Browsing by Author "Zhang L"

Now showing 1 - 7 of 7
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    Astragalosides Supplementation Enhances Intrinsic Muscle Repair Capacity Following Eccentric Exercise-Induced Injury
    (MDPI (Basel, Switzerland), 2022-10) Yeh T-S; Lei T-H; Barnes MJ; Zhang L
    Astragalosides have been shown to enhance endurance exercise capacity in vivo and promote muscular hypertrophy in vitro. However, it remains unknown whether astragalosides supplementation can alter inflammatory response and enhance muscle recovery after damage in humans. We therefore aimed to evaluate the effect of astragalosides supplementation on muscle's intrinsic capacity to regenerate and repair itself after exercise-induced damage. Using a randomized double-blind placebo-controlled cross-over design, eleven male participants underwent 7 days of astragalosides supplementation (in total containing 4 mg of astragalosides per day) or a placebo control, following an eccentric exercise protocol. Serum blood samples and variables related to muscle function were collected prior to and immediately following the muscle damage protocol and also at 2 h, and 1, 2, 3, 5, and 7 days of the recovery period, to assess the pro-inflammatory cytokine response, the secretion of muscle regenerative factors, and muscular strength. Astragalosides supplementation reduced biomarkers of skeletal muscle damage (serum CK, LDH, and Mb), when compared to the placebo, at 1, 2, and 3 days following the muscle damage protocol. Astragalosides supplementation suppressed the secretion of IL-6 and TNF-α, whilst increasing the release of IGF-1 during the initial stages of muscle recovery. Furthermore, following astragaloside supplementation, muscular strength returned to baseline 2 days earlier than the placebo. Astragalosides supplementation shortens the duration of inflammation, enhances the regeneration process and restores muscle strength following eccentric exercise-induced injury.
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    Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices.
    (MDPI (Basel, Switzerland), 2025-01-22) Yan X; Zhang L; Liu B; Qu G; Amerini I; Russo P; Di Ciaccio F
    Traditional limb kinematic analysis relies on manual goniometer measurements. With computer vision advancements, integrating RGB cameras can minimize manual labor. Although deep learning-based cameras aim to offer the same ease as manual goniometers, previous approaches have prioritized accuracy over efficiency and cost on PC-based devices. Nevertheless, healthcare providers require a high-performance, low-cost, camera-based tool for assessing upper and lower limb range of motion (ROM). To address this, we propose a lightweight, fast, deep learning model to estimate a human pose and utilize predicted joints for limb ROM measurement. Furthermore, the proposed model is optimized for deployment on resource-constrained edge devices, balancing accuracy and the benefits of edge computing like cost-effectiveness and localized data processing. Our model uses a compact neural network architecture with 8-bit quantized parameters for enhanced memory efficiency and reduced latency. Evaluated on various upper and lower limb tasks, it runs 4.1 times faster and is 15.5 times smaller than a state-of-the-art model, achieving satisfactory ROM measurement accuracy and agreement with a goniometer. We also conduct an experiment on a Raspberry Pi, illustrating that the method can maintain accuracy while reducing equipment and energy costs. This result indicates the potential for deployment on other edge devices and provides the flexibility to adapt to various hardware environments, depending on diverse needs and resources.
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    Functional response and prey stage preference of Neoseiulus barkeri on Trasonemus confuses
    (Systematic and Applied Acarology Society London, 23/11/2018) Li L; Jiao R; Lichen Y; He XZ; He L; Xu C; Zhang L; Liu J
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    Modelling and mapping of subsurface nitrate-attenuation index in agricultural landscapes
    (Elsevier Ltd, 2025-06) Collins SB; Singh R; Mead SR; Horne DJ; Zhang L
    Environmental management of nutrient losses from agricultural lands is required to reduce their potential impacts on the quality of groundwater and eutrophication of surface waters in agricultural landscapes. However, accurate accounting and management of nitrogen losses relies on a robust modelling of nitrogen leaching and its potential attenuation – specifically, the reduction of nitrate to gaseous forms of nitrogen – in subsurface flow pathways. Subsurface denitrification is a key process in potential nitrate attenuation, but the spatial and temporal dynamics of where and when it occurs remain poorly understood, especially at catchment-scale. In this paper, a novel Landscape Subsurface Nitrate-Attenuation Index (LSNAI) is developed to map spatially variable subsurface nitrate attenuation potential of diverse landscape units across the Manawatū-Whanganui region of New Zealand. A large data set of groundwater quality across New Zealand was collated and analysed to assess spatial and temporal variability of groundwater redox status (based on dissolved oxygen, nitrate and dissolved manganese) across different hydrogeological settings. The Extreme Gradient Boosting algorithm was used to predict landscape unit subsurface redox status by integrating the nationwide groundwater redox status data set with various landscape characteristics. Applying the hierarchical clustering analysis and unsupervised classification techniques, the LSNAI was then developed to identify and map five landscape subsurface nitrate attenuation classes, varying from very low to very high potential, based on the predicted groundwater redox status probabilities and identified soil drainage and rock type as key influencing landscape characteristics. Accuracy of the LSNAI mapping was further investigated and validated using a set of independent observations of groundwater quality and redox assessments in shallow groundwaters in the study area. This highlights the potential for further research in up-scaling mapping and modelling of landscape subsurface nitrate attenuation index to accurately account for spatial variability in subsurface nitrate attenuation potential in modelling and assessment of water quality management measures at catchment-scale in agricultural landscapes.
