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Dilated cardiomyopathy (DCM) is the third most common cause of heart failure and the primary reason for heart transplantation; upward of 70% of DCM cases are considered idiopathic. Our in-vitro experiments showed that reduced hybrid/complex N-glycosylation in mouse cardiomyocytes is linked with DCM. Further, we observed direct effects of reduced N-glycosylation on K v gating. However, it is difficult to rigorously determine the effects of glycosylation on K v activity, because there are multiple K v isoforms in cardiomyocytes contributing to the cardiac excitation. Due to complex functions of K v isoforms, only the sum of K + currents (I Ksum ) can be recorded experimentally and decomposed later using exponential fitting to estimate component currents, such as I Kto , I Kslow , and I Kss . However, such estimation cannot adequately describe glycosylation effects and K v mechanisms. Here, we propose a framework of simulation modeling of K v kinetics in mouse ventricular myocytes and model calibration using the in-vitro data under normal and reduced glycosylation conditions through ablation of the Mgat1 gene (i.e., Mgat1KO). Calibrated models facilitate the prediction of K v characteristics at different voltages that are not directly observed in the in-vitro experiments. A modelmore »calibration procedure is developed based on the genetic algorithm. Experimental results show that, in the Mgat1KO group, both I Kto and I Kslow densities are shown to be significantly reduced and the rate of I Kslow inactivation is much slower. The proposed approach has strong potential to couple simulation models with experimental data for gaining a better understanding of glycosylation effects on K v kinetics.« less

The rapid expansion of K-12 CS education has made it critical to support CS teachers, many of whom are new to teaching CS, with the necessary resources and training to strengthen their understanding of CS concepts and how to effectively teach CS. CS teachers are often tasked with teaching different curricula using different programming languages in different grades or during different school years, and tend to receive different professional development (PD) for each curriculum they are required to teach. This often leads to a lack of deep understanding of the underlying CS concepts and how different curricula address the same concepts in different ways. Empowering teachers to develop a deep understanding of CS standards, and use formative assessments to recognize common student challenges associated with the standards, will enable teachers to provide more effective CS instruction, irrespective of the curriculum and/or programming language they are tasked with using. This position paper advocates supporting CS teacher professional learning by supplementing existing curriculum-specific teacher PD with standards-aligned PD that focuses on teachers' conceptual understanding of CS standards and ability to adapt instruction based on student understanding of concepts underlying the CS standards. We share concrete examples of how to design standards-aligned educativemore »resources and instructionally supportive tools that promote teachers' understanding of CS standards and common student challenges and develop teachers' formative assessment literacy, all essential components of CS pedagogical content knowledge.« less

Photosymbioses, intimate interactions between photosynthetic algal symbionts and heterotrophic hosts, are well known in invertebrate and protist systems. Vertebrate animals are an exception where photosynthetic microorganisms are not often considered part of the normal vertebrate microbiome, with a few exceptions in amphibian eggs. Here, we review the breadth of vertebrate diversity and explore where algae have taken hold in vertebrate fur, on vertebrate surfaces, in vertebrate tissues, and within vertebrate cells. We find that algae have myriad partnerships with vertebrate animals, from fishes to mammals, and that those symbioses range from apparent mutualisms to commensalisms to parasitisms. The exception in vertebrates, compared with other groups of eukaryotes, is that intracellular mutualisms and commensalisms with algae or other microbes are notably rare. We currently have no clear cell-in-cell (endosymbiotic) examples of a trophic mutualism in any vertebrate, while there is a broad diversity of such interactions in invertebrate animals and protists. This functional divergence in vertebrate symbioses may be related to vertebrate physiology or a byproduct of our adaptive immune system. Overall, we see that diverse algae are part of the vertebrate microbiome, broadly, with numerous symbiotic interactions occurring across all vertebrate and many algal clades. These interactions are being studiedmore »for their ecological, organismal, and cellular implications. This synthesis of vertebrate–algal associations may prove useful for the development of novel therapeutics: pairing algae with medical devices, tissue cultures, and artificial ecto- and endosymbioses.« less

Virtual reality (VR) technology allows for the creation of fully immersive environments that enable personalized manufacturing learning. This case study discusses the development of a virtual learning factory that integrates manual and automated manufacturing processes such as welding, fastening, 3D printing, painting, and automated assembly. Two versions of the virtual factory are developed: (1) a multiplayer VR environment for the design and assembly of car toys; which allows for the collaboration of multiple users in the same VR environment, and (2) a virtual plant that utilizes heavy machinery and automated assembly lines for car manufacturing. The virtual factory also includes an intelligent avatar that can interact with the users and guide them to the different sections of the plant. The virtual factory enhances the learning of advanced manufacturing concepts by combining virtual objects with hands-on activities and providing students with an engaging learning experience.

