Synthesis of Light Hydrocarbons from Biogas and Hydrogen: Investigation of a Fe-Mn-K/MgO Catalyst

Fischer-Tropsch synthesis of the CO₂ in biogas aims at producing light hydrocarbons and increasing its calorific value for feeding into the grid. Fe catalysts with Mn and K as promoters are supposed to yield high amounts of light hydrocarbons. Using a Fe-Mn-K/MgO catalyst, a parameter screening and long-term experiments were carried out. The catalyst shows, within the examined range, the highest selectivity to C₂–C₄ hydrocarbons at 450 °C, 8 bar(a), and a gas hourly space velocity of 350 h⁻¹. Calcination of the catalyst resulted in a significant drop of activity and an almost complete loss of selectivity to hydrocarbons. Admixture of steam to the reactant gas lowers the tendency to carbon deposition but also promotes the water-gas shift reaction and results in lower yields of hydrocarbons.     

Crossing the Border: From Keto- to Imine Reduction in Short-Chain Dehydrogenases/Reductases

The family of NAD(P)H-dependent short-chain dehydrogenases/reductases (SDRs) comprises numerous biocatalysts capable of C=O or C=C reduction. The highly homologous noroxomaritidine reductase (NR) from Narcissus sp. aff. pseudonarcissus and Zt_SDR from Zephyranthes treatiae, however, are SDRs with an extended imine substrate scope. Comparison with a similar SDR from Asparagus officinalis (Ao_SDR) exhibiting keto-reducing activity, yet negligible imine-reducing capability, and mining the Short-Chain Dehydrogenase/Reductase Engineering Database indicated that NR and Zt_SDR possess a unique active-site composition among SDRs. Adapting the active site of Ao_SDR accordingly improved its imine-reducing capability. By applying the same strategy, an unrelated SDR from Methylobacterium sp. 77 (M77_SDR) with distinct keto-reducing activity was engineered into a promiscuous enzyme with imine-reducing activity, thereby confirming that the ability to reduce imines can be rationally introduced into members of the “classical” SDR enzyme family. Thus, members of the SDR family could be a promising starting point for protein approaches to generate new imine-reducing enzymes.     

Analysis of Cerebrospinal Fluid Extracellular Vesicles by Proximity Extension Assay: A Comparative Study of Four Isolation Kits

There is a lack of reliable biomarkers for disorders of the central nervous system (CNS), and diagnostics still heavily rely on symptoms that are both subjective and difficult to quantify. The cerebrospinal fluid (CSF) is a promising source of biomarkers due to its close connection to the CNS. Extracellular vesicles are actively secreted by cells, and proteomic analysis of CSF extracellular vesicles (EVs) and their molecular composition likely reflects changes in the CNS to a higher extent compared with total CSF, especially in the case of neuroinflammation, which could increase blood–brain barrier permeability and cause an influx of plasma proteins into the CSF. We used proximity extension assay for proteomic analysis due to its high sensitivity. We believe that this methodology could be useful for de novo biomarker discovery for several CNS diseases. We compared four commercially available kits for EV isolation: MagCapture and ExoIntact (based on magnetic beads), EVSecond L70 (size-exclusion chromatography), and exoEasy (membrane affinity). The isolated EVs were characterized by nanoparticle tracking analysis, ELISA (CD63, CD81 and albumin), and proximity extension assay (PEA) using two different panels, each consisting of 92 markers. The exoEasy samples did not pass the built-in quality controls and were excluded from downstream analysis. The number of detectable proteins in the ExoIntact samples was considerably higher (~150% for the cardiovascular III panel and ~320% for the cell regulation panel) compared with other groups. ExoIntact also showed the highest intersample correlation with an average Pearson’s correlation coefficient of 0.991 compared with 0.985 and 0.927 for MagCapture and EVSecond, respectively. The median coefficient of variation was 5%, 8%, and 22% for ExoIntact, MagCapture, and EVSecond, respectively. Comparing total CSF and ExoIntact samples revealed 70 differentially expressed proteins in the cardiovascular III panel and 17 in the cell regulation panel. To our knowledge, this is the first time that CSF EVs were analyzed by PEA. In conclusion, analysis of CSF EVs by PEA is feasible, and different isolation kits give distinct results, with ExoIntact showing the highest number of identified proteins with the lowest variability.     

Requirements engineering in health care: the example of chemotherapy planning in paediatric oncology

Health care is characterized by highly complex processes of patient care that require unusual amount of communication between different health care professionals of different institutions. Sub-optimal processes can significantly impact on the patient’s health, increase the consumption of services and resources and in severe cases can lead to the patient death. For these reasons, requirements engineering for the development of information technology in health care is a complex process as well: without constant and rigorous evaluation, the impact of new systems on the quality of care is unknown and it is possible that badly designed systems significantly harm patients. To overcome these limitations, we present and discuss an approach to requirements engineering that we applied for the development of applications for chemotherapy planning in paediatric oncology. Chemotherapy planning in paediatric oncology is complex and time-consuming and errors must be avoided by all means. In the multi-hospital/multi-trial-centre environment of paediatric oncology, it is especially difficult and time-consuming to analyse requirements. Our approach combines a grounded theory approach with evolutionary prototyping based on the constant development and refinement of a generic domain model, in this case a domain model for chemotherapy planning in paediatric oncology. The prototypes were introduced in medical centres and final results show that the developed generic domain model is adequate.     

Empirical efficiency and volume relationships for HPGe detectors

We have extended the empirical work of Vano et al.[1] relating the slope of the detector efficiency curve to the active volume for Ge detectors. The analysis was carried out using Monte Carlo techniques and covered a wide range of incident energies (200 keV-20 MeV) and active volumes (19.6 cm³–396 cm³). It is shown that the expression of Vano et al.[1] is only valid over the energy range 200 keV-3 MeV for active volumes <50 cm³. The upper bound decreases to 2 MeV for volumes of a few hundred cm³. The usable energy range can, however, be extended to 6 MeV by introducing higher order terms into the polynomial. Above this energy, the shape of the efficiency curve is better described by a non-linear function since linear forms fail simultaneously to fit large active volumes and high energies. We therefore propose a composite function which reduces to the form given in Vano et al. in the low energy/active volume limit. By comparison with the Monte Carlo results, it is estimated that relative efficiencies can be calculated to within 6% over the energy range 200 keV-20 MeV and active volumes 20 cm³–400 cm³. Since the largest errors occur for the smallest volumes, we recommend that for energies <3 MeV a two-fold approach be followed, i.e. using the expression of Vano et al.[1] for active volumes less than 50 cm³ and the proposed non-linear form for larger volumes. For high energy work (E > 3 MeV), we advocate the non-linear form. In this way, average errors can be kept 3%. Finally, we point out that the real power of the expression of Vano et al. lies not in predicting efficiencies, but active volumes.