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Monitoring of flocculation processes such as those used in downstream processing of a fermentation broth is essential for process control. One approach is to apply microscopic imaging combined with image analysis for characterizing the state of the process. In this work, we investigate and compare the use of supervised feedforward convolutional neural network (CNN) architectures to predict the process states from the image information and compare the results with the traditional alternative of characterizing flocs based on manually engineered image features guided by human expertise. From a well‐defined image data set representing six process states, the objective is to establish end‐to‐end classification models which are accurate but at the same time learn meaningful latent variable space representations. Specifically, we evaluate three different CNN architectures with varying degrees of regularization and compare results with logistic regression models based on inputs from two different traditional feature engineering methods. By applying global average pooling as a structural regularizer to the CNN architecture, we significantly improve the generalization performance in comparison with the classification accuracies of the traditional feature engineered models. Furthermore, we show that by imposing a projection to latent structures (PLS) like regularization framework onto the CNN, it can also learn a latent variable representation that mimics the features selected by human expertise. This work explores the use of convolutional neural network (CNN) to monitor industrial flocculation processes, comparing them with traditional methods guided by human expertise. By evaluating different CNN architectures and traditional feature engineering methods, we aim to establish accurate classification models, while learning meaningful and interpretable latent variable space representations. [ABSTRACT FROM AUTHOR]

Validation of international food products has become increasingly important due to the proliferation of food products shipped globally. This study classified extra virgin olive oils from Portugal and Turkey using [...]

We compare the application of different modeling strategies in order to predict physical properties of five different industrial pectin formulations based on near‐infrared spectral data. Methods from the chemometric toolbox, such as partial least squares regression (PLS1 and PLS2) and ridge regression, were employed and compared to the performance of a 1‐D convolutional neural network (CNN). The pectin formulations were modeled in two major scenarios, individually using local models, and jointly using global models, which resulted in better prediction performance of the 1‐D CNN. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pectin production is complex, and final product quality assessment is generally accomplished at the end of the process using time-consuming off-line laboratory analysis. In this study, pectin was extracted from lime peel either by acid or by enzymes. Fourier transform infrared spectroscopy and carbohydrate microarray analysis were performed directly on the crude lime peel extracts during the time course of the extractions. Multivariate analysis of the data was carried out to predict final pectin yields. Fourier transform infrared spectroscopy (FTIR) was found applicable for determining the optimal extraction time for the enzymatic and acidic extraction processes, respectively. The combined results of FTIR and carbohydrate microarray analysis suggested major differences in the crude pectin extracts obtained by enzymatic and acid extraction, respectively. Enzymatically extracted pectin, thus, showed a higher degree of esterification (DE 82 %) than pectin extracted by acid (DE 67 %) and was moreover found to be more heterogeneously esterified when probed with the monoclonal antibodies JIM5, JIM7, and LM20. The data infer that enzymatic pectin extraction allows for extraction of complex, high DE pectin, and that FTIR and carbohydrate microarray analysis have potential to be developed into online process analysis tools for prediction of pectin extraction yields and pectin features from measurements on crude pectin extracts. [ABSTRACT FROM AUTHOR]

Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in order to gap-fill missing information. EM Tucker belongs to a family of tensor decomposition methods that are known to offer a number of interesting properties, including the ability to directly analyze data stored in multidimensional arrays and to explicitly exploit their multiway structure, which is lost when traditional spatial-, temporal- and spectral-based methods are used. In order to evaluate the gap-filling accuracy of EM Tucker for NDVI images, we used three data sets based on advanced very-high resolution radiometer (AVHRR) imagery over the Iberian Peninsula with artificially added missing data as well as a data set originating from the Iberian Peninsula with natural missing data. The performance of EM Tucker was compared to a simple mean imputation, a spatio-temporal hybrid method, and an iterative method based on principal component analysis (PCA). In comparison, imputation of the missing data using EM Tucker consistently yielded the most accurate results across the three simulated data sets, with levels of missing data ranging from 10 to 90%. [ABSTRACT FROM AUTHOR]

