RPA is a master regulator of DNA metabolism and RPA availability acts as a rate-limiting factor. While numerous studies focused on the post-translational regulations of RPA for its functions, little ...is known regarding how RPA availability is controlled. Here we identify a novel lncRNA Discn as the guardian of RPA availability in stem cells. Discn is induced upon genotoxic stress and binds to neucleolin (NCL) in the nucleolus. This prevents NCL from translocation into nucleoplasm and avoids undesirable NCL-mediated RPA sequestration. Thus, Discn-NCL-RPA pathway preserves a sufficient RPA pool for DNA replication stress response and repair. Discn loss causes massive genome instability in mouse embryonic stem cells and neural stem/progenigor cells. Mice depleted of Discn display newborn death and brain dysfunctions due to DNA damage accumulation and associated inflammatory reactions. Our findings uncover a key regulator of DNA metabolism and provide new clue to understand the chemoresistance in cancer treatment.
Pluripotent stem cells （PSCs） harbor constitutive DNA rePlication stress during their rapid proliferation and the consequent genome instability hampers their applications in regenerative medicine. It ...is therefore important to un- derstand the regulatory mechanisms of replication stress response in PSCs. Here, we report that mouse embryonic stem cells （ESCs） are superior to differentiated cells in resolving replication stress. Specifically, ESCs utilize a unique Filia-Floped protein complex-dependent mechanism to efficiently promote the restart of stalled replication forks, therefore maintaining genomic stability. The ESC-specific Filia-Floped complex resides on replication forks under normal conditions. Replication stress stimulates their recruitment to stalling forks and the serine 151 residue of Filia is phosphorylated in an ATR-dependent manner. This modification enables the Filia-Floped complex to act as a func- tional scaffold, which then promotes the stalling fork restart through a dual mechanism： both enhancing recruitment of the replication fork restart protein, Blm, and stimulating ATR kinase activation. In the Blm pathway, the scaffolds recruit the E3 ubiquitin ligase, Trim25, to the stalled replication forks, and in turn Trim25 tethers and concentrates Blm at stalled replication forks through ubiquitination. In differentiated cells, the recruitment of the Trim25-Blm complex to replication forks and the activation of ATR signaling are much less robust due to lack of the ESC-specific Filia-Floped scaffold. Thus, our study reveals that ESCs utilize an additional and unique regulatory layer to efficient- ly promote the stalled fork restart and maintain genomic stability.
With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current ...lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an intelligent lighting control system based on a distributed architecture, incorporating a dynamic shading system for adjusting the interior lighting environment. The system comprises two subsystems: lighting and shading. The shading subsystem utilizes fuzzy control logic to control lighting based on the room’s temperature and illumination, thereby achieving rapid control with fewer calculations. The lighting subsystem employs a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the luminaire dimming problem based on room illuminance in order to maximize user convenience while achieving uniform illumination. This paper also includes the construction of a prototype box on which the system is evaluated in two distinct circumstances. The results of the tests demonstrate that the system functions properly, has stability and real-time performance, and can adapt to complex and variable outdoor environments. The maximum relative error between actual and expected illuminance is less than 10%, and the average relative error is less than 5% when achieving uniform illuminance.
The Unsupervised Domain Adaptation (UDA) methods aim to enhance feature transferability possibly at the expense of feature discriminability. Recently, contrastive representation learning has been ...applied to UDA as a promising approach. One way is to combine the mainstream domain adaptation method with contrastive self-supervised tasks. The other way uses contrastive learning to align class-conditional distributions according to the semantic structure information of source and target domains. Nevertheless, there are some limitations in two aspects. One is that optimal solutions for the contrastive self-supervised learning and the domain discrepancy minimization may not be consistent. The other is that contrastive learning uses pseudo label information of target domain to align class-conditional distributions, where the pseudo label information contains noise such that false positive and negative pairs would deteriorate the performance of contrastive learning. To address these issues, we propose Noise-robust cross-domain Contrastive Learning (NaCL) to directly realize the domain adaptation task via simultaneously learning the instance-wise discrimination and encoding semantic structures in intra- and inter-domain to the learned representation space. More specifically, we adopt topology-based selection on the target domain to detect and remove false positive and negative pairs in contrastive loss. Theoretically, we demonstrate that not only NaCL can be considered an example of Expectation Maximization (EM), but also accurate pseudo label information is beneficial for reducing the expected error on target domain. NaCL obtains superior results on three public benchmarks. Further, NaCL can also be applied to semi-supervised domain adaptation with only minor modifications, achieving advanced diagnostic performance on COVID-19 dataset. Code is available at
https://github.com/jingzhengli/NaCL
Guanidinoacetic acid can improve pork quality. Previous studies have demonstrated that pork quality is closely linked to the muscle fiber type mediated by PPARGC1A. Therefore, this study aimed to ...evaluate the influence of dietary GAA supplementation on the skeletal muscle fiber type transformation. A total of 180 healthy Duroc × Landrace × Meishan cross castrated male pigs with a similar average weight (90 ± 1.5 kg) were randomly divided into three treatments with five replicates per treatment and 12 pigs per replicate, including a GAA-free basal diet and basal diet with 0.05% or 0.10% GAA for 15 days. Our results showed that 0.10% GAA supplementation increased the contents of Ca2+ in sarcoplasm (p < 0.05). Compared with the control group, both GAA supplementation groups upregulated the expression of Troponin I-ss (p < 0.05), and 0.10% GAA supplementation downregulated the expression of Troponin T3 (p < 0.05). GAA supplementation increased the expression of peroxisome proliferator activated receptor-γ coactivator-1alpha (PPARGC1A) (p < 0.05), and further upregulated the mitochondrial transcription factor A (TFAM), increased the level of membrane potential, and the activities of mitochondrial respiratory chain complex I, III (p < 0.05). The 0.10% GAA supplementation upregulated the protein expression of calcineurin catalytic subunit α (CnAα) and nuclear factor of activated T cells (NFATc1) (p < 0.05). Overall, dietary GAA supplementation promotes skeletal muscle fiber types transformation from fast-to-slow-twitch via increasing the PPARGC1A based mitochondrial function and the activation of CaN/NFAT pathway in finishing pigs.
