A B S T R A C TMost climate projections predict that average surface temperature and precipitation variability will increase at the global scale, triggering hydrological variations and alterations in river flows and groundwater table levels. Climate change impacts on freshwater resources are likely to affect freshwater availability and quality and by extension, the ability of water systems to support natural processes and ensure population needs. As a result, the vulnerability of water systems to adverse conditions (e.g. water shortages, overexploitation, and quality deterioration) is intensified; hence, methods and tools for vulnerability assessment and identification of adaptation measures are necessary. This paper proposes a comprehensive framework for the assessment of water systems' vulnerability to adverse water related conditions and the identification of potential adaptation strategies. The proposed methodology is applied in the four study site areas of the FP7 COROADO project (selected river basins in Argentina, Brazil, Chile, and Mexico), and an indicator-based framework is adopted, expressing natural, physical, socio-economic, and institutional attributes of the examined areas. The vulnerability assessment was conducted following a disaggregated analysis (use of proxy indicators). The vulnerability profiles of the four study sites were formulated, describing the factors shaping vulnerability and the aspects that need improvement. Additionally, the anticipated contribution of alternative strategies to vulnerability mitigation was assessed. The systems' response to alternative strategies (what-if scenarios) was analyzed following an aggregated analysis (estimation of an overall vulnerability index).

Traces of heavy metals found in water resources, due to mining activities and e-waste discharge, pose a global threat. Conventional treatment processes fail to remove toxic heavy metals, such as lead, from drinking water in a resource-efficient manner when their initial concentrations are low. Here, we show that by using the yeast Saccharomyces cerevisiae we can effectively remove trace lead from water via a rapid mass transfer process, called biosorption, achieving an uptake of up to 12 mg lead per gram of biomass in solutions with initial lead concentrations below 1 part per million. Through spectroscopic analyses, we found that the yeast cell wall plays a crucial role in this process, with its mannoproteins and β-glucans being the key potential lead adsorbents. Furthermore, by employing nanomechanical characterization in the yeast biomass, we discovered that biosorption is linked to an increase in cell wall stiffness. These findings open new opportunities for using environmentally friendly and abundant biomaterials for advanced water treatment targeting emerging contaminants.

Prostate cancer is the second leading cause of cancer death in men in the developed world. A more sensitive and specific detection strategy for lethal prostate cancer beyond serum prostate specific antigen (PSA) population screening is urgently needed. Diagnosis by canine olfaction, using dogs trained to detect cancer by smell, has been shown to be both specific and sensitive. While dogs themselves are impractical as scalable diagnostic sensors, machine olfaction for cancer detection is testable. However, studies bridging the divide between clinical diagnostic techniques, artificial intelligence, and molecular analysis remains difficult due to the significant divide between these disciplines. We tested the clinical feasibility of a cross-disciplinary, integrative approach to early prostate cancer biosensing in urine using trained canine olfaction, volatile organic compound (VOC) analysis by gas chromatography-mass spectroscopy (GC-MS) artificial neural network (ANN)-assisted examination, and microbial profiling in a double-blinded pilot study. Two dogs were trained to detect Gleason 9 prostate cancer in urine collected from biopsy-confirmed patients. Biopsy-negative controls were used to assess canine specificity as prostate cancer biodetectors. Urine samples were simultaneously analyzed for their VOC content in headspace via GC-MS and urinary microbiota content via 16S rDNA Illumina sequencing. In addition, the dogs’ diagnoses were used to train an ANN to detect significant peaks in the GC-MS data. The canine olfaction system was 71% sensitive and between 70–76% specific at detecting Gleason 9 prostate cancer. We have also confirmed VOC differences by GC-MS and microbiota differences by 16S rDNA sequencing between cancer positive and biopsy-negative controls. Furthermore, the trained ANN identified regions of interest in the GC-MS data, informed by the canine diagnoses. Methodology and feasibility are established to inform larger-scale studies using canine olfaction, urinary VOCs, and urinary microbiota profiling to develop machine olfaction diagnostic tools. Scalable multi-disciplinary tools may then be compared to PSA screening for earlier, non-invasive, more specific and sensitive detection of clinically aggressive prostate cancers in urine samples.