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Introduction: The rate of concussions in youth soccer is among the highest of all youth sports. Parents play an important role in caring for their children and making decisions regarding whether they should participate in a sport, such as soccer, where concussions are well known. This study examined parental perceptions regarding: (a) coaches’ role in concussion management, (b) heading restriction policies, and (c) overall concussion risk and participation issues. Method: Online surveys were completed by 419 parents of youth soccer players who participated in the largest U.S. youth soccer programs nationwide. Results: Findings indicated 44.5% of the respondents had considered keeping their children from playing organized soccer and 47.2% were concerned about a potential decline in youth soccer participation due to concussions. Nearly 69% of responding parents agreed that heading should be banned for participants 10 years old or younger, while 56.5% thought heading should not be limited for participants 13 or older. Only 35% of parents were very confident about their child’s coach’s ability to properly identify concussions and remove those suspected of a concussion from play. Parents’ socioeconomic status (SES), soccer coaching and playing experience, and previous history of concussion(s) were key predictors of greater perceived risk about concussions. Conclusions: Findings from this study shed light on parents’ perceptions about concussions and related safety issues in youth soccer. Understanding what parents believe about concussions is vital to preserve youth soccer participation and can be used to strengthen education and policies that promote a safer environment for youth sport participants. Practical Applications: Youth soccer coaches can benefit from stronger, comprehensive educational efforts at the league/club level. Additionally, parents of youth athletes who are in the lower SES communities should be targeted to receive concussion safety information and/or interventions that would improve their knowledge, attitude, and practices regarding concussion safety.

An important strategy for reducing the impact of global CO2 emissions involves conversion technologies that transform CO2 into value-added materials. Bimetallic electrocatalysts of Zn and Ag were constructed on polypyrrole-decorated carbon paper (CP/PPy) electrodes for the improved electrochemical reduction of CO2 to CO. Bimetallic catalysts with different Zn and Ag atomic ratios were produced by the partial galvanic replacement of Zn with Ag on a CP/PPy/Zn electrode. Selectivity for the electrochemical reduction of CO2 to CO at the CP/PPy/Zn/Ag was clearly improved compared to those of the single-metal catalysts (CP/PPy(Ag or Zn)), while the hydrogen evolution reaction (HER) was suppressed. The PPy interlayer between the catalyst and CP support reduced the hydrophobicity of the carbon paper electrode, which facilitated the effective electrodeposition of Ag and Zn on the CP and suppressed the HER at the electrode. Herein, we propose Zn:Ag bimetallic composites deposited on PPy surfaces as CO-selective electrocatalysts with the optimal Zn:Ag catalyst composition suggested by electrochemical and physicochemical analyses. We experimentally confirmed that the atomically mixed Ag-Zn in the bimetallic catalysts enhanced the CO selectivity and production rate over each single metal catalyst. The developed electrocatalyst resulted in a maximum Faradaic efficiency of approximately 70% and a maximum current density of 8.6 mA/cm2 for the CO2 reduction reaction to CO in a 1 M KHCO3 aqueous solution.

Summary Palatoplasty is performed with neck extension in patients with a cleft palate. The neck extension required for a better surgical view during palatoplasty can affect intracranial pressure. We evaluated the effect of neck extension on intracranial pressure by measuring the optic nerve sheath diameter using ultrasonography during palatoplasty. The optic nerve sheath diameter was measured in 30 patients at 10 min after anesthetic induction in the supine position (T1), at 10 min after neck extension before preparing for a sterile field (T2), at the end of surgery with neck extension (T3), and at 10 min after the supine position (T4). Hemodynamic and respiratory variables such as systolic blood pressure, heart rate, end-tidal carbon dioxide partial pressure, and peak airway pressure were also measured at the same time points. In comparison with the optic nerve sheath diameter measured at 10 min after anesthetic induction in the supine position (T1), the mean optic nerve sheath diameters were significantly increased at 10 min after neck extension before preparing for a sterile field (T2), at the end of surgery with neck extension (T3), and at 10 min after the supine position (T4; 4.19 ± 0.26, 5.20 ± 0.29, 4.38 ± 0.36, and 4.35 ± 0.30 mm, respectively). However, hemodynamic and respiratory variables were not significantly different at all time points. We found that the optic nerve sheath diameter, an indicator of intracranial pressure, was increased during palatoplasty with neck extension, which suggests that the position may affect the intracranial pressure of patients with a cleft palate.

