Eco-geomorphological processes within grasslands, shrublands and badlands in the semi-arid Karoo, South Africa

Vegetation type and cover play an important role in the operation of geomorphological processes by controlling runoff and sediment dynamics. In drylands, land degradation is particularly sensitive to these eco-geomorphic interactions. Although many geomorphological studies of land degradation focus on the change in hydrological response as a function of vegetation cover, few have investigated how the autogenic response of plants may influence the susceptibility of soil to erosion through a change of soil resources. This study investigates the hypothesis that shrub communities possess greater soil parameter heterogeneity compared with grasslands and assesses how these different scales of heterogeneity can influence the susceptibility of soil to erosion.  Soil samples were taken from seven 60 m × 60 m plots within grasslands, shrublands and badlands situated in the Sneeuberg uplands of the central Karoo. One hundred and eight samples per plot were analysed for bulk density, organic matter, pH, conductivity and available sodium, calcium, magnesium, potassium and phosphorus. Geostatistical analyses determined that the grassland landscape was largely homogenous in its distribution of soil parameters, whereas shrublands demonstrated an increase in heterogeneity. Periodicity in the semi-variograms indicated that regular patterns across the landscape were evident for all parameters and thus likely to represent the differences between shrub and intershrub regions, areas of high and low erodibility. More pronounced patterns were identified in the badlands. This indicates that, if the conditions are right, changes in plant-soil interactions caused by soil parameter redistribution in shrubland landscapes can exacerbate erosion, leading to further degradation in the form of badlands

Trichloromethyl chloroformate ("diphosgene"), ClC(O)OCCl 3: Structure and conformational properties in the gaseous and condensed phases

The conformational properties of gaseous trichloromethyl chloroformate (or “diphosgene”), ClC(O)- OCCl3, have been studied by vibrational spectroscopy [IR (gas), IR (matrix), and Raman (liquid)] and quantum chemical calculations (MP2 and B3LYP with 6-311G* basis sets); in addition, the structure of a single crystal at low temperature has been determined by X-ray diffraction. ClC(O)OCCl3 exhibits only one conformational form having Cs symmetry with a synperiplanar orientation of the C-O single bond relative to the CdO double bond. The calculated energy difference between the syn and anti forms, 5.73 kcal mol-1 (B3LYP) or 7.06 kcal mol-1 (MP2), is consistent with the experimental findings for the gas and liquid phases. The crystalline solid at 150 K [monoclinic, P21/n, a ) 5.5578(5) Å, b ) 14.2895(12) Å, c ) 8.6246(7) Å, â ) 102.443(2)°, Z ) 4] likewise consists only of molecules in the syn form.Fil: Arce, Valeria Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Química Inorgánica "Dr. Pedro J. Aymonino". Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Química Inorgánica "Dr. Pedro J. Aymonino"; ArgentinaFil: Della Vedova, Carlos Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Química Inorgánica "Dr. Pedro J. Aymonino". Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Química Inorgánica "Dr. Pedro J. Aymonino"; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Química. Laboratorio de Servicios a la Industria y al Sistema Científico; ArgentinaFil: Downs, Anthony J.. University of Oxford; Reino UnidoFil: Parsons, Simon. University of Edinburgh; Reino UnidoFil: Romano, Rosana Mariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Química Inorgánica "Dr. Pedro J. Aymonino". Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Química Inorgánica "Dr. Pedro J. Aymonino"; Argentin

Factors affecting the radiation response in glioblastoma.

Glioblastoma (GBM) is a highly invasive primary brain tumor in adults with a 5-year survival rate of less than 10%. Conventional radiotherapy with photons, along with concurrent and adjuvant temozolomide, is the mainstay for treatment of GBM although no significant improvement in survival rates has been observed over the last 20 years. Inherent factors such as tumor hypoxia, radioresistant GBM stem cells, and upregulated DNA damage response mechanisms are well established as contributing to treatment resistance and tumor recurrence. While it is understandable that efforts have focused on targeting these factors to overcome this phenotype, there have also been striking advances in precision radiotherapy techniques, including proton beam therapy and carbon ion radiotherapy (CIRT). These enable higher doses of radiation to be delivered precisely to the tumor, while minimizing doses to surrounding normal tissues and organs at risk. These alternative radiotherapy techniques also benefit from increased biological effectiveness, particularly in the case of CIRT. Although not researched extensively to date, combining these new radiation modalities with radio-enhancing agents may be particularly effective in improving outcomes for patients with GBM

Chemical screen identifies FDA-approved drugs and target pathways that induce precocious pancreatic endocrine differentiation

Pancreatic β-cells are an essential source of insulin and their destruction because of autoimmunity causes type I diabetes. We conducted a chemical screen to identify compounds that would induce the differentiation of insulin-producing β-cells in vivo. To do this screen, we brought together the use of transgenic zebrafish as a model of β-cell differentiation, a unique multiwell plate that allows easy visualization of lateral views of swimming larval fish and a library of clinical drugs. We identified six hits that can induce precocious differentiation of secondary islets in larval zebrafish. Three of these six hits were known drugs with a considerable background of published data on mechanism of action. Using pharmacological approaches, we have identified and characterized two unique pathways in β-cell differentiation in the zebrafish, including down-regulation of GTP production and retinoic acid biosynthesis

