Stir bar sorptive extraction and trace analysis of selected endocrine disruptors in water, biosolids and sludge samples by thermal desorption with gas chromatography-mass spectrometry

Stir bar sorptive extraction (SBSE) in combination with thermal desorption coupled to gas chromatography-mass spectrometry (GC-MS) was successfully applied to analyze a range of endocrine disrupting compounds (EDCs) in wastewater, solids and sludge. The targeted EDCs include sex steroid hormones, phthalates, alkylphenols and tamoxifen. Recovery for the EDCs using this analytical technique ranged from 44% to 128%. Limit of detection (LOD) of the compounds was 2.0 ng/L for water samples and 0.02 ng/g for solid samples, whereas the limit of quantitation (LOQ) was 5.0 ng/L for water samples and 0.06 ng/g for solid samples. When this analytical technique was applied to measure EDC concentration in a biological nutrient removal (BNR) wastewater treatment plant located in South East Queensland, Australia, the results showed that there were high amounts of phthalates, alkylphenols and female hormones present in the raw influent wastewater and solids. These concentrations were dramatically reduced after passing through the various treatment zones of the bioreactor (anaerobic, aerobic and anoxic).     

Increasing Role of Targeted Immunotherapies in the Treatment of AML

Acute myeloid leukemia (AML) is the most common acute leukemia in adults. The standard of care in medically and physically fit patients is intensive induction therapy. The majority of these intensively treated patients achieve a complete remission. However, a high number of these patients will experience relapse. In patients older than 60 years, the results are even worse. Therefore, new therapeutic approaches are desperately needed. One promising approach in high-risk leukemia to prevent relapse is the induction of the immune system simultaneously or after reduction of the initial tumor burden. Different immunotherapeutic approaches such as allogenic stem cell transplantation or donor lymphocyte infusions are already standard therapies, but other options for AML treatment are in the pipeline. Moreover, the therapeutic landscape in AML is rapidly changing, and in the last years, a number of immunogenic targets structures eligible for specific therapy, risk assessment or evaluation of disease course were determined. For example, leukemia-associated antigens (LAA) showed to be critical as biomarkers of disease state and survival, as well as markers of minimal residual disease (MRD). Yet many mechanisms and properties are still insufficiently understood, which also represents a great potential for this form of therapy. Therefore, targeted therapy as immunotherapy could turn into an efficient tool to clear residual disease, improve the outcome of AML patients and reduce the relapse risk. In this review, established but also emerging immunotherapeutic approaches for AML patients will be discussed.     

Fault diagnosis of nonlinear systems using structured augmented state models

This paper presents an internal model approach for modeling and diagnostic functionality design for nonlinear systems operating subject to single- and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems that are switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and exemplified for the fault diagnosis of the well-known three-tank benchmark. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance.     

Energy-aware resource sharing with mobile devices

Ad hoc sharing of resources by offering remote services through an appropriate infrastructure enables new applications for mobile devices. However, the willingness of device owners to contribute resources to such applications remains low as long as they cannot control the amount of energy spent in sharing. In this paper, we present a framework for energy-aware resource sharing among mobile devices of various kinds that comprises (1) energy-aware strategies for selecting remote service providers and (2) a generic energy estimator for forecasting and accounting the energy consumption of a remote service call. To illustrate the benefit of (1), we show by simulation that the battery lifetime of devices running the framework can be extended up to 40% by service selection strategies that take into account the energy cost of a requested service compared to energy-unaware (random) service selection. For providing the energy-related input for service selection, we present (2) a generic estimator that can be customized easily for different hardware-platforms by solving a linear equation system with coefficients derived from benchmark measurements. We present a prototype-based case study for three different platforms, the Nokia N810, the HTC Touch Cruise and the Samsung Galaxy S showing that for all of them the estimation error is below 10% for 90% of the service calls. Furthermore, measurements conducted with a prototype implementation of the resource sharing framework show that battery lifetime can in fact be extended by energy-aware service selection strategies.     

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated K_i ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.     

On Floating‐Point Normal Vectors

In this paper we analyze normal vector representations. We derive the error of the most widely used representation, namely 3D floating‐point normal vectors. Based on this analysis, we show that, in theory, the discretization error inherent to single precision floating‐point normals can be achieved by 2^50.2 uniformly distributed normals, addressable by 51 bits. We review common sphere parameterizations and show that octahedron normal vectors perform best: they are fast and stable to compute, have a controllable error, and require only 1 bit more than the theoretical optimal discretization with the same error.     

Simulation of Fluid Flow and Oscillation of the Argon Oxygen Decarburization (AOD) Process

The oscillation of argon oxygen decarburization (AOD) converters is flow related and depends on the process parameters (e.g., vessel geometry, melt fill height, process gas type and blowing rate, vessel tilting angle, as well as geometry, number, and arrangement of the side-wall nozzles). For a 120-ton AOD converter with seven submerged side-wall nozzles, plant tests, physical simulations on a 1:4 scale water model, and computational fluid dynamics simulations have been done. The investigations show that the penetration depth of an inert gas jet into the melt does not exceed approximately 0.4 m. The plumes are located close to the nozzle-side converter wall and induce a large-scale primary vortex as well as intensive surface movements; both are responsible for the oscillation. Several process mechanisms were investigated. The oscillation is highest in the last stage of the dynamic blow and is still high during the reduction stage. As the amount of inert gas increases, the vibration level also increases. Inert gas has a greater influence on the oscillation than oxygen. Tilting the converter around 8 deg clearly leads to more intensive oscillations. Increasing the blowing rate increases the forces and torques acting on the vessel, whereas the oscillation frequency remains nearly constant. A varying fill level does not influence the vibration level the same way as the blowing rate. The operational test shows, for example, that the maximum torque does not depend on the heat size when the latter varies between –8 pct and +21 pct of the nominal heat size. The water model test shows decreasing forces and torques with a rising fill level.