The equivalence of multi-axis spine systems: Recommended stiffness limits using a standardized testing protocol

Author's accepted manuscriptFinal version available from Elsevier via the DOI in this recordThe complexity of multi-axis spine testing often makes it challenging to compare results from different studies. The aim of this work was to develop and implement a standardized testing protocol across three six-axis spine systems, compare them, and provide stiffness and phase angle limits against which other test systems can be compared. Standardized synthetic lumbar specimens (n = 5), comprising three springs embedded in polymer at each end, were tested on each system using pure moments in flexion–extension, lateral bending, and axial rotation. Tests were performed using sine and triangle waves with an amplitude of 8 Nm, a frequency of 0.1 Hz, and with axial preloads of 0 and 500 N. The stiffness, phase angle, and R2 value of the moment against rotation in the principal axis were calculated at the center of each specimen. The tracking error was adopted as a measure of each test system to minimize non-principal loads, defined as the root mean squared difference between actual and target loads. All three test systems demonstrated similar stiffnesses, with small (<14%) but significant differences in 4 of 12 tests. More variability was observed in the phase angle between the principal axis moment and rotation, with significant differences in 10 of 12 tests. Stiffness and phase angle limits were calculated based on the 95% confidence intervals from all three systems. These recommendations can be used with the standard specimen and testing protocol by other research institutions to ensure equivalence of different spine systems, increasing the ability to compare in vitro spine studies.This research was completed with the support of the Catherine Sharpe Foundation, the Enid Linder Foundation, and the University of Bath Alumni Fun

Comparison of the force exerted by hippocampal and DRG growth cones

Mechanical properties such as force generation are fundamental for neuronal motility, development and regeneration. We used optical tweezers to compare the force exerted by growth cones (GCs) of neurons from the Peripheral Nervous System (PNS), such as Dorsal Root Ganglia (DRG) neurons, and from the Central Nervous System (CNS) such as hippocampal neurons. Developing GCs from dissociated DRG and hippocampal neurons were obtained from P1-P2 and P10-P12 rats. Comparing their morphology, we observed that the area of GCs of hippocampal neurons was 8-10 \ub5m(2) and did not vary between P1-P2 and P10-P12 rats, but GCs of DRG neurons were larger and their area increased from P1-P2 to P10-P12 by 2-4 times. The force exerted by DRG filopodia was in the order of 1-2 pN and never exceeded 5 pN, while hippocampal filopodia exerted a larger force, often in the order of 5 pN. Hippocampal and DRG lamellipodia exerted lateral forces up to 20 pN, but lamellipodia of DRG neurons could exert a vertical force larger than that of hippocampal neurons. Force-velocity relationships (Fv) in both types of neurons had the same qualitative behaviour, consistent with a common autocatalytic model of force generation. These results indicate that molecular mechanisms of force generation of GC from CNS and PNS neurons are similar but the amplitude of generated force is influenced by their cytoskeletal properties

Association Between Ventilatory Settings and Development of Acute Respiratory Distress Syndrome in Mechanically Ventilated Patients Due to Brain Injury

PURPOSE: In neurologically critically ill patients with mechanical ventilation (MV), the development of acute respiratory distress syndrome (ARDS) is a major contributor to morbidity and mortality, but the role of ventilatory management has been scarcely evaluated. We evaluate the association of tidal volume, level of PEEP and driving pressure with the development of ARDS in a population of patients with brain injury. MATERIALS AND METHODS: We performed a secondary analysis of a prospective, observational study on mechanical ventilation. RESULTS: We included 986 patients mechanically ventilated due to an acute brain injury (hemorrhagic stroke, ischemic stroke or brain trauma). Incidence of ARDS in this cohort was 3%. Multivariate analysis suggested that driving pressure could be associated with the development of ARDS (odds ratio for unit increment of driving pressure 1.12; confidence interval for 95%: 1.01 to 1.23) whereas we did not observe association for tidal volume (in ml per kg of predicted body weight) or level of PEEP. ARDS was associated with an increase in mortality, longer duration of mechanical ventilation, and longer ICU length of stay. CONCLUSIONS: In a cohort of brain-injured patients the development of ARDS was not common. Driving pressure was associated with the development of this disease.info:eu-repo/semantics/publishedVersio

Systematic review and meta-analysis of the diagnostic accuracy of ultrasonography for deep vein thrombosis

