Bioimmunological activities of Candida glabrata cellular mannan

Candida glabrata is a second most common human opportunistic pathogen which causes superficial but also life-threatening systemic candidiasis. According to the localization of mannans and mannoproteins in the outermost layer of the cell wall, mannan detection could be one of the first steps in the cell recognition of Candida cells by the host innate immune system. Mannans from the cell wall provide important immunomodulatory activities, compromising stimulation of cytokine production, induction of dendritic cells maturation and T-cell immunity. The model of DCs represents a promising tool to study immunomodulatory interventions throughout the vaccine development. Activated DCs induce, activate and polarize T-cell responses by expression of distinct maturation markers and cytokines regulating the adaptive immune responses. In addition, they are uniquely adept at decoding the fungus-associated information and translate it in qualitatively different T helper responses. We find out, that C. glabrata mannan is able to induce proliferation of splenocytes and to increase the production of TNF-α and IL-4. Next, increased the expression of co-stimulatory molecules CD80 and CD86 and the proportion of CD4+CD25+ and CD4+CD28+ T cells during in vitro stimulation of splenocytes

Microscale Petrographic, Trace Element, and Isotopic Constraints on Glauconite Diagenesis in Altered Sedimentary Sequences: Implications for Glauconite Geochronology

Glauconite is an authigenic clay mineral that is common in marine sedimentary successions. Dating of glauconite to determine the depositional age of sedimentary sequences has a long history but has fallen into disfavor due to the difficulty of obtaining “pure” glauconite separates. Recent advances in sedimentary petrography and reaction cell mass spectrometry permit rapid in situ Rb-Sr dating of carefully screened glauconite grains. However, glauconite remains susceptible to burial alteration so that successful application of in situ Rb-Sr glauconite geochronology requires improved, microscale constraints on the impact of postdepositional alteration on glauconite Rb-Sr systematics and articulation of robust criteria for identifying grains suitable for geochronology. Here, we address these questions by combining SEM-EDS mineral mapping, geochemical characterization, and in situ Rb-Sr dating of glauconite grains in partially altered lower Cambrian sedimentary sequences from the Arrowie and Amadeus basins in Australia. Our approach provides information at high spatial resolution, representing new insights into the interplay between source material, burial fluids, and diagenetic processes. Among the different glauconite classes, which we classify based on alteration and inclusion type, only the primary apatite-bearing “pristine” glauconite returns an age within the error of the expected stratigraphic age. We attribute the preservation of a depositional Rb-Sr age to the influence of Sr-rich, alteration-resistant apatite and the limited permeability of the clay-rich strata hosting these grains. We conclude that our combined petrographic–geochemical screening approach holds considerable potential for identifying the best preserved glauconite grains for in situ Rb-Sr geochronology.M. Rafiei, S. C. Löhr, O. Alard, A. Baldermann, J. Farkaš, and G. A. Broc

Adsorbate-Induced Segregation of Cobalt from PtCo Nanoparticles: Modeling Au Doping and Core AuCo Alloying for the Improvement of Fuel Cell Cathode Catalysts

Platinum, when used as a cathode material for the oxygen reduction reaction, suffers from high overpotential and possible dissolution, in addition to the scarcity of the metal and resulting cost. Although the introduction of cobalt has been reported to improve reaction kinetics and decrease the precious metal loading, surface segregation or complete leakage of Co atoms causes degradation of the membrane electrode assembly, and either of these scenarios of structural rearrangement eventually decreases catalytic power. Ternary PtCo alloys with noble metals could possibly maintain activity with a higher dissolution potential. First-principles-based theoretical methods are utilized to identify the critical factors affecting segregation in Pt–Co binary and Pt–Co–Au ternary nanoparticles in the presence of oxidizing species. With a decreasing share of Pt, surface segregation of Co atoms was already found to become thermodynamically viable in the PtCo systems at low oxygen concentrations, which is assigned to high charge transfer between species. While the introduction of gold as a dopant caused structural changes that favor segregation of Co, creation of CoAu alloy core is calculated to significantly suppress Co leakage through modification of the electronic properties. The theoretical framework of geometrically different ternary systems provides a new route for the rational design of oxygen reduction catalysts

Characterising the economic Proterozoic Glyde Package of the greater McArthur Basin, northern Australia

