Search

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

Economic forests are crucial sources of food and nutrition. To guarantee national grain and oil security, it is extremely important to investigate the spatial association between cutting quotas and the economic forest planting. The logging quota scheme increases uncertainty about the future benefits of forestry management. This limits the scale expansion of the timber forest planting industry and dampens the societal enthusiasm for economic forest planting. The severity of logging control in the northern China has been increasing from the standpoint of spatial distribution, which has caused the economic forest planting center of gravity to shift south. Economic forests can meet citizens' needs for wholesome food and ensure the safety of the nation's food supply under a favorable food outlook. The spatial correlation between the logging quota and economic forest plantations was examined using the centroid model based on data from the fourth to ninth national forest resource inventories. A spatial Dubin model was used to test the spatial spillover effects of logging quotas on economically important forest plantations. Additionally, two tasks were completed to guarantee the reliability of the estimation results. After variable replacement and first-order difference processing, a robustness test was conducted using a spatial Durbin model. The system of the GMM estimation endogeneity test came in second place. The findings indicated that the economic forest planting center primarily moved within the Henan Province, and that its overall migration distance was 235.13 km to the southwest. The ratio of the logging quota to forest stock shifted its center of gravity from Henan to Hubei Province and moved southwest across a migration distance of 300.4 km. The spatial separation of the two barycenters' was stable, but the consistency index fluctuated in an inverted U-shape, and the spatial coupling was typically high. At the regional level, the southwestern and southern forests exhibited considerable spatial- overlap and -coupling, whereas the northern forest experienced considerable fluctuations and an improved trend. The logging quotas and commercial forest planting have a spatial relationship. The two barycenters' local spatial patterns exhibited "high-high" and "low-low" agglomeration traits. The scale of economic forest planting has benefited spatially from logging quotas. In addition to encouraging the growth of the local economic forest planting scale, increasing the relative quantity of the logging quota was helpful in the growth of the economic forest planting scale in nearby areas. At the regional level, the southern forest area had a stronger and less pronounced spatial spillover effect than the northern forest area. The spatial spillover effect of the relaxation of the tending logging quota was obviously greater than the spatial spillover effect of the relaxation of the main logging quota, as seen from the perspective of the logging quota structure. This study enhances the theoretical justification for farmers' planting structure adjustment, in addition to resolving the problem of analyzing the trend of farmers' changing planting structures from a microscopic perspective. The conclusions of this study serve as a guide for improving the spatial layout of economic forest planting and the logging quota system.

[Objective] This study aims to explore the impact of grazing enclosure on plant community structure and soil water conservation function in alpine steppe in the Qinghai Lake region, and to evaluate the effects of grazing enclosure on the restoration of the degraded grassland in Qinghai Lake. [Methods] By setting up free grazing and different years of enclosure in the alpine grassland on the north bank of the Qinghai Lake, we analyzed the effects of enclosure measures on plant species, biomass, functional groups, soil bulk density, water holding capacity, porosity, and other indicators. [Results] (1) With the increasing of enclosure time, the number of species in the alpine steppe was decreased, the vegetation cover, above-ground biomass, and below-ground biomass were first increased and then decreased. The percentage of grasses in the community was gradually increased. (2) The effects of grazing exclusion on soil moisture characteristics of alpine steppe were mainly concentrated in the 0-20 cm depth. With the increase of grazing exclusion time, soil bulk density at 0-20 cm was decreased gradually, soil saturated water holding capacity, capillary water holding capacity, field water holding capacity, and total porosity at 0-10 cm was first increased and then stabilized. (3) Soil bulk density was the most important soil water characteristic factor affecting the vegetation community structure in the alpine steppe. [Conclusion] From the view of vegetation community structure, the grazing exclusion time in the alpine steppe in Qinghai Lake may not exceed 7 years, and moderate disturbance might be allowed after 7 years of grazing exclusion.

