| 1. |
- Li, Shuqi, et al.
(författare)
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Rtt105 functions as a chaperone for replication protein A to preserve genome stability
- 2018
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Ingår i: EMBO Journal. - : Wiley-VCH Verlagsgesellschaft. - 0261-4189 .- 1460-2075. ; 37:17
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Tidskriftsartikel (refereegranskat)abstract
- Generation of single-stranded DNA (ssDNA) is required for the template strand formation during DNA replication. Replication Protein A (RPA) is an ssDNA-binding protein essential for protecting ssDNA at replication forks in eukaryotic cells. While significant progress has been made in characterizing the role of the RPA-ssDNA complex, how RPA is loaded at replication forks remains poorly explored. Here, we show that the Saccharomyces cerevisiae protein regulator of Ty1 transposition 105 (Rtt105) binds RPA and helps load it at replication forks. Cells lacking Rtt105 exhibit a dramatic reduction in RPA loading at replication forks, compromised DNA synthesis under replication stress, and increased genome instability. Mechanistically, we show that Rtt105 mediates the RPA-importin interaction and also promotes RPA binding to ssDNA directly in vitro, but is not present in the final RPA-ssDNA complex. Single-molecule studies reveal that Rtt105 affects the binding mode of RPA to ssDNA These results support a model in which Rtt105 functions as an RPA chaperone that escorts RPA to the nucleus and facilitates its loading onto ssDNA at replication forks.
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| 2. |
- Jin, Yi, et al.
(författare)
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Self-aware distributed deep learning framework for heterogeneous IoT edge devices
- 2021
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Ingår i: Future generations computer systems. - : Elsevier BV. - 0167-739X .- 1872-7115. ; 125, s. 908-920
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Tidskriftsartikel (refereegranskat)abstract
- Implementing artificial intelligence (AI) in the Internet of Things (IoT) involves a move from the cloud to the heterogeneous and low-power edge, following an urgent demand for deploying complex training tasks in a distributed and reliable manner. This work proposes a self-aware distributed deep learning (DDL) framework for IoT applications, which is applicable to heterogeneous edge devices aiming to improve adaptivity and amortize the training cost. The self-aware design including the dynamic self-organizing approach and the self-healing method enhances the system reliability and resilience. Three typical edge devices are adopted with cross-platform Docker deployment: Personal Computers (PC) for general computing devices, Raspberry Pi 4Bs (Rpi) for resource-constrained edge devices, and Jetson Nanos (Jts) for AI-enabled edge devices. Benchmarked with ResNet-32 on CIFAR-10, the training efficiency of tested distributed clusters is increased by 8.44x compared to the standalone Rpi. The cluster with 11 heterogeneous edge devices achieves a training efficiency of 200.4 images/s and an accuracy of 92.45%. Results prove that the self-organizing approach functions well with dynamic changes like devices being removed or added. The self-healing method is evaluated with various stabilities, cluster scales, and breakdown cases, testifying that the reliability can be largely enhanced for extensively distributed deployments. The proposed DDL framework shows excellent performance for training implementation with heterogeneous edge devices in IoT applications with high-degree scalability and reliability.
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| 3. |
- Chen, Jiawei, et al.
(författare)
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Inhibition of aquaporin-9 ameliorates severe acute pancreatitis and associated lung injury by NLRP3 and Nrf2/HO-1 pathways
- 2024
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Ingår i: International Immunopharmacology. - 1567-5769. ; 137
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Tidskriftsartikel (refereegranskat)abstract
- Inflammation, apoptosis and oxidative stress play crucial roles in the deterioration of severe acute pancreatitis-associated acute respiratory distress syndrome (SAP-ARDS). Unfortunately, despite a high mortality rate of 45 %[1], there are limited treatment options available for ARDS outside of last resort options such as mechanical ventilation and extracorporeal support strategies[2]. This study investigated the potential therapeutic role and mechanisms of AQP9 inhibitor RG100204 in two animal models of severe acute pancreatitis, inducing acute respiratory distress syndrome: 1) a sodium-taurocholate induced rat model, and 2) and Cerulein and lipopolysaccharide induced mouse model. RG100204 treatment led to a profound reduction in inflammatory cytokine expression in pancreatic, and lung tissue, in both models. In addition, infiltration of CD68 + and CD11b + cells into these tissues were reduced in RG100204 treated SAP animals, and edema and SAP associated tissue damage were improved. Moreover, we demonstrate that RG100204 reduced apoptosis in the lungs of rat SAP animals, and reduces NF-κB signaling, NLRP3, expression, while profoundly increasing the Nrf2-dependent anti oxidative stress response. We conclude that AQP9 inhibition is a promising strategy for the treatment of pancreatitis and its systemic complications, such as ARDS.
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| 4. |
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| 5. |
- Gao, Shang, 1982-, et al.
