Effective Distillation of Table-based Reasoning Ability from LLMs

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their remarkable parameter size and their impressive high requirement of computing resources pose challenges for their practical deployment. Recent research has revealed that specific capabilities of LLMs, such as numerical reasoning, can be transferred to smaller models through distillation. Some studies explore the potential of leveraging LLMs to perform table-based reasoning. Nevertheless, prior to our work, there has been no investigation into the prospect of specialising table reasoning skills in smaller models specifically tailored for table-to-text generation tasks. In this paper, we propose a novel table-based reasoning distillation, with the aim of distilling distilling LLMs into tailored, smaller models specifically designed for table-based reasoning task. Experimental results have shown that a 0.22 billion parameter model (Flan-T5-base) fine-tuned using distilled data, not only achieves a significant improvement compared to traditionally fine-tuned baselines but also surpasses specific LLMs like gpt-3.5-turbo on the scientific table-to-text generation dataset (SciGen). The code and data are released in https://github.com/Bernard-Yang/TableDistill

Identification and profiling of microRNA between back and belly Skin in Rex rabbits (Oryctolagus cuniculus)

[EN] Skin is an important trait for Rex rabbits and skin development is influenced by many processes, including hair follicle cycling, keratinocyte differentiation and formation of coat colour and skin morphogenesis. We identified differentially expressed microRNAs (miRNAs) between the back and belly skin in Rex rabbits. In total, 211 miRNAs (90 upregulated miRNAs and 121 downregulated miRNAs) were identified with a |log2 (fold change)|>1 and P-value<0.05. Using target gene prediction for the miRNAs, differentially expressed predicted target genes were identified and the functional enrichment and signalling pathways of these target genes were processed to reveal their biological functions. A number of differentially expressed miRNAs were found to be involved in regulation of the cell cycle, skin epithelium differentiation, keratinocyte proliferation, hair follicle development and melanogenesis. In addition, target genes regulated by miRNAs play key roles in the activities of the Hedgehog signalling pathway, Wnt signalling pathway, Osteoclast differentiation and MAPK pathway, revealing mechanisms of skin development. Nine candidate miRNAs and 5 predicted target genes were selected for verification of their expression by quantitative reverse transcription polymerase chain reaction. A regulation network of miRNA and their target genes was constructed by analysing the GO enrichment and signalling pathways. Further studies should be carried out to validate the regulatory relationships between candidate miRNAs and their target genes.This study was supported by the Modern Agricultural Industrial System Special Funding (CARS-44-A-1), the Priority Academic Programme Development of Jiangsu Higher Education Institutions (2014-134) and the General Programme of Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJB230001).Zhao, B.; Chen, Y.; Mu, L.; Hu, S.; Wu, X. (2018). Identification and profiling of microRNA between back and belly Skin in Rex rabbits (Oryctolagus cuniculus). World Rabbit Science. 26(2):179-190. https://doi.org/10.4995/wrs.2018.7058SWORD179190262Adamidi C. 2008. Discovering microRNAs from deep sequencing data using miRDeep. Nature Biotechnol., 26: 407-415. https://doi.org/10.1038/nbt1394Adijanto J., Castorino J.J., Wang Z.X., Maminishkis A., Grunwald G.B., Philp N.J. 2012. 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Effective Distillation of Table-based Reasoning Ability from LLMs

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their enormous parameter size and extremely high requirements for compute power pose challenges for their practical deployment. Recent research has revealed that specific capabilities of LLMs, such as numerical reasoning, can be transferred to smaller models through distillation. Some studies explore the potential of leveraging LLMs to perform table-based reasoning. However, there has been no prior work focusing on table reasoning skills in smaller models specifically tailored for scientific table-to-text generation tasks. In this paper, we propose a novel table-based reasoning distillation approach, with the aim of distilling LLMs into tailored smaller models. Our experimental results have shown that a 220 million parameter model (Flan-T5-base) fine-tuned using distilled data, not only achieves a significant improvement compared to traditionally fine-tuned baselines, but also surpasses specific LLMs on a scientific table-to-text generation dataset. Our code is available at https://github.com/Bernard-Yang/DistillTableCoT

