ESTIMATING A RUNNER\u27S STRIDE LENGTH AND FREQUENCY FROM A RACE VIDEO BY USING GROUND STITCHING

This study estimated stride length and frequency of runners in a 100 m race video. One method for measuring stride length and frequency is using infrared sensors. However, this method is not applicable to real races since numerous markers with infrared-reflective material must be attached to the runner’s entire body. Therefore, we proposed a method using a race video. We generated a panoramic image of the 100 m track to estimate the distance of each frame from the start line. We detected the positions of the runner’s steps from the movement of the leg joints. We projected every step to the overview image of the 100 m track. In the experiment, we applied our method to the video of an IAAF World Championship Track and Field 100 m race and obtained data from Usain Bolt. As a result, we can automatically estimate stride length and frequency of real races

Continuous data assimilation of large eddy simulation by lattice Boltzmann method and local ensemble transform Kalman filter (LBM-LETKF)

We investigate the applicability of the data assimilation (DA) to large eddy simulations (LESs) based on the lattice Boltzmann method (LBM). We carry out the observing system simulation experiment of a two-dimensional (2D) forced isotropic turbulence, and examine the DA accuracy of the nudging and the local ensemble transform Kalman filter (LETKF) with spatially sparse and noisy observation data of flow fields. The advantage of the LETKF is that it does not require computing spatial interpolation and/or an inverse problem between the macroscopic variables (the density and the pressure) and the velocity distribution function of the LBM, while the nudging introduces additional models for them. The numerical experiments with $256\times256$ grids and $10\%$ observation noise in the velocity showed that the root mean square error of the velocity in the LETKF with $8\times 8$ observation points ($\sim 0.1\%$ of the total grids) and 64 ensemble members becomes smaller than the observation noise, while the nudging requires an order of magnitude larger number of observation points to achieve the same accuracy. Another advantage of the LETKF is that it well keeps the amplitude of the energy spectrum, while only the phase error becomes larger with more sparse observation. We also see that a lack of observation data in the LETKF produces a spurious energy injection in high wavenumber regimes, leading to numerical instability. Such numerical instability is known as the catastrophic filter divergence problem, which can be suppressed by increasing the number of ensemble members. From these results, it was shown that the LETKF enables robust and accurate DA for the 2D LBM with sparse and noisy observation data.Comment: 27 pages, 14 figure

Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic model

This paper presents an ensemble data assimilation method using the pseudo ensembles generated by denoising diffusion probabilistic model. Since the model is trained against noisy and sparse observation data, this model can produce divergent ensembles close to observations. Thanks to the variance in generated ensembles, our proposed method displays better performance than the well-established ensemble data assimilation method when the simulation model is imperfect

Activation of cytosolic phospholipase A2 in dorsal root ganglion neurons by Ca2+/calmodulin-dependent protein kinase II after peripheral nerve injury

<p>Abstract</p> <p>Background</p> <p>Peripheral nerve injury leads to a persistent neuropathic pain state in which innocuous stimulation elicits pain behavior (tactile allodynia), but the underlying mechanisms have remained largely unknown. We have previously shown that spinal nerve injury induces the activation of cytosolic phospholipase A₂(cPLA₂) in injured dorsal root ganglion (DRG) neurons that contribute to tactile allodynia. However, little is known about the signaling pathway that activates cPLA₂after nerve injury. In the present study, we sought to determine the mechanisms underlying cPLA₂activation in injured DRG neurons in an animal model of neuropathic pain, focusing on mitogen-activated protein kinases (MAPKs) and Ca²⁺/calmodulin-dependent protein kinase II (CaMKII).</p> <p>Results</p> <p>Pharmacological inhibition of either p38 or extracellular signal-regulated kinase (ERK) in the injured DRG, which led to suppression of the development of tactile allodynia, did not affect cPLA₂phosphorylation and translocation after nerve injury. By contrast, a CaMKII inhibitor prevented the development and expression of nerve injury-induced tactile allodynia and reduced both the level of cPLA₂phosphorylation and the number of DRG neurons showing translocated cPLA₂in response to nerve injury. Applying ATP to cultured DRG neurons increased the level of both phosphorylated cPLA₂and CaMKII in the vicinity of the plasma membrane and caused physical association of these two proteins. In addition, ATP-stimulated cPLA₂and CaMKII phosphorylation were inhibited by both a selective P2X₃R/P2X₂₊₃R antagonist and a nonselective voltage-dependent Ca²⁺channel (VDCC) blocker.</p> <p>Conclusion</p> <p>These results suggest that CaMKII, but not MAPKs, has an important role in cPLA₂activation following peripheral nerve injury, probably through P2X₃R/P2X₂₊₃R and VDCCs in primary afferent neurons.</p

