Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph

The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://github.com/zhourixin/bronze-Ding.Comment: CVPR2023 accepte

TFormer: A Transmission-Friendly ViT Model for IoT Devices

Deploying high-performance vision transformer (ViT) models on ubiquitous Internet of Things (IoT) devices to provide high-quality vision services will revolutionize the way we live, work, and interact with the world. Due to the contradiction between the limited resources of IoT devices and resource-intensive ViT models, the use of cloud servers to assist ViT model training has become mainstream. However, due to the larger number of parameters and floating-point operations (FLOPs) of the existing ViT models, the model parameters transmitted by cloud servers are large and difficult to run on resource-constrained IoT devices. To this end, this paper proposes a transmission-friendly ViT model, TFormer, for deployment on resource-constrained IoT devices with the assistance of a cloud server. The high performance and small number of model parameters and FLOPs of TFormer are attributed to the proposed hybrid layer and the proposed partially connected feed-forward network (PCS-FFN). The hybrid layer consists of nonlearnable modules and a pointwise convolution, which can obtain multitype and multiscale features with only a few parameters and FLOPs to improve the TFormer performance. The PCS-FFN adopts group convolution to reduce the number of parameters. The key idea of this paper is to propose TFormer with few model parameters and FLOPs to facilitate applications running on resource-constrained IoT devices to benefit from the high performance of the ViT models. Experimental results on the ImageNet-1K, MS COCO, and ADE20K datasets for image classification, object detection, and semantic segmentation tasks demonstrate that the proposed model outperforms other state-of-the-art models. Specifically, TFormer-S achieves 5% higher accuracy on ImageNet-1K than ResNet18 with 1.4$\times$ fewer parameters and FLOPs.Comment: IEEE Transactions on Parallel and Distributed System

CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising

Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results. Existing methods have dedicated efforts to restoring degraded character images. However, the denoising results obtained by these methods do not appear to improve character recognition performance. This is mainly because current methods only focus on pixel-level information and ignore critical features of a character, such as its glyph, resulting in character-glyph damage during the denoising process. In this paper, we introduce a novel generic framework based on glyph fusion and attention mechanisms, i.e., CharFormer, for precisely recovering character images without changing their inherent glyphs. Unlike existing frameworks, CharFormer introduces a parallel target task for capturing additional information and injecting it into the image denoising backbone, which will maintain the consistency of character glyphs during character image denoising. Moreover, we utilize attention-based networks for global-local feature interaction, which will help to deal with blind denoising and enhance denoising performance. We compare CharFormer with state-of-the-art methods on multiple datasets. The experimental results show the superiority of CharFormer quantitatively and qualitatively.Comment: Accepted by ACM MM 202

Hydrogen Sulfide Protects against Chemical Hypoxia-Induced Injury by Inhibiting ROS-Activated ERK1/2 and p38MAPK Signaling Pathways in PC12 Cells

Hydrogen sulfide (H2S) has been proposed as a novel neuromodulator and neuroprotective agent. Cobalt chloride (CoCl2) is a well-known hypoxia mimetic agent. We have demonstrated that H2S protects against CoCl2-induced injuries in PC12 cells. However, whether the members of mitogen-activated protein kinases (MAPK), in particular, extracellular signal-regulated kinase1/2(ERK1/2) and p38MAPK are involved in the neuroprotection of H2S against chemical hypoxia-induced injuries of PC12 cells is not understood. We observed that CoCl2 induced expression of transcriptional factor hypoxia-inducible factor-1 alpha (HIF-1α), decreased cystathionine-β synthase (CBS, a synthase of H2S) expression, and increased generation of reactive oxygen species (ROS), leading to injuries of the cells, evidenced by decrease in cell viability, dissipation of mitochondrial membrane potential (MMP) , caspase-3 activation and apoptosis, which were attenuated by pretreatment with NaHS (a donor of H2S) or N-acetyl-L cystein (NAC), a ROS scavenger. CoCl2 rapidly activated ERK1/2, p38MAPK and C-Jun N-terminal kinase (JNK). Inhibition of ERK1/2 or p38MAPK or JNK with kinase inhibitors (U0126 or SB203580 or SP600125, respectively) or genetic silencing of ERK1/2 or p38MAPK by RNAi (Si-ERK1/2 or Si-p38MAPK) significantly prevented CoCl2-induced injuries. Pretreatment with NaHS or NAC inhibited not only CoCl2-induced ROS production, but also phosphorylation of ERK1/2 and p38MAPK. Thus, we demonstrated that a concurrent activation of ERK1/2, p38MAPK and JNK participates in CoCl2-induced injuries and that H2S protects PC12 cells against chemical hypoxia-induced injuries by inhibition of ROS-activated ERK1/2 and p38MAPK pathways. Our results suggest that inhibitors of ERK1/2, p38MAPK and JNK or antioxidants may be useful for preventing and treating hypoxia-induced neuronal injury

HTRA1 variant increases risk to neovascular age-related macular degeneration in Chinese population