Density-on-Density Regression

In this study, a density-on-density regression model is introduced, where the association between densities is elucidated via a warping function. The proposed model has the advantage of a being straightforward demonstration of how one density transforms into another. Using the Riemannian representation of density functions, which is the square-root function (or half density), the model is defined in the correspondingly constructed Riemannian manifold. To estimate the warping function, it is proposed to minimize the average Hellinger distance, which is equivalent to minimizing the average Fisher-Rao distance between densities. An optimization algorithm is introduced by estimating the smooth monotone transformation of the warping function. Asymptotic properties of the proposed estimator are discussed. Simulation studies demonstrate the superior performance of the proposed approach over competing approaches in predicting outcome density functions. Applying to a proteomic-imaging study from the Alzheimer's Disease Neuroimaging Initiative, the proposed approach illustrates the connection between the distribution of protein abundance in the cerebrospinal fluid and the distribution of brain regional volume. Discrepancies among cognitive normal subjects, patients with mild cognitive impairment, and Alzheimer's disease (AD) are identified and the findings are in line with existing knowledge about AD

Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information

The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based automatic evaluation metric (CMN), which can robustly evaluate open-domain dialogues by augmenting Conditional Variational Autoencoders (CVAEs) with a Next Sentence Prediction (NSP) objective and employing Mutual Information (MI) to model the semantic similarity of text in the latent space. Experimental results on two open-domain dialogue datasets demonstrate the superiority of our method compared with a wide range of baselines, especially in handling responses which are distant to the golden reference responses in semantics.Comment: Accepted at ACL202

Characterisation and functional analysis of the WIF1 gene and its role in hair follicle growth and development of the Angora rabbit

[EN] Growth and development of hair follicles (HF) is a complex and dynamic process in most mammals. As HF growth and development regulate rabbit wool yield, exploring the role of genes involved in HF growth and development may be relevant. In this study, the coding sequence of the Angora rabbit (Oryctolagus cuniculus) WIF1 gene was cloned. The length of the coding region sequence was found to be 1140 bp, which encodes 379 amino acids. Bioinformatics analysis indicated that the WIF1 protein was unstable, hydrophilic and located in the extracellular region, contained a putative signal peptide and exhibited a high homology in different mammals. Moreover, WIF1 was significantly downregulated in the high wool production in the Angora rabbit group. Overexpression and knockdown studies revealed that WIF1 regulates HF growth and development-related genes and proteins, such as LEF1 and CCND1. WIF1 activated β-catenin/TCF transcriptional activity, promoted cell apoptosis and inhibited cellular proliferation. These results indicate that WIF1 might be important for HF development. This study, therefore, provides a theoretical foundation for investigating WIF1 in HF growth and development.This research was funded by This research was funded by National Natural Science Foundation of China (Grant No. 32102529), China Agriculture Research System of MOF and MARA (CARS-43-A-1).Zhao, B.; Li, J.; Zhang, X.; Bao, Z.; Chen, Y.; Wu, X. (2022). Characterisation and functional analysis of the WIF1 gene and its role in hair follicle growth and development of the Angora rabbit. World Rabbit Science. 30(3):209-218. https://doi.org/10.4995/wrs.2022.1735320921830

Chromosome-level genome assembly of a high-altitude-adapted frog (Rana kukunoris) from the Tibetan plateau provides insight into amphibian genome evolution and adaptation

Background The high-altitude-adapted frog Rana kukunoris, occurring on the Tibetan plateau, is an excellent model to study life history evolution and adaptation to harsh high-altitude environments. However, genomic resources for this species are still underdeveloped constraining attempts to investigate the underpinnings of adaptation. Results The R. kukunoris genome was assembled to a size of 4.83 Gb and the contig N50 was 1.80 Mb. The 6555 contigs were clustered and ordered into 12 pseudo-chromosomes covering similar to 93.07% of the assembled genome. In total, 32,304 genes were functionally annotated. Synteny analysis between the genomes of R. kukunoris and a low latitude species Rana temporaria showed a high degree of chromosome level synteny with one fusion event between chr11 and chr13 forming pseudo-chromosome 11 in R. kukunoris. Characterization of features of the R. kukunoris genome identified that 61.5% consisted of transposable elements and expansions of gene families related to cell nucleus structure and taste sense were identified. Ninety-five single-copy orthologous genes were identified as being under positive selection and had functions associated with the positive regulation of proteins in the catabolic process and negative regulation of developmental growth. These gene family expansions and positively selected genes indicate regions for further interrogation to understand adaptation to high altitude. Conclusions Here, we reported a high-quality chromosome-level genome assembly of a high-altitude amphibian species using a combination of Illumina, PacBio and Hi-C sequencing technologies. This genome assembly provides a valuable resource for subsequent research on R. kukunoris genomics and amphibian genome evolution in general.Peer reviewe

