Exploiting Milling By-Products in Bread-Making : The Case of Sprouted Wheat

This research investigated the effect of sprouting on wheat bran. Bran from un-sprouted (BUW) and sprouted (BSW) wheat were characterized in terms of chemical composition, enzymatic activities, and hydration properties. In addition, the rheological properties (using GlutoPeak, Farinograph, Extensograph, and Rheofermentometer tests) and bread-making performance (color, texture, volume of bread) of wheat doughs enriched in bran at 20% replacement level were assessed. Sprouting process caused a significant decrease in phytic acid (~20%), insoluble dietary fiber (~11%), and water holding capacity (~8%), whereas simple sugars (~133%) and enzymatic activities significantly increased after processing. As regards the gluten aggregation kinetics, the BSW-blend profile was more similar to wheat than BUW-blend, indicating changes in the fiber and gluten interactions. BSW led to a worsening of the mixing and leavening properties, instead, no significant changes in extensibility were observed. Finally, BSW improved bread volume (~10%) and crumb softness (~52%). Exploiting bran from sprouted wheat might be useful to produce bread rich in fiber with enhanced characteristics

Flour from sprouted wheat as a new ingredient in bread-making

Despite the nutritional and sensory improvements associated with sprouted grains, their use in baking has been limited until recently. Indeed, severe and uncontrolled grain sprouting induces high accumulations of enzymatic activities that negatively affect dough rheology and baking performance. In this study, wheat was sprouted under controlled conditions and the effects of enrichment (i.e. 15%, 25%, 33%, 50%, 75% and 100%) of the related refined flour (SWF) on dough rheological properties, baking performances and starch digestibility were assessed. Adding SWF to flour significantly decreased dough water absorption, development time, and stability during mixing, which suggests a weakening of the gluten network. However, no significant changes in mixing properties and gluten aggregation kinetics were measured from 25 to 75% SWF. Regardless of the amount added, SWF improved dough development and gas production during leavening. Decreases in gas retention did not compromise bread-making performances. The best result – in terms of bread volume and crumb porosity – was obtained with 50% SWF instead of using SWF alone. Interestingly, in 100 % SWF bread the slowly digestible starch fraction significantly increased

Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle \u201ccomplex data\u201d, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences

Benchmark of quasi-linear models against gyrokinetic single scale simulations in deuterium and tritium plasmas for a JET high beta hybrid discharge

A benchmark of the reduced quasi-linear models QuaLiKiz and TGLF with GENE gyrokinetic simulations has been performed for parameters corresponding to a JET high performance hybrid pulse in deuterium. Given the importance of the study of such advanced scenarios in view of ITER and DEMO operations, the dependence of the transport on the ion isotope mass has also been assessed, by repeating the benchmark changing the ion isotope to tritium. TGLF agrees better with GENE on the linear spectra and the flux levels. However, concerning the isotope dependence, only QuaLiKiz reproduces the GENE radial trend of a basically gyro-Bohm (gB) scaling at inner radii and instead anti-gB at outer radii. The physics effects which are responsible of the antigB effect in GENE simulations have been singled out

Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis

In this work, near infrared (NIR) spectroscopy and multivariate data analysis were investigated as a fast and non disruptive method to classify green coffee beans on continents and countries bases. FT-NIR spectra of 191 coffee samples, origin from 2 continents and 9 countries, were acquired by two different laboratories. Laboratory-independent Partial Least Square-Discriminant Analysis and interval PIS-DA models were developed by following a hierarchical approach, i.e. considering at first the continent and then the country of origin as discrimination rule. The best continent-based classification model was able to identify correctly more than 98% in prediction, whereas 100% of them were correctly predicted by the best country-based classification model. The inter-laboratory reliability of the proposed method was confirmed by McNemar test, since no significant differences (P > 0.05) were found. Furthermore, a validation was performed predicting the spectral test set of a laboratory using the model developed by the other one

Preserving the Integrity of Liposomes Prepared by Ethanol Injection upon Freeze-Drying: Insights from Combined Molecular Dynamics Simulations and Experimental Data