Developing hydrologic models based on data-driven approaches (DDA) is very complicated due to the complex nature of meteorological data. For example, a high degree of irregularities, periodicities, jumps, and other forms of stochastic behavior influence the accuracy of river flow forecasting. In this study, M5 model tree (M5Tree) and multivariate adaptive regression spline (MARS) models were developed to forecast one and multi-day-ahead river flow. Moreover, ensemble empirical mode decomposition (EEMD), a robust data pre-processing technique, was used to enhance M5Tree and MARS models’ forecasting. Also, Mallows’ coefficient (CP), one of the procedures to determine the input variables, was used to obtain the optimum values of hydrological time series. The developed models were validated using two different meteorological stations (e.g., Kordkheyl in Iran and Hongcheon in South Korea). Forecasting performance of developed models (e.g., M5Tree, MARS, EEMD-M5Tree, and EEMD-MARS) was evaluated using six different statistical criteria. Comparing the results between standalone and hybrid models indicated that a data pre-processing technique can enhance the performance of standalone models (e.g., M5Tree and MARS). EEMD-MARS model (NSE = 0.819 and RMSE = 7.206 m3/s (Kordkheyl station) and NSE = 0.738 and RMSE = 50.426 m3/s (Hongcheon station)) outperformed M5Tree, MARS, and EEMD-M5Tree models based on two-day-ahead river flow forecasting in validation stage, respectively. Results showed that EEMD-MARS model was an efficient and robust tool to forecast one and multi-day-ahead (e.g., two, three, and four-day-ahead) river flow.

Despite modern advancements in structural engineering, the behavior and design of reinforced concrete beams in shear are still a major concern for structural engineers. In this research, a new Support Vector Regression algorithm coupled with Particle Swarm Optimization (SVR-PSO) is developed to predict the shear strength ( S s ) of steel fiber-reinforced concrete beams (SFRC) using several input combinations denoting the dimensional and material properties. The experimental test data are collected from reliable literature sources. The main variables used to construct the predictive model are related to the dimensional and material properties of the beams. SVR-PSO, the objective predictive model, is validated against a classical neural network model tuned with the same metaheuristic optimizer algorithm. The findings of the modeling study provide a clear evidence of the superior capability of the SVR-PSO used to predict the SFRC shear strength relative to the benchmark model. In addition, the construction of the predictive models with a lesser number of input data attributes are attained, leading an acceptable prediction accuracy of the SVR-PSO compared to the ANN-PSO model. In summary, the proposed SVR-PSO methodology has demonstrates an effective engineering strategy that can be applied in problems of structural and construction engineering prospective, applied to predict shear strength of steel fiber reinforced concrete beam using advanced hybrid artificial intelligence models developed in this study.

Biomass derived carbon via hydrothermal carbonization are critically important for the catalysis research field requiring high activity with low cost. However, most of them present low activity due to low surface areas with large particle size originating from unnecessary compounds in raw status. Here, we report a porous carbon decorated with Fe3C/Fe3O4 nanoparticles via pretreatment of pig blood (PB) by employing solvent pretreatment and pyrolyzing in regulated environment using a Fe-porphyrin-type macrocycle as catalyst precursors. Importantly, the addition of toluene to the raw form of PB as pretreatment plays a significant role in not only producing the nanoparticles with porous carbon materials but also removing impurities that deteriorate the active sites of molecular nitrogen carbon (MNC) type catalysts during high-temperature activation. Furthermore, the temperature for hydrothermal treatment and pyrolysis influences oxygen reduction reaction (ORR) performances. The highest-performing PB-derived catalyst delivered its kinetic current and the degree of degradation (after 10,000 potential cycles) were 1.57 mA/cm2 (at 0.9 V) and 19 mV (half-wave potential), and those of Pt/C were 1.26 mA/cm2 and 43 mV, respectively. The catalysts were prepared by applying pretreatment to the PB and characterized systematically to investigate how such pretreatment influences the physical properties and ORR performances.

