Relationship between cartilage glycosaminoglycan content (assessed with dGEMRIC) and OA risk factors in meniscectomized patients

SummaryObjectiveTo study the relationship between cartilage integrity, assessed with [delayed Gadolinium-Enhanced Magnetic Resonance Imaging of Cartilage (dGEMRIC)] and epidemiologic risk factors for knee osteoarthritis (OA) in meniscectomized patients.MethodsBody mass index (BMI) was calculated in 45 patients (16 women), mean age 46, who underwent an arthroscopic medial meniscectomy 1–6 years earlier. The cartilage glycosaminoglycan (GAG) content was estimated by dGEMRIC Index and tests of isokinetic muscle strength and functional performance (one-leg hop test) were conducted.ResultsBMI ranged from 20.0 to 34.3 (mean: 26.5). The dGEMRIC Index was 14.4% lower in the medial index compartment (374±61ms, mean±SD) than in the lateral reference compartment (437±59ms, mean±SD) (P<0.001).The dGEMRIC Index of the medial diseased compartment correlated positively with both knee flexor (r=0.50, P=0.001) and knee extensor strength (r=0.47, P=0.001) relative to body weight and with the one-leg hop test (r=0.42, P=0.004). Furthermore, a negative correlation was found between the dGEMRIC Index of the medial compartment and BMI (r=−0.35, P=0.019).No significant correlations were found in the lateral reference compartment.ConclusionThe lower dGEMRIC Index of the medial compartment suggests decreased cartilage GAG content after medial meniscectomy, indicating an early stage OA. Furthermore, results suggest that overweight is a factor that deteriorates cartilage, whereas strong and co-ordinated thigh muscles may have a protective effect on the cartilage integrity

Development of a High-Density Linkage Map and Tagging Leaf Spot Resistance in Pearl Millet Using Genotyping-by-Sequencing Markers

Pearl millet [Pennisetum glaucum (L.) R. Br; also Cenchrus americanus (L.) Morrone] is an important crop throughout the world but better genomic resources for this species are needed to facilitate crop improvement. Genome mapping studies are a prerequisite for tagging agronomically important traits. Genotyping-by-sequencing (GBS) markers can be used to build high-density linkage maps, even in species lacking a reference genome. A recombinant inbred line (RIL) mapping population was developed from a cross between the lines ‘Tift 99D2B1’ and ‘Tift 454’. DNA from 186 RILs, the parents, and the F1 was used for 96-plex ApeKI GBS library development, which was further used for sequencing. The sequencing results showed that the average number of good reads per individual was 2.2 million, the pass filter rate was 88%, and the CV was 43%. High-quality GBS markers were developed with stringent filtering on sequence data from 179 RILs. The reference genetic map developed using 150 RILs contained 16,650 single-nucleotide polymorphisms (SNPs) and 333,567 sequence tags spread across all seven chromosomes. The overall average density of SNP markers was 23.23 SNP/cM in the final map and 1.66 unique linkage bins per cM covering a total genetic distance of 716.7 cM. The linkage map was further validated for its utility by using it in mapping quantitative trait loci (QTLs) for flowering time and resistance to Pyricularia leaf spot [Pyricularia grisea (Cke.) Sacc.]. This map is the densest yet reported for this crop and will be a valuable resource for the pearl millet community

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km &lt;sup&gt;2&lt;/sup&gt; resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km &lt;sup&gt;2&lt;/sup&gt; pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Long-term effect of removal of knee joint loading on cartilage quality evaluated by delayed gadolinium-enhanced magnetic resonance imaging of cartilage

SummaryObjectiveAnkle fracture patients were used as a model to study the long-term effect of the removal of joint loading on knee cartilage quality in human subjects.DesignThe knees of 10 patients with ipsilateral ankle fractures were investigated using delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) at the time of ankle injury. After 6 weeks' prescribed unloading of the affected leg, but no restrictions regarding knee movement, the cast was removed from the ankle and the patient underwent a second dGEMRIC examination. Physiotherapy was then initiated. A third dGEMRIC examination was performed 4 months after remobilization, and a final examination 1 year after the injury.ResultsBaseline T1Gd values for the 10 patients were within a narrow range. No significant change in mean T1Gd was observed after 6 weeks' prescribed unloading, but the T1Gd range had increased significantly. Four months after remobilization, the mean T1Gd was significantly lower than in the previous examinations, and the range remained significantly broader than at baseline. At the 1-year follow-up, the mean T1Gd was almost identical to the value after remobilization, and the T1Gd range still showed a significant increase compared to the baseline investigation.ConclusionsRemoval of knee cartilage loading for 6 weeks resulted in a measurable effect on the cartilage matrix, as evidenced by a broader T1Gd range. A decrease in mean T1Gd was observed 4 months after remobilization. These differences persisted a year after injury compared to baseline

Fine-resolved, near-coastal spatiotemporal variation of temperature in response to insolation

This study uses GIS-based modeling of incoming solar radiation to quantify fine-resolved spatiotemporal responses of monthly average temperature, and diurnal temperature variation, at different times and locations within a field study area located on the eastern coast of Sweden. Near-surface temperatures are measured by a network of temperature sensors during the spring and summer of 2011 and then used as the basis for model development and testing. The modeling of finescale spatiotemporal variation considers topography, distance from the sea, and observed variations in atmospheric conditions, accounting for site latitude, elevation, surface orientation, daily and seasonal shifts in sun angle, and effects of shadows from surrounding topography. The authors find a lag time between insolation and subsequent temperature response that follows an exponential decay from coastal to inland locations. They further develop a linear regression model that accounts for this lag time in quantifying fine-resolved spatiotemporal temperature evolution. This model applies in the considered growing season for spatial distribution across the studied near-coastal landscape