GEM Model Research Articles

MODEL ‘GEMS’: SATU META ANALISIS

Voluntary activities in the community should be practiced as a culture because it can form a united and cooperative society. Therefore, this culture is believed to have a strong influence in ensuring the quality of life and setting aside negative elements. This achievement can be fully implemented if the culture of volunteerism becomes an ongoing practice and effort. The GEMS Model approach is an approach in ensuring that voluntary attitudes will always remain better over time. The meta -analysis of this model is described as a spiral model of the voluntary administration profile. The model consists of four sets of processes; 1) generate, 2) educate, 3) mobilize, and 4) retain, which involves the recruitment or withdrawal of volunteers.

Abstract 911: Ablation of rare tumor propagating cells during early lung adenocarcinoma development abrogates tumor growth and progression

Abstract Lung cancer is the leading cause of cancer-related death worldwide. Non-small lung cancer accounts for 85% of diagnoses and the predominant subtype is lung adenocarcinoma (LUAD). Previously, we identified a population of Tumor Propagating Cells (TPCs) in the Kras; TP53 GEM model of LUAD. We demonstrated that TPCs have a greater capacity for secondary tumor formation and are enriched after treatment. The TPC population is defined by the expression of CD24, ITGB4 and Notch, with further functional validation showing that Notch3 contributes to this propagation phenotype in a non-redundant fashion. To investigate the biology of these TPCs, we have produced a KrasFSFG12D; Tp53FRT/FRT; Notch3CreERT2; RFP; inducible Diphtheria toxin receptor mouse (KPFFNRiDTR) for lineage tracing and ablation of Notch3 expressing tumor cells to get a granular understanding of the cell types/progeny generated by Notch3 expressing cells and the effects of removing these cells on the tumor microenvironment.In this model, adenovirus expressing FLP is delivered intra-tracheally resulting in expression of an oncogenic form of Kras and deletion of TP53, initiating LUAD tumors. Upon administration of Tamoxifen, Notch3 expressing cells are labelled by induced expression of RFP and DTR, then these cells can be ablated by subsequent dosing with Diphtheria toxin. By labelling/ablating cells at different disease progressions we can infer the importance of Notch signaling at key timepoints and assess the contribution of Notch3 expressing cells to the tumor microenvironment and tumor progression. We have found that Notch3 is expressed in the epithelial progenitors during early embryogenesis up to ~p5 and then gets turned off in the epithelial compartment of distal lung during alveologenesis, and in adults Notch3 is only expressed in the lung pericytes. Thus, the cell of origin for LUAD does not express Notch3, even if the TPC does, and we see the acquisition of Notch3 expression during early tumorigenesis. If we ablate Notch3 positive cells during this state we effectively prevent further growth of the tumors, however we still see similar tumor numbers at endpoint, suggesting that all lesions that will progress are already present when we ablate the TPCs. Considering that the total number of Notch3 cells in the tumor is approximated 1%, this is a profound effect on the overall growth of the tumor and suggests that the TPCs are fundamentally required for lung adenocarcinoma progression and may be a viable target for treatment. Citation Format: Kieren D. Marini, Emma C. Champion, Phuong T. Dinh, Elizabeth E. Hwang, Leanne C. Sayles, E. Alejandro Sweet-Cordero. Ablation of rare tumor propagating cells during early lung adenocarcinoma development abrogates tumor growth and progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 911.

MEDB-83. A novel epigenetic nanotherapeutic strategy to induce medulloblastoma differentiation

