Chemical,Pharmacological, and in vitro Metabolic Stability Studies on Enantiomerically Pure RC‐33 Compounds: Promising Neuroprotective Agents Acting as σ1 Receptor Agonists

Our recent research efforts identified racemic RC‐33 as a potent and metabolically stable σ₁ receptor agonist. Herein we describe the isolation of pure RC‐33 enantiomers by chiral chromatography, assignment of their absolute configuration, and in vitro biological studies in order to address the role of chirality in the biological activity of these compounds and their metabolic processing. The binding of enantiopure RC‐33 to the σ₁ receptor was also investigated in silico by molecular dynamics simulations. Both RC‐33 enantiomers showed similar affinities for the σ₁ receptor and appeared to be almost equally effective as σ₁ receptor agonists. However, the R‐configured enantiomer showed higher in vitro hepatic metabolic stability in the presence of NADPH than the S enantiomer. Overall, the results presented herein led us to select (R)‐RC‐33 as the optimal candidate for further in vivo studies in an animal model of amyotrophic lateral sclerosis.     

Collaborations with Scientific Partners: The Mediating Role of the Social Context in Fostering Innovation Performance

The success of collaboration between firms and scientific partners is still debated. Although a firm's social context seems to be crucial to explain the success or failure of such collaboration in terms of innovation performance, a deep analysis of the context role is still lacking. Therefore, the aim of this paper is to analyse whether a firm's internal climate as well as the quality of the relationships with scientific partners can mediate the relationship between collaboration and innovation performance. Results, based on survey research developed in Finland, Italy and Sweden, suggest that collaborations with scientific partners achieve good results in terms of innovation performance, through an explanation path which involves a firm's social context.     

Automated classification of non-functional requirements

This paper describes a technique for automating the detection and classification of non-functional requirements related to properties such as security, performance, and usability. Early detection of non-functional requirements enables them to be incorporated into the initial architectural design instead of being refactored in at a later date. The approach is used to detect and classify stakeholders’ quality concerns across requirements specifications containing scattered and non-categorized requirements, and also across freeform documents such as meeting minutes, interview notes, and memos. This paper first describes the classification algorithm and then evaluates its effectiveness through reporting a series of experiments based on 30 requirements specifications developed as term projects by MS students at DePaul University. A new and iterative approach is then introduced for training or retraining a classifier to detect and classify non-functional requirements (NFR) in datasets dissimilar to the initial training sets. This approach is evaluated against a large free-form requirements document obtained from Siemens Logistics and Automotive Organization. Although to the NFR classifier is unable to detect all of the NFRs, it is useful for supporting an analyst in the error-prone task of manually discovering NFRs, and furthermore can be used to quickly analyse large and complex documents in order to search for NFRs.     

Switched nonlinear systems with state-dependent dwell-time

The asymptotic convergence of a switched nonlinear system in the presence of disturbances is studied. The system switches among a family of integral input-to-state stable systems. The time between two consecutive switchings is not less than a value τ_D. This dwell-time τ_D is allowed to take different values according to a function whose argument is the state of the system at the switching times. We propose a dwell-time function which depends on the comparison functions which characterize the integral input-to-state stability property and guarantees the state of the switched system to converge to zero under the action of disturbances with “bounded energy”. The main feature of the analysis is that it does not rely on the property for the switching to stop in finite time. The two important cases of locally exponentially stable and feedforward systems are analyzed in detail.     

Performance analysis of a new real-time elastographic time constant estimator

New elastographic techniques such as poroelastography and viscoelasticity imaging aim at imaging the temporal mechanical behavior of tissues. These techniques usually involve the use of curve fitting methods being applied to noisy data to estimate new elastographic parameters. As of today, however, current elastographic implementations of poroelastography and viscoelasticity imaging methods are in general too slow and not optimized for clinical applications. Furthermore, image quality performance of these new elastographic techniques is still largely unknown due to a paucity of data and the lack of systematic studies that analyze their performance limitations. In this paper, we propose a new elastographic time constant (TC) estimator, which is based on the use of the least square error (LSE) curve-fitting method and the Levenberg-Marquardt (LM) optimization rule as applied to noisy elastographic data obtained from a material in a creep-type experiment. The algorithm is executed on a massively parallel general purpose graphics processing unit (GPGPU) to achieve real-time performance. The estimator's performance is analyzed using simulations. Experimental results obtained from poroelastic phantoms are presented as a proof of principle of the new estimator's technical applicability on real experimental data. The results of this study demonstrate that the newly proposed elastographic estimator can produce highly accurate and sensitive elastographic TC estimates in real-time and at high signal-to-noise ratios.     

Analysis of the volatile fraction of “Pesto Genovese” by headspace sorptive extraction (HSSE)

This paper describes the development and application of a new analytical method for the analysis of the volatile fractions of pesto, a typical Italian basil-based pasta sauce, and its ingredients. The extraction of the volatile fraction was realized by headspace sorptive extraction (HSSE) and the extracted volatiles were then thermodesorbed, cryoconcentrated in a cooled injector and analysed by gas chromatography-mass spectrometry.