| 1. |
- Zhang, Lixiu, et al.
(author)
-
Advances in the Application of Perovskite Materials
- 2023
-
In: NANO-MICRO LETTERS. - : SHANGHAI JIAO TONG UNIV PRESS. - 2311-6706. ; 15:1
-
Research review (peer-reviewed)abstract
- Nowadays, the soar of photovoltaic performance of perovskite solar cells has set off a fever in the study of metal halide perovskite materials. The excellent optoelectronic properties and defect tolerance feature allow metal halide perovskite to be employed in a wide variety of applications. This article provides a holistic review over the current progress and future prospects of metal halide perovskite materials in representative promising applications, including traditional optoelectronic devices (solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices (artificial synapses and memristors) and pressure-induced emission. This review highlights the fundamentals, the current progress and the remaining challenges for each application, aiming to provide a comprehensive overview of the development status and a navigation of future research for metal halide perovskite materials and devices.
|
|
| 2. |
- De Luca, Alberto, et al.
(author)
-
On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types : Chronicles of the MEMENTO challenge
- 2021
-
In: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240
-
Journal article (peer-reviewed)abstract
- Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
|
|
| 3. |
- Sun, Tao, et al.
(author)
-
A test of manganese effects on decomposition in forest and cropland sites
- 2019
-
In: Soil Biology and Biochemistry. - : Elsevier BV. - 0038-0717 .- 1879-3428. ; 129, s. 178-183
-
Journal article (peer-reviewed)abstract
- Litter of plant origin is the main source of soil organic matter, and its physical and chemical quality and decomposition rates are key variables in the prediction and modelling of how litter-derived carbon (C) is cycling through the ecosystem. However, the biological control factors for decomposition are not well understood and often poorly represented in global C models. These are typically run using simple parameters, such as nitrogen (N) and lignin concentrations, characterizing the quality of the organic matter input to soils and its accessibility to decomposer organisms. Manganese (Mn) is a key component for the formation of manganese peroxidase (MnP), an important enzyme for lignin degradation. However, the functional role of Mn on plant litter decomposition has been rarely experimentally examined. Here, using a forest and a cropland site we studied, over 41 months, the effects of Mn fertilization on MnP activity and decomposition of eight substrates ranging in initial lignin concentrations from 9.8 to 44.6%. Asymptotic decomposition models fitted the mass loss data best and allowed us to separately compare the influence of Mn fertilization on different litter stages and pools. Across substrates, Mn fertilization stimulated decomposition rates of the late stage where lignin dominates decomposition, resulting in smaller fraction of slowly decomposing litter. The increased MnP activity caused by Mn fertilization provided the mechanism explaining the stimulated decomposition in the Mn-addition treatments.
|
|
| 4. |
- Xiao, Chao, et al.
(author)
-
RBBP6 increases radioresistance and serves as a therapeutic target for preoperative radiotherapy in colorectal cancer
- 2018
-
In: Cancer Science. - : Blackwell Publishing. - 1347-9032 .- 1349-7006. ; 109:4, s. 1075-1087
-
Journal article (peer-reviewed)abstract
- Radiotherapy (RT) can be used as preoperative treatment to downstage initially unresectable locally rectal carcinoma, but the radioresistance and recurrence remain significant problems. Retinoblastoma binding protein 6 (RBBP6) has been implicated in the regulation of cell cycle, apoptosis and chemoresistance both in vitro and in vivo. This study investigated whether the inhibition of RBBP6 expression would improve radiosensitivity in human colorectal cancer cells. After SW620 and HT29 cells were exposed to radiation, the levels of RBBP6 mRNA and protein increased over time in both two cells. Moreover, a significant reduction in clonogenic survival and a decrease in cell viability in parallel with an obvious increase in cell apoptosis were demonstrated in irradiated RBBP6-knockdown cells. Besides, transfection with RBBP6 shRNA improved levels of G2-M phase arrest which blocked the cells in a more radiosensitive period of the cell cycle. These observations indicated that cell cycle and apoptosis mechanisms may be connected with tumor cell survival following radiotherapy. In vivo, tumor growth rate of nude mice in RBBP6-knockdown group was significantly slower than that in other groups. These results indicated that RBBP6 overexpression could resist colorectal cancer cells against radiation by regulating cell cycle and apoptosis pathways, and inhibition of RBBP6 could enhance radiosensitivity of human colorectal cancer.
|
|
| 5. |
- Chen, Yingxin, et al.
