Context-specific Models Research Articles

Informal Digital Learning of English and willingness to communicate in a second language: self-efficacy beliefs as a mediator

Informal Digital Learning of English (IDLE) is associated with willingness to communicate in a second language (L2 WTC). Although affective mediators (e.g. enjoyment and anxiety) have been found to influence the relationship between IDLE and L2 WTC, it is unclear whether cognitive factors influence IDLE’s effects on L2 WTC. This study aims to fill this gap by determining whether self-efficacy beliefs mediate the relationship between IDLE and L2 WTC among 246 Kazakhstani university EFL students. In contrast to previous research in other Asian contexts, our structural equation modelling results revealed that IDLE was not directly related to L2 WTC. Instead, self-efficacy beliefs acted as a full mediator between IDLE constructs and L2 WTC. These findings suggest that EFL students who participate in IDLE activities more frequently have a stronger belief in their own ability to perform various tasks in English, which leads to a greater willingness to communicate. This is the first research of its kind in Kazakhstani EFL context. This is also the first study to show that IDLE and L2 WTC are linked via a cognitive factor. Our research offers a context-specific pedagogical IDLE model to teachers and teacher educators in Kazakhstan and other similar EFL contexts.

Grid parity analysis of photovoltaic systems considering feed-in tariff and renewable energy certificate schemes in Hong Kong

The Hong Kong government has actively promoted the development of renewable energy with two local utility companies to achieve its renewable energy target. Accordingly, two crucial initiatives, the feed-in tariff (FiT) and renewable energy certificate (REC) schemes, have been launched to boost the installation of renewable energy systems, particularly photovoltaic (PV) systems. However, the efficiency and effectiveness of these schemes on grid parity achievement have not been investigated from the perspectives of different stakeholders. To fill this research gap, this study adopts the levelized cost of electricity (LCOE) models to evaluate the role of FiT and REC schemes on realising the grid parity from the residential PV installers' and utility companies’ perspectives, respectively. As a supplementary indicator, the levelized profit of electricity (LPOE) is applied to investigate the economic profitability. The superiority of this study is to expand a general LCOE model into context-specific LCOE and LPOE models considering different grid parity judgement standards from the two sectors. Through a comprehensive analysis, it is indicated that for residential PV installers, the achievement of grid parity is affected by the government electricity subsidy scheme, the trend of PV system capital cost as well as which utility companies they subscribe. For utility companies, the REC price is found sufficient to compensate the lifetime expenditures on large-scale PV systems. Sensitivity analysis indicates that unit capital cost and annual solar power generation are the most critical factors to reduce LCOE in order to achieve earlier grid parity years.

Developing Economic Loss-Based Thresholds for Improving Context-Specific Crash Prediction Models

Negative binomial-based safety performance functions (SPFs) have been extensively used by United States Department of Transportation professionals for predictive crash analysis. Recently, the Florida Department of Transportation (FDOT) has developed a context classification approach and incorporated it into crash prediction models, which has the potential to significantly enhance their accuracy and reliability. The additional modeling contexts and parameters make it more challenging to diagnose and remedy modeling problems, however. Particularly for roadway segments with low annual average daily traffic (AADT), short lengths, or low counts of severe crashes, the SPF models significantly underestimate the actual number of crashes. This uncertainty in SPF predictions can lead FDOT practitioners to reach misleading conclusions, such as failing to detect sites with genuinely high crash rates. This project intends to establish thresholds for certain SPF parameters to ensure reliable crash predictions are obtained across various context classes. For this purpose, we (a) developed a functional statistical model that quantifies economic loss relative prediction errors as a function of AADT volume and (b) calculated the minimum context-specific AADT threshold for each segment length group, roadway category, context classification, and crash severity combination. Employing the developed AADT thresholds confirmed up to 89% reduction in SPF prediction errors for the most represented context class. In light of the results obtained, we are able to conclude that context-specific AADT thresholds perform well in significantly reducing prediction errors for the thresholded segments and contexts on Florida roadways.

Understanding antenatal care use in pastoralist communities: a focused ethnographic study in Kenya

Background/Aims The provision of high-quality antenatal care improves maternal and child health outcomes. Pastoralist communities face unique challenges in accessing healthcare associated with a nomadic, marginalised lifestyle, and have high rates of maternal morbidity and mortality. There is minimal evidence on antenatal care use globally among this group. To develop optimal services for pastoralist communities, there is an urgent need to understand pastoralist women's use of antenatal care services. This study's aim was to explore experiences of antenatal care among pastoralist communities in Kenya, to identify key barriers and facilitators of uptake. Methods This focused ethnographic qualitative study was underpinned by a philosophy of critical realism. A total of 58 participants (women, husbands, traditional birth attendants and nurses) were included. Data were collected using interviews, focus group discussions and observation. Data were collected for 6 months in six villages and five health facilities in a pastoralist region of northern Kenya. Inductive thematic analysis and retroduction were used to identify concepts, structures and mechanisms that influenced antenatal care use. Results Pastoralist women's antenatal care experiences and use were linked to two main themes. The first was government policies that incentivised antenatal care use by linking the provision of the baby's health and citizenship records to antenatal care uptake and encouraging traditional birth attendants to adopt defined referral roles. The second highlighted multiple structural barriers that impeded consistent antenatal care uptake, including the pastoralist lifestyle, cultural barriers, health system constraints and a gap in information sharing. The dissonance between these led to partial, sub-optimal use patterns of antenatal care. Conclusions Government policy created an impetus for the pastoralist community to use contemporary healthcare services. At the same time, multiple barriers impeded access to services. This explains the partial use of antenatal care services among pastoralist communities. The findings throw doubt on the feasibility of antenatal care guidelines that recommend eight contacts during pregnancy. There is a need to work collaboratively with communities to develop context-specific models of care.