The histone-lysine N-methyltransferase EZH2 is the catalytic component of the PRC2 complex and is overexpressed in several medulloblastoma subtypes. However, its role in medulloblastoma tumorigenesis has been shown to be context-dependent using genetic approaches. Furthermore, pharmacological approaches have been limited by the very poor blood-brain barrier (BBB) penetration of current EZH2 inhibitors in use. Using laser capture microdissection and RNA-Seq analysis of human nodular/desmoplastic SHH medulloblastoma FFPE tissue, we provide data for the spatial epigenetic heterogeneity of primitive/proliferative regions compared to nodular/mature regions. Bioinformatic analysis identifies ~120 differentially expressed genes between primitive and mature regions with enrichment for genes regulated by H3K4me3 and H3K27me3 or SUZ12. ChIP-Seq analysis shows striking differences in H3K27me3 enrichment between primitive and mature medulloblastoma cells including at the EZH2 locus. Utilizing a genetically-engineered mouse model of SHH medulloblastoma, we show that conditional EZH2 genetic ablation within medulloblastoma cells results in wide-spread tumor cell differentiation (n=31 mice; *p=2e-07). Conversely, conditional EZH2 (Y641F) activation in this GEM model prevents tumor cell differentiation. Notably, we have found that the CDNK2A (p16) locus is an important EZH2 target that regulates tumor cell differentiation. qRT-PCR analysis of SHH medulloblastoma in wild-type and Ezh2 knockout settings show significant reduction in Gli1 and CCND1 and increase p15 and p16 expression in Ezh2 knockout mice compared to Ezh2 wildtype mice (*p<0.05). Importantly, genetic ablation of p16 conditionally in SHH MB EZH2 double knockout mice rescues the widespread tumor cell differentiation (n=9 mice; *p=3e-06) seen in Ezh2 single knockout SHH medulloblastoma mice. Finally, we developed a novel fucoidan-based nanoparticle strategy to deliver the EZH2 inhibitor (EPZ-6438) across the intact BBB of this GEM model to achieve significant extension of mouse survival (median 70 days compared to 19 days in control mice; *p=0.01, Mantel-Cox) with potential utility for other pediatric brain tumors.

Characterization of stage-specific tumor progression in TMPRSS2-ERG (fusion)-driven and non-fusion-driven prostate cancer in GEM models.

In the present study, we performed a comparative stage-specific pathological and molecular marker evaluation of TMPRSS2-ERG fusion and PTEN loss-driven (TMPRSS2-ERG. Ptenflox/flox ) versus non-fusion-driven prostate tumorigenesis (Hi-Myc) in mice. Anterior, ventral, and dorsolateral prostates were collected from mice at different ages (or time points post-Cre induction). Results indicated that growth and progression of prostatic intraepithelial lesions to adenocarcinoma stages occurred in both mice models albeit at different rates. In the TMPRSS2-ERG. Ptenflox/flox mice, the initiation of tumorigenesis was slow, but subsequent progression through different stages became increasingly faster. Adenocarcinoma stage was reached early on; however, no high-grade undifferentiated tumors were observed. Conversely, in the Hi-Myc+/- mice, tumorigenesis initiation was rapid; however, progression through different stages was relatively slower and it took a while to reach the more aggressive phenotype stage. Nevertheless, at the advanced stages in the Hi-Myc+/- mice, high-grade undifferentiated tumors were observed compared to the later stage tumors observed in the fusion-driven TMPRSS2-ERG. Ptenflox/flox mice. These results were corroborated by the stage specific-pattern in the molecular expression of proliferation markers (PCNA and c-Myc); androgen receptor (AR); fusion-resultant overexpression of ERG; Prostein (SLC45-A3); and angiogenesis marker (CD-31). Importantly, there was a significant increase in immune cell infiltrations, which increased with the stage of tumorigenesis, in the TMPRSS2-ERG fusion-positive tumors relative to fusion negative tumors. Together, these findings are both novel and highly significant in establishing a working preclinical model for evaluating the efficacy of interventions during different stages of tumorigenesis in TMPRSS2-ERG fusion-driven PCa.

