11 research outputs found
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
Phosphorus Solubilizing and Releasing Bacteria Screening from the Rhizosphere in a Natural Wetland
Inorganic phosphorus (P)-solubilizing bacteria (IPSB) and organic P-mineralizing bacteria (OPMB) were isolated from bacteria that were first extracted from the rhizosphere soil of a natural wetland and then grown on either tricalcium phosphate or lecithin medium. The solubilizing of inorganic P was the major contribution to P availability, since the isolated bacteria released much more available P from inorganic tricalcium phosphate than lecithin. IPSB No. 5 had the highest P release rate, that is, 0.53 mg·L−1·h−1 in 96 h, and R10′s release rate was 0.52 mg·L−1·h−1 in 10 days. The bacteria were identified as Pseudomonas sp. and Pseudomonas knackmussii, respectively. R10 released as much as 125.88 mg·L−1 dissolved P from tricalcium phosphate medium, while R4 released the most dissolved P from organic P medium among the isolates, with a concentration of 1.88 mg·L−1 and a releasing rate of 0.0078 mg·L−1·h−1 in ten days. P releasing increased with a pH decrease only when it was from inorganic P, not organic lecithin, and there was no significant correlation between the culture pH and P solubilizing. High-throughput sequencing analysis revealed that the dominant phylum in the studied wetland rhizosphere consisted of Acidobacteria, Proteobacteria, Bacteroidetes and Chloroflexi, accounting for 34.9%, 34.2%, 8.8% and 4.8%, respectively
Hormonal Regulation and Transcriptomic Insights into Flower Development in <i>Hydrangea paniculata</i> ‘Vanilla Strawberry’
Understanding the molecular mechanisms that regulate flower growth, development, and opening is of paramount importance, yet these processes remain less explored at the genetic level. Flower development in Hydrangea paniculata ‘Vanilla Strawberry’ is finely tuned through hormonal signals, yet the genetic underpinnings are not well defined. This study addresses the gap by examining the influence of gibberellic acid (GA3), salicylic acid (SA), and ethylene (ETH) on the flowering traits and underlying molecular responses. Treatment with 100 mg/L SA significantly improved chlorophyll content and bolstered the accumulation of soluble sugars and proteins, advancing the flowering onset by 6 days and lengthening the flowering period by 11 days. Concurrently, this treatment enhanced inflorescence dimensions, increasing length, width, and petal area by 22.76%, 26.74%, and 27.45%, respectively. Contrastingly, 100 mg/L GA3 expanded inflorescence size but postponed flowering initiation and decreased inflorescence count. Higher concentrations of SA and GA3, as well as any concentration of ETH, resulted in delayed flowering and inferior inflorescence attributes. A physiological analysis over 50 days revealed that these regulators variably affected sugar and protein levels and modified antioxidant enzyme activities. An RNA-seq analysis during floral development highlighted significant transcriptomic reprogramming, with SA treatment downregulating Myb transcription factors, implicating them in the modulation of flowering timing and stress adaptation. These findings illuminate the complex interplay between hormonal treatments, gene expression, and flowering phenotypes in Hydrangea paniculata, offering valuable perspectives for ornamental horticulture optimization
Label-Free Imaging of Blood Vessels in Human Normal Breast and Breast Tumor Tissue Using Multiphoton Microscopy
Multi-omics analyses demonstrate the modulating role of gut microbiota on the associations of unbalanced dietary intake with gastrointestinal symptoms in children with autism spectrum disorder
ABSTRACTOur previous work revealed that unbalanced dietary intake was an important independent factor associated with constipation and gastrointestinal (GI) symptoms in children with autism spectrum disorder (ASD). Growing evidence has shown the alterations in the gut microbiota and gut microbiota-derived metabolites in ASD. However, how the altered microbiota might affect the associations between unbalanced diets and GI symptoms in ASD remains unknown. We analyzed microbiome and metabolomics data in 90 ASD and 90 typically developing (TD) children based on 16S rRNA and untargeted metabolomics, together with dietary intake and GI symptoms assessment. We found that there existed 11 altered gut microbiota (FDR-corrected P-value <0.05) and 397 altered metabolites (P-value <0.05) in children with ASD compared with TD children. Among the 11 altered microbiota, the Turicibacter, Coprococcus 1, and Lachnospiraceae FCS020 group were positively correlated with constipation (FDR-corrected P-value <0.25). The Eggerthellaceae was positively correlated with total GI symptoms (FDR-corrected P-value <0.25). More importantly, three increased microbiota including Turicibacter, Coprococcus 1, and Eggerthellaceae positively modulated the associations of unbalanced dietary intake with constipation and total GI symptoms, and the decreased Clostridium sp. BR31 negatively modulated their associations in ASD children (P-value <0.05). Together, the altered microbiota strengthens the relationship between unbalanced dietary intake and GI symptoms. Among the altered metabolites, ten metabolites derived from microbiota (Turicibacter, Coprococcus 1, Eggerthellaceae, and Clostridium sp. BR31) were screened out, enriched in eight metabolic pathways, and were identified to correlate with constipation and total GI symptoms in ASD children (FDR-corrected P-value <0.25). These metabolomics findings further support the modulating role of gut microbiota on the associations of unbalanced dietary intake with GI symptoms. Collectively, our research provides insights into the relationship between diet, the gut microbiota, and GI symptoms in children with ASD