Network centrality, knowledge searching and creativity: The role of domain

This study aims to determine the role of knowledge searching on creativity in the fields of science research and technology development. Creativity is a process of knowledge combination, thus internal and external knowledge searching is important for creativity in both fields, particularly in the open innovation age. However, the nature of the work across these fields is different. While science research aims to solve theoretical problems and generate new knowledge, technology development aims to apply new knowledge to solve practical problems. Compared to science research, technology development has clear task goals, which make it easier to identify the related external knowledge and integrate this knowledge and in turn improve employee creativity. Thus, employees\u27 attention to external knowledge as well as the influence of external knowledge on creativity might be different in the two fields. Results based on an empirical study of 211 employees from science research and 257 employees from technology development showed that external knowledge searching increased employee creativity in the field of technology development but not in science research. Furthermore, employees\u27 centrality in the intra-team problem-solving network moderated the relationship between external knowledge searching and creativity in the science research field. Suggestions about employee creativity management in science and technology fields are discussed

Pathologically Activated Neuroprotection via Uncompetitive Blockade of \u3cem\u3eN\u3c/em\u3e-Methyl-d-aspartate Receptors with Fast Off-rate by Novel Multifunctional Dimer Bis(propyl)-cognitin

Uncompetitive N-methyl-d-aspartate (NMDA) receptor antagonists with fast off-rate (UFO) may represent promising drug candidates for various neurodegenerative disorders. In this study, we report that bis(propyl)-cognitin, a novel dimeric acetylcholinesterase inhibitor and γ-aminobutyric acid subtype A receptor antagonist, is such an antagonist of NMDA receptors. In cultured rat hippocampal neurons, we demonstrated that bis(propyl)-cognitin voltage-dependently, selectively, and moderately inhibited NMDA-activated currents. The inhibitory effects of bis(propyl)-cognitin increased with the rise in NMDA and glycine concentrations. Kinetics analysis showed that the inhibition was of fast onset and offset with an off-rate time constant of 1.9 s. Molecular docking simulations showed moderate hydrophobic interaction between bis(propyl)-cognitin and the MK-801 binding region in the ion channel pore of the NMDA receptor. Bis(propyl)-cognitin was further found to compete with [3H]MK-801 with a Ki value of 0.27 μm, and the mutation of NR1(N616R) significantly reduced its inhibitory potency. Under glutamate-mediated pathological conditions, bis(propyl)-cognitin, in contrast to bis(heptyl)-cognitin, prevented excitotoxicity with increasing effectiveness against escalating levels of glutamate and much more effectively protected against middle cerebral artery occlusion-induced brain damage than did memantine. More interestingly, under NMDA receptor-mediated physiological conditions, bis(propyl)-cognitin enhanced long-term potentiation in hippocampal slices, whereas MK-801 reduced and memantine did not alter this process. These results suggest that bis(propyl)-cognitin is a UFO antagonist of NMDA receptors with moderate affinity, which may provide a pathologically activated therapy for various neurodegenerative disorders associated with NMDA receptor dysregulation

Effective and fast-screening route to evaluate dynamic elastomer-filler network reversibility for sustainable rubber composite design

.The introduction of self-healing and reprocessability into conventional vulcanized rubbers has been recognized as a promising strategy to promote elastomer circularity. However, the reversibility and recovery of cross-linking polymer networks have often been assessed by static mechanical testing, which highly limits the understanding of the underlying microscale mechanisms. In this work, we investigated the network recovery of natural rubber (NR)/carbon black (CB) nanocomposites using Fourier transform (FT) rheology coupled with large amplitude oscillation shear (LAOS) technology across linear and nonlinear regimes (0.01–500%). The self-healing process of the rubber composite networks was monitored by using a programmed time–temperature oscillation shear measurement. The role of CB particle size in the filler network recovery was also discussed from the perspective of strain-induced crystallization of NR. Coupling FT-rheology and LAOS analysis, two distinct nonlinear enhancement behaviors beyond the linear viscoelastic regime were detected in the rubber nanocomposites, which were ascribed to the filler network disruption followed by the polymer network deformation. The relationship of the nonlinearity parameter I3/1 as a function of strain amplitude was selected to quantify the nonlinear rheological responses, where the role of the filler and polymer on the network recovery can therefore be differentiated. This work provides an efficient method to evaluate the self-healing and reprocessability of cross-linked rubbers and offers a fast-screen route for formulation development and sustainable rubber composite design

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI

Effective and Fast-Screening Route to Evaluate Dynamic Elastomer-Filler Network Reversibility for Sustainable Rubber Composite Design

