Simultaneous monitoring of multiple hormones from human islets of Langerhans using solid-phase extraction-mass spectrometry

Islets of Langerhans release peptide hormones in controlled amounts and patterns to ensure proper maintenance of blood glucose levels. The overall release of the hormones is shaped by external factors and by autocrine and paracrine interactions occurring within the islets. To better understand what controls the secretion of islet-secreted peptides, and how these processes go awry in diabetes, methods to monitor the release of multiple hormones simultaneously are needed. While antibody-based assays are typically used, they are most often applied to quantification of a single hormone. Mass spectrometry (MS), on the other hand, is well suited for quantifying multiple hormones simultaneously but typically requires time-consuming separation steps with biological samples. In this report, response surface methodology was used to identify a set of optimal solid-phase extraction (SPE) conditions for the islet-secreted peptides, insulin, C-peptide, glucagon, and somatostatin. The optimized SPE method was used with multiple reaction monitoring and isotopically labeled standards to quantify secretion levels. Calibrations were linear from 0.5 to 50 nM with < 15% RSD peak area ratios. A microfluidic system was used to perfuse 30 human islets with different glucose conditions, and fractions were collected every 2 min for SPE-MS analysis. Results showed the release dynamics of the individual peptides, as well as patterns, such as positively and negatively correlated release and oscillations. This rapid SPE-MS method is expected to be useful for examining other peptide and small-molecule secretions from islets and could be applied to a number of other biological systems for investigating cellular communication.

The influence of leader-follower cognitive style congruence on organizational citizenship behaviors and the mediating role of trust

This study examined the impact of cognitive style congruence between leaders and followers on followers' organizational citizenship behaviors (OCBs) by integrating the similarity-attraction and signaling theories. We collected dyadic data from 80 leaders and 223 followers in ten manufacturing companies in China. Using polynomial regression analysis and response surface modeling, the study supported the positive influence of cognitive style congruence on followers' OCBs. Specifically, we found that dyads with more intuitive than analytical leader-follower cognitive styles had higher levels of OCBs. However, there were no significant differences in followers' OCBs between dyads consisting of an intuitive leader and an analytic follower versus those consisting of an analytic leader and an intuitive follower under conditions of cognitive style incongruence. Additionally, the study found that interpersonal trust mediated the relationship between leader-follower cognitive style congruence and followers' OCBs, offering valuable insights for promoting OCBs in the workplace.

Application of response surface modeling optimization in UV spectrophotometric determination of 4-aminobenzoic acid by molecularly imprinted polypyrrole

Selective determination of 4-aminobenzoic acid (ABA) in pharmaceutical and human serum samples was performed by molecularly imprinted polypyrrole and ultraviolet (UV) spectrophotometry, based on precipitation polymerization. The molecularly imprinted polymer (MIP) was prepared using pyrrole functional monomer and ABA template molecules. The significant factors controlling the performance of the synthesized ABAMIP sorbent were screened and optimized using Plackett- Burman design (PBD) and central composite design (CCD), respectively. The model was used to obtain the optimal values of the significant response factors. The predicted MIP to NIP response ratio demonstrated an approximate deviation of 5 % from the experimental value. Under the obtained optimal conditions, the calibration curve showed a linear range of 0.05-2 mM with a correlation coefficient (r²) of 0.9920 and a limit of detection (LOD) of 0.0310 mM. The method recovery for the analyte was obtained 88.10-100.5 in the investigated real samples. The proposed ABA-MIP sorbent showed an acceptable selectivity in the presence of some pharmaceuticals.

Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO₂ P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO₂ P25 in the roles such as an adsorbent and photocatalyst. The effect of pH and γ(TiO₂ P25) as variables on the extent of removal of organics by adsorption on TiO₂ P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of the studied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of the studied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO₂ P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO₂ P25, providing insights into influential structural features facilitating adsorption process.

Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China

Quantification of source impacts and contributions is a key element for the design of effective air pollution control policies. In this study, O₃ source impacts and contributions were comprehensively assessed over the Pearl River Delta (PRD) region of China using brute-force method (BFM), response surface modeling with BFM (RSM-BFM) and differential method (RSM-DM) respectively, high-order decoupled direct method (HDDM), and ozone source apportionment technology (OSAT). The multi-modeling comparison results indicated that under typical nonlinear atmospheric conditions during the O₃ formation, BFM, RSM-BFM, and HDDM seemed to be appropriate for assessing the impact of single source emissions; however, the results of HDDM could deviate from those of BFM when the emission reduction ratio was higher than 50 %. Under multi-source control scenarios, the results of source contribution analyses obtained from RSM-DM and OSAT were reasonably well, but the performance of OSAT was limited by its capability in representing the nonlinearity of O₃ response to emission reductions of its precursors, particularly NO_x. The results of this pilot study in the PRD showed that the RSM-DM appeared to replicate the nonlinearity of O₃ chemistry reasonably well (e.g., O₃ disbenefits due to local NO_x emission reductions in Guangzhou city). Based on the source contribution results, on-road mobile (including both NO_x and VOC emissions) and industrial process (mainly VOC emissions) sources were identified as two major contribution sectors by both RSM-DM and OSAT, contributing an average of 31.5 % and 11.4 % (estimated by RSM-DM) and 29.2 % and 13.0 % (estimated by OSAT) respectively to O₃ formation in 9 cities of the PRD. Therefore, the reinforced emission reductions on NO_x and VOC from on-road mobile and industrial process sources in the central cities (i.e., Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan) were suggested to effectively mitigate the ambient O₃ levels in the PRD.