Development of metabolic models with multiple constraints: a review

Constraint-based genome-scale metabolic network models (genome-scale metabolic models, GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric constraints, other constraints such as enzyme availability and thermodynamic feasibility may also limit the cellular phenotype solution space. Recently, extended GEM models considering either enzymatic or thermodynamic constraints have been developed to improve model prediction accuracy. This review summarizes the recent progresses on metabolic models with multiple constraints (MCGEMs). We presented the construction methods and various applications of MCGEMs including the simulation of gene knockout, prediction of biologically feasible pathways and identification of bottleneck steps. By integrating multiple constraints in a consistent modeling framework, MCGEMs can predict the metabolic bottlenecks and key controlling and modification targets for pathway optimization more precisely, and thus may provide more reliable design results to guide metabolic engineering of industrially important microorganisms.

Effective hydraulic properties of 3D virtual stony soils identified by inverse modeling

Abstract. Stony soils that have a considerable amount of rock fragments (RFs) are widespread around the world. However, experiments to determine the effective soil hydraulic properties (SHPs) of stony soils, i.e., the water retention curve (WRC) and hydraulic conductivity curve (HCC), are challenging. Installation of measurement devices and sensors in these soils is difficult, and the data are less reliable because of their high local heterogeneity. Therefore, effective properties of stony soils especially under unsaturated hydraulic conditions are still not well understood. An alternative approach to evaluate the SHPs of these systems with internal structural heterogeneity is numerical simulation. We used the Hydrus 2D/3D software to create virtual stony soils in 3D and simulate water flow for different volumetric fractions of RFs, f. Stony soils with different values of f from 11 % to 37 % were created by placing impermeable spheres as RFs in a sandy loam soil. Time series of local pressure heads at various depths, mean water contents, and fluxes across the upper boundary were generated in a virtual evaporation experiment. Additionally, a multistep unit-gradient simulation was applied to determine effective values of hydraulic conductivity near saturation up to pF=2. The generated data were evaluated by inverse modeling, assuming a homogeneous system, and the effective hydraulic properties were identified. The effective properties were compared with predictions from available scaling models of SHPs for different values of f. Our results showed that scaling the WRC of the background soil based on only the value of f gives acceptable results in the case of impermeable RFs. However, the reduction in conductivity could not be simply scaled by the value of f. Predictions were highly improved by applying the Novák, Maxwell, and GEM models to scale the HCC. The Maxwell model matched the numerically identified HCC best.

Data Driven Explanation of Temporal and Spatial Variability of Maize Yield in the United States.

Maize yield has demonstrated significant variability both temporally and spatially. Numerous models have been presented to explain such variability in crop yield using data from multiple sources with varying temporal and spatial resolutions. Some of these models are data driven, which focus on approximating the complex relationship between explanatory variables and crop yield from massive data sets. Others are knowledge driven, which focus on integrating scientific understanding of crop growth mechanism in the modeling structure. We propose a new model that leverages the computational efficiency and prediction accuracy of data driven models and incorporates agronomic insights from knowledge driven models. Referred to as the GEM model, this model estimates three independent components of (G)enetics, (E)nvironment, and (M)anagement, the product of which is used as the predicted crop yield. The aim of this study is to produce not only accurate crop yield predictions but also insightful explanations of temporal and spatial variability with respect to weather, soil, and management variables. Computational experiments were conducted on a data set that includes maize yield, weather, soil, and management data covering 2,649 counties in the U.S. from 1980 to 2019. Results suggested that the GEM model is able to achieve a comparable prediction performance with state-of-the-art machine learning models and produce meaningful insights such as the estimated growth potential, effectiveness of management practices, and genetic progress.

What do text-based newsgames have to do with journalism? An account from Gem Model perspective

The article aims to discuss text-based newsgames as a multimodal artifact, having in mind the goal to systematize their expressive processing and rhetorical strategies as possible tropes for journalistic processes and practices. For that, we have adapted some of the main layers/elements of the GeM Model (Genre and Multimodality), proposed by Bateman (2008, 2014), to analyze a Brazilian text-based newsgame called “A Teia”, having in mind its material regularities and semiotic modes. The game portrays the reality of a woman who has to decide what to do upon situations of continuous domestic violence and abuse carried out by her partner. In the design of trajectory of “A Teia”, the player follows a rhetorical cluster made of a non-linear sequence, according to an event/circumstance upon which the player has to decide, and so on. Another goal of the paper is to contribute for the assessment on how procedural rhetoric of digital text-based newsgames touches crucial aspect of journalistic practices, functions and values.

