There was no evidence for an association with WMH and either t-tau or p-tau. These data are supportive of a link between amyloid burden and presumed vascular pathology. Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on which we train a uniform deep learning model for brain age estimation. We demonstrate an age estimation accuracy on a hold-out test set (mean absolute error = 4.06 years, r = 0.970) and an independent life span evaluation data set (mean absolute error = 4.21 years, r = 0.960). We further demonstrate the utility of the estimated age in a life span aging analysis of cognitive functions. In summary, we achieve age estimation performance comparable to previous studies, but with a more heterogenous data set confirming the efficacy of this deep learning framework. We also evaluated training with varying age distributions. The analysis of regional contributions to our brain age predictions through multiple analyses, and confirmation of the association of divergence between the estimated and chronological brain age with neuropsychological measures, may be useful in the development and evaluation of similar imaging biomarkers. PURPOSE This case-control study aimed to evaluate the ability to use a panel of IL-31, IL-1ß and NLRP3 to differentiate sepsis from systemic inflammatory response syndrome (SIRS) and to predict septic shock. METHODS Serum levels of IL-31, IL-1ß and NLRP3 were measured by ELISA in 149 participants; 38 with sepsis, 51 with SIRS, 30 with septic shock and 30 healthy controls. RESULTS Lower levels of IL-31 were found in sepsis (10.21 ± 4.34 pg/ml) compared to SIRS (16.74 ± 3.18 pg/ml) and to controls with the lowest levels detected in septic shock (6.26 ± 2.72 pg/ml). IL-1ß and NLRP3 levels were higher in sepsis (54.99 ± 14.11 pg/ml and 9.93 ± 2.38 ng/ml) compared to SIRS (27.8 ± 6.94 pg/ml and 4.86 ± 1.33 ng/ml) with the highest levels seen in septic shock (125.1 ± 32.79 pg/ml and 19.43 ± 6.48 ng/ml) respectively. IL-31 discriminated sepsis in patients showing SIRS with 80% sensitivity and 70% specificity and, identified septic shock with 78.6% sensitivity and 60.3% specificity. CCT251545 inhibitor IL-1ß identified sepsis from SIRS with 93.3% and 83.3% specificity. NLRP3 discriminated sepsis from SIRS with 94.5% sensitivity and 93.3% specificity. And, with sensitivity 99.1% and 90.1% and specificity 98.9% and 80% IL-1ß and NLRP3 could respectively define septic shock. A panel of combined markers provided 100% sensitivity and specificity. The three biomarkers proved to be independent prognostic biomarkers. At 95% CI, IL-31 hazard ratio (HR) was 0.716, p = 0.001; IL-1β HR was 1.023, p ≤ 0.001; and NLRP3 HR was 1.114, p ≤ 0.001. Additionally, IL-1ß proved to be an independent predictor of septic shock (β = 0.355; p = 0.035). CONCLUSION The cross-relation between IL-31, IL-1ß and NLRP3 in sepsis can provide a promising diagnostic and prognostic panel. While driving the regional economy, industrial parks also pose great threats to natural environment due to large quantities of resource consumption and intensive pollutants emissions. Eco-industrial development, including cleaner production, bioproducts or waste interchange, and infrastructure sharing, is key to improving the parks' environmental quality and sustainability. However, how to measure the performance of eco-industrial development is an essential and hard work since the material and energy flows are complex and cannot be compared in various units. The water and non-renewable resources which are very vital materials to sustain industrial activities in the industrial parks were rarely considered in the previous traditional ecological footprint analysis. Therefore, our research depicts a real picture of all the resources including water and non-renewable resources to illustrate the actual environmental impact of a national high technology industrial development zone-Jiangyin high technology Park, using energy based ecological footprint method. Results show that the emergy-ecological footprint deficit and emergy-ecological footprint intensity of the study park decreased by 16.75% and 16.74% due to the implementation of eco-industrial development. In detail, minerals made the largest reduction, 2.00E + 2 ha/capita, followed by fossil fuels with a reduction of 1.01E + 2 ha/capita, and the resources from cropland and pasture did not make a contribution in reducing emergy ecological footprint. Policy implications such as further replenishing and improving the ecological industry chains are proposed based on this survey. This study provides a basis to improve the environmental management and performance of industrial parks. The anaerobic digestion of food waste, can result in large amounts of solid waste digestate, often without methods of disposal. In this study, a biochar was prepared from food waste digestate (FWD) by pyrolysis, and its potential to activate peroxymonosulfate (PMS) for the removal of pollutants from a simulated textile wastewater was evaluated. The results showed that the addition of biochar (0.5 g/L) and PMS (1 mM) to wastewater could remove >99% of a representative azo dye pollutant (reactive brilliant red X-3B, 1 g/L) within 10 min. The efficiency of this removal process was attributed to the catalytic sites in the biochar (graphitic carbon and nitrogen, pyridinium nitrogen and CO structures) which could activate PMS to produce reactive oxygen species (1O2, O2-, OH and SO4-). The results obtained in this study confirmed the activation potential of the biochar derived from FWD on PMS, providing an alternative utilization strategy for anaerobic FWD. Nowadays, increasingly complex sets of indicators are used to compare and diagnose municipal solid waste management (MSWM). These sets incorporate new priorities regarding sustainability and focus on measuring the progress to zero waste. Nevertheless, in developing countries, where MSWM is still striving to protect health from the potential impacts of waste, the MSWM information available is scarce and of low quality. This work proposes a basic set of indicators for analyzing technical aspects of street cleaning, waste collection and disposal in such contexts. Based on the assessment of 66 Mexican municipalities, ten indicators were identified that can be calculated with the information available. For each indicator, reference values were established, and their performance was evaluated by means of a traffic light system. In addition, a method that allows the quality of the information to be classified into four levels according to the data source, its uncertainty, the temporal coverage, and its spatial coverage was applied.