Nudging, formulating new products, and the lifecourse : a qualitative assessment of the viability of three methods for reducing Scottish meat consumption for health, ethical, and environmental reasons

The authors would like to thank all individuals and groups who took part in this research. Research funding was provided by the Scottish Government’s Rural & Environmental Science and Analytical Services division. This study was funded as part of the Scottish Government Rural and Environmental Science and Analytical Services (RESAS). The sponsors had no further role in the research project.Peer reviewedPostprin

The Effects of Exercise Training versus Intensive Insulin Treatment on Skeletal Muscle Fibre Content in Type 1 Diabetes Mellitus Rodents

In patients with Type I diabetes mellitus (T1DM) undergoing intensive insulin therapy, the development of insulin resistance (IR) is linked to the improper storage of intramyocellular lipid (IMCL) species. While both IR and IMCL are improved with combined aerobic and resistance training, it is unclear how these adaptations relate to individual fibre-type transitions and metabolic function. This study aimed to compare the effects of combined exercise training versus conventional and intensive insulin therapy on skeletal muscle fibres in T1DM rodents. Seventeen Sprague-Dawley rats were divided into groups: Control-Sedentary (CS; n=4), conventionally-treated T1DM sedentary (DCT; n=4), intensively-treated T1DM sedentary (DIT; n=5) and combined aerobic/resistance exercise-trained T1DM (DCE; n=4). After twelve weeks, muscle fibre type, IMCL, and muscle glycogen content were analyzed. Significant increases in IMCL storage solely in type I fibres implicate improvements in oxidative capacity rather than a shift towards more oxidative fibres as the primary mechanism for these improvements

Methods to assess the price of diets : a rapid literature review

This work was funded by the Scottish Government’s Rural and Environment Science and Analytical Services Division as part of the Strategic Research Programme 2022-2027 (project RI-B5-9 and Underpinning National Capacity Support for Policy).Publisher PD

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.info:eu-repo/semantics/acceptedVersio

Replication and Oncolytic Activity of an Avian Orthoreovirus in Human Hepatocellular Carcinoma Cells

Oncolytic viruses are cancer therapeutics with promising outcomes in pre-clinical and clinical settings. Animal viruses have the possibility to avoid pre-existing immunity in humans, while being safe and immunostimulatory. We isolated an avian orthoreovirus (ARV-PB1), and tested it against a panel of hepatocellular carcinoma cells. We found that ARV-PB1 replicated well and induced strong cytopathic effects. It was determined that one mechanism of cell death was through syncytia formation, resulting in apoptosis and induction of interferon stimulated genes (ISGs). As hepatitis C virus (HCV) is a major cause of hepatocellular carcinoma worldwide, we investigated the effect of ARV-PB1 against cells already infected with this virus. Both HCV replicon-containing and infected cells supported ARV-PB1 replication and underwent cytolysis. Finally, we generated in silico models to compare the structures of human reovirus- and ARV-PB1-derived S1 proteins, which are the primary targets of neutralizing antibodies. Tertiary alignments confirmed that ARV-PB1 differs from its human homolog, suggesting that immunity to human reoviruses would not be a barrier to its use. Therefore, ARV-PB1 can potentially expand the repertoire of oncolytic viruses for treatment of human hepatocellular carcinoma and other malignancies

Replication and Oncolytic Activity of an Avian Orthoreovirus in Human Hepatocellular Carcinoma Cells

Oncolytic viruses are cancer therapeutics with promising outcomes in pre-clinical and clinical settings. Animal viruses have the possibility to avoid pre-existing immunity in humans, while being safe and immunostimulatory. We isolated an avian orthoreovirus (ARV-PB1), and tested it against a panel of hepatocellular carcinoma cells. We found that ARV-PB1 replicated well and induced strong cytopathic effects. It was determined that one mechanism of cell death was through syncytia formation, resulting in apoptosis and induction of interferon stimulated genes (ISGs). As hepatitis C virus (HCV) is a major cause of hepatocellular carcinoma worldwide, we investigated the effect of ARV-PB1 against cells already infected with this virus. Both HCV replicon-containing and infected cells supported ARV-PB1 replication and underwent cytolysis. Finally, we generated in silico models to compare the structures of human reovirus- and ARV-PB1-derived S1 proteins, which are the primary targets of neutralizing antibodies. Tertiary alignments confirmed that ARV-PB1 differs from its human homolog, suggesting that immunity to human reoviruses would not be a barrier to its use. Therefore, ARV-PB1 can potentially expand the repertoire of oncolytic viruses for treatment of human hepatocellular carcinoma and other malignancies

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and netecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

International audienceThere is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details