Abstract 2766: Hypoxia-induced lactate production by tumor-associated macrophages promote tumorigenesis

Abstract Tumor-associated macrophages (TAMs) are the most prevalent immune cell in the tumor microenvironment (TME). They mainly adopt an M2-like phenotype that supports angiogenesis, attenuates anti-tumor immunity, and promotes metastasis. Blocking M2 activation of TAMs in pre-clinical models attenuates tumor growth and metastasis, and high abundance of M2-like TAMs is associated with poor patient survival across many cancer types. Despite of TAMs as a promising therapeutic target, the mechanisms producing their M2-like phenotype is poorly understood. This understanding is required to develop effective TAM-targeting therapeutics and identify patients that might benefit from them. One potential pathway to influence macrophage polarization is via metabolic reprogramming. The traditional view is that glycolysis supports a pro-inflammatory M1 phenotype in macrophages, while oxidative phosphorylation is required for their M2 phenotypes. Our recent work challenged this paradigm. We showed that treating macrophages with LPS or bacteria (conditions that support M1 activation) induces lactate production, which in turn drives a late phase switch to an M2-like phenotypes. The mechanism underlying this surprising observation involves a novel lactate-induced epigenetic modification termed histone lysine lactylation (Kla) that marks promoters of M2-like genes and directly promote transcription. In tumors, hypoxia is a key environmental stimulus that induces lactate production. The goal of this study was to examine if hypoxia-induced lactate production by TAMs metabolically reprograms them to promote Kla-M2 pathway to influence tumorigenesis. Here, we showed that hypoxia-induced lactate production by macrophages elevates the expression of M2-like genes, marked by Kla at their promotes. We further demonstrated the spatial and quantitative relationship between hypoxia, Kla, and M2-like phenotype within a single tumor (GEM model &amp; human tumors) and across tumors with variable hypoxia (syngeneic models). Moreover, inhibiting endogenous lactate production by TAMs (via Ldha deletion) reduces tumor growth, attenuates M2-like phenotype of TAMs, and increases CD8+ T cells in tumor with high hypoxia, but not low hypoxia. Importantly, lactate level in TME is independent of tumor hypoxia or LDHA status, suggesting that this epigenetic pathway is primarily driven by endogenous lactate production by TAM rather than exogenous lactate in TME. Collectively, our studies demonstrated an important role for a “hypoxia-induced lactate-Kla-M2 pathway” in TAMs to promote tumorigenesis. Citation Format: Chang Cui, kasturi Chakraborty, Kelly Schoenfelt, Guolin Zhou, Xu Anna Tang, Catherine Reardon, Di Zhang, Alexander Muir, Yingming Zhao, Lev Becker. Hypoxia-induced lactate production by tumor-associated macrophages promote tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2766.

Estimating greenhouse emissions from sanitary landfills using Land-GEM and IPCC model based on realistic scenarios of different urban areas: a case study of Iran.

This study examined the emission of greenhouse gases from municipal solid waste disposal centers in different regions of Iran based on different scenarios. Assuming landfill site opening in 2012 and considering the 20-year plan period for its usage, the amount of wastes entering the landfill site was calculated for 2012-2032. For calculating the production of methane (CH4) and other gases during different years of the project, Land-GEM and IPCC model were used. We defined 9 scenarios for these two models based on the growth rate of population and waste generation. The results revealed that the lowest amount of gas emission in nine scenarios by Land- GEM model was related to non-methane organic compounds (NMOCs). According to the results obtained, the total emissions of greenhouse gases from sanitary landfills for Iran in 2032 were 3,844,000 Mg/year, the largest and lowest amounts of gas emission were related to Tehran region, 860,400 Mg/year, and the South Coast area of the country,138,200 Mg/year, respectively. The major section of the gas production in both landfills was related to greenhouse gas and carbon dioxide emissions. The difference in gas production in the studied regions was due to differences in the percentage of moisture and organic compounds.

