Seed Traits Research Is on the Rise: A Bibliometric Analysis from 1991–2020

Seed traits (ST) influence seedling establishment, population dynamics, community composition and ecosystem function and reflect the adaptability of plants and the environmental conditions they experienced. There has been a historical and global accumulation of studies on ST, but with few pertaining to visual and quantitative analyses. To understand the trends in the field of ST research in the past 30 years, we conducted a bibliometric analysis based on the Science Citation Index-Expanded (SCI-E) database. The analysis provided annual publications, time trends for keywords, the most productive journals, authors, institutions and countries, and a comprehensive overview of the ST field. Our results showed that in the past 30 years, the number of publications in ST research has increased at an average annual growth rate of 9.1%, while the average number of citations per paper per year showed a rapid increase–slow increase–decrease trend. Keyword analysis showed that “germination” was the most popular research section. Crop Science ranked first among the top journals and Theoretical and Applied Genetics had greater influence in this area and more citations than other journals. The 10 most productive institutions were mostly located in the United States, China and Australia. Furthermore, the three countries also had the largest number of publications and citations. Our analysis showed that the research interests in ST have evolved from genetics and agricultural science to ecological research over the last thirty years; as more fields embrace ST research, there are opportunities for international and interdisciplinary collaborations, cooperative institutions and new advances in the field

Gravity and magnetic field features and basement relief of the Sanjiang Basin in Heilongjiang Province, China

The Sanjiang Basin has received more attention in Mesozoic stratum and petroleum potential research because of its particularity in geographic and tectonic position. There remains debate on the basement structure of the basin since igneous rocks and faults make the structure and stratigraphy more complicated. In this paper we utilize gravity and magnetic data as well as petrophysical properties and drilling logs to understand the structure of the Sanjiang Basin. The study is focused on the comparison between the western and eastern parts of the basin. The comparison reveals that there are distinct differences in the gravity and magnetic field between the western and eastern parts. The integrated analysis of the gravity, magnetic, geological, petrophysical data and drilling logs indicates that the difference in the gravity and magnetic field results from the different basement structure and caprock formation of the two parts of the basin. The basement consists of three parts from west to east, the Proterozoic crystalline basement, the Neopaleozoic fold basement and the Lower Mesozoic fold basement separately. The Tongjiang–Yingchun Fault and the Qinglongshan–Xiaoheyan Fault controlled the formation and development of depressions and uplifts and also affected the sedimentation and volcanic activities of the basin. The Sanjiang Basin has relatively thin and stable crust thickness, varying around 33 km, and the deep structure has control and constraint over the shallow conformations

Table_1_Exploring the potential of the sit-to-stand test for self-assessment of physical condition in advanced knee osteoarthritis patients using computer vision.DOCX

IntroductionKnee osteoarthritis (KOA) is a prevalent condition often associated with a decline in patients’ physical function. Objective self-assessment of physical conditions poses challenges for many advanced KOA patients. To address this, we explored the potential of a computer vision method to facilitate home-based physical function self-assessments.MethodsWe developed and validated a simple at-home artificial intelligence approach to recognize joint stiffness levels and physical function in individuals with advanced KOA. One hundred and four knee osteoarthritis (KOA) patients were enrolled, and we employed the WOMAC score to evaluate their physical function and joint stiffness. Subsequently, patients independently recorded videos of five sit-to-stand tests in a home setting. Leveraging the AlphaPose and VideoPose algorithms, we extracted time-series data from these videos, capturing three-dimensional spatiotemporal information reflecting changes in key joint angles over time. To deepen our study, we conducted a quantitative analysis using the discrete wavelet transform (DWT), resulting in two wavelet coefficients: the approximation coefficients (cA) and the detail coefficients (cD).ResultsOur analysis specifically focused on four crucial joint angles: “the right hip,” “right knee,” “left hip,” and “left knee.” Qualitative analysis revealed distinctions in the time-series data related to functional limitations and stiffness among patients with varying levels of KOA. In quantitative analysis, we observed variations in the cA among advanced KOA patients with different levels of physical function and joint stiffness. Furthermore, there were no significant differences in the cD between advanced KOA patients, demonstrating different levels of physical function and joint stiffness. It suggests that the primary difference in overall movement patterns lies in the varying degrees of joint stiffness and physical function among advanced KOA patients.DiscussionOur method, designed to be low-cost and user-friendly, effectively captures spatiotemporal information distinctions among advanced KOA patients with varying stiffness levels and functional limitations utilizing smartphones. This study provides compelling evidence for the potential of our approach in enabling self-assessment of physical condition in individuals with advanced knee osteoarthritis.</p

Data_Sheet_1_Exploring the potential of the sit-to-stand test for self-assessment of physical condition in advanced knee osteoarthritis patients using computer vision.ZIP

