Suppression of Superconductivity and Nematic Order in Fe1-ySe1-xSx (0 ≤ x ≤ 1; y ≤ 0.1) Crystals by Anion Height Disorder.

Connections between crystal chemistry and critical temperature Tc have been in the focus of superconductivity, one of the most widely studied phenomena in physics, chemistry, and materials science alike. In most Fe-based superconductors, materials chemistry and physics conspire so that Tc correlates with the average anion height above the Fe plane, i.e., with the geometry of the FeAs4 or FeCh4 (Ch = Te, Se, or S) tetrahedron. By synthesizing Fe1-ySe1-xSx (0 ≤ x ≤ 1; y ≤ 0.1), we find that in alloyed crystals Tc is not correlated with the anion height like it is for most other Fe superconductors. Instead, changes in Tc(x) and tetragonal-to-orthorhombic (nematic) transition Ts(x) upon cooling are correlated with disorder in Fe vibrations in the direction orthogonal to Fe planes, along the crystallographic c-axis. The disorder stems from the random nature of S substitution, causing deformed Fe(Se,S)4 tetrahedra with different Fe-Se and Fe-S bond distances. Our results provide evidence of Tc and Ts suppression by disorder in anion height. The connection to local crystal chemistry may be exploited in computational prediction of new superconducting materials with FeSe/S building blocks.

A new FTIR assay for quantitative measurement of endo-fucoidanase activity.

Endo-fucoidanases, including EC 3.2.1.211 endo-α-1,3-L-fucanase and EC 3.2.1.212 endo-α-1,4-L-fucanase activities, catalyze depolymerization of fucoidans - a group of bioactive, sulfated fucosyl-polysaccharides found primarily in brown macroalgae (brown seaweeds). Quantitative assessment of endo-fucoidanase activity is critical for characterizing endo-fucoidanase kinetics and for comparing the action of different endo-fucoidanases on different types of fucoidans. However, the current state-of-the-art endo-fucoidanase assay consists of a qualitative assessment based on Carbohydrate-Polyacrylamide Gel Electrophoresis. Here, we report a new quantitative endo-fucoidanase assay based on real time spectral evolution profiling of changes in substrate and product during endo-fucoidanase action using Fourier Transform InfraRed spectroscopy (FTIR) combined with Parallel Factor Analysis (PARAFAC). The FTIR-PARAFAC assay was validated by monitoring the reaction progress of three different microbial endo-fucoidanase enzymes, FcnAΔ229, FFA2 and Fhf1Δ470, on two different fucoidan substrates. The substrates were purified from the brown macroalgae Fucus evanescens and Fucus vesiculosus, respectively. The evolution profiling showed that the strongest spectral change of the fucoidans during enzymatic depolymerization occurred in the spectral range 1220-1260 cm-1, but the profiles differed depending on the substrate and the enzyme used. Spectral changes within 1220-1260 cm-1 are in agreement with the enzymatic depolymerization inducing signature changes in the mid-infrared absorption of sulfated fucosyls as sulfate ester bonds and C-O stretching vibrations absorb in this spectral region. Based on the data obtained, we also introduce an activity unit for endo-fucoidanases: One endo-fucoidanase Unit, Uf, is the amount of enzyme able to catalyze a change in the FTIR-PARAFAC score by 0.01 during 498 s of reaction (8.3 min) on 20 g/L pure fucoidan from F. evanescens at 42 °C, pH 7.4, 100 mM NaCl and 10 mM CaCl2. This new quantitative endo-fucoidanase assay can pave the way for better kinetic characterizations as well as novel explorations of endo-fucoidanases.

Spatial patterns of pathogen prevalence in questing Ixodes ricinus nymphs in southern Scandinavia, 2016.

Tick-borne pathogens cause diseases in animals and humans, and tick-borne disease incidence is increasing in many parts of the world. There is a need to assess the distribution of tick-borne pathogens and identify potential risk areas. We collected 29,440 tick nymphs from 50 sites in Scandinavia from August to September, 2016. We tested ticks in a real-time PCR chip, screening for 19 vector-associated pathogens. We analysed spatial patterns, mapped the prevalence of each pathogen and used machine learning algorithms and environmental variables to develop predictive prevalence models. All 50 sites had a pool prevalence of at least 33% for one or more pathogens, the most prevalent being Borrelia afzelii, B. garinii, Rickettsia helvetica, Anaplasma phagocytophilum, and Neoehrlichia mikurensis. There were large differences in pathogen prevalence between sites, but we identified only limited geographical clustering. The prevalence models performed poorly, with only models for R. helvetica and N. mikurensis having moderate predictive power (normalized RMSE from 0.74-0.75, R2 from 0.43-0.48). The poor performance of the majority of our prevalence models suggest that the used environmental and climatic variables alone do not explain pathogen prevalence patterns in Scandinavia, although previously the same variables successfully predicted spatial patterns of ticks in the same area.

