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Hemp and hemp seed press cake­a by-product of hemp oil production­are high-protein, gluten-free raw materials that are often used to enhance the nutritional value of breads. The addition of hemp materials, however, often negatively impacts the technological parameters of breads. Consequently, this study investigated whether and how much the addition of various by-products of hemp seed press cakes to wheat bread mixtures adversely affects the texture and colour profile. The texture profile and colour were determined using a texture analyser and tristimulus measurements. The particle size of raw materials was also measured. Principal component analysis was then used to visualise the correlation between all measured values as well as nutritional parameters. The results showed that the addition of only 1% of some hemp raw materials caused significant technological changes (p > 0.05). Hemp raw materials increased bread hardness and decreased elasticity. The colour of breads containing 1% hemp was also visibly darker than the reference bread. The addition of more hemp led to further darkening and the deterioration of the technological parameters of the products. Consequently, while various hemp materials have high nutritional value, a balance with sensory properties, e.g., textural and colour, has to be reached.

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In this article we extend the framework of explanatory mixed IRT models to a more general class called explanatory additive IRT models. We do this by augmenting the linear predictors in terms of smooth functions. This development offers many new modeling options such as the inclusion of nonlinear covariate effects, the specification of various temporal and spatial dependency patterns, and parameter partitioning across covariates. We use integrated nested Laplace approximation (INLA) for accurate and computationally efficient estimation of the parameters. Uninformative, weakly informative, and informative prior settings for the hyperparameters are discussed. Running time experiments and Monte Carlo parameter recovery simulations are performed in order to study the accuracy and computational efficiency of INLA when applied to the proposed explanatory additive IRT model class. Using a real-life dataset, a variety of application scenarios is explored, and the results are compared with classical maximum likelihood estimation when possible. R code is included in the supplemental materials to allow readers to fully reproduce the examples computed in the paper.

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BACKGROUND: Pregnancy associated cardiovascular pathologies have a significant impact on outcome for mother and child. Bioimpedance cardiography may provide additional outcome-relevant information early in pregnancy and may also be used as a predictive instrument for pregnancy-associated diseases. METHODS: We performed a prospective longitudinal cohort trial in an outpatient setting and included 242 pregnant women. Cardiac output and concomitant hemodynamic data were recorded from 11(th)-13(th) week of gestation every 5(th) week as well as at two occasions post partum employing bioimpedance cardiography. RESULTS: Cardiac output increased during pregnancy and peaked early in the third trimester. A higher heart rate and a decreased systemic vascular resistance were accountable for the observed changes. Women who had a pregnancy-associated disease during a previous pregnancy or developed hypertension or preeclampsia had a significantly increased cardiac output early in pregnancy. Furthermore, an effect of cardiac output on birthweight was found in healthy pregnancies and could be confirmed with multiple linear regression analysis. CONCLUSIONS: Cardiovascular adaptation during pregnancy is characterized by distinct pattern described herein. These may be altered in women at risk for preeclampsia or reduced birthweigth. The assessment of cardiac parameters by bioimpedance cardiography could be performed at low costs without additional risks.

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One of the cornerstones of the R system for statistical computing is the multitude of packages contributed by numerous package authors. This amount of packages makes an extremely broad range of statistical techniques and other quantitative methods freely available. Thus far, no empirical study has investigated psychological factors that drive authors to participate in the R project. This article presents a study of R package authors, collecting data on different types of participation (number of packages, participation in mailing lists, participation in conferences), three psychological scales (types of motivation, psychological values, and work design characteristics), and various socio-demographic factors. The data are analyzed using item response models and subsequent generalized linear models, showing that the most important determinants for participation are a hybrid form of motivation and the social characteristics of the work design. Other factors are found to have less impact or influence only specific aspects of participation.

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PURPOSE: A reduction in quality of life (QoL) during an active phase of disease has been shown for patients with uveitis of various age groups. Adult uveitis patients were shown to also have reduced QoL-values despite disease being quiescent, but adolescents have not been investigated in this respect. METHODS: This clinic-based, cross-sectional pilot study assessed the overall health-related quality of life (HRQoL) in adolescents (11-18 years) with a history of inactive uveitis and good visual function. Two independent generic (non-disease-specific) instruments were applied, the Inventory for Assessing the Quality of Life and the German KINDL-R-questionnaire. RESULTS: Thirty-seven adolescents, 20 females (54%) and 17 males with a mean age of 15 ± 3 years and diagnosis since 5 ± 2 years participated. Subgroups with a reduced HRQoL were those with anterior and intermediate localization of uveitis, bilateral disease, application of immunosuppressive therapy, a short time since diagnosis, and a high number of recurrences. CONCLUSION: Even despite quiescence of disease and good visual function, certain risk factors may result in a decreased HRQoL in juvenile uveitis patients. Our findings support the need of further evaluation of HRQoL in this patient group, the validation of specific psychometric inventories for this disease and the establishment of interdisciplinary collaboration including psychological counselling.

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Carbohydrate-based sensors, that specifically detect sugar binding molecules or cells, are increasingly important in medical diagnostic and drug screening. Here we demonstrate that cantilever arrays functionalized with different mannosides allow the real-time detection of several Escherichia coli strains in solution. Cantilever deflection is thereby dependent on the bacterial strain studied and the glycan used as the sensing molecule. The cantilevers exhibit specific and reproducible deflection with a sensitivity range over four orders of magnitude.

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Carbohydrates are important mediators of many biological processes that underlie cellular communication and disease mechanisms. Therapeutic agents include carbohydrate-based vaccines and the potent anti-viral protein Cyanovirin-N (CV-N). CV-N acts by specifically binding the carbohydrate structures decorating the cell surface of deadly viruses including human immunodeficiency virus (HI-V) or Ebola. In search for new carbohydrate-binding proteins and the development of sensors that exploit carbohydrate-protein interactions the label-free cantilever array technique can provides a fast, parallel and low-cost approach.

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Advances in carbohydrate sequencing technologies have revealed the tremendous complexity of the glycome. This complexity reflects the structural and chemical diversity of carbohydrates and is greater than that of proteins and oligonucleotides. The next step in understanding the biological function of carbohydrates requires the identification and quantification of carbohydrate interactions with other biomolecules, in particular, with proteins. To this end, we have developed a cantilever array biosensor with a self-assembling carbohydrate-based sensing layer that selectively and sensitively detects carbohydrate-protein binding interactions. Specifically, we examined binding of mannosides and the protein cyanovirin-N, which binds and blocks the human immunodeficiency virus (HIV). Cyanovirin-N binding to immobilized oligomannosides on the cantilever resulted in mechanical surface stress that is transduced into a mechanical force and cantilever bending. The degree and duration of cantilever deflection correlates with the interaction's strength, and comparative binding experiments reveal molecular binding preferences. This study establishes that carbohydrate-based cantilever biosensors are a robust, label-free, and scalable means to analyze carbohydrate-protein interactions and to detect picomolar concentrations of carbohydrate-binding proteins.

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An interaction-site model can a priori predict molecular self-organisation on a new substrate in Monte Carlo simulations. This is experimentally confirmed with scanning tunnelling microscopy on Fréchet dendrons of a pentacontane template. Local and global ordering motifs, inclusion molecules and a rotated unit cell are correctly predicted.