Laplacian spectral characterization of some double starlike trees

A tree is called double starlike if it has exactly two vertices of degree greater than two. Let $H(p,n,q)$ denote the double starlike tree obtained by attaching $p$ pendant vertices to one pendant vertex of the path $P_n$ and $q$ pendant vertices to the other pendant vertex of $P_n$. In this paper, we prove that $H(p,n,q)$ is determined by its Laplacian spectrum

Spectral characterizations of sandglass graphs

a b s t r a c t The sandglass graph is obtained by appending a triangle to each pendant vertex of a path. It is proved that sandglass graphs are determined by their adjacency spectra as well as their Laplacian spectra

Plant buffering against the high-light stress-induced accumulation of CsGA2ox8 transcripts via alternative splicing to finely tune gibberellin levels and maintain hypocotyl elongation

Ajuts: this study was supported by The National Key Research and Development Program of China (2019YFD1000300), the International Postdoctoral Exchange Fellowship Program from the China Postdoctoral Council (20170053), the Technology System Construction of Modern Agricultural Industry of Shanghai (19Z113040008), and the Presidential Foundation of Guangdong Academy of Agricultural Sciences (BZ201901).In plants, alternative splicing (AS) is markedly induced in response to environmental stresses, but it is unclear why plants generate multiple transcripts under stress conditions. In this study, RNA-seq was performed to identify AS events in cucumber seedlings grown under different light intensities. We identified a novel transcript of the gibberellin (GA)-deactivating enzyme Gibberellin 2-beta-dioxygenase 8 (CsGA2ox8). Compared with canonical CsGA2ox8.1, the CsGA2ox8.2 isoform presented intron retention between the second and third exons. Functional analysis proved that the transcript of CsGA2ox8.1 but not CsGA2ox8.2 played a role in the deactivation of bioactive GAs. Moreover, expression analysis demonstrated that both transcripts were upregulated by increased light intensity, but the expression level of CsGA2ox8.1 increased slowly when the light intensity was >400 µmol·m −2 ·s −1 PPFD (photosynthetic photon flux density), while the CsGA2ox8.2 transcript levels increased rapidly when the light intensity was >200 µmol·m −2 ·s −1 PPFD. Our findings provide evidence that plants might finely tune their GA levels by buffering against the normal transcripts of CsGA2ox8 through AS

Ask One More Time: Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios

Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality. To address this shortcoming, ensemble-optimization tries to obtain multiple reasoning paths to get the final answer assembly. However, current ensemble-optimization methods either simply employ rule-based post-processing such as \textit{self-consistency}, or train an additional model based on several task-related human annotations to select the best one among multiple reasoning paths, yet fail to generalize to realistic settings where the type of input questions is unknown or the answer format of reasoning paths is unknown. To avoid their limitations, we propose \textbf{self-agreement}, a generalizable ensemble-optimization method applying in almost all scenarios where the type of input questions and the answer format of reasoning paths may be known or unknown. Self-agreement firstly samples from language model's decoder to generate a \textit{diverse} set of reasoning paths, and subsequently prompts the language model \textit{one more time} to determine the optimal answer by selecting the most \textit{agreed} answer among the sampled reasoning paths. Self-agreement simultaneously achieves remarkable performance on six public reasoning benchmarks and superior generalization capabilities.Comment: Work in progres

What Makes an Elite Shooter and Archer? The Critical Role of Interoceptive Attention

It is well-acknowledged that attention is important for expert performance in sports. However, the role of interoceptive attention, i.e., the attentional mechanism of awareness and conscious focus of bodily somatic and visceral signals, in self-paced and far-aiming sports remains to be explored. This study aims to investigate the relationship of expertise level and interoceptive attention ability in shooting and archery, and to examine if interoceptive attention can be improved by mindfulness training in elite athletes of shooting and archery. We tested the performance differences of 41 elite athletes from the Chinese national team of shooting and archery and 43 non-elite athletes from a provincial team in breath detection task (BDT) and dot flash detection task (DDT), which were designed to measure interoceptive and exteroceptive attention (i.e., attention toward information input of primary sensory), respectively. Furthermore, we applied mindfulness training to the 41 elite athletes for 5–8 weeks and remeasured their performances of BDT and DDT. Results showed that elite athletes outperformed non-elite athletes in BDT (but not in DDT) both in accuracy (DiffBDT = 11.50%, p = 0.004) and in discrimination sensitivity (d′, DiffBDT = 1.159, p = 0.002). Difference in accuracy and d′ reached significant level only in BDT (accuracy: DiffBDT = −8.50%, p = 0.001; d′: DiffBDT = −0.822, p = 0.003) before and after mindfulness training. These results indicate that elite athletes of shooting and archery (i.e., relative to non-elite athletes) can better perceive the somatic and visceral responses or changes and discriminate these signals from noises. Moreover, interoceptive attention can be improved by mindfulness training. These results have important implications for the selection and training of athletes of shooting and archery

Systems Biology Analysis of the Effect and Mechanism of Qi-Jing-Sheng-Bai Granule on Leucopenia in Mice

Qi-Jing-Sheng-Bai granule (QJSB) is a newly developed traditional Chinese medicine (TCM) formula. Clinically, it has been used for the treatment of leucopenia. However, its pharmacological mechanism needs more investigation. In this study, we firstly tested the effects of QJSB on leucopenia using mice induced by cyclophosphamide. Our results suggested that QJSB significantly raised the number of peripheral white blood cells, platelets and nucleated bone marrow cells. Additionally, it markedly enhanced the cell viability and promoted the colony formation of bone marrow mononuclear cells. Furthermore, it reversed the serum cytokines IL-6 and G-CSF disorders. Then, using transcriptomics datasets and metabonomic datasets, we integrated transcriptomics-based network pharmacology and metabolomics technologies to investigate the mechanism of action of QJSB. We found that QJSB regulated a series of biological processes such as hematopoietic cell lineage, homeostasis of number of cells, lymphocyte differentiation, metabolic processes (including lipid, amino acid, and nucleotide metabolism), B cell receptor signaling pathway, T cell activation and NOD-like receptor signaling pathway. In a summary, QJSB has protective effects to leucopenia in mice probably through accelerating cell proliferation and differentiation, regulating metabolism response pathways and modulating immunologic function at a system level

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author