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Parchment was used as a writing material in the Middle Ages and was made using animal skins by liming them with Ca(OH)2. During liming, collagen peptides containing Glutamine (Q) undergo deamidation resulting in a mass shift of 0.984 Da. Assessing the extent of deamidation can inform us about parchment production patterns and quality. In this study, we propose a simple three-step workflow, developed as an R package called MALDIpqi(), to estimate deamidation in parchment derived collagen using low-resolution MALDI-TOF spectra. After pre-processing raw spectra, we used weighted least-squares linear regression to estimate Q deamidation levels from the convoluted isotopic envelope for seven collagen-peptide markers. Finally, we employed a linear mixed effect model to predict the overall deamidation level of a parchment sample termed Parchment Glutamine Index (PQI). To test the robustness of the workflow, we applied MALDIpqi() to previously published ZooMS data generated from almost an entire library of the Cistercian monastery at Orval Abbey, Belgium. In addition to reliably predicting PQI, we observed interesting patterns pertaining to parchment production. MALDIpqi() holds excellent potential for biocodicological and other archaeological studies involving collagen, such as bone, but we also foresee its application in the food and biomedical industry.

Deep rooting winter wheat genotypes can reduce nitrate leaching losses and increase N uptake. We aimed to investigate which deep root traits are correlated to deep N uptake and to estimate genetic variation in root traits and deep 15N tracer uptake. In 2 years, winter wheat genotypes were grown in RadiMax, a semifield root‐screening facility. Minirhizotron root imaging was performed three times during the main growing season. At anthesis, 15N was injected via subsurface drip irrigation at 1.8 m depth. Mature ears from above the injection area were analysed for 15N content. From minirhizotron image‐based root length data, 82 traits were constructed, describing root depth, density, distribution and growth aspects. Their ability to predict 15N uptake was analysed with the least absolute shrinkage and selection operator (LASSO) regression. Root traits predicted 24% and 14% of tracer uptake variation in 2 years. Both root traits and genotype showed significant effects on tracer uptake. In 2018, genotype and the three LASSO‐selected root traits predicted 41% of the variation in tracer uptake, in 2019 genotype and one root trait predicted 48%. In both years, one root trait significantly mediated the genotype effect on tracer uptake. Deep root traits from minirhizotron images can predict deep N uptake, indicating the potential to breed deep‐N‐uptake‐genotypes. Summary Statement: Root traits of winter wheat were constructed from minirhizotron image data. Root traits predicted 24% of 15N tracer uptake, which was injected at anthesis at 1.8 m depth, and explained genotypic variation in tracer uptake. [ABSTRACT FROM AUTHOR]

Datasets are sometimes divided into distinct subsets, e.g. due to multi-center sampling, or to variations in instruments, questionnaire item ordering or mode of administration, and the data analyst then needs to assess whether a joint analysis is meaningful. The Principal Component Analysis-based Data Structure Comparisons (PCADSC) tools are three new non-parametric, visual diagnostic tools for investigating differences in structure for two subsets of a dataset through covariance matrix comparisons by use of principal component analysis. The PCADCS tools are demonstrated in a data example using European Social Survey data on psychological well-being in three countries, Denmark, Sweden, and Bulgaria. The data structures are found to be different in Denmark and Bulgaria, and thus a comparison of for example mean psychological well-being scores is not meaningful. However, when comparing Denmark and Sweden, very similar data structures, and thus comparable concepts of well-being, are found. Therefore, inter-country comparisons are warranted for these countries. [ABSTRACT FROM AUTHOR]

Analyzing the progression of Alzheimer's disease (AD) is challenging due to lacking sensitivity in currently available measures. AD stages are typically defined based on cognitive cut-offs, but this results in heterogeneous patient groups. More accurate modeling of the continuous progression of the disease would enable more accurate patient prognosis. To address these issues, we propose a new multivariate continuous-time disease progression (MCDP) model. The model is formulated as a nonlinear mixed-effects model that aligns patients based on their predicted disease progression along a continuous latent disease timeline. The model is evaluated using long-term follow-up data from 2152 participants in the Alzheimer's Disease Neuroimaging Initiative. The MCDP model was used to simultaneously model three cognitive scales; the Alzheimer's Disease Assessment Scale-cognitive subscale, the Mini-Mental State Examination, and the Clinical Dementia Rating scale-sum of boxes. Compared with univariate modeling and previously proposed multivariate disease progression models, the MCDP model showed superior ability to predict future patient trajectories. Finally, based on the multivariate disease timeline estimated using the MCDP model, the sensitivity of the individual items of the cognitive scales along the different stages of disease was analyzed. The analysis showed that delayed memory recall items had the highest sensitivity in the early stages of disease, whereas language and attention items were sensitive later in disease. [ABSTRACT FROM AUTHOR]

Despite Ghana's large contribution to global cocoa production, average yields are low. Policymakers and practitioners are calling for implementation of a climate smart strategy by increasing yields and augmenting shade tree cover in cocoa systems. However, there have been few studies in West Africa on the relationship between shade and cocoa yield under field conditions. The objective of this study was to determine the effect of shade tree cover and other factors on on-farm cocoa yields over a four-year period. The study was conducted on 86 farm plots of 8–28 years' cocoa trees with varied canopy cover (CC) in Ashanti and Western regions of Ghana. A linear mixed model analysis showed that yields increased significantly with increased CC of shade trees, and indicated a doubling of yields when going from zero to approximately 30% crown cover. Fertilizer use gave a yield increase of 7%. Farms located in Western region had higher yields compared to Ashanti, and cocoa systems on short fallows had lower yields than farms cultivated on recent forest clearings and old fallows. Fungicide use, seed sources and land ownership had no significant effects on yield. We conclude that for a sustainable climate-smart cocoa agenda, promotion of shade trees is key. [ABSTRACT FROM AUTHOR]

A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are mutually confusable and hard to see. The model assumes that the visual system's initial sensory response consists in tentative visual categorizations, which are accumulated by leaky integration of both transient and sustained components comparable with those found in spike density patterns of early sensory neurons. The sensory response (tentative categorizations) feeds independent Poisson counters, each of which accumulates tentative object categorizations of a particular type to guide overt identification performance. We tested the model's ability to predict the effect of stimulus duration on observed distributions of responses in a nonspeeded (pure accuracy) identification task with eight response alternatives. The time courses of correct and erroneous categorizations were well accounted for when the event-rates of competing Poisson counters were allowed to vary independently over time in a way that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model provided an explanation for Bloch's law. [ABSTRACT FROM AUTHOR]