Overexpression of Pear (Pyrus pyrifolia) CAD2 in Tomato Affects Lignin Content

PpCAD2 was originally isolated from the ‘Wangkumbae’ pear (Pyrus pyrifolia Nakai), and it encodes for cinnamyl alcohol dehydrogenase (CAD), which is a key enzyme in the lignin biosynthesis pathway. In order to verify the function of PpCAD2, transgenic tomato (Solanum lycopersicum) ‘Micro-Tom’ plants were generated using over-expression constructs via the agrobacterium-mediated transformation method. The results showed that the PpCAD2 over-expression transgenic tomato plant had a strong growth vigor. Furthermore, these PpCAD2 over-expression transgenic tomato plants contained a higher lignin content and CAD enzymatic activity in the stem, leaf and fruit pericarp tissues, and formed a greater number of vessel elements in the stem and leaf vein, compared to wild type tomato plants. This study clearly indicated that overexpressing PpCAD2 increased the lignin deposition of transgenic tomato plants, and thus validated the function of PpCAD2 in lignin biosynthesis

PpNAC187 Enhances Lignin Synthesis in ‘Whangkeumbae’ Pear (Pyrus pyrifolia) ‘Hard-End’ Fruit

A disorder in pears that is known as ‘hard-end’ fruit affects the appearance, edible quality, and market value of pear fruit. RNA-Seq was carried out on the calyx end of ‘Whangkeumbae’ pear fruit with and without the hard-end symptom to explore the mechanism underlying the formation of hard-end. The results indicated that the genes in the phenylpropanoid pathway affecting lignification were up-regulated in hard-end fruit. An analysis of differentially expressed genes (DEGs) identified three NAC transcription factors, and RT-qPCR analysis of PpNAC138, PpNAC186, and PpNAC187 confirmed that PpNAC187 gene expression was correlated with the hard-end disorder in pear fruit. A transient increase in PpNAC187 was observed in the calyx end of ‘Whangkeumbae’ fruit when they began to exhibit hard-end symptom. Concomitantly, the higher level of PpCCR and PpCOMT transcripts was observed, which are the key genes in lignin biosynthesis. Notably, lignin content in the stem and leaf tissues of transgenic tobacco overexpressing PpNAC187 was significantly higher than in the control plants that were transformed with an empty vector. Furthermore, transgenic tobacco overexpressing PpNAC187 had a larger number of xylem vessel elements. The results of this study confirmed that PpNAC187 functions in inducing lignification in pear fruit during the development of the hard-end disorder. View Full-Tex

Context Perception Parallel Decoder for Scene Text Recognition

Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based STR model uses the previously recognized characters to decode the next character iteratively. It shows superiority in terms of accuracy. However, the inference speed is slow also due to this iteration. Alternatively, parallel decoding (PD)-based STR model infers all the characters in a single decoding pass. It has advantages in terms of inference speed but worse accuracy, as it is difficult to build a robust recognition context in such a pass. In this paper, we first present an empirical study of AR decoding in STR. In addition to constructing a new AR model with the top accuracy, we find out that the success of AR decoder lies also in providing guidance on visual context perception rather than language modeling as claimed in existing studies. As a consequence, we propose Context Perception Parallel Decoder (CPPD) to decode the character sequence in a single PD pass. CPPD devises a character counting module and a character ordering module. Given a text instance, the former infers the occurrence count of each character, while the latter deduces the character reading order and placeholders. Together with the character prediction task, they construct a context that robustly tells what the character sequence is and where the characters appear, well mimicking the context conveyed by AR decoding. Experiments on both English and Chinese benchmarks demonstrate that CPPD models achieve highly competitive accuracy. Moreover, they run approximately 7x faster than their AR counterparts, and are also among the fastest recognizers. The code will be released soon

Vibration modes of the rotor system of turbocharger with floating-ring bearing

In this paper, in order to investigate the natural modes of the rotor system of turbocharger, the analytical model of floating-ring bearing, the FEM and modal test technology were employed. Firstly, based on Reynolds equations of the dynamic oil layer of floating-ring bearing, the oil layer pressure was obtained by using the finite difference algorithm. Then the stiffness coefficients and damping coefficients were calculated by using integration method. Next the FEM model of rotor system with supporting stiffness of floating-ring bearings was established. Then the natural modes of rotor system were simulated with considering the oil layer stiffness of the floating-ring bearings and the rotor’s rotating speeds. Lastly, the natural modes were identified with modal test technology and furthermore the FEM calculation results were verified. The investigation shows that the oil layer stiffness of the floating-ring bearings and rotating speeds of the rotor have great influences on vibration modes of the rotor system

Multiparametric MRI combined with liver volume for quantitative evaluation of liver function in patients with cirrhosis

PURPOSEWe aimed to establish a liver function evaluation model by combining multiparametric magnetic resonance imaging (MRI) with liver volume (LV) and further verify the effectiveness of the model to evaluate liver function.METHODSThis retrospective study included 101 consecutive cirrhosis patients (69 cases for modeling group and 32 cases for validation group) who underwent gadoxetic acid-enhanced MRI. Five signal intensity parameters were obtained by measuring the signal intensities of the liver, spleen, and erector spinae before and 20 minutes after gadoxetic acid disodium enhancement. The diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were obtained from intravoxel incoherent motion diffusion-weighted imaging. The LV parameters (Vliver, Vspleen, and Vliver/Vspleen) were obtained using 3-dimensional image generation software. The most effective parameter was selected from each of the 3 methods, and a multivariate regression model for liver function evaluation was established and validated.RESULTSIn the modeling group, relative enhancement (RE), D*, and Vliver/Vspleen showed significant differences among the different liver function groups (P < .001). Receiver operating characteristic analysis showed that these parameters had the highest area under the curve (AUC) values for distinguishing Child-Pugh A from Child-Pugh B and C groups (0.917, 0.929, and 0.885, respectively). The following liver function model was obtained by multivariate regression analysis: F(x)=3.96 − 1.243 (RE) − 0.034 (D*) − 0.080 (Vliver/Vspleen) (R2=0.811, P < .001). In the patients with cirrhosis, the F(x) of Child-Pugh A, B, and C were 1.16 ± 0.44, 1.95 ± 0.29, and 2.79 ± 0.38, respectively. In the validation group, the AUC for F(x) to distinguish Child-Pugh A from Child-Pugh B and C was 0.973.CONCLUSIONCombining multiparametric MRI with LV effectively distinguished patients with different ChildPugh grades. This model could hence be useful as a novel radiological marker to estimate the liver function

SkyMath: Technical Report

Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning. In this work, we present SkyMath, a large language model for mathematics with 13 billion parameters. By applying self-compare fine-tuning, we have enhanced mathematical reasoning abilities of Skywork-13B-Base remarkably. On GSM8K, SkyMath outperforms all known open-source models of similar size and has established a new SOTA performance