Using system dynamics modelling to assess the economic efficiency of innovations in the public sector - a systematic review

© 2022 Jadeja et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Background: Decision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health. Methods and findings: Relevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions. Conclusions: This systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further.Peer reviewedFinal Published versio

Analyzing drop coalescence in microfluidic devices with a deep learning generative model

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering

Room-temperature multiferroic hexagonal LuFeO3_3 films

The crystal and magnetic structures of single-crystalline hexagonal LuFeO$_3$ films have been studied using x-ray, electron and neutron diffraction methods. The polar structure of these films are found to persist up to 1050 K; and the switchability of the polar behavior is observed at room temperature, indicating ferroelectricity. An antiferromagnetic order was shown to occur below 440 K, followed by a spin reorientation resulting in a weak ferromagnetic order below 130 K. This observation of coexisting multiple ferroic orders demonstrates that hexagonal LuFeO$_3$ films are room-temperature multiferroics

The Molecular Mechanism of \u3cem\u3eN\u3c/em\u3e-Acetylglucosamine Side-Chain Attachment to the Lancefield Group A Carbohydrate in \u3cem\u3eStreptococcus pyogenes\u3c/em\u3e

In many Lactobacillales species (i.e. lactic acid bacteria), peptidoglycan is decorated by polyrhamnose polysaccharides that are critical for cell envelope integrity and cell shape and also represent key antigenic determinants. Despite the biological importance of these polysaccharides, their biosynthetic pathways have received limited attention. The important human pathogen, Streptococcus pyogenes, synthesizes a key antigenic surface polymer, the Lancefield group A carbohydrate (GAC). GAC is covalently attached to peptidoglycan and consists of a polyrhamnose polymer, with N-acetylglucosamine (GlcNAc) side chains, which is an essential virulence determinant. The molecular details of the mechanism of polyrhamnose modification with GlcNAc are currently unknown. In this report, using molecular genetics, analytical chemistry, and mass spectrometry analysis, we demonstrated that GAC biosynthesis requires two distinct undecaprenol-linked GlcNAc-lipid intermediates: GlcNAc-pyrophosphoryl-undecaprenol (GlcNAc-P-P-Und) produced by the GlcNAc-phosphate transferase GacO and GlcNAc-phosphate-undecaprenol (GlcNAc-P-Und) produced by the glycosyltransferase GacI. Further investigations revealed that the GAC polyrhamnose backbone is assembled on GlcNAc-P-P-Und. Our results also suggested that a GT-C glycosyltransferase, GacL, transfers GlcNAc from GlcNAc-P-Und to polyrhamnose. Moreover, GacJ, a small membrane-associated protein, formed a complex with GacI and significantly stimulated its catalytic activity. Of note, we observed that GacI homologs perform a similar function in Streptococcus agalactiae and Enterococcus faecalis. In conclusion, the elucidation of GAC biosynthesis in S. pyogenes reported here enhances our understanding of how other Gram-positive bacteria produce essential components of their cell wall

A Transcriptomic Signature of Mouse Liver Progenitor Cells

Liver progenitor cells (LPCs) can proliferate extensively, are able to differentiate into hepatocytes and cholangiocytes, and contribute to liver regeneration. The presence of LPCs, however, often accompanies liver disease and hepatocellular carcinoma (HCC), indicating that they may be a cancer stem cell. Understanding LPC biology and establishing a sensitive, rapid, and reliable method to detect their presence in the liver will assist diagnosis and facilitate monitoring of treatment outcomes in patients with liver pathologies. A transcriptomic meta-analysis of over 400 microarrays was undertaken to compare LPC lines against datasets of muscle and embryonic stem cell lines, embryonic and developed liver (DL), and HCC. Three gene clusters distinguishing LPCs from other liver cell types were identified. Pathways overrepresented in these clusters denote the proliferative nature of LPCs and their association with HCC. Our analysis also revealed 26 novel markers, LPC markers, including Mcm2 and Ltbp3, and eight known LPC markers, including M2pk and Ncam. These markers specified the presence of LPCs in pathological liver tissue by qPCR and correlated with LPC abundance determined using immunohistochemistry. These results showcase the value of global transcript profiling to identify pathways and markers that may be used to detect LPCs in injured or diseased liver

Using system dynamics modelling to assess the economic efficiency of innovations in the public sector - a systematic review.

BackgroundDecision-makers for public policy are increasingly utilising systems approaches such as system dynamics (SD) modelling, which test alternative interventions or policies for their potential impact while accounting for complexity. These approaches, however, have not consistently included an economic efficiency analysis dimension. This systematic review aims to examine how, and in what ways, system dynamics modelling approaches incorporate economic efficiency analyses to inform decision-making on innovations (improvements in products, services, or processes) in the public sector, with a particular interest in health.Methods and findingsRelevant studies (n = 29) were identified through a systematic search and screening of four electronic databases and backward citation search, and analysed for key characteristics and themes related to the analytical methods applied. Economic efficiency analysis approaches within SD broadly fell into two categories: as embedded sub-models or as cost calculations based on the outputs of the SD model. Embdedded sub-models within a dynamic SD framework can reveal a clear allocation of costs and benefits to periods of time, whereas cost calculations based on the SD model outputs can be useful for high-level resource allocation decisions.ConclusionsThis systematic review reveals that SD modelling is not currently used to its full potential to evaluate the technical or allocative efficiency of public sector innovations, particularly in health. The limited reporting on the experience or methodological challenges of applying allocated efficiency analyses with SD, particularly with dynamic embedded models, hampers common learning lessons to draw from and build on. Further application and comprehensive reporting of this approach would be welcome to develop the methodology further

