Plants are masterpieces of evolution that is based on carbon chemistry. In particular, plant leaves are biosynthetic factories able to convert CO into carbohydrates and oxygen. It is worth noting that mimicking the efficiency of a natural plant and natural leaf is still a challenge for contemporary chemistry. We can even better realize this when we notice that a plant and an industrial factory are equivalent in meaning. On the other hand, green technologies are under development in a quest for the artificial leaf. If we could modify the synthetic pathways in leaves, we could also design green chemistry schemes in natural leaves to produce useful chemicals or to digest wastes or toxins. Specifically, can we intensify the potential for capturing atmospheric CO in leaves? Auxins are plant hormones that control the growth and development of plants. Herein, we determined whether we could efficiently transport xenobiotic auxin into leaves and if so, whether this supply could enhance the metabolism and CO capturing ability. By exploring a series of dioxolanes as potential enhancers of auxin transport, we discovered for the first time that a small molecular compound, 2,2-dimethyl-1,3-dioxolane (DMD), enhances the xenobiotic auxin transport to leaves, which boosts the metabolism that is measured by HO production as well as CO capturing ability in leaves.
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http://dx.doi.org/10.1016/j.scitotenv.2020.141032 | DOI Listing |
Nutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
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January 2025
Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.
In sports training, personalized skill assessment and feedback are crucial for athletes to master complex movements and improve performance. However, existing research on skill transfer predominantly focuses on skill evaluation through video analysis, addressing only a single facet of the multifaceted process required for skill acquisition. Furthermore, in the limited studies that generate expert comments, the learner's skill level is predetermined, and the spatial-temporal information of human movement is often overlooked.
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January 2025
School of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
Music generation by AI algorithms like Transformer is currently a research hotspot. Existing methods often suffer from issues related to coherence and high computational costs. To address these problems, we propose a novel Transformer-based model that incorporates a gate recurrent unit with root mean square norm restriction (TARREAN).
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January 2025
School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK.
Objective and continuous monitoring of Parkinson's disease (PD) tremor in free-living conditions could benefit both individual patient care and clinical trials, by overcoming the snapshot nature of clinical assessments. To enable robust detection of tremor in the context of limited amounts of labeled training data, we propose to use prototypical networks, which can embed domain expertise about the heterogeneous tremor and non-tremor sub-classes. We evaluated our approach using data from the Parkinson@Home Validation study, including 8 PD patients with tremor, 16 PD patients without tremor, and 24 age-matched controls.
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January 2025
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model-PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection.
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