Identification and validation of a gap junction protein related signature for predicting the prognosis of renal clear cell carcinoma

BackgroundGap junction proteins (GJPs) are a class of channel proteins that are closely related to cell communication and tumor development. The objective of this study was to screen out GJPs related prognostic signatures (GRPS) associated with clear cell renal cell carcinoma (ccRCC).Materials and MethodsGJPs microarray data for ccRCC patients were obtained from The Gene Expression Omnibus (GEO) database, along with RNA sequencing data for tumor and paired normal tissues from The Cancer Genome Atlas (TCGA) database. In the TCGA database, least absolute shrinkage and selection Operator (LASSO) and Cox regression models were used to identify GJPs with independent prognostic effects as GRPS in ccRCC patients. According to the GRPS expression and regression coefficient from the multivariate Cox regression model, the risk score (RS) of each ccRCC patient was calculated, to construct the RS prognostic model to predict survival. Overall survival (OS) and progression-free survival (PFS) analyses; gene pan-cancer analysis; single gene survival analysis; gene joint effect analysis; functional enrichment analysis; tumor microenvironment (TME) analysis; tumor mutational burden (TMB) analysis; and drug sensitivity analysis were used to explore the biological function, mechanism of action and clinical significance of GRPS in ccRCC. Further verification of the genetic signature was performed with data from the GEO database. Finally, the cytofunctional experiments were used to verify the biological significance of GRPS associated GJPs in ccRCC cell lines.ResultsGJA5 and GJB1, which are GRPS markers of ccRCC patients, were identified through LASSO and Cox regression models. Low expression of GJA5 and GJB1 is associated with poor patient prognosis. Patients with high-RS had significantly shorter OS and PFS than patients with low-RS (p&lt; 0.001). The risk of death for individuals with high-RS was 1.695 times greater than that for those with low-RS (HR = 1.695, 95%CI= 1.439-1.996, p&lt; 0.001). Receiver Operating Characteristic (ROC) curve showed the great predictive power of the RS prognostic model for the survival rate of patients. The area under curve (AUC) values for predicting 1-year, 3-year and 5-year survival rates were 0.740, 0.781 and 0.771, respectively. The clinical column chart was also reliable for predicting the survival rate of patients, with AUC values of 0.859, 0.846 and 0.796 for predicting 1-year, 3-year and 5-year survival, respectively. The GRPS was associated with immune cell infiltration, the TME, the TMB, and sensitivity to chemotherapy drugs. Further in vitro experiments showed that knockdown of GJA5 or GJB1 could promote the proliferation, migration and epithelial-mesenchymal transition (EMT) and inhibit apoptosis of ccRCC cells.ConclusionGJA5 and GJB1 could be potential biological markers for predicting survival in patients with ccRCC

Systematic Analysis of Non-coding RNAs Involved in the Angora Rabbit (Oryctolagus cuniculus) Hair Follicle Cycle by RNA Sequencing

The hair follicle (HF) cycle is a complicated and dynamic process in mammals, associated with various signaling pathways and gene expression patterns. Non-coding RNAs (ncRNAs) are RNA molecules that are not translated into proteins but are involved in the regulation of various cellular and biological processes. This study explored the relationship between ncRNAs and the HF cycle by developing a synchronization model in Angora rabbits. Transcriptome analysis was performed to investigate ncRNAs and mRNAs associated with the various stages of the HF cycle. One hundred and eleven long non-coding RNAs (lncRNAs), 247 circular RNAs (circRNAs), 97 microRNAs (miRNAs), and 1,168 mRNAs were differentially expressed during the three HF growth stages. Quantitative real-time PCR was used to validate the ncRNA transcriptome analysis results. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses provided information on the possible roles of ncRNAs and mRNAs during the HF cycle. In addition, lncRNA–miRNA–mRNA and circRNA–miRNA–mRNA ceRNA networks were constructed to investigate the underlying relationships between ncRNAs and mRNAs. LNC_002919 and novel_circ_0026326 were found to act as ceRNAs and participated in the regulation of the HF cycle as miR-320-3p sponges. This research comprehensively identified candidate regulatory ncRNAs during the HF cycle by transcriptome analysis, highlighting the possible association between ncRNAs and the regulation of hair growth. This study provides a basis for systematic further research and new insights on the regulation of the HF cycle