Conventional spark versus nanosecond repetitively pulsed discharge for a turbulence facilitated ignition phenomenon

This work applies both conventional-single-spark-discharge (CSSD) at 500-µs pulse duration time and nanosecond-repetitively-pulsed-discharge (NRPD) at various pulsed-repetitive-frequency PRF = 5–70 kHz to explore a turbulence facilitated ignition (TFI) phenomenon using a pair of pin-to-pin electrodes at an inter-electrode gap of 0.8 mm in randomly-stirred lean n-butane/air mixture with Lewis number ≫ 1. For CSSD, measured laminar and turbulent minimum ignition energies (MIE$_{L}$ and MIE$_{T}$) at 50% ignitability show that MIE$_{L}$≈ 23 mJ > the smallest MIE$_{T}$≈ 19.7 mJ at u′ = 0.9 m/s (TFI) and then MIE$_{T}$≈ 28.6/30.8/36.8 mJ at u′ = 1.4/2.1/2.8 m/s (no TFI), where u′ is the r.m.s turbulent fluctuating velocity. For comparison, all NRPD experiments apply the same total ignition energy E$_{tot}$≈ 23 mJ via a fixed train of 11 pulses, each pulse with 2.2 mJ except for the first pulse with 1 mJ. NRPD results show a cumulatively synergistic effect depending on the coherence between PRF and an inward reactant flow recirculation frequency (f$_{RC}$) inside the torus-like kernel induced by the discharge that could enhance ignition. When PRF is approximately synchronizing with f$_{RC}$, the synergistic effect is most profound at PRF = 20-kHz/40-kHz with very high ignition probability P$_{ig}$ = 90%/85% > 50% in quiescence, whereas lower values of P$_{ig}$ = 42%/34% are found at PRF = 10-kHz/60-kHz. Further, P$_{ig}$ = 0 at PRF = 5-kHz even when 5000 pulses (E$_{tot}$≈ 10 J) are applied. We discover that P$_{ig}$ decreases significantly with increasing u′ for most PRFs (no TFI) except at higher PRF ≥ 60 kHz showing possible TFI. These results are attributed to the interactions between turbulent dissipation, differential diffusion, and synergistic influence, which are substantiated by Schlieren images of initial kernel development and the ignition time determined at one half of the flame critical radius that leads to a self-sustained spherical flame propagation

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($\Omega_m$ and $\sigma_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $\Omega_{\text{M}}$ and $\sigma_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.Comment: 40 pages, 22 figures (11 made of subfigures

A CADM3 variant causes Charcot-Marie-Tooth disease with marked upper limb involvement

The CADM family of proteins consists of four neuronal specific adhesion molecules (CADM1, CADM2, CADM3 and CADM4) that mediate the direct contact and interaction between axons and glia. In the peripheral nerve, axon-Schwann cell interaction is essential for the structural organization of myelinated fibres and is primarily mediated by the binding of CADM3, expressed in axons, to CADM4, expressed by myelinating Schwann cells. We have identified—by whole exome sequencing—three unrelated families, including one de novo patient, with axonal Charcot-Marie-Tooth disease (CMT2) sharing the same private variant in CADM3, Tyr172Cys. This variant is absent in 230 000 control chromosomes from gnomAD and predicted to be pathogenic. Most CADM3 patients share a similar phenotype consisting of autosomal dominant CMT2 with marked upper limb involvement. High resolution mass spectrometry analysis detected a newly created disulphide bond in the mutant CADM3 potentially modifying the native protein conformation. Our data support a retention of the mutant protein in the endoplasmic reticulum and reduced cell surface expression in vitro. Stochastic optical reconstruction microscopy imaging revealed decreased co-localization of the mutant with CADM4 at intercellular contact sites. Mice carrying the corresponding human mutation (Cadm3Y170C) showed reduced expression of the mutant protein in axons. Cadm3Y170C mice showed normal nerve conduction and myelin morphology, but exhibited abnormal axonal organization, including abnormal distribution of Kv1.2 channels and Caspr along myelinated axons. Our findings indicate the involvement of abnormal axon-glia interaction as a disease-causing mechanism in CMT patients with CADM3 mutations. A correction has been published: Brain, Volume 144, Issue 7, July 2021, Page e64, https://doi.org/10.1093/brain/awab18