The P72R Polymorphism in R248Q/W p53 Mutants Modifies the Mutant Effect on Epithelial to Mesenchymal Transition Phenotype and Cell Invasion via CXCL1 Expression

High-grade serous carcinoma (HGSC), the most lethal subtype of epithelial ovarian cancer (EOC), is characterized by widespread TP53 mutations (\u3e90%), most of which are missense mutations (\u3e70%). The objective of this study was to investigate differential transcriptional targets affected by a common germline P72R SNP (rs1042522) in two p53 hotspot mutants, R248Q and R248W, and identify the mechanism through which the P72R SNP affects the neomorphic properties of these mutants. Using isogenic cell line models, transcriptomic analysis, xenografts, and patient data, we found that the P72R SNP modifies the effect of p53 hotspot mutants on cellular morphology and invasion properties. Most importantly, RNA sequencing studies identified CXCL1 a critical factor that is differentially affected by P72R SNP in R248Q and R248W mutants and is responsible for differences in cellular morphology and functional properties observed in these p53 mutants. We show that the mutants with the P72 SNP promote a reversion of the EMT phenotype to epithelial characteristics, whereas its R72 counterpart promotes a mesenchymal transition via the chemokine CXCL1. These studies reveal a new role of the P72R SNP in modulating the neomorphic properties of p53 mutants via CXCL1, which has significant implications for tumor invasion and metastasis

Room-Temperature Multiferroic Hexagonal LuFeO\u3csub\u3e3\u3c/sub\u3e Films

The crystal and magnetic structures of single-crystalline hexagonal LuFeO3 films have been studied using x-ray, electron, and neutron diffraction methods. The polar structure of these films are found to persist up to 1050 K; and the switchability of the polar behavior is observed at room temperature, indicating ferroelectricity. An antiferromagnetic order was shown to occur below 440 K, followed by a spin reorientation resulting in a weak ferromagnetic order below 130 K. This observation of coexisting multiple ferroic orders demonstrates that hexagonal LuFeO3 films are room-temperature multiferroics

Fluctuation-driven, topology-stabilized order in a correlated nodal semimetal

The interplay between strong electron correlation and band topology is at the forefront of condensed matter research. As a direct consequence of correlation, magnetism enriches topological phases and also has promising functional applications. However, the influence of topology on magnetism remains unclear, and the main research effort has been limited to ground state magnetic orders. Here we report a novel order above the magnetic transition temperature in magnetic Weyl semimetal (WSM) CeAlGe. Such order shows a number of anomalies in electrical and thermal transport, and neutron scattering measurements. We attribute this order to the coupling of Weyl fermions and magnetic fluctuations originating from a three-dimensional Seiberg-Witten monopole, which qualitatively agrees well with the observations. Our work reveals a prominent role topology may play in tailoring electron correlation beyond ground state ordering, and offers a new avenue to investigate emergent electronic properties in magnetic topological materials.Comment: 32 pages, 15 figure

Changing Patterns of Bloodstream Infections in the Community and Acute Care Across 2 Coronavirus Disease 2019 Epidemic Waves: A Retrospective Analysis Using Data Linkage

BackgroundWe examined community- and hospital-acquired bloodstream infections (BSIs) in coronavirus disease 2019 (COVID-19) and non-COVID-19 patients across 2 epidemic waves.MethodsWe analyzed blood cultures of patients presenting to a London hospital group between January 2020 and February 2021. We reported BSI incidence, changes in sampling, case mix, healthcare capacity, and COVID-19 variants.ResultsWe identified 1047 BSIs from 34 044 blood cultures, including 653 (62.4%) community-acquired and 394 (37.6%) hospital-acquired. Important pattern changes were seen. Community-acquired Escherichia coli BSIs remained below prepandemic level during COVID-19 waves, but peaked following lockdown easing in May 2020, deviating from the historical trend of peaking in August. The hospital-acquired BSI rate was 100.4 per 100 000 patient-days across the pandemic, increasing to 132.3 during the first wave and 190.9 during the second, with significant increase in elective inpatients. Patients with a hospital-acquired BSI, including those without COVID-19, experienced 20.2 excess days of hospital stay and 26.7% higher mortality, higher than reported in prepandemic literature. In intensive care, the BSI rate was 421.0 per 100 000 intensive care unit patient-days during the second wave, compared to 101.3 pre-COVID-19. The BSI incidence in those infected with the severe acute respiratory syndrome coronavirus 2 Alpha variant was similar to that seen with earlier variants.ConclusionsThe pandemic have impacted the patterns of community- and hospital-acquired BSIs, in COVID-19 and non-COVID-19 patients. Factors driving the patterns are complex. Infection surveillance needs to consider key aspects of pandemic response and changes in healthcare practice