The freeze-drying of complex formulations, such as liposomes, is challenging, particularly if dispersions contain residual organic solvents. This work aimed to investigate the effects of possible protectants, namely sucrose, trehalose and/or poly(vinyl pyrrolidone) (PVP), on the main features of the dried product using a 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC)-based liposomal dispersion prepared by ethanol injection and containing ethanol up to 6%, as a model. The interactions among vesicles and protectants were preliminary screened by Molecular Dynamics (MD) simulations, which have been proved useful in rationalizing the selection of protectant(s). The freeze-drying protocol was based on calorimetric results. Overall data suggested a stronger cryo-protectant effect of trehalose, compared with sucrose, due to stronger interactions with the DPPC bilayer and the formation of highly ordered clusters around the lipids. The effect further improved in the presence of PVP. Differently from the other tested protectants, the selected trehalose/PVP combination allows to preserve liposome size, even in the presence of 6% ethanol, as demonstrated by Nanoparticle Tracking Analysis (NTA). Nevertheless, it should be also underlined that cakes blew out at an ethanol concentration higher than 1% v/v, probably due to the poor cohesion within the cake and solvent vapour pressure upon sublimation

A bit stickier, a bit slower, a lot stiffer: Specific vs. nonspecific binding of gal4 to dna

Transcription factors regulate gene activity by binding specific regions of genomic DNA thanks to a subtle interplay of specific and nonspecific interactions that is challenging to quantify. Here, we exploit Reflective Phantom Interface (RPI), a label-free biosensor based on optical reflectivity, to investigate the binding of the N-terminal domain of Gal4, a well-known gene regulator, to double-stranded DNA fragments containing or not its consensus sequence. The analysis of RPI-binding curves provides interaction strength and kinetics and their dependence on temperature and ionic strength. We found that the binding of Gal4 to its cognate site is stronger, as expected, but also markedly slower. We performed a combined analysis of specific and nonspecific binding— equilibrium and kinetics—by means of a simple model based on nested potential wells and found that the free energy gap between specific and nonspecific binding is of the order of one kcal/mol only. We investigated the origin of such a small value by performing all-atom molecular dynamics simulations of Gal4–DNA interactions. We found a strong enthalpy–entropy compensation, by which the binding of Gal4 to its cognate sequence entails a DNA bending and a striking conformational freezing, which could be instrumental in the biological function of Gal4

The Parkinson-related E193K LRRK2 variant impacts neuronal vesicles dynamics through perturbed protein interactions

The Leucine-Rich Repeat Kinase 2 (LRRK2) is a complex protein, expressed in neurons and implicated in Parkinson disease (PD). LRRK2 contains a dual enzymatic activity and several structural domains that constitute a versatile platform for multiple protein interactions at the synapses. In this study, we characterize the functional role of the N-terminal Armadillo repeats domain of LRRK2 and the impact on synaptic vesicle (SV) dynamics of a novel variant, E193K, harboured within this domain and identified in an Italian family affected by PD. Using a genetically encoded sensor of recycling, synaptopHluorine, and total internal reflection fluorescence microscopy, we visualized SV trafficking in the N2A neuroblastoma cells expressing the wild type LRRK2 protein, a mutant lacking the Armadillo domain (\u394N LRRK2) or the E193K variant. We found that expression of the \u394N construct increased the frequency and the amplitude of spontaneous synaptic events. A similar phenotype was detected in the presence of the E193K variant, suggesting that this mutation behaves as a loss-of-function mutation. A domain-based pulldown approach demonstrated that the LRRK2 N-terminus binds to cytoskeletal (\u3b2-actin and \u3b1-tubulin) and SV (synapsin I) proteins and the E193K substitution alters strength and quality of LRRK2 interactions. The results support a role of the Armadillo domain in interaction with synaptic proteins and suggest that the E193K mutation affects LRRK2 function via perturbation of its physiological network of interactors, resulting in impaired vesicular trafficking. These findings may have important implications for understanding the role of LRRK2 at the synapses and the pathophysiological mechanism for LRRK2-linked disease