Background Ultrasound (US) has largely replaced contrast venography as the definitive diagnostic test for deep vein thrombosis (DVT). We aimed to derive a definitive estimate of the diagnostic accuracy of US for clinically suspected DVT and identify study-level factors that might predict accuracy. Methods We undertook a systematic review, meta-analysis and meta-regression of diagnostic cohort studies that compared US to contrast venography in patients with suspected DVT. We searched Medline, EMBASE, CINAHL, Web of Science, Cochrane Database of Systematic Reviews, Cochrane Controlled Trials Register, Database of Reviews of Effectiveness, the ACP Journal Club, and citation lists (1966 to April 2004). Random effects meta-analysis was used to derive pooled estimates of sensitivity and specificity. Random effects meta-regression was used to identify study-level covariates that predicted diagnostic performance. Results We identified 100 cohorts comparing US to venography in patients with suspected DVT. Overall sensitivity for proximal DVT (95% confidence interval) was 94.2% (93.2 to 95.0), for distal DVT was 63.5% (59.8 to 67.0), and specificity was 93.8% (93.1 to 94.4). Duplex US had pooled sensitivity of 96.5% (95.1 to 97.6) for proximal DVT, 71.2% (64.6 to 77.2) for distal DVT and specificity of 94.0% (92.8 to 95.1). Triplex US had pooled sensitivity of 96.4% (94.4 to 97.1%) for proximal DVT, 75.2% (67.7 to 81.6) for distal DVT and specificity of 94.3% (92.5 to 95.8). Compression US alone had pooled sensitivity of 93.8 % (92.0 to 95.3%) for proximal DVT, 56.8% (49.0 to 66.4) for distal DVT and specificity of 97.8% (97.0 to 98.4). Sensitivity was higher in more recently published studies and in cohorts with higher prevalence of DVT and more proximal DVT, and was lower in cohorts that reported interpretation by a radiologist. Specificity was higher in cohorts that excluded patients with previous DVT. No studies were identified that compared repeat US to venography in all patients. Repeat US appears to have a positive yield of 1.3%, with 89% of these being confirmed by venography. Conclusion Combined colour-doppler US techniques have optimal sensitivity, while compression US has optimal specificity for DVT. However, all estimates are subject to substantial unexplained heterogeneity. The role of repeat scanning is very uncertain and based upon limited data

An Efficient Coding Hypothesis Links Sparsity and Selectivity of Neural Responses

To what extent are sensory responses in the brain compatible with first-order principles? The efficient coding hypothesis projects that neurons use as few spikes as possible to faithfully represent natural stimuli. However, many sparsely firing neurons in higher brain areas seem to violate this hypothesis in that they respond more to familiar stimuli than to nonfamiliar stimuli. We reconcile this discrepancy by showing that efficient sensory responses give rise to stimulus selectivity that depends on the stimulus-independent firing threshold and the balance between excitatory and inhibitory inputs. We construct a cost function that enforces minimal firing rates in model neurons by linearly punishing suprathreshold synaptic currents. By contrast, subthreshold currents are punished quadratically, which allows us to optimally reconstruct sensory inputs from elicited responses. We train synaptic currents on many renditions of a particular bird's own song (BOS) and few renditions of conspecific birds' songs (CONs). During training, model neurons develop a response selectivity with complex dependence on the firing threshold. At low thresholds, they fire densely and prefer CON and the reverse BOS (REV) over BOS. However, at high thresholds or when hyperpolarized, they fire sparsely and prefer BOS over REV and over CON. Based on this selectivity reversal, our model suggests that preference for a highly familiar stimulus corresponds to a high-threshold or strong-inhibition regime of an efficient coding strategy. Our findings apply to songbird mirror neurons, and in general, they suggest that the brain may be endowed with simple mechanisms to rapidly change selectivity of neural responses to focus sensory processing on either familiar or nonfamiliar stimuli. In summary, we find support for the efficient coding hypothesis and provide new insights into the interplay between the sparsity and selectivity of neural responses

Jerarca: Efficient Analysis of Complex Networks Using Hierarchical Clustering

Background: How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use. Methodology/Principal Findings: We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized. Conclusions/Significance: The four main advantages of Jerarca in respect to UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluatio

The Born supremacy: quantum advantage and training of an Ising Born machine

The search for an application of near-term quantum devices is widespread. Quantum Machine Learning is touted as a potential utilisation of such devices, particularly those which are out of the reach of the simulation capabilities of classical computers. In this work, we propose a generative Quantum Machine Learning Model, called the Ising Born Machine (IBM), which we show cannot, in the worst case, and up to suitable notions of error, be simulated efficiently by a classical device. We also show this holds for all the circuit families encountered during training. In particular, we explore quantum circuit learning using non-universal circuits derived from Ising Model Hamiltonians, which are implementable on near term quantum devices. We propose two novel training methods for the IBM by utilising the Stein Discrepancy and the Sinkhorn Divergence cost functions. We show numerically, both using a simulator within Rigetti's Forest platform and on the Aspen-1 16Q chip, that the cost functions we suggest outperform the more commonly used Maximum Mean Discrepancy (MMD) for differentiable training. We also propose an improvement to the MMD by proposing a novel utilisation of quantum kernels which we demonstrate provides improvements over its classical counterpart. We discuss the potential of these methods to learn `hard' quantum distributions, a feat which would demonstrate the advantage of quantum over classical computers, and provide the first formal definitions for what we call `Quantum Learning Supremacy'. Finally, we propose a novel view on the area of quantum circuit compilation by using the IBM to `mimic' target quantum circuits using classical output data only.Comment: v3 : Close to journal published version - significant text structure change, split into main text & appendices. See v2 for unsplit version; v2 : Typos corrected, figures altered slightly; v1 : 68 pages, 39 Figures. Comments welcome. Implementation at https://github.com/BrianCoyle/IsingBornMachin