ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods

TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods.Comment: MICCAI 202

Two-Stream Regression Network for Dental Implant Position Prediction

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods

Experimental Study of Broadcatching in BitTorrent

Abstract—Broadcatching is a promising mechanism to improve the experience of BitTorrent users by automatically downloading files advertised through RSS feeds. However, though widely used, the mechanism itself has not been well studied. In this paper, we conducted extensive experiments on PlanetLab to evaluate the performance of Broadcatching under different typical scenarios. The results demonstrated the effectiveness of the Broadcatching: it reduces the average completion time and downloading failure ratio. It also improves the overall fairness of the system: the subscribers are encouraged to share more while downloading faster, which results in the increased share ratio. Our study is the first work to systematically evaluate the benefit of Broadcatching and sheds lights on how to improve performance of BitTorrrent by manipulating peer’s behavior like Broadcatching. I

Identification of TonB-dependent siderophore receptor inhibitors against Flavobacterium columnare using a structure-based high-throughput virtual screening method

TonB-dependent siderophore receptors play a critical transport role for Flavobacterium columnare virulence formation and growth, and have become valuable targets for the development of novel antimicrobial agents. Traditional Chinese medicine has demonstrated notable efficacy in the treatment of fish diseases and includes potential antibacterial agents. Herein, we performed molecular docking-based virtual screening to discover novel TonB-dependent siderophore receptor inhibitors from traditional Chinese medicine and provide information for developing novel antibacterial agents. Firstly, we efficiently obtained 11 potential inhibitors with desirable drug-like characteristics from thousands of compounds in the TCM library based on virtual screening and property prediction. The antibacterial activity of Enoxolone, along with its interaction characteristics, were determined via an MIC assay and molecular dynamic simulation. Transcriptional profiling, along with validation experiments, subsequently revealed that an insufficient uptake of iron ions by bacteria upon binding to the TonB-dependent siderophore receptors is the antibacterial mechanism of Enoxolone. Finally, Enoxolone's acceptable toxicity was illustrated through immersion experiments. In summary, we have used virtual screening techniques for the first time in the development of antimicrobial agents in aquaculture. Through this process, we have identified Enoxolone as a promising compound targeting the TonB-dependent siderophore receptor of F. columnare. In addition, our findings will provide new ideas for the advancement of innovative antimicrobial medications in aquaculture

Characteristics of pathology and transcriptome profiling reveal features of immune response of acutely infected and asymptomatic infected of carp edema virus in Koi

Koi sleepy disease (KSD) is a high mortality and infection viral disease caused by carp edema virus (CEV), which was a serious threat to aquaculture of common carp and export trade of Koi worldwide. Asymptomatic infection is an important cause of the difficulty in preventing KSD and its worldwide spread, because asymptomatic infection can be activated under appropriate condition. However, the understanding of the molecular correlates of these infections is still unknown. The purpose of this study was to compare the pathology change, enzyme activity, immunoglobulin activity, host and viral gene expression differences in acutely infected and cohabiting asymptomatic Koi infected with CEV. Healthy Koi were used as a control. The gross pathology, histopathology and ultrastructural pathology showed the difference and characteristics damage to the tissues of Koi under different infection conditions. Periodic Acid-Schiff stain (PAS), enzyme activity and immunoglobulin activity revealed changes in the immune response of gill tissue between acutely infected, asymptomatic infected and healthy Koi. A total of 111 and 2484 upregulated genes and 257 and 4940 downregulated genes were founded in healthy Koi vs asymptomatic infected Koi and healthy Koi vs acutely infected Koi, respectively. Additionally, 878 upregulated genes and 1089 downregulated genes were identified in asymptomatic vs. acutely infected Koi. Immune gene categories and their corresponding genes in different comparison groups were revealed. A total of 3, 59 and 28 immune-related genes were identified in the group of healthy Koi vs asymptomatic infected Koi, healthy Koi vs acutely infected Koi and asymptomatic infected Koi vs acutely infected Koi, respectively. Nineteen immune-related genes have the same expression manner both in healthy Koi vs acutely infected Koi and asymptomatic Koi vs acutely infected Koi, while 9 immune-related genes were differentially expressed only in asymptomatic Koi vs acutely infected Koi, which may play a role in viral reactivation. In addition, 8 differentially expressed genes (DEGs) were validated by quantitative reverse transcription PCR (RT-qPCR), and the results were consistent with the RNA-Seq results. In conclusion, the data obtained in this study provide new evidence for further elucidating CEV-host interactions and the CEV infection mechanism and will facilitate the implementation of integrated strategies for controlling CEV infection and spread

Prevalence and Characterization of Staphylococcus aureus Isolated From Women and Children in Guangzhou, China

The prevalent Staphylococcus aureus clones and antibiotic susceptibility profiles are known to change dynamically and geographically; however, recent S. aureus strains causing infections in women and children in China have not been characterized. In this study, we analyzed the molecular epidemiology and antimicrobial resistance of S. aureus isolated from patients in four centers for women and children in Guangzhou, China. In total, 131 S. aureus isolates (100 from children and 31 from women) were analyzed by spa typing, multi-locus sequence typing, virulence gene and antimicrobial resistance profiling, staphylococcal chromosomal cassette mec typing, and mutation analyses of rpoB. A total of 58 spa types, 27 sequence types (STs), and 10 clonal complexes (CCs) were identified. While CC59 (ST59-IV, 48.8%; ST338-III, 35.7%) and CC45 (ST45-IV, 100%) were the major clones (84.4%) among MRSA isolates, CC5 (ST188, 24.3%; ST1, 21.6%) and CC398 (ST398, 70%) were the major ones (70.1%) among MSSA isolates. ST338-MRSA-III mostly found in pus but hardly in respiratory tract samples while ST45-MRSA-IV was on the opposite, even though they both found in blood and cerebrospinal fluid sample frequently. Staphylococcal enterotoxin genes seb-seq-sek were strongly associated with ST59 and ST338, while sec was associated with ST45, ST121, ST22, and ST30. All ST338, ST1232, and SCCmec III isolates carried lukF/S-PV genes. A total of 80% of ST338 isolates were resistant to erythromycin, clindamycin, and tetracycline. All ST45 isolates exhibited intermediate or complete resistance to rifampicin. In total, 481 HIS/ASN mutations in rpoB were found in rifampicin-resistant or intermediate-resistant isolates. ST338-III and ST45-IV emerged as two of three major clones in MRSA isolates from women and children in Guangzhou, China, though ST59-MRSA-IV remained the most prevalent MRSA clone. Clonal distribution of S. aureus varied, depending on the specimen source. Virulence genes and antibiograms were closely associated with the clonal lineage. These results clarified the molecular epidemiology of S. aureus from women and children in Guangzhou, China, and provide critical information for the control and treatment of S. aureus infections