Molecular composition in surface and subsurface sediments of the Helgoland mud area, North Sea

The role of microorganisms in the cycling of sedimentary organic carbon is a crucial one. To better understand relationships between molecular composition of a potentially bioavailable fraction of organic matter and microbial populations, bacterial and archaeal communities were characterized using pyrosequencing-based 16S rRNA gene analysis in surface (top 30 cm) and subsurface/deeper sediments (30-530 cm) of the Helgoland mud area, North Sea. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) was used to characterize a potentially bioavailable organic matter fraction (hot-water extractable organic matter, WE-OM). Algal polymer-associated microbial populations such as members of the Gammaproteobacteria, Bacteroidetes, and Verrucomicrobia were dominant in surface sediments while members of the Chloroflexi (Dehalococcoidales and candidate order GIF9) and Miscellaneous Crenarchaeota Groups (MCG), both of which are linked to degradation of more recalcitrant, aromatic compounds and detrital proteins, were dominant in subsurface sediments. Microbial populations dominant in subsurface sediments (Chloroflexi, members of MCG, and Thermoplasmata) showed strong correlations to total organic carbon (TOC) content. Changes of WE-OM with sediment depth reveal molecular transformations from oxygen-rich [high oxygen to carbon (O/C), low hydrogen to carbon (H/C) ratios] aromatic compounds and highly unsaturated compounds toward compounds with lower O/C and higher H/C ratios. The observed molecular changes were most pronounced in organic compounds containing only CHO atoms. Our data thus, highlights classes of sedimentary organic compounds that may serve as microbial energy sources in methanic marine subsurface environments.

Library of medium-resolution Fiber Optic Echelle spectra of F, G, K, and M field dwarfs to giant stars

We present a library of Penn State Fiber Optic Echelle (FOE) observations of a sample of field stars with spectral types F to M and luminosity classes V to I. The spectral coverage is from 3800 to 10000 Å with a nominal resolving power of 12,000. These spectra include many of the spectral lines most widely used as optical and near-infrared indicators of chromospheric activity such as the Balmer lines (Hα to H epsilon), Ca II H & K, the Mg I b triplet, Na I D_1, D_2, He I D_3, and Ca II IRT lines. There are also a large number of photospheric lines, which can also be affected by chromospheric activity, and temperature-sensitive photospheric features such as TiO bands. The spectra have been compiled with the goal of providing a set of standards observed at medium resolution. We have extensively used such data for the study of active chromosphere stars by applying a spectral subtraction technique. However, the data set presented here can also be utilized in a wide variety of ways ranging from radial velocity templates to study of variable stars and stellar population synthesis. This library can also be used for spectral classification purposes and determination of atmospheric parameters (T_eff, log g, [Fe/H]). A digital version of all the fully reduced spectra is available via ftp and the World Wide Web (WWW) in FITS format.

Methicillin-resistant Staphylococcus aureus genotyping using a small set of polymorphisms

The aim of this study was to identify a set of genetic polymorphisms that efficiently divides methicillin-resistant Staphylococcus aureus (MRSA) strains into groups consistent with the population structure. The rationale was that such polymorphisms could underpin rapid real-time PCR or low-density array-based methods for monitoring MRSA dissemination in a cost-effective manner. Previously, the authors devised a computerized method for identifying sets of single nucleoticle polymorphisms (SNPs) with high resolving power that are defined by multilocus sequence typing (MLST) databases, and also developed a real-time PCR method for interrogating a seven-member SNP set for genotyping S. aureus. Here, it is shown that these seven SNPs efficiently resolve the major MRSA lineages and define 27 genotypes. The SNP-based genotypes are consistent with the MRSA population structure as defined by eBURST analysis. The capacity of binary markers to improve resolution was tested using 107 diverse MRSA isolates of Australian origin that encompass nine SNP-based genotypes. The addition of the virulence-associated genes cna, pvl and bbplsdrE, and the integrated plasmids pT181, p1258 and pUB110, resolved the nine SNP-based genotypes into 21 combinatorial genotypes. Subtyping of the SCCmec locus revealed new SCCmec types and increased the number of combinatorial genotypes to 24. It was concluded that these polymorphisms provide a facile means of assigning MRSA isolates into well-recognized lineages.

The construction and optimization of an ion mobility spectrometer for the analysis of explosives and drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs.^ In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug.^ Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). ^ The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.^

The Construction and Optimization on an Ion Mobility Spectrometer for the Analysis of Explosives and Drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs. In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug. Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.

Characterization of cation exchanger stationary phases applied for the separations of therapeutic monoclonal antibodies.