Role of PAF Receptor in Proinflammatory Cytokine Expression in the Dorsal Root Ganglion and Tactile Allodynia in a Rodent Model of Neuropathic Pain

BACKGROUND: Neuropathic pain is a highly debilitating chronic pain following damage to peripheral sensory neurons and is often resistant to all treatments currently available, including opioids. We have previously shown that peripheral nerve injury induces activation of cytosolic phospholipase A(2) (cPLA(2)) in injured dorsal root ganglion (DRG) neurons that contribute to tactile allodynia, a hallmark of neuropathic pain. However, lipid mediators downstream of cPLA(2) activation to produce tactile allodynia remain to be determined. PRINCIPAL FINDINGS: Here we provide evidence that platelet-activating factor (PAF) is a potential candidate. Pharmacological blockade of PAF receptors (PAFRs) reduced the development and expression of tactile allodynia following nerve injury. The expression of PAFR mRNA was increased in the DRG ipsilateral to nerve injury, which was seen mainly in macrophages. Furthermore, mice lacking PAFRs showed a reduction of nerve injury-induced tactile allodynia and, interestingly, a marked suppression of upregulation of tumor necrosis factor alpha (TNFalpha) and interleukin-1beta (IL-1beta) expression in the injured DRG, crucial proinflammatory cytokines involved in pain hypersensitivity. Conversely, a single injection of PAF near the DRG of naïve rats caused a decrease in the paw withdrawal threshold to mechanical stimulation in a dose-dependent manner and an increase in the expression of mRNAs for TNFalpha and IL-1beta, both of which were inhibited by pretreatment with a PAFR antagonist. CONCLUSIONS: Our results indicate that the PAF/PAFR system has an important role in production of TNFalpha and IL-1beta in the DRG and tactile allodynia following peripheral nerve injury and suggest that blocking PAFRs may be a viable therapeutic strategy for treating neuropathic pain

Ultrasensitive detection of SARS-CoV-2 nucleocapsid protein using large gold nanoparticle-enhanced surface plasmon resonance

The COVID-19 pandemic has created urgent demand for rapid detection of the SARS-CoV-2 coronavirus. Herein, we report highly sensitive detection of SARS-CoV-2 nucleocapsid protein (N protein) using nanoparticle-enhanced surface plasmon resonance (SPR) techniques. A crucial plasmonic role in significantly enhancing the limit of detection (LOD) is revealed for exceptionally large gold nanoparticles (AuNPs) with diameters of hundreds of nm. SPR enhanced by these large nanoparticles lowered the LOD of SARS-CoV-2 N protein to 85 fM, resulting in the highest SPR detection sensitivity ever obtained for SARS-CoV-2 N protein

Continuous Repetition Motor Imagery Training and Physical Practice Training Exert the Growth of Fatigue and Its Effect on Performance

Continuous repetition of motor imagery leads to mental fatigue. This study aimed to examine whether fatigue caused by motor imagery training affects improvement in performance and the change in corticospinal excitability. The participants were divided into “physical practice training” and “motor imagery training” groups, and a visuomotor task (set at 50% of maximal voluntary contraction in participants) was performed to assess the training effect on fatigue. The measurements were recorded before and after training. Corticospinal excitability at rest was measured by transcranial magnetic stimulation according to the Neurophysiological Index. Subjective mental fatigue and muscle fatigue were assessed by using the visual analog scale and by measuring the pinch force, respectively. Additionally, the error area was evaluated and calculated at pre-, mid-, and post-terms after training, using a visuomotor task. After training, muscle fatigue, subjective mental fatigue, and decreased corticospinal excitability were noted in both of the groups. Moreover, the visuomotor task decreased the error area by training; however, there was no difference in the error area between the mid- and post-terms. In conclusion, motor imagery training resulted in central fatigue by continuous repetition, which influenced the improvement in performance in the same manner as physical practice training