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...

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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.

Widespread death of transplanted mesenchymal stem cells (MSCs) hampers the development of stem cell therapy for Alzheimer disease (AD). Cell pre-conditioning might help cope with this challenge. We tested whether let-7f-5p-modified MSCs could prolong the survival of MSCs after transplantation. When exposed to Aβ25−35 in vitro, MSCs showed significant early apoptosis with decrease in the let-7f-5p levels and increased caspase-3 expression. Upregulating microRNA let-7f-5p in MSCs alleviated Aβ25−35-induced apoptosis by decreasing the caspase-3 levels. After computerized analysis and the luciferase reporter assay, we identified that caspases-3 was the target gene of let-7f-5p. In vivo, hematoxylin and eosin staining confirmed the success of MSCs transplantation into the lateral ventricles, and the let-7f-5p upregulation group showed the lowest apoptotic rate of MSCs detected by TUNEL immunohistochemistry analysis and immunofluorescence. Similarly, bioluminescent imaging showed that let-7f-5p upregulation moderately prolonged the retention of MSCs in brain. In summary, we identified the anti-apoptotic role of let-7f-5p in Aβ25−35-induced cytotoxicity, as well as the protective effect of let-7f-5p on survival of grafted MSCs by targeting caspase-3 in AD models. These findings show a promising approach of microRNA-modified MSCs transplantation as a therapy for neurodegenerative diseases.

One of the major challenges in training text-to-image generation models is the need of a large number of highquality image-text pairs. While image samples are often easily accessible, the associated text descriptions typically require careful human captioning, which is particularly time-and cost-consuming. In this paper, we propose the first work to train text-to-image generation models without any text data. Our method leverages the well-aligned multimodal semantic space of the powerful pre-trained CLIP model: the requirement of text-conditioning is seamlessly alleviated via generating text features from image features. Extensive experiments are conducted to illustrate the effectiveness of the proposed method. We obtain state-of-theart results in the standard text-to-image generation tasks. Importantly, the proposed language-free model outperforms most existing models trained with full image-text pairs. Furthermore, our method can be applied in fine-tuning pretrained models, which saves both training time and cost in training text-to-image generation models. Our pre-trained model obtains competitive results in zero-shot text-to-image generation on the MS-COCO dataset, yet with around only 1% of the model size and training data size relative to the recently proposed large DALL-E model.

Spontaneous intracerebral haemorrhage (ICH) is a devastating type of stroke with high mortality and morbidity and for which no effective treatments are available to date. Much experimental and clinical research have been performed to explore its mechanisms regard the subsequent inflammatory cascade and to seek the potential therapeutic strategies. The aim of this review is to discuss insights from clinical settings that have led to the development of numerous animal models of ICH. Some of the current and future challenges for clinicians to understand ICH are also surveyed.

Customizing pre-trained text-to-image generation model has attracted massive research interest recently, due to its huge potential in real-world applications. Although existing methods are able to generate creative content for a novel concept contained in single user-input image, their capability are still far from perfection. Specifically, most existing methods require fine-tuning the generative model on testing images. Some existing methods do not require fine-tuning, while their performance are unsatisfactory. Furthermore, the interaction between users and models are still limited to directive and descriptive prompts such as instructions and captions. In this work, we build a customization assistant based on pre-trained large language model and diffusion model, which can not only perform customized generation in a tuning-free manner, but also enable more user-friendly interactions: users can chat with the assistant and input either ambiguous text or clear instruction. Specifically, we propose a new framework consists of a new model design and a novel training strategy. The resulting assistant can perform customized generation in 2-5 seconds without any test time fine-tuning. Extensive experiments are conducted, competitive results have been obtained across different domains, illustrating the effectiveness of the proposed method.