High-resolution permittivity estimation of ground penetrating radar data by migration with isolated hyperbolic diffractions and local focusing analyses

Ground penetrating radar (GPR) is important for detecting shallow subsurface structures, which has been successfully used on the Earth, Moon, and Mars. It is difficult to analyze the underground permittivity from GPR data because its observation system is almost zero-offset. Traditional velocity analysis methods can work well with separable diffractions but fail with strong-interfered diffractions. However, in most situations, especially for lunar or Martian exploration, the diffractions are highly interfered, or even buried in reflections. Here, we proposed a new method to estimate the underground permittivity and apply it to lunar penetrating radar data. First, we isolate a group of diffractions with a hyperbolic time window determined by a given velocity. Then, we perform migration using the given velocity and evaluate the focusing effects of migration results. Next, we find the most focused results after scanning a series of velocities and regard the corresponding velocity as the best estimation. Finally, we assemble all locally focused points and derive the best velocity model. Tests show that our method has high spatial resolution and can handle strong noises, thus can achieve velocity analyses with high accuracy, especially for complex materials. The permittivity of lunar regolith at Chang’E-4 landing area is estimated to be ∼4 within 12 m, ranging from 3.5 to 4.2 with a local perturbation of ∼2.3%, consistent with ∼3% obtained by numerical simulations using self-organization random models. This suggests that the lunar regolith at Chang’E-4 landing area is mature and can be well described by self-organization random models

A note on (α,β)-derivations

AbstractWe show that every multiplicative (α,β)-derivation of a ring R is additive if there exists an idempotent e′ (e′≠0,1) in R satisfying the conditions (C1)–(C3): (C1) β(e′)Rx=0 implies x=0; (C2) β(e′)xα(e′)R(1-α(e′))=0 implies β(e′)xα(e′)=0; (C3) xR=0 implies x=0. In particular, every multiplicative (α,β)-derivation of a prime ring with a nontrivial idempotent is additive. As applications, we could decompose a multiplicative (α,β)-derivation of the algebra Mn(C) of all the n×n complex matrices into a sum of an (α,β)-inner derivation and an (α,β)-derivation on Mn(C) given by an additive derivation f on C

Detecting differential item functioning using the DINA model

"DIF occurs for an item when one group (the focal group) of examinees is more or less likely to give the correct response to that item when compared to another group (the reference group) after controlling for the primary ability measured in a test. Cognitive assessment models generally deal with a more complex goal than linearly ordering examinees in a low-dimensional Euclidean space. In cognitive diagnostic modeling, ability is no longer represented by the overall test scores or a single continuous ability estimate. Instead, each examinee receives a diagnostic profile indicating mastery or non-mastery of the set of skills required for the test, namely the attribute mastery pattern. The purpose of the study had three objectives; first to define DIF from a cognitive diagnostic model perspective; second, to identify possible types of DIF occurring in the cognitive diagnostic context introduced into the data simulation design; finally, this study compared traditional matching criteria for DIF procedures, (e.g., total score) to new conditioning variable for DIF detection, namely the attribute mastery patterns or examinee profile scores derived from the DINA model. Two popular DIF detection procedures were used: Mantel-Haenszel procedure (MH) and the Simultaneous Item Bias Test (SIBTEST) based on total test score and profile score matching. Four variables were manipulated in a simulation study: two sample sizes (400 and 800 examinees in each group), five types of DIF introduced by manipulating the item parameters in the DINA model, two levels of DIF amount on a 25-item test (moderate and large DIF), and three correlations between skill attributes for both groups (no association, medium association and high association). The simulation study and the real data application demonstrated that, assuming cognitive diagnostic model was correct and the Q-matrix was correctly specified, attribute pattern matching appeared to be more effective than the traditional total test score matching observed by lower Type I error rates and higher power rates under comparable test conditions."--Abstract from author supplied metadata

High Mobility Group Box 1 Contributes to the Acute Rejection of Liver Allografts by Activating Dendritic Cells

Acute rejection induced by the recognition of donor alloantigens by recipient T cells leads to graft failure in liver transplant recipients. The role of high mobility group box 1 (HMGB1), an inflammatory mediator, in the acute allograft rejection of liver transplants is unknown. Here, rat orthotopic liver transplantation was successfully established to analyze the expression pattern of HMGB1 in liver allografts and its potential role in promoting the maturation of dendritic cells (DCs) to promote T cell proliferation and differentiation. Five and 10 days after transplantation, allografts showed a marked upregulation of HMGB1 expression accompanied by elevated levels of serum transaminase and CD3+ and CD86+ inflammatory cell infiltration. Furthermore, in vitro experiments showed HMGB1 increased the expressions of co-stimulatory molecules (CD80, CD83, and MHC class II) on bone marrow-derived DCs. HMGB1-pulsed DCs induced naive CD4+ T cells to differentiate to Th1 and Th17 subsets secreting IFN-γ and IL-17, respectively. Further in vivo experiments confirmed the administration of glycyrrhizic acid, a natural HMGB1 inhibitor, during donor liver preservation had therapeutic effects by reducing inflammation and hepatocyte damage reflected by a decline in serum transaminase and prolonged allograft survival time. These results suggest the involvement of HMBG1 in acute liver allograft rejection and that it might be a candidate therapeutic target to avoid acute rejection after liver transplantation

Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012

Changes in vegetation phenology are among the most sensitive biological responses to global change. While land surface phenological changes in the Northern Hemisphere have been extensively studied from the widely used long-term AVHRR (Advanced Very High Resolution Radiometer) data, current knowledge on land surface phenological trends and the associated drivers remains uncertain for the tropics. This uncertainty is partly due to the well-known challenges of applying satellite-derived vegetation indices from the optical domain in areas prone to frequent cloud cover. The long-term vegetation optical depth (VOD) product from satellite passive microwaves features less sensitivity to atmospheric perturbations and measures different vegetation traits and functioning as compared to optical sensors. VOD thereby provides an independent and complementary data source for studying land surface phenology and here we performed a combined analysis of the VOD and AVHRR NDVI (Normalized Difference Vegetation Index) datasets for the dry tropics (25°N to 25°S) during 1992–2012. We find a general delay in the VOD derived start of season (SOS) and end of season (EOS) as compared to NDVI derived metrics, however with clear differences among land cover and continents. Pixels characterized by significant phenological trends (P < 0.05) account for up to 20% of the study area for each phenological metric of NDVI and VOD, with large spatial difference between the two sensor systems. About 50% of the pixels studied show significant phenological changes in either VOD or NDVI metrics. Drivers of phenological changes were assessed for pixels of high agreement between VOD and NDVI phenological metrics (serving as a means of reducing noise-related uncertainty). We find rainfall variability and woody vegetation change to be the main forcing variables of phenological trends for most of the dry tropical biomes, while fire events and land cover change are recognized as second-order drivers. Taken together, our study provides new insights on land surface phenological changes and the associated drivers in the dry tropics, as based on the complementary long-term data sources of VOD and NDVI, sensitive to changes in vegetation water content and greenness, respectively