Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training

Introduction: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of R-2: 0.38 with an error (sigma: 12.8). Next, we evaluate its reliability (r = 0.89 for test-retest), longitudinal external validity (95% true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements (R-2: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory (R-2: 0.40) and Barthel Index (R-2: 0.35). Conclusions: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers

Statistical mechanics of clonal expansion in lymphocyte networks modelled with slow and fast variables

We study the Langevin dynamics of the adaptive immune system, modelled by a lymphocyte network in which the B cells are interacting with the T cells and antigen. We assume that B clones and T clones are evolving in different thermal noise environments and on different timescales. We derive stationary distributions and use statistical mechanics to study clonal expansion of B clones in this model when the B and T clone sizes are assumed to be the slow and fast variables respectively and vice versa. We derive distributions of B clone sizes and use general properties of ferromagnetic systems to predict characteristics of these distributions, such as the average B cell concentration, in some regimes where T cells can be modelled as binary variables. This analysis is independent of network topologies and its results are qualitatively consistent with experimental observations. In order to obtain full distributions we assume that the network topologies are random and locally equivalent to trees. The latter allows us to employ the Bethe-Peierls approach and to develop a theoretical framework which can be used to predict the distributions of B clone sizes. As an example we use this theory to compute distributions for the models of immune system defined on random regular networks.Comment: A more recent version (accepted for publication in Journal of Physics A: Mathematical and Theoretical) with improved figures, references, et

Protein Networks Reveal Detection Bias and Species Consistency When Analysed by Information-Theoretic Methods

We apply our recently developed information-theoretic measures for the characterisation and comparison of protein–protein interaction networks. These measures are used to quantify topological network features via macroscopic statistical properties. Network differences are assessed based on these macroscopic properties as opposed to microscopic overlap, homology information or motif occurrences. We present the results of a large–scale analysis of protein–protein interaction networks. Precise null models are used in our analyses, allowing for reliable interpretation of the results. By quantifying the methodological biases of the experimental data, we can define an information threshold above which networks may be deemed to comprise consistent macroscopic topological properties, despite their small microscopic overlaps. Based on this rationale, data from yeast–two–hybrid methods are sufficiently consistent to allow for intra–species comparisons (between different experiments) and inter–species comparisons, while data from affinity–purification mass–spectrometry methods show large differences even within intra–species comparisons

A randomised controlled trial of intravenous zoledronic acid in malignant pleural disease: A proof of principle pilot study

© 2015 Clive et al. Introduction: Animal studies have shown Zoledronic Acid (ZA) may diminish pleural fluid accumulation and tumour bulk in malignant pleural disease (MPD). We performed a pilot study to evaluate its effects in humans. Methods: We undertook a single centre, double-blind, placebo-controlled trial in adults with MPD. Patients were randomised (1:1) to receive 2 doses of intravenous ZA or placebo, 3 weeks apart and were followed-up for 6 weeks. The co-primary outcomes were change in Visual Analogue Scale (VAS) score measured breathlessness during trial follow-up and change in the initial area under the curve (iAUC) on thoracic Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) from randomisation to week 5. Multiple secondary endpoints were also evaluated. Results: Between January 2010 and May 2013, 30 patients were enrolled, 24 randomised and 4 withdrew after randomisation (1 withdrew consent; 3 had a clinical decline). At baseline, the ZA group were more breathless, had more advanced disease on radiology and worse quality of life than the placebo group. There was no significant difference between the groups with regards change in breathlessness (Adjusted mean difference (AMD) 4.16 (95%CI -4.7 to 13.0)) or change in DCE-MRI iAUC (AMD -15.4 (95%CI -58.1 to 27.3). Two of nine (22%) in the ZA arm had a >10% improvement by modified RECIST (vs 0/11 who received placebo). There was no significant difference in quality of life measured by the QLQ-C30 score (global QOL: AMD -4.1 (-13.0 to 4.9)), side effects or serious adverse event rates. Conclusions: This is the first human study to evaluate ZA in MPD. The study is limited by small numbers and imbalanced baseline characteristics. Although no convincing treatment effect was identified, potential benefits for specific subgroups of patients cannot be excluded. This study provides important information regarding the feasibility of future trials to evaluate the effects of ZA further. Trial Registration: UK Clinical Research Network ID 8877 ISRCTN17030426 www.isrctn.com

Study of pallial neurogenesis in shark embryos and the evolutionary origin of the subventricular zone

The dorsal part of the developing telencephalon is one of the brain areas that has suffered most drastic changes throughout vertebrate evolution. Its evolutionary increase in complexity was thought to be partly achieved by the appearance of a new neurogenic niche in the embryonic subventricular zone (SVZ). Here, a new kind of amplifying progenitors (basal progenitors) expressing Tbr2, undergo a second round of divisions, which is believed to have contributed to the expansion of the neocortex. Accordingly, the existence of a pallial SVZ has been classically considered exclusive of mammals. However, the lack of studies in ancient vertebrates precludes any clear conclusion about the evolutionary origin of the SVZ and the neurogenic mechanisms that rule pallial development. In this work, we explore pallial neurogenesis in a basal vertebrate, the shark Scyliorhinus canicula, through the study of the expression patterns of several neurogenic markers. We found that apical progenitors and radial migration are present in sharks, and therefore, their presence must be highly conserved throughout evolution. Surprisingly, we detected a subventricular band of ScTbr2-expressing cells, some of which also expressed mitotic markers, indicating that the existence of basal progenitors should be considered an ancestral condition rather than a novelty of mammals or amniotes. Finally, we report that the transcriptional program for the specification of glutamatergic pallial cells (Pax6, Tbr2, NeuroD, Tbr1) is also present in sharks. However, the segregation of these markers into different cell types is not clear yet, which may be linked to the lack of layering in anamniotesThis work was supported by the Spanish Ministerio de Economía y Competitividad-FEDER (BFU2014-5863-1P)S

