Zeta potentials of the rare earth element fluorcarbonate minerals focusing on bastnäsite and parisite

Rare earth elements (REE) are critical to a wide range of technologies ranging from mobile phones to wind turbines. Processing and extraction of REE minerals from ore bodies is, however, both challenging and relatively poorly understood, as the majority of deposits contain only limited enrichment of REEs. An improved understanding of the surface properties of the minerals is important in informing and optimising their processing, in particular for separation by froth flotation. The measurement of zeta potential can be used to extract information regarding the electrical double layer, and hence surface properties of these minerals. There are over 34 REE fluorcarbonate minerals currently identified, however bastnäsite, synchysite and parisite are of most economic importance. Bastnäsite-(Ce), the most common REE fluorcarbonate, supplies over 50% of the world's REE. Previous studies of bastnäsite have showed a wide range of surface behaviour, with the iso-electric point (IEP), being measured between pH values of 4.6 and 9.3. In contrast, no values of IEP have been reported for parisite or synchysite. In this work, we review previous studies of the zeta potentials of bastnäsite to investigate the effects of different methodologies and sample preparation. In addition, measurements of zeta potentials of parisite under water, collector and supernatant conditions were conducted, the first to be reported. These results showed an iso-electric point for parisite of 5.6 under water, with a shift to a more negative zeta potential with both collector (hydroxamic and fatty acids) and supernatant conditions. The IEP with collectors and supernatant was <3.5. As zeta potential measurements in the presence of reagents and supernatants are the most rigorous way of determining the efficiency of a flotation reagent, the agreement between parisite zeta potentials obtained here and previous work on bastnäsite suggests that parisite may be processed using similar reagent schemes to bastnäsite. This is important for future processing of REE deposits, comprising of more complex REE mineralogy

Supersymmetry of hyperbolic monopoles

We investigate what supersymmetry says about the geometry of the moduli space of hyperbolic monopoles. We construct a three-dimensional supersymmetric Yang-Mills-Higgs theory on hyperbolic space whose half-BPS configurations coincide with (complexified) hyperbolic monopoles. We then study the action of the preserved supersymmetry on the collective coordinates and show that demanding closure of the supersymmetry algebra constraints the geometry of the moduli space of hyperbolic monopoles, turning it into a so-called pluricomplex manifold, thus recovering a recent result of Bielawski and Schwachh\"ofer.Comment: 22 page

Feasibility of the porous zone approach to modelling vegetation in CFD

Vegetation within stormwater ponds varies seasonly and its presence affects the flow field, which in turn affects the pond’s Residence Time Distribution and its effectiveness at pollutant removal. Vegetated flows are complex and, as a result, few suitable tools exist for evaluating realistic stormwater pond designs. Recent research has suggested using a porous zone to represent vegetation within a CFD model, and this paper investigates the feasibility of this approach using ANSYS Fluent. One of the main benefits of using a porous zone is the ability to derive the relevant parameters from the known physical characteristics of stem diameter and porosity using the Ergun equation. A sensitivity analysis on the viscous resistance factor 1/α1/α and the inertial resistance factor C2C2 has been undertaken by comparing model results to data collected from an experimental vegetated channel. Best fit values of C2C2 were obtained for a range of flow conditions including emergent and submerged vegetation. Results show the CFD model to be insensitive to 1/α1/α but very sensitive to values of C2C2. For submerged vegetation, values of C2C2 derived from the Ergun equation are under-predictions of best-fit C2C2 values as only the turbulence due to the shear layer is represented. The porous zone approach does not take into account turbulence generated from stem wakes such that no meaningful predictions for emergent vegetation were obtained. C2C2 values calculated using a force balance show better agreement with best-fit C2C2 values than those derived from the Ergun equation. Manually fixing values of kk and εε within the porous zone of the model shows initial promise as a means of taking stem wakes into account

Deaminase-Independent Inhibition of Parvoviruses by the APOBEC3A Cytidine Deaminase