Extracting the GEMs: Genotype, Environment, and Microbiome Interactions Shaping Host Phenotypes.

One of the fundamental tenets of biology is that the phenotype of an organism (Y) is determined by its genotype (G), the environment (E), and their interaction (GE). Quantitative phenotypes can then be modeled as Y = G + E + GE + e, where e is the biological variance. This simple and tractable model has long served as the basis for studies investigating the heritability of traits and decomposing the variability in fitness. The importance and contribution of microbe interactions to a given host phenotype is largely unclear, nor how this relates to the traditional GE model. Here we address this fundamental question and propose an expansion of the original model, referred to as GEM, which explicitly incorporates the contribution of the microbiome (M) to the host phenotype, while maintaining the simplicity and tractability of the original GE model. We show that by keeping host, environment, and microbiome as separate but interacting variables, the GEM model can capture the nuanced ecological interactions between these variables. Finally, we demonstrate with an in vitro experiment how the GEM model can be used to statistically disentangle the relative contributions of each component on specific host phenotypes.

Abstract B09: Lineage tracing of tumor-propagating cells in LUAD

Abstract Lung cancer is the leading cause of cancer-related death worldwide. Non-small lung cancer accounts for 85% of diagnoses, and the predominant subtype is lung adenocarcinoma (LUAD). LUAD has a less than 20% response rate to conventional chemotherapy, and despite identification of several therapeutic targets, the overall 5-year survival is only 23%. Previously, we identified a population of tumor propagating cells (TPCs) in the Kras; TP53 GEM model of LUAD. We demonstrated that TPCs have a greater capacity for secondary tumor formation and are enriched after treatment with cisplatin. The TPC population is defined by the expression of CD24, ITGB4, and Notch high, with further functional validation showing that Notch3 contributes to this propagation phenotype in a nonredundant fashion. Currently, we are attempting to use mouse modeling to get a more granular understanding of Notch3 signaling and TPC biology. Specifically, we want to examine the cell types/progeny generated by Notch3 expressing cells and the effects of removing these cells on the tumor microenvironment. Further to this, we want to determine the dynamism of Notch3 expression in lung adenocarcinoma and how this affects cell state. To investigate the biology of these TPCs, we have a KrasFSFG12D; Tp53FRT/FRT; Notch3CreERT2; RFP; inducible diphtheria toxin receptor mouse (KPFFNRiDTR) to perform lineage tracing and ablation of Notch3-expressing tumor cells. In this model, adenovirus expressing FLP is delivered intratracheally, leading to expression of an oncogenic form of Kras and deletion of TP53, initiating LUAD tumors. Subsequently, upon administration of tamoxifen, Notch3-expressing cells are specifically labeled with RFP and simultaneously induce expression of the DTR. Dosing with diphtheria toxin then eliminates cells that were Notch positive at the time of tamoxifen labeling. By labeling/ablating cells at different disease progressions, we can infer the importance of Notch signaling at key timepoints and assess the contribution of Notch3-expressing cells to the tumor microenvironment. Preliminary experiments show that Notch3 is not expressed in the epithelial compartment of the adult lung but instead is expressed in the lung pericytes. The Notch3CreER was crossed into the standard KP model to test if Notch3-expressing cells had the capacity to form LUAD, and 12 weeks after initiation with tamoxifen, no formation of LUAD tumors was observed, suggesting that the cell of origin for LUAD does not express Notch3, even if the TPC does. Despite Notch3 not being epithelial in the adult lung, we do see colocalization with known AT1/AT2 cell type markers after tumor initiation in the KPFFNRiDTR model, suggesting that Notch3 expression is gained by tumors after initiation. Interestingly, from the patterning of Notch3 positivity observed in these tumors, it does not seem that the TPCs are maintained in a quiescent state, suggesting that the TPC state is not dormant and instead may be an active signaling process. Citation Format: Kieren D. Marini, Elizabeth Hwang, Alex Lee, Leanne Sayles, Alejandro Sweet-Cordero. Lineage tracing of tumor-propagating cells in LUAD [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr B09.