The human papillomavirus E7 proteins associate with p190RhoGAP and alter its function

Using mass spectrometry, we identified p190RhoGAP (p190) as a binding partner of human papillomavirus 16 (HPV16) E7. p190 belongs to the GTPase activating protein (GAP) family and is one of the primary GAPs for RhoA. GAPs stimulate the intrinsic GTPase activity of the Rho proteins, leading to Rho inactivation and influencing numerous biological processes. RhoA is one of the best-characterized Rho proteins and is specifically involved in formation of focal adhesions and stress fibers, thereby regulating cell migration and cell spreading. Since this is the first report that E7 associates with p190, we carried out detailed interaction studies. We show that E7 proteins from other HPV types also bind p190. Furthermore, we found that conserved region 3 (CR3) of E7 and the middle domain of p190 are important for this interaction. More specifically, we identified two residues in CR3 of E7 that are necessary for p190 binding and used mutants of E7 with mutations of these residues to determine the biological consequences of the E7-p190 interaction. Our data suggest that the interaction of E7 with p190 dysregulates this GAP and alters the actin cytoskeleton. We also found that this interaction negatively regulates cell spreading on a fibronectin substrate and therefore likely contributes to important aspects of the HPV life cycle or HPV-induced tumorigenesis. © 2014, American Society for Microbiology

Impacts of Climate Change on indirect human exposure to pathogens and chemicals from agriculture

Objective: Climate change is likely to affect the nature of pathogens and chemicals in the environment and their fate and transport. Future risks of pathogens and chemicals could therefore be very different from those of today. In this review, we assess the implications of climate change for changes in human exposures to pathogens and chemicals in agricultural systems in the United Kingdom and discuss the subsequent effects on health impacts. Data sources: In this review, we used expert input and considered literature on climate change ; health effects resulting from exposure to pathogens and chemicals arising from agriculture ; inputs of chemicals and pathogens to agricultural systems ; and human exposure pathways for pathogens and chemicals in agricultural systems. Data synthesis: We established the current evidence base for health effects of chemicals and pathogens in the agricultural environment ; determined the potential implications of climate change on chemical and pathogen inputs in agricultural systems ; and explored the effects of climate change on environmental transport and fate of different contaminant types. We combined these data to assess the implications of climate change in terms of indirect human exposure to pathogens and chemicals in agricultural systems. We then developed recommendations on future research and policy changes to manage any adverse increases in risks. Conclusions: Overall, climate change is likely to increase human exposures to agricultural contaminants. The magnitude of the increases will be highly dependent on the contaminant type. Risks from many pathogens and particulate and particle-associated contaminants could increase significantly. These increases in exposure can, however, be managed for the most part through targeted research and policy changes

Phenotypic and functional characteristics of highly differentiated CD57 +NKG2C + NK cells in HIV-1- infected individuals

Natural killer (NK) cells are important anti-viral effector cells. The function and phenotype of the NK cells that constitute an individual’s NK cell repertoire can be influenced by ongoing and/or previous viral infections. Indeed, infection with human cytomegalovirus (HCMV) drives the expansion of a highly differentiated NK cell population characterized by expression of CD57 and the activating NKG2C receptor. This NK cell population has also been noted to occur in HIV-1-infected individuals. We evaluated the NK cells of HIV-1-infected and –uninfected individuals to determine the relative frequency of highly differentiated CD57 +NKG2C + NK cells and characterize these cells for their receptor expression and responsiveness to diverse stimuli. Highly differentiated CD57 +NKG2C + NK cells occurred at higher frequencies in HCMV-infected donors relative to HCMV-uninfected donors and were dramatically expanded in HIV-1/HCMV co-infected donors. The expanded CD57 +NKG2C + NK cell population in HIV-1-infected donors remained stable following antiretroviral therapy. CD57 +NKG2C + NK cells derived from HIV-1-infected individuals were robustly activated by antibody-dependent stimuli that contained anti-HIV-1 antibodies or therapeutic anti-CD20 antibody, and these NK cells mediated cytolysis through NKG2C. Lastly, CD57 +NKG2C + NK cells from HIV-1-infected donors were characterized by reduced expression of the inhibitory NKG2A receptor. The abundance of highly functional CD57 +NKG2C + NK cells in HIV-1-infected individuals raises the possibility that these NK cells could play a role in HIV-1 pathogenesis or serve as effector cells for therapeutic/cure strategies

Experimental Quantum Hamiltonian Learning

Efficiently characterising quantum systems, verifying operations of quantum devices and validating underpinning physical models, are central challenges for the development of quantum technologies and for our continued understanding of foundational physics. Machine-learning enhanced by quantum simulators has been proposed as a route to improve the computational cost of performing these studies. Here we interface two different quantum systems through a classical channel - a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen-vacancy centre - and use the former to learn the latter's Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately $10^{-5}$. Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model itself. We go on to implement an interactive version of the protocol and experimentally show its ability to characterise the operation of the quantum photonic device. This work demonstrates powerful new quantum-enhanced techniques for investigating foundational physical models and characterising quantum technologies