Notes

Nucleic Chemical p Automatic Proteins Arrays (NAPPA) for the Discovery associated with Autoantibodies within Osteoarthritis.
5, PM10, SO2, CO, and NO2 were positively correlated with hospital visits for depression. The strongest effects all occurred on lag0 (the same) day, and the corresponding excess risks (ERs) were 2.59 (95%CI 0.72, 4.49) for PM2.5, 3.08 (95%CI 1.05, 5.16) for PM10, 3.22 (95%CI 1.16, 5.32) for SO2, 4.38 (95%CI 1.83, 6.99) for CO, and 4.94 (95%CI 2.03, 7.92) for NO2 per IQR increase, respectively. The associations were found to be stronger in the elderly (≥65 years) and cold season. Furthermore, the effects of CO and NO2 remained significant in most two-pollutant models, suggesting that traffic-related air pollutants might be more important triggers of depression. Waste, especially biomass in general, is a large reservoir of nutrients that can be recovered through different technologies and used to produce biofertilizers. In the present study, environmental impacts of the production of microalgae biomass-based phosphate biofertilizer compared to triple superphosphate through life-cycle assessment conducted in the Simapro® software were investigated. The functional unit of the analysis was 163 g of P for both fertilizers. Phosphorus was recovered from a meat processing industry effluent in a high-rate algal pond. Impacts related to the entire biofertilizer chain impacted mainly on climate changes (3.17 kg CO2eq). Microalgae biofertilizer had higher environmental impact than conventional fertilizer in all impact categories, highlighting climate change and terrestrial ecotoxicity. An ideal scenario was created considering that all energy used comes from photovoltaic panels; in the separation step a physical method will be used, without energy expenditure (i.e. gravimetric sedimentation) and; biomass will be dried in a drying bed instead of the thermal drying. In this scenario, the impact of biofertilizer approached considerably those of triple superphosphate. When impacts of biomass cultivation and concentration stages were disregarded, drying step was of great relevance, contributing to increase biofertilizer impacts. More research is needed to optimize the algae production chain and determine the possibility of obtaining higher added value products more environmental attractive. Mercury (Hg) removal by six different living marine macroalgae, namely, Ulva intestinalis, Ulva lactuca, Fucus spiralis, Fucus vesiculosus, Gracilaria sp., and Osmundea pinnatifida was investigated in mono and multi-contamination scenarios. All macroalgae were tested under the same experimental conditions, evaluating the competition effects with all elements at the same initial molar concentration of 1 μmol dm-3. The presence of the main potentially toxic elements (Cd, Cr, Cu, Ni, and Pb) and rare earth elements (La, Ce, Pr, Nd, Eu, Gd, Tb, and Y) has not affected the removal of Hg. Characterizations of the macroalgae by FTIR before and after the biosorption/bioaccumulation assays suggest that Hg was mainly linked to sulfur-functional groups, while the removal of other elements was related with other functional groups. The mechanisms involved point to biosorption of Hg on the macroalgae surface followed by possible incorporation of this metal into the macroalgae by metabolically active processes. Globally, the green macroalgae (Ulva intestinalis, Ulva lactuca) showed the best performances for Hg, potential toxic elements and rare earth elements removal from synthetic seawater spiked with 1 μmol dm-3 of each element, at room temperature and pH 8.5. Metal release from mining wastes is a major environmental problem affecting ecosystems that requires effective, low-cost strategies for prevention and reclamation. The capacity of two strains (UB3 and UB5) of Sporosarcina luteola was investigated to induce the sequestration of metals by precipitation of carbonates in vitro and under microcosm conditions. These strains carry the ureC gene and have high urease activity. Also, they are highly resistant to metals and have the capacity for producing metallophores and arsenophores. SEM, EDX and XRD reveal that the two strains induced precipitation of calcite, vaterite and magnesian calcite as well as several (M2+)CO3 such as hydromagnesite (Mg2+), rhodochrosite (Mn2+), cerussite (Pb2+), otavite (Cd2+), strontianite (Sr2+), witherite (Ba2+) and hydrozincite (Zn2+) in vitro. Inoculation of the mixed culture of UB3+UB5 in tailings increased the pH and induced the precipitation of vaterite, calcite and smithsonite enhancing biocementation and reducing pore size and permeability slowing down the oxidation of residual sulfides. Results further demonstrated that the strains of S. luteola immobilize bioavailable toxic elements through the precipitation and coprecipitation of thermodynamically stable (M2+)CO3, Fe-Mn oxyhydroxides and organic chelates. Colombia is the fourth contributor to the Amazon River Basin (ARB) by surface, and the third by mean annual runoff. The Yahuarcaca Lakes System (YLS), consisting of four large interconnected water bodies situated on the floodplain of Amazon River, was identified as one of the key areas for the conservation of freshwater biodiversity in the Colombian ARB. This review aimed to provide a general overview of YLS, present its environmental and biological features, identify main ecological and health threats, and propose mitigation strategies and future research prospects. A systematic search was performed using various databases. In summary, YLS harbors significant biodiversity and provides a number of ecological services for local communities, encompassing fish and drinking water supply and utilization of the floodplain for agriculture. Ensuring its sustainability requires attention from local and international authorities, collaboration with indigenous communities and future interdisciplinary research. Cities are severely affected by air pollution. Ziprasidone Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO2, O3, PM2.5 and PM10 concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R2 of 0.59, 0.72, 0.76 and 0.75 for NO2, PM10, PM2.5 and O3, respectively, improving results from CTM alone.
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