catena-Poly[[[bis­(4-amino­benzoato-κO)copper(II)]-μ-1,1′-(pentane-1,5-di­yl)diimidazole] trihydrate]

In the title compound, {[Cu(C7H6NO2)2(C11H16N4)]·3H2O}n, each CuII atom is coordinated by two O atoms from two 4-amino­benzoate anions, and two N atoms from two different 1,1′-(pentane-1,5-di­yl)diimidazole (biim-5) ligands, to furnish a distorted square-planar geometry. The biim-5 ligand coordinates to two copper(II) cations, acting as a bridging ligand; as a result the copper(II) cations are connected to form an infinite chain structure. The polymeric chains are linked through a variety of hydrogen bonds to form a three-dimensional structure

An expert consensus for the management of chronic hepatitis B in Asian Americans.

BACKGROUND: Hepatitis B virus (HBV) infection is common with major clinical consequences. In Asian Americans, the HBsAg carrier rate ranges from 2% to 16% which approximates the rates from their countries of origin. Similarly, HBV is the most important cause of cirrhosis, hepatocellular carcinoma (HCC) and liver related deaths in HBsAg positive Asians worldwide. AIM: To generate recommendations for the management of Asian Americans infected with HBV. METHODS: These guidelines are based on relevant data derived from medical reports on HBV from Asian countries as well as from studies in the HBsAg positive Asian Americans. The guidelines herein differ from other recommendations in the treatment of both HBeAg positive and negative chronic hepatitis B (CHB), in the approach to HCC surveillance, and in the management of HBV in pregnant women. RESULTS: Asian American patients, HBeAg positive or negative, with HBV DNA levels \u3e2000 IU/mL (\u3e10 CONCLUSIONS: Application of the recommendations made based on a review of the relevant literature and the opinion of a panel of Asian American physicians with expertise in HBV treatment will inform physicians and improve patient outcomes

Large fine‐scale spatiotemporal variations of CH4 diffusive fluxes from shrimp aquaculture ponds affected by organic matter supply and aeration in Southeast China

Mariculture shrimp ponds are important CH4 sources to the atmosphere. However, the spatiotemporal variations of CH4 concentration and flux at fine spatial scales in mariculture ponds are poorly known, particularly in China, worlds largest aquaculture producer. In this study, the plot‐scale spatiotemporal variations of water CH4 concentration and flux, both within and among ponds, were researched in shrimp ponds in Shanyutan wetland, Min River Estuary, Southeast China. The average water CH4 concentration and diffusion flux across the water‐air interface in the shrimp ponds over the shrimp aquaculture period varied from 2.29 ± 0.29 to 50.48 ± 20.91 μM and from 0.09 ± 0.01 to 2.32 ± 0.95 mmol·m−2·hr−1, respectively. The CH4 emissions from the estuarine ponds varied greatly between seasons, with peaks in August and September, which was similar to the trend of water temperature and dissolved oxygen concentrations. There was no remarkable difference in CH4 concentration and flux between shrimp ponds but significantly spatiotemporal differences in CH4 concentration and flux within the ponds. Significantly higher emissions occurred in the feeding zone, accounting for approximately 60% of total CH4 emission flux, while much lower CH4 emissions appeared in aeration zone, contributing 14% to total flux. This study suggests the importance of considering spatiotemporal variation in the whole‐pond estimates of CH4 concentration and flux. In light of such high spatial variation within ponds, improving aeration and feed utilization efficiency would help to mitigate CH4 emissions from mariculture ponds

TM-NET: Deep Generative Networks for Textured Meshes

We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a part-aware manner. Once trained, the network can generate novel textured meshes from scratch or predict textures for a given 3D mesh, without image guidance. Plausible and diverse textures can be generated for the same mesh part, while texture compatibility between parts in the same shape is achieved via conditional generation. Specifically, our method produces texture maps for individual shape parts, each as a deformable box, leading to a natural UV map with minimal distortion. The network separately embeds part geometry (via a PartVAE) and part texture (via a TextureVAE) into their respective latent spaces, so as to facilitate learning texture probability distributions conditioned on geometry. We introduce a conditional autoregressive model for texture generation, which can be conditioned on both part geometry and textures already generated for other parts to achieve texture compatibility. To produce high-frequency texture details, our TextureVAE operates in a high-dimensional latent space via dictionary-based vector quantization. We also exploit transparencies in the texture as an effective means to model complex shape structures including topological details. Extensive experiments demonstrate the plausibility, quality, and diversity of the textures and geometries generated by our network, while avoiding inconsistency issues that are common to novel view synthesis methods

