A generalized linear model with smoothing effects for claims reserving

In this paper, we continue the development of the ideas introduced in England and Verrall (2001) by suggesting the use of a reparameterized version of the generalized linear model (GLM) which is frequently used in stochastic claims reserving. This model enables us to smooth the origin, development and calendar year parameters in a similar way as is often done in practice, but still keep the GLM structure. Specifically, we use this model structure in order to obtain reserve estimates and to systemize the model selection procedure that arises in the smoothing process. Moreover, we provide a bootstrap procedure to achieve a full predictive distribution.     

Variance computations for functional of absolute risk estimates

We present a simple influence function based approach for computing the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates.     

Effect of acoustic frequency and power density on the aqueous ultrasonic-assisted extraction of grape pomace (Vitis vinifera L.) – A response surface approach

Aqueous ultrasound-assisted extraction (UAE) of grape pomace was investigated by Response Surface Methodology (RSM) to evaluate the effect of acoustic frequency (40, 80, 120 kHz), ultrasonic power density (50, 100, 150 W/L) and extraction time (5, 15, 25 min) on total phenolics, total flavonols and antioxidant capacity. All the process variables showed a significant effect on the aqueous UAE of grape pomace (p < 0.05). The Box–Behnken Design (BBD) generated satisfactory mathematical models which accurately explain the behavior of the system; allowing to predict both the extraction yield of phenolic and flavonol compounds, and also the antioxidant capacity of the grape pomace extracts. The optimal UAE conditions for all response factors were a frequency of 40 kHz, a power density of 150 W/L and 25 min of extraction time. Under these conditions, the aqueous UAE would achieve a maximum of 32.31 mg GA/100 g fw for total phenolics and 2.04 mg quercetin/100 g fw for total flavonols. Regarding the antioxidant capacity, the maximum predicted values were 53.47 and 43.66 mg Trolox/100 g fw for CUPRAC and FRAP assays, respectively. When comparing with organic UAE, in the present research, from 12% to 38% of total phenolic bibliographic values were obtained, but using only water as the extraction solvent, and applying lower temperatures and shorter extraction times. To the best of the authors’ knowledge, no studies specifically addressing the optimization of both acoustic frequency and power density during aqueous-UAE of plant materials have been previously published.     

Data analysis in stability studies of biopharmaceutical drugs with isothermal and non-isothermal assays

Stability is a particular problem for biopharmaceutical products because the efficacy of peptides and proteins as therapeutic or diagnostic agents can be affected during preparation, shipping, and storage. A particular formulation may have no immediately apparent effect on physical or chemical stability, and the time required for these studies at ambient temperature can be very lengthy because chemical reactions proceed relatively slowly at low temperatures. Undoubtedly, accelerated and stress testing of stability can provide useful information for future product development. The many methods used to study kinetics in aqueous solution may be experimental or computational. Experimental approaches may be isothermal or non-isothermal. Non-linear and linear regression methods can be used to analyze data from these experimental approaches, and the Monte Carlo method could be useful to obtain information about uncertainties in experimental data.The purpose of this review is to describe and to discuss options for the accelerated study of peptide and protein drugs. These options are not necessarily the same as those used for regulatory testing to set expiration dates. We also review statistical techniques to estimate kinetic parameters (rate constant, activation energy, and pre-exponential factor). Further, we establish the advantages and the limitations of both thermal approaches. We analyze and discuss all aspects using the most recent examples of peptide and protein stability.     

Protein digestion optimization for characterization of drug-protein adducts using response surface modeling

The formation of drug-protein adducts in vivo may have important clinical and toxicological implications. Consequently, there is a great interest in the detection of these adducts and the elucidation of their role in the processes leading to adverse and idiosyncratic drug reactions. Enzymatic digestion is a crucial step in bottom-up proteomics strategies for the analysis of drug-protein adducts. The chosen proteolytic enzyme and digestion conditions have a large influence on the protein coverage of the modified protein and identification of its modification site. In this work, the enzymatic digestion conditions (pH, temperature and time) of trypsin and thermolysin were optimized specifically for the characterization of Human Serum Albumin (HSA) adducts. Using a Design of Experiments (DOE), it was found that of the three optimized parameters mainly pH and temperature showed strong effects on both responses. The optimized digestion conditions were different from those obtained from the suppliers or literature. Their application to HSA adducts resulted in improved protein coverage and signal intensity regarding the peptide containing the modification site, thereby highlighting the importance of a detailed optimization of digestion conditions.     

Pathway and mechanism study on improvement of N2 selectivity of catalytic denitrification

Recently, nitrate pollution has attracted more public attention. In order to truly remove nitrate and reduce total nitrate content (TN) in water body, more harmless N₂ should be converted from nitrate. Studies on catalytic removal of nitrate in wastewater have been carried out. However, the catalytic performance still needs to be significantly improved, especially the N₂ selectivity. According to these, strategies that enable to efficiently improve N₂ selectivity of catalytic denitrification were explored in this paper. Results implied that the catalyst with composite carrier that possesses porous structure, large surface area, excellent electronic properties, and stable physical–chemical property tends to have better catalytic performance. It is suggested that acid washing by 2 mol/L HCl for certain carriers be applied to enhancement of N₂ selectivity. Additionally, higher N₂ conversion was also achieved by addition of sodium bis-2-ethylhexyl sulphosuccinate (AOT) onto Pd with the formation of Pd_AOT-Cu catalyst, which may be ascribed to the AOT that partially shields Pd active sites and inhibits hydrogen spillover from Pd to Cu. Response Surface Methodology (RSM) was utilized for experimental design and prediction of the optimal parameters. More N₂ was obtained under the predicted optimal conditions: 5.0 pH, 135 min time, 3.1 Pd: Cu, and 3.1 g/L Fe⁽⁰⁾.