Mesenchymal stem cell (MSC) transplantation has been shown to represent a potential treatment for traumatic spinal cord injury (SCI). However, there are several obstacles that need to be overcome before MSCs can be considered for clinical application, such as failure of MSCs to reach the spinal cord lesion core and possible tumor formation. Recent studies have suggested that MSC treatment is beneficial owing to paracrine-secreted factors. Extracellular vesicles are considered to be some of the most valuable paracrine molecules. However, the therapeutic mechanism of extracellular vesicles on spinal cord injury has not been studied clearly. Therefore, our study investigated the effect of systemic administration of extracellular vesicles on the loss of motor function after SCI and examined the potential mechanisms underlying their effects. Disruption of the blood-spinal cord barrier (BSCB) is a crucial factor that can be detrimental to motor function recovery. Pericytes are an important component of the neurovascular unit, and play a pivotal role in maintaining the structural integrity of the BSCB. Our study demonstrated that administration of bone mesenchymal stem cell-derived extracellular vesicles (BMSC-EV) reduced brain cell death, enhanced neuronal survival and regeneration, and improved motor function compared with the administration of BMSC-EV free culture media (EV-free CM). Besides, the BSCB was attenuated and pericyte coverage was significantly decreased in vivo. Furthermore, we found that exosomes reduced pericyte migration via downregulation of NF-κB p65 signaling, with a consequent decrease in the permeability of the BSCB. In summary, we identified that extracellular vesicles treatment suppressed the migration of pericytes and further improved the integrity of the BSCB via NF-κB p65 signaling in pericytes. Our data suggest that extracellular vesicles may serve as a promising treatment strategy for SCI.
We propose a topic-guided variational autoencoder (TGVAE) model for text generation. Distinct from existing variational autoencoder (VAE) based approaches, which assume a simple Gaussian prior for the latent code, our model specifies the prior as a Gaussian mixture model (GMM) parametrized by a neural topic module. Each mixture component corresponds to a latent topic, which provides guidance to generate sentences under the topic. The neural topic module and the VAE-based neural sequence module in our model are learned jointly. In particular, a sequence of invertible Householder transformations is applied to endow the approximate posterior of the latent code with high flexibility during model inference. Experimental results show that our TGVAE outperforms alternative approaches on both unconditional and conditional text generation, which can generate semantically-meaningful sentences with various topics. µ log 2 ✓ < l a t e x i t s h a 1 _ b a s e 6 4 = " 2 U n l 4 N N J R P X a u u g M u c 4 E V m Z 8 m A 8 = " > A A A B + n i c b V B P S 8 M w H E 3 n v z n / 1 X n 0 E h y C p 9 G K o N 6 G X j x O s D p Y y 0 j T d A t L 0 5 L 8 K o 6 y r + L F g 4 p X P 4 k 3 v 4 3 p 1 o N u P g h 5 v P f 7 k Z c X Z o J r c J x v q 7 a y u r a + U d 9 s b G 3 v 7 O 7 Z + 8 1 7 n e a K M o + m I l W 9 k G g m u G Q e c B C s l y l G k l C w h 3 B 8 X f o P j 0 x p n s o 7 m G Q s S M h Q 8 p h T A k Y a 2 E 0 / T E W k J 4 m 5 s A 8 j B m R g t 5 y 2 M w N e J m 5 F W q h C d 2 B / + V F K 8 4 R J o I J o 3 Neural Topic Model (NTM) d t < l a t e x i t s h a 1 _ b a s e 6 4 = " u o 7 N n e 9 4 D t v o M i f d 7 m u o d O j 9 Q X M = " > A A A B 8 3 i c b V B N S w M x E J 2 t X 7 V + V T 1 6 C R b B U 9 k V Q b 0 V v X i s 4 G q h X U o 2 m 2 1 D s 8 m a Z A t l 6 e / w 4 k H F q 3 / G m / / G b L s H b R 0 I e b w 3 w 7 x 5 Y c q Z N q 7 7 7 V R W V t f W N 6 q b t a 3 t n d 2 9 + v 7 B g 5 a Z I t Q n k k v V C b G m n A n q G 2 Y 4 7 a S K 4 i T k 9 D E c 3 R T 6 4 5 g q z a S 4 N 5 O U B g k e C B Y z g o 2 l g l 4 o e a Q n i f 24 K 3 e P I y 8 M + a V 0 3 v 7 r z R u i 7 T q M I R H M M p e H A B L b i F N v h A 4 A m e 4 R X e n L H z 4 r w 7 H / P W i l P O H M K f c j 5 / A E S M k f c = < / l a t e x i t > < l a t e x i t s h a 1 _ b a s e 6 4 = " u o 7 N n e 9 4 D t v o M i f d 7 m u o d O j 9 Q X M = " > A A A B 8 3 i c b V B N S w M x E J 2 t X 7 V + V T 1 6 C R b B U 9 k V Q b 0 V v X i s 4 G q h X U o 2 m 2 1 D s 8 m a Z A t l 6 e / w 4 k H F q 3 / G m / / G b L s H b R 0 I e b w 3 w 7 x 5 Y c q Z N q 7 7 7 V R W V t f W N 6 q b t a 3 t n d 2 9 + v 7 B g 5 a Z I t Q n k k v V C b G m n A n q G 2 Y 4 7 a S K 4 i T k 9 D E c 3 R T 6 4 5 g q z a S 4 N 5 O U B g k e C B Y z g o 2 l g l 4 o e a Q n i f 2 Q 6 d c b b t O d F V o G X g k a U F a 7 X / / q R Z J k C R W G c K x 1 1 3 N T E + R Y G U Y 4 n d Z 6 m a Y p J i M 8 o F 0 L B U 6 o D v K Z 6 S k 6 s U y E Y q n s E w b N 2 N 8 T O U 5 0 4 c x 2 J t g M 9 a J W k P 9 p 3 c z E l 0 H O R J o Z K s h 8 U Z z Z + y Q q E k A R U 5 Q Y P r E A E 8 W s V 0 S G W G F...
Interactive recommendation with natural-language feedback can provide richer user feedback and has demonstrated advantages over traditional recommender systems. However, the classical online paradigm involves iteratively collecting experience via interaction with users, which is expensive and risky. We consider an offline interactive recommendation to exploit arbitrary experience collected by multiple unknown policies. A direct application of policy learning with such fixed experience suffers from the distribution shift. To tackle this issue, we develop a behavior-agnostic off-policy correction framework to make offline interactive recommendation possible. Specifically, we leverage the conservative Q-function to perform off-policy evaluation, which enables learning effective policies from fixed datasets without further interactions. Empirical results on the simulator derived from real-world datasets demonstrate the effectiveness of our proposed offline training framework.
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