During water stress, crops undertake adjustments in functional, structural, and biochemical traits. Hyperspectral data and machine learning techniques (PLS-R) can be used to assess water stress responses in plant physiology. In this study, we investigated the potential of hyperspectral optical (VNIR) measurements supplemented with thermal remote sensing and canopy height (hc) to detect changes in leaf physiology of soybean (C3) and maize (C4) plants under three levels of soil moisture in controlled environmental conditions. We measured canopy evapotranspiration (ET), leaf transpiration (Tr), leaf stomatal conductance (gs), leaf photosynthesis (A), leaf chlorophyll content and morphological properties (hc and LAI), as well as vegetation cover reflectance and radiometric temperature (TL,Rad). Our results showed that water stress caused significant ET decreases in both crops. This reduction was linked to tighter stomatal control for soybean plants, whereas LAI changes were the primary control on maize ET. Spectral vegetation indices (VIs) and TL,Rad were able to track these different responses to drought, but only after controlling for confounding changes in phenology. PLS-R modeling of gs, Tr, and A using hyperspectral data was more accurate when pooling data from both crops together rather than individually. Nonetheless, separated PLS-R crop models are useful to identify the most relevant variables in each crop such as TL,Rad for soybean and hc for maize under our experimental conditions. Interestingly, the most important spectral bands sensitive to drought, derived from PLS-R analysis, were not exactly centered at the same wavelengths of the studied VIs sensitive to drought, highlighting the benefit of having contiguous narrow spectral bands to predict leaf physiology and suggesting different wavelength combinations based on crop type. Our results are only a first but a promising step towards larger scale remote sensing applications (e.g., airborne and satellite). PLS-R estimates of leaf physiology could help to parameterize canopy level GPP or ET models and to identify different photosynthetic paths or the degree of stomatal closure in response to drought. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Resolving the microscopic pairing mechanism and its experimental identification in unconventional superconductors is among the most vexing problems of contemporary condensed matter physics. We show that Raman spectroscopy provides an avenue towards this aim by probing the structure of the pairing interaction at play in an unconventional superconductor. As we study the spectra of the prototypical Fe-based superconductor Ba1−xKxFe2As2 for 0.22 ≤ x ≤ 0.70 in all symmetry channels, Raman spectroscopy allows us to distill the leading s-wave state. In addition, the spectra collected in the B1g symmetry channel reveal the existence of two collective modes which are indicative of the presence of two competing, yet sub-dominant, pairing tendencies of d x 2 - y 2 symmetry type. A comprehensive functional Renormalization Group and random-phase approximation study on this compound confirms the presence of the two sub-leading channels, and consistently matches the experimental doping dependence of the related modes. The consistency between the experimental observations and the theoretical modeling suggests that spin fluctuations play a significant role in superconducting pairing. Iron-based superconductors: competing pairing interactions Two collective Raman modes are observed in an iron-based superconductor, indicative of the presence of two competing pairing tendencies alongside the dominant s-wave state. An international team led by R. Hackl from the Walther Meissner Institut perform Raman spectroscopy measurements to probe the structure of pairing interactions in Ba1−xKxFe2As2 for 0.22 ≤ x ≤ 0.70 for all symmetry channels. The Raman spectra not only shows the dominant peak marking the dominant s-wave superconducting pairing state, but also reveals the existence of two collective modes in the B1g symmetry channel, indicative of two competing, sub-dominant, paring tendencies of d x 2 - y 2 symmetry type. Numerical calculations confirm the finding and consistently match the doping dependencies of the related modes. The results suggest a significant role of spin-fluctuations in superconducting pairing. [ABSTRACT FROM AUTHOR]

Connections between crystal chemistry and critical temperature T c 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 T c correlates with the average anion height above the Fe plane, i.e., with the geometry of the FeAs 4 or FeCh 4 (Ch = Te, Se, or S) tetrahedron. By synthesizing Fe 1- y Se 1 -x S x (0 ≤ x ≤ 1; y ≤ 0.1), we find that in alloyed crystals T c is not correlated with the anion height like it is for most other Fe superconductors. Instead, changes in T c ( x ) and tetragonal-to-orthorhombic (nematic) transition T s ( 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 T c and T s 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.

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.