The research on promoting characteristic agricultural industry competitiveness based on GEM model - making JinJiang carrot industry as an example

Enhancing the competitiveness of characteristic industries plays an important role in promoting the development of regional economy. Taking the carrot industry in Jinjiang City as an example, this paper analyses the market competitiveness status of county industries and products from the three factors of 'base-enterprise-market', and realises the potential of agricultural industry transformation and upgrading by applying GEM competitiveness model and the related theories. The analysis results show that the development of the six factors of the competitiveness of carrot characteristic industries in Jinjiang City is uneven, and it has advantages in resources, facilities, and external markets, but it is relatively insufficient in three aspects: manufacturer structure/strategy, suppliers/related enterprises, and local market. The overall level of competitiveness is normal. From the analysis of industrial development potential, due to the improvement of the diet structure will promote the expansion of the industrial market, the carrot industry in Jinjiang City has a large space for development, especially the demand for carrot processing products.

Комунікативно-прагматичний зміст контамінованої лексичної когезії в англійських мультимодальних літературних казках Філіпа Арда

Introduction: the paper focuses on the communicative and pragmatic content of contaminated lexical cohesion in English multimodal literary fairy tales by Philip Ardagh. The purpose of the paper is to describe semiotic, structural-semantic and narrative aspects as the dominant components concerning modes of contaminated lexical cohesion in English multimodal literary fairy tales. Methods: semiotic modes of contaminated lexical cohesion in modern English literary fairy tales by Philip Ardagh have been singled out and analysed according to the micro-, meso- and macro inclusions within the semiotic and narrative interpretation of the multimodal fictional text on the general principles of structural and semiotic, narrative, poetic and interpretative, quantitative analyses and GeM model. Results: the communicative and pragmatic content of contaminated lexical cohesion in the text of English multimodal literary fairy tales by Philip Ardagh has been actualised by micro inclusions (88%) and meso inclusions (12 %) but for macro inclusions (0 %). Conclusion: the communicative and pragmatic content of contaminated lexical cohesion is based on the actualisation of the semantic potential of antonomasia. In English multimodal literary fairy tale the character’s name is the link between the conception of the addressant Philip Ardagh and the embodied idea of a fairy tale, fiction and reality, historical past and present. The character’s proper name performs a communicative function grounded on the interaction between the addressant and the addressee. Contaminated antonomasia is based on a verbal explanation of the character’s name in the text of English multimodal literary fairy tale, which is absorbed by its iconic component for a detailed image of character’s appearance. Parentheses, separated by brackets, indicate predominantly the presence of antonomasia in the context of English multimodal literary fairy tale by the lexeme (the) name. The indefinite article the in the function of the language marker that accompanies the lexeme name identifies the characters in the illustrations mentioned previously in the context of the verbal component. Contaminated antonomasia is the result of an implicit semantic correlation between the verbal and iconic components, which provides the connection between the iconic component and the supra-phrasal unit or fragment of English multimodal literary fairy tale. In prospect the suggested semiotic and narrative interpretation of modes of contaminated lexical cohesion in English multimodal literary fairy tales by Philip Ardagh can serve as a model for further linguistic text research on other categories of different text genres according to the modern linguistic, stylistic and discourse studies.

