Contents and colophon : Philological Studies of the Ainu Language 2

Alignment results of 5S gene NTS sequences from all kinds of groupers. a: The 266 bp NTS sequences of E. coioides, diploid hybrid and triploid hybrid; b: The 272 bp NTS sequences of E. coioides, diploid hybrid and triploid hybrid; c: The 275 bp NTS sequences of E. lanceolatus, diploid hybrid and triploid hybrid; d: The 284 bp NTS sequences of E. lanceolatus, diploid hybrid and triploid hybrid. The TATA sequences were framed in boxes. Dots indicated the identical nucleotides. In bold letters were shown the nucleotide substitutions. (TIF 2265 kb

Combining Mendelian Randomization and Network Deconvolution for Inference of Causal Networks With GWAS Summary Data

Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer’s disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest

Multimodal Information Interaction for Medical Image Segmentation

The use of multimodal data in assisted diagnosis and segmentation has emerged as a prominent area of interest in current research. However, one of the primary challenges is how to effectively fuse multimodal features. Most of the current approaches focus on the integration of multimodal features while ignoring the correlation and consistency between different modal features, leading to the inclusion of potentially irrelevant information. To address this issue, we introduce an innovative Multimodal Information Cross Transformer (MicFormer), which employs a dual-stream architecture to simultaneously extract features from each modality. Leveraging the Cross Transformer, it queries features from one modality and retrieves corresponding responses from another, facilitating effective communication between bimodal features. Additionally, we incorporate a deformable Transformer architecture to expand the search space. We conducted experiments on the MM-WHS dataset, and in the CT-MRI multimodal image segmentation task, we successfully improved the whole-heart segmentation DICE score to 85.57 and MIoU to 75.51. Compared to other multimodal segmentation techniques, our method outperforms by margins of 2.83 and 4.23, respectively. This demonstrates the efficacy of MicFormer in integrating relevant information between different modalities in multimodal tasks. These findings hold significant implications for multimodal image tasks, and we believe that MicFormer possesses extensive potential for broader applications across various domains. Access to our method is available at https://github.com/fxxJuses/MICForme

ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer

Vision Transformers (ViTs) have shown impressive performance and have become a unified backbone for multiple vision tasks. But both attention and multi-layer perceptions (MLPs) in ViTs are not efficient enough due to dense multiplications, resulting in costly training and inference. To this end, we propose to reparameterize the pre-trained ViT with a mixture of multiplication primitives, e.g., bitwise shifts and additions, towards a new type of multiplication-reduced model, dubbed $\textbf{ShiftAddViT}$, which aims for end-to-end inference speedups on GPUs without the need of training from scratch. Specifically, all $\texttt{MatMuls}$ among queries, keys, and values are reparameterized by additive kernels, after mapping queries and keys to binary codes in Hamming space. The remaining MLPs or linear layers are then reparameterized by shift kernels. We utilize TVM to implement and optimize those customized kernels for practical hardware deployment on GPUs. We find that such a reparameterization on (quadratic or linear) attention maintains model accuracy, while inevitably leading to accuracy drops when being applied to MLPs. To marry the best of both worlds, we further propose a new mixture of experts (MoE) framework to reparameterize MLPs by taking multiplication or its primitives as experts, e.g., multiplication and shift, and designing a new latency-aware load-balancing loss. Such a loss helps to train a generic router for assigning a dynamic amount of input tokens to different experts according to their latency. In principle, the faster experts run, the larger amount of input tokens are assigned. Extensive experiments consistently validate the effectiveness of our proposed ShiftAddViT, achieving up to \textbf{5.18\times} latency reductions on GPUs and \textbf{42.9%} energy savings, while maintaining comparable accuracy as original or efficient ViTs.Comment: Accepted by NeurIPS 202

Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays

Retrieving 3D bone anatomy from biplanar X-ray images is crucial since it can significantly reduce radiation exposure compared to traditional CT-based methods. Although various deep learning models have been proposed to address this complex task, they suffer from two limitations: 1) They employ voxel representation for bone shape and exploit 3D convolutional layers to capture anatomy prior, which are memory-intensive and limit the reconstruction resolution. 2) They overlook the prevalent occlusion effect within X-ray images and directly extract features using a simple loss, which struggles to fully exploit complex X-ray information. To tackle these concerns, we present Spatial-division Augmented Occupancy Field~(SdAOF). SdAOF adopts the continuous occupancy field for shape representation, reformulating the reconstruction problem as a per-point occupancy value prediction task. Its implicit and continuous nature enables memory-efficient training and fine-scale surface reconstruction at different resolutions during the inference. Moreover, we propose a novel spatial-division augmented distillation strategy to provide feature-level guidance for capturing the occlusion relationship. Extensive experiments on the pelvis reconstruction dataset show that SdAOF outperforms state-of-the-art methods and reconstructs fine-scale bone surfaces.The code is available at https://github.com/xmed-lab/SdAOFComment: Accepted to MICCAI 2024. Project link: https://github.com/xmed-lab/SdAO

Glucose Enhances Leptin Signaling through Modulation of AMPK Activity

Leptin exerts its action by binding to and activating the long form of leptin receptors (LEPRb). LEPRb activates JAK2 that subsequently phosphorylates and activates STAT3. The JAK2/STAT3 pathway is required for leptin control of energy balance and body weight. Defects in leptin signaling lead to leptin resistance, a primary risk factor for obesity. Body weight is also regulated by nutrients, including glucose. Defects in glucose sensing also contribute to obesity. Here we report crosstalk between leptin and glucose. Glucose starvation blocked the ability of leptin to stimulate tyrosyl phosphorylation and activation of JAK2 and STAT3 in a variety of cell types. Glucose dose-dependently enhanced leptin signaling. In contrast, glucose did not enhance growth hormone-stimulated phosphorylation of JAK2 and STAT5. Glucose starvation or 2-deoxyglucose-induced inhibition of glycolysis activated AMPK and inhibited leptin signaling; pharmacological inhibition of AMPK restored the ability of leptin to stimulate STAT3 phosphorylation. Conversely, pharmacological activation of AMPK was sufficient to inhibit leptin signaling and to block the ability of glucose to enhance leptin signaling. These results suggest that glucose and/or its metabolites play a permissive role in leptin signaling, and that glucose enhances leptin sensitivity at least in part by attenuating the ability of AMPK to inhibit leptin signaling