Chlorophyll-a is one of the main indicators for water quality (WQ) analysis in environmental monitoring of aquatic ecosystems. WQ degradation is mostly a result of the increase of the concentration of chlorophyll-a in a waterbody, however, proper estimation of daily chlorophyll-a concentration is a complex problem. In this study, the classic extreme learning machine (ELM), group method of data handling (GMDH), random forest (RF), classification and regression tree (CART), and a novel integrated Bat-ELM model (with the bat optimization algorithm) were developed and applied to predict daily chlorophyll-a (Chl-a) concentration in river and lake ecosystems. Input parameters including turbidity (TU), pH, specific conductance (SC), water temperature (TE), and periodicity were applied as the influential elements for estimating daily Chl-a concentration for two different USGS stations. General results based on RMSE values indicated that the Bat-ELM as the most robust model improved the performance of standard ELM, GMDH, RF, and CART models during the testing procedure by 20.7%, 23.9%, 18.3%, and 27.4% in USGS no. 05543010 and 13.8%, 16.8%, 17.5%, and 52.0% in USGS no. 09014050 in terms of the 7th input-combination, respectively. Moreover, the results revealed that periodicity is the most effective input parameter that considered as the last scenario (input combination) on daily chlorophyll-a (Chl-a) concentration.

Interleukin (IL)-17D is a proinfiammatory cytokine with currently largely unknown biological functions. Here we provide the description of the sequence, bioactivity, and mRNA expression profile of duck IL-17D homologue. A full-length duck IL-17D (duIL-17D) cDNA with a 624-bp coding region was identified from the large intestine. dull-17D shares approximately 94.7% identity with its chicken counterpart, which is also identified in this work. duIL-17D exhibits 62.6-68.4% and 52.1-53.1% identity with mammalian and piscine homologues. Recombinant duIL-17D promoted the expression of proinfiammatory cytokines such as IL-6, IL-8, and IL-1 beta in duck embryo fibroblast cells. Very low levels of dull-17D transcript were observed in healthy lymphoid tissues, including bursa, thymus, and spleen, while duIL-17D expression was relatively high in the heart. The dull-17D expression profiles were examined in mitogen-stimulated splenic lymphocytes, as well as tissues affected by Riemerella anatipestifer infection. The levels of duIL-17D were mostly upregulated in mitogen-activated splenic lymphocytes but downregulated in the liver and spleen of R. anatipestifer-infected ducks. These results provide new insights into the roles of IL-17D in host protective immune responses to Riemerella infection, which can therefore lead to further studies of its biological functions in different disease models of ducks and other avian species. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2015R1D1A1A02059953] This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A02059953). Public domain – authored by a U.S. government employee

The objective of this study is to develop the hybrid model for estimating solar irradiance and investigate its accuracy for optimal photovoltaic system. The hybrid model is wavelet-based support vector machines (WSVMs) and wavelet-based adaptive-neuro fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose the global solar irradiance time series components into approximation and detail components. These decomposed time series are then used as input to support vector machines (SVMs) modules in the WSVMs model and adaptive-neuro fuzzy inference system (ANFIS) modules in the WANFIS. Based on statistical indices, results indicate that WSVMs and WANFIS can successfully be used for the estimation of global solar irradiance at Big bend, Carbondale, Champaign, and Springfield stations in Illinois.

Nutrients are absorbed in the small intestine through a variety of transporter proteins, which have not been as well characterized in turkeys as in chickens. The objective of this study was to profile the mRNA expression of amino acid and monosaccharide transporters in the small intestine of male and female turkeys. Jejunum was collected during embryonic development (embryonic d 21 and 24, and d of hatch (DOH)) and duodenum, jejunum, and ileum were collected in a separate experiment during posthatch development (DOH, d 7, 14, 21, and 28). Real-time PCR was used to determine expression of aminopeptidase N (APN), one peptide (PepT1), 6 amino acid (ASCT1, b(o,+)AT, CAT1, EAAT3, LAT1, y(+)LAT2) and 3 monosaccharide (GLUT2, GLUT5, SGLT1) transporters. Data were analyzed by ANOVA using JMP Pro 11.0. APN, b(o,+)AT, PepT1, y(+)LAT2, GLUT5, and SGLT1 showed increased expression from embryonic d 21 and 24 to DOH. During posthatch, all genes except GLUT2 and SGLT1 were expressed greater in females than males. GLUT2 was expressed the same in males as females and SGLT1 was expressed greater in males than females. All basolateral membrane transporters were expressed greater during early development then decreased with age, while the brush border membrane transporters EAAT3, GLUT5, and SGLT1 showed increased expression later in development. Because turkeys showed high-level expression of the anionic amino acid transporter EAAT3, a direct comparison of tissue-specific expression of EAAT3 between chicken and turkey was conducted. The anionic amino acid transporter EAAT3 showed 6-fold greater expression in the ileum of turkeys at d 14 compared to chickens. This new knowledge can be used not only to better formulate turkey diets to accommodate increased glutamate transport, but also to optimize nutrition for both sexes.

This study develops and applies hybrid models combining wavelet packet decomposition and data-driven models for forecasting river stage and investigating their accuracy. The hybrid models are wavelet packet-based artificial neural network (WPANN) and wavelet packet-based adaptive neuro-fuzzy inference system (WPANFIS). Wavelet packet decomposition splits an input time series into approximation and detail components, and the decomposed time series are used as inputs to artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WPANN and WPANFIS models, respectively. The forecasting performances of WPANN and WPANFIS models are compared with those of ANN and ANFIS models. Based on performance evaluation indexes and graphical comparison, the WPANN and WPANFIS models produce better performance than ANN and ANFIS models in terms of model efficiency, and WPANFIS-coif18 model is also found to yield the best performance among all other models. Therefore, wavelet packet decomposition improves the accuracy of ANN and ANFIS models for forecasting river stage. The results indicate that river stage forecasting models combining wavelet packet decomposition and data-driven models can be used as an effective tool for forecasting river stage accuracy.

The objective of this study is to develop the hybrid models by combining neural computation, including support vector machines (SVM) and generalized regression neural networks (GRNN), and wavelet technique for rainfall modeling. The wavelet-based support vector machines (WSVM) and wavelet-based generalized regression neural networks (WGRNN) models are obtained using mother wavelets, including db8, db10, sym8, sym10, coif6, and coif12. The developed models are evaluated in the Bocheong-stream catchment, an International Hydrological Program (IHP) representative catchment, Republic of Korea. Results obtained from this study indicate that the combination of neural computing and wavelet technique can be a useful tool for modeling of rainfall satisfactorily and can yield better efficiency than neural computing.

The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of BOD. This study investigated the capability of a novel deep learning-based model, Deep Echo State Network (Deep ESN), for predicting BOD, based on various water quality variables, at Gongreung and Gyeongan stations, South Korea. The model was compared with the Extreme Learning Machine (ELM) and two ensemble tree models comprising the Gradient Boosting Regression Tree (GBRT) and Random Forests (RF). Diverse water quality variables (i.e., BOD, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P)) were utilized for developing the Deep ESN, ELM, GBRT, and RF with five input combinations (i.e., Categories 1–5). These models were evaluated by root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and correlation coefficient (R). Overall evaluations suggested that the Deep ESN5 model provided the most reliable predictions of BOD among all the models at both stations.

In this study, the potential of six different machine learning models, gradient boosting tree (GBT), multilayer perceptron neural network (MLPNN), two types of adaptive neuro-fuzzy inference systems (ANFIS) based on fuzzy c-means clustering (ANFIS-FCM) and subtractive clustering (ANFIS-SC), multivariate adaptive regression spline (MARS), and classification and regression tree (CART) were used for forecasting solar radiation from two stations of two different locations, Turkey and USA. Wind speed, maximum air temperature, minimum air temperature and relative humidity were used as inputs to the developed models. For accurate evaluation of performance of models, four statistical indicators, root mean squared error (RMSE), coefficient of correlation (R), mean absolute error (MAE) and Nash–Sutcliffe efficiency coefficient (NS) were employed to evaluate accuracy of the developed models. Comparison of results showed that the GBT model performed better than the MLPNN, ANFIS, MARS, and CART in modeling solar radiation. The average RMSE of MLPNN, ANFIS-FCM, ANFIS-SC, MARS and CART models was decreased by 0.26%, 1.5%, 0.51%, 2.5%, and 19.34% using GBT model at Fairfield Station, 4%, 1.37%, 0.24%, 4.12%, and 24.4% at Monmouth Station, 11.99%, 48.7%, 41.6%, 8.23%, and 33.41% at Antalya Station, 11%, 54.8%, 51.9%, 19.65%, and 37.1% at Mersin Station, respectively. The overall results indicated that the GBT model could be successfully applied in forecasting solar radiation by using climatic parameters as inputs.

Summary Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

The accuracy of four evolutionary neuro fuzzy methods, adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO), ANFIS with genetic algorithm (ANFIS-GA), ANFIS with ant colony algorithm (ANFIS-ACO) and ANFIS with butterfly optimization algorithm (ANFIS-BOA), is investigated and compared with classical ANFIS method in forecasting various time scales of standard precipitation index (SPI). Monthly precipitation data of Abbasabad, Biarjmand and Ebrahim-Abad stations, Iran are used in the case study. The comparison is made according to the three indexes, root mean square error (RMSE), mean absolute error and index of agreement. It is observed that the evolutionary neuro fuzzy methods perform superior to the classical ANFIS in forecasting all SPI indexes (SPI-3, SPI-6, SPI-9 and SPI12) in all three stations. The RMSE of the classic method is increased roughly by 11.4–16.7%, 11.3–32.4%, 9.8–34.4% 30.6–46.7% for the SPI-3, SPI-6, SPI-9 and SPI-12, respectively. In Ebrahim-Abad Station, the best accuracy is observed from the ANFIS-PSO method for all drought indexes while there is not a dominant method in Abbasabad and Biarjmand stations.

As a member of the interleukin (IL)-10 family, IL-22 is an important mediator in modulating tissue responses during inflammation. Through activation of STAT3-signaling cascades, IL-22 induces proliferative and anti-apoptotic pathways, as well as antimicrobial peptides (AMPs), that help prevent tissue damage and aid in its repair. This study reports the cloning and expression of recombinant chicken IL-22 (rChIL-22) and its soluble receptor, rChIL22BP, and characterization of biological effects of rChIL-22 during inflammatory responses. Similar to observations with mammalian IL-22, purified rChIL-22 had no effect on either peripheral blood mononuclear cells (PBMCs) or lymphocytes. This was due to the low expression of the receptor ChIL22RA1 chain compared to ChIL10RB chain. rChIL-22 alone did not affect chicken embryo kidney cells (CEKCs); however, co-stimulation of CEKCs with LPS and rChIL-22 enhanced the production of pro-inflammatory cytokines, chemokines and AMPs. Furthermore, rChIL-22 alone stimulated and induced acute phase reactants in chicken embryo liver cells (CELCs). These effects of rChIL-22 were abolished by pre-incubation of rChIL-22 with rChIL22BP. Together, this study indicates an important role of ChIL-22 on epithelial cells and hepatocytes during inflammation.

In dye-sensitized solar cells (DSSCs), dye coating is one of the important factors in the fabrication process. Dye coating takes more than several hours in the conventional methods when Ru-based dyes, especially N719, the main dye for DSSC applications, are applied. In this paper, we applied a new coating system to maximize the adsorption reaction between the dye and TiO 2 nanocrystalline surface. Applied solvents are from mono alcohol to triols with various temperatures and concentrations of the dye. According to our experiments, dye coating time can be shortened down from several hours to 3 min. To compare the new system to the conventional system, various analysis tools were applied for further investigation. The new solvent system with ethylene glycol, gives many advantages such as saving solvent usage, short coating time, and easy procedure for mass fabrication without toxicity.

We synthesized oleic acid capped tetrapod nanocrystals of CdSe, CdTe and type II heterostructured CdTe/CdSe to investigate the effects of nanocrystal compositions and type II heterojunction on the photovoltaic properties of hybrid solar cells. The hybrid solar cell based on the blend of CdSe tetrapod nanocrystals and P3HT with a weight ratio of 6:1 showed the maximum power conversion efficiency of 1.03% under AM 1.5 G condition, and the maximum incident photon to current conversion efficiency of the solar cell was 43% at 415 nm. Although CdTe and CdTe/CdSe tetrapod nanocrystals showed relatively poor performance, the power conversion efficiency and the short circuit current density of the hybrid solar cell based on type II heterostructured CdTe/CdSe tetrapod nanocrystals was 4.4 and 3.9 times higher than that of the solar cell based on CdTe tetrapod nanocrystals, respectively. These results can be explained by the effects of nanocrystal compositions and type II heterojunction on the photovoltaic properties of hybrid solar cells.

Macrophage migration inhibitory factor (MIF) is recognized as a soluble factor produced by sensitized T lymphocytes and inhibits the random migration of macrophages. Recent studies have revealed a more prominent role for MIF as a multi-functional cytokine mediating both innate and adaptive immune responses. This study describes the cloning and functional characterization of avian MIF in an effort to better understand its role in innate and adaptive immunity, and potential use in poultry health applications. The full-length avian MIF gene was amplified from stimulated chicken lymphocytes and cloned into a prokaryotic expression vector. The confirmed 115 amino acid sequence of avian MIF has 71% identity with human and murine MIF. The bacterially expressed avian recombinant MIF (rChMIF) was purified, followed by endotoxin removal, and then tested by chemotactic assay and quantitative real-time PCR (qRT-PCR). Diff-Quick staining revealed a substantial decrease in migration of macrophages in the presence of 0.01microg/ml rChMIF. qRT-PCR analysis revealed that the presence of rChMIF enhanced levels of IL-1beta and iNOS during PBMCs stimulation with LPS. Additionally, the Con A-stimulated lymphocytes showed enhanced interferon (IFN)-gamma and IL-2 transcripts in the presence of rChMIF. Interestingly, addition of rChMIF to the stimulated PBMCs, in the presence of lymphocytes, showed anti-inflammatory function of rChMIF. To our knowledge, this study represents the first report for the functional characterization of avian MIF, demonstrating the inhibition of macrophage migration, similar to mammalian MIF, and the mediation of inflammatory responses during antigenic stimulation.

Recently, a variety of bioactive protein drugs have been available in large quantities as a result of advances in biotechnology. Such availability has prompted development of long-term protein delivery systems. Biodegradable microparticulate systems have been used widely for controlled release of protein drugs for days and months. The most widely used biodegradable polymer has been poly(d,l-lactic-co-glycolic acid) (PLGA). Protein-containing microparticles are usually prepared by the water/oil/water (W/O/W) double emulsion method, and variations of this method, such as solid/oil/water (S/O/W) and water/oil/oil (W/O/O), have also been used. Other methods of preparation include spray drying, ultrasonic atomization, and electrospray methods. The important factors in developing biodegradable microparticles for protein drug delivery are protein release profile (including burst release, duration of release, and extent of release), microparticle size, protein loading, encapsulation efficiency, and bioactivity of the released protein. Many studies used albumin as a model protein, and thus, the bioactivity of the release protein has not been examined. Other studies which utilized enzymes, insulin, erythropoietin, and growth factors have suggested that the right formulation to preserve bioactivity of the loaded protein drug during the processing and storage steps is important. The protein release profiles from various microparticle formulations can be classified into four distinct categories (Types A, B, C, and D). The categories are based on the magnitude of burst release, the extent of protein release, and the protein release kinetics followed by the burst release. The protein loading (i.e., the total amount of protein loaded divided by the total weight of microparticles) in various microparticles is 6.7+/-4.6%, and it ranges from 0.5% to 20.0%. Development of clinically successful long-term protein delivery systems based on biodegradable microparticles requires improvement in the drug loading efficiency, control of the initial burst release, and the ability to control the protein release kinetics.

