In clinical practice, panoramic dental radiography is a widely employed imaging technique that can provide a detailed and comprehensive view of dental structures and surrounding tissues for identifying various oral anomalies. However, due to the complexity of oral anomalies and the scarcity of available data, existing research still suffers from substantial challenges in automated oral anomaly detection. To this end, this paper presents a new hospital-scale panoramic X-ray benchmark, namely "OralXrays-91", which consists of 12,688 panoramic X-ray images with 84,113 meticulously annotated instances across nine common oral anomalies. Correspondingly, we propose a personalized Multi-Object Query-Aware Mining (MOQAM) paradigm, which jointly incorporates the Distribution-IoU Region Proposal Network (DI-RPN) and Class-Balanced Spherical Contrastive Regularization (CB-SCR) mechanisms to address the challenges posed by multi-scale variations and class-imbalanced distributions. To the best of our knowledge, this is the first attempt to develop AI-driven diagnostic systems specifically designed for multi-object oral anomaly detection, utilizing publicly available data resources. Extensive experiments on the newly-published OralXrays-9 dataset and real-world nature scenarios consistently demonstrate the superiority of our MOQAM in revolutionizing oral healthcare practices.

Accelerating the MRI acquisition process is always a key issue in modern medical practice, and great efforts have been devoted to fast MR imaging. Among them, multi-contrast MR imaging is a promising and effective solution that utilizes and combines information from different contrasts. However, existing methods may ignore the importance of the high-frequency priors among different contrasts. Moreover, they may lack an efficient method to fully utilize the information from the reference contrast. In this paper, we propose a lightweight and accurate High-frequency Modulated Transformer (HFMT) for multi-contrast MRI super-resolution. The key ideas of HFMT are high-frequency prior enhancement and its fusion with global features. Specifically, we employ an enhancement module to enhance and amplify the high-frequency priors in the reference and target modalities. In addition, we utilize the Rectangle Window Transformer Block (RWTB) to capture global information in the target contrast. Meanwhile, we propose a novel cross-attention mechanism to fuse the high-frequency enhanced features with the global features sequentially, which assists the network in recovering clear texture details from the low-resolution inputs. Extensive experiments show that our proposed method can reconstruct high-quality images with fewer parameters and faster inference time.

Autism spectrum disorder (ASD) is characterized by poor social communication abilities and repetitive behaviors or restrictive interests, which has brought a heavy burden to families and society. In many attempts to understand ASD neurobiology, resting-state functional magnetic resonance imaging (rs-fMRI) has been an effective tool. However, current ASD diagnosis methods based on rs-fMRI have two major defects. First, the instability of rs-fMRI leads to functional connectivity (FC) uncertainty, affecting the performance of ASD diagnosis. Second, many FCs are involved in brain activity, making it difficult to determine effective features in ASD classification. In this study, we propose an interpretable ASD classifier DeepTSK, which combines a multi-output Takagi-Sugeno-Kang (MO-TSK) fuzzy inference system (FIS) for composite feature learning and a deep belief network (DBN) for ASD classification in a unified network. To avoid the suboptimal solution of DeepTSK, a joint optimization procedure is employed to simultaneously learn the parameters of MO-TSK and DBN. The proposed DeepTSK was evaluated on datasets collected from three sites of the Autism Brain Imaging Data Exchange (ABIDE) database. The experimental results showed the effectiveness of the proposed method, and the discriminant FCs are presented by analyzing the consequent parameters of Deep MO-TSK.

Knee osteoarthritis (KOA) has been developed to be one of the most popular chronic joint disease in elder. Muscle shrink or force reduction of lower limb muscles such as gastrocnemius and quadriceps is a pathogenic factor about KOA. Therefore, imaging techniques such as MRI and CT for observing muscles morphological difference become popular in diagnosing KOA in clinic. However, the image information in different populations may vary according to age, weight, sex, and may be influenced by the subjective judgment according to doctor's experience. Viscoelasticity is a quantitative indicator for assessing the biomechanical properties of muscle, but there's lack of research to describe the relationship between viscoelasticity of lower limb muscles and KOA. The purpose of this study is to discuss the difference of viscoelasticity during lower limb muscles paining. We designed an experiment called muscle perfusion to simulate the muscle pain of KOA patients, and measured the lower limb muscles' viscoelasticity using shear-wave dispersion ultra-sound vibrometry (SDUV). Besides, we detected the viscoelasticity changes of a volunteer who had lower limb pain (which was similar to the symptom of KOA) during Fu's Subcutaneous Needling (FSN) treatment. Results show that during FSN treatment as well as perfusion, the elasticity and viscosity of lower limb muscles changed significantly, showing that viscoelasticity was a potential quantitative index to diagnose and qualify the disease of KOA.

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions.

Dental pulp stem cells (DPSCs) are stem cellspresent in the dental pulp and have same renewal anddifferentiating properties as bone marrow cells. Hence, DPSCs canbe used for future regenerative therapies of various diseases.Previous studies show that DPSCs would lose their differentiationcapability during long-term passage when they were cultured inmedium promoting differentiation. Researchers have beenworking to explore the key genes that influence their differentiation capability. However, the differentiation of dental pulp stem cells is accompanied by a complex biological process involving multiple genes and multi-element interactions. In this study, we used the weighted gene co-expression network (WGCNA) to obtain gene network modules based on a gene microarray data set from a DPSCs experiment, which is available at the Gene Expression Omnibus (GEO) public database. We identified two modules (yellow module and salmon module) significantly associated with DPSC passage.

Oral squamous cell carcinomas (OSCC) is the most common head and neck cancer worldwide, with more than 300,000 new cases being diagnosed annually. Studies have shown that miRNAs are involved in the process of growth, differentiation, apoptosis, invasion and metastasis of OSCC tumor cells. How miRNAs work together to contribute to this process is still largely unknown. The goal of our study was to characterize the coexpression network of miRNAs and to identify the miRNA subnetworks (modules) that were significantly associated with the OSCC cancer status. We also searched hub miRNAs that might play a vital role in the development of OSCC. We applied the weighted gene co-expression network analysis (WGCNA) to the miRNA expression profile data from a paired design study contributed by Shiah et al. To account for the within-pair correlation, a linear mixed model (LMM) was constructed to test the associations of miRNA modules to cancer status. Two significant modules (turquoise module with 254 miRNAs and grey module with 309 miRNAs) were identified. The miRNA miR-let-7c was the hub miRNA in the turquoise module in terms of node degree. Finally, we used miRsystem to perform the target gene prediction and KEGG pathway enrichment analysis of miRNAs within the two modules. Interestingly, the two modules have similar sets of target genes so that the top 6 enriched KEGG pathways for the 2 modules were the same. Compared with the probe-wise test used by Shiah et al., we took the network approach and identified significant OSCC-associated miRNA modules, which could help uncover the mechanism that miRNAs interplay each other to contribute to OSCC.

Model-based reconstruction algorithms have shown potentials over conventional strain-based methods in static elas-tographic image by using ”accurate” finite element(FE) or bio-mechanical models. Strictly speaking, however, the measurement noise are always exists and thus do not meet basic assumptions of these algorithms. In addition, the difficulty in determining the proper system response model also greatly affects the quality of the reconstructed images. In this paper, we explore the usage of state space principles for the estimation of materials properties in elastographic imaging. The model-data discrepancy is modeled as uncertainties, i.e. Gaussian white noise, and the measurement noise is treated as another independent Gaussian white noise in the stochastic state space space, and an optimal estimation is computed of full displacement field and Young's modulus simultaneously using an extended Kalman filter (EKF). The performance of the proposed framework is evaluated using phantom data and real data with favorable results.

The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.