Positive emotion in posttraumatic stress disorder: A global or context-specific problem?

Problems with positive emotion are an important component of posttraumatic stress disorder (PTSD), with competing perspectives as to why. The global model suggests that people with PTSD experience a relatively permanent shift in their capacity for positive emotion regardless of context, whereas the context-specific model posits access to the full repertoire of positive emotion that only becomes reduced during exposure to trauma reminders. We tested the global versus context-specific models using ecological momentary assessment (EMA). Trauma-exposed adult community members (N = 80) with (n = 39) and without diagnosed PTSD completed 3 days of EMA (n = 2,158 observations). Participants with PTSD reported lower average momentary levels of positive emotion, B = -0.947, 95% CI [-1.35, -0.54], p < .001, and positive situations, B = -0.607, 95% CI [-1.16, -0.05], p = .032, and more thinking about trauma reminders, B = 0.360, 95% CI [0.21, 0.51], p < .001. There was no between-group difference in positive emotion reactivity (degree of positive emotion derived from positive situations), B = 0.03, 95% CI [-0.09, 0.14], p = .635. Increased thinking about trauma reminders predicted lower momentary levels of positive emotion, B = -0.55, 95% CI [-0.83, -0.26], p < .001, but not reactivity, B = 0.02, 95% CI [-0.35, 0.40], p = .906, irrespective of PTSD status. Findings supported the global model and were inconsistent with the context-specific model. This study helps clarify positive emotional functioning in trauma-exposed adults and highlights future directions to better understand problems with positive emotion in PTSD.

Strengthening climate resilience of rural communities by co-producing landscape-specific integrated farming systems in Cambodia

ABSTRACT Climate change poses a major threat to the livelihoods of rural smallholder farmers in Cambodia. Adaptation measures through sustainable land management (SLM) and farming practices can help farmers to increase their resilience to climate change and secure their livelihoods. This paper presents a novel approach for promoting landscape-specific integrated farming systems (IFS) through multi-stakeholder engagement, knowledge-based decision-making and improved land use planning. It presents a stepwise participatory approach, applied under an IFAD-funded project, to define context-specific IFS models. Through co-production processes with multiple stakeholders, three landscape units and seven landscape-specific IFS models consisting of different SLM technologies were defined and demonstrated on 1,500 farms in two case study sites. The process included training and awareness raising to enhance local stakeholder engagement in developing integrated farm plans. This paper provides insights into how such a novel approach can be embedded in rural development projects to enhance smallholders’ resilience and livelihoods.

Integrating Palliative Care into Oncology Care Worldwide: The Right Care in the Right Place at the Right Time.

While the benefits of early palliative care are indisputable, most of the current evidence has emerged from resource-rich settings in urban areas of high-income countries, with an emphasis on solid tumors in outpatient settings; this model of palliative care integration is not currently scalable internationally. A shortage of specialist palliative care clinicians means that in order to meet the needs of all patients who require support at any point along their advanced cancer trajectory, palliative care must also be provided by family physicians and oncology clinicians who require training and mentorship. Models of care that facilitate the timely provision of seamless palliative care across all settings (inpatient, outpatient, and home-based care), with clear communication between clinicians, are crucial to the provision of patient-centred palliative care. The unique needs of patients with hematological malignancies must be further explored and existing models of palliative care provision modified to meet these needs. Finally, care must be provided in an equitable and culturally sensitive manner, recognizing the challenges associated with the delivery of high-quality palliative care to both patients in high-income countries who live in rural areas, as well as to those in low- and middle-income countries. A one-size-fits-all model will not suffice, and there is an urgent need to develop innovative context-specific models of palliative care integration worldwide, in order to provide the right care, in the right place, and at the right time.

Aspirations and realities of intergovernmental collaboration in national- level interventions: insights from maternal, neonatal and child health policy processes in Nigeria, 2009–2019

In Nigeria’s federal government system, national policies assign concurrent healthcare responsibilities across constitutionally arranged government levels. Hence, national policies, formulated for adoption by states for implementation, require collaboration. This study...

