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

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

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

Capacitive Touch Panel with Low Sensitivity to Water Drop employing Mutual-coupling Electrical Field Shaping Technique

This paper proposes a novel method to reduce the water interference on the touch panel based on mutual-capacitance sensing in human finger detection. As the height of a finger (height >10 mm) is far larger than that of a water-drop (height 10 mm) and low in the low-height space (height <1 mm), the sensing cell can be designed to distinguish the finger from the water-drop. To achieve this density distribution of the electrical field, the mutual-coupling electrical field shaping (MEFS) technique is employed to build the sensing cell. The drawback of the MEFS sensing cell is large parasitic capacitance, which can be overcome by a readout IC with low sensitivity to parasitic capacitance. Experiments show that the output of the IC with the MEFS sensing cell is 1.11 V when the sensing cell is touched by the water-drop and 1.23 V when the sensing cell is touched by the finger, respectively. In contrast, the output of the IC with the traditional sensing cell is 1.32 and 1.33 V when the sensing cell is touched by the water-drop and the finger, respectively. This demonstrates that the MEFS sensing cell can better distinguish the finger from the water-drop than the traditional sensing cell does.National Research Foundation (NRF)Accepted versionThis work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61771363, in part by the China Scholarship Council (CSC) under Grant 201706960042, and in part by the National Research Foundation of Singapore under Grant NRF-CRP11-2012-01

Origin of mitochondrial DNA diversity of domestic yaks

BACKGROUND: The domestication of plants and animals was extremely important anthropologically. Previous studies have revealed a general tendency for populations of livestock species to include deeply divergent maternal lineages, indicating that they were domesticated in multiple, independent events from genetically discrete wild populations. However, in water buffalo, there are suggestions that a similar deep maternal bifurcation may have originated from a single population. These hypotheses have rarely been rigorously tested because of a lack of sufficient wild samples. To investigate the origin of the domestic yak (Poephagus grunnies), we analyzed 637 bp of maternal inherited mtDNA from 13 wild yaks (including eight wild yaks from a small population in west Qinghai) and 250 domesticated yaks from major herding regions. RESULTS: The domestic yak populations had two deeply divergent phylogenetic groups with a divergence time of > 100,000 yrs BP. We here show that haplotypes clustering with two deeply divergent maternal lineages in domesticated yaks occur in a single, small, wild population. This finding suggests that all domestic yaks are derived from a single wild gene pool. However, there is no clear correlation of the mtDNA phylogenetic clades and the 10 morphological types of sampled yaks indicating that the latter diversified recently. Relatively high diversity was found in Qinghai and Tibet around the current wild distribution, in accordance with previous suggestions that the earliest domestications occurred in this region. Conventional molecular clock estimation led to an unrealistic early dating of the start of the domestication. However, Bayesian estimation of the coalescence time allowing a relaxation of the mutation rate are better in agreement with a domestication during the Holocene as supported by archeological records. CONCLUSION: The information gathered here and the previous studies of other animals show that the demographic histories of domestication of livestock species were highly diverse despite the common general feature of deeply divergent maternal lineages. The results further suggest that domestication of local wild prey ungulate animals was a common occurrence during the development of human civilization following the postglacial colonization in different locations of the world, including the high, arid Qinghai-Tibetan Plateau

Willingness to receive the second booster of COVID-19 vaccine among older adults with cancer: a stratified analysis in four provinces of China

BackgroundDespite the elevated COVID-19 risk for older adults with cancer, vaccine hesitancy poses a significant barrier to their immunization. Intriguingly, there is limited research on the prevalence of willingness to receive the second booster dose and associated determinants in older adults with cancer.ObjectiveOur objective was to ascertain the level of awareness about COVID-19 vaccines and to uncover the factors influencing the willingness to receive the second booster among Chinese cancer patients aged 65 years and over.MethodsTo achieve our objective, we conducted a multicenter cross-sectional study in four tertiary hospitals from four provinces of China. This involved using a Health Belief Model (HBM) based self-administered questionnaire and medical records. Subsequently, we employed multivariable logistic regression to identify factors influencing the second COVID-19 booster vaccine willingness.ResultsOur results showed that among 893 eligible participants, 279 (31.24%) were aged 65 years and over, and 614 (68.76%) were younger. Interestingly, the willingness to receive the second COVID-19 booster vaccine was 34.1% (95/279) (OR: 1.043, 95% CI: 0.858, 1.267) in participants aged 65 years and over, which was similar to participants aged under 65 years (34.1% vs. 35.5%, p = 0.673). Furthermore, our findings revealed that a positive attitude toward the booster and recommendations from healthcare providers and family members were positively associated with vaccine willingness. Conversely, perceptions of negative impacts on cancer control and vaccine accessibility regarding the second COVID-19 booster were inversely related to the outcome event (all p &lt; 0.05).ConclusionOur study concludes with the finding of a low willingness toward the second COVID-19 booster in Chinese cancer patients, particularly in the older adults, a fact which warrants attention. This reluctance raises their risk of infection and potential for severe outcomes. Consequently, we recommend using media and community outreach to dispel misconceptions, promote the booster’s benefits, and encourage vaccine discussions with healthcare providers and family members

Chitosan treatment reduces softening and chilling injury in cold-stored Hami melon by regulating starch and sucrose metabolism

Cold-stored Hami melon is susceptible to chilling injury, resulting in quality deterioration and reduced sales. Pre-storage treatment with chitosan reduces fruit softening and chilling injury in melon; however, the underlying mechanism remains unclear. In this study, Gold Queen Hami melons were treated with 1.5% chitosan solution for 10 min before cold storage at 3°C and then the effect of chitosan was examined on fruit firmness, weight loss, chilling injury, soluble solid content (SSC), pectin, and soluble sugar contents of melon fruit. Also, the enzyme activities and gene expressions related to fruit softening and starch and sucrose metabolism were investigated. Chitosan treatment reduced the fruit softening and chilling injury, maintained the high levels of starch and sucrose contents, and regulated the enzyme activities and gene expressions related to starch and sucrose metabolism. Fruit firmness was significantly positively correlated with sucrose and starch contents. Altogether, we uncovered the potential mechanism of chitosan coating mitigating melon softening and chilling injury through the regulation of starch and sucrose metabolism