Biophysical effects from deforestation have the potential to amplify carbon losses but are often neglected in carbon accounting systems. Here we use both Earth system model simulations and satellite–derived estimates of aboveground biomass to assess losses of vegetation carbon caused by the influence of tropical deforestation on regional climate across different continents. In the Amazon, warming and drying arising from deforestation result in an additional 5.1 ± 3.7% loss of aboveground biomass. Biophysical effects also amplify carbon losses in the Congo (3.8 ± 2.5%) but do not lead to significant additional carbon losses in tropical Asia due to its high levels of annual mean precipitation. These findings indicate that tropical forests may be undervalued in carbon accounting systems that neglect climate feedbacks from surface biophysical changes and that the positive carbon–climate feedback from deforestation-driven climate change is higher than the feedback originating from fossil fuel emissions.

The use of network models to study the spread of infectious diseases is gaining increasing interests. They allow the flexibility to represent epidemic systems as networks of components with complex and interconnected structures. However, most of previous studies are based on networks of individuals as nodes and their social relationships (e.g., friendship, workplace connections) as links during the virus spread process. Notably, the transmission and spread of infectious viruses are more pertinent to human dynamics (e.g., their movements and interactions with others) in the spatial environment. This paper presents a novel network-based simulation model of human traffic and virus spread in community networks. We represent spatial points of interests (POI) as nodes where human subjects interact and perform activities, while edges connect these POIs to form a community network. Specifically, we derive the spatial network from the geographical information systems (GIS) data to provide a detailed representation of the underlying community network, on which human subjects perform activities and form traffics that impact the process of virus transmission and spread. The proposed framework is evaluated and validated in a community of university campus. Experimental results showed that the proposed simulation model is capable of describing interactive human activities at anmore »individual level, as well as capturing the spread dynamics of infectious diseases. This framework can be extended to a wide variety of infectious diseases and shows strong potentials to aid the design of intervention policies for epidemic control.« less

Since the pandemic of COVID-19 began in January 2020, the world has witnessed drastic social-economic changes. To harness the virus spread, several studies have been done to study contributing factors that are pertinent to COVID-19 transmission risks. However, little has been done to investigate how human activities on the spatial network are correlated to the virus transmission and spread. This paper performs a statistical analysis to examine interrelationships between spatial network characteristics and cumulative cases of COVID-19 in US counties. Specifically, both county-level transportation profiles (e.g., the total number of commute workers, route miles of freight railroad) and road network characteristics of US counties are considered. Then, the lasso regression model is utilized to identify a sparse set of significant variables that are sensitive to the response variable of COVID-19 cases. Finally, the fixed-effect model is built to capture the relationship between the selected set of predictors and the response variable. This work helps identify and determine salient features from spatial network characteristics and transportation profiles, thereby improving the understanding of COVID-19 spread dynamics. These significant variables can also be utilized to develop simulation models for the prediction of real-time positions of virus spread and the optimization of intervention strategies.

The COVID-19 preparedness plans by the Centers for Disease Control and Prevention strongly underscores the need for efficient and effective testing strategies. This, in turn, calls upon the design and development of statistical sampling and testing of COVID-19 strategies. However, the evaluation of operational details requires a detailed representation of human behaviors in epidemic simulation models. Traditional epidemic simulations are mainly based upon system dynamic models, which use differential equations to study macro-level and aggregated behaviors of population subgroups. As such, individual behaviors (e.g., personal protection, commute conditions, social patterns) can’t be adequately modeled and tracked for the evaluation of health policies and action strategies. Therefore, this paper presents a network-based simulation model to optimize COVID-19 testing strategies for effective identifications of virus carriers in a spatial area. Specifically, we design a data-driven risk scoring system for statistical sampling and testing of COVID-19. This system collects real-time data from simulated networked behaviors of individuals in the spatial network to support decision-making during the virus spread process. Experimental results showed that this framework has superior performance in optimizing COVID-19 testing decisions and effectively identifying virus carriers from the population.