Author Correction: Spatial data of Ixodes ricinus instar abundance and nymph pathogen prevalence, Scandinavia, 2016-2017.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Spatial data of Ixodes ricinus instar abundance and nymph pathogen prevalence, Scandinavia, 2016-2017.

Ticks carry pathogens that can cause disease in both animals and humans, and there is a need to monitor the distribution and abundance of ticks and the pathogens they carry to pinpoint potential high risk areas for tick-borne disease transmission. In a joint Scandinavian study, we measured Ixodes ricinus instar abundance at 159 sites in southern Scandinavia in August-September, 2016, and collected 29,440 tick nymphs at 50 of these sites. We additionally measured abundance at 30 sites in August-September, 2017. We tested the 29,440 tick nymphs in pools of 10 in a Fluidigm real-time PCR chip to screen for 17 different tick-associated pathogens, 2 pathogen groups and 3 tick species. We present data on the geolocation, habitat type and instar abundance of the surveyed sites, as well as presence/absence of each pathogen in all analysed pools from the 50 collection sites and individual prevalence for each site. These data can be used alone or in combination with other data for predictive modelling and mapping of high-risk areas.

Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning.

BACKGROUND: Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe. METHODS: We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km2 resolution. We used independent test sets for validation and to assess model performance. RESULTS: The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish differences in abundance between countries but was not able to predict vector abundance at individual farm level. CONCLUSIONS: The models and maps presented here represent an initial attempt to capture large scale geographical and temporal variations in Culicoides abundance. The models are a first step towards producing abundance inputs for R0 modelling of Culicoides-borne infections at a continental scale.

Short-Term Effects of Growth Hormone on Lipolysis, Glucose and Amino Acid Metabolism Assessed in Serum and Microdialysate of Healthy Young Men.

OBJECTIVE: We investigated direct effects of a therapeutic growth hormone dose on lipolysis, glucose and amino acid metabolism. METHODS: This crossover microdialysis trial involved six healthy male volunteers receiving single subcutaneous injections of both growth hormone (0.035 mg/kg) and placebo (0.9% sodium chloride). The investigation comprised three test days with standard diet. The first day served for adaptation, the second and third one for determining study data during 9 night hours with or without growth hormone. Abdominal subcutaneous microdialysate and blood were continuously collected and forwarded to a separate room next door where hourly taken samples were centrifuged and frozen until analysed. RESULTS: Growth hormone achieved the peak serum level after 3 h followed by a plateau-like course for the next 6 h. Glycerol in microdialysate started to rise 2 h following growth hormone injection achieving significance compared to placebo after 9 h (P<0.05). Serum glycerol increased 4 h after growth hormone administration achieving significance after 6 h (P<0.05). Glucose and amino acid concentrations showed neither in microdialysate nor in serum significant differences between growth hormone and placebo. Serum values of insulin and C-peptide revealed no significant difference between growth hormone and placebo. SUMMARY AND CONCLUSION: As the result of a high single subcutaneous dose of GH, persistent lipolysis can be shown in continuously collected microdialysate and blood, but no indication for gluconeogenesis or protein anabolism.

Predicting the spatial abundance of Ixodes ricinus ticks in southern Scandinavia using environmental and climatic data.

Recently, focus on tick-borne diseases has increased as ticks and their pathogens have become widespread and represent a health problem in Europe. Understanding the epidemiology of tick-borne infections requires the ability to predict and map tick abundance. We measured Ixodes ricinus abundance at 159 sites in southern Scandinavia from August-September, 2016. We used field data and environmental variables to develop predictive abundance models using machine learning algorithms, and also tested these models on 2017 data. Larva and nymph abundance models had relatively high predictive power (normalized RMSE from 0.65-0.69, R2 from 0.52-0.58) whereas adult tick models performed poorly (normalized RMSE from 0.94-0.96, R2 from 0.04-0.10). Testing the models on 2017 data produced good results with normalized RMSE values from 0.59-1.13 and R2 from 0.18-0.69. The resulting 2016 maps corresponded well with known tick abundance and distribution in Scandinavia. The models were highly influenced by temperature and vegetation, indicating that climate may be an important driver of I. ricinus distribution and abundance in Scandinavia. Despite varying results, the models predicted abundance in 2017 with high accuracy. The models are a first step towards environmentally driven tick abundance models that can assist in determining risk areas and interpreting human incidence data.

