Case report: Anti-IgLON5 disease and anti-LGI1 encephalitis following COVID-19

Anti-IgLON family member 5 (IgLON5) disease is a rare autoimmune encephalitis, characterized by sleep problems, cognitive decline, gait abnormalities, and bulbar dysfunction. Anti–leucine-rich glioma-inactivated 1 (LGI1) autoimmune encephalitis is characterized by cognitive dysfunction, mental disorders, faciobrachial dystonic seizures (FBDS), and hyponatremia. Various studies report that coronavirus disease 2019 (COVID-19) have an effect on the nervous system and induce a wide range of neurological symptoms. Autoimmune encephalitis is one of the neurological complications in severe acute respiratory syndrome coronavirus 2 infection. Until now, autoimmune encephalitis with both anti-IgLON5 and anti-LGI1 receptor antibodies following COVID-19 is rarely reported. The case report described a 40-year-old man who presented with sleep behavior disorder, daytime sleepiness, paramnesia, cognitive decline, FBDS, and anxiety following COVID-19. Anti-IgLON5 and anti-LGI1 receptor antibodies were positive in serum, and anti-LGI1 receptor antibodies were positive in cerebrospinal fluid. The patient presented with typical symptoms of anti-IgLON5 disease such as sleep behavior disorder, obstructive sleep apnea, and daytime sleepiness. Moreover, he presented with FBDS, which is common in anti-LGI1 encephalitis. Therefore, the patient was diagnosed with anti-IgLON5 disease and anti-LGI1 autoimmune encephalitis. The patient turned better after high-dose steroid and mycophenolate mofetil therapy. The case serves to increase the awareness of rare autoimmune encephalitis after COVID-19

Block copolymer-quantum dot micelles for multienzyme colocalization

To mimic the structure and functionality of multienzyme complexes, which are widely present in Nature, Pluronic-based micelles were designed to colocalize multiple enzymes. To stabilize the micelles as well as to enable characterization of single enzyme immobilization and multienzyme colocalization by Förster resonance energy transfer (FRET), quantum dots (QDs) were incorporated into the micelles to form Pluronic-QD micelles using a novel microreactor. Model enzymes glucose oxidase (GOX) and horseradish peroxidase (HRP) were respectively labeled with fluorescent dyes. The results indicated that FRET occurred between the QDs and dyes that labeled each type of enzyme in single enzyme immobilization studies as well as between the dyes in colocalization studies. These observations were consistent with increases in micelle size after adsorption of dye-enzymes as verified by dynamic light scattering. In addition, the activity of single enzymes was retained after immobilization. An optimized colocalization process improved the overall conversion rate by approximately 100% compared to equivalent concentrations of free enzymes in solution. This study demonstrates a versatile platform for multienzyme colocalization and an effective strategy to characterize multienzyme immobilization and colocalization, which can be applicable to many other multienzyme systems

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI

Diffusion models are a leading method for image generation and have been successfully applied in magnetic resonance imaging (MRI) reconstruction. Current diffusion-based reconstruction methods rely on coil sensitivity maps (CSM) to reconstruct multi-coil data. However, it is difficult to accurately estimate CSMs in practice use, resulting in degradation of the reconstruction quality. To address this issue, we propose a self-consistency-driven diffusion model inspired by the iterative self-consistent parallel imaging (SPIRiT), namely SPIRiT-Diffusion. Specifically, the iterative solver of the self-consistent term in SPIRiT is utilized to design a novel stochastic differential equation (SDE) for diffusion process. Then $\textit{k}$-space data can be interpolated directly during the reverse diffusion process, instead of using CSM to separate and combine individual coil images. This method indicates that the optimization model can be used to design SDE in diffusion models, driving the diffusion process strongly conforming with the physics involved in the optimization model, dubbed model-driven diffusion. The proposed SPIRiT-Diffusion method was evaluated on a 3D joint Intracranial and Carotid Vessel Wall imaging dataset. The results demonstrate that it outperforms the CSM-based reconstruction methods, and achieves high reconstruction quality at a high acceleration rate of 10

Expression changes of DSCAM in induction of MSCs to differentiate into neurons

Abstract Objective To explore the role of Down syndrome cellular adhesion molecule (DSCAM) in the course of the rat marrow mesenchymal stem cells (MSCs) differentiated to neurons in vitro. Methods MSCs from Sprague-Dawley rats were induced into neurons by baicalin. Immunocytochemistry, Western blot and other methods were performed to detect DSCAM in neurons. At the same time, RNA interfere technique was performed to observe the induction and differentiation after DSCAM-siRNA was transfected into MSCs. Results Before induction, the expression of DSCAM was not detectable in MSCs. After 24h pre-induction, DSCAM was slightly expressed in MSCs(1.71 ±0.67 ).After 6h induction by baicalin ,the expression of DSCAM increased (15.79 ±4.24 ) and reached the peak (53.16 ±5.94 ) after 3d induction. After 6d induction, DSCAM expression obviously decreased (28.99 ±6.72 ). After DSCAM-siRNA was transfected into MSCs, DSCAM expression obviously decreased. However, MSCs did not express neuron-specific β-III-tubulin, expression of β-III-tubulin was (1.40 ±0.79 )after 6h induction, 41.59%±3.17% after 3d induction and (59.11 ±4.76 )after 6d induction. But after DSCAM-siRNA was transfected into MSCs, expression of β-III-tubulin obviously decreased 28.57%±2.91% 43.90%±12.31% after 3d and 6d induction. Conclusion

Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks