To establish the mechanism(s) and site(s) of action of cholecystokinin (CCK) on pancreatic secretion under physiological conditions, we used an in vivo model using anesthetized rats with pancreaticobiliary cannulas. Infusion of CCK-8 (10-160 pmol/kg per h) produced a dose-dependent increase in plasma CCK levels. CCK-8 infusion at 40 pmol/kg per h produced a plasma CCK level of7.9±1.5 pM and an80% increase in pancreatic protein output over basal. This level was closely approximated by a postprandial peak plasma CCK level by 6.2±1.

In this study, we developed a pH-responsive shape-memory polymer nanocomposite by blending poly(ethylene glycol)-poly(ε-caprolactone)-based polyurethane (PECU) with functionalized cellulose nanocrystals (CNCs). CNCs were functionalized with pyridine moieties (CNC-C6H4NO2) through hydroxyl substitution of CNCs with pyridine-4-carbonyl chloride and with carboxyl groups (CNC-CO2H) via 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) mediated surface oxidation, respectively. At a high pH value, the CNC-C6H4NO2 had attractive interactions from the hydrogen bonding between pyridine groups and hydroxyl moieties; at a low pH value, the interactions reduced or disappeared due to the protonation of pyridine groups, which are a Lewis base. The CNC-CO2H responded to pH variation in an opposite manner. The hydrogen bonding interactions of both CNC-C6H4NO2 and CNC-CO2H can be readily disassociated by altering pH values, endowing the pH-responsiveness of CNCs. When these functionalized CNCs were added in PECU polymer matrix to form nanocomposite network which was confirmed with rheological measurements, the mechanical properties of PECU were not only obviously improved but also the pH-responsiveness of CNCs could be transferred to the nanocomposite network. The pH-sensitive CNC percolation network in polymer matrix served as the switch units of shape-memory polymers (SMPs). Furthermore, the modified CNC percolation network and polymer molecular chains also had strong hydrogen bonding interactions among hydroxyl, carboxyl, pyridine moieties, and isocyanate groups, which could be formed or destroyed through changing pH value. The shape memory function of the nanocomposite network was only dependent on the pH variation of the environment. Therefore, this pH-responsive shape-memory nancomposite could be potentially developed into a new smart polymer material.

China is characterized by a low fertility intention, a strong preference for sons, as well as a stringent birth control policy. In this study, we used data from a Fertility Intention and Behavior Survey of 2101 questionnaires conducted in 2013 in Shaanxi Province of northwestern China, and event history analysis methods to examine the effect of fertility intention and preference for sons on the probability of having a second child. The results not only validate the correlation of fertility intention with having a second child empirically, even in the low fertility intention and stringent birth control context of China, but also show that women with a preference for sons were less likely to have a second child. Women with son preference turn to sex-selective abortion to ensure that their first child is a son, thus reducing the likelihood of a second child and decreasing the fertility rate. Our findings also shed light on China’s potential fertility policy adjustment.

The role of TiO2 nanoparticle surfaces in affecting the crystalline structure of low‐density polyethylene (LDPE) has been investigated by varying the nanoparticle surface from hydrophilic (as‐received) to less hydrophilic (dried) or more hydrophilic (polar silane treated). Differential scanning calorimetry (DSC) and wide‐angle X‐ray diffraction (WXRD) were used to determine the degree of crystallinity and crystalline structure. The impact of nanoparticle aggregates on the nanometer to micrometer organization of LDPE crystals was studied with atomic force microscopy (AFM) and small‐angle light scattering (SALS). This characterization showed that the presence of the TiO2 nanoparticles, with the various different surface conditions investigated, did not alter the degree of LDPE crystallinity, the unit cell dimensions, the average lamellar thickness, or the average spherulite size. However, the nanoparticles did affect the internal arrangement of intraspherulitic crystalline aggregates by decreasing the relative optic axis orientation of these crystals, usually referred to as internal spherulite disorder. The LDPE filled with the nanoparticles treated with a polar silane (N‐(2‐aminoethyl) 3‐aminopropyl‐trimethoxysilane (AEAPS)) showed the highest internal spherulitic disorder and exhibited the most poorly developed spherulite structure. The combination of SALS with AFM has allowed a detailed characterization of the morphology of the semicrystalline polymer nanocomposites. Information on the internal organization of the spherulites, the size of the nanoparticle aggregates, and the location of the nanoparticle aggregates can be uniquely obtained when both techniques are used. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 488–497, 2005

Feature selection, which eliminates irrelevant and redundant features, is one of the most efficient classification methods. However, searching for an optimal subset from the original set is still a challenging problem. This paper proposes a novel feature selection algorithm named hybrid improved dragonfly algorithm (HIDA) which combines the advantages of both mRMR and improved dragonfly algorithm (IDA) in order to generate promising candidate subset and achieve higher classification accuracy rate. Firstly, to generate promising subset, features with small weight have chance to be selected into candidate subset with a small probability in mRMR. Secondly, to balance the exploitation and exploration capabilities of IDA, dynamic swarming factors are proposed to balance global and local capability. Lastly, to enhance the exploitation capability of IDA, quantum local optimum and global optimum are introduced in the position updating mechanism. The performance of HIDA is investigated on ten gene expression datasets and eight UCI data sets from the UCI Machine Learning Data Repository. Results show that the performance of HIDA is superior to BBA, BDA, CDA, LBPSO, MPMDWOA and MSMCCS.