Characterization and relative quantitation of wheat, rye, and barley gluten protein types by liquid chromatography–tandem mass spectrometry

The consumption of wheat, rye, and barley may cause adverse reactions to wheat such as celiac disease, non-celiac gluten/wheat sensitivity, or wheat allergy. The storage proteins (gluten) are known as major triggers, but also other functional protein groups such as α-amylase/trypsin-inhibitors or enzymes are possibly harmful for people suffering of adverse reactions to wheat. Gluten is widely used as a collective term for the complex protein mixture of wheat, rye or barley and can be subdivided into the following gluten protein types (GPTs): α-gliadins, γ-gliadins, ω5-gliadins, ω1,2-gliadins, high- and low-molecular-weight glutenin subunits of wheat, ω-secalins, high-molecular-weight secalins, γ-75k-secalins and γ-40k-secalins of rye, and C-hordeins, γ-hordeins, B-hordeins, and D-hordeins of barley. GPTs isolated from the flours are useful as reference materials for clinical studies, diagnostics or in food analyses and to elucidate disease mechanisms. A combined strategy of protein separation according to solubility followed by preparative reversed-phase high-performance liquid chromatography was employed to purify the GPTs according to hydrophobicity. Due to the heterogeneity of gluten proteins and their partly polymeric nature, it is a challenge to obtain highly purified GPTs with only one protein group. Therefore, it is essential to characterize and identify the proteins and their proportions in each GPT. In this study, the complexity of gluten from wheat, rye, and barley was demonstrated by identification of the individual proteins employing an undirected proteomics strategy involving liquid chromatography–tandem mass spectrometry of tryptic and chymotryptic hydrolysates of the GPTs. Different protein groups were obtained and the relative composition of the GPTs was revealed. Multiple reaction monitoring liquid chromatography–tandem mass spectrometry was used for the relative quantitation of the most abundant gluten proteins. These analyses also allowed the identification of known wheat allergens and celiac disease-active peptides. Combined with functional assays, these findings may shed light on the mechanisms of gluten/wheat-related disorders and may be useful to characterize reference materials for analytical or diagnostic assays more precisely

Influence of molecular weight on the phase behavior and structure formation of branched side-chain hairy-rod polyfluorene in bulk phase.

We report on an experimental study of the self-organization and phase behavior of hairy-rod π -conjugated branched side-chain polyfluorene, poly[9,9-bis(2-ethylhexyl)-fluorene-2,7-diyl]—i.e., poly[2,7–(9,9–bis(2–ethylhexyl)fluorene] (PF2∕6) —as a function of molecular weight (Mn) . The results have been compared to those of phenomenological theory. Samples for which Mn=3–147 kg∕mol were used. First, the stiffness of PF2∕6 , the assumption of the theory, has been probed by small-angle neutron scattering in solution. Thermogravimetry has been used to show that PF2∕6 is thermally stable over the conditions studied. Second, the existence of nematic and hexagonal phases has been phenomenologically identified for lower and higher Mn (LMW, Mn<Mn* and HMW, Mn>Mn* ) regimes, respectively, based on free-energy argument of nematic and hexagonal hairy rods and found to correspond to the experimental x-ray diffraction (XRD) results for PF2∕6 . By using the lattice parameters of PF2∕6 as an experimental input, the nematic-hexagonal transition has been predicted in the vicinity of glassification temperature (Tg) of PF2∕6 . Then, by taking the orientation parts of the free energies into account the nematic-hexagonal transition has been calculated as a function of temperature and Mn and a phase diagram has been formed. Below Tg of 80 °C only (frozen) nematic phase is observed for Mn<Mn*=104 g∕mol and crystalline hexagonal phase for Mn>Mn* . The nematic-hexagonal transition upon heating is observed for the HMW regime depending weakly on Mn , being at 140–165 °C for Mn>Mn* . Third, the phase behavior and structure formation as a function of Mn have been probed using powder and fiber XRD and differential scanning calorimetry and reasonable semiquantitative agreement with theory has been found for Mn≥3 kg∕mol . Fourth, structural characteristics are widely discussed. The nematic phase of LMW materials has been observed to be denser than high-temperature nematic phase of HMW compounds. The hexagonal phase has been found to be paracrystalline in the (ab0) plane but a genuine crystal meridionally. We also find that all these materials including the shortest 10-mer possess the formerly observed rigid five-helix hairy-rod molecular structure