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    Test flakiness’ causes, detection, impact and responses: A multivocal review
    (Elsevier Inc, 2023-12) Tahir A; Rasheed S; Dietrich J; Hashemi N; Zhang L
    Flaky tests (tests with non-deterministic outcomes) pose a major challenge for software testing. They are known to cause significant issues, such as reducing the effectiveness and efficiency of testing and delaying software releases. In recent years, there has been an increased interest in flaky tests, with research focusing on different aspects of flakiness, such as identifying causes, detection methods and mitigation strategies. Test flakiness has also become a key discussion point for practitioners (in blog posts, technical magazines, etc.) as the impact of flaky tests is felt across the industry. This paper presents a multivocal review that investigates how flaky tests, as a topic, have been addressed in both research and practice. Out of 560 articles we reviewed, we identified and analysed a total of 200 articles that are focused on flaky tests (composed of 109 academic and 91 grey literature articles/posts) and structured the body of relevant research and knowledge using four different dimensions: causes, detection, impact and responses. For each of those dimensions, we provide categorization and classify existing research, discussions, methods and tools With this, we provide a comprehensive and current snapshot of existing thinking on test flakiness, covering both academic views and industrial practices, and identify limitations and opportunities for future research.
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    The Value Cocreation Influence Mechanism of Network Freight Transport Platform in IoT-Based Environments: Under the Service-Dominant Logic
    (John Wiley and Sons, 2021-05-24) Bai P; Wu Q; Li Q; Zhang L; Jiang Y; Chen B; Wang W
    The Internet of Things (IoT) has brought many benefits to the development of industries, from manufacturing to services. In the context of IoT, the network freight transport platform is equivalent to an integrator, integrating the scattered resources such as carriers and shippers in the supply chain, providing a solution, improving the use efficiency of vehicles, effectively reducing logistics cost, and creating value for the freight transport platform and bilateral customers. The purpose of this research is to explain the influence mechanism of transport demand subjects' participation in value cocreation (VCC) under the service-dominant logic (SDL). We proposed a conceptual framework and 10 hypotheses and used SEM to measure the direct and mediating relationship among SDL, transport demand subject (TDS) participation, relational embeddedness (RE), platform VCC, and TDS value in the online business environment. The findings show that (1) SDL, TDS participation, and RE do not directly affect the TDS value, but indirectly through platform VCC; (2) SDL affects platform VCC not only directly but also indirectly through TDS participation and RE; (3) TDS participation affects platform VCC not only directly but also indirectly through RE; (4) RE indirectly affects TDS value by affecting platform VCC; and (5) platform VCC plays a mediating role in SDL, TDS participation, RE, and TDS value. Hence, transport enterprises should pay attention to the innovation concept, guide TDS participation, jointly improve platform VCC, give play to the network effect of the freight transport platform, and achieve TDS value through the deepening of TDS relationship embedding.
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    Unique rumen micromorphology and microbiota-metabolite interactions: features and strategies for Tibetan sheep adaptation to the plateau.
    (Frontiers Media S.A., 2024-10-09) Chen Q; Sha Y; Liu X; He Y; Chen X; Yang W; Gao M; Huang W; Wang J; He J; Wang L; Zhang L
    The rumen microbiota-a symbiont to its host and consists of critical functional substances-plays a vital role in the animal body and represents a new perspective in the study of adaptive evolution in animals. This study used Slide Viewer slicing analysis system, gas chromatography, RT-qPCR and other technologies, as well as 16S and metabolomics determination methods, to measure and analyze the microstructure of rumen epithelium, rumen fermentation parameters, rumen transport genes, rumen microbiota and metabolites in Tibetan sheep and Hu sheep. The results indicate that the rumen nipple height and cuticle thickness of Tibetan sheep are significantly greater than those of Hu sheep (p < 0.01) and that the digestion and absorption of forage are greater. The levels of carbohydrate metabolism, lipid metabolism, and protein turnover were increased in Tibetan sheep, which enabled them to ferment efficiently, utilize forage, and absorb metabolic volatile fatty acids (VFAs). Tibetan sheep rumen metabolites are related to immune function and energy metabolism, which regulate rumen growth and development and gastrointestinal homeostasis. Thus, compared with Hu sheep, Tibetan sheep have more rumen papilla and cuticle corneum, and the synergistic effect of the microbiota and its metabolites is a characteristic and strategy for adapting to high-altitude environments.