Requirement of dendritic Akt degradation by the ubiquitin–proteasome system for neuronal polarity

Asymmetric distributions of activities of the protein kinases Akt and glycogen synthase kinase 3β (GSK-3β) are critical for the formation of neuronal polarity. However, the mechanisms underlying polarized regulation of this pathway remain unclear. In this study, we report that the instability of Akt regulated by the ubiquitin–proteasome system (UPS) is required for neuron polarity. Preferential distribution in the axons was observed for Akt but not for its target GSK-3β. A photoactivatable GFP fused to Akt revealed the preferential instability of Akt in dendrites. Akt but not p110 or GSK-3β was ubiquitinated. Suppressing the UPS led to the symmetric distribution of Akt and the formation of multiple axons. These results indicate that local protein degradation mediated by the UPS is important in determining neuronal polarity

Interleukin-1-induced Ether-linked Diglycerides Inhibit Calcium-insensitive Protein Kinase C Isotypes IMPLICATIONS FOR GROWTH SENESCENCE

It is hypothesized that inflammatory cytokines and vasoactive peptides stimulate distinct species of diglycerides that differentially regulate protein kinase C isotypes. In published data, we demonstrated that interleukin-1, in contrast to endothelin, selectively generates ether-linked diglyceride species (alkyl, acyl- and alkenyl, acylglycerols) in rat mesangial cells, a smooth muscle-like pericyte in the glomerulus. We now demonstrate both in intact cell and in cell-free preparations that these interleukin-1 receptor-generated ether-linked diglycerides inhibit immunoprecipitated protein kinase C delta and epsilon but not zeta activity. Neither interleukin-1 nor endothelin affect de novo protein expression of these protein kinase C isotypes. As down-regulation of calcium-insensitive protein kinase C isotypes has been linked to antimitogenic activity, we investigated growth arrest as a functional correlate for IL-1-generated ether-linked diglycerides. Cell-permeable ether-linked diglycerides mimic the effects of interleukin-1 to induce a growth-arrested state in both G-protein-linked receptor- and tyrosine kinase receptor-stimulated mesangial cells. This signaling mechanism implicates cytokine receptor-induced ether-linked diglycerides as second messengers that inhibit the bioactivity of calcium-insensitive protein kinase C isotypes resulting in growth arrest

Continual Segmentation with Disentangled Objectness Learning and Class Recognition

Most continual segmentation methods tackle the problem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones, as objectness has strong transfer ability and forgetting resistance. Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning, a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes, we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://github.com/jordangong/CoMasTRe.Comment: Accepted to CVPR 202

SBSM-Pro: Support Bio-sequence Machine for Proteins

Proteins play a pivotal role in biological systems. The use of machine learning algorithms for protein classification can assist and even guide biological experiments, offering crucial insights for biotechnological applications. We propose a support bio-sequence machine for proteins, a model specifically designed for biological sequence classification. This model starts with raw sequences and groups amino acids based on their physicochemical properties. It incorporates sequence alignment to measure the similarities between proteins and uses a novel MKL approach to integrate various types of information, utilizing support vector machines for classification prediction. The results indicate that our model demonstrates commendable performance across 10 datasets in terms of the identification of protein function and posttranslational modification. This research not only showcases state-of-the-art work in protein classification but also paves the way for new directions in this domain, representing a beneficial endeavour in the development of platforms tailored for biological sequence classification. SBSM-Pro is available for access at http://lab.malab.cn/soft/SBSM-Pro/.Comment: 38 pages, 9 figure

Lie algebras with differential operators of any weights

In this paper, we define a cohomology theory for differential Lie algebras of any weight. As applications of the cohomology, we study abelian extensions and formal deformations of differential Lie algebras of any weight. Finally, we consider homotopy differential operators on $\mathrm{L}_{\infty}$ algebras and 2-differential operators of any weight on Lie 2-algebras, and we prove that the category of 2-term $\mathrm{L}_{\infty}$ algebras with homotopy differential operators of any weight is same as the category of Lie 2-algebras with 2-differential operators of any weight