Cartilage MRI-based T1rho and T2 compositional measurements have been developed to characterize cartilage matrix quality and diagnose cartilage damage before irreversible defects are found, allowing intervention at an early, potentially reversible disease stage. Over the last 2 decades, this technology was investigated in numerous studies and was validated using specimen studies and arthroscopy; and longitudinal studies documented its ability to predict progression of degenerative disease and radiographic osteoarthritis (OA). While T1rho and T2 measurements have shown promise in early disease stages, several hurdles have been encountered to apply this technology clinically. These include (i) challenges with cartilage segmentation, (ii) long image acquisition times, (iii) a lack of standardization of imaging, and (iv) an absence of reference databases and definitions of abnormal cut-off values. Progress has been made by developing deep-learning based automatic cartilage segmentation and faster imaging methods, enabling the feasibility of T1rho and T2 imaging for clinical and scientific trial applications. Also, the Radiological Society of North America (RSNA) Quantitative Imaging Biomarker Alliance mechanism was used to establish standardized profiles for compositional T1rho and T2 imaging, and multi-center feasibility testing is work in progress. The last hurdles are the development of reference databases and establishing a definition of normal versus abnormal cartilage T1rho and T2 values. Finally, effective treatments for prevention and slowing progression of OA are required in order to establish T1rho and T2 as imaging biomarkers for initiating and monitoring therapies, analogous to the role of dual X-ray absorptiometry (DXA) bone mineral density measurements in the management of osteoporosis. KEY POINTS: • T1rho and T2 cartilage measurements have been validated in characterizing cartilage degenerative change using histology and arthroscopy as a reference. • They have also been shown to predict progression of cartilage degeneration and incidence of radiographic OA. • Advances have been made to facilitate clinical and trial application of T1rho and T2 by improved standardization of imaging and by establishing deep learning-based automatic cartilage segmentation. • Effective treatments with disease-modifying OA specific drugs may establish T1rho and T2 cartilage compositional measurements as biomarkers to initiate and monitor treatment.

As UAV and swarms applications continue to deepen and become increasingly widespread, UAV payloads have characteristics such as diverse types, heterogeneous forms, and complex interfaces. At the same time, it is required that the system can quickly access and efficiently manage payloads. In order to decouple the close relationship between the payloads and the system platforms, and expand the compatibility with heterogeneous payloads, a novel payload device virtualization method is proposed. Firstly, a layered driver model is proposed to decouple the device hardware and device driver, standardize the payload access process, and the driver model is implemented by using the software and hardware. Secondly, a payload abstraction based on "capability" is proposed to decouple load application and device. Finally, specific examples for the verification of the virtualization method are implemented. The present UAV payload virtualization method can not only achieve software and hardware decoupling, but also enhance the compatibility and scalability of system, improving the efficiency of unified payload management.

Missing data complicates statistical analyses in multi-site studies, especially when it is not feasible to centrally pool individual-level data across sites. We combined meta-analysis with within-site multiple imputation for one-step estimation of the average causal effect (ACE) of a target population comprised of all individuals from all data-contributing sites within a multi-site distributed data network, without the need for sharing individual-level data to handle missing data. We considered two orders of combination and three choices of weights for meta-analysis, resulting in six approaches. The first three approaches, denoted as RR + metaF, RR + metaR and RR + std, first combined results from imputed data sets within each site using Rubin's rules and then meta-analyzed the combined results across sites using fixed-effect, random-effects and sample-standardization weights, respectively. The last three approaches, denoted as metaF + RR, metaR + RR and std + RR, first meta-analyzed results across sites separately for each imputation and then combined the meta-analysis results using Rubin's rules. Simulation results confirmed very good performance of RR + std and std + RR under various missing completely at random and missing at random settings. A direct application of the inverse-variance weighted meta-analysis based on site-specific ACEs can lead to biased results for the targeted network-wide ACE in the presence of treatment effect heterogeneity by site, demonstrating the need to clearly specify the target population and estimand and properly account for potential site heterogeneity in meta-analyses seeking to draw causal interpretations. An illustration using a large administrative claims database is presented.

OBJECTIVE: To evaluate whether the deep learning (DL) segmentation methods from the six teams that participated in the IWOAI 2019 Knee Cartilage Segmentation Challenge are appropriate for quantifying cartilage loss in longitudinal clinical trials. DESIGN: We included 556 subjects from the Osteoarthritis Initiative study with manually read cartilage volume scores for the baseline and 1-year visits. The teams used their methods originally trained for the IWOAI 2019 challenge to segment the 1130 knee MRIs. These scans were anonymized and the teams were blinded to any subject or visit identifiers. Two teams also submitted updated methods. The resulting 9,040 segmentations are available online.The segmentations included tibial, femoral, and patellar compartments. In post-processing, we extracted medial and lateral tibial compartments and geometrically defined central medial and lateral femoral sub-compartments. The primary study outcome was the sensitivity to measure cartilage loss as defined by the standardized response mean (SRM). RESULTS: For the tibial compartments, several of the DL segmentation methods had SRMs similar to the gold standard manual method. The highest DL SRM was for the lateral tibial compartment at 0.38 (the gold standard had 0.34). For the femoral compartments, the gold standard had higher SRMs than the automatic methods at 0.31/0.30 for medial/lateral compartments. CONCLUSION: The lower SRMs for the DL methods in the femoral compartments at 0.2 were possibly due to the simple sub-compartment extraction done during post-processing. The study demonstrated that state-of-the-art DL segmentation methods may be used in standardized longitudinal single-scanner clinical trials for well-defined cartilage compartments.