The use of a noninvasive and nondestructive method, microcomputed tomography, to evaluate the anti-osteoporotic activity of erxian decoction, a Chinese medicinal formula, in a rat model of menopausal osteoporosis

Aim of the study: The anti-osteoporotic activity of Erxian Decoction, a Chinese medicinal formula, in a rat model of menopausal osteoporosis was evaluated by microcomputed tomography (microCT). Materials and methods: Menopause causes a decline in both endocrine function and bone mineral density in human. In this study, 20-month-old female Sprague-Dawley-rats (SD-rats) with a low serum estradiol level and bone mineral density were employed. The anti-osteoporotic activity of EXD was assessed by the determination of trabecular material bone mineral density at the L2 mid-vertebral body after treatment. Serum estrogen levels were also determined to assess the effect of EXD on the endocrine status. Results: Results revealed a significant elevation in serum estradiol level and trabecular bone mineral density at the L2 mid-vertebral body in the EXD-treated menopausal rat model. Conclusions: The results obtained from the present investigation revealed that the EXD had anti-osteoporotic activity as evidenced by an increase of serum estradiol level and bone mineral density. ©2009 IEEE.published_or_final_versionProceedings of the 2009 2nd International Conference On Biomedical Engineering And Informatics (BMEI 2009), Tianjin, China, 17-19 October 2009, v. 1 p. 47-49, article number 530482

STAR-TM: STructure aware reconstruction of textured mesh from single image

We present a novel method for single-view 3D reconstruction of textured meshes , with a focus to address the primary challenge surrounding texture inference and transfer. Our key observation is that learning textured reconstruction in a structure-aware and globally consistent manner is effective in handling the severe ill-posedness of the texturing problem and significant variations in object pose and texture details. Specifically, we perform structured mesh reconstruction, via a retrieval-and-assembly approach, to produce a set of genus-zero parts parameterized by deformable boxes and endowed with semantic information. For texturing, we first transfer visible colors from the input image onto the unified UV texture space of the deformable boxes. Then we combine a learned transformer model for per-part texture completion with a global consistency loss to optimize inter-part texture consistency. Our texture completion model operates in a VQ-VAE embedding space and is trained end-to-end, with the transformer training enhanced with retrieved texture instances to improve texture completion performance amid significant occlusion. Extensive experiments demonstrate higher-quality textured mesh reconstruction obtained by our method over state-of-the-art alternatives, both quantitatively and qualitatively, as reflected by a better recovery of texture coherence and details

SDM-NET: Deep Generative Network for Structured Deformable Mesh

We introduce SDM-NET, a deep generative neural network which produces structured deformable meshes. Specifically, the network is trained to generate a spatial arrangement of closed, deformable mesh parts, which respect the global part structure of a shape collection, e.g., chairs, airplanes, etc. Our key observation is that while the overall structure of a 3D shape can be complex, the shape can usually be decomposed into a set of parts, each homeomorphic to a box, and the finer-scale geometry of the part can be recovered by deforming the box. The architecture of SDM-NET is that of a two-level variational autoencoder (VAE). At the part level, a PartVAE learns a deformable model of part geometries. At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring a coherence between global shape structure and surface details. Through extensive experiments and comparisons with the state-of-the-art deep generative models of shapes, we demonstrate the superiority of SDM-NET in generating meshes with visual quality, flexible topology, and meaningful structures, which benefit shape interpolation and other subsequently modeling tasks.Comment: Conditionally Accepted to Siggraph Asia 201

3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods

Bioactive proteins and peptides isolated from Chinese medicines with pharmaceutical potential.

Some protein pharmaceuticals from Chinese medicine have been developed to treat cardiovascular diseases, genetic diseases, and cancer. Bioactive proteins with various pharmacological properties have been successfully isolated from animals such as Hirudo medicinalis (medicinal leech), Eisenia fetida (earthworm), and Mesobuthus martensii (Chinese scorpion), and from herbal medicines derived from species such as Cordyceps militaris, Ganoderma, Momordica cochinchinensis, Viscum album, Poria cocos, Senna obtusifolia, Panax notoginseng, Smilax glabra, Ginkgo biloba, Dioscorea batatas, and Trichosanthes kirilowii. This article reviews the isolation methods, molecular characteristics, bioactivities, pharmacological properties, and potential uses of bioactive proteins originating from these Chinese medicines.published_or_final_versio