The Wood Properties of an Intergeneric Hybrid － Taxodiomeria peizhongii (Taxodium mucronatum × Cryptomeria fortunei)

Taxodiomeria peizhongii is an intergeneric hybrid between Taxodium mucronatum and Cryptomeria fortunei. By more than 30 years investigation, it is found that the hybrid is well suited for the site and climate of Shanghai area, and it will be one of the main landscape trees in near few years. So it is necessary to know its basic wood properties. In this research, we harvested 6 sample trees of Taxodiomeria peizhongii and studied the elementary wood properties. The results showed that its mean annual ring width was 7.0mm, mean basic density 0.32g/cm3, and the mean percentage of latewood 24.3%. The mean treacheid length of latewood was 3.1mm, and mean treacheid width 35.1μm. Compared with other usual coniferous trees, the values of these indices were at a medium level. The period of juvenile wood was about 15 years, and the fast growing period appeared in first 10 years. The basic density, altering less in radial growth, showed a significant minus relation with annual ring width. The percentage of latewood did not related to wood density.OtherShinshu University International Symposium 2010 : Sustainable Agriculture and Environment : Asian Networks II 　信州大学国際シンポジウム2010 : 持続的農業と環境 : アジアネットワークII ― アジアネットワークの発展をめざして―. 信州大学農学部, 2010, 65-70conference pape

Effective dosing of L-carnitine in the secondary prevention of cardiovascular disease: a systematic review and meta-analysis

BACKGROUND: L-carnitine supplementation has been associated with a significant reduction in all-cause mortality, ventricular arrhythmia, and angina in the setting of acute myocardial infarction (MI). However, on account of strict homeostatic regulation of plasma L-carnitine concentrations, higher doses of L-carnitine supplementation may not provide additional therapeutic benefits. This study aims to evaluate the effects of various oral maintenance dosages of L-carnitine on all-cause mortality and cardiovascular morbidities in the setting of acute MI. METHODS: After a systematic review of several major electronic databases (PubMed, EMBASE, and the Cochrane Library) up to November 2013, a meta-analysis of five controlled trials (n = 3108) was conducted to determine the effects of L-carnitine on all-cause mortality and cardiovascular morbidities in the setting of acute MI. RESULTS: The interaction test yielded no significant differences between the effects of the four daily oral maintenance dosages of L-carnitine (i.e., 2 g, 3 g, 4 g, and 6 g) on all-cause mortality (risk ratio [RR] = 0.77, 95% CI [0.57-1.03], P = 0.08) with a statistically insignificant trend favoring the 3 g dose (RR = 0.48) over the lower 2 g dose (RR = 0.62), which was favored over the higher 4 g and 6 g doses (RR = 0.78, 0.78). There was no significant differences between the effects of the daily oral maintenance dosages of 2 g and 6 g on heart failure (RR = 0.53, 95% CI [0.25-1.13], P = 0.10), unstable angina (RR = 0.90, 95% CI [0.51-1.58], P = 0.71), or myocardial reinfarction (RR = 0.74, 95% CI [0.30-1.80], P = 0.50). CONCLUSIONS: There appears to be no significant marginal benefit in terms of all-cause mortality, heart failure, unstable angina, or myocardial reinfarction in the setting of acute MI for oral L-carnitine maintenance doses of greater or less than 3 g per day

MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation

We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps. Our approach enables the generation of controllable multi-view images and view-consistent 3D content. To achieve controllable multi-view image generation, we leverage MVDream as our base model, and train a new neural network module as additional plugin for end-to-end task-specific condition learning. To precisely control the shapes and views of generated images, we innovatively propose a new conditioning mechanism that predicts an embedding encapsulating the input spatial and view conditions, which is then injected to the network globally. Once MVControl is trained, score-distillation (SDS) loss based optimization can be performed to generate 3D content, in which process we propose to use a hybrid diffusion prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and our trained MVControl for additional guidance. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content. Code available at https://github.com/WU-CVGL/MVControl/.Comment: Project page: https://lizhiqi49.github.io/MVControl

Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification

Multi-spectral vehicle re-identification aims to address the challenge of identifying vehicles in complex lighting conditions by incorporating complementary visible and infrared information. However, in harsh environments, the discriminative cues in RGB and NIR modalities are often lost due to strong flares from vehicle lamps or sunlight, and existing multi-modal fusion methods are limited in their ability to recover these important cues. To address this problem, we propose a Flare-Aware Cross-modal Enhancement Network that adaptively restores flare-corrupted RGB and NIR features with guidance from the flare-immunized thermal infrared spectrum. First, to reduce the influence of locally degraded appearance due to intense flare, we propose a Mutual Flare Mask Prediction module to jointly obtain flare-corrupted masks in RGB and NIR modalities in a self-supervised manner. Second, to use the flare-immunized TI information to enhance the masked RGB and NIR, we propose a Flare-Aware Cross-modal Enhancement module that adaptively guides feature extraction of masked RGB and NIR spectra with prior flare-immunized knowledge from the TI spectrum. Third, to extract common informative semantic information from RGB and NIR, we propose an Inter-modality Consistency loss that enforces semantic consistency between the two modalities. Finally, to evaluate the proposed FACENet in handling intense flare, we introduce a new multi-spectral vehicle re-ID dataset, called WMVEID863, with additional challenges such as motion blur, significant background changes, and particularly intense flare degradation. Comprehensive experiments on both the newly collected dataset and public benchmark multi-spectral vehicle re-ID datasets demonstrate the superior performance of the proposed FACENet compared to state-of-the-art methods, especially in handling strong flares. The code and dataset will be released soon

Application of High-Resolution DNA Melting for Genotyping in Lepidopteran Non-Model Species: Ostrinia furnacalis (Crambidae)

Development of an ideal marker system facilitates a better understanding of the genetic diversity in lepidopteran non-model organisms, which have abundant species, but relatively limited genomic resources. Single nucleotide polymorphisms (SNPs) discovered within single-copy genes have proved to be desired markers, but SNP genotyping by current techniques remain laborious and expensive. High resolution melting (HRM) curve analysis represents a simple, rapid and inexpensive genotyping method that is primarily confined to clinical and diagnostic studies. In this study, we evaluated the potential of HRM analysis for SNP genotyping in the lepidopteran non-model species Ostrinia furnacalis (Crambidae). Small amplicon and unlabeled probe assays were developed for the SNPs, which were identified in 30 females of O. furnacalis from 3 different populations by our direct sequencing. Both assays were then applied to genotype 90 unknown female DNA by prior mixing with known wild-type DNA. The genotyping results were compared with those that were obtained using bi-directional sequencing analysis. Our results demonstrated the efficiency and reliability of the HRM assays. HRM has the potential to provide simple, cost-effective genotyping assays and facilitates genotyping studies in any non-model lepidopteran species of interest

Tomographic Alcock-Paczynski Method with Redshift Errors

The tomographic Alcock-Paczynski (AP) method is a promising method that uses the redshift evolution of the anisotropic clustering in redshift space to calibrate cosmology. It extends the applicable range of AP method to substantially nonlinear scales, yielding very tight cosmological constraints. For future stage-IV slitless spectroscopic surveys, the non-negligible redshift errors might reduce the advantage of the tomographic AP method by suppressing the resolution of the nonlinear structure along the line of sight. The present work studies how redshift errors propagate to cosmological parameters in the tomographic AP analysis. We use a formula $\sigma_z = \sigma(1+z)^{\alpha}$ to model the redshift errors, with $\sigma$ varying from 0.001 to 0.006 and $\alpha$ varying from 0.5 to 1.5. The redshift errors produce a signal of anisotropic clustering that is similar to a strong finger-of-god effect, which smears out both the AP signal and the contamination caused by the redshift space distortions (RSD). For the target precision of the Chinese Space Station Telescope optical survey ($\sigma\lesssim 0.002$), the decrement of constraining power on the dark energy equation of state is mild ($\lesssim 50\%$), and the suppression of RSD contamination leads to a smaller bias-to-signal ratio. Our results indicate that the tomographic AP method will remain a useful and complementary tool for analyses of future slitless spectroscopic surveys.Comment: 9 pages and 6 figure

Mixline: A Hybrid Reinforcement Learning Framework for Long-horizon Bimanual Coffee Stirring Task

Bimanual activities like coffee stirring, which require coordination of dual arms, are common in daily life and intractable to learn by robots. Adopting reinforcement learning to learn these tasks is a promising topic since it enables the robot to explore how dual arms coordinate together to accomplish the same task. However, this field has two main challenges: coordination mechanism and long-horizon task decomposition. Therefore, we propose the Mixline method to learn sub-tasks separately via the online algorithm and then compose them together based on the generated data through the offline algorithm. We constructed a learning environment based on the GPU-accelerated Isaac Gym. In our work, the bimanual robot successfully learned to grasp, hold and lift the spoon and cup, insert them together and stir the coffee. The proposed method has the potential to be extended to other long-horizon bimanual tasks.Comment: 10 pages, conferenc