Background: Gadolinium (Gd)-enhanced Magnetic Resonance Imaging (MRI) is crucial in several applications, including oncology, cardiac imaging, and musculoskeletal inflammatory imaging. One use case is rheumatoid arthritis (RA), a widespread autoimmune condition for which Gd MRI is crucial in imaging synovial joint inflammation, but Gd administration has well-documented safety concerns. As such, algorithms that could synthetically generate post-contrast peripheral joint MR images from non-contrast MR sequences would have immense clinical utility. Moreover, while such algorithms have been investigated for other anatomies, they are largely unexplored for musculoskeletal applications such as RA, and efforts to understand trained models and improve trust in their predictions have been limited in medical imaging. Methods: A dataset of 27 RA patients was used to train algorithms that synthetically generated post-Gd IDEAL wrist coronal T1-weighted scans from pre-contrast scans. UNets and PatchGANs were trained, leveraging an anomaly-weighted L1 loss and global generative adversarial network (GAN) loss for the PatchGAN. Occlusion and uncertainty maps were also generated to understand model performance. Results: UNet synthetic post-contrast images exhibited stronger normalized root mean square error (nRMSE) than PatchGAN in full volumes and the wrist, but PatchGAN outperformed UNet in synovial joints (UNet nRMSEs: volume = 6.29 ± 0.88, wrist = 4.36 ± 0.60, synovial = 26.18 ± 7.45; PatchGAN nRMSEs: volume = 6.72 ± 0.81, wrist = 6.07 ± 1.22, synovial = 23.14 ± 7.37; n = 7). Occlusion maps showed that synovial joints made substantial contributions to PatchGAN and UNet predictions, while uncertainty maps showed that PatchGAN predictions were more confident within those joints. Conclusions: Both pipelines showed promising performance in synthesizing post-contrast images, but PatchGAN performance was stronger and more confident within synovial joints, where an algorithm like this would have maximal clinical utility. Image synthesis approaches are therefore promising for RA and synthetic inflammatory imaging.

Fracture risk is increased in type 2 diabetes, which may in part be due to altered bone marrow adiposity. Cross sectional studies have reported that people with type 2 diabetes have lower unsaturated BMAT lipid levels than people without diabetes, although there are limited data on longitudinal changes. We hypothesized that Roux-en-Y gastric bypass (RYGB), which dramatically improves glycemic status, would have differential effects on BMAT composition, with increases in the unsaturated lipid index in people with diabetes. Given reports that axial BMAT is responsive to metabolic stimuli while appendicular BMAT is stable, we hypothesized that BMAT changes would occur at the spine but not the tibia. We enrolled 30 obese women, stratified by diabetes status, and used magnetic resonance spectroscopy to measure BMAT at the spine in all participants, and the tibia in a subset (n = 19). At baseline, BMAT parameters were similar between those with and without diabetes, except tibial marrow fat content was lower in women with diabetes (97.4 % ± 1.0 % versus 98.2 % ± 0.4 %, p = 0.04). Six months after surgery, both groups experienced similar weight loss of 27 kg ± 7 kg. At the spine, there was a significant interaction between diabetes status and changes in both marrow fat content and the unsaturated lipid index (p = 0.02, p

ObjectiveTo summarize proceedings of a workshop convened to discuss the current state of science in the disease of osteoarthritis (OA), identify the knowledge gaps, and examine the developmental and regulatory challenges in bringing these products to market.DesignSummary of the one-day workshop held virtually on June 22nd, 2021.ResultsSpeakers selected by the Planning Committee presented data on the current approach to assessment of OA therapies, biomarkers in OA drug development, and the assessment of disease progression and long-term benefit.ConclusionsDemonstrated by numerous failed clinical trials, OA is a challenging disease for which to develop therapeutics. The challenge is magnified by the slow time of onset of disease and the need for clinical trials of long duration and/or large sample size to demonstrate the effect of an intervention. The OA science community, including academia, pharmaceutical companies, regulatory agencies, and patient communities, must continue to develop and test better clinical endpoints that meaningfully reflect disease modification related to long-term patient benefit.