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissuesalgorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Achieving Governance Disclosure – the Surprising Case of an Emerging Market Family Business

Since 1961, when the first paper to clearly recognize and study family firms was published, the business literature has seen an increasing trend in the number of articles relating to family firms. One reason why interest in this field has grown may be because family-owned firms do not behave as expected by the predominant theoretical framework. Family-ownership has been shown to be unique in its effect on the firm when compared to other firm ownership structures. Such divergent behavior is disconcerting, knowing that, globally, family firms may be the most common firm structure. Governance is one of the largest streams of research within the family business literature. Regrettably, most research on family businesses is focused on Western developed economies suggesting that theory building when it comes to family business governance has been largely driven by the Anglo-Saxon corporate governance model. This is unfortunate because we know that context plays a vital role in governance outcomes and in family firm behavior. One of the important challenges faced by developing countries is governance disclosure, with potentially grave repercussions for their economic futures. The aim of this research is to help understand the motivations behind the decision of a family, non-listed firm, in a developing country context, to disclose governance information on its board members. We propose a larger role for institutional isomorphism at the intersection of the family, governance, disclosure, and developing country literatures. To this end, we identified a family business in the emerging market of Lebanon that has demonstrated this phenomenon. Despite the odds, this firm was able to overcome multiple challenges - family firm structure, secretive culture, no legal requirements, information sensitivity - to ultimately share this information with the public. Our case study helps us suggest a context-specific governance disclosure model based on the institutional theoretical framework.

Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae

Infections due to carbapenem-resistant Enterobacteriaceae have recently emerged as one of the most urgent threats to hospitalized patients within the United States and Europe. By far the most common etiological agent of these infections is Klebsiella pneumoniae, frequently manifesting in hospital-acquired pneumonia with a mortality rate of ~50% even with antimicrobial intervention. We performed transcriptomic analysis of data collected previously from in vitro characterization of both laboratory and clinical isolates which revealed shifts in expression of multiple master metabolic regulators across isolate types. Metabolism has been previously shown to be an effective target for antibacterial therapy, and genome-scale metabolic network reconstructions (GENREs) have provided a powerful means to accelerate identification of potential targets in silico. Combining these techniques with the transcriptome meta-analysis, we generated context-specific models of metabolism utilizing a well-curated GENRE of K. pneumoniae (iYL1228) to identify novel therapeutic targets. Functional metabolic analyses revealed that both composition and metabolic activity of clinical isolate-associated context-specific models significantly differs from laboratory isolate-associated models of the bacterium. Additionally, we identified increased catabolism of L-valine in clinical isolate-specific growth simulations. These findings warrant future studies for potential efficacy of valine transaminase inhibition as a target against K. pneumoniae infection.

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models

Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis.

Statistical Evaluation of Power-Aware Routing Protocols for Wireless Networks

Distributed wireless networks use low-power nodes, battery-powered routers, and base-station nodes. Routing strategies lose energy due to distance-dependent transmission and reception. Researchers design low-power routing solutions for wireless networks. Each technique has unique advantages, restrictions, and research options. Protocols vary in energy consumption, throughput, latency, packet delivery ratio (PDR), scalability, and computational complexity. Researchers can't choose ideal context-aware network models due to diverse performance measurements. This article addresses application-specific deployment strengths to reduce uncertainty. This discussion may help researchers choose context-specific routing models. This article compares power-aware routing model performance measures. This comparison may be used to construct routing models for low-delay, high-throughput, high PDR installations, etc. This paper proposes an algorithm rank score (ARS) with performance metrics. Network designers may employ high-ARS routing models to achieve performance balance over numerous assessments.

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

Code and data for "Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data"

DeePROG: Deep Attention-Based Model for Diseased Gene Prognosis by Fusing Multi-Omics Data.

An in-depth exploration of gene prognosis using different methodologies aids in understanding various biological regulations of genes in disease pathobiology and molecular functions. Interpreting gene functions at biological and molecular levels remains a daunting yet crucial task in domains such as drug design, personalized medicine, and next-generation diagnostics. Recent advancements in omics technologies have produced diverse heterogeneous genomic datasets like micro-array gene expression, miRNA expression, DNA sequence, 3D structures, which are significant resources for understanding the gene functions. In this paper, we propose a novel self-attention based deep multi-modal model, named DeePROG, for the prognosis of disease affected genes based on heterogeneous omics data. We use three NCBI datasets covering three modalities, namely gene expression profile, the underlying DNA sequence, and the 3D protein structures. To extract useful features from each modality, we develop several context-specific deep learning models. Besides, we develop three attention-based deep bi-modal architectures along with DeePROG to leverage the prognosis of the underlying biomedical data. We assess the performance of the models' in terms of computational assessment of function annotation (CAFA2) metrics. Moreover, we analyze the results in terms of receiver operating characteristics (ROC) curve in high-class imbalance data setting and perform statistical significance tests in terms of Welch's t-test. Experiment results show that DeePROG significantly outperforms baseline models across in terms of performance metrics. The source code and all preprocessed datasets used in this study are available at https://github.com/duttaprat/DeePROG.

Chronic disease care integration into primary care services in sub-Saharan Africa: a ‘best fit’ framework synthesis and new conceptual model

ObjectiveTo examine the relevance of existing chronic care models to the integration of chronic disease care into primary care services in sub-Saharan Africa and determine whether additional context-specific model elements...