Real-time source contribution analysis of ambient ozone using an enhanced meta-modeling approach over the Pearl River Delta Region of China

The nonlinear response of O₃ to nitrogen oxides (NO_x) and volatile organic compounds (VOC) is not conducive to accurately identify the various source contributions and O₃-NO_x-VOC relationships. An enhanced meta-modeling approach, polynomial functions based response surface modeling coupled with the sectoral linear fitting technique (pf-ERSM-SL), integrating a new differential method (DM), was proposed to break through the limitation. The pf-ERSM-SL with DM was applied for analysis of O₃ formation regime and real-time source contributions in July and October 2015 over the Pearl River Delta Region (PRD) of Mainland China. According to evaluations, the pf-ERSM-SL with DM was proven to be effective in source apportionment when the traditional sensitivity analysis was unsuitable for deriving the source contributions in the nonlinear system. After diagnosing the O₃-NO_x-VOC relationships, O₃ formation in most regions of the PRD was identified as a distinctive NO_x-limited regime in July; in October, the initial VOC-limited regime was found at small emission reductions (less than 22-44%), but it will transit to NO_x-limited when further reductions were implemented. Investigation of the source contributions suggested that NO_x emissions were the dominated contributor when turning-off the anthropogenic emissions, occupying 85.41-94.90% and 52.60-75.37% of the peak O₃ responses in July and October respectively in the receptor regions of the PRD; NO_x emissions from the on-road mobile source (NO_x_ORM) in Guangzhou (GZ), Dongguan&Shenzhen (DG&SZ) and Zhongshan (ZS) were identified as the main contributors. Consequently, the reinforced control of NO_x_ORM is highly recommended to lower the ambient O₃ in the PRD effectively.

Automatic soft independent modeling for class analogies

Soft independent modeling of class analogy (SIMCA) is an important method for authentication. The key parameters for SIMCA, the number of principal components and the decision threshold, determine the model's performance. In this report, a self-optimizing SIMCA that automatically determines these two parameters is devised and referred to as automatic SIMCA (aSIMCA). An efficient optimization is obtained by incorporating response surface modeling (RSM) and bootstrapped Latin partitions with the model-building dataset. A set of design points over the ranges of the two parameters are evaluated with respect to sensitivity and specificity by using the model-building data from target and non-target classes. Averages of the sensitivity and specificity are used as responses for the design points. A 2-dimensional interpolation and a bivariate cubic polynomial were used to model the response surface. As a control method, a grid search that evaluates all combinations of the two parameters over the same ranges was performed in parallel to determine the best conditions for SIMCA and the modeling performance was compared to aSIMCA with RSM. The developed aSIMCA methods were evaluated by authenticating two botanical extracts sets, i.e., marijuana and hemp, with spectral datasets collected from various spectroscopic techniques, including nuclear magnetic resonance, high-resolution mass, and ultraviolet spectrometry. Results of a paired t-test indicated that the aSIMCA with the RSM had similar performance with the one optimized by the grid search for modeling marijuana and hemp, while the RSM was more computationally efficient. The 2-dimensional interpolation is preferred because the better efficiency and the fit to the response surface is more precise.

Environmentally benign nanometric neem-laced urea emulsion for controlling mosquito population in environment

The increasing risk of vector-borne diseases and the environmental pollution in the day-to-day life due to the usage of the conventional pesticides makes the role of nanotechnology to come into the action. The current study deals with one of the applications of nanotechnology through the formulation of neem urea nanoemulsion (NUNE). NUNE was formulated using neem oil, Tween 20, and urea using the microfluidization method. Prior to the development of nanoemulsion, the ratio of oil/surfactant/urea was optimized using the response surface modeling method. The mean droplet size of the nanoemulsion was found to be 19.3 ± 1.34 nm. The nanoemulsion was found to be stable for the period of 4 days in the field conditions which aids to its mosquitocidal activity. The nanoemulsion exhibited a potent ovicidal and larvicidal activity against A. aegypti and C. tritaeniorhynchus vectors. This result was corroborated with the histopathological analysis of the NUNE-treated larvae. Further, the effect of NUNE on the biochemical profile of the target host was assessed and was found to be efficacious compared to the bulk counterpart. The nanoemulsion was then checked for its biosafety towards the non-target species like plant beneficial bacterium (E. ludwigii), and phytotoxicity was assessed towards the paddy plant (O. sativa). Nanometric emulsion at the concentration used for the mosquitocidal application was found to be potentially safe towards the environment. Therefore, the nanometric neem-laced urea emulsion tends to be an efficient mosquito control agent with an environmentally benign property.

