Persistent changes in spinal cord gene expression after recovery from inflammatory hyperalgesia: A preliminary study on pain memory

Background  

Previous studies found that rats subjected to carrageenan injection develop hyperalgesia, and despite complete recovery in several days, they continue to have an enhanced hyperalgesic response to a new noxious challenge for more than 28d. The study's aim was to identify candidate genes that have a role in the formation of the long-term hyperalgesia-related imprint in the spinal cord. This objective was undertaken with the understanding that the long-lasting imprint of acute pain in the central nervous system may contribute to the transition of acute pain to chronicity.     

Structure-activity relationship in the case of intramolecular ortho-cyclization of aromatic nitroso oxides: Inverted steric effect of substituent in the 2-R-C6H4NOO transformation

A systematic theoretical study at the M06L/6-311+G(d, p) level of theory was carried out to calculate the activation barriers ΔH^≠ for the intramolecular ortho-cyclization of aromatic nitroso oxides 2-R-C₆H₄NOO and to reveal the effect of substituent nature and position in the benzene ring on the nitroso oxides reactivity. A set of 24 substituents with widely differing spatial and electronic properties (inductive, resonant, steric effects of R) was studied. The para-substituent was shown to have little effect on the ΔH^≠ value. The full set of effects of the R substituent contributes to the reactivity of ArNOO for 3-substituted aromatic nitroso oxides. In the case of 5-substituted ArNOO the Hammett-type relationship was obtain to describe inductive and resonant effects of R on the ortho-cyclization reactivity. The ortho-cyclization for 2-substituted nitroso oxides is a nontrivial example of the existence of an “inverted” steric effect, when an increase in substituent size accelerates intramolecular transformation. The substituent in position 6 also exhibits an “inverted” steric effect, but it is noticeably weaker than that for 2-R-C₆H₄NOO.     

Correlation of Electronic and Magnetic Properties of Thin Polymer Layers with Cobalt Nanoparticles

Nanoparticles (NPs) of cobalt are synthesized in shallow layers of polyimide using 40 keV implantation of Co⁺ ions with a few different fluences at various ion current densities. Nucleation of individual NPs at low fluencies and their percolation at high fluencies are crucial processes governing the electrical and magnetic properties of the metal/polymer nanocomposites that can be controlled by the implantation regimes. In particular, one can tune the magnetoresistance between negative and positive through appropriate choice of ion fluence and current density. The found non‐monotonous dependence of the magnetoresistance on the applied magnetic field allows suggestion of spin‐dependent domain wall scattering affecting the electron transport. The samples implanted with low fluencies demonstrate superparamagnetic behavior down to very low blocking temperatures. For high fluence (1.25 × 10¹⁷ cm⁻²) the transition to ferromagnetic ordering is observed that is related to the increased magnetic interaction of NPs.     

Bovine serum albumin as a universal suppressor of non-specific peptide binding in vials prior to nano-chromatography coupled mass-spectrometry analysis

Non-specific binding (NSB) is a well-known problem for any application that deals with ultralow analyte quantities. The modern nano-flow chromatography coupled tandem mass-spectrometry (nanoLC-MS/MS) works with the lowest conceivable analyte concentrations. However, while the NSB problem is widely accepted and investigated for metabolomics and single-peptide medicine-related assays, its impact is not studied for complex peptide mixtures in proteomic applications. In this work peptide NSB to a common plastic autosampler vial was studied for a model mixture of 46 synthetic peptides. A significant NSB level was demonstrated for total peptide concentrations of up to 1 mg mL⁻¹. Different agents were tried for NSB suppression and compatibility with nanoLC-MS/MS analysis: a chaotropic agent, an amino acid mixture, a peptide mixture and a protein solution. The first two were inefficacious. The peptide matrix blocked NSB, however, it also led to analyte ionization suppression in nanoLC-MS/MS. The protein solution (0.1% BSA) efficiently eliminated NSB, while a trap-elute nanoHPLC configuration together with a small-pore reverse-phased sorbent effectively and quantitatively extracted the model peptides and depleted protein material from the sample. Higher protein concentration partially impeded peptide extraction. Thus, the 0.1% BSA solution might be regarded as an effective non-interfering blockader of NSB for sample resuspension and storage in an autosampler prior to LC-MS/MS analysis.     

Application of machine learning techniques for supply chain demand forecasting

Full collaboration in supply chains is an ideal that the participant firms should try to achieve. However, a number of factors hamper real progress in this direction. Therefore, there is a need for forecasting demand by the participants in the absence of full information about other participants’ demand. In this paper we investigate the applicability of advanced machine learning techniques, including neural networks, recurrent neural networks, and support vector machines, to forecasting distorted demand at the end of a supply chain (bullwhip effect). We compare these methods with other, more traditional ones, including naïve forecasting, trend, moving average, and linear regression. We use two data sets for our experiments: one obtained from the simulated supply chain, and another one from actual Canadian Foundries orders. Our findings suggest that while recurrent neural networks and support vector machines show the best performance, their forecasting accuracy was not statistically significantly better than that of the regression model.