IntroductionKnee osteoarthritis (KOA) is a prevalent condition often associated with a decline in patients’ physical function. Objective self-assessment of physical conditions poses challenges for many advanced KOA patients. To address this, we explored the potential of a computer vision method to facilitate home-based physical function self-assessments.MethodsWe developed and validated a simple at-home artificial intelligence approach to recognize joint stiffness levels and physical function in individuals with advanced KOA. One hundred and four knee osteoarthritis (KOA) patients were enrolled, and we employed the WOMAC score to evaluate their physical function and joint stiffness. Subsequently, patients independently recorded videos of five sit-to-stand tests in a home setting. Leveraging the AlphaPose and VideoPose algorithms, we extracted time-series data from these videos, capturing three-dimensional spatiotemporal information reflecting changes in key joint angles over time. To deepen our study, we conducted a quantitative analysis using the discrete wavelet transform (DWT), resulting in two wavelet coefficients: the approximation coefficients (cA) and the detail coefficients (cD).ResultsOur analysis specifically focused on four crucial joint angles: “the right hip,” “right knee,” “left hip,” and “left knee.” Qualitative analysis revealed distinctions in the time-series data related to functional limitations and stiffness among patients with varying levels of KOA. In quantitative analysis, we observed variations in the cA among advanced KOA patients with different levels of physical function and joint stiffness. Furthermore, there were no significant differences in the cD between advanced KOA patients, demonstrating different levels of physical function and joint stiffness. It suggests that the primary difference in overall movement patterns lies in the varying degrees of joint stiffness and physical function among advanced KOA patients.DiscussionOur method, designed to be low-cost and user-friendly, effectively captures spatiotemporal information distinctions among advanced KOA patients with varying stiffness levels and functional limitations utilizing smartphones. This study provides compelling evidence for the potential of our approach in enabling self-assessment of physical condition in individuals with advanced knee osteoarthritis.</p

C-Structures in Mesospheric Na and K Layers and Their Relations with Dynamical and Convective Instabilities

We analyzed the C-structures in the mesospheric metal layers. We used two datasets: one from a narrow band Sodium (Na) Density and Temperature LIDAR and the other from a high-resolution dual band Na and Potassium (K) LIDAR, both operated at São José dos Campos, Brazil (23° S, 46° W). We also investigated the Es layer occurrence and wind shear influences forming these structures. We found three C-type events over 82 analyzed nights in the first data set. They all showed lower temperatures inside C-structures compared to the borders. The squared Brunt-Väissälä frequency analyses showed positive values in the region of C-structures. In two out of three cases, dynamical instability was present (Ri < 0.25). We compared these results with the nine simultaneous C-type events identified in the 185 nights from the second data set. They showed height and time simultaneity correspondence as observed in the Na and K layers. Our results showed a low correlation between Es occurrence and C-structures. Additionally, strong wind shears in the altitude and time where C-structures appeared were always present. The advection of a metal cloud to the LIDAR station and a wind distortion seems to be the plausible mechanism that can explain all the observations

Analysis of the Sporadic-E Layer Behavior in Different American Stations during the Days around the September 2017 Geomagnetic Storm

The development of sporadic-E (Es) layers over five Digisonde stations in the American sector is analyzed. This work aims to investigate the dynamic of such layers during the days around the geomagnetic storm that occurred on 8 September 2017. Therefore, a numerical model (MIRE) and Radio Occultation (RO) technique are used to analyze the E layer dynamics. The results show a downward movement in low-middle latitudes due to the wind components that had no significant changes before, during, and after the geomagnetic storm. In fact, our data and simulations showed weak Es layers over Boulder, Cachoeira Paulista, and Santa Maria, even though the winds were not low. However, the RO data show the terdiurnal and quarterdiurnal influence in the Es layer formation, which can explain this behavior. In addition, we observed an atypical Es layer type, slant Es layer (Ess), during the main phase of the magnetic storm over Boulder. The possible cause of the Ess layers was gravity waves. Another interesting point is the spreading Es layer occurrence associated with the Kelvin&ndash;Helmholtz Instability (KHI). Finally, it is confirmed that the disturbed electric field only influenced the Es layer dynamics in regions near the magnetic equator

Analysis of the Sporadic-E Layer Behavior in Different American Stations during the Days around the September 2017 Geomagnetic Storm

The development of sporadic-E (Es) layers over five Digisonde stations in the American sector is analyzed. This work aims to investigate the dynamic of such layers during the days around the geomagnetic storm that occurred on 8 September 2017. Therefore, a numerical model (MIRE) and Radio Occultation (RO) technique are used to analyze the E layer dynamics. The results show a downward movement in low-middle latitudes due to the wind components that had no significant changes before, during, and after the geomagnetic storm. In fact, our data and simulations showed weak Es layers over Boulder, Cachoeira Paulista, and Santa Maria, even though the winds were not low. However, the RO data show the terdiurnal and quarterdiurnal influence in the Es layer formation, which can explain this behavior. In addition, we observed an atypical Es layer type, slant Es layer (Ess), during the main phase of the magnetic storm over Boulder. The possible cause of the Ess layers was gravity waves. Another interesting point is the spreading Es layer occurrence associated with the Kelvin–Helmholtz Instability (KHI). Finally, it is confirmed that the disturbed electric field only influenced the Es layer dynamics in regions near the magnetic equator