CD8+ Trms against malaria liver-stage: prospects and challenges

Attenuated sporozoites provide a valuable model for exploring protective immunity against the malarial liver stage, guiding the design of highly efficient vaccines to prevent malaria infection. Liver tissue-resident CD8+ T cells (CD8+ Trm cells) are considered the host front-line defense against malaria and are crucial to developing prime-trap/target strategies for pre-erythrocytic stage vaccine immunization. However, the spatiotemporal regulatory mechanism of the generation of liver CD8+ Trm cells and their responses to sporozoite challenge, as well as the protective antigens they recognize remain largely unknown. Here, we discuss the knowledge gap regarding liver CD8+ Trm cell formation and the potential strategies to identify predominant protective antigens expressed in the exoerythrocytic stage, which is essential for high-efficacy malaria subunit pre-erythrocytic vaccine designation

Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension

In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or from the target to the source to aid inference. However, in cross-lingual machine reading comprehension (MRC), it is difficult to perform a deep level of assistance to enhance cross-lingual transfer because of the variation of answer span positions in different languages. In this paper, we propose X-STA, a new approach for cross-lingual MRC. Specifically, we leverage an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. A Gradient-Disentangled Knowledge Sharing technique is proposed as an improved cross-attention block. In addition, we force the model to learn semantic alignments from multiple granularities and calibrate the model outputs with teacher guidance to enhance cross-lingual transferability. Experiments on three multi-lingual MRC datasets show the effectiveness of our method, outperforming state-of-the-art approaches.Comment: emnlp 202

BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

Recently, diffusion-based deep generative models (e.g., Stable Diffusion) have shown impressive results in text-to-image synthesis. However, current text-to-image models often require multiple passes of prompt engineering by humans in order to produce satisfactory results for real-world applications. We propose BeautifulPrompt, a deep generative model to produce high-quality prompts from very simple raw descriptions, which enables diffusion-based models to generate more beautiful images. In our work, we first fine-tuned the BeautifulPrompt model over low-quality and high-quality collecting prompt pairs. Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores. Our results demonstrate that learning from visual AI feedback promises the potential to improve the quality of generated prompts and images significantly. We further showcase the integration of BeautifulPrompt to a cloud-native AI platform to provide better text-to-image generation service in the cloud.Comment: emnlp 202

Can acoustic indices reflect the characteristics of public recreational behavioral in urban green spaces?

Acoustic indicators serve as an effective means of assessing the quality of urban green space soundscape. The informative, easy accessibility and non-invasive nature of acoustic monitoring renders it an excellent tool for studying the interaction among the natural environment, wildlife, and human activities. Urban green space is essential in the urban ecosystem and constitutes the primary location for public outdoor recreation. However, the existing methods for monitoring public recreational behavior, such as on-site observation, drone observation, or questionnaire interviews, require significant labor or professional expertise. All of these methods have their limitations, so there is still much to be researched in the acoustic indices and recreational behavior. As a result, the potential for using acoustic characteristics to monitor public recreational behavior remains underexplored. To address this gap, this study investigates the potential of 5 widely used acoustic indices and acoustic intensity for monitoring public recreational behavior: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Richness (AR), Normalized Difference Soundscape Index (NDSI), and Power Spectral Density (PSD). Data were collected from 35 monitoring points in urban green spaces during the opening hours (6:00–22:00) to analyze the relationship between these indices and public recreational behavior. The findings indicate that (1) ACI, ADI, and AR daily exhibited multi-peak daily variation characteristics similar to those of public recreational behavior, displaying a “W” shape, while NDSI exhibits opposite variation characteristics; (2) the spatial variation characteristics of ACI, ADI, and AR change in response to the green space, and these changes align with public recreational behavior; (3) the correlation analysis and generalized linear mixed model construction further demonstrate that acoustic indices are effective in capturing the dynamic activities of visitor behavior; and (4) PSD undergoes significant temporal dynamic changes along the frequency gradient, with different frequency intervals reflecting the activity information of different recreational behaviors. In conclusion, this research highlights the effectiveness of using acoustic indices to analyze both the spatial and temporal variation characteristics of public recreational behavior in urban green spaces. The results can provide valuable data support for the enhancement and renovation of urban green spaces

Removing Orbital Debris with Lasers

Orbital debris in low Earth orbit (LEO) are now sufficiently dense that the use of LEO space is threatened by runaway collisional cascading. A problem predicted more than thirty years ago, the threat from debris larger than about 1 cm demands serious attention. A promising proposed solution uses a high power pulsed laser system on the Earth to make plasma jets on the objects, slowing them slightly, and causing them to re-enter and burn up in the atmosphere. In this paper, we reassess this approach in light of recent advances in low-cost, light-weight modular design for large mirrors, calculations of laser-induced orbit changes and in design of repetitive, multi-kilojoule lasers, that build on inertial fusion research. These advances now suggest that laser orbital debris removal (LODR) is the most cost-effective way to mitigate the debris problem. No other solutions have been proposed that address the whole problem of large and small debris. A LODR system will have multiple uses beyond debris removal. International cooperation will be essential for building and operating such a system.Comment: 37 pages, 15 figures, in preparation for submission to Advances in Space Researc

Adverse drug events associated with linezolid administration: a real-world pharmacovigilance study from 2004 to 2023 using the FAERS database

Introduction: Linezolid is an oxazolidinone antibiotic that is active against drug-resistant Gram-positive bacteria and multidrug-resistant Mycobacterium tuberculosis. Real-world studies on the safety of linezolid in large populations are lacking. This study aimed to determine the adverse events associated with linezolid in real-world settings by analyzing data from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS).Methods: We retrospectively extracted reports on adverse drug events (ADEs) from the FAERS database from the first quarter of 2004 to that of 2023. By using disproportionality analysis including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), along with the multi-item gamma Poisson shrinker (MGPS), we evaluated whether there was a significant association between linezolid and ADE. The time to onset of ADE was further analyzed in the general population and within each age, weight, reporting population, and weight subgroups.Results: A total of 11,176 reports of linezolid as the “primary suspected” drug and 263 significant adverse events of linezolid were identified, including some common adverse events such as thrombocytopenia (n = 1,139, ROR 21.98), anaemia (n = 704, ROR 7.39), and unexpected signals that were not listed on the drug label such as rhabdomyolysis (n = 90, ROR 4.33), and electrocardiogram QT prolonged (n = 73, ROR 4.07). Linezolid-induced adverse reactions involved 27 System Organ Class (SOC). Gender differences existed in ADE signals related to linezolid. The median onset time of all ADEs was 6 days, and most ADEs (n = 3,778) occurred within the first month of linezolid use but some may continue to occur even after a year of treatment (n = 46).Conclusion: This study reports the time to onset of adverse effects in detail at the levels of SOC and specific preferred term (PT). The results of our study provide valuable insights for optimizing the use of linezolid and reducing potential side effects, expected to facilitate the safe use of linezolid in clinical settings