Joint angle affects volitional and magnetically-evoked neuromuscular performance differentially

This study examined the volitional and magnetically-evoked neuromuscular performance of the quadriceps femoris at functional knee joint angles adjacent to full extension. Indices of volitional and magnetically-evoked neuromuscular performance (N= 15 healthy males; 23.5 ± 2.9 years; 71.5 ± 5.4 kg; 176.5 ± 5.5 cm) were obtained at 25°; 35° and 45° of knee flexion. Results showed that volitional and magnetically-evoked peak force (PFV; PTFE, respectively) and electromechanical delay (EMDV; EMDE, respectively) were enhanced by increased knee flexion. However, greater relative improvements in volitional compared to evoked indices of neuromuscular performance were observed with increasing flexion from 25° to 45° (e.g. EMDV; EMDE: 36% vs. 11% improvement, respectively; F[2,14] = 6.8; p < 0.05). There were no significant correlations between EMDV and EMDE or PFV and PTFE, respectively at analogous joint positions. These findings suggest that the extent of the relative differential between volitional and evoked neuromuscular performance capabilities is joint angle-specific and not correlated with performance capabilities at adjacent angles, but tends to be smaller with increased flexion. As such, effective prediction of volitional from evoked performance capabilities at both analogous and adjacent knee joint positions would lack robustness

Repeated exercise stress impairs volitional but not magnetically evoked electromechanical delay of the knee flexors

The effects of serial episodes of fatigue and recovery on volitional and magnetically evoked neuromuscular performance of the knee flexors were assessed in twenty female soccer players during: (i) an intervention comprising 4x35s maximal static exercise; (ii) a control condition. Volitional peak force (PFV) was impaired progressively (-16 % vs. baseline: 235.3±54.7 to 198.1±38.5 N) by the fatiguing exercise and recovered to within -97 % of baseline values following six-minutes of rest. Evoked peak twitch force (PTFE) was diminished subsequent to the fourth episode of exercise (23.3 %: 21.4±13.8 vs. 16.4±14.6 N) and remained impaired at this level throughout the recovery. Impairment of volitional electromechanical delay performance (EMDV) following the first episode of exercise (25.5 % :55.3±11.9 vs. 69.5±24.5 ms) contrasted with concurrent improvement (10.0 %: 24.5±4.7 vs. 22.1±5.0 ms) in evoked electromechanical delay (EMDE) (p <0.05) and this increased disparity between EMDE and EMDV remained during subsequent periods of intervention and recovery. The fatiguing exercise provoked substantial impairments to volitional strength and EMDV that showed differential patterns of recovery. However, improved EMDE performance might identify a dormant capability for optimal muscle responses during acute stressful exercise and an improved capacity to maintain dynamic joint stabilty during critical episodes of loading

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model

Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D

Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows

High Resolution Crystal Structures of the Wild Type and Cys-55 right-arrow Ser and Cys-59 right-arrow Ser Variants of the Thioredoxin-like [2Fe-2S] Ferredoxin from Aquifex aeolicus

The [2Fe-2S] ferredoxin (Fd4) from Aquifex aeolicus adopts a thioredoxin-like polypeptide fold that is distinct from other [2Fe-2S] ferredoxins. Crystal structures of the Cys-55 right-arrow Ser (C55S) and Cys-59 right-arrow Ser (C59S) variants of this protein have been determined to 1.25 Å and 1.05 Å resolution, respectively, whereas the resolution of the wild type (WT) has been extended to 1.5 Å. The improved WT structure provides a detailed description of the [2Fe-2S] cluster, including two features that have not been noted previously in any [2Fe-2S] cluster-containing protein, namely, pronounced distortions in the cysteine coordination to the cluster and a Calpha -H-Sgamma hydrogen bond between cluster ligands Cys-55 and Cys-9. These features may contribute to the unusual electronic and magnetic properties of the [2Fe-2S] clusters in WT and variants of this ferredoxin. The structures of the two variants of Fd4, in which single cysteine ligands to the [2Fe-2S] cluster are replaced by serine, establish the metric details of serine-ligated Fe-S active sites with unprecedented accuracy. Both the cluster and its surrounding protein matrix change in subtle ways to accommodate this ligand substitution, particularly in terms of distortions of the Fe2S2 inorganic core from planarity and displacements of the polypeptide chain. These high resolution structures illustrate how the interactions between polypeptide chains and Fe-S active sites reflect combinations of flexibility and rigidity on the part of both partners; these themes are also evident in more complex systems, as exemplified by changes associated with serine ligation of the nitrogenase P cluster

The origin of heterogeneous nanoparticle uptake by cells

Understanding nanoparticle uptake by biological cells is fundamentally important to wide-ranging fields from nanotoxicology to drug delivery. It is now accepted that the arrival of nanoparticles at the cell is an extremely complicated process, shaped by many factors including unique nanoparticle physico-chemical characteristics, protein-particle interactions and subsequent agglomeration, diffusion and sedimentation. Sequentially, the nanoparticle internalisation process itself is also complex, and controlled by multiple aspects of a cell’s state. Despite this multitude of factors, here we demonstrate that the statistical distribution of the nanoparticle dose per endosome is independent of the initial administered dose and exposure duration. Rather, it is the number of nanoparticle containing endosomes that are dependent on these initial dosing conditions. These observations explain the heterogeneity of nanoparticle delivery at the cellular level and allow the derivation of simple, yet powerful probabilistic distributions that accurately predict the nanoparticle dose delivered to individual cells across a population.J.W.W. would like to acknowledge Girton College and the Herchel Smith Fund of Cambridge for providing him with a post-doctoral Fellowship. The authors are grateful to J.J. Powell and S. H. Doak for their critical insights. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) (grant number EP/H008683/1). P.R. and H.D.S. would also like to acknowledge the support of the Biotechnology and Biological Sciences Research Council (BBSRC) under grants BB/N005163/1 and BB/P026818/1

Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events

The purpose of this work was to develop an automated measurement technique for the assessment of both the form and intensity of physical activity undertaken by children during play. Further to this, our aim was to profile the varying activity across a cohort of children using a multivariate analysis of their movement patterns. Methods: Ankle-worn accelerometers were used to record 40-minutes of activity during a school recess, for 24 children over 5 consecutive days. Epochs of 1.1 second duration were identified within the acceleration time trace and compared to a reference motif, consisting of a single walking stride acceleration trace, obtained in a controlled setting of the motion lab. Dynamic time warping (DTW) of motif and activity events provided metrics of comparative movement duration and intensity, which formed the data set for multivariate mapping of the cohort activity using a principal component analysis (PCA). Results: The 2-D PCA plot provided clear differentiation of children displaying diverse activity profiles and clustering of those with similar movement patterns. The 1 component of the PCA correlated to the integrated intensity of movement over the 40 min. period whilst the 2 component informed on the temporal phasing of activity. Conclusion: By defining movement events and then quantifying them by reference to a motion-standard, meaningful assessment of highly varied activity within free play can be obtained. This allows detailed profiling of individual children's activity and provides an insight on social aspects of play through identification of matched activity time profiles for children participating in conjoined play