The autophagic degradation of cytosolic pools of peroxisomal proteins by a new selective pathway.

Damaged or redundant peroxisomes and their luminal cargoes are removed by pexophagy, a selective autophagy pathway. In yeasts, pexophagy depends mostly on the pexophagy receptors, such as Atg30 for Pichia pastoris and Atg36 for Saccharomyces cerevisiae, the autophagy scaffold proteins, Atg11 and Atg17, and the core autophagy machinery. In P. pastoris, the receptors for peroxisomal matrix proteins containing peroxisomal targeting signals (PTSs) include the PTS1 receptor, Pex5, and the PTS2 receptor and co-receptor, Pex7 and Pex20, respectively. These shuttling receptors are predominantly cytosolic and only partially peroxisomal. It remains unresolved as to whether, when and how the cytosolic pools of peroxisomal receptors, as well as the peroxisomal matrix proteins, are degraded under pexophagy conditions. These cytosolic pools exist both in normal and mutant cells impaired in peroxisome biogenesis. We report here that Pex5 and Pex7, but not Pex20, are degraded by an Atg30-independent, selective autophagy pathway. To enter this selective autophagy pathway, Pex7 required its major PTS2 cargo, Pot1. Similarly, the degradation of Pex5 was inhibited in cells missing abundant PTS1 cargoes, such as alcohol oxidases and Fox2 (hydratase-dehydrogenase-epimerase). Furthermore, in cells deficient in PTS receptors, the cytosolic pools of peroxisomal matrix proteins, such as Pot1 and Fox2, were also removed by Atg30-independent, selective autophagy, under pexophagy conditions. In summary, the cytosolic pools of PTS receptors and their cargoes are degraded via a pexophagy-independent, selective autophagy pathway under pexophagy conditions. These autophagy pathways likely protect cells from futile enzymatic reactions that could potentially cause the accumulation of toxic cytosolic products.Abbreviations: ATG: autophagy related; Cvt: cytoplasm to vacuole targeting; Fox2: hydratase-dehydrogenase-epimerase; PAGE: polyacrylamide gel electrophoresis; Pot1: thiolase; PMP: peroxisomal membrane protein; Pgk1: 3-phosphoglycerate kinase; PTS: peroxisomal targeting signal; RADAR: receptor accumulation and degradation in the absence of recycling; RING: really interesting new gene; SDS: sodium dodecyl sulphate; TCA, trichloroacetic acid; Ub: ubiquitin; UPS: ubiquitin-proteasome system Vid: vacuole import and degradation

Distributionally Robust Performative Optimization

In this paper, we propose a general distributionally robust framework for performative optimization, where the selected decision can influence the probabilistic distribution of uncertain parameters. Our framework facilitates safe decision-making in scenarios with incomplete information about the underlying decision-dependent distributions, relying instead on accessible reference distributions. To tackle the challenge of decision-dependent uncertainty, we introduce an algorithm named repeated robust risk minimization. This algorithm decouples the decision variables associated with the ambiguity set from the expected loss, optimizing the latter at each iteration while keeping the former fixed to the previous decision. By leveraging the strong connection between distributionally robust optimization and regularization, we establish a linear convergence rate to a performatively stable point and provide a suboptimality performance guarantee for the proposed algorithm. Finally, we examine the performance of our proposed model through an experimental study in strategic classification

Grasp Stability Assessment Through Attention-Guided Cross-Modality Fusion and Transfer Learning

Extensive research has been conducted on assessing grasp stability, a crucial prerequisite for achieving optimal grasping strategies, including the minimum force grasping policy. However, existing works employ basic feature-level fusion techniques to combine visual and tactile modalities, resulting in the inadequate utilization of complementary information and the inability to model interactions between unimodal features. This work proposes an attention-guided cross-modality fusion architecture to comprehensively integrate visual and tactile features. This model mainly comprises convolutional neural networks (CNNs), self-attention, and cross-attention mechanisms. In addition, most existing methods collect datasets from real-world systems, which is time-consuming and high-cost, and the datasets collected are comparatively limited in size. This work establishes a robotic grasping system through physics simulation to collect a multimodal dataset. To address the sim-to-real transfer gap, we propose a migration strategy encompassing domain randomization and domain adaptation techniques. The experimental results demonstrate that the proposed fusion framework achieves markedly enhanced prediction performance (approximately 10%) compared to other baselines. Moreover, our findings suggest that the trained model can be reliably transferred to real robotic systems, indicating its potential to address real-world challenges.Comment: Accepted by IROS 202

Automated cropping intensity extraction from isolines of wavelet spectra

Timely and accurate monitoring of cropping intensity (CI) is essential to help us understand changes in food production. This paper aims to develop an automatic Cropping Intensity extraction method based on the Isolines of Wavelet Spectra (CIIWS) with consideration of intra- class variability. The CIIWS method involves the following procedures: (1) characterizing vegetation dynamics from time–frequency dimensions through a continuous wavelet transform performed on vegetation index temporal profiles; (2) deriving three main features, the skeleton width, maximum number of strong brightness centers and the intersection of their scale intervals, through computing a series of wavelet isolines from the wavelet spectra; and (3) developing an automatic cropping intensity classifier based on these three features. The proposed CIIWS method improves the understanding in the spectral–temporal properties of vegetation dynamic processes. To test its efficiency, the CIIWS method is applied to China’s Henan province using 250 m 8 days composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series datasets. An overall accuracy of 88.9% is achieved when compared with in-situ observation data. The mapping result is also evaluated with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data and an overall accuracy of 86.7% is obtained. At county level, the MODIS-derived sown areas and agricultural statistical data are well correlated (r2 = 0.85). The merit and uniqueness of the CIIWS method is the ability to cope with the complex intra-class variability through continuous wavelet transform and efficient feature extraction based on wavelet isolines. As an objective and meaningful algorithm, it guarantees easy applications and greatly contributes to satellite observations of vegetation dynamics and food security efforts

Synthesis and Reactivity of the [NCCCO]– Cyanoketenate Anion

Cyanoketene is a fundamental molecule that is actively being searched for in the interstellar medium. Its deprotonated form (cyanoketenate) is a heterocumulene that is isoelectronic to carbon suboxide whose structure has been the subject of debate. These research questions are hampered by a lack of useful synthetic pathways to these molecules. We report the first synthesis of the cyanoketenate anion in [K(18-crown-6)][NCCCO] (1) as a stable molecule on a multigram scale in excellent yields (>90%). The structure of this molecule is probed crystallographically and computationally. We also explore the protonation of 1, and its reaction with triphenylsilylchloride and carbon dioxide. In all cases, anionic dimers are formed. The cyanoketene could be synthesized and crystallographically characterized when stabilized by a N-heterocyclic carbene. The cyanoketenate is a very useful unsaturated building block containing N, C and O atoms that can now be explored with relative ease and will undoubtedly unlock more interesting reactivity

Railway ballast stabilising agents: Comparison of mechanical properties

Expensive and time-consuming maintenance operations are routinely performed to preserve the ballast mechanical properties in railway lines. Binding agents are used for ballast stabilisation. Four different additives based on bitumen, organosilane, lignosulphonate and polyurethane are investigated in the laboratory by means of repeated load triaxial tests. The parameters that are directly relevant for use in railway structures are assessed. Each binder type significantly influences both the resilient modulus and the resistance to permanent deformation of the treated specimens. The ballast mechanical properties can be conveniently modified, thus being beneficial to track stability and railway maintenance programme.publishedVersionThis is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited