Origin, criterion, and mechanism of vortex-core reversals in soft magnetic nanodisks under perpendicular bias fields

We studied dynamics of vortex-core reversals driven by circular rotating fields along with static perpendicular magnetic fields of different direction and strength. We found that the application of perpendicular fields H p modifies the starting ground state of vortex magnetizations, thereby instigating the development of a magnetization dip mz,dip in the vicinity of the original core up to its threshold value, m z,dip cri ???-p, which is necessary for vortex-core reversals, where p is the initial core polarization. We found the relationship of the dynamic evolutions of the mz,dip and the out-of-plane gyrofields hz, which was induced, in this case, by vortex-core motion of velocity ??, thereby their critical value relation ??crihz cri. The simulation results indicated that the variation of the critical core velocity ??cri with Hp can be expressed explicitly as ??cri / ?? cri 0 = (??/ ??0) | -p- m z,dip g |, with the core size ?? and the starting ground-state magnetization dip m z,dip g variable with H p, and for the values of ?? cri 0 and ??0 at H p =0. This work offers deeper and/or new insights into the origin, criterion and mechanism of vortex-core reversals under application of static perpendicular bias fields.open7

RELATIONSHIP BETWEEN THE SEQUENTIAL MOVEMENT, MUSCLE CO-CONTRACTION AND KLNEMATLC VARIABLES OF FLYING DISC BACKHAND THROWING

The aim of this study was to analyse sequential movement of segments and co-contraction index and its correlation with kinematic variables. Disc trajectory and velocity (DV) variables calculated according to the medio-lateral (DOM) and vertical (DOV) displacement. The time differences calculated according to maximum linear velocity of segments from proximal to distal and reported as shoulder & elbow (S-E), elbow & wrist (E-W), wrist & finger (W-F). Co-contraction index (CI) were calculated for flexor and extensor carpi radialis. DV and S-E, W-F, DOM showed correlation which may be caused by the sequential movement of S-E, W-F and the displacement of the disc. Also, there was a correlation between the CI, DOM and DOV. Therefore, decrease of time gap between the segment movements and higher CI may increase the DV effectively

Finding Kinematics-Driven Latent Neural States From Neuronal Population Activity for Motor Decoding

While intracortical brain-machine interfaces (BMIs) demonstrate feasibility to restore mobility to people with paralysis, it is still challenging to maintain high-performance decoding in clinical BMIs. One of the main obstacles for high-performance BMI is the noise-prone nature of traditional decoding methods that connect neural response explicitly with physical quantity, such as velocity. In contrast, the recent development of latent neural state model enables a robust readout of large-scale neuronal population activity contents. However, these latent neural states do not necessarily contain kinematic information useful for decoding. Therefore, this study proposes a new approach to finding kinematics-dependent latent factors by extracting latent factors' kinematics-dependent components using linear regression. We estimated these components from the population activity through nonlinear mapping. The proposed kinematics-dependent latent factors generate neural trajectories that discriminate latent neural states before and after the motion onset. We compared the decoding performance of the proposed analysis model with the results from other popular models. They are factor analysis (FA), Gaussian process factor analysis (GPFA), latent factor analysis via dynamical systems (LFADS), preferential subspace identification (PSID), and neuronal population firing rates. The proposed analysis model results in higher decoding accuracy than do the others (>17% improvement on average). Our approach may pave a new way to extract latent neural states specific to kinematic information from motor cortices, potentially improving decoding performance for online intracortical BMIs

Decoding Kinematic Information From Primary Motor Cortex Ensemble Activities Using a Deep Canonical Correlation Analysis

