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Abstract Of the ions involved in myocardial function, Ca2+ is the most important. Ca2+ is crucial to the process that allows myocardium to repeatedly contract and relax in a well-organized fashion; it is the process called excitation–contraction coupling. In order, therefore, for accurate comprehension of the physiology of the heart, it is fundamentally important to understand the detailed mechanism by which the intracellular Ca2+ concentration is regulated to elicit excitation–contraction coupling. Aequorin was discovered by Shimomura, Johnson and Saiga in 1962. By taking advantage of the fact that aequorin emits blue light when it binds to Ca2+ within the physiologically relevant concentration range, in the 1970s and 1980s, physiologists microinjected it into myocardial preparations. By doing so, they proved that Ca2+ transients occur upon membrane depolarization, and tension development (i.e., actomyosin interaction) subsequently follows, dramatically advancing the research on cardiac excitation–contraction coupling.

Multimodal transfer learning aims to transform pretrained representations of diverse modalities into a common domain space for effective multimodal fusion. However, conventional systems are typically built on the assumption that all modalities exist, and the lack of modalities always leads to poor inference performance. Furthermore, extracting pretrained embeddings for all modalities is computationally inefficient for inference. In this work, to achieve high efficiency-performance multimodal transfer learning, we propose VideoAdviser, a video knowledge distillation method to transfer multimodal knowledge of video-enhanced prompts from a multimodal fundamental model (teacher) to a specific modal fundamental model (student). With an intuition that the best learning performance comes with professional advisers and smart students, we use a CLIP-based teacher model to provide expressive multimodal knowledge supervision signals to a RoBERTa-based student model via optimizing a step-distillation objective loss—first step: the teacher distills multimodal knowledge of video-enhanced prompts from classification logits to a regression logit—second step: the multimodal knowledge is distilled from the regression logit of the teacher to the student. We evaluate our method in two challenging multimodal tasks: video-level sentiment analysis (MOSI and MOSEI datasets) and audio-visual retrieval (VEGAS dataset). The student (requiring only the text modality as input) achieves an MAE score improvement of up to 12.3% for MOSI and MOSEI. Our method further enhances the state-of-the-art method by 3.4% mAP score for VEGAS without additional computations for inference. These results suggest the strengths of our method for achieving high efficiency-performance multimodal transfer learning.

Video-level sentiment analysis is a challenging task and requires systems to obtain discriminative multimodal representations that can capture difference in sentiments across various modalities. However, due to diverse distributions of various modalities and the unified multimodal labels are not always adaptable to unimodal learning, the distance difference between unimodal representations increases, and prevents systems from learning discriminative multimodal representations. In this paper, to obtain more discriminative multimodal representations that can further improve systems’ performance, we propose a VAE-based adversarial multimodal domain transfer (VAE-AMDT) and jointly train it with a multi-attention module to reduce the distance difference between unimodal representations. We first perform variational autoencoder (VAE) to make visual, linguistic and acoustic representations follow a common distribution, and then introduce adversarial training to transfer all unimodal representations to a joint embedding space. As a result, we fuse various modalities on this joint embedding space via the multi-attention module, which consists of self-attention, cross-attention and triple-attention for highlighting important sentimental representations over time and modality. Our method improves F1-score of the state-of-the-art by 3.6% on MOSI and 2.9% on MOSEI datasets, and prove its efficacy in obtaining discriminative multimodal representations for video-level sentiment analysis.

Abstract Background The association of glycated albumin (GA) with mortality is unclear in chronic hemodialysis patients with diabetes. We investigated the usefulness of GA by comparing it with hemoglobin A1c (HbA1c) in this patient population. Research design and methods This was a multi-center, prospective cohort study of 841 Japanese chronic hemodialysis patients with diabetes. There were 235 women and 606 men included, with a mean age of 64 years. The primary and secondary endpoints were the incidence of all-cause and cause-specific mortality, respectively. The hazard ratios of GA and HbA1c for the endpoints were estimated using the values at baseline and during the study period. Results During the mean follow-up period of 3.1 years, there were 184 deceased cases, in which 30 and 154 resulted from atherosclerotic cardiovascular disease (ASCVD) and non-ASCVD, respectively. The hazard ratio for a 1% increase in GA was 1.033 (95% confidence interval 1.006–1.060, p = 0.017) for all-cause mortality with a statistical significance when GA was treated as a time dependent variable, but not when the baseline levels or the mean levels during the follow-up period were used in the analysis (p = 0.815 and 0.517, respectively). GA was a significant predictor for ASCVD-related mortality in the above 3 models, but was not for non-ASCVD mortality. Higher levels of HbA1c were only associated with ASCVD-related mortality when HbA1c was treated as a time-dependent variable. Conclusions GA may be useful compared to HbA1c for predicting all-cause and ASCVD-related mortality in Japanese patients with diabetes undergoing chronic hemodialysis.

Ants are known to use a colony-specific blend of cuticular hydrocarbons (CHCs) as a pheromone to discriminate between nestmates and non-nestmates and the CHCs were sensed in the basiconic type of antennal sensilla (S. basiconica). To investigate the functional design of this type of antennal sensilla, we observed the ultra-structures at 2D and 3D in the Japanese carpenter ant, Camponotus japonicus, using a serial block-face scanning electron microscope (SBF-SEM), and conventional and high-voltage transmission electron microscopes. Based on the serial images of 352 cross sections of SBF-SEM, we reconstructed a 3D model of the sensillum revealing that each S. basiconica houses > 100 unbranched dendritic processes, which extend from the same number of olfactory receptor neurons (ORNs). The dendritic processes had characteristic beaded-structures and formed a twisted bundle within the sensillum. At the “beads,” the cell membranes of the processes were closely adjacent in the interdigitated profiles, suggesting functional interactions via gap junctions (GJs). Immunohistochemistry with anti-innexin (invertebrate GJ protein) antisera revealed positive labeling in the antennae of C. japonicus. Innexin 3, one of the five antennal innexin subtypes, was detected as a dotted signal within the S. basiconica as a sensory organ for nestmate recognition. These morphological results suggest that ORNs form an electrical network via GJs between dendritic processes. We were unable to functionally certify the electric connections in an olfactory sensory unit comprising such multiple ORNs; however, with the aid of simulation of a mathematical model, we examined the putative function of this novel chemosensory information network, which possibly contributes to the distinct discrimination of colony-specific blends of CHCs or other odor detection.