Homogeneous methylation of wood pulp cellulose dissolved in LiOH/urea/H2O

Spruce sulphite cellulose (number average degree of polymerization 620) dissolved in an aqueous solution of 8% (w/w) LiOH*H₂O and 12% (w/w) urea was methylated with dimethyl sulphate (DMS). By varying the reaction temperature between 22 and 50 °C, the molar ratio between 9 and 15 mol DMS per mol anhydroglucose unit, and the reaction time from 4 to 24 h, methyl cellulose (MC) with degree of substitution (DS) values in the range of 1.07 and 1.59 was prepared. The chemical structure of MC was analysed by FTIR and ¹H NMR spectroscopy. The turbidity (given in nephelometric turbidity units, NTU) of the aqueous solution of MC reached an optimum of 10 NTU for a product obtained with 12 mol DMS/mol AGU at 50 °C. GPC measurements revealed polymer degradation to a certain extent. The intrinsic viscosity and the Huggins constant k of the MC samples increased with increasing DS value. The MC samples possess k values higher than 0.8, indicating association of the polymer chain. The zero-shear viscosity decreased with increase of both temperature and the amount of methylation agent due to the depolymerization. During the heating/cooling cycle (20-90 °C) of the aqueous solutions of MC, it was observed that samples synthesized at 22 °C with DS values lower than 1.3 did not undergo phase separation in aqueous solution. Phase separation hysteresis with a precipitation temperature up to 80 °C was obtained for aqueous solutions of MC with DS values between 1.07 and 1.66 synthesized at higher temperatures. The functionalization pattern determined by GLC of the corresponding partially methylated glucitol acetates is close to randomness and comparable with those of commercial MC samples.     

N,N′‐Diethyl‐4‐nitrobenzene‐1,3‐diamine, 2,6‐bis(ethylamino)‐3‐nitrobenzonitrile and bis(4‐ethylamino‐3‐nitrophenyl) sulfone

N,N′‐Diethyl‐4‐nitrobenzene‐1,3‐diamine, C₁₀H₁₅N₃O₂, (I), crystallizes with two independent molecules in the asymmetric unit, both of which are nearly planar. The molecules differ in the conformation of the ethylamine group trans to the nitro group. Both molecules contain intramolecular N—H...O hydrogen bonds between the adjacent amine and nitro groups and are linked into one‐dimensional chains by intermolecular N—H...O hydrogen bonds. The chains are organized in layers parallel to (101) with separations of ca 3.4 Å between adjacent sheets. The packing is quite different from what was observed in isomeric 1,3‐bis(ethylamino)‐2‐nitrobenzene. 2,6‐Bis(ethylamino)‐3‐nitrobenzonitrile, C₁₁H₁₄N₄O₂, (II), differs from (I) only in the presence of the nitrile functionality between the two ethylamine groups. Compound (II) crystallizes with one unique molecule in the asymmetric unit. In contrast with (I), one of the ethylamine groups, which is disordered over two sites with occupancies of 0.75 and 0.25, is positioned so that the methyl group is directed out of the plane of the ring by approximately 85°. This ethylamine group forms an intramolecular N—H...O hydrogen bond with the adjacent nitro group. The packing in (II) is very different from that in (I). Molecules of (II) are linked by both intermolecular amine–nitro N—H...O and amine–nitrile N—H...N hydrogen bonds into a two‐dimensional network in the (10) plane. Alternating molecules are approximately orthogonal to one another, indicating that π–π interactions are not a significant factor in the packing. Bis(4‐ethylamino‐3‐nitrophenyl) sulfone, C₁₆H₁₈N₄O₆S, (III), contains the same ortho nitro/ethylamine pairing as in (I), with the position para to the nitro group occupied by the sulfone instead of a second ethylamine group. Each 4‐ethylamino‐3‐nitrobenzene moiety is nearly planar and contains the typical intramolecular N—H...O hydrogen bond. Due to the tetrahedral geometry about the S atom, the molecules of (III) adopt an overall V shape. There are no intermolecular amine–nitro hydrogen bonds. Rather, each amine H atom has a long (H...O ca 2.8 Å) interaction with one of the sulfone O atoms. Molecules of (III) are thus linked by amine–sulfone N—H...O hydrogen bonds into zigzag double chains running along [001]. Taken together, these structures demonstrate that small changes in the functionalization of ethylamine–nitroarenes cause significant differences in the intermolecular interactions and packing.     

Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in‐gel electrophoresis

Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor‐specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non‐cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in‐gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine‐rich α2 glycoprotein‐1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.     

Modelling of Surfactant Concentration in a Coupled Bulk Surface Problem

Surface active agents (surfactants) are on high interest for physical and chemical industries. Their presence lowers the surface tension of fluids. This is used to achieve special flow properties. These mechanisms are not only influenced by the overall amount of surfactant. They highly depend on the local surfactant distribution. The surfactant concentration is modeled by two convection diffusion equations, one in the bulk and the other on its surface. Adsorption- and desorption processes are included and lead to a (non linear) coupling of both equations. We present a numerical scheme for solving the coupled problem. Additionally, first analytical results will be given. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

MODELING THE DISTRIBUTION OF FISH SCHOOLS IN THE BERING SEA: MORPHOLOGICAL SCHOOL IDENTIFICATION

In this paper we demonstrate how fish schools can be automatically identified from acoustic backscatter data using a simple morphological and threshold algorithm. The algorithm includes: 1) seabed identification and removal; 2) bubble layer removal; 3) choosing a threshold for fish backscatter and setting all pixels below it to 0; and 4) identifying the school boundaries and smoothing the images by using morphological operations. We compared the school sizes and boundaries found by using our algorithms with those found by experts to evaluate our choice of threshold, structuring element and morphological operations. Results showed two classes of images. The algorithm performed well for images having distinct, well defined fish schools. Images having more amorphous aggregations showed significant disparity between the algorithm and experts' choice. Differences appear to involve separating higher intensity aggregations from surrounding lower intensity aggregations. In these cases, the algorithm provides a more consistent and arguably more accurate estimate than the experts. The shoal image data is translated, via a connected-surface-identifier algorithm into a table of information about shoal size, shape and location that allows further analysis of the shoal information with a much smaller data set than the original acoustic images. Such analysis can lead to observing patterns in fish shoal distribution and its relationship to environmental factors and to point-process tests for randomness at different spatial scales which, in turn, allows stochastic modeling of the schooling process. These topics will be presented in subsequent papers.