Characterisation of novel lung cancer cell lines for immuno-inhibitory markers

The present study investigates the expression of immune biomarkers, PD-L1 and HLA-1 on novel lung cancer cell lines (H838, H838-EGFR, A549, A549-ALK, HCC 827, NCI 1650, TWIT, Jacket). PD-L1 and HLA-1 characterisation were initially performed and analysed via flow cytometry. These results showed that the expression of PD-L1 and HLA-1 varies across the cell lines from high percent to low. The effect of IFNy on biomarkers expression was also investigated following a 48 hour incubation period. of the cell lines analysed the expression of PD-L1 increases with IFNy stimulation whilst HLA-1 remains relatively unchanged. Trypan blue assays for cell viability were performed, showing that when stimulated, cells were 100% viable whereas viability decreases upon IFNy exposure

Unequally unequal? Contextual-level status inequality and social cohesion moderating the association between individual-level socioeconomic position and systemic chronic inflammation

Background: Status inequality is hypothesised to increase socioeconomic inequalities in health by creating an environment in which social cohesion erodes and social comparisons intensify. Such an environment may cause systemic chronic inflammation. Although these are often-used explanations in social epidemiology, empirical tests remain rare. Methods: We analysed data from the West of Scotland Twenty-07 Study. Our sample consisted of 1977 participants in 499 small residential areas. Systemic chronic inflammation was measured by high-sensitivity C-reactive protein (hs-CRP; <10 mg/L). An area-level measurement of status inequality was created using census data and contextual-level social cohesion was measured applying ecometrics. We estimated linear multilevel models with cross-level interactions between socioeconomic position (SEP), status inequality, and social cohesion adjusted for age and gender. Our main analysis on postcode sector-level was re-estimated on three smaller spatial levels. Results: The difference in hs-CRP between disadvantaged and advantaged SEPs (0.806 mg/L; p = 0.063; [95%CI: −0.044; 1.656]) was highest among participants living in areas where most residents were in advantaged SEPs. In these status distributions, high social cohesion was associated with a shallower socioeconomic gradient in hs-CRP and low social cohesion was associated with a steeper gradient. In areas with an equal mix of SEPs or most residents in disadvantaged SEPs, the estimated difference in hs-CRP between disadvantaged and advantaged SEPs was −0.039 mg/L (p = 0.898; [95%CI: 0.644; 0.566]) and −0.257 mg/L (p = 0.568; [95%CI: 1.139; 0.625]) respectively. In these status distributions, the gradient in hs-CRP appeared steeper when social cohesion was high and potentially reversed when social cohesion was low. Results were broadly consistent when using area-levels smaller than postcode sectors. Conclusions: Inequalities in hs-CRP were greatest among participants living in areas wherein a majority of residents were in advantaged SEPs and social cohesion was low. In other combinations of these contextual characteristics, inequalities in systemic chronic inflammation were not detectable or potentially even reversed

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

AbstractA large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.</jats:p

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products. </jats:p

Global scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle but is challenging to estimate over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (ERA5 global reanalysis (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERCCDR)) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against more than 1800 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly and daily time scales, MSWEP followed by ERA5 demonstrated a higher CC and KGE than other datasets for more than 50 % of the stations. Whilst, ERA5 was the second-highest performing dataset, it showed the highest error and bias in about 20 % of the stations. The PERCCDR is the least well performing dataset with large bias (percentage of bias up to 99 %) and errors (normalised root mean square error up  to 247 %) with a higher KGE and CC than the other products in less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.  </jats:p

From warrior to guardian: An autoethnographic study of how consumers think about and interact with the natural world

Consumers are increasingly concerned about how their interactions with the natural world affect both the health of that environment, and their own well-being and enjoyment of life. More aware consumers seek to make sense of the natural world around them and consider how their consumer behavior impacts this environment. How actors notice and bracket ecologically material cues from a stream of experience and build connections and causal networks between these has been referred to as ecological sensemaking. This research examines ecological sensemaking in a specific context, that being in the experience of catch-and-release fishing. Data were gathered through a process of autoethnographic inquiry obtained over the course of four fishing trips. The results reflect the process of ecological sensemaking pertaining to the experience. Through the findings, we propose a new concept, ecological reasoning, which seeks to provide a critical link between ecological sensemaking and ecological embeddedness. Using this new concept, the research contributes to extant understanding of how consumers think about and interact with the natural world. Apart from constructing an overarching narrative of the experience, four subnarratives are also identified, in a chronological sequence that comprises the entire experience of catch-and-release fishing. The findings have implications for the broader management and marketing disciplines seeking to establish better ways of interacting with the natural world, both for themselves and their consumers

Confronting Indifference Toward Truth: Dealing with Workplace Bullshit

Many organizations are drowning in a flood of corporate bullshit, and this is particularly true of organizations in trouble, whose managers tend to make up stuff on the fly and with little regard for future consequences. Bullshitting and lying are not synonymous. While the liar knows the truth and wittingly bends it to suit their purpose, the bullshitter simply does not care about the truth. Managers can actually do something about organizational bullshit, and this Executive Digest provides a sequential framework that enables them to do so. They can comprehend it, they can recognize it for what it is, they can act against it, and they can take steps to prevent it from happening in the future. While it is unlikely that any organization will ever be able to rid itself of bullshit entirely, this article argues that by taking these steps, astute managers can work toward stemming its flood

Hybrid Closed-Loop with Faster Insulin Aspart Compared with Standard Insulin Aspart in Very Young Children with Type 1 Diabetes: A Double-Blind, Multicenter, Randomized, Crossover Study.

We evaluated the use of hybrid closed-loop (HCL) insulin delivery with faster insulin aspart (Fiasp) in very young children with type 1 diabetes (T1D). In a double-blind, multicenter, randomized, crossover study, children aged 2-6 years with T1D underwent two 8-week periods of HCL using CamAPS FX with Fiasp and standard insulin aspart (IAsp), in random order. Primary endpoint was between-treatment difference in time in target range 3.9-10.0 mmol/L. We randomized 25 participants: mean (±standard deviation) age 5.1 ± 1.3 years, baseline HbA1c 55 ± 9 mmol/mol. Time in range was not significantly different between interventions (64% ± 9% vs. 65% ± 9% for HCL with Fiasp vs. IAsp; mean difference -0.33% [95% confidence interval: -2.13 to 1.47; P = 0.71]). There was no significant difference in time with glucose <3.9 mmol/L. No post-randomization severe hypoglycemia or diabetic ketoacidosis events occurred. Use of Fiasp with CamAPS FX HCL demonstrated no significant difference in glycemic outcomes compared with IAsp in very young children with T1D. Clinical trials registration: NCT04759144