The introduction of self-healing and reprocessability into conventional vulcanized rubbers has been recognized as a promising strategy to promote elastomer circularity. However, the reversibility and recovery of cross-linking polymer networks have often been assessed by static mechanical testing, which highly limits the understanding of the underlying microscale mechanisms. In this work, we investigated the network recovery of natural rubber (NR)/carbon black (CB) nanocomposites using Fourier transform (FT) rheology coupled with large amplitude oscillation shear (LAOS) technology across linear and nonlinear regimes (0.01–500%). The self-healing process of the rubber composite networks was monitored by using a programmed time–temperature oscillation shear measurement. The role of CB particle size in the filler network recovery was also discussed from the perspective of strain-induced crystallization of NR. Coupling FT-rheology and LAOS analysis, two distinct nonlinear enhancement behaviors beyond the linear viscoelastic regime were detected in the rubber nanocomposites, which were ascribed to the filler network disruption followed by the polymer network deformation. The relationship of the nonlinearity parameter I3/1 as a function of strain amplitude was selected to quantify the nonlinear rheological responses, where the role of the filler and polymer on the network recovery can therefore be differentiated. This work provides an efficient method to evaluate the self-healing and reprocessability of cross-linked rubbers and offers a fast-screen route for formulation development and sustainable rubber composite design

Uncovering vein patterns from color skin images for personal identification in forensic investigation

Recent technological advances have allowed for a proliferation of digital evidence images. Using these images as evidence in legal cases (e.g. child sexual abuse, child pornography and masked gunmen) can be very challenging, because the faces of criminals or victims are not visible. Although large skin marks and tattoos have been used, they are ineffective in some legal cases, because the skin exposed in evidence images have neither unique tattoos nor enough skin marks for identification. The blood vessel between the skin and the muscle covering most parts of the human body is a powerful biometric trait, because of its universality, permanence and distinctiveness. Traditionally, it was impossible to use vein patterns for forensic identification, because they were not visible in color images. All the current vein recognition systems developed by companies and research laboratories rely on near infrared (NIR) imaging devices to capture high quality vein patterns from hand and wrist, where skin is relatively thin, for commercial applications. Up until now, no one studies vein patterns for forensic identification because no method has been developed to visualize vein patterns hidden in color images. The primary aim of this research is to develop algorithms for visualizing vein patterns hidden in color images so that criminal and victim identification can be performed based on vein patterns. The secondary aim of this research is to develop algorithms for removing JPEG blocking artifacts in skin images that adversely affect forensic recognition. We propose two approaches for uncovering vein patterns from color skin images. The first approach is based on RGB-NIR mapping. It extracts information from a pair of synchronized color and NIR images and uses a neural network (NN) to map RGB values to NIR intensities. Furthermore, we design an automatic intensity adjustment scheme for illumination compensation and an NN weight adjustment scheme for improving the robustness of the approach. Using an automatic matching algorithm, we match resultant images from the RGB-NIR mapping approach and find that its matching result is comparable to the result from matching NIR images, which are always considered as ground truth of vein patterns. In the second approach, we use principles of optics and skin biophysics for uncovering vein patterns. It inverses the process of skin color formation in an image and derives the corresponding biophysical parameters, where veins can be observed. Based on this approach we develop four optical models for simulating skin color formation. They are all based on the radiative transfer equation which quantitatively describes transport of light in the human skin. The first and second optical models use the Kubelka-Munk (K-M) model to approximate the solution of the radiative transfer equation, whose exact analytical solution has not yet been obtained for complex and multiple scattering media such as human skin. In these two models, we assume that the optical properties of human skin is determined by three layers – the stratum corneum, the epidermis, and the dermis, and veins are located in the dermis. To overcome the limits of the first model, the second optical model uses a color optimization scheme and the automatic intensity adjustment scheme. The third and fourth optical models use Reichman’s solution to the radiative transfer equation. In the fourth optical model, we add the fourth layer, the hypodermis consisting of adipose and blood vessels to the skin structure. Because none of the models can provide exact and complete vein patterns, we propose a method to fuse the vein patterns obtained from different models. Experimental evaluations show that the fusion results are much better than any of the single models and also the RGB-NIR mapping approach. Its matching result is even better than matching NIR images. Furthermore, we develop two specific approaches to remove blocking artifacts in JPEG-compressed skin images. The first one is a maximum-a-posteriori (MAP)-based approach which formulates skin image deblocking as an estimation problem, and embeds statistical information of skin images into a MAP model to perform the estimation. The second one is a knowledge-based approach which extracts prior knowledge of skin images from a training set, and uses it to infer original blocks in compressed evidence images. Two inference schemes, a block synthesis algorithm and an indexing mechanism are also proposed for this approach. Both approaches guarantee that the resultant and compressed images have the same quantized DCT coefficients. Experimental results demonstrate that the approaches perform better than other methods. In this research, we break the limit of traditional vein recognition and show its potential for forensic analysis. According to our best knowledge, no one did similar research before.DOCTOR OF PHILOSOPHY (SCE