An increase in the chemical oxygen demand (COD) has been noticed in most Korean reservoirs. Therefore, this research systematically investigated the causes of organic accumulation. Samples of soil affecting the quality of water of reservoirs were collected at various sources and analyzed for their organic characteristics. The COD to biochemical oxygen demand (BOD) ratio was used as the key parameter in the evaluation of non-biodegradable (NBD) organic accumulation in the reservoirs. Soil samples containing plant roots were agitated, with the supernatant showing COD/BOD ratios of less than 2.8, while those of the composted tree leaves were greater than 5.0, suggesting that humic substances produced in forest areas are a major cause of NBD organic accumulation in reservoirs. In addition, the organic fractionation of the leachate from leaching tests showed that of the various types of hydrophobic natural organic matter (NOM), the larger molecular weight humic acid makes a greater contribution than fulvic acid to the increase in the NBD COD in Korean reservoirs.

Summary The purpose of this study is to develop and apply the generalized regression neural networks model (GRNNM) embedding the genetic algorithm (GA) in order to estimate and calculate the pan evaporation (PE) and the alfalfa reference evapotranspiration (ET r ), Republic of Korea. Since the observed data of the alfalfa ET r using lysimeter have not been measured for a long period, Penman–Monteith (PM) method was used to estimate the observed alfalfa ET r . The COMBINE-GRNNM-GA (Type-1) was developed to calculate a reasonable PE and the alfalfa ET r . The COMBINE-GRNNM-GA (Type-1) was evaluated through the training, the testing, and the reproduction performances, respectively. An uncertainty analysis was used to eliminate the climatic variables of the input layer nodes and to construct the optimal COMBINE-GRNNM-GA (Type-1). The climatic variable with the lowest smoothing factor during the training performance was eliminated from the original COMBINE-GRNNM-GA (Type-1). The climatic variable with the lowest smoothing factor implies the most useless input layer node for the model output. Therefore, the optimal COMBINE-GRNNM-GA (Type-1) can estimate and calculate the PE, which is missed or ungaged, and the alfalfa ET r , which is not measured, with the least cost and endeavor. Furthermore, it is possible to derive a linear regression statistical model between the measured PE and the corresponding alfalfa ET r . Finally, the PE and the alfalfa ET r maps could be constructed to provide the reference data for a drought analysis and an irrigation networks system using the optimal COMBINE-GRNNM-GA (Type-1), Republic of Korea.

The aim of this study was to assess the efficacy of ectopic bone formation in a three-dimensional hybrid scaffold in combination with hydroxyapatite (HA) and poly(NiPAAm-co-AAc) as an injectable vehicle in the form of a supporting matrix for the osteogenic differentiation of rabbit mesenchymal stem cells (MSCs). Osteogenic differentiation of MSCs in the hybrid scaffold was greatly influenced by the addition of growth factors. When the osteoinduction activity of hybrid scaffold was studied following implantation into the back subcutis of nude mouse in terms of histological and biochemical examinations, significantly homogeneous bone formation was histologically observed throughout the hybrid scaffolds containing growth factor (BMP-2: bone morphogenic protein-2). The level of alkaline phosphatase activity and osteocalcin content at the implanted sites of hybrid scaffolds were significantly high for the perfusion group compared with those in static culture group. We conclude that combination of MSC-seeded hybrid scaffold containing BMP-2 was a promising method by which to enhance in vitro osteogenic differentiation of MSC and in vivo ectopic bone formation.

Chondroitin sulfate (CsA) is an acidic mucopolysaccharide, which is able to form ionic complexes with positively charged proteins. In this study, a protein-CsA complex was constructed to nano-sized particles. Zeta potential measurements revealed that a CsA-to-protein fraction of greater than 0.1 results in a neutralization of the positive charge on lysozyme (Lys). Based on this preliminary study, we have prepared poly(lactide-co-glycolide) (PLGA) microspheres harboring Lys/CsA complexes via the multi-emulsion method. Protein stability in the PLGA microspheres was preserved during both microsphere preparation and protein release. The profiles of Lys release from the PLGA microspheres evidenced nearly zero-order kinetics, depending on the quantity of CsA. An in vivo fluorescent image of experimental mouse tissue showed that the PLGA microspheres with the Lys/CsA complex had released the entirety of their Lys without no residual amount after 23 days, but microspheres without the complex harbored a great deal of residual Lys, which is attributable to its degradation by acidic PLGA degradates. The tissue reaction evidenced by the PLGA microspheres stabilized with CsA showed minimal foreign body reaction and little configuration of immune cells including neutrophils and macrophages, but the reactions of the PLGA microspheres without CsA were characterized by a relatively elevated inflammation. These results show that CsA is a viable candidate for long-acting micro-particular protein delivery.