Use of Mixture Designs to Investigate Contribution of Minor Sex Pheromone Components to Trap Catch of the Carpenterworm Moth, Chilecomadia valdiviana

Field experiments were carried out to study responses of male moths of the carpenterworm, Chilecomadia valdiviana (Lepidoptera: Cossidae), a pest of tree and fruit crops in Chile, to five compounds previously identified from the pheromone glands of females. Previously, attraction of males to the major component, (7Z,10Z)-7,10-hexadecadienal, was clearly demonstrated while the role of the minor components was uncertain due to the use of an experimental design that left large portions of the design space unexplored. We used mixture designs to study the potential contributions to trap catch of the four minor pheromone components produced by C. valdiviana. After systematically exploring the design space described by the five pheromone components, we concluded that the major pheromone component alone is responsible for attraction of male moths in this species. The need for appropriate experimental designs to address the problem of assessing responses to mixtures of semiochemicals in chemical ecology is described. We present an analysis of mixture designs and response surface modeling and an explanation of why this approach is superior to commonly used, but statistically inappropriate, designs.

The effect of temperature and methanol-water mixture on pressurized hot water extraction (PHWE) of anti-HIV analogoues from Bidens pilosa

Background

Pressurized hot water extraction (PHWE) technique has recently gain much attention for the extraction of biologically active compounds from plant tissues for analytical purposes, due to the limited use of organic solvents, its cost-effectiveness, ease-of-use and efficiency. An increase in temperature results in higher yields, however, issues with degradation of some metabolites (e.g. tartrate esters) when PHWE is conditioned at elevated temperatures has greatly limited its use. In this study, we considered possibilities of optimizing PHWE of some specific functional metabolites from Bidens pilosa using solvent compositions of 0, 20, 40 and 60 % methanol and a temperature profile of 50, 100 and 150 °C.

Results

The extracts obtained were analyzed using UPLC-qTOF-MS/MS and the results showed that both temperature and solvent composition were critical for efficient recovery of target metabolites, i.e., dicaffeoylquinic acid (diCQA) and chicoric acid (CA), which are known to possess anti-HIV properties. It was also possible to extract different isomers (possibly cis-geometrical isomers) of these molecules. Significantly differential (p ≤ 0.05) recovery patterns corresponding to the extraction conditions were observed as recovery increased with increase in methanol composition as well as temperature. The major compounds recovered in descending order were 3,5-diCQA with relative peak intensity of 204.23 ± 3.16 extracted at 50 °C and 60 % methanol; chicoric acid (141.00 ± 3.55) at 50 °C and 60 % methanol; 4,5-diCQA (108.05 ± 4.76) at 150 °C and 0 % methanol; 3,4-diCQA (53.04 ± 13.49) at 150 °C and 0 % methanol; chicoric acid isomer (40.01 ± 1.14) at 150 °C and 20 % methanol; and cis-3,5-diCQA (12.07 ± 5.54) at 100 °C and 60 % methanol. Fitting the central composite design response surface model to our data generated models that fit the data well with R² values ranging from 0.57 to 0.87. Accordingly, it was possible to observe on the response surface plots the effects of temperature and solvent composition on the recovery patterns of these metabolites as well as to establish the optimum extraction conditions. Furthermore, the pareto charts revealed that methanol composition had a stronger effect on extraction yield than temperature.

Conclusion

Using methanol as a co-solvent resulted in significantly higher (p ≤ 0.05) even at temperatures as low as 50 °C, thus undermining the limitation of thermal degradation at higher temperatures during PHWE.

Development and case study of a science-based software platform to support policy making on air quality

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling (RSM) methodology and serves as a visualization and analysis tool (VAT) for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S. demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias <2% and assisting in air quality policy making in near real time.

Optimization of recovery patterns in common carp exposed to roundup using response surface methodology: evaluation of neurotoxicity and genotoxicity effects and biochemical parameters

The present study is the first report on optimization of recovery conditions of fishes exposed to pesticides using response surface methodology-central composite rotatable design (RSM-CCRD). The sub-lethal toxicity bioassay of Roundup® (2 ppm ~10 percent LC₅₀, 96 h) in common carp (1, 4, 9, 16, 25, 35 and 40 day) was investigated. After exposure for 16 days to Roundup®, some the fishes were introduced to herbicide-free water. The effects of four recovery parameters including time (5-25 d), temperature (18-26 °C), water exchange rate (WER, 10-30), and salinity (0-8 ppt) on the levels of biomarkers of genotoxicity (DNA damage), neurotoxicity (acetylcholinesterase activity (AChE)), and the serum alanine (ALT) and aspartate (AST) aminotransferase in plasma were studied. The polynomial equations were significantly fitted for all response variables with high R² values (>0.95), which revealed no indication of lack of fit. The optimum conditions for the maximum AChE activity (37.14 nmol/min/mg protein) and the minimum levels of DNA damage (8.00 percent tail DNA), ALT (27.0 IU/L) and AST (91.0 IU/L) were time of 20 d, temperature of 20 °C, WER of 25 and water salinity of 6 ppt. Thus, a promising improvement for the recovery trend of fishes exposed to Roundup® stress was obtained under the optimized conditions using RSM-CCRD.