(författare)
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An Empirical Study on the Adoption of Online Household e-waste Collection Services in China
- 2015
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Ingår i: Open and Big Data Management and Innovation. - Cham : Springer. - 9783319250137 - 9783319250120 ; , s. 36-47
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Konferensbidrag (refereegranskat)abstract
- Online household e-waste collection services are emerging as new solutions to disposing household e-waste in China. This study aims to investigate the adoption of online household e-waste collection services in China. Based on the previous technology diffusion theories (e.g., TAM, UTAUT), a research model with six research hypotheses was proposed in this research. The research model was empirically tested with a sample of 203 users of online household e-waste collection services in China. The results indicated that five of the six research hypotheses were significantly supported. And the most significant determinant for the behavioral intention to use online household e-waste service was effort expectancy. However, facilitating condition did not have significant impact on users’ behavior of using online household e-waste collection services.
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| 6. |
- Gehrmann, Sebastian, et al.
(författare)
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GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
- 2022
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Ingår i: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. - : Association for Computational Linguistics (ACL). ; , s. 266-281
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Konferensbidrag (refereegranskat)abstract
- Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. The compatibility, often facilitated through leaderboards, thus leads to outdated but standardized evaluation practices. We pose that the standardization is taking place in the wrong spot. Evaluation infrastructure should enable researchers to use the latest methods and what should be standardized instead is how to incorporate these new evaluation advances.We introduce GEMv2, the new version of the Generation, Evaluation, and Metrics Benchmark which uses a modular infrastructure for dataset, model, and metric developers to benefit from each other’s work. GEMv2 supports 40 documented datasets in 51 languages, ongoing online evaluation for all datasets, and our interactive tools make it easier to add new datasets to the living benchmark.
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| 7. |
- Gehrmann, Sebastian, et al.
(författare)
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The GEM Benchmark : Natural Language Generation, its Evaluation and Metrics
- 2021
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Ingår i: The 1st Workshop on Natural Language Generation, Evaluation, and Metrics. - Stroudsburg, PA, USA : Association for Computational Linguistics. ; , s. 96-120
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Konferensbidrag (refereegranskat)abstract
- We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.
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| 8. |
- Hu, Chang-Kang, et al.
(författare)
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Native Conditional iSWAP Operation with Superconducting Artificial Atoms
- 2023
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Ingår i: Physical Review Applied. - 2331-7019. ; 20:3
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Tidskriftsartikel (refereegranskat)abstract
- Controlling the flow of quantum information is a fundamental task for quantum computers, which is unfeasible to realize on classical devices. Coherent devices, which can process quantum states are thus required to route the quantum states that encode information. In this paper we demonstrate experimentally the smallest quantum transistor with a superconducting quantum processor, which is composed of a collector qubit, an emitter qubit, and a coupler (transistor gate). The interaction strength between the collector and emitter qubits is controlled by the frequency and state of the coupler, effectively implementing a quantum switch. Through the coupler-state-dependent Heisenberg (inherent) interaction between the qubits, a single-step (native) conditional iSWAP operation can be applied. To this end, we find that it is useful to take into consideration the higher-energy level for achieving a native and high-fidelity transistor operation. By reconstructing the quantum process tomography, we obtain an operation fidelity of 92.36% when the transistor gate is open (iSWAP implementation) and 95.23% in the case of closed gate (identity gate implementation). The architecture has strong potential in quantum information processing applications with superconducting qubits.
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| 9. |
- Hu, Chang-Kang, et al.
(författare)
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Optimal charging of a superconducting quantum battery
- 2022
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Ingår i: Quantum Science and Technology. - : IOP Publishing. - 2058-9565. ; 7:4
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Tidskriftsartikel (refereegranskat)abstract
- Quantum batteries are miniature energy storage devices and play a very important role in quantum thermodynamics. In recent years, quantum batteries have been extensively studied, but limited in theoretical level. Here we report the experimental realization of a quantum battery based on superconducting qutrit. Our model explores dark and bright states to achieve stable and powerful charging processes, respectively. Our scheme makes use of the quantum adiabatic brachistochrone, which allows us to speed up the battery ergotropy injection. Due to the inherent interaction of the system with its surrounding, the battery exhibits a self-discharge, which is shown to be described by a supercapacitor-like self-discharging mechanism. Our results paves the way for proposals of new superconducting circuits able to store extractable work for further usage.
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| 10. |
- Huan, Yuxiang, et al.
(författare)
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A 3D Tiled Low Power Accelerator for Convolutional Neural Network
- 2018
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Ingår i: 2018 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS). - : IEEE. - 9781538648810
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Konferensbidrag (refereegranskat)abstract
- It remains a challenge to run Deep Learning in devices with stringent power budget in the Internet-of-Things. This paper presents a low-power accelerator for processing Convolutional Neural Networks on the embedded devices. The power reduction is realized by exploring data reuse in three different aspects, with regards to convolution, filter and input features. A systolic-like data flow is proposed and applied to rows of Processing Elements (PEs), which facilitate reusing the data during convolution. Reuse of input features and filters is achieved by arranging the PE array in a 3D tiled architecture, whose dimension is 3 x 14 x 4. Local storage within PEs is therefore reduced and only cost 17.75 kB, which is 20% of the state-of-the-art. With dedicated delay chains in each PE, this accelerator is reconfigurable to suit various parameter settings of convolutional layers. Evaluated in UMC 65 nm low leakage process, the accelerator can reach a peak performance of 84 GOPS and consume only 136 mW at 250 Mhz.
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