In this study, a hydrogel composite, based on the thermo-reversible hydrogel of p(NiPAAm-co-AAc) and hyaluronic acid (HA) was used as an injectable cell and growth factor carrier for cartilage tissue engineering applications. Rabbit chondrocytes were embedded in blended hydrogel composites co-encapsulated with the transforming growth factor beta-3 (TGFbeta-3). The blended hydrogel with the embedded chondrocytes and HA co-encapsulating unloaded growth factors and those with the thermo-reversible hydrogel were used as the controls to examine the effects of TGFbeta-3 on neocartilage formation. The blended hydrogel loaded with TGFbeta-3 embedded with chondrocytes were injected subcutaneously into the nude mice. The mice were monitored for 8 weeks after the injection. Both the differentiation and level of cartilage-specific ECM production were significantly higher in the presence of HA and growth factor than in the control without the growth factor. The level of cartilage associated ECM proteins was examined by immunohistochemical staining (collagen types II and X) as well as by Safranin-O and Alcian blue (GAG) staining. The results showed the potential application of blended hydrogel mixed with the growth factor to neocartilage formation.

Unexpected optical contrast at antiparallel domain walls is observed in non-stoichiometric lithium niobate. This is imaged using near-field scanning optical microscopy. A detailed modeling of the imaging process is performed, and a comparison of the experimental and simulation images is used to extract the index profile across a single antiparallel domain wall.

In order to increase the functionality of islets encapsulated in a biohybrid artificial pancreas (BAP), it was proposed that co-encapsulation with insulinotropic agents would improve insulin secretion from islets. To prevent agents from leaking out, conjugation with high-molecular-weight polymers was inevitable. In this study, synthetic glucagon-like peptide-1 (GLP-1) (7-37) was conjugated to a water-soluble polymer, poly(N-vinyl-2-pyrroridone-co-acrylic acid) (5 mol% acrylic acid, M(w) 445 kDa), via poly(ethylene glycol, M(w) 3.4 kDa) spacer. The chemical conjugation was confirmed by reverse phase-HPLC and the GLP-1 content in the GLP-1/polymer conjugate (VAPG) was determined by UV spectrophotometry at 280 nm (ca. 29 wt/wt%). In a static insulin secretion test, the VAPG increased insulin secretion up to 200% over a control (no stimulation) at high glucose levels, although the insulinotropic activity of VAPG was slightly lower than that of native GLP-1. The bioactivity of VAPG was prolonged for at least 2 weeks, which was examined by co-encapsulation of the conjugate into islet microcapsules. Dose-response curve revealed that the half-maximal effective dose (ED(50)) of VAPG was about 55 nm (25 nm for native GLP-1). By N-terminal analysis using aminopeptidase and RP-HPLC, it was confirmed that the lowered bioactivity of VAPG stemmed from the polymer conjugation to N-terminal histidine moieties, which actively participate in binding to GLP-1 receptors, resulting in only 16% of N-terminal histidine remaining intact after the conjugation reaction. Finally, the specific interaction of the VAPG with isolated rat islets was investigated. Total cellular cyclic AMP levels were measured and confocal microscopy was conducted using GLP-1 and VAPG labeled with fluorescent probes. It was found that VAPG effectively increased the cAMP level in islet cells in a glucose concentration-dependent manner. Moreover, the confocal microscopy study showed that the binding of VAPG occurs at the same location where GLP-1 binds but with less affinity than that of native GLP-1. In summary, a GLP-1/polymer conjugate was synthesized for the first time, and its bioactivity was examined, which must result from its specific interaction with isolated islets.

It is shown that the Einstein-Cartan-Liouville theory is understood as a theory which is the relativistic kinetic theory associating the spin effects in the space-time manifold with torsion.