Transmembrane protease serine 5: a novel Schwann cell plasma marker for CMT1A

OBJECTIVE: Development of biomarkers for Charcot-Marie-Tooth (CMT) disease is critical for implementing effective clinical trials. The most common form of CMT, type 1A, is caused by a genomic duplication surrounding the PMP22 gene. A recent report (Neurology 2018;90:e518-3524) showed elevation of neurofilament light (NfL) in plasma of CMT1A disease patients, which correlated with disease severity. However, no plasma/serum biomarker has been identified that is specific to Schwann cells, the most directly affected cells in CMT1A. METHODS: We used the Olink immuno PCR platform to profile CMT1A patient (n = 47, 2 cohorts) and normal control plasma (n = 41, two cohorts) on five different Olink panels to screen 398 unique proteins. RESULTS: The TMPRSS5 protein (Transmembrane protease serine 5) was elevated 2.07-fold (P = <0.0001) in two independent cohorts of CMT1A samples relative to controls. TMPRSS5 is most highly expressed in Schwann cells of peripheral nerve. Consistent with early myelination deficits in CMT1A, TMPRSS5 was not significantly correlated with disease score (CMTES-R, CMTNS-R), nerve conduction velocities (Ulnar CMAP, Ulnar MNCV), or with age. TMPRSS5 was not significantly elevated in smaller sample sets from patients with CMT2A, CMT2E, CMT1B, or CMT1X. The Olink immuno PCR assays confirmed elevated levels of NfL (average 1.58-fold, P < 0.0001), which correlated with CMT1A patient disease score. INTERPRETATION: These data identify the first Schwann cell-specific protein that is elevated in plasma of CMT1A patients, and may provide a disease marker and a potentially treatment-responsive biomarker with good disease specificity for clinical trials

Tube Models for Rubber-Elastic Systems

In the first part of the paper we show that the constraining potentials introduced to mimic entanglement effects in Edwards' tube model and Flory's constrained junction model are diagonal in the generalized Rouse modes of the corresponding phantom network. As a consequence, both models can formally be solved exactly for arbitrary connectivity using the recently introduced constrained mode model. In the second part, we solve a double tube model for the confinement of long paths in polymer networks which is partially due to crosslinking and partially due to entanglements. Our model describes a non-trivial crossover between the Warner-Edwards and the Heinrich-Straube tube models. We present results for the macroscopic elastic properties as well as for the microscopic deformations including structure factors.Comment: 15 pages, 8 figures, Macromolecules in pres

Laser excitation of the 1s-hyperfine transition in muonic hydrogen

The CREMA collaboration is pursuing a measurement of the ground-state hyperfine splitting (HFS) in muonic hydrogen ($\mu$p) with 1 ppm accuracy by means of pulsed laser spectroscopy to determine the two-photon-exchange contribution with $2\times10^{-4}$ relative accuracy. In the proposed experiment, the $\mu$p atom undergoes a laser excitation from the singlet hyperfine state to the triplet hyperfine state, {then} is quenched back to the singlet state by an inelastic collision with a H$_2$ molecule. The resulting increase of kinetic energy after the collisional deexcitation is used as a signature of a successful laser transition between hyperfine states. In this paper, we calculate the combined probability that a $\mu$p atom initially in the singlet hyperfine state undergoes a laser excitation to the triplet state followed by a collisional-induced deexcitation back to the singlet state. This combined probability has been computed using the optical Bloch equations including the inelastic and elastic collisions. Omitting the decoherence effects caused by {the laser bandwidth and }collisions would overestimate the transition probability by more than a factor of two in the experimental conditions. Moreover, we also account for Doppler effects and provide the matrix element, the saturation fluence, the elastic and inelastic collision rates for the singlet and triplet states, and the resonance linewidth. This calculation thus quantifies one of the key unknowns of the HFS experiment, leading to a precise definition of the requirements for the laser system and to an optimization of the hydrogen gas target where $\mu$p is formed and the laser spectroscopy will occur.Comment: 21 pages, 4 figure

The next generation of laser spectroscopy experiments using light muonic atoms

Precision spectroscopy of light muonic atoms provides unique information about the atomic and nuclear structure of these systems and thus represents a way to access fundamental interactions, properties and constants. One application comprises the determination of absolute nuclear charge radii with unprecedented accuracy from measurements of the 2S$\,$-$\,$2P Lamb shift. Here, we review recent results of nuclear charge radii extracted from muonic hydrogen and helium spectroscopy and present experiment proposals to access light muonic atoms with $Z \geq 3$. In addition, our approaches towards a precise measurement of the Zemach radii in muonic hydrogen ($\mu$p) and helium ($\mu$$^{3}$He$^{+}$) are discussed. These results will provide new tests of bound-state quantum-electrodynamics in hydrogen-like systems and can be used as benchmarks for nuclear structure theories.Comment: 17 pages, 8 figure

Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD

Biallelic mutations in SORD cause a common and potentially treatable hereditary neuropathy with implications for diabetes

Here we report biallelic mutations in the sorbitol dehydrogenase gene (SORD) as the most frequent recessive form of hereditary neuropathy. We identified 45 individuals from 38 families across multiple ancestries carrying the nonsense c.757delG (p.Ala253GlnfsTer27) variant in SORD, in either a homozygous or compound heterozygous state. SORD is an enzyme that converts sorbitol into fructose in the two-step polyol pathway previously implicated in diabetic neuropathy. In patient-derived fibroblasts, we found a complete loss of SORD protein and increased intracellular sorbitol. Furthermore, the serum fasting sorbitol levels in patients were dramatically increased. In Drosophila, loss of SORD orthologs caused synaptic degeneration and progressive motor impairment. Reducing the polyol influx by treatment with aldose reductase inhibitors normalized intracellular sorbitol levels in patient-derived fibroblasts and in Drosophila, and also dramatically ameliorated motor and eye phenotypes. Together, these findings establish a novel and potentially treatable cause of neuropathy and may contribute to a better understanding of the pathophysiology of diabetes