SGTA Recognizes a Noncanonical Ubiquitin-like Domain in the Bag6-Ubl4A-Trc35 Complex to Promote Endoplasmic Reticulum-Associated Degradation

SummaryElimination of aberrantly folded polypeptides from the endoplasmic reticulum (ER) by the ER-associated degradation (ERAD) system promotes cell survival under stress conditions. This quality control mechanism requires movement of misfolded proteins across the ER membrane for targeting to the cytosolic proteasome, a process facilitated by a “holdase” complex, consisting of Bag6 and the cofactors Ubl4A and Trc35. This multiprotein complex also participates in several other protein quality control processes. Here, we report SGTA as a component of the Bag6 system, which cooperates with Bag6 to channel dislocated ERAD substrates that are prone to aggregation. Using nuclear magnetic resonance spectroscopy and biochemical assays, we demonstrate that SGTA contains a noncanonical ubiquitin-like-binding domain that interacts specifically with an unconventional ubiquitin-like protein/domain in Ubl4A at least in part via electrostatics. This interaction helps recruit SGTA to Bag6, enhances substrate loading to Bag6, and thus prevents the formation of nondegradable protein aggregates in ERAD

UNIDEAL: Curriculum Knowledge Distillation Federated Learning

Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, where clients possess data from different domains or distributions, remain a challenging problem due to the inherent heterogeneity. In this paper, we present UNIDEAL, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios and heterogeneous model architectures. The proposed method introduces Adjustable Teacher-Student Mutual Evaluation Curriculum Learning, which significantly enhances the effectiveness of knowledge distillation in FL settings. We conduct extensive experiments on various datasets, comparing UNIDEAL with state-of-the-art baselines. Our results demonstrate that UNIDEAL achieves superior performance in terms of both model accuracy and communication efficiency. Additionally, we provide a convergence analysis of the algorithm, showing a convergence rate of O(1/T) under non-convex conditions.Comment: Submitted to ICASSP 202

Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance

Proprietary Large Language Models (LLMs), such as ChatGPT, have garnered significant attention due to their exceptional capabilities in handling a diverse range of tasks. Recent studies demonstrate that open-sourced smaller foundational models, such as 7B-size LLaMA, can also display remarkable proficiency in tackling diverse tasks when fine-tuned using instruction-driven data. In this work, we investigate a practical problem setting where the primary focus is on one or a few particular tasks rather than general-purpose instruction following, and explore whether LLMs can be beneficial and further improved for such targeted scenarios. We choose the writing-assistant scenario as the testbed, which includes seven writing tasks. We collect training data for these tasks, reframe them in an instruction-following format, and subsequently refine the LLM, specifically LLaMA, via instruction tuning. Experimental results show that fine-tuning LLaMA on writing instruction data significantly improves its ability on writing tasks. We also conduct more experiments and analyses to offer insights for future work on effectively fine-tuning LLaMA for specific scenarios. Finally, we initiate a discussion regarding the necessity of employing LLMs for only one targeted task, taking into account the efforts required for tuning and the resources consumed during deployment

Prominent edge detection with deep metric expression and multi-scale features

Abstract(#br)Edge detection is one of today’s hottest computer vision issues with widely applications. It is beneficial for improving the capability of many vision systems, such as semantic segmentation, salient object detection and object recognition. Deep convolution neural networks (CNNs) recently have been employed to extract robust features, and have achieved a definite improvement. However, there is still a long run to study this hotspot with the main reason that CNNs-based approaches may cause the edges thicker. To address this problem, a novel semantic edge detection algorithm using multi-scale features is proposed. Our model is deep symmetrical metric learning network, which includes 3 key parts. Firstly, the deep detail layer, as a preprocessing layer and a guide module, is..

Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation

Automated segmentation of cells from cervical smears poses great challenge to biomedical image analysis because of the noisy and complex background, poor cytoplasmic contrast and the presence of fuzzy and overlapping cells. In this paper, we propose an automated segmentation method for the nucleus and cytoplasm in a cluster of cervical cells based on distinctive local features and guided sparse shape deformation. Our proposed approach is performed in two stages: segmentation of nuclei and cellular clusters, and segmentation of overlapping cytoplasm. In the rst stage, a set of local discriminative shape and appearance cues of image superpixels is incorporated and classi ed by the Support Vector Machine (SVM) to segment the image into nuclei, cellular clusters, and background. In the second stage, a robust shape deformation framework is proposed, based on Sparse Coding (SC) theory and guided by representative shape features, to construct the cytoplasmic shape of each overlapping cell. Then, the obtained shape is re ned by the Distance Regularized Level Set Evolution (DRLSE) model. We evaluated our approach using the ISBI 2014 challenge dataset, which has 135 synthetic cell images for a total of 810 cells. Our results show that our approach outperformed existing approaches in segmenting overlapping cells and obtaining accurate nuclear boundaries. Keywords: overlapping cervical smear cells, feature extraction, sparse coding, shape deformation, distance regularized level set