A large-scale screening for the taiga tick, Ixodes persulcatus, and the meadow tick, Dermacentor reticulatus, in southern Scandinavia, 2016.

The taiga tick, Ixodes persulcatus, has previously been limited to eastern Europe and northern Asia, but recently its range has expanded to Finland and northern Sweden. The species is of medical importance, as it, along with a string of other pathogens, may carry the Siberian and Far Eastern subtypes of tick-borne encephalitis virus. These subtypes appear to cause more severe disease, with higher fatality rates than the central European subtype. Until recently, the meadow tick, Dermacentor reticulatus, has been absent from Scandinavia, but has now been detected in Denmark, Norway and Sweden. Dermacentor reticulatus carries, along with other pathogens, Babesia canis and Rickettsia raoultii. Babesia canis causes severe and often fatal canine babesiosis, and R. raoultii may cause disease in humans. We collected 600 tick nymphs from each of 50 randomly selected sites in Denmark, southern Norway and south-eastern Sweden in August-September 2016. We tested pools of 10 nymphs in a Fluidigm real time PCR chip to screen for I. persulcatus and D. reticulatus, as well as tick-borne pathogens. Of all the 30,000 nymphs tested, none were I. persulcatus or D. reticulatus. Our results suggest that I. persulcatus is still limited to the northern parts of Sweden, and have not expanded into southern parts of Scandinavia. According to literature reports and supported by our screening results, D. reticulatus may yet only be an occasional guest in Scandinavia without established populations.

Use of image analysis to understand enzyme stability in an aerated stirred reactor.

Efficient regeneration of NAD(P)+ cofactors is essential for large-scale application of alcohol dehydrogenases due to the high cost and chemical instability of these cofactors. NAD(P)+ can be regenerated effectively using NAD(P)H oxidases (NOXs) that require molecular oxygen as a cosubstrate. In large-scale biocatalytic processes, agitation and aeration are needed for sufficient oxygen transfer into the liquid phase, both of which have been shown to significantly increase the rate of enzyme deactivation. As such, the aim of this study was to identify the existence of a correlation between enzyme stability and gas-liquid interfacial area inside the bioreactor. This was done by measuring gas-liquid interfacial areas inside an aerated stirred reactor, using an in situ optical probe, and simultaneously measuring the kinetic stability of NOXs. Following enzyme incubation at various power inputs and gas-phase compositions, the residual activity was assessed and video samples were analyzed through an image processing algorithm. Enzyme deactivation was found to be proportional to an increase in interfacial area up to a certain limit, where power input appears to have a higher impact. Furthermore, the presence of oxygen increased enzyme deactivation rates at low interfacial areas. The areas were validated with defined glass beads and found to be in the range of those in large-scale bioreactors. Finally, a correlation between the enzyme half-life and specific interfacial area was obtained. Therefore, we conclude that the method developed in this contribution can help to predict the behavior of biocatalyst stability under industrially relevant conditions, concerning specific gas-liquid interfacial areas.

Predicting and mapping human risk of exposure to Ixodes ricinus nymphs using climatic and environmental data, Denmark, Norway and Sweden, 2016.

BackgroundTick-borne diseases have become increasingly common in recent decades and present a health problem in many parts of Europe. Control and prevention of these diseases require a better understanding of vector distribution.AimOur aim was to create a model able to predict the distribution of Ixodes ricinus nymphs in southern Scandinavia and to assess how this relates to risk of human exposure.MethodsWe measured the presence of I. ricinus tick nymphs at 159 stratified random lowland forest and meadow sites in Denmark, Norway and Sweden by dragging 400 m transects from August to September 2016, representing a total distance of 63.6 km. Using climate and remote sensing environmental data and boosted regression tree modelling, we predicted the overall spatial distribution of I. ricinus nymphs in Scandinavia. To assess the potential public health impact, we combined the predicted tick distribution with human density maps to determine the proportion of people at risk.ResultsOur model predicted the spatial distribution of I. ricinus nymphs with a sensitivity of 91% and a specificity of 60%. Temperature was one of the main drivers in the model followed by vegetation cover. Nymphs were restricted to only 17.5% of the modelled area but, respectively, 73.5%, 67.1% and 78.8% of the human populations lived within 5 km of these areas in Denmark, Norway and Sweden.ConclusionThe model suggests that increasing temperatures in the future may expand tick distribution geographically in northern Europe, but this may only affect a small additional proportion of the human population.

Laccase activity measurement by FTIR spectral fingerprinting.

