CARMA3: Scaffold Protein Involved in NF-κB Signaling

Scaffold proteins are defined as pivotal molecules that connect upstream receptors to specific effector molecules. Caspase recruitment domain protein 10 (CARD10) gene encodes a scaffold protein CARMA3, belongs to the family of CARD and membrane-associated guanylate kinase-like protein (CARMA). During the past decade, investigating the function of CARMA3 has revealed that it forms a complex with BCL10 and MALT1 to mediate different receptors-dependent signaling, including GPCR and EGFR, leading to activation of the transcription factor NF-κB. More recently, CARMA3 and its partners are also reported to be involved in antiviral innate immune response and DNA damage response. In this review, we summarize the biology of CARMA3 in multiple receptor-induced NF-κB signaling. Especially, we focus on discussing the function of CARMA3 in regulating NF-κB activation and antiviral IFN signaling in the context of recent progress in the field

Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search

Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our observations reveal that only applying adapter modules on cascaded model can not achieve considerable performance as fine-tuning. We propose an automatic and effective adaptive learning method to optimize end-to-end cascaded multi-task models based on Neural Architecture Search (NAS) framework. The candidate adaptive operations on each specific module consist of frozen, inserting an adapter and fine-tuning. We further add a penalty item on the loss to limit the learned structure which takes the amount of trainable parameters into account. The penalty item successfully restrict the searched architecture and the proposed approach is able to search similar tuning scheme with hand-craft, compressing the optimizing parameters to 8.7% corresponding to full fine-tuning on SLURP with an even better performance

Histone modifications in embryo implantation and placentation: insights from mouse models

Embryo implantation and placentation play pivotal roles in pregnancy by facilitating crucial maternal-fetal interactions. These dynamic processes involve significant alterations in gene expression profiles within the endometrium and trophoblast lineages. Epigenetics regulatory mechanisms, such as DNA methylation, histone modification, chromatin remodeling, and microRNA expression, act as regulatory switches to modulate gene activity, and have been implicated in establishing a successful pregnancy. Exploring the alterations in these epigenetic modifications can provide valuable insights for the development of therapeutic strategies targeting complications related to pregnancy. However, our current understanding of these mechanisms during key gestational stages remains incomplete. This review focuses on recent advancements in the study of histone modifications during embryo implantation and placentation, while also highlighting future research directions in this field

VE-KWS: Visual Modality Enhanced End-to-End Keyword Spotting

The performance of the keyword spotting (KWS) system based on audio modality, commonly measured in false alarms and false rejects, degrades significantly under the far field and noisy conditions. Therefore, audio-visual keyword spotting, which leverages complementary relationships over multiple modalities, has recently gained much attention. However, current studies mainly focus on combining the exclusively learned representations of different modalities, instead of exploring the modal relationships during each respective modeling. In this paper, we propose a novel visual modality enhanced end-to-end KWS framework (VE-KWS), which fuses audio and visual modalities from two aspects. The first one is utilizing the speaker location information obtained from the lip region in videos to assist the training of multi-channel audio beamformer. By involving the beamformer as an audio enhancement module, the acoustic distortions, caused by the far field or noisy environments, could be significantly suppressed. The other one is conducting cross-attention between different modalities to capture the inter-modal relationships and help the representation learning of each modality. Experiments on the MSIP challenge corpus show that our proposed model achieves 2.79% false rejection rate and 2.95% false alarm rate on the Eval set, resulting in a new SOTA performance compared with the top-ranking systems in the ICASSP2022 MISP challenge.Comment: 5 pages. Accepted at ICASSP202