Gallegosclemons7109

029 kg DM m-2). Zeolite used in contaminated soil significantly affected root biomass and root morphology parameters. Zeolite application resulted in significantly higher root biomass (2.30 mg cm-3) and root length (76.61 cm cm-3) than those in the treatments without zeolite (0.29 mg cm-3 and 6.90 cm cm-3). Biochar as a soil amendment did not affect most root morphometric parameters. The application of biochars only slightly reduced the root diameter of cocksfoot. The root diameter of tall fescue was similar in all treatments (0.075 mm) except the control (0.063 mm) and biochar 550 treatments (0.067 mm), in which slightly thinner roots were observed.The International Federation of Gynecology and Obstetrics (FIGO) cervical cancer staging system was modified in 2018, introducing new stage IB subdivisions and new lymph node status considerations in stage IIIC. We compared cervical cancer survival outcomes according to the 2014 and 2018 FIGO staging systems. We selected 10% of cervical cancer cases (2010-2015) from the Korean national cancer registry (2010-2015) through a systematic sampling method. We collected information using a collaborative stage data collection system and evaluated the results according to both staging systems. The log-rank test was used to analyze overall survival differences. No significant difference in survival was observed between 2018 subdivisions IB1/IB2/IB3 (P = 0.069), whereas a considerable difference was observed between these subdivisions according to histological subtypes. In the 2018 FIGO staging system, stage IIIC had better survival than stage IIIA/IIIB (P  less then  0.001). We observed considerable heterogeneity in 2018 stage IIIC related to the corresponding stages of the 2014 staging system (stages IA1-IIIB). The size of the primary cervical mass was related to survival (P  less then  0.001). In conclusion, using lymph node status to define stage IIIC captured a broad range of prognoses. The inclusion of primary tumor size considerations may improve the staging accuracy of advanced cervical cancer.Individual estimates of cochlear compression may provide complementary information to traditional audiometric hearing thresholds in disentangling different types of peripheral cochlear damage. Here we investigated the use of the slope of envelope following response (EFR) magnitude-level functions obtained from four simultaneously presented amplitude modulated tones with modulation frequencies of 80-100 Hz as a proxy of peripheral level compression. Compression estimates in individual normal hearing (NH) listeners were consistent with previously reported group-averaged compression estimates based on psychoacoustical and distortion-product oto-acoustic emission (DPOAE) measures in human listeners. Immunology inhibitor They were also similar to basilar membrane (BM) compression values measured invasively in non-human mammals. EFR-based compression estimates in hearing-impaired listeners were less compressive than those for the NH listeners, consistent with a reduction of BM compression. Cochlear compression was also estimated using DPOAEs in the same NH listeners. DPOAE estimates were larger (less compressive) than EFRs estimates, showing no correlation. Despite the numerical concordance between EFR-based compression estimates and group-averaged estimates from other methods, simulations using an auditory nerve (AN) model revealed that compression estimates based on EFRs might be highly influenced by contributions from off-characteristic frequency (CF) neural populations. This compromises the possibility to estimate on-CF (i.e., frequency-specific or "local") peripheral level compression with EFRs.Globally, the conditions and time scales underlying coastal ecosystem recovery following disturbance remain poorly understood, and post-disturbance examples of resilience based on long-term studies are particularly rare. Here, we documented the recovery of a marine foundation species (turtlegrass) following a hypersalinity-associated die-off in Florida Bay, USA, one of the most spatially extensive mortality events for seagrass ecosystems on record. Based upon annual sampling over two decades, foundation species recovery across the landscape was demonstrated by two ecosystem responses the range of turtlegrass biomass met or exceeded levels present prior to the die-off, and turtlegrass regained dominance of seagrass community structure. Unlike reports for most marine taxa, recovery followed without human intervention or reduction to anthropogenic impacts. Our long-term study revealed previously uncharted resilience in subtropical seagrass landscapes but warns that future persistence of the foundation species in this iconic ecosystem will depend upon the frequency and severity of drought-associated perturbation.Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. link2 We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model  less then  0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p  less then  0.001) and the T1 + T2 FLAIR radiomics model (p  less then  0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.Qualitative analysis of fundus photographs enables straightforward pattern recognition of advanced pathologic myopia. However, it has limitations in defining the classification of the degree or extent of early disease, such that it may be biased by subjective interpretation. In this study, we used the fovea, optic disc, and deepest point of the eye (DPE) as the three major markers (i.e., key indicators) of the posterior globe to quantify the relative tomographic elevation of the posterior sclera (TEPS). Using this quantitative index from eyes of 860 myopic patients, support vector machine based machine learning classifier predicted pathologic myopia an AUROC of 0.828, with 77.5% sensitivity and 88.07% specificity. Axial length and choroidal thickness, the existing quantitative indicator of pathologic myopia only reached an AUROC of 0.758, with 75.0% sensitivity and 76.61% specificity. When all six indices were applied (four TEPS, AxL, and SCT), the discriminative ability of the SVM model was excellent, demonstrating an AUROC of 0.868, with 80.0% sensitivity and 93.58% specificity. Our model provides an accurate modality for identification of patients with pathologic myopia and may help prioritize these patients for further treatment.Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013-2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.Brittleness is a major limitation of polymer-derived ceramics (PDCs). link3 Different concentrations of three nanofillers (carbon nanotubes, Si3N4 and Al2O3 nanoparticles) were evaluated to improve both toughness and modulus of a commercial polysilazane (PSZ) PDC. The PSZs were thermally cross-linked and pyrolyzed under isostatic pressure in nitrogen. A combination of mechanical, chemical, density, and microscopy characterizations was used to determine the effects of these fillers. Si3N4 and Al2O3 nanoparticles (that were found to be active fillers) were more effective than nanotubes and improved the elastic modulus, hardness, and fracture toughness (JIC) of the PDC by ~ 1.5 ×, ~ 3 ×, and ~ 2.5 ×, respectively. Nanotubes were also effective in maintaining the integrity of the samples during pyrolysis. The modulus and hardness of PDCs correlated positively with their apparent density; this can provide a fast way to assess future PDCs. The improvement in fracture toughness was attributed to crack deflection and bridging observed in the micro-indentation cracks in the modified PDCs. The specific toughness of the modified PDCs was 4 × higher than that of high-purity alumina, and its specific modulus reached that of commercially available technical ceramics. These PDCs can also easily take different shapes and therefore are of interest in protective armor, propulsion, thermal protection, device packaging and biomaterial systems.Recently, it has been suggested that network temporality can be exploited to substantially reduce the energy required to control complex networks. This somewhat counterintuitive finding was explained through an evocative example of the advantage of temporal networks when navigating a sailboat, we raise the sails when the wind helps us while lowering them when it works against us. Unfortunately, controlling complex networks inherits a further analogy with navigating a sailboat having to face the inherent uncertainty of future winds. We rarely, if ever, have deterministic knowledge of the evolution of the network we want to control. Here, our challenge is to exploit the potential advantages of temporality when only a probabilistic description of the future is available. We prove that, in this more realistic setting, exploiting temporality is no more a panacea for network control, but rather an asset of a wider toolbox made available by the scientific community. One that can indeed turn out useful, provided that the temporality of the network structure matches the intrinsic time scales of the nodes we want to control.