A conceptual framework for a multi-criteria decision support tool to select technologies for resource recovery from urban wastewater.

In the context of circular economy, wastewater can be used to address some of the 21st century's challenges regarding the transition to renewable resources for water, energy, and nutrients. Despite all the research, development, and experience with resource recovery from urban wastewater, its implementation is still limited. The transition from treatment to resource recovery is complex due to the difficulty of selecting unit processes from a large number of candidate processes considering the operational limitations of each process, and sustainability objectives.

Mathematically formulated key performance indicators for design and evaluation of treatment trains for resource recovery from urban wastewater.

While urban wastewater infrastructure is aging and no longer adequate, climate change and sustainability are urging the transition from pollution management to resource recovery. Lacking evidence-based quantitative evaluation of the potential benefits and consequences of resource recovery from wastewater hinders the negotiation amongst stakeholders and slows down the transition. This study proposes mathematical formulations for technical, environmental, economic, and social key performance indicators (KPIs) that can be used to quantify the benefits and the risks of resource recovery.

Ecosystem state change in the Arabian Sea fuelled by the recent loss of snow over the Himalayan-Tibetan Plateau region.

The recent trend of global warming has exerted a disproportionately strong influence on the Eurasian land surface, causing a steady decline in snow cover extent over the Himalayan-Tibetan Plateau region. Here we show that this loss of snow is undermining winter convective mixing and causing stratification of the upper layer of the Arabian Sea at a much faster rate than predicted by global climate models. Over the past four decades, the Arabian Sea has also experienced a profound loss of inorganic nitrate.

Probing and Enhancing Ligand-Mediated Active Targeting of Tumors Using Sub-5 nm Ultrafine Iron Oxide Nanoparticles.

: "Active targeting" based on the ligand-target affinity is a common strategy to precisely deliver nanoparticle (NP) imaging probes or drug carriers to the diseased tissue. However, such ligand-mediated active targeting inevitably takes place with prerequisite "passive targeting", driven by the enhanced permeability and retention (EPR) effect. Thus, the efficiency of active targeting in relation to off-targeted unbound NPs is of great importance in quantitative imaging of tumor biomarkers and delivery.

Evaluation of preclinical efficacy of everolimus and pasireotide in thyroid cancer cell lines and xenograft models.

Background: Signaling through mTOR and somatostatin pathway is implicated in thyroid cancer development.

Method: We evaluated everolimus, an mTOR inhibitor and pasireotide, a multi receptor somatostatin analogue as potential therapy of thyroid cancer focusing on the in vitro and in vivo efficacy, as well as possible mechanism to explain any observed interaction.

Results: Both everolimus and pasireotide inhibit the growth of thyroid cancer cell lines in vitro with varied efficacy that correlates with tumor origin and somatostatin receptor (SSTR) expression profile of the cell lines.