Polyfluorene as a model system for space-charge-limited conduction

Ethyl-hexyl substituted polyfluorene (PF) with its high level of molecular disorder can be described very well by one-carrier space-charge-limited conduction for a discrete set of trap levels with energy $\sim$ 0.5 eV above the valence band edge. Sweeping the bias above the trap-filling limit in the as-is polymer generates a new set of exponential traps, which is clearly seen in the density of states calculations. The trapped charges in the new set of traps have very long lifetimes and can be detrapped by photoexcitation. Thermal cycling the PF film to a crystalline phase prevents creation of additional traps at higher voltages.Comment: 13 pages, 4 figures. Physical Review B (accepted, 2007

Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learning

Background: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. Methods: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2NetPig) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (SJI and SBF, respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. Results: On the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = −0.2 {95% conf. int. −0.23 : −0.16} and SBF = −0.1 {−0.5 : −0.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = −0.29 {−0.36 : −0.22} and SBF = −0.43 {−0.54 : −0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness SJI = −0.46 {−0.52 : −0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = −0.48 {−0.59 : −0.36}. u2NetTransfer improved JI compared to u2NetHuman in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2NetTransfer segmentations exhibited < 5% volume difference compared to MS. Conclusion: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning

Background: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. Methods: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2NetPig) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (SJI and SBF, respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. Results: On the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = 120.2 {95% conf. int. 120.23 : 120.16} and SBF = 120.1 { 120.5 : 120.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = 120.29 { 120.36 : 120.22} and SBF = 120.43 { 120.54 : 120.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P &lt; 0.001, but reduced robustness SJI = 120.46 { 120.52 : 120.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = 120.48 { 120.59 : 120.36}. u2NetTransfer improved JI compared to u2NetHuman in segmenting healthy (P = 0.008), ARDS (P &lt; 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2NetTransfer segmentations exhibited &lt; 5% volume difference compared to MS. Conclusion: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability

Development of Quantum Dot (QD) Based Color Converters for Multicolor Display.

Many displays involve the use of color conversion layers. QDs are attractive candidates as color converters because of their easy processability, tuneable optical properties, high photoluminescence quantum yield, and good stability. Here, we show that emissive QDs with narrow emission range can be made in-situ in a polymer matrix, with properties useful for color conversion. This was achieved by blending the blue-emitting pyridine based polymer with a cadmium selenide precursor and baking their films at different temperatures. To achieve efficient color conversion, blend ratio and baking temperature/time were varied. We found that thermal decomposition of the precursor leads to highly emissive QDs whose final size and emission can be controlled using baking temperature/time. The formation of the QDs inside the polymer matrix was confirmed through morphological studies using atomic force microscopy (AFM) and transmission electron microscopy (TEM). Hence, our approach provides a cost-effective route to making highly emissive color converters for multi-color displays

Polyfluorene as a model system for space-charge-limited conduction

Ethyl-hexyl substituted polyfluorene (PF) with its high level of molecular disorder can be described very well by one-carrier space-charge-limited conduction for a discrete set of trap levels with energy $\sim$ 0.5 eV above the valence band edge. Sweeping the bias above the trap-filling limit in the as-is polymer generates a new set of exponential traps, which is clearly seen in the density of states calculations. The trapped charges in the new set of traps have very long lifetimes and can be detrapped by photoexcitation. Thermal cycling the PF film to a crystalline phase prevents creation of additional traps at higher voltages.We gratefully acknowledge the support of this work through the National Science Foundation under grant Nos. ECS-0523656 and DMR-0413601

Harvesting triplet excitons for application in polymer solar cells

doi:10.1063/1.3082081Triplet enhanced ladder-type poly (para-phenylene) polymer (PhLPPP) with covalently bound trace amounts of palladium blended with a fullerene derivative [[6,6]-phenyl C61-butyric acid methyl ester (PCBM)] shows power conversion efficiencies (PCE) almost ten times greater than with pristine ladder-type polymer (with no palladium atom) blended with PCBM. The steady state optical properties of the triplet and nontriplet-enhanced polymers are comparable; the enhanced PCE and external quantum efficiency in PhLPPP photovoltaics are attributed to the presence of long-lived mobile triplet excitons. Furthermore, the luminescence from PhLPPP blends measured in a delayed setup correlates very well with the efficiency of the solar cells.This research was partially supported by grants from the U.S. National Science Foundation Grant No. ECCS-0823563 and the U.S. Army