164 research outputs found
MicroRNA-141-3p mediates epithelial cell proliferation, apoptosis, and epithelial-mesenchymal transition and alleviates pulmonary fibrosis in mice via Spred2
Objective. This study probed the mechanism of microRNA (miR)-141-3p in the progression of pulmonary fibrosis (PF). Methods. Mice were intratracheally administered with bleomycin (BLM) to establish a PF mouse model. To investigate the effects of miR-141-3p/Spred2 on PF in mice, PF mice received tail vein injections with agomir-141-3p and/or adenovirus vectors overexpressing Spred2 one week after BLM treatment. Then, the pathological changes of lung tissues were analyzed with H&E and Masson’s trichrome staining, and hydroxyproline contents in lung tissues were measured. For cell experiments, after loss- and gain-of-function assays, the role of miR-141-3p/Spred2 in the apoptosis and viability of TGF-β1-stimulated MLE-12 cells was examined by flow cytometry and CCK-8 assay, respectively. miR-141-3p, Spred2, COl 1, and α-SMA expression was determined in cells and mice. Then, the binding of miR-141-3p to Spred2 was tested with a dualluciferase reporter assay. Results. There were abnormally upregulated Spred2 and downregulated miR-141-3p in lung tissues of PF mice. TGF-β1 decelerated viability and augmented apoptosis and COl 1 and α-SMA expression in MLE-12 cells. Spred2 knockdown diminished apoptosis and αSMA and COl 1 expression while enhancing proliferation in TGF-β1-treated MLE-12 cells. Mechanistically, Spred2 was a target gene of miR-1413p. miR-141-3p upregulation accelerated proliferation and repressed apoptosis and α-SMA and COl 1 expression in TGF-β1-treated MLE-12 cells, which was nullified by further overexpressing Spred2. miR-141-3p alleviated PF in mice by targeting Spred2. Conclusion. miR-141-3p negatively modulates Spred2 to promote proliferation and repress epithelialmesenchymal transition and apoptosis of epithelial cells, as well as ameliorating PF in mic
Optimal treatment allocation for efficient policy evaluation in sequential decision making
A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately. We propose three optimal allocation strategies in a dynamic setting where treatments are sequentially assigned over time. These strategies are designed to minimize the variance of the treatment effect estimator when data follow a non-Markov decision process or a (time-varying) Markov decision process. We further develop estimation procedures based on existing off-policy evaluation (OPE) methods and conduct extensive experiments in various environments to demonstrate the effectiveness of the proposed methodologies. In theory, we prove the optimality of the proposed treatment allocation design and establish upper bounds for the mean squared errors of the resulting treatment effect estimator
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation
The task of Question Generation over Knowledge Bases (KBQG) aims to convert a
logical form into a natural language question. For the sake of expensive cost
of large-scale question annotation, the methods of KBQG under low-resource
scenarios urgently need to be developed. However, current methods heavily rely
on annotated data for fine-tuning, which is not well-suited for few-shot
question generation. The emergence of Large Language Models (LLMs) has shown
their impressive generalization ability in few-shot tasks. Inspired by
Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for
reasoning, we formulate KBQG task as a reasoning problem, where the generation
of a complete question is splitted into a series of sub-question generation.
Our proposed prompting method KQG-CoT first retrieves supportive logical forms
from the unlabeled data pool taking account of the characteristics of the
logical form. Then, we write a prompt to explicit the reasoning chain of
generating complicated questions based on the selected demonstrations. To
further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the
logical forms by their complexity. We conduct extensive experiments over three
public KBQG datasets. The results demonstrate that our prompting method
consistently outperforms other prompting baselines on the evaluated datasets.
Remarkably, our KQG-CoT+ method could surpass existing few-shot SoTA results of
the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4,
METEOR, and ROUGE-L, respectively.Comment: Accepted by EMNLP 2023 main conferenc
Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability
Out-of-distribution (OOD) detection is an indispensable aspect of secure AI
when deploying machine learning models in real-world applications. Previous
paradigms either explore better scoring functions or utilize the knowledge of
outliers to equip the models with the ability of OOD detection. However, few of
them pay attention to the intrinsic OOD detection capability of the given
model. In this work, we generally discover the existence of an intermediate
stage of a model trained on in-distribution (ID) data having higher OOD
detection performance than that of its final stage across different settings,
and further identify one critical data-level attribution to be learning with
the atypical samples. Based on such insights, we propose a novel method,
Unleashing Mask, which aims to restore the OOD discriminative capabilities of
the well-trained model with ID data. Our method utilizes a mask to figure out
the memorized atypical samples, and then finetune the model or prune it with
the introduced mask to forget them. Extensive experiments and analysis
demonstrate the effectiveness of our method. The code is available at:
https://github.com/tmlr-group/Unleashing-Mask.Comment: accepted by ICML 202
Poly[diaqua(μ3-8-oxidoquinoline-5-sulfonato-κ4 N,O 8:O 5:O 8)nickel(II)]
In title compound, [Ni(C9H5NO4S)(H2O)2]n, the NiII atom is coordinated by one N atom and two bridging O atoms from two 8-oxidoquinoline-5-sulfonate ligands, one sulfonate O atom from a third ligand, and two water molecules in a distorted octahedral geometry. The two NiII atoms are linked to each other through the bridging O atoms, forming a dimer. Adjacent dimers are connected through the coordination of the sulfonate O atom into a two-dimensional coordination network parallel to (010). Hydrogen bonds between the coordinated water molecules and the uncoordinated O atoms of the sulfonate groups result in the construction of a three-dimensional supramolecular structure
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation
Out-of-distribution (OOD) detection is important for deploying reliable
machine learning models on real-world applications. Recent advances in outlier
exposure have shown promising results on OOD detection via fine-tuning model
with informatively sampled auxiliary outliers. However, previous methods assume
that the collected outliers can be sufficiently large and representative to
cover the boundary between ID and OOD data, which might be impractical and
challenging. In this work, we propose a novel framework, namely, Diversified
Outlier Exposure (DivOE), for effective OOD detection via informative
extrapolation based on the given auxiliary outliers. Specifically, DivOE
introduces a new learning objective, which diversifies the auxiliary
distribution by explicitly synthesizing more informative outliers for
extrapolation during training. It leverages a multi-step optimization method to
generate novel outliers beyond the original ones, which is compatible with many
variants of outlier exposure. Extensive experiments and analyses have been
conducted to characterize and demonstrate the effectiveness of the proposed
DivOE. The code is publicly available at: https://github.com/tmlr-group/DivOE.Comment: accepted by NeurIPS 202
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