Laccases (EC 1.10.3.2) are enzymes known for their ability to catalyze the oxidation of phenolic compounds using molecular oxygen as the final electron acceptor. Laccase activity is commonly determined by monitoring spectrophotometric changes (absorbance) of the product or substrate during the enzymatic reaction. Fourier Transform Infrared Spectroscopy (FTIR) is a fast and versatile technique where spectral evolution profiling, i.e. assessment of the spectral changes of both substrate and products during enzymatic conversion in real time, can be used to assess enzymatic activity when combined with multivariate data analysis. We employed FTIR to monitor enzymatic oxidation of monolignols (sinapyl, coniferyl and p-coumaryl alcohol), sinapic acid, and sinapic aldehyde by four different laccases: three fungal laccases from Trametes versicolor, Trametes villosa and Ganoderma lucidum, respectively, and one bacterial laccase from Meiothermus ruber. By coupling the FTIR measurements with Parallel Factor Analysis (PARAFAC) we established a quantitative assay for assessing laccase activity. By combining PARAFAC modelling with Principal Component Analysis we show the usefulness of this technology as a multivariate tool able to compare and distinguish different laccase reaction patterns. We also demonstrate how the FTIR approach can be used to create a reference system for laccase activity comparison based on a relatively low number of measurements. Such a reference system has potential to function as a high-throughput method for comparing reaction pattern similarities and differences between laccases and hereby identify new and interesting enzyme candidates in large sampling pools.

Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance.

BACKGROUND: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe. METHODS: We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present. RESULTS: The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups. CONCLUSIONS: The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain.

Simultaneous measurement of two enzyme activities using infrared spectroscopy: A comparative evaluation of PARAFAC, TUCKER and N-PLS modeling.

Enzymes are used in many processes to release fermentable sugars for green production of biofuel, or the refinery of biomass for extraction of functional food ingredients such as pectin or prebiotic oligosaccharides. The complex biomasses may, however, require a multitude of specific enzymes which are active on specific substrates generating a multitude of products. In this paper we use the plant polymer, pectin, to present a method to quantify enzyme activity of two pectolytic enzymes by monitoring their superimposed spectral evolutions simultaneously. The data is analyzed by three chemometric multiway methods, namely PARAFAC, TUCKER3 and N-PLS, to establish simultaneous enzyme activity assays for pectin lyase and pectin methyl esterase. Correlation coefficients Rpred(2) for prediction test sets are 0.48, 0.96 and 0.96 for pectin lyase and 0.70, 0.89 and 0.89 for pectin methyl esterase, respectively. The retrieved models are compared and prediction test sets show that especially TUCKER3 performs well, even in comparison to the supervised regression method N-PLS.

Enzyme activity measurement via spectral evolution profiling and PARAFAC.

The recent advances in multi-way analysis provide new solutions to traditional enzyme activity assessment. In the present study enzyme activity has been determined by monitoring spectral changes of substrates and products in real time. The method relies on measurement of distinct spectral fingerprints of the reaction mixture at specific time points during the course of the whole enzyme catalyzed reaction and employs multi-way analysis to detect the spectral changes. The methodology is demonstrated by spectral evolution profiling of Fourier Transform Infrared (FTIR) spectral fingerprints using parallel factor analysis (PARAFAC) for pectin lyase, glucose oxidase, and a cellulase preparation.

Rapid near infrared spectroscopy for prediction of enzymatic hydrolysis of corn bran after various pretreatments.

Efficient generation of a fermentable hydrolysate is a primary requirement in the utilization of fibrous plant biomass as feedstocks in bioethanol processes. The first biomass conversion step usually involves a hydrothermal pretreatment before enzymatic hydrolysis. The purpose of the pretreatment step is to increase the responsivity of the substrate to enzymatic attack and the type of pretreatment affects the enzymatic conversion efficiency. Destarched corn bran is a fibrous, heteroxylan-rich side-stream from the starch industry which may be used as a feedstock for bioethanol production or as a source of xylose for other purposes. In the present study we demonstrate the use of diffuse reflectance near infrared spectroscopy (NIR) as a rapid and non-destructive analytical tool for evaluation of pretreatment effects on destarched corn bran. NIR was used to achieve classification between 43 differently pretreated corn bran samples using principal component analysis (PCA) and hierarchal clustering algorithms. Quantification of the enzymatically released monosaccharides by HPLC was used to design multivariate calibration models (biPLS) on the NIR spectra. The models could predict the enzymatic release of different levels of arabinose, xylose and glucose from all the differently pretreated destarched corn bran samples. The present study also demonstrates a generic, non-destructive solution to determine the enzymatic monosaccharide release from polymers in biomass side-streams, thereby potentially replacing the cumbersome HPLC analysis.