Cation exchange chromatography (CEX) is a well established strategy for the characterization of monoclonal antibodies (mAbs). The optimization of mobile phase conditions is well described in the literature, but there is a lack of information about CEX stationary phases for the analysis of therapeutic proteins. The aim of this study was to compare five state-of-the-art CEX stationary phases based on the retention, selectivity and resolving power achieved in pH- and salt-gradient modes, with various therapeutic mAbs and their variants. The Sepax Antibodix WCX-NP3, Thermo MAbPac SCX-10 RS, YMC BioPro SP-F, Waters Protein-Pak Hi Res SP and Agilent Bio mAb NP1.7 SS were considered in this study. In terms of retention, the YMC Bio Pro SP-F material was the less retentive one, while the Agilent Bio mAb NP1.7 SS provides the highest retention. Regarding the selectivity achieved between the main mAbs isoforms and their variants, the Thermo MabPac SCX column generally gave the highest selectivity. Finally, it was hard to rank columns in term of kinetic performance since their performance is strongly solute (mAb) and elution mode (pH or salt gradient) dependent. However, the highest resolution--in most cases--was observed on the strong cation exchanger YMC Bio Pro SP-F material.

Role of Shiga toxins in the pathogenesis of hemolytic uremic syndrome

During the pathogenesis of hemolytic uremic syndrome (HUS), a severe sequela of Shiga toxin (Stx)-producing Escherichia coli (STEC) gastrointestinal infections, before the toxin acts on the target endothelial cells of the kidney and brain, several Stx forms are transported in the bloodstream: free Stx; Stx bound to circulating cells through Gb3Cer and TLR4 receptors; and Stx associated to blood cell-derived microvesicles. The latter form is mainly responsible for the development of life-threatening HUS in 15% of STEC-infected patients. Stx consist of five B subunits non-covalently bound to a single A subunit (uncleaved Stx) which can be cleaved in two fragments (A1 and A2) held by a disulfide bond (cleaved Stx). After reduction, the enzymatically active A1 fragment responsible for toxicity is released. Cleaved and uncleaved Stx are biologically active but functionally different, thus their presence in patients’ blood could affect the onset of HUS. Currently, there are no effective therapies for the treatment of STEC-infected patients and the gold standard strategies available for the diagnosis are very expensive and time-consuming. In this thesis, by exploiting the resolving power of SERS technology (Amplified Raman Spectroscopy on Surfaces), a plasmonic biosensor was developed as effective diagnostic tool for early detection of Stx in patients’ sera. An acellular protein synthesis system for detecting cleaved Stx2a in human serum based on its greater translation inhibition after treatment with reducing agents was developed and used to identify cleaved Stx in STEC-infected patients’ sera. Pathogenic microvesicles from Stx2a-challenged blood from healthy donors were isolated and characterized. The antibiotic NAB815, acting as inhibitor of toxin binding to TLR4 expressed by circulating cells, was found to be effective in impairing the formation of blood cell-derived microvesicles containing Stx2a, also having a protective effect in cellular models. This approach could be proposed as an innovative treatment for HUS prevention.

Grant-Free Access for Massive IoT Based on Interference Cancellation and Massive MIMO

Recent years have witnessed an increasing evolution of wireless mobile networks, with an intensive research work aimed at developing new efficient techniques for the future 6G standards. In the framework of massive machine-type communication (mMTC), emerging Internet of Things (IoT) applications, in which sensor nodes and smart devices transmit unpredictably and sporadically short data packets without coordination, are gaining an increasing interest. In this work, new medium access control (MAC) protocols for massive IoT, capable of supporting a non-instantaneous feedback from the receiver, are studied. These schemes guarantee an high time for the acknowledgment (ACK) messages to the base station (BS), without a significant performance loss. Then, an error floor analysis of the considered protocols is performed in order to obtain useful guidelines for the system design. Furthermore, non-orthogonal multiple access (NOMA) coded random access (CRA) schemes based on power domain are here developed. The introduction of power diversity permits to solve more packet collision at the physical (PHY) layer, with an important reduction of the packet loss rate (PLR) in comparison to the number of active users in the system. The proposed solutions aim to improve the actual grant-free protocols, respecting the stringent constraints of scalability, reliability and latency requested by 6G networks.

Optimizing the renewable generation mix in the Portuguese power system based on temporal and spatial diversity

Renewable energy sources (RES) have unique characteristics that grant them preference in energy and environmental policies. However, considering that the renewable resources are barely controllable and sometimes unpredictable, some challenges are faced when integrating high shares of renewable sources in power systems. In order to mitigate this problem, this paper presents a decision-making methodology regarding renewable investments. The model computes the optimal renewable generation mix from different available technologies (hydro, wind and photovoltaic) that integrates a given share of renewable sources, minimizing residual demand variability, therefore stabilizing the thermal power generation. The model also includes a spatial optimization of wind farms in order to identify the best distribution of wind capacity. This methodology is applied to the Portuguese power system.

Optimizing the renewable generation mix in the portuguese power system based on temporal and spatial diversity

Renewable energy sources (RES) have unique characteristics that grant them preference in energy and environmental policies. However, considering that the renewable resources are barely controllable and sometimes unpredictable, some challenges are faced when integrating high shares of renewable sources in power systems. In order to mitigate this problem, this paper presents a decision-making methodology regarding renewable investments. The model computes the optimal renewable generation mix from different available technologies (hydro, wind and photovoltaic) that integrates a given share of renewable sources, minimizing residual demand variability, therefore stabilizing the thermal power generation. The model also includes a spatial optimization of wind farms in order to identify the best distribution of wind capacity. This methodology is applied to the Portuguese power system.