Shock-Augmented Ignition Approach to Laser Inertial Fusion

Shock ignition enables high gain at low implosion velocity, reducing ablative Rayleigh-Taylor instability growth, which can degrade conventional direct drive. With this method, driving a strong shock requires high laser power and intensity, resulting in inefficiencies in the drive and the generation of hot electrons that can preheat the fuel. A new "shock-augmented ignition"pulse shape is described that, by preconditioning the ablation plasma before launching a strong shock, enables the shock ignition of thermonuclear fuel, but importantly, with substantially reduced laser power and intensity requirements. The reduced intensity requirement with respect to shock ignition limits laser-plasma instabilities, such as stimulated Raman and Brillouin scatter, reducing the risk of hot-electron preheat and restoring the laser coupling advantages of conventional direct drive. Simulations indicate that, due to the reduced power requirements, high gain (∼100) ignition of large-scale direct drive implosions (outer radius ∼1750 μm, deuterium-tritium ice thickness ∼165 μm) may be possible within the power and energy limits of existing facilities such as the National Ignition Facility. Moreover, this concept extends to indirect drive implosions, which exhibit substantial yield increases at reduced implosion velocity. Shock-augmented ignition expands the viable design space of laser inertial fusion

Proton deflectometry of a capacitor coil target along two axes

A developing application of laser-driven currents is the generation of magnetic fields of picosecond-nanosecond duration with magnitudes exceeding. Single-loop and helical coil targets can direct laser-driven discharge currents along wires to generate spatially uniform, quasi-static magnetic fields on the millimetre scale. Here, we present proton deflectometry across two axes of a single-loop coil ranging from 1 to 2 mm in diameter. Comparison with proton tracking simulations shows that measured magnetic fields are the result of kiloampere currents in the coil and electric charges distributed around the coil target. Using this dual-axis platform for proton deflectometry, robust measurements can be made of the evolution of magnetic fields in a capacitor coil target

Wakefields in a cluster plasma

We report the first comprehensive study of large amplitude Langmuir waves in a plasma of nanometer-scale clusters. Using an oblique angle single-shot frequency domain holography diagnostic, the shape of these wakefields is captured for the first time. The wavefronts are observed to curve backwards, in contrast to the forwards curvature of wakefields in uniform plasma. Due to the expansion of the clusters, the first wakefield period is longer than those trailing it. The features of the data are well described by fully relativistic two-dimensional particle-in-cell simulations and by a quasianalytic solution for a one-dimensional, nonlinear wakefield in a cluster plasma

Shock Ignition Laser-Plasma Interactions in Ignition-Scale Plasmas

We use a subignition scale laser, the 30 kJ Omega, and a novel shallow-cone target to study laser-plasma interactions at the ablation-plasma density scale lengths and laser intensities anticipated for direct drive shock-ignition implosions at National Ignition Facility scale. Our results show that, under these conditions, the dominant instability is convective stimulated Raman scatter with experimental evidence of two plasmon decay (TPD) only when the density scale length is reduced. Particle-in-cell simulations indicate this is due to TPD being shifted to lower densities, removing the experimental back-scatter signature and reducing the hot-electron temperature. The experimental laser energy-coupling to hot electrons was found to be 1%-2.5%, with electron temperatures between 35 and 45 keV. Radiation-hydrodynamics simulations employing these hot-electron characteristics indicate that they should not preheat the fuel in MJ-scale shock ignition experiments

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

International audienceWe present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

Predictors of Poststroke Aphasia Recovery A Systematic Review-Informed Individual Participant Data Meta-Analysis