Available online 24 May 2023The greater McArthur Basin is an informal term for a Palaeo-to-Mesoproterozoic sedimentary system that consists of terranes from the McArthur Basin, Birrindudu Basin, and the Tomkinson Province. These spatially distant basins are interpreted to connect in the subsurface based on geophysical, lithological, and geochronological evidence. The coeval sedimentary units of the greater McArthur Basin were subdivided into non-genetic depositional ‘packages’ bookended by regional unconformities. In ascending order, these packages are the: Redbank, Goyder, Glyde, Favenc, and Wilton Packages. The ca. 1660–1610 Ma Glyde Package is the focus of this study and includes the economically important Barney Creek Formation, found in the McArthur Basin sensu stricto. The Barney Creek Formation hosts the world-class, sediment-hosted, Zn-Pb-Ag McArthur River deposit. Importantly, it is also a key petroleum source rock and unconventional hydrocarbon reservoir, containing Australia’s geologically oldest oil and gas discoveries and forming a part of the McArthur Petroleum Supersystem. Consequently, identifying chronostratigraphically similar units elsewhere in the greater McArthur Basin is important for explorers in finding analogous economic resources. In situ Rb–Sr geochronological results of the Barney Creek Formation shales sourced from borehole LV09001 yielded ages of 1634 ± 59 Ma and 1635 ± 67 Ma. Shale samples from Fraynes Formation in borehole Manbulloo S1 were dated at 1630 ± 57 Ma and 1636 ± 42 Ma using the same approach. These ages are in good agreement with U–Pb ages of tuffaceous layers from the same units, suggesting that they represent their early burial histories and not secondary, post-depositional events. These results indicate that the Fraynes Formation and the Barney Creek Formation are direct chronostratigraphic equivalents, with ages within analytical error of each other. In addition to the geochronological similarities, the δ13Ccarb, 87Sr/86Sr, and δ88/86Sr isotopic constraints from both units also display parallel geochemical fingerprints up-section. These include a positive δ13Ccarb excursion of ∼2.0 ‰, a trend towards more crustal-dominated 87Sr/86Sr ratios, and a negative δ88/86Sr excursion of ∼-0.25 ‰. These findings further support the application of isotopic chemostratigraphy as a powerful tool to correlate distal carbonaceous rocks in the basin system. Importantly, these geochemical fingerprints also show that the McArthur Group and the Limbunya Group experienced similar changes in palaeoenvironments during the evolution of the basin system. However, trace element data collated in this study indicates that they may have recorded different, heterogeneous palaeoredox histories. Geochemical models based on redox-sensitive trace elements V and Mo suggest that the Fraynes Formation sustained a much more euxinic water column as opposed to the Barney Creek Formation. These differences may have implications for the accumulation and preservation of base metals and hydrocarbons within the sediment.Darwinaji Subarkah, Alan S. Collins Juraj Farkaš, Morgan L. Blades, Sarah E. Gilbert, Amber J.M. Jarrett, Maxwell M. Bullen, William Giulian

N and C Isotope Variations Along an Extreme Eutrophication and Salinity Gradient in the Coorong Lagoon, South Australia

The Coorong Lagoon is a unique hydrological and depositional system at the terminus of the Murray–Darling Basin, the largest river system in Australia. It exhibits large salinity, nutrient, and organic matter gradients, providing a modern analogue to study and validate the use of δ15N and δ13C as tracers of past and contemporary geochemical cycles in estuarine environments. To this end, water and surface sediment samples were analyzed for particulate organic nitrogen (PON) and carbon (POC) concentrations, and the respective δ15N and δ13C signatures of particulate nitrogen and carbon. PON and POC exhibited positive relationships to chlorophyll-a, indicating the dominance of phytoplankton production upon suspended organic matter. There was also a general trend of increasing δ15N of PON (δ15NPON) values and decreasing δ13C of particulate carbon (δ13CPC) values with increasing salinity and eutrophication in the restricted South Lagoon. In a multiple linear regression for δ15NPON, the best two predictors in combination are PON and C:N molar ratio, highlighting the importance of productivity and the type or source of organic matter. For δ13CPC, the best two predictors are total dissolved phosphorus and latitude, suggesting influences from productivity and proximity to the ocean. Sediment δ15N values across the Coorong Lagoon overlap with the δ15NPON in the water column, suggesting that PON derived from algal material represents the main source of nitrogen to lagoon sediments. We hypothesize that limited N loss via denitrification leads to PON being recycled almost exclusively to ammonium, due to low rates of nitrification and dominance of dissimilatory nitrate reduction to ammonium (DNRA). We propose that preferential volatilization of 14N in ammonia increases the δ15N of ammonium assimilated by phytoplankton, thereby increasing the δ15N within suspended organic matter and surface sediment in the South Lagoon. By contrast, the gradient exhibited in δ13CPC data was countered by a relatively constant sedimentary organic carbon δ13C. Data from the Coorong, therefore, suggest that δ15N values in sediments can be used to infer palaeoproductivity in this hypereutrophic and hypersaline depositional environment, however, the measured δ13CPC may be influenced by δ13CDIC or preferential loss of 13C during sedimentation that alter the sedimentary δ13C record of organic carbon.Stacey C. Priestley, Jonathan Tyler, Savannah R. Liebelt, Luke M. Mosley, Wei Wen Wong, Yuexiao Shao, Zara Woolston, Mark Farrell, David T. Welsh, Justin D. Brookes, Alan S. Collins, Chris Keneally, and Juraj Farka

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Selected isotope ratio measurements of light metallic elements (Li, Mg, Ca, and Cu) by multiple collector ICP-MS

The unique capabilities of multiple collector inductively coupled mass spectrometry (MC-ICP-MS) for high precision isotope ratio measurements in light elements as Li, Mg, Ca, and Cu are reviewed in this paper. These elements have been intensively studied at the Geological Survey of Israel (GSI) and other laboratories over the past few years, and the methods used to obtain high precision isotope analyses are discussed in detail. The scientific study of isotopic fractionation of these elements is significant for achieving a better understanding of geochemical and biochemical processes in nature and the environment