(author)
-
A Case Study on Knowledge Driven Code Generation for Software-Defined Industrial Cyber-Physical Systems
- 2018
-
In: Proceedings IECON 2018. - : IEEE. ; , s. 4687-4692
-
Conference paper (peer-reviewed)abstract
- Industrial Cyber-Physical Systems (iCPS) enables coordination between various subsystems and devices based on real-time feedback data from sensors. iCPS must react rapidly to new requirements and adjust itself to fulfill new functionalities in no time. On the software side, control programs of iCPS need to be reconfigured dynamically. An efficient way for massive reconfiguration is automatic code generation. In this paper, a knowledge-driven code generation method is experimented for software-defined iCPS. Based on sensor values, actuators are controlled by the reasoning process with support of ontological knowledge base. The results demonstrate that iCPS could be driven by rules completely without programming control software.
|
|
| 6. |
- Dai, Wenbin, et al.
(author)
-
Modelling Industrial Cyber-Physical Systems using IEC 61499 and OPC UA
- 2018
-
Conference paper (peer-reviewed)abstract
- Industrial Cyber-Physical Systems (iCPS) are considered as the enabling technology for achieve Industry 4.0. One main characteristic of the iCPS is the information transparency to allow interoperability among various devices and systems. The OPC UA provides a common information model for connecting Industry 4.0 components. On the other hand, the IEC 61499 is commonly used as an executable modeling language for iCPS. The IEC 61499 function block network provides an abstract view of the system configuration. By combining IEC 61499 and OPC UA, a visual executable model for iCPS is completed. In this paper, the mapping between two standards are provided and a case study of the proposed mapping is given.
|
|
| 7. |
- Dai, Wenbin, et al.
(author)
-
Service-oriented data acquisition and management for industrial cyber-physical systems
- 2017
-
In: Proceedings. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781538608371 ; , s. 759-764
-
Conference paper (peer-reviewed)abstract
- With rapid improvement in information and communication technologies, industrial automation systems are under the revolution. Legacy industrial automation systems lack flexibility and interoperability due to multi-layered architecture. From industrial cyber-physical system point of view, a new system architecture is needed to allow vertical and horizontal integration between all devices and systems from enterprise level to sensor level. In this paper, a RESTful service-oriented architecture is proposed for industrial controllers. By adopting RESTful services over HTTP methods, better efficiency for data acquisition from sensors, actuators, and controllers is achieved. In addition, service-oriented device management for industrial controllers is also implemented along with data acquisition. The reference architecture experiments on a car manufacturing demonstration line
|
|
| 8. |
- Kehoe, Laura, et al.
(author)
-
Make EU trade with Brazil sustainable
- 2019
-
In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 364:6438, s. 341-
-
Journal article (other academic/artistic)
|
|
| 9. |
- Li, Jiyun, et al.
(author)
-
A method for document image enhancement to improve template-based classification
- 2020
-
In: ACM International Conference Proceeding Series. - New York, NY, USA : ACM Digital Library. - 9781450375603 ; , s. 87-91
-
Conference paper (peer-reviewed)abstract
- Document classification is one of the significant procedure in paper document recognition. This article proposed a method for document image enhancement to improve the performance of classification in the convolutional neural network. An enhanced document image was generated by extracting the table frame, text region, and shape of the raw document. The template-based classification experiment on 414 customs documents and more than one thousand generated images showed the enhanced image could help CNN model achieve higher accuracies compared to the original images. It could also diminish the interference of noise and unrelated features in document classification optimizing the robustness of networks. The proposed method also demonstrated the channels of the image could provide more information except for color in deep neural networks. As the similarity in the whole image classification tasks, the conclusion might provide ideas for the training of the neural networks in other fields such as street view recognition, medical image recognition, etc.
|
|
| 10. |
- Liu, Lihui, et al.
(author)
-
Ablation of ERO1A induces lethal endoplasmic reticulum stress responses and immunogenic cell death to activate anti-tumor immunity
- 2023
-
In: Cell Reports Medicine. - : Cell Press. - 2666-3791. ; 4:10
-
Journal article (peer-reviewed)abstract
- Immunophenotyping of the tumor microenvironment (TME) is essential for enhancing immunotherapy effi-cacy. However, strategies for characterizing the TME exhibit significant heterogeneity. Here, we show that endoplasmic reticular oxidoreductase-1a (ERO1A) mediates an immune-suppressive TME and attenuates the response to PD-1 blockade. Ablation of ERO1A in tumor cells substantially incites anti-tumor T cell im-munity and promotes the efficacy of aPD-1 in therapeutic models. Single-cell RNA-sequencing analyses confirm that ERO1A correlates with immunosuppression and dysfunction of CD8+ T cells along anti-PD-1 treatment. In human lung cancer, high ERO1A expression is associated with a higher risk of recurrence following neoadjuvant immunotherapy. Mechanistically, ERO1A ablation impairs the balance between IRE1a and PERK signaling activities and induces lethal unfolded protein responses in tumor cells undergoing endoplasmic reticulum stress, thereby enhancing anti-tumor immunity via immunogenic cell death. These findings reveal how tumor ERO1A induces immunosuppression, highlighting its potential as a therapeutic target for cancer immunotherapy.
|
|