The potential of optical proteomic technologies to individualize prognosis and guide rational treatment for cancer patients

Genomics and proteomics will improve outcome prediction in cancer and have great potential to help in the discovery of unknown mechanisms of metastasis, ripe for therapeutic exploitation. Current methods of prognosis estimation rely on clinical data, anatomical staging and histopathological features. It is hoped that translational genomic and proteomic research will discriminate more accurately than is possible at present between patients with a good prognosis and those who carry a high risk of recurrence. Rational treatments, targeted to the specific molecular pathways of an individual’s high-risk tumor, are at the core of tailored therapy. The aim of targeted oncology is to select the right patient for the right drug at precisely the right point in their cancer journey. Optical proteomics uses advanced optical imaging technologies to quantify the activity states of and associations between signaling proteins by measuring energy transfer between fluorophores attached to specific proteins. Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM) assays are suitable for use in cell line models of cancer, fresh human tissues and formalin-fixed paraffin-embedded tissue (FFPE). In animal models, dynamic deep tissue FLIM/FRET imaging of cancer cells in vivo is now also feasible. Analysis of protein expression and post-translational modifications such as phosphorylation and ubiquitination can be performed in cell lines and are remarkably efficiently in cancer tissue samples using tissue microarrays (TMAs). FRET assays can be performed to quantify protein-protein interactions within FFPE tissue, far beyond the spatial resolution conventionally associated with light or confocal laser microscopy. Multivariate optical parameters can be correlated with disease relapse for individual patients. FRET-FLIM assays allow rapid screening of target modifiers using high content drug screens. Specific protein-protein interactions conferring a poor prognosis identified by high content tissue screening will be perturbed with targeted therapeutics. Future targeted drugs will be identified using high content/throughput drug screens that are based on multivariate proteomic assays. Response to therapy at a molecular level can be monitored using these assays while the patient receives treatment: utilizing re-biopsy tumor tissue samples in the neoadjuvant setting or by examining surrogate tissues. These technologies will prove to be both prognostic of risk for individuals when applied to tumor tissue at first diagnosis and predictive of response to specifically selected targeted anticancer drugs. Advanced optical assays have great potential to be translated into real-life benefit for cancer patients

Dynamics of neural networks and disordered spin systems

I obtain a number of results for the dynamics of several disordered spin systems, of successively greater complexity. I commence with the generalised Hopfield model trained with an intensive number of patterns, where in the thermodynamic limit macroscopic, deterministic equations of motion can be derived exactly for both the synchronous discrete time and asynchronous continuous time dynamics. I show that for symmetric embedding matrices Lyapunov functions exist at the macroscopic level of description in terms of pattern overlaps. I then show that for asymmetric embedding matrices several types of bifurcation phenomena to complex non-transient dynamics occur, even in this simplest model. Extending a recent result of Coolen and Sherrington, I show how the dynamics of the generalised Hopfield model trained with extensively many patterns and non-trivial embedding matrix can be described by the evolution of a small number of overlaps and the disordered contribution to the 'energy', upon calculation of a noise distribution by the replica method. The evaluation of the noise distribution requires two key assumptions: that the flow equations are self averaging, and that equipartitioning of probability occurs within the macroscopic sub-shells of the ensemble. This method is inexact on intermediate time scales, due to the microscopic information integrated out in order to derive a closed set of equations. I then show how this theory can be improved in a systematic manner by introducing an order parameter function - the joint distribution of spins and local alignment fields, which evolves in time deterministically, according to a driven diffusion type equation. I show how the coefficients in this equation can be evaluated for the generalised Sherrington-Kirkpatrick model, both within the replica symmetric ansatz, and using Parisi's ultrametric ansatz for the replica matrices, upon making once again the two key assumptions (self averaging and equipartitioning). Since the order parameter is now a continuous function, however, the assumption of equipartitioning within the macroscopic sub-shells is much less restricting.</p

Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM

The Gaussian Process Latent Variable Model (GP-LVM) is a non-linear probabilistic method of embedding a high dimensional dataset in terms low dimensional ‘latent ’ variables. In this paper we illustrate that maximum a posteriori (MAP) estimation of the latent variables and hyperparameters can be used for model selection and hence we can determine the optimal number or latent variables and the most appropriate model. This is an alternative to the variational approaches developed recently and may be useful when we want to use a non-Gaussian prior or kernel functions that don’t have automatic relevance determination (ARD) parameters. Using a second order expansion of the latent variable posterior we can marginalise the latent variables and obtain an estimate for the hyperparameter posterior. Secondly, we use the GP-LVM to integrate multiple data sources by simultaneously embedding them in terms of common latent variables. We present results from synthetic data to illustrate the successful detection and retrieval of low dimensional structure from high dimensional data. We demonstrate that the integration of multiple data sources leads to more robust performance. Finally, we show that when the data are used for binary classification tasks we can attain a significant gain in prediction accuracy when the low dimensional representation is used