The control of arm movements through intracortical brain-machine interfaces (BMIs) mainly relies on the activities of the primary motor cortex (M1) neurons and mathematical models that decode their activities. Recent research on decoding process attempts to not only improve the performance but also simultaneously understand neural and behavioral relationships. In this study, we propose an efficient decoding algorithm using a deep canonical correlation analysis (DCCA), which maximizes correlations between canonical variables with the non-linear approximation of mappings from neuronal to canonical variables via deep learning. We investigate the effectiveness of using DCCA for finding a relationship between M1 activities and kinematic information when non-human primates performed a reaching task with one arm. Then, we examine whether using neural activity representations from DCCA improves the decoding performance through linear and non-linear decoders: a linear Kalman filter (LKF) and a long short-term memory in recurrent neural networks (LSTM-RNN). We found that neural representations of M1 activities estimated by DCCA resulted in more accurate decoding of velocity than those estimated by linear canonical correlation analysis, principal component analysis, factor analysis, and linear dynamical system. Decoding with DCCA yielded better performance than decoding the original FRs using LSTM-RNN (6.6 and 16.0% improvement on average for each velocity and position, respectively; Wilcoxon rank sum test, p < 0.05). Thus, DCCA can identify the kinematics-related canonical variables of M1 activities, thus improving the decoding performance. Our results may help advance the design of decoding models for intracortical BMIs

Extensive Emphysematous Pyelonephritis in a Nondiabetic Female Cat - Treatment with Unilateral Nephroureterectomy

Background: Emphysematous pyelonephritis (EPN) is an acute, severe necrotizing infection of the renal parenchyma and surrounding tissues that results in gas formation in the kidney, collecting system, or surroundings. EPN is a rare condition in veterinary medicine and occurs most frequently in dogs with diabetes mellitus. Although the prognosis of medical management in animals is poor, the standardized treatment protocol according to EPN severity is unclear. This report describes the first case of a nondiabetic female cat with extensive EPN and good prognosis following direct nephroureterectomy (NU). Case: A 10-year-old spayed female cat presented with the chief complaint of an acute loss of weight within 1 week, vomiting, and disorientation including stumbling, discoordination, circling, wobbling, head tilting, and difficulties in standing. At presentation, the patient had a body condition score of 1/9 and weighed 2.6 kg. Blood examination revealed leukocytosis, anemia, and hypoproteinemia. Abdominal radiography revealed severely decreased serosal details. A massive gas silhouette observed in the peritoneal and retroperitoneal cavities, was diagnosed as abdominal free gas. Abdominal ultrasound showed an accumulation of moderately anechoic fluid mixed with gas and cyst-like capsules around the left kidney. Left partial ureteral obstruction and dilation were also observed. Computed tomography (CT) was performed without sedatives or anesthetic drugs. The findings showed severe inflammatory changes in the peritoneum and a loss of the normal inner structure in the left kidney. A pyelogram of the left kidney was not observed after injection of the contrast material. Diffuse fat stranding and free gas observed in the mesentery of the entire abdominal cavity and around the left kidney were considered septic peritonitis. Urinalysis revealed proteinuria and hematuria. Numerous neutrophils with rod-type bacteria were observed in the ascites. Following diagnostic examinations, the patient was diagnosed with extensive left EPN, including inflammatory ascites and abdominal free gas. Therefore, emergency NU of the nonfunctional left kidney and ruptured ureter and thorough abdominal lavage were conducted. Diffuse inflammation and a nephrolith were observed in the section of the harvested kidney. The nephrolith was composed of 100% calcium oxalate monohydrate. The real-time polymerase chain reaction (RT-PCR) test for feline infectious peritonitis (FIP) was negative. Escherichia coli was detected in the ascites, and antibiotic therapy was administered following the antibiotic sensitivity test. The histological findings from the left kidney and ureter included marked chronic inflammation and fibrosis. The patient was discharged 4 days after surgery. During the 8-month follow-up period, the patient’s condition improved. Discussion: This was a unique case of EPN in a nondiabetic cat and the first reported case of EPN with a ruptured ureter, including abdominal free gas, inflammatory ascites, and peritonitis. This patient had a bacterial urinary tract infection with E. coli, which is the most frequently isolated pathogen in humans. This gas-forming bacteria produced a massive amount of gas and inflammation that were considered to have ruptured the urinary tract, so that the gas was released into the abdomen. This case corresponded to class 3B, with two risk factors according to the human EPN classification system. Direct NU and abdominal lavage were performed as emergency surgeries. The patient stabilized gradually and showed a good prognosis. Immediate surgical intervention is recommended in animal patients showing the extensive EPN stage. Keywords: kidney, nephroureterectomy, emphysematous pyelonephritis, peritonitis, cat, E. coli.