The APOBEC3 proteins form a multigene family of cytidine deaminases with inhibitory activity against viruses and retrotransposons. In contrast to APOBEC3G (A3G), APOBEC3A (A3A) has no effect on lentiviruses but dramatically inhibits replication of the parvovirus adeno-associated virus (AAV). To study the contribution of deaminase activity to the antiviral activity of A3A, we performed a comprehensive mutational analysis of A3A. By mutation of non-conserved residues, we found that regions outside of the catalytic active site contribute to both deaminase and antiviral activities. Using A3A point mutants and A3A/A3G chimeras, we show that deaminase activity is not required for inhibition of recombinant AAV production. We also found that deaminase-deficient A3A mutants block replication of both wild-type AAV and the autonomous parvovirus minute virus of mice (MVM). In addition, we identify specific residues of A3A that confer activity against AAV when substituted into A3G. In summary, our results demonstrate that deaminase activity is not necessary for the antiviral activity of A3A against parvoviruses

DR6 as a Diagnostic and Predictive Biomarker in Adult Sarcoma

The Death Receptor 6 (DR6) protein is elevated in the serum of ovarian cancer patients. We tested DR6 serum protein levels as a diagnostic/predictive biomarker in several epithelial tumors and sarcomas.DR6 gene expression profiles were screened in publically available arrays of solid tumors. A quantitative immunofluorescent western blot analysis was developed to test the serum of healthy controls and patients with sarcoma, uterine carcinosarcoma, bladder, liver, and pancreatic carcinomas. Change in DR6 serum levels was used to assay the ability of DR6 to predict the response to therapy of sarcoma patients.DR6 mRNA is highly expressed in all tumor types assayed. Western blot analysis of serum DR6 protein demonstrated high reproducibility (r = 0.97). Compared to healthy donor controls, DR6 serum levels were not elevated in patients with uterine carcinosarcoma, bladder, liver, or pancreatic cancers. Serum DR6 protein levels from adult sarcoma patients were significantly elevated (p<0.001). This was most evident for patients with synovial sarcoma. Change in serum DR6 levels during therapy correlated with clinical benefit from therapy (sensitivity 75%, and positive predictive value 87%).DR6 may be a clinically useful diagnostic and predictive serum biomarker for some adult sarcoma subtypes.Diagnosis of sarcoma can be difficult and can lead to improper management of these cancers. DR6 serum protein may be a tool to aid in the diagnosis of some sarcomatous tumors to improve treatment planning. For patients with advanced disease, rising DR6 levels predict non-response to therapy and may expedite therapeutic decision making and reduce reliance on radiologic imaging

A Landscape and Climate Data Logistic Model of Tsetse Distribution in Kenya

, biologically transmitted by the tsetse fly in Africa, are a major cause of illness resulting in both high morbidity and mortality among humans, cattle, wild ungulates, and other species. However, tsetse fly distributions change rapidly due to environmental changes, and fine-scale distribution maps are few. Due to data scarcity, most presence/absence estimates in Kenya prior to 2000 are a combination of local reports, entomological knowledge, and topographic information. The availability of tsetse fly abundance data are limited, or at least have not been collected into aggregate, publicly available national datasets. Despite this limitation, other avenues exist for estimating tsetse distributions including remotely sensed data, climate information, and statistical tools.Here we present a logistic regression model of tsetse abundance. The goal of this model is to estimate the distribution of tsetse fly in Kenya in the year 2000, and to provide a method by which to anticipate their future distribution. Multiple predictor variables were tested for significance and for predictive power; ultimately, a parsimonious subset of variables was identified and used to construct the regression model with the 1973 tsetse map. These data were validated against year 2000 Food and Agriculture Organization (FAO) estimates. Mapcurves Goodness-Of-Fit scores were used to evaluate the modeled fly distribution against FAO estimates and against 1973 presence/absence data, each driven by appropriate climate data.Logistic regression can be effectively used to produce a model that projects fly abundance under elevated greenhouse gas scenarios. This model identifies potential areas for tsetse abandonment and expansion