Unique hue data for colour appearance models. Part III: Comparison with NCS unique hues

In this study, Swedish Natural Color System (NCS) unique hue data were used to evaluate the performance of unique hue predictions by the CIECAM02 colour appearance model. The colour appearance of 108 NCS unique hue stimuli was predicted using CIECAM02, and their distributions were represented in a CIECAM02 a_c–b_c chromatic diagram. The best‐fitting line for each of the four unique hues was found using orthogonal distance regression in the a_c–b_c chromatic diagram. Comparison of these predicted unique hue lines (based on the NCS data) with the default unique hue loci in CIECAM02 showed that there were significant differences in both unique yellow (UY) and unique blue (UB). The same tendency was found for hue uniformity: hue uniformity is worse for UY and UB stimuli in comparison with unique red (UR) and unique green (UG). A comparison between NCS unique hue stimuli and another set of unique hue stimuli (obtained on a calibrated cathode ray tube) was conducted in CIECAM02 to investigate possible media differences that might affect unique hue predictions. Data for UY and UB are in very good agreement; largest deviations were found for UR. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 256–263, 2015     

Effects of Variations in Ligand Density on Cell Signaling

Multiple simultaneous interactions between receptors and ligands dictate the extracellular and intracellular activities of cells. The concept of programmable ligand display is generally used to study the interaction between ligands, displayed on surfaces at various densities, with receptors present on cell surfaces. Various strategies are discussed here to display ligands on surfaces to study their effect on cell behavior. Only very few strategies have been reported where this display combines precise control over density with lateral spacing of ligands on surfaces. In this review, selected examples of strategies to control ligand density and spacing and their implications for biological functions of cells are discussed.     

Modeling the recrystallization process using inverse cellular automata and genetic algorithms: Studies using differential evolution

An inverse modeling approach was taken up in this work to model the process of recrystallization using cellular automata (CA). Using this method after formulating a CA model of recrystallization, differential evaluation (DE), a real-coded variant of genetic algorithms, was used to search for the value of nucleation rate, providing an acceptable matching between the theoretical and experimentally observed values of fraction-recrystallized (X). Initially, the inverse modeling was attempted with a simple CA strategy, in which each of the CA cells had an equal probability of becoming nucleated. DE searched for the value of the nucleation rate yielding the best results for single-crystal iron at 550 °C. A good match could not be simultaneously achieved this way for the early stages of recrystallization as well as for the later stages. To overcome this difficulty, the CA grid was divided into two zones, having lower and higher probabilities of nucleation. This resulted in good correspondence between the predicted and experimental values of X for the entire duration of recrystallization. The introduction of a distribution in the probability of nucleation made the model even closer to the actual process, in which the probability of nucleation is often nonuniform due to nonuniformity in dislocation density as well as the presence of grain/interface boundaries.     

A Size‐Selective Intracellular Delivery Platform

Identifying and separating a subpopulation of cells from a heterogeneous mixture are essential elements of biological research. Current approaches require detailed knowledge of unique cell surface properties of the target cell population. A method is described that exploits size differences of cells to facilitate selective intracellular delivery using a high throughput microfluidic device. Cells traversing a constriction within this device undergo a transient disruption of the cell membrane that allows for cytoplasmic delivery of cargo. Unique constriction widths allow for optimization of delivery to cells of different sizes. For example, a 4 μm wide constriction is effective for delivery of cargo to primary human T‐cells that have an average diameter of 6.7 μm. In contrast, a 6 or 7 μm wide constriction is best for large pancreatic cancer cell lines BxPc3 (10.8 μm) and PANC‐1 (12.3 μm). These small differences in cell diameter are sufficient to allow for selective delivery of cargo to pancreatic cancer cells within a heterogeneous mixture containing T‐cells. The application of this approach is demonstrated by selectively delivering dextran‐conjugated fluorophores to circulating tumor cells in patient blood allowing for their subsequent isolation and genomic characterization.     

Oriented Nanofibrous Polymer Scaffolds Containing Protein‐Loaded Porous Silicon Generated by Spray Nebulization

Oriented composite nanofibers consisting of porous silicon nanoparticles (pSiNPs) embedded in a polycaprolactone or poly(lactide‐co‐glycolide) matrix are prepared by spray nebulization from chloroform solutions using an airbrush. The nanofibers can be oriented by an appropriate positioning of the airbrush nozzle, and they can direct growth of neurites from rat dorsal root ganglion neurons. When loaded with the model protein lysozyme, the pSiNPs allow the generation of nanofiber scaffolds that carry and deliver the protein under physiologic conditions (phosphate‐buffered saline (PBS), at 37 °C) for up to 60 d, retaining 75% of the enzymatic activity over this time period. The mass loading of protein in the pSiNPs is 36%, and in the resulting polymer/pSiNP scaffolds it is 3.6%. The use of pSiNPs that display intrinsic photoluminescence (from the quantum‐confined Si nanostructure) allows the polymer/pSiNP composites to be definitively identified and tracked by time‐gated photoluminescence imaging. The remarkable ability of the pSiNPs to protect the protein payload from denaturation, both during processing and for the duration of the long‐term aqueous release study, establishes a model for the generation of biodegradable nanofiber scaffolds that can load and deliver sensitive biologics.     

A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research span from online gaming to surgical robotics. The location of the hands, the alignment of the fingers, and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. In this scenario, it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling. When a user performs the same dynamic gesture, the hand shapes and speeds of each user, as well as those often generated by the same user, vary. A machine-learning-based Gesture Recognition Framework (ML-GRF) for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation. We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions, and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes. The findings from the simulation support the accuracy, precision, gesture recognition, sensitivity, and efficiency rates. The Machine Learning-based Gesture Recognition Framework (ML-GRF) had an accuracy rate of 98.97%, a precision rate of 97.65%, a gesture recognition rate of 98.04%, a sensitivity rate of 96.99%, and an efficiency rate of 95.12%.     

Generalized pointwise bias error bounds for response surface approximations

This paper proposes a generalized pointwise bias error bounds estimation method for polynomial‐based response surface approximations when bias errors are substantial. A relaxation parameter is introduced to account for inconsistencies between the data and the assumed true model. The method is demonstrated with a polynomial example where the model is a quadratic polynomial while the true function is assumed to be cubic polynomial. The effect of relaxation parameter is studied. It is demonstrated that when bias errors dominate, the bias error bounds characterize the actual error field better than the standard error. The bias error bound estimates also help to identify regions in the design space where the accuracy of the response surface approximations is inadequate. It is demonstrated that this information can be utilized for adaptive sampling in order to improve accuracy in such regions. Copyright © 2005 John Wiley & Sons, Ltd.