Learning from multiple annotators aims to induce a high-quality classifier from training instances, where each of them is associated with a set of observed labels provided by multiple annotators ...under the impact of their varying abilities and own biases. When modeling the probability transition process from latent true labels to observed labels, most existing methods adopt class-level confusion matrices of annotators which assume that observed labels do not depend on the instance features and are just determined by the true labels. However, in practice the labeling process of annotators is impacted not only by the correlation between classes but also by the content of instances. Thus using only class-level confusion matrices to characterize the probability transition process may limit the performance that the classifier can achieve. In this work, we propose the noise transition matrix, that incorporates the impact of instance features on annotators’ performance based on confusion matrices. Furthermore, we propose a simple and effective learning framework, which consists of a classifier module and a noise transition matrix module in a unified neural network architecture. Experimental results on synthetic and real datasets demonstrate the noise transition matrix is better than the confusion matrix for modeling multiple annotators and the superiority of our method in comparison with state-of-the-art methods.
The grain boundary and lattice mismatch among CuO and Cu2O work properly to facilitate the formation of ethylene and C2+ products during CO2RR.
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•Cu catalysts were prepared by thermal ...reduction of organic copper reagent complexation.•CuOAm reduced at 180 °C exhibits FEs as 79% for C2+ products.•Grain boundary and lattice mismatch formed due to different crystal structures.•In-situ Raman measurements confirms catalyst reconstruction and reaction pathways.
Cu-based catalysts for CO2 electrochemical reduction reaction suffer from both activity and selectivity towards C2+ products. Here, Cu-Oleylamine (CuOAm) based catalysts with adjustable Cu chemical state are synthesized through thermal reducing method with oleylamine as the only surfactant, solvent, and reducing agent. The catalyst exhibits a high faradaic efficiency of 54% and 79% with a partial current density of 186 mA cm−2 and 245 mA cm−2 at −1.0 V (vs. RHE) for C2H4 and C2+ products, respectively. Further characterizations of the size, morphology and surface composition of CuOAm by XRPD, XPS, SEM, HRTEM, and EDS showed that the presence of different crystal structures (CuO and Cu2O) with comparable amounts were vital for the formation of grain boundary and lattice mismatch during CO2RR. The sequential reduction of CuOx and CO2 at abundant grain boundary and lattice mismatch could facilitate the formation of ethylene and C2+ products, which was confirmed by in-situ Raman measurements under varied potentials.
Though attribute reduction defined by neighborhood decision error rate can improve the classification performance of neighborhood classifier via deleting redundant attributes, such reduction does not ...take the variations of classification results into account. To fill this gap, a multi-criterion based attribute reduction is proposed, which considers both neighborhood decision error rate and neighborhood decision consistency. The neighborhood decision consistency is used to measure the variations of classification results if attributes change. Following the novel attribute reduction, a heuristic algorithm is also designed to derive reduct which aims to obtain less error rate and higher consistency simultaneously. The experimental results on 10 UCI data sets show that the multi-criterion based reduction can not only improve the decision consistencies without decreasing the classification accuracies significantly, but also bring us more stable reducts. This study suggests new trends concerning criteria and constraints in attribute reduction.
Industrial-robot assisted abrasive cloth wheel polishing blades aim to reduce surface roughness and improve machining consistency of blades. Since the blade is the complex free-form surface, the ...blade surface after offline programming has “over-polishing,” “under-polishing,” and machining allowance uneven phenomenon. In this paper, fuzzy impedance force control technology is proposed to solve the precision problem in the blade polishing process. First, the position-based impedance control algorithm is analyzed, and reasonable impedance parameters are obtained based on the actual robot model. Then, the fuzzy variable impedance control combining fuzzy theory and impedance control is proposed to solve the problems of poor trajectory tracking ability and force instability, when the traditional impedance control faces environmental changes and unknown environments. Finally, the simulation platform is built with the help of MATLAB Simulink tool to verify the effectiveness and rationality of the strategy, and the comparative experiment is conducted for robot-assisted abrasive cloth wheel polishing blade under fuzzy variable impedance force control and without force control. The results show that after superimposing the displacement compensation controlled by the fuzzy variable impedance force on the blade surface, the blade surface roughness is below 0.4 μm, the polishing machining allowance is within ± 0.06 mm, and the uniformity and consistency of the blade polishing surface are better.
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