Mendelian randomization (MR) method utilizes genetic variants as instrumental variables to infer the causal effect of an exposure on an outcome. However, the impact of rare variants on traits is often neglected, and traditional MR assumptions can be violated by correlated horizontal pleiotropy (CHP) and uncorrelated horizontal pleiotropy (UHP). To address these issues, we propose a multivariable MR approach, an extension of the standard MR framework: MVMR incorporating Rare variants Accounting for multiple Risk factors and shared horizontal plEiotropy (RARE). In the simulation studies, we demonstrate that RARE effectively detects the causal effects of exposures on outcome with accounting for the impact of rare variants on causal inference. Additionally, we apply RARE to study the effects of high density lipoprotein and low density lipoprotein on type 2 diabetes and coronary atherosclerosis, respectively, thereby illustrating its robustness and effectiveness in real data analysis.

In this work, a Chaos Division Multiple Access (Chaos-DMA) communication system based on chaotic filter groups are proposed. A Chaotic Quasi-orthogonal Shape-forming Filter (CQSF) group is designed at the transmitter to generate quasi-orthogonal signals for multiuser, achieving a good balance between spectrum efficiency and high bit transmission rate. At the receiver, two groups of filters, namely Chaotic Correlation Filter (CCF) group and Chaotic Matched Filter (CMF) group, are designed to decode the information. The transmitted information bits are recovered by averaging the sampled sequence from the CMF group outputs to derive the numbers of different bit polarity and sorting the sampling sequence from the CCF group outputs. Compared with traditional Code Division Multiple Access (CDMA) and Frequency Division Multiple Access (FDMA) communication systems, the proposed Chaos-DMA communication system not only provides high reliability and high data transmission rate, but also achieves higher spectrum efficiency and throughput. The Bit Error Rate (BER) expressions are derived for both additive white Gaussian noise (AWGN) channel and wireless multipath fading channel. The effectiveness and superiority of the proposed Chaos-DMA are demonstrated through numerical simulations and experimental tests based on software-defined radio platform.

Virtual standard patient (VSP) is in high demand for medical students' diagnosis ability training in an efficient manner. Different from the traditional conversation system in medical dialogue generation, VSP needs a novel conversation paradigm to act as the patient instead of the doctor. However, existing conversation techniques still have limited ability in terms of generation of symptoms exhibited by patients with the personalized and knowledge-centered expressions. To alleviate these problems, we propose to construct a novel oral knowledge graph, which sufficiently provides medical clues of the certain disease. Accordingly, the VSP could accurately interact with the dentists for their underlying intention and express the symptoms characters in a natural style. To efficiently retrieve the related disease clues, the symptoms descriptions of the oral diseases are encoded into the oral knowledge graph, which could well organize the disease-centered symptom entities and speaking styles. Moreover, to transfer the common sense knowledge from existing large scale of medical knowledge graph to the specific oral knowledge graph, a coupled pre-trained Bert models is further designed to learn the related medical knowledge from coarse-level to fine-level hierarchically. Finally, a series of well-designed personalized templates are proposed to generate plausible and realistic answers in condition of the certain disease. We also conduct extensive user studies to demonstrate that the VSP satisfies the medical students' diagnosis practice requirement in terms of naturalness, realism, and topic relevance.