AbstractAge-related macular degeneration (AMD) is a leading cause of irreversible visual impairment in the world. Advanced AMD can be divided into wet AMD (choroidal neovascularization) and dry AMD (geographic atrophy, GA). Drusen is characterized by deposits in the macula without visual loss and is an early AMD sign in the Caucasian population. rs11200638 in the promoter of HTRA1 has recently been shown to increases the risk for wet AMD in both Caucasian and Hong Kong Chinese populations. In order to replicate these results in a different cohort, we genotyped rs11200638 for 164 Chinese patients (90 wet AMD and 74 drusen) and 106 normal controls in a Han Mainland Chinese cohort. The genotypes were compared using chi square analysis for an additive allelic model. rs11200638 was significantly associated with wet AMD (p=5.00×10−12). Unlike in the Caucasian population, the risk allele of rs11200638 was not associated with drusen in our Chinese population. These findings confirm the association of HTRA1 with wet AMD

Relationship between chemical composition of native forage and nutrient digestibility by Tibetan sheep on the Qinghai–Tibetan Plateau

Publication history: Accepted - 06 March 2018; Published - 02 april 2018.To better utilize native pasture at the high altitude region, three-consecutive-year feeding experiments and a total of seven metabolism trials were conducted to evaluate the impact of three forage stages of maturity on the chemical composition, nutrient digestibility, and energy metabolism of native forage in Tibetan sheep on the Qinghai–Tibetan Plateau (QTP). Forages were harvested from June to July, August to October, and November to December of 2011 to 2013, corresponding to the vegetative, bloom, and senescent stages of the annual forages. Twenty male Tibetan sheep were selected for each study and fed native forage ad libitum. The digestibility of DM, OM, CP, NDF, ADF, DE, DE/GE, and ME/GE were greatest (P < 0.01) from the vegetative stage, intermediate (P < 0.01) from the bloom stage, and least (P < 0.01) from the senescent stage. Nutrient digestibility and energy parameters correlated positively (linear, 0.422 to 0.778; quadratic, 0.568 to 0.815; P < 0.01) with the CP content of forage but correlated negatively with the content of NDF (linear, 0.343 to 0.689; quadratic, 0.444 to 0.777; P ≤ 0.02), ADF (linear, 0.563 to 0.766; quadratic, 0.582 to 0.770; P < 0.01), and ether extract (EE, linear, 0.283 to 0.574; quadratic, 0.366 to 0.718; P ≤ 0.04) of forage. For each predicted variable, the prediction of DMI expressed as grams per kilogram of BW (g/kg BW·d) yielded a greater R2 value (0.677 to 0.761 vs. 0.616 to 0.711) compared with the equations of DMI expressed as g/kg metabolic BW by step-wise regression. The results suggest that parameters of forage CP, NDF, and ADF content were most closely related to nutrient digestibility. Contrary to previous studies, in this study, ADF content had a greater linear relationship (0.766 vs. 0.563 to 0.732) with OM digestibility than the other parameters of nutrient digestibility. The quadratic relationship between forage CP content and CP digestibility indicates that when forage CP content exceeds the peak point (9.7% DM in the present study), increasing forage CP content could decrease CP digestibility when Tibetan sheep were offered native forage alone on the QTP. Additionally, using the forage CP, EE, NDF, and ADF content to predict DMI (g/kg BW·d) yielded the best fit equation for Tibetan sheep living in the northeast portion of the QTPThis work was supported by the National Key Project of Scientific and Technical Supporting (2014CB138706), National Natural Science Foundation of China (31672472), Program for Changjiang Scholars and Innovative Research Team in University (IRT13019), and the 111 project (B12002)

Removal of a Cationic Dye by Adsorption/Photodegradation Using Electrospun PAN/O-MMT Composite Nanofibrous Membranes Coated with TiO

Polyacrylonitrile (PAN)/organic-modified montmorillonite (O-MMT) composite nanofibrous membranes were firstly prepared by electrospinning and then coated with titanium dioxide (TiO2) using spin coating technique. The structural morphology of the nanofibrous membranes with different mass ratio of O-MMT before and after spin coating was investigated by scanning electron microscope (SEM) and transmission electron microscope (TEM). The chemical property of adsorbed methylene blue (MB) was analyzed by infrared spectroscopy (IR). The adsorption and photodegradation capability of the TiO2-coated PAN/O-MMT composite nanofibrous membranes were evaluated by adsorption rate of MB and K/S values of the membranes before and after UV irradiation. The experimental results indicated that with the increase of O-MMT amount, the diameters of the nanofibers decreased and the adsorption rate of MB was evidently improved. Besides, with the increase of TiO2 film layers, the photocatalytic properties were enhanced while the adsorption process was slowed down

Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location

Microbial communities, demonstrating dynamic changes in cadavers and the surroundings, provide invaluable insights for forensic investigations. Conventional methodologies for microbiome sequencing data analysis face obstacles due to subjectivity and inefficiency. Artificial Intelligence (AI) presents an efficient and accurate tool, with the ability to autonomously process and analyze high-throughput data, and assimilate multi-omics data, encompassing metagenomics, transcriptomics, and proteomics. This facilitates accurate and efficient estimation of the postmortem interval (PMI), detection of crime location, and elucidation of microbial functionalities. This review presents an overview of microorganisms from cadavers and crime scenes, emphasizes the importance of microbiome, and summarizes the application of AI in high-throughput microbiome data processing in forensic microbiology