Background and Purpose: The factors associated with recovery of language domains after stroke remain uncertain. We described recovery of overall-language-ability, auditory comprehension, naming, and functional-communication across participants’ age, sex, and aphasia chronicity in a large, multilingual, international aphasia dataset. / Methods: Individual participant data meta-analysis of systematically sourced aphasia datasets described overall-language ability using the Western Aphasia Battery Aphasia-Quotient; auditory comprehension by Aachen Aphasia Test (AAT) Token Test; naming by Boston Naming Test and functional-communication by AAT Spontaneous-Speech Communication subscale. Multivariable analyses regressed absolute score-changes from baseline across language domains onto covariates identified a priori in randomized controlled trials and all study types. Change-from-baseline scores were presented as estimates of means and 95% CIs. Heterogeneity was described using relative variance. Risk of bias was considered at dataset and meta-analysis level. / Results: Assessments at baseline (median=43.6 weeks poststroke; interquartile range [4–165.1]) and first-follow-up (median=10 weeks from baseline; interquartile range [3–26]) were available for n=943 on overall-language ability, n=1056 on auditory comprehension, n=791 on naming and n=974 on functional-communication. Younger age (<55 years, +15.4 Western Aphasia Battery Aphasia-Quotient points [CI, 10.0–20.9], +6.1 correct on AAT Token Test [CI, 3.2–8.9]; +9.3 Boston Naming Test points [CI, 4.7–13.9]; +0.8 AAT Spontaneous-Speech Communication subscale points [CI, 0.5–1.0]) and enrollment <1 month post-onset (+19.1 Western Aphasia Battery Aphasia-Quotient points [CI, 13.9–24.4]; +5.3 correct on AAT Token Test [CI, 1.7–8.8]; +11.1 Boston Naming Test points [CI, 5.7–16.5]; and +1.1 AAT Spontaneous-Speech Communication subscale point [CI, 0.7–1.4]) conferred the greatest absolute change-from-baseline across each language domain. Improvements in language scores from baseline diminished with increasing age and aphasia chronicity. Data exhibited no significant statistical heterogeneity. Risk-of-bias was low to moderate-low. / Conclusions: Earlier intervention for poststroke aphasia was crucial to maximize language recovery across a range of language domains, although recovery continued to be observed to a lesser extent beyond 6 months poststroke

Utilising a systematic review-based approach to create a database of individual participant data for meta- and network meta-analyses: the RELEASE database of aphasia after stroke

Background: Collation of aphasia research data across settings, countries and study designs using big data principles will support analyses across different language modalities, levels of impairment, and therapy interventions in this heterogeneous population. Big data approaches in aphasia research may support vital analyses, which are unachievable within individual trial datasets. However, we lack insight into the requirements for a systematically created database, the feasibility and challenges and potential utility of the type of data collated. Aim: To report the development, preparation and establishment of an internationally agreed aphasia after stroke research database of individual participant data (IPD) to facilitate planned aphasia research analyses. Methods: Data were collated by systematically identifying existing, eligible studies in any language (≥10 IPD, data on time since stroke, and language performance) and included sourcing from relevant aphasia research networks. We invited electronic contributions and also extracted IPD from the public domain. Data were assessed for completeness, validity of value-ranges within variables, and described according to pre-defined categories of demographic data, therapy descriptions, and language domain measurements. We cleaned, clarified, imputed and standardised relevant data in collaboration with the original study investigators. We presented participant, language, stroke, and therapy data characteristics of the final database using summary statistics. Results: From 5256 screened records, 698 datasets were potentially eligible for inclusion; 174 datasets (5928 IPD) from 28 countries were included, 47/174 RCT datasets (1778 IPD) and 91/174 (2834 IPD) included a speech and language therapy (SLT) intervention. Participants’ median age was 63 years (interquartile range [53, 72]), 3407 (61.4%) were male and median recruitment time was 321 days (IQR 30, 1156) after stroke. IPD were available for aphasia severity or ability overall (n = 2699; 80 datasets), naming (n = 2886; 75 datasets), auditory comprehension (n = 2750; 71 datasets), functional communication (n = 1591; 29 datasets), reading (n = 770; 12 datasets) and writing (n = 724; 13 datasets). Information on SLT interventions were described by theoretical approach, therapy target, mode of delivery, setting and provider. Therapy regimen was described according to intensity (1882 IPD; 60 datasets), frequency (2057 IPD; 66 datasets), duration (1960 IPD; 64 datasets) and dosage (1978 IPD; 62 datasets). Discussion: Our international IPD archive demonstrates the application of big data principles in the context of aphasia research; our rigorous methodology for data acquisition and cleaning can serve as a template for the establishment of similar databases in other research areas