Revisiting Galaxy Evolution in Morphology in the COSMOS field (COSMOS-ReGEM):I. Merging Galaxies

We revisit the evolution of galaxy morphology in the COSMOS field over the redshift range $0.2\leq z \leq 1$, using a large and complete sample of 33,605 galaxies with a stellar mass of log($M_{\ast}$/M$_{\odot} )>9.5$ with significantly improved redshifts and comprehensive non-parametric morphological parameters. Our sample has 13,881 ($\sim41.3\%$) galaxies with reliable spectroscopic redshifts and has more accurate photometric redshifts with a $\sigma_{\rm NMAD} \sim 0.005$. This paper is the first in a series that investigates merging galaxies and their properties. We identify 3,594 major merging galaxies through visual inspection and find 1,737 massive galaxy pairs with log($M_\ast$/M$_\odot$)$>10.1$. Among the family of non-parametric morphological parameters including $C$, $A$, $S$, $Gini$, $M_{\rm 20}$, $A_{\rm O}$, and $D_{\rm O}$, we find that the outer asymmetry parameter $A_{\rm O}$ and the second-order momentum parameter $M_{\rm 20}$ are the best tracers of merging features than other combinations. Hence, we propose a criterion for selecting candidates of violently star-forming mergers: $M_{\rm 20}> -3A_{\rm O}+3$ at $0.2 -6A_{\rm O}+3.7$ at $0.6<z<1.0$. Furthermore, we show that both the visual merger sample and the pair sample exhibit a similar evolution in the merger rate at $z<1$, with $\Re \sim(1+z)^{1.79 \pm 0.13}$ for the visual merger sample and $\Re \sim(1+z)^{2.02\pm 0.42}$ for the pair sample. The visual merger sample has a specific star formation rate that is about 0.16\,dex higher than that of non-merger galaxies, whereas no significant star formation excess is observed in the pair sample. This suggests that the effects of mergers on star formation differ at different merger stages.Comment: 21 pages, 12 figures; accepted for publication in Ap

XPrompt: Exploring the Extreme of Prompt Tuning

Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner. While prompt tuning has gradually reached the performance level of fine-tuning as the model scale increases, there is still a large performance gap between prompt tuning and fine-tuning for models of moderate and small scales (typically less than 11B parameters). In this paper, we empirically show that the trained prompt tokens can have a negative impact on a downstream task and thus degrade its performance. To bridge the gap, we propose a novel Prompt tuning model with an eXtremely small scale (XPrompt) under the regime of lottery tickets hypothesis. Specifically, XPrompt eliminates the negative prompt tokens at different granularity levels through a hierarchical structured pruning, yielding a more parameter-efficient prompt yet with a competitive performance. Comprehensive experiments are carried out on SuperGLUE tasks, and the extensive results indicate that XPrompt is able to close the performance gap at smaller model scales.Comment: 15 pages, accepted to EMNLP 2022 main conferenc

LLM-Rec: Personalized Recommendation via Prompting Large Language Models

We investigate various prompting strategies for enhancing personalized recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct prompting strategies: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that incorporating the augmented input text generated by LLM leads to improved recommendation performance. Recommendation-driven and engagement-guided prompting strategies are found to elicit LLM's understanding of global and local item characteristics. This finding highlights the importance of leveraging diverse prompts and input augmentation techniques to enhance the recommendation capabilities with LLMs

Prioritizing human cancer microRNAs based on genes’ functional consistency between microRNA and cancer

The identification of human cancer-related microRNAs (miRNAs) is important for cancer biology research. Although several identification methods have achieved remarkable success, they have overlooked the functional information associated with miRNAs. We present a computational framework that can be used to prioritize human cancer miRNAs by measuring the association between cancer and miRNAs based on the functional consistency score (FCS) of the miRNA target genes and the cancer-related genes. This approach proved successful in identifying the validated cancer miRNAs for 11 common human cancers with area under ROC curve (AUC) ranging from 71.15% to 96.36%. The FCS method had a significant advantage over miRNA differential expression analysis when identifying cancer-related miRNAs with a fine regulatory mechanism, such as miR-27a in colorectal cancer. Furthermore, a case study examining thyroid cancer showed that the FCS method can uncover novel cancer-related miRNAs such as miR-27a/b, which were showed significantly upregulated in thyroid cancer samples by qRT-PCR analysis. Our method can be used on a web-based server, CMP (cancer miRNA prioritization) and is freely accessible at http://bioinfo.hrbmu.edu.cn/CMP. This time- and cost-effective computational framework can be a valuable complement to experimental studies and can assist with future studies of miRNA involvement in the pathogenesis of cancers

Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material

To effectively reuse waste limestone powder, which is a major solid waste around mines, we replaced limestone powder back into a part of cement in solid waste cemented paste backfill (SWCPB) and studied the parameters of pore structures. To optimize the pore microstructure characteristics of SWCPB in mines, two different components and grade tailings were selected. The samples were characterized by scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR) to examine the pore properties and microstructure of SWCPB. The results showed that (1) at the later curing stage, with the optimization of pore characteristics and microstructure through the limestone powder admixture, the strength of SWCFB was guaranteed at a 20% replacement degree of cement. (2) Porosity, macropore proportion, and the average pore radius all negatively correlated with limestone powder content, which were reduced by 7.15%, 46.35%, and 16.37%, respectively. (3) Limestone powder as a crystal nucleus participated in the hydration reaction and was embedded into the product to enhance the strength

Strength Characteristics and the Reaction Mechanism of Stone Powder Cement Tailings Backfill

Stone powder cement (SPC) is widely used as a novel cement substitute material in concrete for its good gelling performance and low cost. In order to reduce the backfilling cost and assess the potential of SPC backfilling materials, a series of experiments were conducted to analyze the strength and hydration reaction mechanism of stone powder cement tailings backfill (SPCTB). The analysis was based on SPC and tailings, which were used as the gelling agent and the aggregate, respectively. The results showed that the strength of the backfill was greatly reduced at an early stage and slightly reduced in the final stages. The stone powder content was less than 15%, which met the requirement of mining procedure. The addition of stone powder reduced the content of adsorbed water and capillary water in the early stages, while it increased in the middle stages. The SiO2 contained in stone powder reacted with the hydration products at later stages, which is the reason why the growth of strength is rapid between the groups with the addition of stone powder. The addition of stone powder improved the microstructure of backfill and produced a denser three-dimensional (3D) network structure; however, the plane porosities of Groups A and B gradually increased with the increase in the content of stone powder. The cement powder mixed appropriately with the stone power could meet the strength requirement and reduce the cost of backfilling materials

Establishing a prognostic model based on immune-related genes and identification of BIRC5 as a potential biomarker for lung adenocarcinoma patients

Abstract Background Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction. Methods Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments. Results Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation. Conclusion This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients