-Frequency Cepstral Coefficients (MFCCs) and Discrete Wavelet Transform-based (DWT) features was conducted. Classification was determined using a Support Vector Machine (SVM) model. High accuracy was achieved for all data sets (sheep: 99.29%, cattle: 95.78%, dogs: 99.67%). Classification performance alone was insufficient to determine the most suitable feature extraction method for each data set. Computational timing results revealed the DWT-based features to be markedly faster to produce (14.81 - 15.38% decrease in execution time). The results indicate the development of a highly accurate livestock vocalisation classification algorithm, which forms the foundation for an automated livestock vocalisation detection system

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI

1,4-Dichloro­naphthalene-2,3-diol

The achiral planar (maximum deviation 0.014 Å) title compound, C10H6Cl2O2, crystallizes in the chiral space group P212121 in an arrangement incorporating conventional O—H⋯O hydrogen bonding leading to a supra­molecular chain

Serotonin 5-HT2A receptors underlie increased motor behaviors induced in dopamine-depleted rats by intrastriatal 5-HT2A/2C agonism

ABSTRACT Gene expression studies have suggested that dopamine (DA) depletion increases the sensitivity of striatal direct pathway neurons to the effects of serotonin (5-HT) via the 5-HT 2 receptor. The present study examined the possible influence(s) of 5-HT 2A or 5-HT 2C receptor-mediated signaling locally within the striatum on motor behavior triggered by 5-HT 2 receptor agonism in the neonatal DA-depleted rat. Male Sprague-Dawley rats were treated with 6-hydroxydopamine (6-OHDA; 60 g in 5 l per lateral ventricle) on postnatal day 3 to achieve near-total DA depletion bilaterally. Sixty days later, sham-operated (saline-injected) or 6-OHDA-treated rats were challenged with the 5-HT 2A/2C agonist DOI [(Ϯ)-1-(4-iodo-2,5-dimethoxyphenyl)-2-aminopropane] or saline either by systemic treatment or bilateral intrastriatal infusion. Motor behavior was quantified for 60 min after agonist injection using computerized activity monitors. Systemic DOI treatment (0.2 or 2.0 mg/kg i.p.) was more effective in inducing motor activity in the DA-depleted group compared with intact controls. Intrastriatal DOI infusion (1.0 or 10.0 g/side) also produced a significant rise in motor activity in the DA-depleted group during the 30-to 60-min period of behavioral analysis but did not influence behavior in intact animals. The effects of intrastriatal DOI infusion were blocked by intrastriatal coinfusion of the 5-HT 2 antagonist ketanserin (1. A loss of dopamine (DA) transmission to the rodent striatum during early postnatal development results in a compensatory increase in serotonin (5-HT) innervation to the dorsal striatum Several studies indicate that 5-HT 2A receptors are positioned to mediate the influences of enhanced 5-HT signaling in the DA-depleted striatum. First, 5-HT release agents and 5-HT 2 receptor agonists gain potency in inducing striatal preprotachykinin (PPT; encodes substance P and neurokini