Systematic analysis of leucine-rich repeat disease resistance genes in maize

Leucine-rich repeat disease resistance (LRRDR) genes are important for defending plants from a range of pathogens. However, little information has been reported on the systematic analysis of LRRDR genes in maize. In this study, 235 LRRDR genes were identified in the complete genome sequence of maize (Zea mays cv. B73), classified as six different structural types, and then characterized based on conserved protein motifs, chromo- somal locations and gene duplications. Subsequent phylogenetic comparisons indicated that ~20 pairs of maize LRRDR proteins possessed high similarities to LRRDR proteins with known functions. Analyses of the physical locations and duplications of LRRDR genes indicated that gene duplication events involving LRRDR genes were high in maize and 84% occurred between chromosomes, which may ensure the functional performance and en- hancement of maize LRRDR genes. Meanwhile, the functions and expression patterns of the LRRDR genes were associated with their conserved protein secondary structures, suggesting that different conserved domains might distinguish their biological functions. Transcripts of 13 genes were regulated by two or more fungal pathogens, respectively, indicating that one LRRDR gene might mediate resistance to multiple fungal pathogens, suggest- ing that the signal networks of the maize-fungal pathogen interactions were partially crossed. Additionally, we screened five candidate LRRDR genes for ear rot resistance. The results reported in this study contribute to an improved understanding of the LRRDR gene family in maize

How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective

Pre-trained code generation models (PCGMs) have been widely applied in neural code generation which can generate executable code from functional descriptions in natural languages, possibly together with signatures. Despite substantial performance improvement of PCGMs, the role of method names in neural code generation has not been thoroughly investigated. In this paper, we study and demonstrate the potential of benefiting from method names to enhance the performance of PCGMs, from a model robustness perspective. Specifically, we propose a novel approach, named RADAR (neuRAl coDe generAtor Robustifier). RADAR consists of two components: RADAR-Attack and RADAR-Defense. The former attacks a PCGM by generating adversarial method names as part of the input, which are semantic and visual similar to the original input, but may trick the PCGM to generate completely unrelated code snippets. As a countermeasure to such attacks, RADAR-Defense synthesizes a new method name from the functional description and supplies it to the PCGM. Evaluation results show that RADAR-Attack can reduce the CodeBLEU of generated code by 19.72% to 38.74% in three state-of-the-art PCGMs (i.e., CodeGPT, PLBART, and CodeT5) in the fine-tuning code generation task, and reduce the Pass@1 of generated code by 32.28% to 44.42% in three state-of-the-art PCGMs (i.e., Replit, CodeGen, and CodeT5+) in the zero-shot code generation task. Moreover, RADAR-Defense is able to reinstate the performance of PCGMs with synthesized method names. These results highlight the importance of good method names in neural code generation and implicate the benefits of studying model robustness in software engineering.Comment: UNDER REVIE

Maximized string order parameters in the valence bond solid states of quantum integer spin chains

We propose a set of maximized string order parameters to describe the hidden topological order in the valence bond solid states of quantum integer spin-S chains. These optimized string order parameters involve spin-twist angles corresponding to $Z_{S+1}$ rotations around $z$ or $x$-axes, suggesting a hidden $Z_{S+1}\times Z_{S+1}$ symmetry. Our results also suggest that a local triplet excitation in the valence bond solid states carries a $Z_{S+1}$ topological charge measured by these maximized string order parameters.Comment: 5 pages, 1 figur

String order and hidden topological symmetry in the SO(2n+1) symmetric matrix product states

We have introduced a class of exactly soluble Hamiltonian with either SO(2n+1) or SU(2) symmetry, whose ground states are the SO(2n+1) symmetric matrix product states. The hidden topological order in these states can be fully identified and characterized by a set of nonlocal string order parameters. The Hamiltonian possesses a hidden $(Z_{2}\times Z_{2})^{n}$ topological symmetry. The breaking of this hidden symmetry leads to $4^{n}$ degenerate ground states with disentangled edge states in an open chain system. Such matrix product states can be regarded as cluster states, applicable to measurement-based quantum computation.Comment: 5 pages, 1 figur

Four new isoflavanones from Tadehagi triquetrum

Four new isoflavanones with isoprenoid units, named triquetrumones E-H (1–4), were isolated from the whole plants of Tadehagi triquetrum. The structures were elucidated on the basis of spectroscopic analyses, including application of MS, UV, IR, 1D and 2D NMR spectroscopic techniques. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at 10.1007/s13659-011-0033-5 and is accessible for authorized users

Distinct expression of synaptic NR2A and NR2B in the central nervous system and impaired morphine tolerance and physical dependence in mice deficient in postsynaptic density-93 protein

Postsynaptic density (PSD)-93, a neuronal scaffolding protein, binds to and clusters N-methyl-D-aspartate receptor (NMDAR) subunits NR2A and NR2B at cellular membranes in vitro. However, the roles of PSD-93 in synaptic NR2A and NR2B targeting in the central nervous system and NMDAR-dependent physiologic and pathologic processes are still unclear. We report here that PSD-93 deficiency significantly decreased the amount of NR2A and NR2B in the synaptosomal membrane fractions derived from spinal cord dorsal horn and forebrain cortex but did not change their levels in the total soluble fraction from either region. However, PSD-93 deficiency did not markedly change the amounts of NR2A and NR2B in either synaptosomal or total soluble fractions from cerebellum. In mice deficient in PSD-93, morphine dose-dependent curve failed to shift significantly rightward as it did in wild type (WT) mice after acute and chronic morphine challenge. Unlike WT mice, PSD-93 knockout mice also showed marked losses of NMDAR-dependent morphine analgesic tolerance and associated abnormal sensitivity in response to mechanical, noxious thermal, and formalin-induced inflammatory stimuli after repeated morphine injection. In addition, PSD-93 knockout mice displayed dramatic loss of jumping activity, a typical NMDAR-mediated morphine withdrawal abstinence behavior. These findings indicate that impaired NMDAR-dependent neuronal plasticity following repeated morphine injection in PSD-93 knockout mice is attributed to PSD-93 deletion-induced alterations of synaptic NR2A and NR2B expression in dorsal horn and forebrain cortex neurons. The selective effect of PSD-93 deletion on synaptic NMDAR expression in these two major pain-related regions might provide the better strategies for the prevention and treatment of opioid tolerance and physical dependence