A model for the accurate estimation of methane emissions in landfills

Landfills are one of the major sources of methane (CH 4 ) emissions. Prediction of CH4 emissions from landfills is important in estimating power generation potential and greenhouse gas (GHG) emissions from landfills. The most widely used landfill gas (LFG) models developed based on the first order decay (FOD) reaction do not take into account changing waste composition and landfill site conditions in methane estimations. The aim of this study was therefore to develop a LFG model for estimation of methane emissions from landfills in Lagos metropolis. Field investigations were carried out to determine waste composition, waste disposal rates and site conditions relevant for methane emissions estimation. Waste composition studies were conducted and waste fractions were divided into rapidly, moderately and slowly degrading. The output of the model was verified with the US EPA Landfill Gas Emission model (Land GEM). Results revealed maximum CH 4 emissions estimated occurred at the end of landfill’s closure. Methane generation potential (𝑳𝒐) and methane generation rate (𝒌) parameters were dependent on waste composition and site conditions. Model verification also showed methane emissions peaked at the end of landfill’s closure for both models and variation in modelling parameters by Land GEM model resulted in significant change in methane emissions. Keywords : Methane emissions, Landfills, Municipal solid waste, landfill gas, Land GEM model

Simulation of snow accumulation and melting in the Kama river basin using data from global prognostic models

Currently, the improvement of numerical models of weather forecasting allows using them for hydrological problems, including calculations of snow water equivalent (SWE) or snow storage. In this paper, we discuss the applicability of daily precipitation forecasts for three global atmospheric models: GFS (USA), GEM (Canada) and PL-AV (Russia) for calculating snow storage (SWE) in the Kama river basin for the cold season of 2017–2018. As the main components of the balance of snow storages the following parameters were taken into account: precipitation (with regard for the phase); snow melting during thaws; evaporation from the surface of the snow cover; interception of solid precipitation by forest vegetation. The calculation of snow accumulation and melting was based on empirical methods and performed with the GIS technologies. The degree-day factor was used to calculate snowmelt intensity, and snow sublimation was estimated by P.P. Kuz’min formula. The accuracy of numerical precipitation forecasts was estimated by comparing the results with the data of 101 weather stations. Materials of 40 field and 27 forest snow-measuring routes were taken into account to assess the reliability of the calculation of snow storages (SWE). During the snowmelt period, the part of the snow-covered area of the basin was also calculated using satellite images of Terra/Aqua MODIS on the basis of the NDFSI index. The most important result is that under conditions of 2017/18 the mean square error of calculating the maximum snow storage by the GFS, GEM and PL-AB models was less than 25% of its measured values. It is difficult to determine which model provides the maximum accuracy of the snow storage calculation since each one has individual limitations. According to the PL-AV model, the mean square error of snow storage calculation was minimal, but there was a significant underestimation of snow accumulation in the mountainous part of the basin. According to the GEM model, snow storages were overestimated by 10–25%. When calculating with use of the GFS model data, a lot of local maximums and minimums are detected in the field of snow storages, which are not confirmed by the data of weather stations. The main sources of uncertainty in the calculation are possible systematic errors in the numerical forecasts of precipitation, as well as the empirical coefficients used in the calculation of the intensity of snowmelt and evaporation from the snow cover surface.

Entrepreneurial Ecosystem, Entrepreneurial Activity and Economic Performance

In this research, we have empirically tested the impact of Entrepreneurial Framework Conditions (EFC) on entrepreneurial activity and ultimately on economic growth of a nation. In our sample all the 54 countries participating in the GEM study in 2017 are taken. 67.8% of the world’s population and 86.0% of the world’s GDP is represented by the economies included in GEM 2017. The entrepreneurial ecosystem is captured through 12 EFCs used in the GEM model that includes: Financial environment, government policy of support and relevance, government policy of taxes and bureaucracy, government programs, entrepreneurial education at school stage and post school stage, R&amp;D transfer, commercial and legal infrastructure, internal market dynamics, access to physical infrastructure, and social and cultural norms. The impact of these EFCs and their significance in creating entrepreneurial activity (TEA) in a nation is analyzed using OLS estimation technique with TEA as dependent variable. Secondly, the impact of entrepreneurial activity (TEA) on economic growth ( ∆GDP) is estimated using OLS regression model with control variables such as global competitiveness index (GCI) and Gross national income per capita, expressed in purchasing power parity (GNIC). The results suggest that entrepreneurship education at post school stage, entry burdens and cultural and social norms are significant framework conditions that support entrepreneurial activity, while R&amp;D transfer, internal market dynamics and commercial and legal infrastructure hinders the process of entrepreneurship. Furthermore, TEA is significantly contributing to the economic growth of factor driven and efficiency driven economies.

