Spinal Excitatory Dynorphinergic Interneurons Contribute to Burn Injury-Induced Nociception Mediated by Phosphorylated Histone 3 at Serine 10 in Rodents

The phosphorylation of serine 10 in histone 3 (p-S10H3) has recently been demonstrated to participate in spinal nociceptive processing. However, superficial dorsal horn (SDH) neurons involved in p-S10H3-mediated nociception have not been fully characterized. In the present work, we combined immunohistochemistry, in situ hybridization with the retrograde labeling of projection neurons to reveal the subset of dorsal horn neurons presenting an elevated level of p-S10H3 in response to noxious heat (60 °C), causing burn injury. Projection neurons only represented a small percentage (5%) of p-S10H3-positive cells, while the greater part of them belonged to excitatory SDH interneurons. The combined immunolabeling of p-S10H3 with markers of already established interneuronal classes of the SDH revealed that the largest subset of neurons with burn injury-induced p-S10H3 expression was dynorphin immunopositive in mice. Furthermore, the majority of p-S10H3-expressing dynorphinergic neurons proved to be excitatory, as they lacked Pax-2 and showed Lmx1b-immunopositivity. Thus, we showed that neurochemically heterogeneous SDH neurons exhibit the upregulation of p-S10H3 shortly after noxious heat-induced burn injury and consequential tissue damage, and that a dedicated subset of excitatory dynorphinergic neurons is likely a key player in the development of central sensitization via the p-S10H3 mediated pathway.     

The Electron–Phonon Interaction of Low-Dimensional and Multi-Dimensional Materials from He Atom Scattering

Atom scattering is becoming recognized as a sensitive probe of the electron–phonon interaction parameter λ at metal and metal-overlayer surfaces. Here, the theory is developed, linking λ to the thermal attenuation of atom scattering spectra (in particular, the Debye–Waller factor), to conducting materials of different dimensions, from quasi-1D systems such as W(110):H(1 × 1) and Bi(114), to quasi-2D layered chalcogenides, and high-dimensional surfaces such as quasicrystalline 2ML-Ba(0001)/Cu(001) and d-AlNiCo(00001). Values of λ obtained using He atoms compare favorably with known values for the bulk materials. The corresponding analysis indicates in addition, the number of layers contributing to the electron–phonon interaction, which is measured in an atom surface collision.     

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

Stability analysis of a feedback-controlled resonant DC-DC converter

This paper reports on the stability analysis of one member of a dual-channel resonant DC-DC converter family. The study is confined to the buck configuration in symmetrical operation. The output voltage of the converter is controlled by a closed loop applying constant-frequency pulsewidth modulation. The dynamic analysis reveals that a bifurcation cascade develops as a result of increasing the loop gain. The trajectory of the variable-structure piecewise-linear nonlinear system pierces through the Poincare plane at the fixed point in state space when the loop gain is small. For stability criterion the positions of the characteristic multipliers of the Jacobian matrix belonging to the Poincare map function defined around the fixed point located in the Poincare plane is applied. In addition to the stability analysis, a bifurcation diagram is developed showing the four possible states of the feedback loop: the periodic, the quasi-periodic, the subharmonic, and the chaotic states. Simulation and test results verify the theory.     

Neural networks-then and now

The emergence of neural networks as a significant subdiscipline with corresponding attempts at application to engineering problems is traced back to the 1960s, when Frank Rosenblatt, a Cornell University psychologist, showed by mathematical analysis, digital computer simulation, and experiments with special-purpose parallel analog systems that neural networks with variable-weight connections could be trained to classify spatial patterns into prespecified categories. In his attempts to provide biologically plausible explanations of the function of the central nervous system, he investigated relatively simple networks that were amenable to analysis and more complex networks whose behavior could be predicted only in terms of gross characteristics. He assembled a sizable group involving theoreticians, experimentalists, technologists, and, later, biologists. His work caught the imagination of the press and led to the wave of febrile activity that subsided at the end of that decade.     

On sorting triangles in a delaunay tessellation

In a two-dimensional Delaunay-triangulated domain, there exists a partial ordering of the triangles (with respect to a vertex) that is consistent with the two-dimensional visibility of the triangles from that vertex. An equivalent statement is that a polygon that is star-shaped with respect to a given vertex can be extended, one triangle at a time, until it includes the entire domain. Arbitrary planar triangulations do not possess this useful property which allows incremental processing of the triangles.This work was partially supported by the National Science Foundation's US-Italy Collaborative Research Program under Grant INT-8714578 and Information, Robotics, and Intelligent Research Grant IRI-8704781.     

Defect detection in analog and mixed circuits by neural networks using wavelet analysis

An efficient defect-oriented parametric test method for analog & mixed-signal integrated circuits based on neural network classification of a selected circuit's parameter using wavelet decomposition preprocessing is proposed in this paper. The neural network has been used for detecting catastrophic defects in two experimental analog & mixed-signal CMOS circuits by sensing the abnormalities in selected parameters, observed under defective conditions and by their consequent classification into a proper category. To reduce complexity of the neural network, wavelet decomposition is used to perform preprocessing of the analyzed parameter. Moreover, we show that wavelet analysis brings significant enhancement in the correct classification, and makes the neural network-based test method extremely efficient & versatile for detecting hard-detectable catastrophic defects in analog & mixed-signal circuits.     

Asymmetries in simple feature searches for color

Observers were required to search for and find a target stimulus that differed from distractor stimuli only in chromaticity. Pairs of target and distractor colors were chosen so that in some conditions the two members of a pair differed in hue, in saturation, or in both hue and saturation. For each type of condition, a number of pairs of colors representing varying degrees of perceptual difference were chosen. Each member of each pair of colors served as both the target and distractor color while the other member of the pair served in the remaining role. The largest asymmetries in search times occurred when the target and distractors differed in saturation. Somewhat smaller asymmetries occurred with differences in both saturation and hue, and no asymmetries occurred with hue differences. Results suggest that the asymmetries are related to the time required to encode and transmit the chromatic information centrally rather than the properties of the search process itself.