Protease Substrate-Independent Universal Assay for Monitoring Digestion of Native Unmodified Proteins

Proteases are a group of enzymes with a catalytic function to hydrolyze peptide bonds of proteins. Proteases regulate the activity, signaling mechanism, fate, and localization of many proteins, and their dysregulation is associated with various pathological conditions. Proteases have been identified as biomarkers and potential therapeutic targets for multiple diseases, such as acquired immunodeficiency syndrome, cardiovascular diseases, osteoporosis, type 2 diabetes, and cancer, where they are essential to disease progression. Thus, protease inhibitors and inhibitor-like molecules are interesting drug candidates. To study proteases and their substrates and inhibitors, simple, rapid, and sensitive protease activity assays are needed. Existing fluorescence-based assays enable protease monitoring in a high-throughput compatible microtiter plate format, but the methods often rely on either molecular labeling or synthetic protease targets that only mimic the hydrolysis site of the true target proteins. Here, we present a homogenous, label-free, and time-resolved luminescence utilizing the protein-probe method to assay proteases with native and denatured substrates at nanomolar sensitivity. The developed protein-probe method is not restricted to any single protein or protein target class, enabling digestion and substrate fragmentation studies with the natural unmodified substrate proteins. The versatility of the assay for studying protease targets was shown by monitoring the digestion of a substrate panel with different proteases. These results indicate that the protein-probe method not only monitors the protease activity and inhibition, but also studies the substrate specificity of individual proteases.     

Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks

Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.     

Modeling of the fluid structure interaction of a human trachea under different ventilation conditions

This work is focused on the analysis of the response of the tracheal wall to different ventilation conditions. Thus, a finite element model of a human trachea is developed and used to analyze its deformability under normal breathing and mechanical ventilation. The geometry of the trachea is obtained from computed tomography (CT) images of a healthy man. A fluid structure interaction approach is used to analyze the deformation of the wall when the fluid (in this case, air) moves inside the trachea. A structured hexahedral-based grid for the tracheal walls and an unstructured tetrahedral-based mesh with coincident nodes for the fluid are used to perform the simulations with the finite element-based commercial software code (ADINA R & D Inc.). The tracheal wall is modeled as a fiber reinforced hyperelastic solid material in which the anisotropy due to the orientation of the fibers is introduced. Deformation of the tracheal walls is analyzed under different conditions. Normal breathing is performed assuming a sinus shape of the pressure at the inlet and air speed at the outlet based on real data which represent the inspiration and the expiration processes respectively. Mechanical ventilation is simulated as smooth square shape velocity airflow considering positive values of pressure using data from a mechanical ventilation machine. Deformations of the tracheal cartilage rings and of the muscle membrane, as well as the maximum principal stresses in the wall, are analyzed. The results show that, although the deformation and stresses are quite small for both conditions, forced ventilation does not exactly imitate the physiological response of the trachea, since with always positive pressure values the trachea does not collapse during mechanical breathing.     

Structural,Micromechanical and Tribological Analyses of Electrodeposited Nickel–Graphite Coatings with Different Fractions of Graphite Microparticles

Nickel–graphite composite coatings were electrodeposited on a steel substrate from typical watts bath and with specific operating conditions for different weight fractions of graphite microparticles. The weight percentage of particles in coatings and the microstructure were studied, respectively, by atomic absorption, scanning electron microscope and X-ray diffraction. The influence of graphite fraction on roughness and microhardness were also investigated. It was found that roughness increases with the increase of particle content while microhardness decreases. The tribological response was examined against high chromium steel ball using linear reciprocating tribometer. The results indicated that the friction coefficient decreases when graphite content increases. This was ascribed to the development of tribo-layer on the wear track and transfer film on the counterface. However, it was found that the improvement of wear resistance was obtained until an optimal value of graphite concentration, which provided the best condition that promoted the tribo-layer stability and maintained the matrix integrity.     

Thermal Shift Assay for Small GTPase Stability Screening: Evaluation and Suitability

Thermal unfolding methods are commonly used as a predictive technique by tracking the protein’s physical properties. Inherent protein thermal stability and unfolding profiles of biotherapeutics can help to screen or study potential drugs and to find stabilizing or destabilizing conditions. Differential scanning calorimetry (DSC) is a ‘Gold Standard’ for thermal stability assays (TSA), but there are also a multitude of other methodologies, such as differential scanning fluorimetry (DSF). The use of an external probe increases the assay throughput, making it more suitable for screening studies, but the current methodologies suffer from relatively low sensitivity. While DSF is an effective tool for screening, interpretation and comparison of the results is often complicated. To overcome these challenges, we compared three thermal stability probes in small GTPase stability studies: SYPRO Orange, 8-anilino-1-naphthalenesulfonic acid (ANS), and the Protein-Probe. We studied mainly KRAS, as a proof of principle to obtain biochemical knowledge through TSA profiles. We showed that the Protein-Probe can work at lower concentration than the other dyes, and its sensitivity enables effective studies with non-covalent and covalent drugs at the nanomolar level. Using examples, we describe the parameters, which must be taken into account when characterizing the effect of drug candidates, of both small molecules and Designed Ankyrin Repeat Proteins.     

Fully probabilistic control for uncertain nonlinear stochastic systems

This paper develops a novel probabilistic framework for stochastic nonlinear and uncertain control problems. The proposed framework exploits the Kullback–Leibler divergence to measure the divergence between the distribution of the closed-loop behavior of a dynamical system and a predefined ideal distribution. To facilitate the derivation of the analytic solution of the randomized controllers for nonlinear systems, transformation methods are applied such that the dynamics of the controlled system becomes affine in the state and control input. Additionally, knowledge of uncertainty is taken into consideration in the derivation of the randomized controller. The derived analytic solution of the randomized controller is shown to be obtained from a generalized state-dependent Riccati solution that takes into consideration the state- and control-dependent functional uncertainty of the controlled system. The proposed framework is demonstrated on an inverted pendulum on a cart problem, and the results are obtained.