Structure of focal adhesion kinase in healthy heart versus pathological cardiac hypertrophy: A modeling and simulation study

Focal adhesion kinase (FAK) is required for signaling in the heart. S910 phosphorylated FAK is known to cause pathological cardiac hypertrophy. The switching of FAK between its inactive (-i), activated (-a) and hyperactive (-h) state is controlled by phosphorylation. FAK consists of three domains, namely: FERM, Kinase, and FAT joined by linkers L1 and L2. The structural basis of FAK phosphorylation and signaling to the downstream pathways is not understood. In this work, we carried out homology modeling and domain assembly of full length human iFAK and aFAK. 100 ns classical molecular dynamic simulations were performed using AMBER14 and effect of S910 phosphorylation on FAK was investigated. The iFAK model superposed on a small angel X-ray scattering (SAXS) derived model with RMSD of 1.18 Å for 590 Cα atoms. aFAK showed S910 phosphorylation site in L2 shielded by FERM. S910 phosphorylation in hFAK led to its exposure accompanied by a large conformational change and exposing the previously buried Grb2 interaction site responsible for causing cardiac hypertrophy. The models of FAK are in agreement with diverse experimental data and observed differences in biological action. Understanding the structure activity relationships of FAK in response to phosphorylation is important for its future therapeutic modulation.     

The impact of electronic environmental knowledge on the environmental behaviors of people

Nowadays, we examined the connection between individuals' relationships with the natural environment, their environmental behaviors, and their environmental knowledge with a focus on environmental attitudes, environmental awareness, environmental values, public information, environmental skills, and environmental responsibility is very interesting. Environmental knowledge involves human discourse about interrelationships with the environment. The impact of people to perceive and interpret the relative health of environmental systems and to take appropriate action to maintain, restore, or improve the health of those systems is very important. Therefore, environmental teaching and learning is very necessary and can help to improve the environment behaviors of the people. The purpose of this paper is to investigate the impact of e-learning system the environmental of the behavior. Data were collected from 330 students of Tabriz Branch, Islamic Azad University. After performing satisfactory reliability and validity checks, the structural model was tested with the use of smart PLS 2.0. Results indicate that electronic environmental knowledge learning has a direct effect on person's environmental behaviors.     

Coping knowledge boundaries between information system and business disciplines: An intellectual capital perspective

Information system development can be considered a collaboration between users and developers. The inability to leverage the localized knowledge embedded in these two stakeholders hinders software development work to achieve high performance. Exploring the ways to counter this difficulty is then critical. This study applies an intellectual capital perspective to address the issues around spanning the knowledge boundary between developers and users. Our findings highlighted how important effective knowledge boundary spanning is to both product and project quality. Furthermore, three dimensions of intellectual capital increased the degree to which knowledge boundary spanning was effective.     

Regional evolution and waves of growth: A knowledge-based perspective

The evolution of a regional economy and its competitiveness capacity may involve multiple independent trajectories through which different sets of resources and capabilities evolve together. However, there is a dearth of evidence concerning how these trends are occurring across the globe. Based on the underlying tenets of the streams of research relating to regional competitiveness, knowledge cities/regions, and knowledge-based urban development, this paper seeks to present an empirical approach to establishing such evidence in relation to the recent development of the globe’s most productive regions from the viewpoint of their growth trajectories and the particular form of growth they are experiencing. The aim is to uncover the underlying structure of the changes in knowledge-based resources, capabilities and outputs across regions, and offer an analysis of these regions according to an uncovered set of key trends. The analysis identifies three key trends by which the economic evolution and growth patterns of these regions are differentiated – namely Fifth Wave Growth, Third & Fourth Wave Growth, and Government-led Third Wave Growth. Overall, spectacular knowledge-based growth of leading Chinese regions is evident, highlighting a continued shift of knowledge-based resources to Asia. In addition, a superstructure is observed at the global scale, consisting of two separate continuums that explicitly distinguish Chinese regions from the rest in terms of regional growth trajectories.     

Innovation quality in knowledge cities: Empirical evidence of innovation award competitions in Finland

Innovation awards have for long attracted policy makers as a method for innovation promotion. Still, academic research on innovation awards has thus far received little attention. In particular, empirical studies on the motives to enter award competitions and the realized impacts of winning an innovation award are scarce. This study addresses this research gap. Firm-level evidence, questionnaire data on innovation award winning companies of the Finnish national Innofinland and Quality Innovation of the Year award competitions, indicate that the motives for companies to participate in award competitions and the realized impacts of winning an award are largely the same: media coverage and a credibility boost. The importance of innovation awards in innovation policy was, however, considered only as mediocre or modest. As a conclusion it can be stated that innovation awards are an additional tool for innovation promotion, alongside innovation inducement policies including tax reductions and direct funding, as they produce significant positive effects for the award winning companies, and an additional indicator of innovation quality in the context of knowledge cities.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

Enhanced energy storage performance of BNT-ST based ceramics under low electric field via domain engineering

Lead-free bulk ceramics for advanced pulse power capacitors possess low recoverable energy storage density (W_rec) under low electric field. Sodium bismuth titanate (Bi_0.5Na_0.5TiO₃, BNT)-based ferroelectrics have attracted great attention due to their large maximum polarization (P_m) and high power density. The BNT-ST: xAlN ceramics are designed and fabricated to get high W_rec and large P_m under low electric field simultaneously. An excellent large P_m (49.04 μC/cm²) and W_rec (2.07 J/cm³) under low electric field (160 kV/cm) are acquired in BNT-ST: 0.1 wt% AlN. The domain structure evolution and polarization switching are investigated systematically using piezoresponse force microscopy (PFM). The introduction of AlN promotes the formation of thermal conductive network and the crystallization of ceramics, thus improving thermal stability and increasing P_m significantly. The higher density of domain walls and the larger negative built-in voltage may be beneficial to increase breakdown field strength (E_b), while the more 180° domains induce by electric field and the better domain switching behavior contribute to a significant increase in P_m. The enhanced E_b and super high P_m are favorable for obtaining high W_rec under low electric field which will boost the application of BNT-based ferroelectrics in advanced pulse power capacitors.     

Development of a knowledge library for automated watershed modeling

In this work, we develop a library of components for building semi-distributed watershed models. The library incorporates basic modeling knowledge that allows us to adequately model different water fluxes and nutrient loadings on a watershed scale. It is written in a formalism compliant with the equation discovery tool ProBMoT, which can automatically construct watershed models from the components in the library, given a conceptual model specification and measured data. We apply the proposed modeling methodology to the Ribeira da Foupana catchment to extract a set of viable hydrological models. By specifying the conceptual model and using the knowledge library, two different hydrological models are generated. Both models are automatically calibrated against measurements and the model with the lower root mean squared error (RMSE) value is selected as an appropriate hydrological model for the selected study area.