Postural control strategies can be investigated by kinematic analysis of joint movements. However, current research is focussing mainly on the analysis of centre of pressure excursion and lacks consensus on how to assess joint movement during postural control tasks. This study introduces a new signal processing technique to comprehensively quantify joint sway during standing and evaluates its reproducibility. Fifteen patients with non-specific low back pain and ten asymptomatic participants performed three repetitions of a 60-second standing task on foam surface. This procedure was repeated on a second day. Lumbar spine movement was recorded using an inertial measurement system. The signal was temporally divided into six sections. Two outcome variables (mean absolute sway and sways per second) were calculated for each section. The reproducibility of single and averaged measurements was quantified with linear mixed-effects models and the generalizability theory. A single measurement of ten seconds duration revealed reliability coefficients of .75 for mean absolute sway and .76 for sways per second. Averaging a measurement of 40 seconds duration on two different days revealed reliability coefficients higher than .90 for both outcome variables. The outcome variables' reliability compares favourably to previously published results using different signal processing techniques or centre of pressure excursion. The introduced signal processing technique with two outcome variables to quantify joint sway during standing proved to be a highly reliable method. Since different populations, tasks or measurement tools could influence reproducibility, further investigation in other settings is still necessary. Nevertheless, the presented method has been shown to be highly promising.
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Sci Data
January 2025
Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, 04103, Germany.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.
View Article and Find Full Text PDFJ Colloid Interface Sci
January 2025
College of Light Industry and Food Engineering, Nanjing Forestry University, Jiangsu Co-Innovation Center for Efficient Processing and Utilization of Forest Resources, Nanjing 210037 China. Electronic address:
Surface-enhanced Raman scattering (SERS) is a highly sensitive technology to detect target analytes. The construction of dynamic "hot-spots" represents a significant approach to enhancing detection sensitivity. Herein, a hybrid plasma platform with dynamic "hot-spots" was developed for SERS recognition based on the assembly of gold nanospheres (AuNSs) on temperature-sensitive bacterial cellulose (BC) film grafted with poly(N-isopropylacrylamide) (PNIPAM).
View Article and Find Full Text PDFPlants (Basel)
January 2025
College of Life Science, Dezhou University, Dezhou 253023, China.
Thioredoxin z (TRX z) plays a significant role in chloroplast development by regulating the transcription of chloroplast genes. In this study, we identified a pentatricopeptide repeat (PPR) protein, rice albino seedling-lethal (RAS), that interacts with OsTRX z. This interaction was initially discovered by using a yeast two-hybrid (Y2H) screening technique and was further validated through Y2H and bimolecular fluorescence complementation (BiFC) experiments.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Biomedical and Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea.
With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China.
Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available.
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