A Multiphase, Multicomponent Reservoir-Simulation Framework for Miscible Gas and Steam Coinjection

SummaryThe solvent thermal resource innovation process (STRIP), a downhole steam-generation technology, has the capacity to show improved recovery factors with a significantly reduced environmental footprint compared with traditional thermal-enhanced-oil-recovery (TEOR) methods, most notably by delivering all the combustion heat to the pay zone. In this effort, a quarter-symmetry inverse-five-spot model and a multiphase, multicomponent reservoir-simulation framework were used to simulate the STRIP technology. Commercial simulators such as STARS - Thermal and Advanced Processes Reservoir Simulator [Computer Modelling Group Ltd. (CMG), Calgary, Alberta, Canada; CMG 2015b] often use the K-value approach to simulate TEOR. However, the method cannot simulate STRIP's carbon dioxide (CO2) and steam coinjection because the K-value method does not consider miscible gas injection. On the other hand, CMG's GEM - Compositional and Unconventional Simulator (CMG 2015a) includes the effects of miscible gases but does not provide comprehensive support for steam-injection processes, which are better handled by STARS. The novel simulation framework developed here leverages and combines the individual strengths of STARS (thermal features) and GEM (compositional features). In this framework, STARS simulated steam injection (but cannot directly simulate the effects of CO2) and was the governing model that synchronized temperature, pressure, and phase saturations for two parallel iterations of the GEM models (GEM-1 and GEM-2) at each timestep. Immiscible methane (CH4) was added to GEM models to maintain gas saturations equivalent to the STARS model. GEM-1 simulated hot-water and CH4 injection, but at increased rates to yield a pressure field and gas saturations equivalent to STARS. A final run of GEM-1 injected both CO2 and hot water and demonstrated the expected increase in oil production. Calibrated injection rates from GEM-1 were specified in GEM-2 to ensure equivalence of the pressure field. Next, the GEM-2 model also simulated hot-water and CH4 injection, but matched both water and oil productions along with oil saturations from the final GEM-1 run by altering relative permeabilities. Finally, the updated relative permeabilities were fed back to STARS, and iteration proceeded. Results from this framework were verified against a STARS steam-injection simulation. Finally, when considering coinjection of CO2, STRIP's superior performance was demonstrated through increased oil recovery and a lower steam/oil ratio (SOR).

The GEM model of health: a model based on generalized empirical method –Part 4 – Comparisons and contrasts

In this paper, I compare and contrast in summary fashion (1) the GEM definition of health with that of the World Health Organization (WHO); (2) the methodical integration of judgments of fact and value in the GEM model with their incommensurability in most naturalist and normativist theories of health; (3) the significance of differentiating risk factors and disease relative to states of health in the GEM model with the tendency to blur any such difference in current multifactorial accounts of disability and dysfunction; (4) the GEM model’s emphasis on the common core of operations underlying health science and healthcare with the gap separating hermeneutic understanding and scientific explanation that is often the rule in humanistic accounts of health and (5) the role of the ordered and eco-socially conditioned set of relationships in the GEM model of health with the multilevel perspective on health in the developing field of global bioethics. In conclusion, I note that the GEM model offers a unique framework - a higher viewpoint - for integrating in dynamic fashion the manifold viewpoints of clinical practice, the humanities, health science and health policy.

The GEM Model: A model of health based on generalized empirical method – Part 1 – Introduction

This is the first of a four-part essay in which I present a comprehensive model of health based on the philosophy of Bernard Lonergan. In this introductory article, I situate the model within my overall project to develop a philosophy of health based on Lonergan’s philosophy and within contemporary efforts to understand the relationship between health science and healthcare. In the following set of three articles I discuss the philosophical background of the model before fleshing the model out and then comparing it to other accounts of health and disease.